Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother

USDA-ARS?s Scientific Manuscript database

This study demonstrated a new method for mapping high-resolution (spatial: 1 m, and temporal: 1 h) soil moisture by assimilating distributed temperature sensing (DTS) observed soil temperatures at intermediate scales. In order to provide robust soil moisture and property estimates, we first proposed...

Introduction of digital soil mapping techniques for the nationwide regionalization of soil condition in Hungary; the first results of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project

NASA Astrophysics Data System (ADS)

Pásztor, László; Laborczi, Annamária; Szatmári, Gábor; Takács, Katalin; Bakacsi, Zsófia; Szabó, József; Dobos, Endre

2014-05-01

Due to the former soil surveys and mapping activities significant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfilled with these available datasets either by their direct usage or after certain specific and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable imperfection as for the accuracy and reliability of the delivered products. With a recently started project (DOSoReMI.hu; Digital, Optimized, Soil Related Maps and Information in Hungary) we would like to significantly extend the potential, how countrywide soil information requirements could be satisfied in Hungary. We started to compile digital soil related maps which fulfil optimally the national and international demands from points of view of thematic, spatial and temporal accuracy. The spatial resolution of the targeted countrywide, digital, thematic maps is at least 1:50.000 (approx. 50-100 meter raster resolution). DOSoReMI.hu results are also planned to contribute to the European part of GSM.net products. In addition to the auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened incorporating and eventually integrating geostatistical, data mining and GIS tools. In our paper we will present the first results. - Regression kriging (RK) has been used for the spatial inference of certain quantitative data, like particle size distribution components, rootable depth and organic matter content. In the course of RK-based mapping spatially segmented categorical information provided by the SMUs of Digital Kreybig Soil Information System (DKSIS) has been also used in the form of indicator variables. - Classification and regression trees (CART) were used to improve the spatial resolution of category-type soil maps (thematic downscaling), like genetic soil type and soil productivity maps. The approach was justified by the fact that certain thematic soil maps are not available in the required scale. Decision trees were applied for the understanding of the soil-landscape models involved in existing soil maps, and for the post-formalization of survey/compilation rules. The relationships identified and expressed in decision rules made the creation of spatially refined maps possible with the aid of high resolution environmental auxiliary variables. Among these co-variables, a special role was played by larger scale spatial soil information with diverse attributes. As a next step, the testing of random forests for the same purposes has been started. - Due to the simultaneous richness of available Hungarian legacy soil data, spatial inference methods and auxiliary environmental information, there is a high versatility of possible approaches for the compilation of a given soil (related) map. This suggests the opportunity of optimization. For the creation of an object specific soil (related) map with predefined parameters (resolution, accuracy, reliability etc.) one might intend to identify the optimum set of soil data, method and auxiliary co-variables optimized for the resources (data costs, computation requirements etc.). The first findings on the inclusion and joint usage of spatial soil data as well as on the consistency of various evaluations of the result maps will be also presented. Acknowledgement: Our work has been supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Development of predictive mapping techniques for soil survey and salinity mapping

NASA Astrophysics Data System (ADS)

Elnaggar, Abdelhamid A.

Conventional soil maps represent a valuable source of information about soil characteristics, however they are subjective, very expensive, and time-consuming to prepare. Also, they do not include explicit information about the conceptual mental model used in developing them nor information about their accuracy, in addition to the error associated with them. Decision tree analysis (DTA) was successfully used in retrieving the expert knowledge embedded in old soil survey data. This knowledge was efficiently used in developing predictive soil maps for the study areas in Benton and Malheur Counties, Oregon and accessing their consistency. A retrieved soil-landscape model from a reference area in Harney County was extrapolated to develop a preliminary soil map for the neighboring unmapped part of Malheur County. The developed map had a low prediction accuracy and only a few soil map units (SMUs) were predicted with significant accuracy, mostly those shallow SMUs that have either a lithic contact with the bedrock or developed on a duripan. On the other hand, the developed soil map based on field data was predicted with very high accuracy (overall was about 97%). Salt-affected areas of the Malheur County study area are indicated by their high spectral reflectance and they are easily discriminated from the remote sensing data. However, remote sensing data fails to distinguish between the different classes of soil salinity. Using the DTA method, five classes of soil salinity were successfully predicted with an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data compared to that predicted by using DTA. Hence, DTA could be a very helpful approach in developing soil survey and soil salinity maps in more objective, effective, less-expensive and quicker ways based on field data.

Goal oriented soil mapping: applying modern methods supported by local knowledge: A review

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Brevik, Eric; Oliva, Marc; Estebaranz, Ferran; Depellegrin, Daniel; Novara, Agata; Cerda, Artemi; Menshov, Oleksandr

2017-04-01

In the recent years the amount of soil data available increased importantly. This facilitated the production of better and accurate maps, important for sustainable land management (Pereira et al., 2017). Despite these advances, the human knowledge is extremely important to understand the natural characteristics of the landscape. The knowledge accumulated and transmitted generation after generation is priceless, and should be considered as a valuable data source for soil mapping and modelling. The local knowledge and wisdom can complement the new advances in soil analysis. In addition, farmers are the most interested in the participation and incorporation of their knowledge in the models, since they are the end-users of the study that soil scientists produce. Integration of local community's vision and understanding about nature is assumed to be an important step to the implementation of decision maker's policies. Despite this, many challenges appear regarding the integration of local and scientific knowledge, since in some cases there is no spatial correlation between folk and scientific classifications, which may be attributed to the different cultural variables that influence local soil classification. The objective of this work is to review how modern soil methods incorporated local knowledge in their models. References Pereira, P., Brevik, E., Oliva, M., Estebaranz, F., Depellegrin, D., Novara, A., Cerda, A., Menshov, O. (2017) Goal Oriented soil mapping: applying modern methods supported by local knowledge. In: Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B. (Eds.) Soil mapping and process modelling for sustainable land use management (Elsevier Publishing House) ISBN: 9780128052006

Soils [Chapter 5

Treesearch

R. W. E. Hopper; P. M. Walthall

1994-01-01

This report describes the soils of the Lost Lake, West Glacier Lake, and East Glacier Lake watersheds of GLEES and presents the methods used in conducting both the field and laboratory work. In addition, general statements about the nature of the mapping units used in making the soil maps are provided.

Soil classification based on cone penetration test (CPT) data in Western Central Java

NASA Astrophysics Data System (ADS)

Apriyono, Arwan; Yanto, Santoso, Purwanto Bekti; Sumiyanto

2018-03-01

This study presents a modified friction ratio range for soil classification i.e. gravel, sand, silt & clay and peat, using CPT data in Western Central Java. The CPT data was obtained solely from Soil Mechanic Laboratory of Jenderal Soedirman University that covers more than 300 sites within the study area. About 197 data were produced from data filtering process. IDW method was employed to interpolated friction ratio values in a regular grid point for soil classification map generation. Soil classification map was generated and presented using QGIS software. In addition, soil classification map with respect to modified friction ratio range was validated using 10% of total measurements. The result shows that silt and clay dominate soil type in the study area, which is in agreement with two popular methods namely Begemann and Vos. However, the modified friction ratio range produces 85% similarity with laboratory measurements whereby Begemann and Vos method yields 70% similarity. In addition, modified friction ratio range can effectively distinguish fine and coarse grains, thus useful for soil classification and subsequently for landslide analysis. Therefore, modified friction ratio range proposed in this study can be used to identify soil type for mountainous tropical region.

Suitability aero-geophysical methods for generating conceptual soil maps and their use in the modeling of process-related susceptibility maps

NASA Astrophysics Data System (ADS)

Tilch, Nils; Römer, Alexander; Jochum, Birgit; Schattauer, Ingrid

2014-05-01

In the past years, several times large-scale disasters occurred in Austria, which were characterized not only by flooding, but also by numerous shallow landslides and debris flows. Therefore, for the purpose of risk prevention, national and regional authorities also require more objective and realistic maps with information about spatially variable susceptibility of the geosphere for hazard-relevant gravitational mass movements. There are many and various proven methods and models (e.g. neural networks, logistic regression, heuristic methods) available to create such process-related (e.g. flat gravitational mass movements in soil) suszeptibility maps. But numerous national and international studies show a dependence of the suitability of a method on the quality of process data and parameter maps (f.e. Tilch & Schwarz 2011, Schwarz & Tilch 2011). In this case, it is important that also maps with detailed and process-oriented information on the process-relevant geosphere will be considered. One major disadvantage is that only occasionally area-wide process-relevant information exists. Similarly, in Austria often only soil maps for treeless areas are available. However, in almost all previous studies, randomly existing geological and geotechnical maps were used, which often have been specially adapted to the issues and objectives. This is one reason why very often conceptual soil maps must be derived from geological maps with only hard rock information, which often have a rather low quality. Based on these maps, for example, adjacent areas of different geological composition and process-relevant physical properties are razor sharp delineated, which in nature appears quite rarly. In order to obtain more realistic information about the spatial variability of the process-relevant geosphere (soil cover) and its physical properties, aerogeophysical measurements (electromagnetic, radiometric), carried out by helicopter, from different regions of Austria were interpreted. Previous studies show that, especially with radiometric measurements, the two-dimensional spatial variability of the nature of the process-relevant soil, close to the surface can be determined. In addition, the electromagnetic measurements are more important to obtain three-dimensional information of the deeper geological conditions and to improve the area-specific geological knowledge and understanding. The validation of these measurements is done with terrestrial geoelectrical measurements. So both aspects, radiometric and electromagnetic measurements, are important and subsequently, interpretation of the geophysical results can be used as the parameter maps in the modeling of more realistic susceptibility maps with respect to various processes. Within this presentation, results of geophysical measurements, the outcome and the derived parameter maps, as well as first process-oriented susceptibility maps in terms of gravitational soil mass movements will be presented. As an example results which were obtained with a heuristic method in an area in Vorarlberg (Western Austria) will be shown. References: Schwarz, L. & Tilch, N. (2011): Why are good process data so important for the modelling of landslide susceptibility maps?- EGU-Postersession "Landslide hazard and risk assessment, and landslide management" (NH 3.6), Vienna. [http://www.geologie.ac.at/fileadmin/user_upload/dokumente/pdf/poster/poster_2011_egu_schwarz_tilch_1.pdf] Tilch, N. & Schwarz, L. (2011): Spatial and scale-dependent variability in data quality and their influence on susceptibility maps for gravitational mass movements in soil, modelled by heuristic method.- EGU-Postersession "Landslide hazard and risk assessment, and landslide management" (NH 3.6); Vienna. [http://www.geologie.ac.at/fileadmin/user_upload/dokumente/pdf/poster/poster_2011_egu_tilch_schwarz.pdf

Multifractal and Singularity Maps of soil surface moisture distribution derived from 2D image analysis.

NASA Astrophysics Data System (ADS)

Cumbrera, Ramiro; Millán, Humberto; Martín-Sotoca, Juan Jose; Pérez Soto, Luis; Sanchez, Maria Elena; Tarquis, Ana Maria

2016-04-01

Soil moisture distribution usually presents extreme variation at multiple spatial scales. Image analysis could be a useful tool for investigating these spatial patterns of apparent soil moisture at multiple resolutions. The objectives of the present work were (i) to describe the local scaling of apparent soil moisture distribution and (ii) to define apparent soil moisture patterns from vertical planes of Vertisol pit images. Two soil pits (0.70 m long × 0.60 m width × 0.30 m depth) were excavated on a bare Mazic Pellic Vertisol. One was excavated in April/2011 and the other pit was established in May/2011 after 3 days of a moderate rainfall event. Digital photographs were taken from each Vertisol pit using a Kodak™ digital camera. The mean image size was 1600 × 945 pixels with one physical pixel ≈373 μm of the photographed soil pit. For more details see Cumbrera et al. (2012). Geochemical exploration have found with increasingly interests and benefits of using fractal (power-law) models to characterize geochemical distribution, using the concentration-area (C-A) model (Cheng et al., 1994; Cheng, 2012). This method is based on the singularity maps of a measure that at each point define areas with self-similar properties that are shown in power-law relationships in Concentration-Area plots (C-A method). The C-A method together with the singularity map ("Singularity-CA" method) define thresholds that can be applied to segment the map. We have applied it to each soil image. The results show that, in spite of some computational and practical limitations, image analysis of apparent soil moisture patterns could be used to study the dynamical change of soil moisture sampling in agreement with previous results (Millán et al., 2016). REFERENCES Cheng, Q., Agterberg, F. P. and Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51, 109-130. Cheng, Q. (2012). Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. Journal of Geochemical Exploration, 122, 55-70. Cumbrera, R., Ana M. Tarquis, Gabriel Gascó, Humberto Millán (2012) Fractal scaling of apparent soil moisture estimated from vertical planes of Vertisol pit images. Journal of Hydrology (452-453), 205-212. Martin Sotoca; J.J. Antonio Saa-Requejo, Juan Grau and Ana M. Tarquis (2016). Segmentation of singularity maps in the context of soil porosity. Geophysical Research Abstracts, 18, EGU2016-11402. Millán, H., Cumbrera, R. and Ana M. Tarquis (2016) Multifractal and Levy-stable statistics of soil surface moisture distribution derived from 2D image analysis. Applied Mathematical Modelling, 40(3), 2384-2395.

Characterizing Watersheds with Geophysical Methods: Some uses of GPR and EMI in Hydropedological Investigations.

NASA Astrophysics Data System (ADS)

Doolittle, J.; Lin, H.; Jenkinson, B.; Zhou, X.

2006-05-01

The USDA-NRCS and its cooperators use ground-penetrating radar (GPR) and electromagnetic induction (EMI) as rapid, noninvasive tools to support soil surveys at different scales and levels of resolution. The effective use of GPR is site-specific and generally restricted to soils having low electrical conductivity (e.g., soils with low clay and soluble salt contents). In suitable soils, GPR provides high resolution data, which are used to estimate depths to soil horizons and geologic layers that restrict, redirect, and/or concentrate the flow of water through landscapes. In areas of coarse-textured soils, GPR has been used to map spatiotemporal variations in water-table depths and local ground-water flow patterns. Compared with GPR, EMI can be effectively used across a broader spectrum of soils and spatial scales, but provides lower resolution of subsurface features. EMI is used to refine and improve soil maps prepared with traditional soil survey methods. Differences in apparent conductivity (ECa) are associated with different soils and soil properties (e.g., clay, moisture and soluble salt contents). Apparent conductivity maps provide an additional layer of information, which directs soil sampling, aids the identification and delineation of some soil polygons, and enhances the quality of soil maps. More recently, these tools were used to characterize the hydropedological character of a small, steeply sloping, forested watershed. Within the watershed, EMI was used to characterize the principal soil-landscape components, and GPR was used to provide high resolution data on soil depth and layering within colluvial deposits located in swales and depressional areas.

GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth.

PubMed

Mulder, V L; Lacoste, M; Richer-de-Forges, A C; Arrouays, D

2016-12-15

This work presents the first GlobalSoilMap (GSM) products for France. We developed an automatic procedure for mapping the primary soil properties (clay, silt, sand, coarse elements, pH, soil organic carbon (SOC), cation exchange capacity (CEC) and soil depth). The procedure employed a data-mining technique and a straightforward method for estimating the 90% confidence intervals (CIs). The most accurate models were obtained for pH, sand and silt. Next, CEC, clay and SOC were found reasonably accurate predicted. Coarse elements and soil depth were the least accurate of all models. Overall, all models were considered robust; important indicators for this were 1) the small difference in model diagnostics between the calibration and cross-validation set, 2) the unbiased mean predictions, 3) the smaller spatial structure of the prediction residuals in comparison to the observations and 4) the similar performance compared to other developed GlobalSoilMap products. Nevertheless, the confidence intervals (CIs) were rather wide for all soil properties. The median predictions became less reliable with increasing depth, as indicated by the increase of CIs with depth. In addition, model accuracy and the corresponding CIs varied depending on the soil variable of interest, soil depth and geographic location. These findings indicated that the CIs are as informative as the model diagnostics. In conclusion, the presented method resulted in reasonably accurate predictions for the majority of the soil properties. End users can employ the products for different purposes, as was demonstrated with some practical examples. The mapping routine is flexible for cloud-computing and provides ample opportunity to be further developed when desired by its users. This allows regional and international GSM partners with fewer resources to develop their own products or, otherwise, to improve the current routine and work together towards a robust high-resolution digital soil map of the world. Copyright © 2016 Elsevier B.V. All rights reserved.

Geostatistics, remote sensing and precision farming.

PubMed

Mulla, D J

1997-01-01

Precision farming is possible today because of advances in farming technology, procedures for mapping and interpolating spatial patterns, and geographic information systems for overlaying and interpreting several soil, landscape and crop attributes. The key component of precision farming is the map showing spatial patterns in field characteristics. Obtaining information for this map is often achieved by soil sampling. This approach, however, can be cost-prohibitive for grain crops. Soil sampling strategies can be simplified by use of auxiliary data provided by satellite or aerial photo imagery. This paper describes geostatistical methods for estimating spatial patterns in soil organic matter, soil test phosphorus and wheat grain yield from a combination of Thematic Mapper imaging and soil sampling.

Geostatistical interpolation of available copper in orchard soil as influenced by planting duration.

PubMed

Fu, Chuancheng; Zhang, Haibo; Tu, Chen; Li, Lianzhen; Luo, Yongming

2018-01-01

Mapping the spatial distribution of available copper (A-Cu) in orchard soils is important in agriculture and environmental management. However, data on the distribution of A-Cu in orchard soils is usually highly variable and severely skewed due to the continuous input of fungicides. In this study, ordinary kriging combined with planting duration (OK_PD) is proposed as a method for improving the interpolation of soil A-Cu. Four normal distribution transformation methods, namely, the Box-Cox, Johnson, rank order, and normal score methods, were utilized prior to interpolation. A total of 317 soil samples were collected in the orchards of the Northeast Jiaodong Peninsula. Moreover, 1472 orchards were investigated to obtain a map of planting duration using Voronoi tessellations. The soil A-Cu content ranged from 0.09 to 106.05 with a mean of 18.10 mg kg -1 , reflecting the high availability of Cu in the soils. Soil A-Cu concentrations exhibited a moderate spatial dependency and increased significantly with increasing planting duration. All the normal transformation methods successfully decreased the skewness and kurtosis of the soil A-Cu and the associated residuals, and also computed more robust variograms. OK_PD could generate better spatial prediction accuracy than ordinary kriging (OK) for all transformation methods tested, and it also provided a more detailed map of soil A-Cu. Normal score transformation produced satisfactory accuracy and showed an advantage in ameliorating smoothing effect derived from the interpolation methods. Thus, normal score transformation prior to kriging combined with planting duration (NSOK_PD) is recommended for the interpolation of soil A-Cu in this area.

Spectral analysis of charcoal on soils: Implications for wildland fire severity mapping methods

Treesearch

Alistair M. S. Smith; Jan U. H. Eitel; Andrew T. Hudak

2010-01-01

Recent studies in the Western United States have supported climate scenarios that predict a higher occurrence of large and severe wildfires. Knowledge of the severity is important to infer long-term biogeochemical, ecological, and societal impacts, but understanding the sensitivity of any severity mapping method to variations in soil type and increasing charcoal (char...

High-resolution soil moisture mapping in Afghanistan

NASA Astrophysics Data System (ADS)

Hendrickx, Jan M. H.; Harrison, J. Bruce J.; Borchers, Brian; Kelley, Julie R.; Howington, Stacy; Ballard, Jerry

2011-06-01

Soil moisture conditions have an impact upon virtually all aspects of Army activities and are increasingly affecting its systems and operations. Soil moisture conditions affect operational mobility, detection of landmines and unexploded ordinance, natural material penetration/excavation, military engineering activities, blowing dust and sand, watershed responses, and flooding. This study further explores a method for high-resolution (2.7 m) soil moisture mapping using remote satellite optical imagery that is readily available from Landsat and QuickBird. The soil moisture estimations are needed for the evaluation of IED sensors using the Countermine Simulation Testbed in regions where access is difficult or impossible. The method has been tested in Helmand Province, Afghanistan, using a Landsat7 image and a QuickBird image of April 23 and 24, 2009, respectively. In previous work it was found that Landsat soil moisture can be predicted from the visual and near infra-red Landsat bands1-4. Since QuickBird bands 1-4 are almost identical to Landsat bands 1- 4, a Landsat soil moisture map can be downscaled using QuickBird bands 1-4. However, using this global approach for downscaling from Landsat to QuickBird scale yielded a small number of pixels with erroneous soil moisture values. Therefore, the objective of this study is to examine how the quality of the downscaled soil moisture maps can be improved by using a data stratification approach for the development of downscaling regression equations for each landscape class. It was found that stratification results in a reliable downscaled soil moisture map with a spatial resolution of 2.7 m.

Mapping soil total nitrogen of cultivated land at county scale by using hyperspectral image

NASA Astrophysics Data System (ADS)

Gu, Xiaohe; Zhang, Li Yan; Shu, Meiyan; Yang, Guijun

2018-02-01

Monitoring total nitrogen content (TNC) in the soil of cultivated land quantitively and mastering its spatial distribution are helpful for crop growing, soil fertility adjustment and sustainable development of agriculture. The study aimed to develop a universal method to map total nitrogen content in soil of cultivated land by HSI image at county scale. Several mathematical transformations were used to improve the expression ability of HSI image. The correlations between soil TNC and the reflectivity and its mathematical transformations were analyzed. Then the susceptible bands and its transformations were screened to develop the optimizing model of map soil TNC in the Anping County based on the method of multiple linear regression. Results showed that the bands of 14th, 16th, 19th, 37th and 60th with different mathematical transformations were screened as susceptible bands. Differential transformation was helpful for reducing the noise interference to the diagnosis ability of the target spectrum. The determination coefficient of the first order differential of logarithmic transformation was biggest (0.505), while the RMSE was lowest. The study confirmed the first order differential of logarithm transformation as the optimal inversion model for soil TNC, which was used to map soil TNC of cultivated land in the study area.

Semi-automated landform classification for hazard mapping of soil liquefaction by earthquake

NASA Astrophysics Data System (ADS)

Nakano, Takayuki

2018-05-01

Soil liquefaction damages were caused by huge earthquake in Japan, and the similar damages are concerned in near future huge earthquake. On the other hand, a preparation of soil liquefaction risk map (soil liquefaction hazard map) is impeded by the difficulty of evaluation of soil liquefaction risk. Generally, relative soil liquefaction risk should be able to be evaluated from landform classification data by using experimental rule based on the relationship between extent of soil liquefaction damage and landform classification items associated with past earthquake. Therefore, I rearranged the relationship between landform classification items and soil liquefaction risk intelligibly in order to enable the evaluation of soil liquefaction risk based on landform classification data appropriately and efficiently. And I developed a new method of generating landform classification data of 50-m grid size from existing landform classification data of 250-m grid size by using digital elevation model (DEM) data and multi-band satellite image data in order to evaluate soil liquefaction risk in detail spatially. It is expected that the products of this study contribute to efficient producing of soil liquefaction hazard map by local government.

Landscape scale estimation of soil carbon stock using 3D modelling.

PubMed

Veronesi, F; Corstanje, R; Mayr, T

2014-07-15

Soil C is the largest pool of carbon in the terrestrial biosphere, and yet the processes of C accumulation, transformation and loss are poorly accounted for. This, in part, is due to the fact that soil C is not uniformly distributed through the soil depth profile and most current landscape level predictions of C do not adequately account the vertical distribution of soil C. In this study, we apply a method based on simple soil specific depth functions to map the soil C stock in three-dimensions at landscape scale. We used soil C and bulk density data from the Soil Survey for England and Wales to map an area in the West Midlands region of approximately 13,948 km(2). We applied a method which describes the variation through the soil profile and interpolates this across the landscape using well established soil drivers such as relief, land cover and geology. The results indicate that this mapping method can effectively reproduce the observed variation in the soil profiles samples. The mapping results were validated using cross validation and an independent validation. The cross-validation resulted in an R(2) of 36% for soil C and 44% for BULKD. These results are generally in line with previous validated studies. In addition, an independent validation was undertaken, comparing the predictions against the National Soil Inventory (NSI) dataset. The majority of the residuals of this validation are between ± 5% of soil C. This indicates high level of accuracy in replicating topsoil values. In addition, the results were compared to a previous study estimating the carbon stock of the UK. We discuss the implications of our results within the context of soil C loss factors such as erosion and the impact on regional C process models. Copyright © 2014 Elsevier B.V. All rights reserved.

Potential Electrokinetic Remediation Technologies of Laboratory Scale into Field Application- Methodology Overview

NASA Astrophysics Data System (ADS)

Ayuni Suied, Anis; Tajudin, Saiful Azhar Ahmad; Nizam Zakaria, Muhammad; Madun, Aziman

2018-04-01

Heavy metal in soil possesses high contribution towards soil contamination which causes to unbalance ecosystem. There are many ways and procedures to make the electrokinetic remediation (EKR) method to be efficient, effective, and potential as a low cost soil treatment. Electrode compartment for electrolyte is expected to treat the contaminated soil through electromigration and enhance metal ions movement. The electrokinetic is applicable for many approaches such as electrokinetic remediation (EKR), electrokinetic stabilization (EKS), electrokinetic bioremediation and many more. This paper presents a critical review on comparison of laboratory scale between EKR, EKS and EK bioremediation treatment by removing the heavy metal contaminants. It is expected to propose one framework of contaminated soil mapping. Electrical Resistivity Method (ERM) is one of famous indirect geophysical tools for surface mapping and subsurface profiling. Hence, ERM is used to mapping the migration of heavy metal ions by electrokinetic.

[Extracting black soil border in Heilongjiang province based on spectral angle match method].

PubMed

Zhang, Xin-Le; Zhang, Shu-Wen; Li, Ying; Liu, Huan-Jun

2009-04-01

As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classification precision based on remote sensing (RS) technique is unsatisfied. Under RS and geographic information systems (GIS) environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest (ROI) for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilongjiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying accuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification accuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate (temperature, precipitation, etc.), parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.

Comparing different approaches - data mining, geostatistic, and deterministic pedology - to assess the frequency of WRB Reference Soil Groups in the Italian soil regions

NASA Astrophysics Data System (ADS)

Lorenzetti, Romina; Barbetti, Roberto; L'Abate, Giovanni; Fantappiè, Maria; Costantini, Edoardo A. C.

2013-04-01

Estimating frequency of soil classes in map unit is always affected by some degree of uncertainty, especially at small scales, with a larger generalization. The aim of this study was to compare different possible approaches - data mining, geostatistic, deterministic pedology - to assess the frequency of WRB Reference Soil Groups (RSG) in the major Italian soil regions. In the soil map of Italy (Costantini et al., 2012), a list of the first five RSG was reported in each major 10 soil regions. The soil map was produced using the national soil geodatabase, which stored 22,015 analyzed and classified pedons, 1,413 soil typological unit (STU) and a set of auxiliary variables (lithology, land-use, DEM). Other variables were added, to better consider the influence of soil forming factors (slope, soil aridity index, carbon stock, soil inorganic carbon content, clay, sand, geography of soil regions and soil systems) and a grid at 1 km mesh was set up. The traditional deterministic pedology assessed the STU frequency according to the expert judgment presence in every elementary landscape which formed the mapping unit. Different data mining techniques were firstly compared in their ability to predict RSG through auxiliary variables (neural networks, random forests, boosted tree, supported vector machine (SVM)). We selected SVM according to the result of a testing set. A SVM model is a representation of the examples as points in space, mapped so that examples of separate categories are divided by a clear gap that is as wide as possible. The geostatistic algorithm we used was an indicator collocated cokriging. The class values of the auxiliary variables, available at all the points of the grid, were transformed in indicator variables (values 0, 1). A principal component analysis allowed us to select the variables that were able to explain the largest variability, and to correlate each RSG with the first principal component, which explained the 51% of the total variability. The principal component was used as collocated variable. The results were as many probability maps as the estimated WRB classes. They were summed up in a unique map, with the most probable class at each pixel. The first five more frequent RSG resulting from the three methods were compared. The outcomes were validated with a subset of the 10% of the pedons, kept out before the elaborations. The error estimate was produced for each estimated RSG. The first results, obtained in one of the most widespread soil region (plains and low hills of central and southern Italy) showed that the first two frequency classes were the same for all the three methods. The deterministic method differed from the others at the third position, while the statistical methods inverted the third and fourth position. An advantage of the SVM was the possibility to use in the same elaboration numeric and categorical variable, without any previous transformation, which reduced the processing time. A Bayesian validation indicated that the SVM method was as reliable as the indicator collocated cokriging, and better than the deterministic pedological approach.

Improvements to the DRASTIC ground-water vulnerability mapping method

USGS Publications Warehouse

Rupert, Michael G.

1999-01-01

Ground-water vulnerability maps are designed to show areas of greatest potential for ground-water contamination on the basis of hydrogeologic and anthropogenic (human) factors. The maps are developed by using computer mapping hardware and software called a geographic information system (GIS) to combine data layers such as land use, soils, and depth to water. Usually, ground-water vulnerability is determined by assigning point ratings to the individual data layers and then adding the point ratings together when those layers are combined into a vulnerability map. Probably the most widely used ground-water vulnerability mapping method is DRASTIC, named for the seven factors considered in the method: Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone media, and hydraulic Conductivity of the aquifer (Aller and others, 1985, p. iv). The DRASTIC method has been used to develop ground-water vulnerability maps in many parts of the Nation; however, the effectiveness of the method has met with mixed success (Koterba and others, 1993, p. 513; U.S. Environmental Protection Agency, 1993; Barbash and Resek, 1996; Rupert, 1997). DRASTIC maps usually are not calibrated to measured contaminant concentrations. The DRASTIC ground-water vulnerability mapping method was improved by calibrating the point rating scheme to measured nitrite plus nitrate as nitrogen (NO2+NO3–N) concentrations in ground water on the basis of statistical correlations between NO2+NO3–N concentrations and land use, soils, and depth to water (Rupert, 1997). This report describes the calibration method developed by Rupert and summarizes the improvements in results of this method over those of the uncalibrated DRASTIC method applied by Rupert and others (1991) in the eastern Snake River Plain, Idaho.

Mapping Soil Erosion Factors and Potential Erosion Risk for the National Park "Central Balkan"

NASA Astrophysics Data System (ADS)

Ilieva, Diliana; Malinov, Ilia

2014-05-01

Soil erosion is widely recognised environmental problem. The report aims at presenting the main results from assessment and mapping of the factors of sheet water erosion and the potential erosion risk on the territory of National Park "Central Balkan". For this purpose, the Universal Soil Loss Equation (USLE) was used for predicting soil loss from erosion. The influence of topography (LS-factor) and soil erodibility (K-factor) was assessed using small-scale topographic and soil maps. Rainfall erosivity (R-factor) was calculated from data of rainfalls with amounts exceeding 9.5 mm from 14 hydro-meteorological stations. The values of the erosion factors (R, K and LS) were presented for the areas of forest, sub-alpine and alpine zones. Using the methods of GIS, maps were plotted presenting the area distribution among the classes of the soil erosion factors and the potential risk in the respective zones. The results can be used for making accurate decisions for soil conservation and sustainable land management in the park.

How to feed environmental studies with soil information to address SDG 'Zero hunger'

NASA Astrophysics Data System (ADS)

Hendriks, Chantal; Stoorvogel, Jetse; Claessens, Lieven

2017-04-01

As pledged by UN Sustainable Development Goal (SDG) 2, there should be zero hunger, food security, improved food nutrition and sustainable agriculture by 2030. Environmental studies are essential to reach SDG 2. Soils play a crucial role, especially in addressing 'Zero hunger'. This study aims to discuss the connection between the supply and demand of soil data for environmental studies and how this connection can be improved illustrating different methods. As many studies are resource constrained, the options to collect new soil data are limited. Therefore, it is essential to use existing soil information, auxiliary data and collected field data efficiently. Existing soil data are criticised in literature as i) being dominantly qualitative, ii) being often outdated, iii) being not spatially exhaustive, iv) being only available at general scales, v) being inconsistent, and vi) lacking quality assessments. Additional field data can help to overcome some of these problems. Outdated maps can, for example, be improved by collecting additional soil data in areas where changes in soil properties are expected. Existing soil data can also provide insight in the expected soil variability and, as such, these data can be used for the design of sampling schemes. Existing soil data are also crucial input for studies on digital soil mapping because they give information on parent material and the relative age of soils. Digital soil mapping is commonly applied as an efficient method to quantitatively predict the spatial variation of soil properties. However, the efficiency of digital soil mapping may increase if we look at functional soil properties (e.g. nutrient availability, available water capacity) for the soil profile that vary in a two-dimensional space rather than at basic soil properties of individual soil layers (e.g. texture, organic matter content, nitrogen content) that vary in a three-dimensional space. Digital soil mapping techniques are based on statistical relations between soil properties and environmental variables. However, in some cases a more mechanistic approach, based on pedological knowledge, might be more convincing to predict soil properties. This study showed that the soil science community is able to provide the required soil information for environmental studies. However, there is not a single solution that provides the required soil data. Case studies are needed to prove that certain methods meet the data requirements, whereafter these case studies function as a lighthouse to other studies. We illustrate data availability and methodological innovations for a case study in Kenya, where the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) aims to contribute to SDG 2.

Potential of EnMAP spaceborne imaging spectroscopy for the prediction of common surface soil properties and expected accuracy

NASA Astrophysics Data System (ADS)

Chabrillat, Sabine; Foerster, Saskia; Steinberg, Andreas; Stevens, Antoine; Segl, Karl

2016-04-01

There is a renewed awareness of the finite nature of the world's soil resources, growing concern about soil security, and significant uncertainties about the carrying capacity of the planet. As a consequence, soil scientists are being challenged to provide regular assessments of soil conditions from local through to global scales. However, only a few countries have the necessary survey and monitoring programs to meet these new needs and existing global data sets are out-of-date. A particular issue is the clear demand for a new area-wide regional to global coverage with accurate, up-to-date, and spatially referenced soil information as expressed by the modeling scientific community, farmers and land users, and policy and decision makers. Soil spectroscopy from remote sensing observations based on studies from the laboratory scale to the airborne scale has been shown to be a proven method for the quantitative prediction of key soil surface properties in local areas for exposed soils in appropriate surface conditions such as low vegetation cover and low water content. With the upcoming launch of the next generation of hyperspectral satellite sensors in the next 3 to 5 years (EnMAP, HISUI, PRISMA, SHALOM), a great potential for the global mapping and monitoring of soil properties is appearing. Nevertheless, the capabilities to extend the soil properties current spectral modeling from local to regional scales are still to be demonstrated using robust methods. In particular, three central questions are at the forefront of research nowadays: a) methodological developments toward improved algorithms and operational tools for the extraction of soil properties, b) up scaling from the laboratory into space domain, and c) demonstration of the potential of upcoming satellite systems and expected accuracy of soil maps. In this study, airborne imaging spectroscopy data from several test sites are used to simulate EnMAP satellite images at 30 m scale. Then, different soil algorithms are examined based on the analyses of chemical-physical features from the soil spectral reflectance and/or multivariate established techniques such as Partial-Least Squares PLS, Support-Vector Machine SVM, to determine common surface soil properties, in particular soil organic carbon (SOC), clay and iron oxide content. Results show that EnMAP is able to predict clay, free iron oxide, and SOC with an RV2 between 0.53 and 0.67 compared to airborne imagery with RV2 between 0.64 and 0.74. The correlation between EnMAP and airborne imagery prediction results is high (Pearson coefficients between 0.84 and 0.91). Furthermore, spatial distribution is coherent between the airborne mapping and simulated EnMAP mapping as shown with a spatial structure analysis. In general, this paper demonstrates the high potential of upcoming spaceborne hyperspectral missions for soil science studies but also shows the need for future adapted strategies to fulfill the entire potential of soil spectroscopy for orbital utilization.

High-resolution mapping and spatial variability of soil organic carbon storage of permafrost-affected soils

NASA Astrophysics Data System (ADS)

Siewert, Matthias; Hugelius, Gustaf

2017-04-01

Permafrost-affected soils store large amounts of soil organic carbon (SOC). Mapping of this SOC provides a first order spatial input variable for research that relates carbon stored in permafrost regions to carbon cycle dynamics. High-resolution satellite imagery is becoming increasingly available even in circum-polar regions. The presented research highlights findings of high-resolution mapping efforts of SOC from five study areas in the northern circum-polar permafrost region. These study areas are located in Siberia (Kytalyk, Spasskaya Pad /Neleger, Lena delta), Northern Sweden (Abisko) and Northwestern Canada (Herschel Island). Our high spatial resolution analyses show how geomorphology has a strong influence on the distribution of SOC. This is organized at different spatial scales. Periglacial landforms and processes dictate local scale SOC distribution due to patterned ground. Such landforms are non-sorted circles and ice-wedge polygons of different age and scale. Palsas and peat plateaus are formed and can cover larger areas in Sub-Arctic environments. Study areas that have not been affected by Pleistocene glaciation feature ice-rich Yedoma sediments that dominate the local relief through thermokarst formation and create landscape scale macro environments that dictate the distribution of SOC. A general trend indicates higher SOC storage in Arctic tundra soils compared to forested Boreal or Sub-Arctic taiga soils. Yet, due to the shallower active layer depth in the Arctic, much of the SOC may be permanently frozen and thus not be available to ecosystem processes. Significantly more SOC is stored in soils compared to vegetation, indicating that vegetation growth and incorporation of the carbon into the plant phytomass alone will not be able to offset SOC released from permafrost. This contribution also addresses advances in thematic mapping methods and digital soil mapping of SOC in permafrost terrain. In particular machine-learning methods, such as support vector machines, artificial neural networks and random forests show promising results as a toolbox for mapping permafrost-affected soils. Yet, these new methods do not decrease our dependency from soil pedon data from the field. In contrary, soil pedon data represents an urgent research priority. Statistical analyses are provided as an indication for best practice of soil pedon sampling for the quantification and the model representation of SOC stored in permafrost-affected soils.

Mapping pocket gopher burrow systems with expanding polyurethane foam

DOE Office of Scientific and Technical Information (OSTI.GOV)

Felthauser, M.; McInroy, D.

The impetus for this study arose from the need to isolate buried chemical and radioactive waste from burrowing animals. In a study of barrier materials that inhibit burrowing by pocket gophers (Thomomys spp.) into waste material, it was necessary to map tunnel systems as a function of depth and soil type. A method of mapping burrow systems was needed that would be economical, portable, useful in a variety of soil types, and give accurate, permanent records of burrow configurations. A method is described for injecting an expanding polyurethane foam to map burrow systems in situ.

Mapping soil texture targeting predefined depth range or synthetizing from standard layers?

NASA Astrophysics Data System (ADS)

Laborczi, Annamária; Dezső Kaposi, András; Szatmári, Gábor; Takács, Katalin; Pásztor, László

2017-04-01

There are increasing demands nowadays on spatial soil information in order to support environmental related and land use management decisions. Physical soil properties, especially particle size distribution play important role in this context. A few of the requirements can be satisfied by the sand-, silt-, and clay content maps compiled according to global standards such as GlobalSoilMap (GSM) or Soil Grids. Soil texture classes (e. g. according to USDA classification) can be derived from these three fraction data, in this way texture map can be compiled based on the proper separate maps. Soil texture class as well as fraction information represent direct input of crop-, meteorological- and hydrological models. The model inputs frequently require maps representing soil features of 0-30 cm depth, which is covered by three consecutive depth intervals according to standard specifications: 0-5 cm, 5-15 cm, 15-30 cm. Becoming GSM and SoilGrids the most detailed freely available spatial soil data sources, the common model users (e. g. meteorologists, agronomists, or hydrologists) would produce input map from (the weighted mean of) these three layers. However, if the basic soil data and proper knowledge is obtainable, a soil texture map targeting directly the 0-30 cm layer could be independently compiled. In our work we compared Hungary's soil texture maps compiled using the same reference and auxiliary data and inference methods but for differing layer distribution. We produced the 0-30 cm clay, silt and sand map as well as the maps for the three standard layers (0-5 cm, 5-15 cm, 15-30 cm). Maps of sand, silt and clay percentage were computed through regression kriging (RK) applying Additive Log-Ratio (alr) transformation. In addition to the Hungarian Soil Information and Monitoring System as reference soil data, digital elevation model and its derived components, soil physical property maps, remotely sensed images, land use -, geological-, as well as meteorological data were applied as auxiliary variables. We compared the directly compiled and the synthetized clay-, sand content, and texture class maps by different tools. In addition to pairwise comparison of basic statistical features (histograms, scatter plots), we examined the spatial distribution of the differences. We quantified the taxonomical distances of the textural classes, in order to investigate the differences of the map-pairs. We concluded that the directly computed and the synthetized maps show various differences. In the case of clay-, and sand content maps, the map-pairs have to be considered statistically different. On the other hand, the differences of the texture class maps are not significant. However, in all cases, the differences rather concern the extreme ranges and categories. Using of synthetized maps can intensify extremities by error propagation in models and scenarios. Based on our results, we suggest the usage of the directly composed maps.

Three-Dimensional Mapping of Soil Organic Carbon by Combining Kriging Method with Profile Depth Function.

PubMed

Chen, Chong; Hu, Kelin; Li, Hong; Yun, Anping; Li, Baoguo

2015-01-01

Understanding spatial variation of soil organic carbon (SOC) in three-dimensional direction is helpful for land use management. Due to the effect of profile depths and soil texture on vertical distribution of SOC, the stationary assumption for SOC cannot be met in the vertical direction. Therefore the three-dimensional (3D) ordinary kriging technique cannot be directly used to map the distribution of SOC at a regional scale. The objectives of this study were to map the 3D distribution of SOC at a regional scale by combining kriging method with the profile depth function of SOC (KPDF), and to explore the effects of soil texture and land use type on vertical distribution of SOC in a fluvial plain. A total of 605 samples were collected from 121 soil profiles (0.0 to 1.0 m, 0.20 m increment) in Quzhou County, China and SOC contents were determined for each soil sample. The KPDF method was used to obtain the 3D map of SOC at the county scale. The results showed that the exponential equation well described the vertical distribution of mean values of the SOC contents. The coefficients of determination, root mean squared error and mean prediction error between the measured and the predicted SOC contents were 0.52, 1.82 and -0.24 g kg(-1) respectively, suggesting that the KPDF method could be used to produce a 3D map of SOC content. The surface SOC contents were high in the mid-west and south regions, and low values lay in the southeast corner. The SOC contents showed significant positive correlations between the five different depths and the correlations of SOC contents were larger in adjacent layers than in non-adjacent layers. Soil texture and land use type had significant effects on the spatial distribution of SOC. The influence of land use type was more important than that of soil texture in the surface soil, and soil texture played a more important role in influencing the SOC levels for 0.2-0.4 m layer.

Three-Dimensional Mapping of Soil Organic Carbon by Combining Kriging Method with Profile Depth Function

PubMed Central

Chen, Chong; Hu, Kelin; Li, Hong; Yun, Anping; Li, Baoguo

2015-01-01

Understanding spatial variation of soil organic carbon (SOC) in three-dimensional direction is helpful for land use management. Due to the effect of profile depths and soil texture on vertical distribution of SOC, the stationary assumption for SOC cannot be met in the vertical direction. Therefore the three-dimensional (3D) ordinary kriging technique cannot be directly used to map the distribution of SOC at a regional scale. The objectives of this study were to map the 3D distribution of SOC at a regional scale by combining kriging method with the profile depth function of SOC (KPDF), and to explore the effects of soil texture and land use type on vertical distribution of SOC in a fluvial plain. A total of 605 samples were collected from 121 soil profiles (0.0 to 1.0 m, 0.20 m increment) in Quzhou County, China and SOC contents were determined for each soil sample. The KPDF method was used to obtain the 3D map of SOC at the county scale. The results showed that the exponential equation well described the vertical distribution of mean values of the SOC contents. The coefficients of determination, root mean squared error and mean prediction error between the measured and the predicted SOC contents were 0.52, 1.82 and -0.24 g kg-1 respectively, suggesting that the KPDF method could be used to produce a 3D map of SOC content. The surface SOC contents were high in the mid-west and south regions, and low values lay in the southeast corner. The SOC contents showed significant positive correlations between the five different depths and the correlations of SOC contents were larger in adjacent layers than in non-adjacent layers. Soil texture and land use type had significant effects on the spatial distribution of SOC. The influence of land use type was more important than that of soil texture in the surface soil, and soil texture played a more important role in influencing the SOC levels for 0.2-0.4 m layer. PMID:26047012

Mapping the spatial patterns of field traffic and traffic intensity to predict soil compaction risks at the field scale

NASA Astrophysics Data System (ADS)

Duttmann, Rainer; Kuhwald, Michael; Nolde, Michael

2015-04-01

Soil compaction is one of the main threats to cropland soils in present days. In contrast to easily visible phenomena of soil degradation, soil compaction, however, is obscured by other signals such as reduced crop yield, delayed crop growth, and the ponding of water, which makes it difficult to recognize and locate areas impacted by soil compaction directly. Although it is known that trafficking intensity is a key factor for soil compaction, until today only modest work has been concerned with the mapping of the spatially distributed patterns of field traffic and with the visual representation of the loads and pressures applied by farm traffic within single fields. A promising method for for spatial detection and mapping of soil compaction risks of individual fields is to process dGPS data, collected from vehicle-mounted GPS receivers and to compare the soil stress induced by farm machinery to the load bearing capacity derived from given soil map data. The application of position-based machinery data enables the mapping of vehicle movements over time as well as the assessment of trafficking intensity. It also facilitates the calculation of the trafficked area and the modeling of the loads and pressures applied to soil by individual vehicles. This paper focuses on the modeling and mapping of the spatial patterns of traffic intensity in silage maize fields during harvest, considering the spatio-temporal changes in wheel load and ground contact pressure along the loading sections. In addition to scenarios calculated for varying mechanical soil strengths, an example for visualizing the three-dimensional stress propagation inside the soil will be given, using the Visualization Toolkit (VTK) to construct 2D or 3D maps supporting to decision making due to sustainable field traffic management.

Ecosystem Services in Agricultural Landscapes: A Spatially Explicit Approach to Support Sustainable Soil Management

PubMed Central

Crossman, Neville D.; MacEwan, Richard J.; Wallace, D. Dugal; Bennett, Lauren T.

2014-01-01

Soil degradation has been associated with a lack of adequate consideration of soil ecosystem services. We demonstrate a broadly applicable method for mapping changes in the supply of two priority soil ecosystem services to support decisions about sustainable land-use configurations. We used a landscape-scale study area of 302 km2 in northern Victoria, south-eastern Australia, which has been cleared for intensive agriculture. Indicators representing priority soil services (soil carbon sequestration and soil water storage) were quantified and mapped under both a current and a future 25-year land-use scenario (the latter including a greater diversity of land uses and increased perennial crops and irrigation). We combined diverse methods, including soil analysis using mid-infrared spectroscopy, soil biophysical modelling, and geostatistical interpolation. Our analysis suggests that the future land-use scenario would increase the landscape-level supply of both services over 25 years. Soil organic carbon content and water storage to 30 cm depth were predicted to increase by about 11% and 22%, respectively. Our service maps revealed the locations of hotspots, as well as potential trade-offs in service supply under new land-use configurations. The study highlights the need to consider diverse land uses in sustainable management of soil services in changing agricultural landscapes. PMID:24616632

Ecosystem services in agricultural landscapes: a spatially explicit approach to support sustainable soil management.

PubMed

Forouzangohar, Mohsen; Crossman, Neville D; MacEwan, Richard J; Wallace, D Dugal; Bennett, Lauren T

2014-01-01

Soil degradation has been associated with a lack of adequate consideration of soil ecosystem services. We demonstrate a broadly applicable method for mapping changes in the supply of two priority soil ecosystem services to support decisions about sustainable land-use configurations. We used a landscape-scale study area of 302 km(2) in northern Victoria, south-eastern Australia, which has been cleared for intensive agriculture. Indicators representing priority soil services (soil carbon sequestration and soil water storage) were quantified and mapped under both a current and a future 25-year land-use scenario (the latter including a greater diversity of land uses and increased perennial crops and irrigation). We combined diverse methods, including soil analysis using mid-infrared spectroscopy, soil biophysical modelling, and geostatistical interpolation. Our analysis suggests that the future land-use scenario would increase the landscape-level supply of both services over 25 years. Soil organic carbon content and water storage to 30 cm depth were predicted to increase by about 11% and 22%, respectively. Our service maps revealed the locations of hotspots, as well as potential trade-offs in service supply under new land-use configurations. The study highlights the need to consider diverse land uses in sustainable management of soil services in changing agricultural landscapes.

Using electrical resistance tomography to map subsurface temperatures

DOEpatents

Ramirez, A.L.; Chesnut, D.A.; Daily, W.D.

1994-09-13

A method is provided for measuring subsurface soil or rock temperatures remotely using electrical resistivity tomography (ERT). Electrical resistivity measurements are made using electrodes implanted in boreholes driven into the soil and/or at the ground surface. The measurements are repeated as some process changes the temperatures of the soil mass/rock mass. Tomographs of electrical resistivity are calculated based on the measurements using Poisson's equation. Changes in the soil/rock resistivity can be related to changes in soil/rock temperatures when: (1) the electrical conductivity of the fluid trapped in the soil's pore space is low, (2) the soil/rock has a high cation exchange capacity and (3) the temperature changes are sufficiently high. When these three conditions exist the resistivity changes observed in the ERT tomographs can be directly attributed to changes in soil/rock temperatures. This method provides a way of mapping temperature changes in subsurface soils remotely. Distances over which the ERT method can be used to monitor changes in soil temperature range from tens to hundreds of meters from the electrode locations. 1 fig.

Using electrical resistance tomography to map subsurface temperatures

DOEpatents

Ramirez, Abelardo L.; Chesnut, Dwayne A.; Daily, William D.

1994-01-01

A method is provided for measuring subsurface soil or rock temperatures remotely using electrical resistivity tomography (ERT). Electrical resistivity measurements are made using electrodes implanted in boreholes driven into the soil and/or at the ground surface. The measurements are repeated as some process changes the temperatures of the soil mass/rock mass. Tomographs of electrical resistivity are calculated based on the measurements using Poisson's equation. Changes in the soil/rock resistivity can be related to changes in soil/rock temperatures when: (1) the electrical conductivity of the fluid trapped in the soil's pore space is low, (2) the soil/rock has a high cation exchange capacity and (3) the temperature changes are sufficiently high. When these three conditions exist the resistivity changes observed in the ERT tomographs can be directly attributed to changes in soil/rock temperatures. This method provides a way of mapping temperature changes in subsurface soils remotely. Distances over which the ERT method can be used to monitor changes in soil temperature range from tens to hundreds of meters from the electrode locations.

Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields

NASA Astrophysics Data System (ADS)

Xu, Yiming; Smith, Scot E.; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P.

2017-01-01

Soil prediction models based on spectral indices from some multispectral images are too coarse to characterize spatial pattern of soil properties in small and heterogeneous agricultural lands. Image pan-sharpening has seldom been utilized in Digital Soil Mapping research before. This research aimed to analyze the effects of pan-sharpened (PAN) remote sensing spectral indices on soil prediction models in smallholder farm settings. This research fused the panchromatic band and multispectral (MS) bands of WorldView-2, GeoEye-1, and Landsat 8 images in a village in Southern India by Brovey, Gram-Schmidt and Intensity-Hue-Saturation methods. Random Forest was utilized to develop soil total nitrogen (TN) and soil exchangeable potassium (Kex) prediction models by incorporating multiple spectral indices from the PAN and MS images. Overall, our results showed that PAN remote sensing spectral indices have similar spectral characteristics with soil TN and Kex as MS remote sensing spectral indices. There is no soil prediction model incorporating the specific type of pan-sharpened spectral indices always had the strongest prediction capability of soil TN and Kex. The incorporation of pan-sharpened remote sensing spectral data not only increased the spatial resolution of the soil prediction maps, but also enhanced the prediction accuracy of soil prediction models. Small farms with limited footprint, fragmented ownership and diverse crop cycle should benefit greatly from the pan-sharpened high spatial resolution imagery for soil property mapping. Our results show that multiple high and medium resolution images can be used to map soil properties suggesting the possibility of an improvement in the maps' update frequency. Additionally, the results should benefit the large agricultural community through the reduction of routine soil sampling cost and improved prediction accuracy.

Hyperspectral Soil Mapper (HYSOMA) software interface: Review and future plans

NASA Astrophysics Data System (ADS)

Chabrillat, Sabine; Guillaso, Stephane; Eisele, Andreas; Rogass, Christian

2014-05-01

With the upcoming launch of the next generation of hyperspectral satellites that will routinely deliver high spectral resolution images for the entire globe (e.g. EnMAP, HISUI, HyspIRI, HypXIM, PRISMA), an increasing demand for the availability/accessibility of hyperspectral soil products is coming from the geoscience community. Indeed, many robust methods for the prediction of soil properties based on imaging spectroscopy already exist and have been successfully used for a wide range of soil mapping airborne applications. Nevertheless, these methods require expert know-how and fine-tuning, which makes them used sparingly. More developments are needed toward easy-to-access soil toolboxes as a major step toward the operational use of hyperspectral soil products for Earth's surface processes monitoring and modelling, to allow non-experienced users to obtain new information based on non-expensive software packages where repeatability of the results is an important prerequisite. In this frame, based on the EU-FP7 EUFAR (European Facility for Airborne Research) project and EnMAP satellite science program, higher performing soil algorithms were developed at the GFZ German Research Center for Geosciences as demonstrators for end-to-end processing chains with harmonized quality measures. The algorithms were built-in into the HYSOMA (Hyperspectral SOil MApper) software interface, providing an experimental platform for soil mapping applications of hyperspectral imagery that gives the choice of multiple algorithms for each soil parameter. The software interface focuses on fully automatic generation of semi-quantitative soil maps such as soil moisture, soil organic matter, iron oxide, clay content, and carbonate content. Additionally, a field calibration option calculates fully quantitative soil maps provided ground truth soil data are available. Implemented soil algorithms have been tested and validated using extensive in-situ ground truth data sets. The source of the HYSOMA code was developed as standalone IDL software to allow easy implementation in the hyperspectral and non-hyperspectral communities. Indeed, within the hyperspectral community, IDL language is very widely used, and for non-expert users that do not have an ENVI license, such software can be executed as a binary version using the free IDL virtual machine under various operating systems. Based on the growing interest of users in the software interface, the experimental software was adapted for public release version in 2012, and since then ~80 users of hyperspectral soil products downloaded the soil algorithms at www.gfz-potsdam.de/hysoma. The software interface was distributed for free as IDL plug-ins under the IDL-virtual machine. Up-to-now distribution of HYSOMA was based on a close source license model, for non-commercial and educational purposes. Currently, the HYSOMA is being under further development in the context of the EnMAP satellite mission, for extension and implementation in the EnMAP Box as EnSoMAP (EnMAP SOil MAPper). The EnMAP Box is a freely available, platform-independent software distributed under an open source license. In the presentation we will focus on an update of the HYSOMA software interface status and upcoming implementation in the EnMAP Box. Scientific software validation, associated publication record and users responses as well as software management and transition to open source will be discussed.

Measuring spatial variability in soil characteristics

DOEpatents

Hoskinson, Reed L.; Svoboda, John M.; Sawyer, J. Wayne; Hess, John R.; Hess, J. Richard

2002-01-01

The present invention provides systems and methods for measuring a load force associated with pulling a farm implement through soil that is used to generate a spatially variable map that represents the spatial variability of the physical characteristics of the soil. An instrumented hitch pin configured to measure a load force is provided that measures the load force generated by a farm implement when the farm implement is connected with a tractor and pulled through or across soil. Each time a load force is measured, a global positioning system identifies the location of the measurement. This data is stored and analyzed to generate a spatially variable map of the soil. This map is representative of the physical characteristics of the soil, which are inferred from the magnitude of the load force.

Three-Dimensional Mapping of Soil Chemical Characteristics at Micrometric Scale by Combining 2D SEM-EDX Data and 3D X-Ray CT Images.

PubMed

Hapca, Simona; Baveye, Philippe C; Wilson, Clare; Lark, Richard Murray; Otten, Wilfred

2015-01-01

There is currently a significant need to improve our understanding of the factors that control a number of critical soil processes by integrating physical, chemical and biological measurements on soils at microscopic scales to help produce 3D maps of the related properties. Because of technological limitations, most chemical and biological measurements can be carried out only on exposed soil surfaces or 2-dimensional cuts through soil samples. Methods need to be developed to produce 3D maps of soil properties based on spatial sequences of 2D maps. In this general context, the objective of the research described here was to develop a method to generate 3D maps of soil chemical properties at the microscale by combining 2D SEM-EDX data with 3D X-ray computed tomography images. A statistical approach using the regression tree method and ordinary kriging applied to the residuals was developed and applied to predict the 3D spatial distribution of carbon, silicon, iron, and oxygen at the microscale. The spatial correlation between the X-ray grayscale intensities and the chemical maps made it possible to use a regression-tree model as an initial step to predict the 3D chemical composition. For chemical elements, e.g., iron, that are sparsely distributed in a soil sample, the regression-tree model provides a good prediction, explaining as much as 90% of the variability in some of the data. However, for chemical elements that are more homogenously distributed, such as carbon, silicon, or oxygen, the additional kriging of the regression tree residuals improved significantly the prediction with an increase in the R2 value from 0.221 to 0.324 for carbon, 0.312 to 0.423 for silicon, and 0.218 to 0.374 for oxygen, respectively. The present research develops for the first time an integrated experimental and theoretical framework, which combines geostatistical methods with imaging techniques to unveil the 3-D chemical structure of soil at very fine scales. The methodology presented in this study can be easily adapted and applied to other types of data such as bacterial or fungal population densities for the 3D characterization of microbial distribution.

Three-Dimensional Mapping of Soil Chemical Characteristics at Micrometric Scale by Combining 2D SEM-EDX Data and 3D X-Ray CT Images

PubMed Central

Hapca, Simona; Baveye, Philippe C.; Wilson, Clare; Lark, Richard Murray; Otten, Wilfred

2015-01-01

There is currently a significant need to improve our understanding of the factors that control a number of critical soil processes by integrating physical, chemical and biological measurements on soils at microscopic scales to help produce 3D maps of the related properties. Because of technological limitations, most chemical and biological measurements can be carried out only on exposed soil surfaces or 2-dimensional cuts through soil samples. Methods need to be developed to produce 3D maps of soil properties based on spatial sequences of 2D maps. In this general context, the objective of the research described here was to develop a method to generate 3D maps of soil chemical properties at the microscale by combining 2D SEM-EDX data with 3D X-ray computed tomography images. A statistical approach using the regression tree method and ordinary kriging applied to the residuals was developed and applied to predict the 3D spatial distribution of carbon, silicon, iron, and oxygen at the microscale. The spatial correlation between the X-ray grayscale intensities and the chemical maps made it possible to use a regression-tree model as an initial step to predict the 3D chemical composition. For chemical elements, e.g., iron, that are sparsely distributed in a soil sample, the regression-tree model provides a good prediction, explaining as much as 90% of the variability in some of the data. However, for chemical elements that are more homogenously distributed, such as carbon, silicon, or oxygen, the additional kriging of the regression tree residuals improved significantly the prediction with an increase in the R2 value from 0.221 to 0.324 for carbon, 0.312 to 0.423 for silicon, and 0.218 to 0.374 for oxygen, respectively. The present research develops for the first time an integrated experimental and theoretical framework, which combines geostatistical methods with imaging techniques to unveil the 3-D chemical structure of soil at very fine scales. The methodology presented in this study can be easily adapted and applied to other types of data such as bacterial or fungal population densities for the 3D characterization of microbial distribution. PMID:26372473

SOIL RADON POTENTIAL MAPPLING OF TWELVE COUNTIES IN NORTH-CENTRAL FLORIDA

EPA Science Inventory

The report describes the approach, methods, and detailed data used to prepare soil radon potential maps of 12 counties in North-Central Florida. he maps were developed under the Florida Radon Research Program to provide a scientific basis for implementing radon-protective buildin...

Evaluation of digital soil mapping approaches with large sets of environmental covariates

NASA Astrophysics Data System (ADS)

Nussbaum, Madlene; Spiess, Kay; Baltensweiler, Andri; Grob, Urs; Keller, Armin; Greiner, Lucie; Schaepman, Michael E.; Papritz, Andreas

2018-01-01

The spatial assessment of soil functions requires maps of basic soil properties. Unfortunately, these are either missing for many regions or are not available at the desired spatial resolution or down to the required soil depth. The field-based generation of large soil datasets and conventional soil maps remains costly. Meanwhile, legacy soil data and comprehensive sets of spatial environmental data are available for many regions. Digital soil mapping (DSM) approaches relating soil data (responses) to environmental data (covariates) face the challenge of building statistical models from large sets of covariates originating, for example, from airborne imaging spectroscopy or multi-scale terrain analysis. We evaluated six approaches for DSM in three study regions in Switzerland (Berne, Greifensee, ZH forest) by mapping the effective soil depth available to plants (SD), pH, soil organic matter (SOM), effective cation exchange capacity (ECEC), clay, silt, gravel content and fine fraction bulk density for four soil depths (totalling 48 responses). Models were built from 300-500 environmental covariates by selecting linear models through (1) grouped lasso and (2) an ad hoc stepwise procedure for robust external-drift kriging (georob). For (3) geoadditive models we selected penalized smoothing spline terms by component-wise gradient boosting (geoGAM). We further used two tree-based methods: (4) boosted regression trees (BRTs) and (5) random forest (RF). Lastly, we computed (6) weighted model averages (MAs) from the predictions obtained from methods 1-5. Lasso, georob and geoGAM successfully selected strongly reduced sets of covariates (subsets of 3-6 % of all covariates). Differences in predictive performance, tested on independent validation data, were mostly small and did not reveal a single best method for 48 responses. Nevertheless, RF was often the best among methods 1-5 (28 of 48 responses), but was outcompeted by MA for 14 of these 28 responses. RF tended to over-fit the data. The performance of BRT was slightly worse than RF. GeoGAM performed poorly on some responses and was the best only for 7 of 48 responses. The prediction accuracy of lasso was intermediate. All models generally had small bias. Only the computationally very efficient lasso had slightly larger bias because it tended to under-fit the data. Summarizing, although differences were small, the frequencies of the best and worst performance clearly favoured RF if a single method is applied and MA if multiple prediction models can be developed.

Soil mapping and process modeling for sustainable land use management: a brief historical review

NASA Astrophysics Data System (ADS)

Brevik, Eric C.; Pereira, Paulo; Muñoz-Rojas, Miriam; Miller, Bradley A.; Cerdà, Artemi; Parras-Alcántara, Luis; Lozano-García, Beatriz

2017-04-01

Basic soil management goes back to the earliest days of agricultural practices, approximately 9,000 BCE. Through time humans developed soil management techniques of ever increasing complexity, including plows, contour tillage, terracing, and irrigation. Spatial soil patterns were being recognized as early as 3,000 BCE, but the first soil maps didn't appear until the 1700s and the first soil models finally arrived in the 1880s (Brevik et al., in press). The beginning of the 20th century saw an increase in standardization in many soil science methods and wide-spread soil mapping in many parts of the world, particularly in developed countries. However, the classification systems used, mapping scale, and national coverage varied considerably from country to country. Major advances were made in pedologic modeling starting in the 1940s, and in erosion modeling starting in the 1950s. In the 1970s and 1980s advances in computing power, remote and proximal sensing, geographic information systems (GIS), global positioning systems (GPS), and statistics and spatial statistics among other numerical techniques significantly enhanced our ability to map and model soils (Brevik et al., 2016). These types of advances positioned soil science to make meaningful contributions to sustainable land use management as we moved into the 21st century. References Brevik, E., Pereira, P., Muñoz-Rojas, M., Miller, B., Cerda, A., Parras-Alcantara, L., Lozano-Garcia, B. Historical perspectives on soil mapping and process modelling for sustainable land use management. In: Pereira, P., Brevik, E., Muñoz-Rojas, M., Miller, B. (eds) Soil mapping and process modelling for sustainable land use management (In press). Brevik, E., Calzolari, C., Miller, B., Pereira, P., Kabala, C., Baumgarten, A., Jordán, A. 2016. Historical perspectives and future needs in soil mapping, classification and pedological modelling, Geoderma, 264, Part B, 256-274.

Mapping Soil Salinity/Sodicity by using Landsat OLI Imagery and PLSR Algorithm over Semiarid West Jilin Province, China

PubMed Central

Liu, Mingyue; Du, Baojia; Zhang, Bai

2018-01-01

Soil salinity and sodicity can significantly reduce the value and the productivity of affected lands, posing degradation, and threats to sustainable development of natural resources on earth. This research attempted to map soil salinity/sodicity via disentangling the relationships between Landsat 8 Operational Land Imager (OLI) imagery and in-situ measurements (EC, pH) over the west Jilin of China. We established the retrieval models for soil salinity and sodicity using Partial Least Square Regression (PLSR). Spatial distribution of the soils that were subjected to hybridized salinity and sodicity (HSS) was obtained by overlay analysis using maps of soil salinity and sodicity in geographical information system (GIS) environment. We analyzed the severity and occurring sizes of soil salinity, sodicity, and HSS with regard to specified soil types and land cover. Results indicated that the models’ accuracy was improved by combining the reflectance bands and spectral indices that were mathematically transformed. Therefore, our results stipulated that the OLI imagery and PLSR method applied to mapping soil salinity and sodicity in the region. The mapping results revealed that the areas of soil salinity, sodicity, and HSS were 1.61 × 106 hm2, 1.46 × 106 hm2, and 1.36 × 106 hm2, respectively. Also, the occurring area of moderate and intensive sodicity was larger than that of salinity. This research may underpin efficiently mapping regional salinity/sodicity occurrences, understanding the linkages between spectral reflectance and ground measurements of soil salinity and sodicity, and provide tools for soil salinity monitoring and the sustainable utilization of land resources. PMID:29614727

Mapping soil particle-size fractions: A comparison of compositional kriging and log-ratio kriging

NASA Astrophysics Data System (ADS)

Wang, Zong; Shi, Wenjiao

2017-03-01

Soil particle-size fractions (psf) as basic physical variables need to be accurately predicted for regional hydrological, ecological, geological, agricultural and environmental studies frequently. Some methods had been proposed to interpolate the spatial distributions of soil psf, but the performance of compositional kriging and different log-ratio kriging methods is still unclear. Four log-ratio transformations, including additive log-ratio (alr), centered log-ratio (clr), isometric log-ratio (ilr), and symmetry log-ratio (slr), combined with ordinary kriging (log-ratio kriging: alr_OK, clr_OK, ilr_OK and slr_OK) were selected to be compared with compositional kriging (CK) for the spatial prediction of soil psf in Tianlaochi of Heihe River Basin, China. Root mean squared error (RMSE), Aitchison's distance (AD), standardized residual sum of squares (STRESS) and right ratio of the predicted soil texture types (RR) were chosen to evaluate the accuracy for different interpolators. The results showed that CK had a better accuracy than the four log-ratio kriging methods. The RMSE (sand, 9.27%; silt, 7.67%; clay, 4.17%), AD (0.45), STRESS (0.60) of CK were the lowest and the RR (58.65%) was the highest in the five interpolators. The clr_OK achieved relatively better performance than the other log-ratio kriging methods. In addition, CK presented reasonable and smooth transition on mapping soil psf according to the environmental factors. The study gives insights for mapping soil psf accurately by comparing different methods for compositional data interpolation. Further researches of methods combined with ancillary variables are needed to be implemented to improve the interpolation performance.

Regional mapping of soil parent material by machine learning based on point data

NASA Astrophysics Data System (ADS)

Lacoste, Marine; Lemercier, Blandine; Walter, Christian

2011-10-01

A machine learning system (MART) has been used to predict soil parent material (SPM) at the regional scale with a 50-m resolution. The use of point-specific soil observations as training data was tested as a replacement for the soil maps introduced in previous studies, with the aim of generating a more even distribution of training data over the study area and reducing information uncertainty. The 27,020-km 2 study area (Brittany, northwestern France) contains mainly metamorphic, igneous and sedimentary substrates. However, superficial deposits (aeolian loam, colluvial and alluvial deposits) very often represent the actual SPM and are typically under-represented in existing geological maps. In order to calibrate the predictive model, a total of 4920 point soil descriptions were used as training data along with 17 environmental predictors (terrain attributes derived from a 50-m DEM, as well as emissions of K, Th and U obtained by means of airborne gamma-ray spectrometry, geological variables at the 1:250,000 scale and land use maps obtained by remote sensing). Model predictions were then compared: i) during SPM model creation to point data not used in model calibration (internal validation), ii) to the entire point dataset (point validation), and iii) to existing detailed soil maps (external validation). The internal, point and external validation accuracy rates were 56%, 81% and 54%, respectively. Aeolian loam was one of the three most closely predicted substrates. Poor prediction results were associated with uncommon materials and areas with high geological complexity, i.e. areas where existing maps used for external validation were also imprecise. The resultant predictive map turned out to be more accurate than existing geological maps and moreover indicated surface deposits whose spatial coverage is consistent with actual knowledge of the area. This method proves quite useful in predicting SPM within areas where conventional mapping techniques might be too costly or lengthy or where soil maps are insufficient for use as training data. In addition, this method allows producing repeatable and interpretable results, whose accuracy can be assessed objectively.

Cokriging of Electromagnetic Induction Soil Electrical Conductivity Measurements and Soil Textural Properties to Demarcate Sub-field Management Zones for Precision Irrigation.

NASA Astrophysics Data System (ADS)

Ding, R.; Cruz, L.; Whitney, J.; Telenko, D.; Oware, E. K.

2017-12-01

There is the growing need for the development of efficient irrigation management practices due to increasing irrigation water scarcity as a result of growing population and changing climate. Soil texture primarily controls the water-holding capacity of soils, which determines the amount of irrigation water that will be available to the plant. However, while there are significant variabilities in the textural properties of the soil across a field, conventional irrigation practices ignore the underlying variability in the soil properties, resulting in over- or under-irrigation. Over-irrigation leaches plant nutrients beyond the root-zone leading to fertilizer, energy, and water wastages with dire environmental consequences. Under-irrigation, in contrast, causes water stress of the plant, thereby reducing plant quality and yield. The goal of this project is to leverage soil textural map of a field to create water management zones (MZs) to guide site-specific precision irrigation. There is increasing application of electromagnetic induction methods to rapidly and inexpensively map spatially continuous soil properties in terms of the apparent electrical conductivity (ECa) of the soil. ECa is a measure of the bulk soil properties, including soil texture, moisture, salinity, and cation exchange capacity, making an ECa map a pseudo-soil map. Data for the project were collected from a farm site at Eden, NY. The objective is to leverage high-resolution ECa map to predict spatially dense soil textural properties from limited measurements of soil texture. Thus, after performing ECa mapping, we conducted particle-size analysis of soil samples to determine the textural properties of soils at selected locations across the field. We cokriged the high-resolution ECa measurements with the sparse soil textural data to estimate a soil texture map for the field. We conducted irrigation experiments at selected locations to calibrate representative water-holding capacities of each estimated soil textural unit. Estimated soil units with similar water-holding characteristics were merged to create sub-field water MZs to guide precision irrigation of each MZ, instructed by each MZ's calibrated water-holding properties.

Mapping of soil erosion and redistribution on two agricultural areas in Czech Republic by using of magnetic parameters.

NASA Astrophysics Data System (ADS)

Kapicka, Ales; Stejskalova, Sarka; Grison, Hana; Petrovsky, Eduard; Jaksik, Ondrej; Kodesova, Radka

2015-04-01

Soil erosion is one of the major concerns in sustainability of agricultural systems in different areas. Therefore there is a need to develop suitable innovative indirect methods of soil survey. One of this methods is based on well established differentiation in magnetic signature with depth in soil profile. Magnetic method can be applied in the field as well as in the laboratory on collected soil samples. The aim of this study is to evaluate suitability of magnetic method to assess soil degradation and construct maps of cumulative soil loss due to erosion at two morphologically diverse areas with different soil types. Dominant soil unit in the first locality (Brumovice) is chernozem, which is gradually degraded on slopes to regosols. In the second site (Vidim), the dominant soil unit is luvisol, gradualy transformed to regosol due to erosion. Field measurements of magnetic susceptibility were carried out on regular grid, resulting in 101 data points in Brumovice and 65 in Vidim locality. Mass specific magnetic susceptibility χ and its frequency dependence χFD was used to estimate the significance of SP ferrimagnetic particles of pedogenic origin in top soil horizons. Strong correlation was found between the volume magnetic susceptibility (field measurement) and mass- specific magnetic susceptibility measured in the laboratory (Kapicka et al 2013). Values of magnetic susceptibility are spatially distributed depending on terrain position. Higher values were measured at the flat parts (where the original topsoil horizon remained). The lowest values magnetic susceptibility were obtained on the steep valley sides. Here the original topsoil was eroded and mixed by tillage with the soil substrate (loess). Positive correlation between the organic carbon content and volume magnetic susceptibility (R2= 0.89) was found for chernozem area. The differences between the values of susceptibility in the undisturbed soil profile and the magnetic signal after uniform mixing of the soil material as a result of tillage and erosion are fundamental for the estimation of soil loss in the studied test field (Royall 2001). The map of soil erosion shows maximum removal of soil material in the steepest parts of the testing localities. The magnetic method is very well suitable for mapping at the chernozem locality (Brumovice) and measurement of soil magnetic susceptibility is in this case a useful and fast technique for quantitative estimation of soil loss caused by erosion and tillage. However, it is less suitable (probably due to high terrain heterogeneity) for mapping in areas with luvisol as dominant soil unit. Acknowledgement: This study was supported by NAZV Agency of the Ministry of Agriculture of the Czech Republic through grant No QJ1230319. References : Royall, D. (2001). Use of mineral magnetic measurements to investigate soil erosion and sediment delivery in small agricultural catchment in limestone terrain. Catena, 46, 15-34. Kapicka, A., Dlouha, S., Grison, H., Jaksik, O., Kodesova, R., Petrovsky, E. (2013) Magnetism of soils applied for estimation of erosion at an agricultural land. Geophys Res Abstr Vol. 15, EGU2013 -4774.

Geomorphically based predictive mapping of soil thickness in upland watersheds

NASA Astrophysics Data System (ADS)

Pelletier, Jon D.; Rasmussen, Craig

2009-09-01

The hydrologic response of upland watersheds is strongly controlled by soil (regolith) thickness. Despite the need to quantify soil thickness for input into hydrologic models, there is currently no widely used, geomorphically based method for doing so. In this paper we describe and illustrate a new method for predictive mapping of soil thicknesses using high-resolution topographic data, numerical modeling, and field-based calibration. The model framework works directly with input digital elevation model data to predict soil thicknesses assuming a long-term balance between soil production and erosion. Erosion rates in the model are quantified using one of three geomorphically based sediment transport models: nonlinear slope-dependent transport, nonlinear area- and slope-dependent transport, and nonlinear depth- and slope-dependent transport. The model balances soil production and erosion locally to predict a family of solutions corresponding to a range of values of two unconstrained model parameters. A small number of field-based soil thickness measurements can then be used to calibrate the local value of those unconstrained parameters, thereby constraining which solution is applicable at a particular study site. As an illustration, the model is used to predictively map soil thicknesses in two small, ˜0.1 km2, drainage basins in the Marshall Gulch watershed, a semiarid drainage basin in the Santa Catalina Mountains of Pima County, Arizona. Field observations and calibration data indicate that the nonlinear depth- and slope-dependent sediment transport model is the most appropriate transport model for this site. The resulting framework provides a generally applicable, geomorphically based tool for predictive mapping of soil thickness using high-resolution topographic data sets.

Fate of 1-(1',4'-cyclohexadienyl)-2-methylaminopropane (CMP) in soil: route-specific by-product in the clandestine manufacture of methamphetamine.

PubMed

Pal, Raktim; Megharaj, Mallavarapu; Kirkbride, K Paul; Naidu, Ravi

2012-02-01

We investigated the fate of 1-(1',4'-cyclohexadienyl)-2-methylaminopropane (CMP) in soil. CMP is the major route-specific byproduct in the clandestine manufacture of methamphetamine (MAP) by the use of excess alkali metal (e.g., lithium) in liquid ammonia, which is commonly referred to as the "Nazi method". This is one of the most common methods used in many countries for the illicit production of MAP. Knowledge on the fate of CMP in the terrestrial environment is essential to combat potential threats arising from illegal dumping of clandestine laboratory wastes. We report on the sorption-desorption, degradation, and metabolism patterns of CMP in three South Australian soils investigated in laboratory scale. CMP sorption in the test soils followed a Freundlich isotherm in the concentration range of 5 to 100μgmL(-1). Degradation studies showed that CMP was fairly unstable in both non-sterile and sterile soils, with half-life values typically less than one week. The role of biotic and abiotic soil processes in the degradation of CMP also varied significantly between the different soils, and with the length of the incubation period. Interestingly, but not surprisingly, the results showed that the CMP was not actually degraded to any simpler compounds but transformed to more persistent MAP. Thus, the main concern with Nazi method is the potential hazard from MAP rather than CMP if wastes are disposed of into the environment. Copyright © 2011 Elsevier B.V. All rights reserved.

Digital mapping of soil properties in Zala County, Hungary for the support of county-level spatial planning and land management

NASA Astrophysics Data System (ADS)

Pásztor, László; Laborczi, Annamária; Szatmári, Gábor; Fodor, Nándor; Bakacsi, Zsófia; Szabó, József; Illés, Gábor

2014-05-01

The main objective of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project is to significantly extend the potential, how demands on spatial soil related information could be satisfied in Hungary. Although a great amount of soil information is available due to former mappings and surveys, there are more and more frequently emerging discrepancies between the available and the expected data. The gaps are planned to be filled with optimized DSM products heavily based on legacy soil data, which still represent a valuable treasure of soil information at the present time. Impact assessment of the forecasted climate change and the analysis of the possibilities of the adaptation in the agriculture and forestry can be supported by scenario based land management modelling, whose results can be incorporated in spatial planning. This framework requires adequate, preferably timely and spatially detailed knowledge of the soil cover. For the satisfaction of these demands in Zala County (one of the nineteen counties of Hungary), the soil conditions of the agricultural areas were digitally mapped based on the most detailed, available recent and legacy soil data. The agri-environmental conditions were characterized according to the 1:10,000 scale genetic soil mapping methodology and the category system applied in the Hungarian soil-agricultural chemistry practice. The factors constraining the fertility of soils were featured according to the biophysical criteria system elaborated for the delimitation of naturally handicapped areas in the EU. Production related soil functions were regionalized incorporating agro-meteorological modelling. The appropriate derivatives of a 20m digital elevation model were used in the analysis. Multitemporal MODIS products were selected from the period of 2009-2011 representing different parts of the growing season and years with various climatic conditions. Additionally two climatic data layers, the 1:100.000 Geological Map of Hungary and the map of groundwater depth were used as auxiliary environmental covariables. Various soil related information were mapped in three distinct sets: (i) basic soil properties determining agri-environmental conditions (soil type according to the Hungarian genetic classification, rootable depth, sand and clay content for the 1st and 2nd soil layers, pH, OM and carbonate content for the plough layer); (ii) biophysical criteria of natural handicaps defined by common European system and (iii) agro-meteorologically modelled yield values for different crops, meteorological and management scenarios. The applied method(s) for the spatial inference of specific themes was/were suitably selected: regression and classification trees for categorical data, indicator kriging for probabilistic management of criterion information; and typically regression kriging for quantitative data. Our paper will present the mapping processes themselves, the resulted maps and some conclusions drawn from the experiences. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167) and by the European Union with the co-financing of the European Social Fund (TÁMOP-4.2.2.A-11/1/KONV-2012-0013.).

Application of Thermal Infrared Remote Sensing for Quantitative Evaluation of Crop Characteristics

NASA Technical Reports Server (NTRS)

Shaw, J.; Luvall, J.; Rickman, D.; Mask, P.; Wersinger, J.; Sullivan, D.; Arnold, James E. (Technical Monitor)

2002-01-01

Evidence suggests that thermal infrared emittance (TIR) at the field-scale is largely a function of the integrated crop/soil moisture continuum. Because soil moisture dynamics largely determine crop yields in non-irrigated farming (85 % of Alabama farms are non-irrigated), TIR may be an effective method of mapping within field crop yield variability, and possibly, absolute yields. The ability to map yield variability at juvenile growth stages can lead to improved soil fertility and pest management, as well as facilitating the development of economic forecasting. Researchers at GHCC/MSFC/NASA and Auburn University are currently investigating the role of TIR in site-specific agriculture. Site-specific agriculture (SSA), or precision farming, is a method of crop production in which zones and soils within a field are delineated and managed according to their unique properties. The goal of SSA is to improve farm profits and reduce environmental impacts through targeted agrochemical applications. The foundation of SSA depends upon the spatial and temporal characterization of soil and crop properties through the creation of management zones. Management zones can be delineated using: 1) remote sensing (RS) data, 2) conventional soil testing and soil mapping, and 3) yield mapping. Portions of this research have concentrated on using remote sensing data to map yield variability in corn (Zea mays L.) and soybean (Glycine max L.) crops. Remote sensing data have been collected for several fields in the Tennessee Valley region at various crop growth stages during the last four growing seasons. Preliminary results of this study will be presented.

Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran.

PubMed

Mahmoudabadi, Ebrahim; Karimi, Alireza; Haghnia, Gholam Hosain; Sepehr, Adel

2017-09-11

Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.

A method for testing land resource area concepts

USDA-ARS?s Scientific Manuscript database

Land Resource Units (LRUs) are defined by the National Soil Survey Handbook as aggregations of soil map units and subunits of Major Land Resource Areas (MLRAs). In the USDA NRCS Land Resource Hierarchy, LRUs are defined as the level between MLRAs and STATSGO and are mapped at 1:1 million scale. They...

Maps of averaged spectral deviations from soil lines and their comparison with traditional soil maps

NASA Astrophysics Data System (ADS)

Rukhovich, D. I.; Rukhovich, A. D.; Rukhovich, D. D.; Simakova, M. S.; Kulyanitsa, A. L.; Bryzzhev, A. V.; Koroleva, P. V.

2016-07-01

The analysis of 34 cloudless fragments of Landsat 5, 7, and 8 images (1985-2014) on the territory of Plavsk, Arsen'evsk, and Chern districts of Tula oblast has been performed. It is shown that bare soil surface on the RED-NIR plots derived from the images cannot be described in the form of a sector of spectral plane as it can be done for the NDVI values. The notion of spectral neighborhood of soil line (SNSL) is suggested. It is defined as the sum of points of the RED-NIR spectral space, which are characterized by spectral characteristics of the bare soil applied for constructing soil lines. The way of the SNSL separation along the line of the lowest concentration density of points on the RED-NIR spectral space is suggested. This line separates bare soil surface from vegetating plants. The SNSL has been applied to construct soil line (SL) for each of the 34 images and to delineate bare soil surface on them. Distances from the points with averaged RED-NIR coordinates to the SL have been calculated using the method of moving window. These distances can be referred to as averaged spectral deviations (ASDs). The calculations have been performed strictly for the SNSL areas. As a result, 34 maps of ASDs have been created. These maps contain ASD values for 6036 points of a grid used in the study. Then, the integral map of normalized ASD values has been built with due account for the number of points participating in the calculation (i.e., lying in the SNSL) within the moving window. The integral map of ASD values has been compared with four traditional soil maps on the studied territory. It is shown that this integral map can be interpreted in terms of soil taxa: the areas of seven soil subtypes (soddy moderately podzolic, soddy slightly podzolic, light gray forest. gray forest, dark gray forest, podzolized chernozems, and leached chernozems) belonging to three soil types (soddy-podzolic, gray forest, and chernozemic soils) can be delineated on it.

VARIABLE RATE APPLICATION OF SOIL HERBICIDES IN ARABLE CROPS: FROM THEORY TO PRACTICE.

PubMed

Heijting, S; Kempenaar, C

2014-01-01

Soil herbicides are applied around crop emergence and kill germinating weeds in the surface layer of the soil. These herbicides play an important role in the chemical management of weeds in major arable crops. From an environmental point of view there is a clear need for smarter application of these chemicals. This paper presents research done in The Netherlands on Variable Rate Application (VRA) of soil herbicides by taking into account spatial variation of the soil. Herbicides adsorbed to soil parameters such as clay or organic matter are not available for herbicidal activity. Decision Support Rules (DSR) describe the relation between the soil parameter and herbicide dosage needed for effectively controlling weeds. Research methods such as greenhouse trials, models and on farm research to develop DSR are discussed and results are presented. Another important ingredient for VRA of soil herbicides is an accurate soil map of the field. Sampling and subsequent interpolation is costly. Soil scans measuring a proxy that is subsequently translated into soil properties such as clay fraction and soil organic matter content offer a quicker way to achieve such maps but validation is needed. DSR is applied to the soil map to get the variable dosage map. The farmer combines this map with the routing, spray volume and spray boom width in the Farm Management Information System (FMIS), resulting in a task file. This task file can subsequently be read by the board computer resulting in a VRA spray map. Reduction in soil herbicide depends on the DSR, the spatial variation and pattern of the soil, the spatial configuration of the routing and the technical advances of the spray equipment. Recently, within the framework the Programma Precisie Landbouw, first steps were made to test and implement this in practice. Currently, theory and practice of VRA of soil herbicides is developed within the research program IJKakker in close cooperation with pioneering farmers in The Netherlands.

Combining local scaling and global methods to detect soil pore space

NASA Astrophysics Data System (ADS)

Martin-Sotoca, Juan Jose; Saa-Requejo, Antonio; Grau, Juan B.; Tarquis, Ana M.

2017-04-01

The characterization of the spatial distribution of soil pore structures is essential to obtain different parameters that will influence in several models related to water flow and/or microbial growth processes. The first step in pore structure characterization is obtaining soil images that best approximate reality. Over the last decade, major technological advances in X-ray computed tomography (CT) have allowed for the investigation and reconstruction of natural porous media architectures at very fine scales. The subsequent step is delimiting the pore structure (pore space) from the CT soil images applying a thresholding. Many times we could find CT-scan images that show low contrast at the solid-void interface that difficult this step. Different delimitation methods can result in different spatial distributions of pores influencing the parameters used in the models. Recently, new local segmentation method using local greyscale value (GV) concentration variabilities, based on fractal concepts, has been presented. This method creates singularity maps to measure the GV concentration at each point. The C-A method was combined with the singularity map approach (Singularity-CA method) to define local thresholds that can be applied to binarize CT images. Comparing this method with classical methods, such as Otsu and Maximum Entropy, we observed that more pores can be detected mainly due to its ability to amplify anomalous concentrations. However, it delineated many small pores that were incorrect. In this work, we present an improve version of Singularity-CA method that avoid this problem basically combining it with the global classical methods. References Martín-Sotoca, J.J., A. Saa-Requejo, J.B. Grau, A.M. Tarquis. New segmentation method based on fractal properties using singularity maps. Geoderma, 287, 40-53, 2017. Martín-Sotoca, J.J, A. Saa-Requejo, J.B. Grau, A.M. Tarquis. Local 3D segmentation of soil pore space based on fractal properties using singularity maps. Geoderma, http://dx.doi.org/10.1016/j.geoderma.2016.11.029. Torre, Iván G., Juan C. Losada and A.M. Tarquis. Multiscaling properties of soil images. Biosystems Engineering, http://dx.doi.org/10.1016/j.biosystemseng.2016.11.006.

Evaluating the new soil erosion map of Hungary

NASA Astrophysics Data System (ADS)

Waltner, István; Centeri, Csaba; Takács, Katalin; Pirkó, Béla; Koós, Sándor; László, Péter; Pásztor, László

2017-04-01

With growing concerns on the effects of climate change and land use practices on our soil resources, soil erosion by water is becoming a significant issue internationally. Since the 1964 publication of the first soil erosion map of Hungary, there have been several attempts to provide a countrywide assessment of erosion susceptibility. However, there has been no up-to-date map produced in the last decade. In 2016, a new, 1:100 000 scale soil erosion map was published, based on available soil, elevation, land use and meteorological data for the extremely wet year of 2010. The map utilized combined outputs for two spatially explicit methods: the widely used empirical Universal Soil Loss Equation (USLE) and the process-based Pan-European Soil Erosion Risk Assessment (PESERA) models. The present study aims to provide a detailed analysis of the model results. In lieu of available national monitoring data, information from other sources were used. The Soil Degradation Subsystem (TDR) of the National Environmental Information System (OKIR) is a digital database based on a soil survey and farm dairy data collected from representative farms in Hungary. During the survey all kind of degradation forms - including soil erosion - were considered. Agricultural and demographic data was obtained from the Hungarian Central Statistical Office (KSH). Data from an interview-based survey was also used in an attempt to assess public awareness of soil erosion risks. Point-based evaluation of the model results was complemented with cross-regional assessment of soil erosion estimates. This, combined with available demographic information provides us with an opportunity to address soil erosion on a community level, with the identification of regions with the highest risk of being affected by soil erosion.

Ensemble learning for spatial interpolation of soil potassium content based on environmental information.

PubMed

Liu, Wei; Du, Peijun; Wang, Dongchen

2015-01-01

One important method to obtain the continuous surfaces of soil properties from point samples is spatial interpolation. In this paper, we propose a method that combines ensemble learning with ancillary environmental information for improved interpolation of soil properties (hereafter, EL-SP). First, we calculated the trend value for soil potassium contents at the Qinghai Lake region in China based on measured values. Then, based on soil types, geology types, land use types, and slope data, the remaining residual was simulated with the ensemble learning model. Next, the EL-SP method was applied to interpolate soil potassium contents at the study site. To evaluate the utility of the EL-SP method, we compared its performance with other interpolation methods including universal kriging, inverse distance weighting, ordinary kriging, and ordinary kriging combined geographic information. Results show that EL-SP had a lower mean absolute error and root mean square error than the data produced by the other models tested in this paper. Notably, the EL-SP maps can describe more locally detailed information and more accurate spatial patterns for soil potassium content than the other methods because of the combined use of different types of environmental information; these maps are capable of showing abrupt boundary information for soil potassium content. Furthermore, the EL-SP method not only reduces prediction errors, but it also compliments other environmental information, which makes the spatial interpolation of soil potassium content more reasonable and useful.

The use of crop rotation for mapping soil organic content in farmland

NASA Astrophysics Data System (ADS)

Yang, Lin; Song, Min; Zhu, A.-Xing; Qin, Chengzhi

2017-04-01

Most of the current digital soil mapping uses natural environmental covariates. However, human activities have significantly impacted the development of soil properties since half a century, and therefore become an important factor affecting soil spatial variability. Many researches have done field experiments to show how soil properties are impacted and changed by human activities, however, spatial variation data of human activities as environmental covariates have been rarely used in digital soil mapping. In this paper, we took crop rotation as an example of agricultural activities, and explored its effectiveness in characterizing and mapping the spatial variability of soil. The cultivated area of Xuanzhou city and Langxi County in Anhui Province was chosen as the study area. Three main crop rotations,including double-rice, wheat-rice,and oilseed rape-cotton were observed through field investigation in 2010. The spatial distribution of the three crop rotations in the study area was obtained by multi-phase remote sensing image interpretation using a supervised classification method. One-way analysis of variance (ANOVA) for topsoil organic content in the three crop rotation groups was performed. Factor importance of seven natural environmental covariates, crop rotation, Land use and NDVI were generated by variable importance criterion of Random Forest. Different combinations of environmental covariates were selected according to the importance rankings of environmental covariates for predicting SOC using Random Forest and Soil Landscape Inference Model (SOLIM). A cross validation was generated to evaluated the mapping accuracies. The results showed that there were siginificant differences of topsoil organic content among the three crop rotation groups. The crop rotation is more important than parent material, land use or NDVI according to the importance ranking calculated by Random Forest. In addition, crop rotation improved the mapping accuracy, especially for the flat clutivated area. This study demonstrates the usefulness of human activities in digital soil mapping and thus indicates the necessity for human activity factors in digital soil mapping studies.

Interpolation Approaches for Characterizing Spatial Variability of Soil Properties in Tuz Lake Basin of Turkey

NASA Astrophysics Data System (ADS)

Gorji, Taha; Sertel, Elif; Tanik, Aysegul

2017-12-01

Soil management is an essential concern in protecting soil properties, in enhancing appropriate soil quality for plant growth and agricultural productivity, and in preventing soil erosion. Soil scientists and decision makers require accurate and well-distributed spatially continuous soil data across a region for risk assessment and for effectively monitoring and managing soils. Recently, spatial interpolation approaches have been utilized in various disciplines including soil sciences for analysing, predicting and mapping distribution and surface modelling of environmental factors such as soil properties. The study area selected in this research is Tuz Lake Basin in Turkey bearing ecological and economic importance. Fertile soil plays a significant role in agricultural activities, which is one of the main industries having great impact on economy of the region. Loss of trees and bushes due to intense agricultural activities in some parts of the basin lead to soil erosion. Besides, soil salinization due to both human-induced activities and natural factors has exacerbated its condition regarding agricultural land development. This study aims to compare capability of Local Polynomial Interpolation (LPI) and Radial Basis Functions (RBF) as two interpolation methods for mapping spatial pattern of soil properties including organic matter, phosphorus, lime and boron. Both LPI and RBF methods demonstrated promising results for predicting lime, organic matter, phosphorous and boron. Soil samples collected in the field were used for interpolation analysis in which approximately 80% of data was used for interpolation modelling whereas the remaining for validation of the predicted results. Relationship between validation points and their corresponding estimated values in the same location is examined by conducting linear regression analysis. Eight prediction maps generated from two different interpolation methods for soil organic matter, phosphorus, lime and boron parameters were examined based on R2 and RMSE values. The outcomes indicate that RBF performance in predicting lime, organic matter and boron put forth better results than LPI. However, LPI shows better results for predicting phosphorus.

Creating soil moisture maps based on radar satellite imagery

NASA Astrophysics Data System (ADS)

Hnatushenko, Volodymyr; Garkusha, Igor; Vasyliev, Volodymyr

2017-10-01

The presented work is related to a study of mapping soil moisture basing on radar data from Sentinel-1 and a test of adequacy of the models constructed on the basis of data obtained from alternative sources. Radar signals are reflected from the ground differently, depending on its properties. In radar images obtained, for example, in the C band of the electromagnetic spectrum, soils saturated with moisture usually appear in dark tones. Although, at first glance, the problem of constructing moisture maps basing on radar data seems intuitively clear, its implementation on the basis of the Sentinel-1 data on an industrial scale and in the public domain is not yet available. In the process of mapping, for verification of the results, measurements of soil moisture obtained from logs of the network of climate stations NOAA US Climate Reference Network (USCRN) were used. This network covers almost the entire territory of the United States. The passive microwave radiometers of Aqua and SMAP satellites data are used for comparing processing. In addition, other supplementary cartographic materials were used, such as maps of soil types and ready moisture maps. The paper presents a comparison of the effect of the use of certain methods of roughening the quality of radar data on the result of mapping moisture. Regression models were constructed showing dependence of backscatter coefficient values Sigma0 for calibrated radar data of different spatial resolution obtained at different times on soil moisture values. The obtained soil moisture maps of the territories of research, as well as the conceptual solutions about automation of operations of constructing such digital maps, are presented. The comparative assessment of the time required for processing a given set of radar scenes with the developed tools and with the ESA SNAP product was carried out.

Mapping Soil Organic Carbon Resources Across Agricultural Land Uses in Highland Lesotho Using High Resolution Satellite Imagery

NASA Astrophysics Data System (ADS)

Knight, J.; Adam, E.

2015-12-01

Mapping spatial patterns of soil organic carbon (SOC) using high resolution satellite imagery is especially important in inaccessible or upland areas that have limited field measurements, where land use and land cover (LULC) are changing rapidly, or where the land surface is sensitive to overgrazing and high rates of soil erosion and thus sediment, nutrient and carbon export. Here we outline the methods and results of mapping soil organic carbon in highland areas (~2400 m) of eastern Lesotho, southern Africa, across different land uses. Bedrock summit areas with very thin soils are dominated by xeric alpine grassland; terrace agriculture with strip fields and thicker soils is found within river valleys. Multispectral Worldview 2 imagery was used to map LULC across the region. An overall accuracy of 88% and kappa value of 0.83 were achieved using a support vector machine model. Soils were examined in the field from different LULC areas for properties such as soil depth, maturity and structure. In situ soils in the field were also evaluated using a portable analytical spectral device (ASD) in order to ground truth spectral signatures from Worldview. Soil samples were examined in the lab for chemical properties including organic carbon. Regression modeling was used in order to establish a relationship between soil characteristics and soil spectral reflectance. We were thus able to map SOC across this diverse landscape. Results show that there are notable differences in SOC between upland and agricultural areas which reflect both soil thickness and maturity, and land use practices such as manuring of fields by cattle. Soil erosion and thus carbon (nutrient) export is significant issue in this region, which this project will now be examining.

Using geophysical images of a watershed subsurface to predict soil textural properties

USDA-ARS?s Scientific Manuscript database

Subsurface architecture, in particular changes in soil type across the landscape, is an important control on the hydrological and ecological function of a watershed. Traditional methods of mapping soils involving subjective assignment of soil boundaries are inadequate for studies requiring a quantit...

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS

PubMed Central

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data. PMID:26090852

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

PubMed

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

NASA Astrophysics Data System (ADS)

DY, C. Y.; Fung, J. C. H.

2016-08-01

A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the soil map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the soil moisture improves near-surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

Spatial variability of soil carbon stock in the Urucu river basin, Central Amazon-Brazil.

PubMed

Ceddia, Marcos Bacis; Villela, André Luis Oliveira; Pinheiro, Érika Flávia Machado; Wendroth, Ole

2015-09-01

The Amazon Forest plays a major role in C sequestration and release. However, few regional estimates of soil organic carbon (SOC) stock in this ecoregion exist. One of the barriers to improve SOC estimates is the lack of recent soil data at high spatial resolution, which hampers the application of new methods for mapping SOC stock. The aims of this work were: (i) to quantify SOC stock under undisturbed vegetation for the 0-30 and the 0-100 cm under Amazon Forest; (ii) to correlate the SOC stock with soil mapping units and relief attributes and (iii) to evaluate three geostatistical techniques to generate maps of SOC stock (ordinary, isotopic and heterotopic cokriging). The study site is located in the Central region of Amazon State, Brazil. The soil survey covered the study site that has an area of 80 km(2) and resulted in a 1:10,000 soil map. It consisted of 315 field observations (96 complete soil profiles and 219 boreholes). SOC stock was calculated by summing C stocks by horizon, determined as a product of BD, SOC and the horizon thickness. For each one of the 315 soil observations, relief attributes were derived from a topographic map to understand SOC dynamics. The SOC stocks across 30 and 100 cm soil depth were 3.28 and 7.32 kg C m(-2), respectively, which is, 34 and 16%, lower than other studies. The SOC stock is higher in soils developed in relief forms exhibiting well-drained soils, which are covered by Upland Dense Tropical Rainforest. Only SOC stock in the upper 100 cm exhibited spatial dependence allowing the generation of spatial variability maps based on spatial (co)-regionalization. The CTI was inversely correlated with SOC stock and was the only auxiliary variable feasible to be used in cokriging interpolation. The heterotopic cokriging presented the best performance for mapping SOC stock. Copyright © 2015 Elsevier B.V. All rights reserved.

The status of soil mapping for the Idaho National Engineering Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olson, G.L.; Lee, R.D.; Jeppesen, D.J.

This report discusses the production of a revised version of the general soil map of the 2304-km{sup 2} (890-mi{sup 2}) Idaho National Engineering Laboratory (INEL) site in southeastern Idaho and the production of a geographic information system (GIS) soil map and supporting database. The revised general soil map replaces an INEL soil map produced in 1978 and incorporates the most current information on INEL soils. The general soil map delineates large soil associations based on National Resources Conservation Services [formerly the Soil Conservation Service (SCS)] principles of soil mapping. The GIS map incorporates detailed information that could not be presentedmore » on the general soil map and is linked to a database that contains the soil map unit descriptions, surficial geology codes, and other pertinent information.« less

Mapping polycyclic aromatic hydrocarbon and total toxicity equivalent soil concentrations by visible and near-infrared spectroscopy.

PubMed

Okparanma, Reuben N; Coulon, Frederic; Mayr, Thomas; Mouazen, Abdul M

2014-09-01

In this study, we used data from spectroscopic models based on visible and near-infrared (vis-NIR; 350-2500 nm) diffuse reflectance spectroscopy to develop soil maps of polycyclic aromatic hydrocarbons (PAHs) and total toxicity equivalent concentrations (TTEC) of the PAH mixture. The TTEC maps were then used for hazard assessment of three petroleum release sites in the Niger Delta province of Nigeria (5.317°N, 6.467°E). As the paired t-test revealed, there were non-significant (p > 0.05) differences between soil maps of PAH and TTEC developed with chemically measured and vis-NIR-predicted data. Comparison maps of PAH showed a slight to moderate agreement between measured and predicted data (Kappa coefficient = 0.19-0.56). Using proposed generic assessment criteria, hazard assessment showed that the degree of action for site-specific risk assessment and/or remediation is similar for both measurement methods. This demonstrates that the vis-NIR method may be useful for monitoring hydrocarbon contamination in a petroleum release site. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Brief History of Soil Mapping and Classification in the USA

NASA Astrophysics Data System (ADS)

Brevik, Eric C.; Hartemink, Alfred E.

2014-05-01

Soil maps show the distribution of soils across an area but also depict soil science theory and ideas on soil formation and classification at the time the maps were created. The national soil mapping program in the USA was established in 1899. The first nation-wide soil map was published by M. Whitney in 1909 and showed soil provinces that were largely based on geology. In 1912, G.N. Coffey published the first country-wide map based on soil properties. The map showed 5 broad soil units that used parent material, color and drainage as diagnostic criteria. The 1913 national map was produced by C.F. Marbut, H.H. Bennett, J.E. Lapham, and M.H. Lapham and showed broad physiographic units that were further subdivided into soil series, soil classes and soil types. In 1935, Marbut drafted a series of maps based on soil properties, but these maps were replaced as official U.S. soil maps in 1938 with the work of M. Baldwin, C.E. Kellogg, and J. Thorp. A series of soil maps similar to modern USA maps appeared in the 1960s with the 7th Approximation followed by revisions with the 1975 and 1999 editions of Soil Taxonomy. This review has shown that soil maps in the United States produced since the early 1900s moved initially from a geologic-based concept to a pedologic concept of soils. Later changes were from property-based systems to process-based, and then back to property-based. The information in this presentation is based on Brevik and Hartemink (2013). Brevik, E.C., and A.E. Hartemink. 2013. Soil Maps of the United States of America. Soil Science Society of America Journal 77:1117-1132. doi:10.2136/sssaj2012.0390.

Landmarks of History of Soil Science in Sri Lanka

NASA Astrophysics Data System (ADS)

Mapa, R.

2012-04-01

Sri Lanka is a tropical Island in the Southern tip of Indian subcontinent positioned at 50 55' to 90 50' N latitude and 790 42' to 810 53' E longitude surrounded by the Indian Ocean. It is an island 435 km in length and 224 km width consisting of a land are of 6.56 million ha with a population of 20 million. In area wise it is ranked as 118th in the world, where at present ranked as 47 in population wise and ranked 19th in population density. The country was under colonial rule under Portuguese, Dutch and British from 1505 to 1948. The majority of the people in the past and present earn their living from activities based on land, which indicates the important of the soil resource. The objective of this paper is to describe the landmarks of the history of Soil Science to highlight the achievements and failures, which is useful to enrich our present understanding of Sri Lankan soils. The landmarks of the history of Soil Science in Sri Lanka can be divided to three phases namely, the early period (prior to 1956), the middle period (1956 to 1972) and the present period (from 1972 onwards). During the early period, detailed analytical studies of coffee and tea soils were compiled, and these gave mainly information on up-country soils which led to fertilizer recommendations based on field trials. In addition, rice and forest soils were also studied in less detail. The first classification of Sri Lankan soils and a provisional soil map based on parent material was published by Joachim in 1945 which is a major landmark of history of Soil Science in Sri Lanka. In 1959 Ponnamperuma proposed a soil classification system for wetland rice soils. From 1963 to 1968 valuable information on the land resource was collected and documented by aerial resource surveys funded by Canada-Ceylon Colombo plan aid project. This covered 18 major river basins and about 1/4th of Sri Lanka, which resulted in producing excellent soil maps and information of the areas called the Kelani Aruvi Ara and Walawe basins. The provisional soil map was updated by many other workers as Moorman and Panabokke in 1961 and 1972 using this information. The soil map produced by De Alwis and Panabokke in 1972 at a scale of 1:500,000 was the soil maps mostly used during the past years During the present era, the need for classification of Soils of Sri Lanka according to international methods was felt. A major leap forward in Soil Survey, Classification leading to development of a soil data base was initiated in 1995 with the commencement of the "SRICANSOL" project which was a twining project between the Soil Science Societies of Sri Lanka and Canada. This project is now completed with detail soil maps at a scale of 1:250,000 and soil classified according to international methods for the Wet, Intermediate and Dry zones of Sri Lanka. A digital database consisting of soil profile description and physical and chemical data is under preparation for 28, 40 and 51 benchmark sites of the Wet, Intermediate and Dry zones respectively. The emphases on studies on Soil Science in the country at present is more towards environmental conservation related to soil erosion control, reducing of pollution of soil and water bodies from nitrates, pesticide residues and heavy metal accumulation. Key words: Sri Lanka, Provisional soil map

Understanding the effect of watershed characteristic on the runoff using SCS curve number

NASA Astrophysics Data System (ADS)

Damayanti, Frieta; Schneider, Karl

2015-04-01

Runoff modeling is a key component in watershed management. The temporal course and amount of runoff is a complex function of a multitude of parameters such as climate, soil, topography, land use, and water management. Against the background of the current rapid environmental change, which is due to both i) man-made changes (e.g. urban development, land use change, water management) as well as ii) changes in the natural systems (e.g. climate change), understanding and predicting the impacts of these changes upon the runoff is very important and affects the wellbeing of many people living in the watershed. A main tool for predictions is hydrologic models. Particularly process based models are the method of choice to assess the impact of land use and climate change. However, many regions which experience large changes in the watersheds can be described as rather data poor, which limits the applicability of such models. This is particularly also true for the Telomoyo Watershed (545 km2) which is located in southern part of Central Java province. The average annual rainfall of the study area reaches 2971 mm. Irrigated paddy field are the dominating land use (35%), followed by built-up area and dry land agriculture. The only available soil map is the FAO soil digital map of the world, which provides rather general soil information. A field survey accompanied by a lab analysis 65 soil samples of was carried out to provide more detailed soil texture information. The soil texture map is a key input in the SCS method to define hydrological soil groups. In the frame of our study on 'Integrated Analysis on Flood Risk of Telomoyo Watershed in Response to the Climate and Land Use Change' funded by the German Academic Exchange service (DAAD) we analyzed the sensitivity of the modeled runoff upon the choice of the method to estimate the CN values using the SCS-CN method. The goal of this study is to analyze the impact of different data sources on the curve numbers and the estimated runoff. CN values were estimated using the field measurements of soil textures for different combinations of land use and topography. To transfer the local soil texture measurements to the watershed domain a statistical analysis using the frequency distribution of the measured soil textures is applied and used to derive the effective CN value for a given land use, topography and soil texture combination. Since the curve numbers change as a function of parameter combinations, the effect of different methods to estimate the curve number upon the runoff is analyzed and compared to the straight forward method of using the data from the FAO soil map.

Comparison of different landform classification methods for digital landform and soil mapping of the Iranian loess plateau

NASA Astrophysics Data System (ADS)

Hoffmeister, Dirk; Kramm, Tanja; Curdt, Constanze; Maleki, Sedigheh; Khormali, Farhad; Kehl, Martin

2016-04-01

The Iranian loess plateau is covered by loess deposits, up to 70 m thick. Tectonic uplift triggered deep erosion and valley incision into the loess and underlying marine deposits. Soil development strongly relates to the aspect of these incised slopes, because on northern slopes vegetation protects the soil surface against erosion and facilitates formation and preservation of a Cambisol, whereas on south-facing slopes soils were probably eroded and weakly developed Entisols formed. While the whole area is intensively stocked with sheep and goat, rain-fed cropping of winter wheat is practiced on the valley floors. Most time of the year, the soil surface is unprotected against rainfall, which is one of the factors promoting soil erosion and serious flooding. However, little information is available on soil distribution, plant cover and the geomorphological evolution of the plateau, as well as on potentials and problems in land use. Thus, digital landform and soil mapping is needed. As a requirement of digital landform and soil mapping, four different landform classification methods were compared and evaluated. These geomorphometric classifications were run on two different scales. On the whole area an ASTER GDEM and SRTM dataset (30 m pixel resolution) was used. Likewise, two high-resolution digital elevation models were derived from Pléiades satellite stereo-imagery (< 1m pixel resolution, 10 by 10 km). The high-resolution information of this dataset was aggregated to datasets of 5 and 10 m scale. The applied classification methods are the Geomorphons approach, an object-based image approach, the topographical position index and a mainly slope based approach. The accuracy of the classification was checked with a location related image dataset obtained in a field survey (n ~ 150) in September 2015. The accuracy of the DEMs was compared to measured DGPS trenches and map-based elevation data. The overall derived accuracy of the landform classification based on the high-resolution DEM with a resolution of 5 m is approximately 70% and on a 10 m resolution >58%. For the 30 m resolution datasets is the achieved accuracy approximately 40%, as several small scale features are not recognizable in this resolution. Thus, for an accurate differentiation between different important landform types, high-resolution datasets are necessary for this strongly shaped area. One major problem of this approach are the different classes derived by each method and the various class annotations. The result of this evaluation will be regarded for the derivation of landform and soil maps.

Assessment of possibilities and conditions of irrigation in Hungary by digital soil map products

NASA Astrophysics Data System (ADS)

Laborczi, Annamária; Bakacsi, Zsófia; Takács, Katalin; Szatmári, Gábor; Szabó, József; Pásztor, László

2016-04-01

Sustaining proper soil moisture is essentially important in agricultural management. However, irrigation can be really worth only, if we lay sufficient emphasis on soil conservation. Nationwide planning of irrigation can be taken place, if we have spatially exhaustive maps and recommendations for the different areas. Soil moisture in the pores originate from 'above' (precipitation), or from 'beneath' (from groundwater by capillary lift). The level of groundwater depends on topography, climatic conditions and water regime of the nearby river. The thickness of capillary zone is basicly related to the physical and water management properties of the soil. Accordingly the capillary rise of sandy soils - with very high infiltration rate and very poor water retaining capacity - are far smaller than in the case of clay soils - with very poor infiltration rate and high water retaining capacity. Applying irrigation water can be considered as a reinforcement from 'above', and it affects the salinity and sodicity as well as the soil structure, nutrient supply and soil formation. We defined the possibilities of irrigation according to the average salt content of the soil profile. The nationwide mapping of soil salinity was based on legacy soil profile data, and it was carried out by regression kriging. This method allows that environmental factors with exhaustive spatial extension, such as climatic-, vegetation-, topographic-, soil- and geologic layers can be taken into consideration to the spatial extension of the reference data. According to soil salinity content categories, the areas were delineated as 1. to be irrigated, 2. to be irrigated conditionally, 3. not to be irrigated. The conditions of irrigation was determined by the comparison of the 'actual' and the 'critical' depth of the water table. Since, if the water rises above the critical level, undesirable processes, such as salinization and alkalinization can be developed. The critical depth of the water table was calculated according to the literature, and based on average soil content of the soil profile, the water regime category of soil, salt content of the groundwater, and soil pH. The water regime category map originated from legacy polygon-based map of physical soil properties. The soil content, and the actual level of groundwater as well as the soil pH map - similarly to the soil salinity map - was compiled by regression kriging. The conditions are classified into the following three categories: 1. level of groundwater have to be sinked, 2. rising of groundwater level have to be hindered, 3. level of groundwater have to be regularly controlled. The newly compiled maps can help decision makers to improve land use management, taking soil conservation into consideration. Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167) and the Research Institute of Agricultural Economics.

Evaluating Soil Health Using Remotely Sensed Evapotranspiration on the Benchmark Barnes Soils of North Dakota

NASA Astrophysics Data System (ADS)

Bohn, Meyer; Hopkins, David; Steele, Dean; Tuscherer, Sheldon

2017-04-01

The benchmark Barnes soil series is an extensive upland Hapludoll of the northern Great Plains that is both economically and ecologically vital to the region. Effects of tillage erosion coupled with wind and water erosion have degraded Barnes soil quality, but with unknown extent, distribution, or severity. Evidence of soil degradation documented for a half century warrants that the assumption of productivity be tested. Soil resilience is linked to several dynamic soil properties and National Cooperative Soil Survey initiatives are now focused on identifying those properties for benchmark soils. Quantification of soil degradation is dependent on a reliable method for broad-scale evaluation. The soil survey community is currently developing rapid and widespread soil property assessment technologies. Improvements in satellite based remote-sensing and image analysis software have stimulated the application of broad-scale resource assessment. Furthermore, these technologies have fostered refinement of land-based surface energy balance algorithms, i.e. Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) algorithm for evapotranspiration (ET) mapping. The hypothesis of this study is that ET mapping technology can differentiate soil function on extensive landscapes and identify degraded areas. A recent soil change study in eastern North Dakota resampled legacy Barnes pedons sampled prior to 1960 and found significant decreases in organic carbon. An ancillary study showed that evapotranspiration (ET) estimates from METRIC decreased with Barnes erosion class severity. An ET raster map has been developed for three eastern North Dakota counties using METRIC and Landsat 5 imagery. ET pixel candidates on major Barnes soil map units were stratified into tertiles and classified as ranked ET subdivisions. A sampling population of randomly selected points stratified by ET class and county proportion was established. Morphologic and chemical data will be recorded at each sampling site to test whether soil properties correlate to ET, thus serving as a non-biased proxy for soil health.

The Use of Electrical Resistivity Method to Mapping The Migration of Heavy Metals by Electrokinetic

NASA Astrophysics Data System (ADS)

Azhar, A. T. S.; Ayuni, S. A.; Ezree, A. M.; Nizam, Z. M.; Aziman, M.; Hazreek, Z. A. M.; Norshuhaila, M. S.; Zaidi, E.

2017-08-01

The presence of heavy metals contamination in soil environment highly needs innovative remediation. Basically, this contamination was resulted from ex-mining sites, motor workshop, petrol station, landfill and industrial sites. Therefore, soil treatment is very important due to metal ions are characterized as non-biodegradable material that may be harmful to ecological system, food chain, human health and groundwater sources. There are various techniques that have been proposed to eliminate the heavy metal contamination from the soil such as bioremediation, phytoremediation, electrokinetic remediation, solidification and stabilization. The selection of treatment needs to fulfill some criteria such as cost-effective, easy to apply, green approach and high remediation efficiency. Electrokinetic remediation technique (EKR) offers those solutions in certain area where other methods are impractical. While, electrical resistivity method offers an alternative geophysical technique for soil subsurface profiling to mapping the heavy metals migration by the influece of electrical gradient. Consequently, this paper presents an overview of the use of EKR to treat contaminated soil by using ERM method to verify their effectiveness to remove heavy metals.

Comparison of crop stress and soil maps to enhance variable rate irrigation prescriptions

USDA-ARS?s Scientific Manuscript database

Soil textural variability within many irrigated fields diminishes the effectiveness of conventional irrigation management, and scheduling methods that assume uniform soil conditions may produce less than satisfactory results. Furthermore, benefits of variable-rate application of agrochemicals, seeds...

Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques.

PubMed

Triki Fourati, Hela; Bouaziz, Moncef; Benzina, Mourad; Bouaziz, Samir

2017-04-01

Traditional surveying methods of soil properties over landscapes are dramatically cost and time-consuming. Thus, remote sensing is a proper choice for monitoring environmental problem. This research aims to study the effect of environmental factors on soil salinity and to map the spatial distribution of this salinity over the southern east part of Tunisia by means of remote sensing and geostatistical techniques. For this purpose, we used Advanced Spaceborne Thermal Emission and Reflection Radiometer data to depict geomorphological parameters: elevation, slope, plan curvature (PLC), profile curvature (PRC), and aspect. Pearson correlation between these parameters and soil electrical conductivity (EC soil ) showed that mainly slope and elevation affect the concentration of salt in soil. Moreover, spectral analysis illustrated the high potential of short-wave infrared (SWIR) bands to identify saline soils. To map soil salinity in southern Tunisia, ordinary kriging (OK), minimum distance (MD) classification, and simple regression (SR) were used. The findings showed that ordinary kriging technique provides the most reliable performances to identify and classify saline soils over the study area with a root mean square error of 1.83 and mean error of 0.018.

MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY

EPA Science Inventory

This paper presents a fuzzy set-based method of mapping spatial accuracy of thematic map and computing several ecological indicators while taking into account spatial variation of accuracy associated with different land cover types and other factors (e.g., slope, soil type, etc.)...

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

Segmentation of singularity maps in the context of soil porosity

NASA Astrophysics Data System (ADS)

Martin-Sotoca, Juan J.; Saa-Requejo, Antonio; Grau, Juan; Tarquis, Ana M.

2016-04-01

Geochemical exploration have found with increasingly interests and benefits of using fractal (power-law) models to characterize geochemical distribution, including concentration-area (C-A) model (Cheng et al., 1994; Cheng, 2012) and concentration-volume (C-V) model (Afzal et al., 2011) just to name a few examples. These methods are based on the singularity maps of a measure that at each point define areas with self-similar properties that are shown in power-law relationships in Concentration-Area plots (C-A method). The C-A method together with the singularity map ("Singularity-CA" method) define thresholds that can be applied to segment the map. Recently, the "Singularity-CA" method has been applied to binarize 2D grayscale Computed Tomography (CT) soil images (Martin-Sotoca et al, 2015). Unlike image segmentation based on global thresholding methods, the "Singularity-CA" method allows to quantify the local scaling property of the grayscale value map in the space domain and determinate the intensity of local singularities. It can be used as a high-pass-filter technique to enhance high frequency patterns usually regarded as anomalies when applied to maps. In this work we will put special attention on how to select the singularity thresholds in the C-A plot to segment the image. We will compare two methods: 1) cross point of linear regressions and 2) Wavelets Transform Modulus Maxima (WTMM) singularity function detection. REFERENCES Cheng, Q., Agterberg, F. P. and Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51, 109-130. Cheng, Q. (2012). Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. Journal of Geochemical Exploration, 122, 55-70. Afzal, P., Fadakar Alghalandis, Y., Khakzad, A., Moarefvand, P. and Rashidnejad Omran, N. (2011) Delineation of mineralization zones in porphyry Cu deposits by fractal concentration-volume modeling. Journal of Geochemical Exploration, 108, 220-232. Martín-Sotoca, J. J., Tarquis, A. M., Saa-Requejo, A. and Grau, J. B. (2015). Pore detection in Computed Tomography (CT) soil images through singularity map analysis. Oral Presentation in PedoFract VIII Congress (June, La Coruña - Spain).

A history of Soil Survey in England and Wales

NASA Astrophysics Data System (ADS)

Hallett, S.; Deeks, L.

2012-04-01

Early soil mapping in Britain was dominated, as in the USA, by soil texture with maps dating back to the early 1900's identifying surface texture and parent rock materials. Only in the 1920's did Dokuchaev's work in Russia involving soil morphology and the development of the soil profile start to gain popularity, drawing in the influence of climate and topography on pedogenesis. Intentions to create a formal body at this time responsible for soil survey were not implemented and progress remained slow. However, in 1939 definite steps were taken to address this and the soil survey was created. In 1947, its activities were transferred from Bangor to the research branch of the Rothamsted experimental station in Hertfordshire under Professor G.W. Robinson. Soon after, a number of regional offices were also established to act as a link with the National Agricultural Advisory Service. At this time a Pedology Department was established at Rothamsted, focussing on petrological, X-ray, spectrographic and chemical analyses. Although not a Rothamsted Department itself, the Survey did fall under the 'Lawes Agricultural Trust'. A Soil Survey Research Advisory Board was also formed to act as a liaison with the Agricultural Field Council. In Scotland by contrast, soil survey activities became centred on the Macaulay Institute in Aberdeen. Developments in the survey of British soils were accompanied in parallel by the development of soil classification systems. In 1930 a Soils Correlation Committee had been formed to ensure consistency in methods and naming of soil series and to ensure the classification was applied uniformly. In England and Wales the zonal system adopted was similar to that used in the USA, where soil series were named after the location where they were first described. American soil scientists such as Veitch and Lee provided stimulus to the development of mapping methods. In Scotland a differing classification was adopted, being similar to that used in Canada, recognising the importance of the soil drainage characteristics within areas of similar parent material. This led to the adoption of the soil catena approach and the usage of soil 'associations'. With Britain entering the Second World War in 1939, there followed the almost complete cessation of survey activities and it was only in the aftermath of that war that recruitment of surveyors could re-commence. The first Soil Survey Field Handbook was published in 1940. Systematic and formal national soil survey activities across both England and Wales can be dated back to 1947 when work commenced to provide a complete picture of the soil resources of the two countries. Mapping at 1:25,000 scale, almost half the land was covered when, in 1979, the survey received instructions, together with the Scottish survey, to complete respective national maps at 1:250,000, which were published in the early 1980s. Attention then turned again to mapping lowland areas in more detail as well as specialised and thematic maps. However, in 1987 systematic survey was terminated and staff of the Soil Survey of England and Wales disbanded to form the Soil Survey and Land Research Centre (SSLRC) at what became Cranfield University - where its successor, the National Soil Resources Institute (NSRI) operates currently.

The Use of Electromagnetic Induction Techniques for Soil Mapping

NASA Astrophysics Data System (ADS)

Brevik, Eric C.; Doolittle, Jim

2015-04-01

Soils have high natural spatial variability. This has been recognized for a long time, and many methods of mapping that spatial variability have been investigated. One technique that has received considerable attention over the last ~30 years is electromagnetic induction (EMI). Particularly when coupled with modern GPS and GIS systems, EMI techniques have allowed the rapid and relatively inexpensive collection of large spatially-related data sets that can be correlated to soil properties that either directly or indirectly influence electrical conductance in the soil. Soil electrical conductivity is directly controlled by soil water content, soluble salt content, clay content and mineralogy, and temperature. A wide range of indirect controls have been identified, such as soil organic matter content and bulk density; both influence water relationships in the soil. EMI techniques work best in areas where there are large changes in one soil property that influences soil electrical conductance, and don't work as well when soil properties that influence electrical conductance are largely homogenous. This presentation will present examples of situations where EMI techniques were successful as well as a couple of examples of situations where EMI was not so useful in mapping the spatial variability of soil properties. Reasons for both the successes and failures will be discussed.

Seasonal variation in apparent conductivity and soil salinity at two Narragansett Bay salt marshes

EPA Science Inventory

Measurement of the apparent conductivity of salt marsh sediments using electromagnetic induction (EMI) is a rapid alternative to traditional methods of salinity determination that can be used to map soil salinity across a marsh surface. Soil salinity measures can provide informat...

Mapping Pesticide Partition Coefficients By Electromagnetic Induction

USDA-ARS?s Scientific Manuscript database

A potential method for reducing pesticide leaching is to base application rates on the leaching potential of a specific chemical and soil combination. However, leaching is determined in part by the partitioning of the chemical between the soil and soil solution, which varies across a field. Standard...

Turning soil survey data into digital soil maps in the Energy Region Eger Research Model Area

NASA Astrophysics Data System (ADS)

Pásztor, László; Dobos, Anna; Kürti, Lívia; Takács, Katalin; Laborczi, Annamária

2015-04-01

Agria-Innoregion Knowledge Centre of the Eszterházy Károly College has carried out targeted basic researches in the field of renewable energy sources and climate change in the framework of TÁMOP-4.2.2.A-11/1/KONV project. The project has covered certain issues, which require the specific knowledge of the soil cover; for example: (i) investigation of quantitative and qualitative characteristics of natural and landscape resources; (ii) determination of local amount and characteristics of renewable energy sources; (iii) natural/environmental risk analysis by surveying the risk factors. The Energy Region Eger Research Model Area consists of 23 villages and is located in North-Hungary, at the Western part of Bükkalja. Bükkalja is a pediment surface with erosional valleys and dense river network. The diverse morphology of this area results diversity in soil types and soil properties as well. There was large-scale (1:10,000 and 1:25,000 scale) soil mappings in this area in the 1960's and 1970's which provided soil maps, but with reduced spatial coverage and not with fully functional thematics. To achive the recent tasks (like planning suitable/optimal land-use system, estimating biomass production and development of agricultural and ecomonic systems in terms of sustainable regional development) new survey was planned and carried out by the staff of the College. To map the soils in the study area 10 to 22 soil profiles were uncovered per settlement in 2013 and 2014. Field work was carried out according to the FAO Guidelines for Soil Description and WRB soil classification system was used for naming soils. According to the general goal of soil mapping the survey data had to be spatially extended to regionalize the collected thematic local knowledge related to soil cover. Firstly three thematic maps were compiled by digital soil mapping methods: thickness of topsoil, genetic soil type and rate of surface erosion. High resolution digital elevation model, Earth observation imagery, geology and land cover maps were used as spatial ancillary environmental variables related to soil forming processes. Regression kriging (RK) has been used for the spatial inference of quantitative data (thickness of topsoil); classification and regression trees (CART) were applied for the spatial inference of category type information (genetic soil type and rate of surface erosion) with the aid of the available and properly preprocessed auxiliary co-variables. The applied spatial resolution was 25 meters. The deduced digital soil maps hopefully will significantly promote to plan sustainable economic model in the region which can provide protection and regeneration of local natural conditions and potentials for local inhabitants for a long time. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167) and TÁMOP-4.2.2.A-11/1/KONV project.

Mapping soil textural fractions across a large watershed in north-east Florida.

PubMed

Lamsal, S; Mishra, U

2010-08-01

Assessment of regional scale soil spatial variation and mapping their distribution is constrained by sparse data which are collected using field surveys that are labor intensive and cost prohibitive. We explored geostatistical (ordinary kriging-OK), regression (Regression Tree-RT), and hybrid methods (RT plus residual Sequential Gaussian Simulation-SGS) to map soil textural fractions across the Santa Fe River Watershed (3585 km(2)) in north-east Florida. Soil samples collected from four depths (L1: 0-30 cm, L2: 30-60 cm, L3: 60-120 cm, and L4: 120-180 cm) at 141 locations were analyzed for soil textural fractions (sand, silt and clay contents), and combined with textural data (15 profiles) assembled under the Florida Soil Characterization program. Textural fractions in L1 and L2 were autocorrelated, and spatially mapped across the watershed. OK performance was poor, which may be attributed to the sparse sampling. RT model structure varied among textural fractions, and the model explained variations ranged from 25% for L1 silt to 61% for L2 clay content. Regression residuals were simulated using SGS, and the average of simulated residuals were used to approximate regression residual distribution map, which were added to regression trend maps. Independent validation of the prediction maps showed that regression models performed slightly better than OK, and regression combined with average of simulated regression residuals improved predictions beyond the regression model. Sand content >90% in both 0-30 and 30-60 cm covered 80.6% of the watershed area. Copyright 2010 Elsevier Ltd. All rights reserved.

DSM for soil erosion risk in Scotland

NASA Astrophysics Data System (ADS)

Poggio, Laura; Gimona, Alessandro; McLeaod, Jim; Castellazzi, Marie; Baggio Compagnucci, Andrea; Irvine, Justin

2017-04-01

Soils play a crucial role in ecosystem functioning, and modelling its risk of degradation is fundamental, especially in the context of climate change. In this work we used continuous 3D soil information derived from digital soil mapping (DSM) approaches to map sediment erosion and deposition patterns due to rainfall. The test area covers the whole of mainland Scotland, excluding the Northern Islands. Soil profiles data were interpolated using a geo-statistical hybrid Generalised Additive Models method for a range of soil properties such as organic matter, texture, soil depth and peat presence. The same method was used to interpolate climatic data and management information. Remote sensing data were integrated in the process and land use data included. Information on grazing (sheep and deer) pressure was taken into account in the modelling. The uncertainty was accounted and propagated across the whole process. The Scottish test case highlights the differences in roles between mineral and organic soils with an assessment adapted to each of them. The results and intermediate steps were compared with available continental scale results. The results show the importance of the use of DSM approaches for modeling soils and ecosystem functions and assessing uncertainty propagation.

Detecting surface runoff location in a small catchment using distributed and simple observation method

NASA Astrophysics Data System (ADS)

Dehotin, Judicaël; Breil, Pascal; Braud, Isabelle; de Lavenne, Alban; Lagouy, Mickaël; Sarrazin, Benoît

2015-06-01

Surface runoff is one of the hydrological processes involved in floods, pollution transfer, soil erosion and mudslide. Many models allow the simulation and the mapping of surface runoff and erosion hazards. Field observations of this hydrological process are not common although they are crucial to evaluate surface runoff models and to investigate or assess different kinds of hazards linked to this process. In this study, a simple field monitoring network is implemented to assess the relevance of a surface runoff susceptibility mapping method. The network is based on spatially distributed observations (nine different locations in the catchment) of soil water content and rainfall events. These data are analyzed to determine if surface runoff occurs. Two surface runoff mechanisms are considered: surface runoff by saturation of the soil surface horizon and surface runoff by infiltration excess (also called hortonian runoff). The monitoring strategy includes continuous records of soil surface water content and rainfall with a 5 min time step. Soil infiltration capacity time series are calculated using field soil water content and in situ measurements of soil hydraulic conductivity. Comparison of soil infiltration capacity and rainfall intensity time series allows detecting the occurrence of surface runoff by infiltration-excess. Comparison of surface soil water content with saturated water content values allows detecting the occurrence of surface runoff by saturation of the soil surface horizon. Automatic records were complemented with direct field observations of surface runoff in the experimental catchment after each significant rainfall event. The presented observation method allows the identification of fast and short-lived surface runoff processes at a small spatial and temporal resolution in natural conditions. The results also highlight the relationship between surface runoff and factors usually integrated in surface runoff mapping such as topography, rainfall parameters, soil or land cover. This study opens interesting prospects for the use of spatially distributed measurement for surface runoff detection, spatially distributed hydrological models implementation and validation at a reasonable cost.

The methods of geomorphometry and digital soil mapping for assessing spatial variability in the properties of agrogray soils on a slope

NASA Astrophysics Data System (ADS)

Gopp, N. V.; Nechaeva, T. V.; Savenkov, O. A.; Smirnova, N. V.; Smirnov, V. V.

2017-01-01

The relationships between the morphometric parameters (MPs) of topography calculated on the basis of digital elevation model (ASTER GDEM, 30 m) and the properties of the plow layer of agrogray soils on a slope were analyzed. The contribution of MPs to the spatial variability of the soil moisture reached 42%; to the content of physical clay (<0.01 mm particles), 59%; to the humus content, 46%; to the total nitrogen content, 31%; to the content of nitrate nitrogen, 28%; to the content of mobile phosphorus, 40%; to the content of exchangeable potassium, 45%; to the content of exchangeable calcium, 67%; to the content of exchangeable magnesium, 40%; and to the soil pH, 42%. A comparative analysis of the plow layer within the eluvial and transitional parts of the slope was performed with the use of geomorphometric methods and digital soil mapping. The regression analysis showed statistically significant correlations between the properties of the plow layer and the MPs describing surface runoff, geometric forms of surface, and the soil temperature regime.

Production of high-resolution forest-ecosite maps based on model predictions of soil moisture and nutrient regimes over a large forested area.

PubMed

Yang, Qi; Meng, Fan-Rui; Bourque, Charles P-A; Zhao, Zhengyong

2017-09-08

Forest ecosite reflects the local site conditions that are meaningful to forest productivity as well as basic ecological functions. Field assessments of vegetation and soil types are often used to identify forest ecosites. However, the production of high-resolution ecosite maps for large areas from interpolating field data is difficult because of high spatial variation and associated costs and time requirements. Indices of soil moisture and nutrient regimes (i.e., SMR and SNR) introduced in this study reflect the combined effects of biogeochemical and topographic factors on forest growth. The objective of this research is to present a method for creating high-resolution forest ecosite maps based on computer-generated predictions of SMR and SNR for an area in Atlantic Canada covering about 4.3 × 10 6 hectares (ha) of forestland. Field data from 1,507 forest ecosystem classification plots were used to assess the accuracy of the ecosite maps produced. Using model predictions of SMR and SNR alone, ecosite maps were 61 and 59% correct in identifying 10 Acadian- and Maritime-Boreal-region ecosite types, respectively. This method provides an operational framework for the production of high-resolution maps of forest ecosites over large areas without the need for data from expensive, supplementary field surveys.

Magnetic soil mapping and modelling for sustainable land use management in Ukraine

NASA Astrophysics Data System (ADS)

Menshov, Oleksandr; Kruglov, Oleksandr; Pereira, Paulo; Sukhorada, Anatoliy

2015-04-01

The agricultural activities need to be monitored in order to observe if they respect the sustainability principles. During the last 15 years we have been using the magnetic susceptibility measurements for the identification of soil properties and degradation risks. This method can be used to measure soil fertility. We observed a decrease of soil magnetic susceptibility values in the areas with high erosion risk. Magnetic susceptibility can be used as an indicator in identifying rates and depths of soil erosion. Compared to other conventional methods, this one, have a low cost and is time saving. This opens new possibilities to have a better cover of the studied area, collect more samples, hence, a better spatial and temporal resolution. Another field of the soil magnetic properties study is the land use change a result of the urban sprawl and technogenic pollution. The increased risk of the soil degradation is connected to soil pollution and the high concentrations of heavy metals and other dangerous chemical elements and compounds to the environment. The main sources of the anthropogenic pollution are the vehicle circulation, power plants, cement and chemical industry. The components released by these sources contain magnetic properties, which can be identified in soils. In this way we can identify the negative impacts of these activities on the ecosystems sustainability and services and promote measures to recover it. We obtained new results on an example of the urban and industry developed sites of Ukraine. The interpretation of soil magnetic parameter measurements depends on knowledge of a reference value. It is influenced by the type of soils and landscape topography. Magnetic methods are an effective method for temporal and spatial soil mapping and modeling. The results of the soils magnetic studies are valuable to sustainable land use management.

Regional Characterization of Soil Properties via a Combination of Methods from Remote Sensing, Geophysics and Geopedology

NASA Astrophysics Data System (ADS)

Meyer, Uwe; Fries, Elke; Frei, Michaela

2016-04-01

Soil is one of the most precious resources on Earth. Preserving, using and enriching soils are most complex processes that fundamentally need a sound regional data base. Many countries lack this sort of extensive data or the existing data must be urgently updated when land use recently changed in major patterns. The project "RECHARBO" (Regional Characterization of Soil Properties) aims at the combination of methods from remote sensing, geophysics and geopedology in order to develop a new system to map soils on a regional scale in a quick and efficient manner. First tests will be performed on existing soil monitoring districts, using newly available sensing systems as well as established techniques. Especially hyperspectral and infrared data measured from satellites or airborne platforms shall be combined. Moreover, a systematic correlation between hyperspectral imagery and gamma-ray spectroscopy shall be established. These recordings will be compared and correlated to measurements upon ground and on soil samples to get hold of properties such as soil moisture, soil density, specific resistance plus analytic properties like clay content, anorganic background, organic matter etc. The goal is to generate a system that enables users to map soil patterns on a regional scale using airborne or satellite data and to fix their characteristics with only a limited number of soil samples.

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

USDA-ARS?s Scientific Manuscript database

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote sensing methods have the spatial and temporal resolution required to...

Mapping variability of soil water content and flux across 1-1000 m scales using the Actively Heated Fiber Optic method

NASA Astrophysics Data System (ADS)

Sayde, Chadi; Buelga, Javier Benitez; Rodriguez-Sinobas, Leonor; El Khoury, Laureine; English, Marshall; van de Giesen, Nick; Selker, John S.

2014-09-01

The Actively Heated Fiber Optic (AHFO) method is shown to be capable of measuring soil water content several times per hour at 0.25 m spacing along cables of multiple kilometers in length. AHFO is based on distributed temperature sensing (DTS) observation of the heating and cooling of a buried fiber-optic cable resulting from an electrical impulse of energy delivered from the steel cable jacket. The results presented were collected from 750 m of cable buried in three 240 m colocated transects at 30, 60, and 90 cm depths in an agricultural field under center pivot irrigation. The calibration curve relating soil water content to the thermal response of the soil to a heat pulse of 10 W m-1 for 1 min duration was developed in the lab. This calibration was found applicable to the 30 and 60 cm depth cables, while the 90 cm depth cable illustrated the challenges presented by soil heterogeneity for this technique. This method was used to map with high resolution the variability of soil water content and fluxes induced by the nonuniformity of water application at the surface.

Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia.

PubMed

Pradhan, Biswajeet; Chaudhari, Amruta; Adinarayana, J; Buchroithner, Manfred F

2012-01-01

In this paper, an attempt has been made to assess, prognosis and observe dynamism of soil erosion by universal soil loss equation (USLE) method at Penang Island, Malaysia. Multi-source (map-, space- and ground-based) datasets were used to obtain both static and dynamic factors of USLE, and an integrated analysis was carried out in raster format of GIS. A landslide location map was generated on the basis of image elements interpretation from aerial photos, satellite data and field observations and was used to validate soil erosion intensity in the study area. Further, a statistical-based frequency ratio analysis was carried out in the study area for correlation purposes. The results of the statistical correlation showed a satisfactory agreement between the prepared USLE-based soil erosion map and landslide events/locations, and are directly proportional to each other. Prognosis analysis on soil erosion helps the user agencies/decision makers to design proper conservation planning program to reduce soil erosion. Temporal statistics on soil erosion in these dynamic and rapid developments in Penang Island indicate the co-existence and balance of ecosystem.

Three-dimensional mapping of soil chemical characteristics at micrometric scale: Statistical prediction by combining 2D SEM-EDX data and 3D X-ray computed micro-tomographic images

NASA Astrophysics Data System (ADS)

Hapca, Simona

2015-04-01

Many soil properties and functions emerge from interactions of physical, chemical and biological processes at microscopic scales, which can be understood only by integrating techniques that traditionally are developed within separate disciplines. While recent advances in imaging techniques, such as X-ray computed tomography (X-ray CT), offer the possibility to reconstruct the 3D physical structure at fine resolutions, for the distribution of chemicals in soil, existing methods, based on scanning electron microscope (SEM) and energy dispersive X-ray detection (EDX), allow for characterization of the chemical composition only on 2D surfaces. At present, direct 3D measurement techniques are still lacking, sequential sectioning of soils, followed by 2D mapping of chemical elements and interpolation to 3D, being an alternative which is explored in this study. Specifically, we develop an integrated experimental and theoretical framework which combines 3D X-ray CT imaging technique with 2D SEM-EDX and use spatial statistics methods to map the chemical composition of soil in 3D. The procedure involves three stages 1) scanning a resin impregnated soil cube by X-ray CT, followed by precision cutting to produce parallel thin slices, the surfaces of which are scanned by SEM-EDX, 2) alignment of the 2D chemical maps within the internal 3D structure of the soil cube, and 3) development, of spatial statistics methods to predict the chemical composition of 3D soil based on the observed 2D chemical and 3D physical data. Specifically, three statistical models consisting of a regression tree, a regression tree kriging and cokriging model were used to predict the 3D spatial distribution of carbon, silicon, iron and oxygen in soil, these chemical elements showing a good spatial agreement between the X-ray grayscale intensities and the corresponding 2D SEM-EDX data. Due to the spatial correlation between the physical and chemical data, the regression-tree model showed a great potential in predicting chemical composition in particular for iron, which is generally sparsely distributed in soil. For carbon, silicon and oxygen, which are more densely distributed, the additional kriging of the regression tree residuals improved significantly the prediction, whereas prediction based on co-kriging was less consistent across replicates, underperforming regression-tree kriging. The present study shows a great potential in integrating geo-statistical methods with imaging techniques to unveil the 3D chemical structure of soil at very fine scales, the framework being suitable to be further applied to other types of imaging data such as images of biological thin sections for characterization of microbial distribution. Key words: X-ray CT, SEM-EDX, segmentation techniques, spatial correlation, 3D soil images, 2D chemical maps.

Spatial correlation of shear-wave velocity in the San Francisco Bay Area sediments

USGS Publications Warehouse

Thompson, E.M.; Baise, L.G.; Kayen, R.E.

2007-01-01

Ground motions recorded within sedimentary basins are variable over short distances. One important cause of the variability is that local soil properties are variable at all scales. Regional hazard maps developed for predicting site effects are generally derived from maps of surficial geology; however, recent studies have shown that mapped geologic units do not correlate well with the average shear-wave velocity of the upper 30 m, Vs(30). We model the horizontal variability of near-surface soil shear-wave velocity in the San Francisco Bay Area to estimate values in unsampled locations in order to account for site effects in a continuous manner. Previous geostatistical studies of soil properties have shown horizontal correlations at the scale of meters to tens of meters while the vertical correlations are on the order of centimeters. In this paper we analyze shear-wave velocity data over regional distances and find that surface shear-wave velocity is correlated at horizontal distances up to 4 km based on data from seismic cone penetration tests and the spectral analysis of surface waves. We propose a method to map site effects by using geostatistical methods based on the shear-wave velocity correlation structure within a sedimentary basin. If used in conjunction with densely spaced shear-wave velocity profiles in regions of high seismic risk, geostatistical methods can produce reliable continuous maps of site effects. ?? 2006 Elsevier Ltd. All rights reserved.

Leveraging Machine Learning to Estimate Soil Salinity through Satellite-Based Remote Sensing

NASA Astrophysics Data System (ADS)

Welle, P.; Ravanbakhsh, S.; Póczos, B.; Mauter, M.

2016-12-01

Human-induced salinization of agricultural soils is a growing problem which now affects an estimated 76 million hectares and causes billions of dollars of lost agricultural revenues annually. While there are indications that soil salinization is increasing in extent, current assessments of global salinity levels are outdated and rely heavily on expert opinion due to the prohibitive cost of a worldwide sampling campaign. A more practical alternative to field sampling may be earth observation through remote sensing, which takes advantage of the distinct spectral signature of salts in order to estimate soil conductivity. Recent efforts to map salinity using remote sensing have been met with limited success due to tractability issues of managing the computational load associated with large amounts of satellite data. In this study, we use Google Earth Engine to create composite satellite soil datasets, which combine data from multiple sources and sensors. These composite datasets contain pixel-level surface reflectance values for dates in which the algorithm is most confident that the surface contains bare soil. We leverage the detailed soil maps created and updated by the United States Geological Survey as label data and apply machine learning regression techniques such as Gaussian processes to learn a smooth mapping from surface reflection to noisy estimates of salinity. We also explore a semi-supervised approach using deep generative convolutional networks to leverage the abundance of unlabeled satellite images in producing better estimates for salinity values where we have relatively fewer measurements across the globe. The general method results in two significant contributions: (1) an algorithm that can be used to predict levels of soil salinity in regions without detailed soil maps and (2) a general framework that serves as an example for how remote sensing can be paired with extensive label data to generate methods for prediction of physical phenomenon.

Identification of land degradation evidences in an organic farm using probability maps (Croatia)

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Bogunovic, Igor; Estebaranz, Ferran

2017-04-01

Land degradation is a biophysical process with important impacts on society, economy and policy. Areas affected by land degradation do not provide services in quality and with capacity to full-field the communities that depends on them (Amaya-Romero et al., 2015; Beyene, 2015; Lanckriet et al., 2015). Agricultural activities are one of the main causes of land degradation (Kraaijvanger and Veldkamp, 2015), especially when they decrease soil organic matter (SOM), a crucial element for soil fertility. In temperate areas, the critical level of SOM concentration in agricultural soils is 3.4%. Below this level there is a potential decrease of soil quality (Loveland and Weeb, 2003). However, no previous work was carried out in other environments, such as the Mediterranean. The spatial distribution of potential degraded land is important to be identified and mapped, in order to identify the areas that need restoration (Brevik et al., 2016; Pereira et al., 2017). The aim of this work is to assess the spatial distribution of areas with evidences of land degradation (SOM bellow 3.4%) using probability maps in an organic farm located in Croatia. In order to find the best method, we compared several probability methods, such as Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), Indicator Kriging (IK), Probability Kriging (PK) and Disjunctive Kriging (DK). The study area is located on the Istria peninsula (45°3' N; 14°2' E), with a total area of 182 ha. One hundred eighty-two soil samples (0-30 cm) were collected during July of 2015 and SOM was assessed using wet combustion procedure. The assessment of the best probability method was carried out using leave one out cross validation method. The probability method with the lowest Root Mean Squared Error (RMSE) was the most accurate. The results showed that the best method to predict the probability of potential land degradation was SK with an RMSE of 0.635, followed by DK (RMSE=0.636), UK (RMSE=0.660), OK (RMSE=0.660), IK (RMSE=0.722) and PK (RMSE=1.661). According to the most accurate method, it is observed that the majority of the area studied has a high probability to be degraded. Measures are needed to restore this area. References Amaya-Romero, M., Abd-Elmabod, S., Munoz-Rojas, M., Castellano, G., Ceacero, C., Alvarez, S., Mendez, M., De la Rosa, D. (2015) Evaluating soil threats under climate change scenarios in the Andalusia region, Southern Spain. Land Degradation and Development, 26, 441-449. Beyene, F. (2015) Incentives and challenges in community based rangeland management: Evidence from Eastern Ethiopia. Land Degradation and Development, 26, 502-509. Brevik, E., Calzolari, C., Miller, B., Pereira, P., Kabala, C., Baumgarten, A., Jordán, A. (2016) Historical perspectives and future needs in soil mapping, classification and pedological modelling, Geoderma, 264, Part B, 256-274. Kraaijvanger, R., Veldkamp, T. (2015) Grain productivity, fertilizer response and nutrient balance of farming systems in Tigray, Ethiopia: A Multiprespective view in relation do soil fertility degradation. Land Degradation and Development, 26, 701-710. Lanckriet, S., Derudder, B., Naudts, J., Bauer, H., Deckers, J., Haile, M., Nyssen, J. (2015) A political ecology perspective of land degradation in the North Ethiopian Highlands. Land Degradation and Development, 26, 521-530. Loveland, P., Weeb, J. (2003) Is there a critical level of organic matter in the agricultural soils of temperate regions: a review. Soil & Tillage Research, 70, 1-18. Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B., Smetanova, A., Depellegrin, D., Misiune, I., Novara, A., Cerda, A. Soil mapping and process modelling for sustainable land management. In: Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B. (Eds.) Soil mapping and process modelling for sustainable land use management (Elsevier Publishing House) ISBN: 9780128052006

Application of cattle slurry containing Mycobacterium avium subsp. paratuberculosis (MAP) to grassland soil and its effect on the relationship between MAP and free-living amoeba.

PubMed

Salgado, M; Alfaro, M; Salazar, F; Badilla, X; Troncoso, E; Zambrano, A; González, M; Mitchell, R M; Collins, M T

2015-01-30

Slurry from dairy farms is commonly used to fertilize crops and pastures. This mixture of manure, urine and water can harbor multiple microbial pathogens among which Mycobacterium avium subsp. paratuberculosis (MAP) is a major concern. Persistence of MAP in soil and infection of soil Acanthamoeba was evaluated by culture, real-time IS900 PCR, and by staining of amoeba with acid-fast and vital stains comparing soils irrigated with MAP-spiked or control dairy farm slurry. MAP DNA was detected in soil for the 8 month study duration. MAP was detected by PCR from more soil samples for plots receiving MAP-spiked slurry (n=61/66) than from soils receiving control slurry (n=10/66 samples). Vital stains verified that intracellular MAP in amoeba was viable. More MAP was found in amoeba at the end of the study than immediately after slurry application. There was no relationship between MAP presence in soil and in amoeba over time. Infection of amoeba by MAP provides a protected niche for the persistence and even possibly the replication of MAP in soils. As others have suggested, MAP-infected amoeba may act like a "Trojan horse" providing a means for persistence in soils and potentially a source of infection for grazing animals. Copyright © 2014 Elsevier B.V. All rights reserved.

Effects of anthropogenic particles on the chemical and geophysical properties of urban soils, Detroit, Michigan

NASA Astrophysics Data System (ADS)

Orlicki, Katharine M.

There is a great need in many cities for a better quality of urban soil maps. This is due to the increasing interest in repurposing vacant land for urban redevelopment, agriculture, and green infrastructure. Mapping vacant urban land in Detroit can be very difficult because anthropogenic soils were often highly variable and frequently contained demolition debris (such as brick), making it difficult to use a hand auger. This study was undertaken in Detroit, MI to create a more efficient way to map urban soils based on their geophysical and chemical properties. This will make the mapping process faster, less labor intensive, and therefore more cost effective. Optical and chemical criteria for the identification and classification of microartifacts (MAs) were made from a set of reference artifacts of a known origin. These MAs were then observed and tested in urban topsoil samples from sites in Detroit, Michigan that represent three different land use types (residential demolition, fly ash-impacted, and industrial). Optical analyses, SEM, EDAX, and XRD showed that reference MAs may be classified into five basic compositional types (carbonaceous, calcareous, siliceous, ferruginous and miscellaneous). Reference MAs were generally distinguishable using optical microscopy by color, luster, fracture and microtexture. MAs that were more difficult to classify were further differentiable when using SEM, EDAX, and XRD. MAs were found in all of the anthropogenic soils studied, but were highly variable. All three study sites had concentrations coal-related wastes were the most common types of MAs observed and often included coal, ash (microspheres, microagglomerate), cinders, and burnt shale. MAs derived from waste building materials such as brick, mortar, and glass, were typically found on residential demolition sites. Manufacturing waste MAs, which included iron-making slag and coked coal were commonly observed on industrial sites. Fly ash-impacted sites were composed of only microspheres and microagglomerate that were concentrated within the soils by airborne deposition, making it widespread. These results support the hypothesis that MA assemblages of distinct composition vary with land use. Therefore, it seems likely that magnetic susceptibility surveying and other geophysical methods will prove effective for mapping anthropogenic soils on vacant urban land. Anthropogenic soils and MAs were assessed for pH, electrical conductivity (EC), and magnetic susceptibility (MS). The A horizons of urban soils at residential demolition, industrial-impacted, and fly ash-impacted sites were found to be distinguishable from those of native soils. Anthropogenic soils were higher by one pH unit or more than the background level, had an EC value two to three times the background level, and had MS measurements up to 20 times greater than the background level. The analysis of reference artifacts suggested that the elevated pH of anthropogenic soils was caused by calcareous building material wastes, the elevated EC were the result of both calcareous and ferruginous wastes, and elevated MS were attributable to ferromagnetic materials. Anthropogenic soils collected at residential demolition sites were differentiable by EC, whereas those at collected form industrial sites were distinguishable using MS. Therefore, anthropogenic soils and native soils have a unique chemical and geophysical signature which can be highly dependent on the concentration of MAs. This suggests that EC and MS surveying methods may be used to remotely sense and map urban soils more effectively than using traditional methods alone.

Soil maps as data input for soil erosion models: errors related to map scales

NASA Astrophysics Data System (ADS)

van Dijk, Paul; Sauter, Joëlle; Hofstetter, Elodie

2010-05-01

Soil erosion rates depend in many ways on soil and soil surface characteristics which vary in space and in time. To account for spatial variations of soil features, most distributed soil erosion models require data input derived from soil maps. Ideally, the level of spatial detail contained in the applied soil map should correspond to the objective of the modelling study. However, often the model user has only one soil map available which is then applied without questioning its suitability. The present study seeks to determine in how far soil map scale can be a source of error in erosion model output. The study was conducted on two different spatial scales, with for each of them a convenient soil erosion model: a) the catchment scale using the physically-based Limbourg Soil Erosion Model (LISEM), and b) the regional scale using the decision-tree expert model MESALES. The suitability of the applied soil map was evaluated with respect to an imaginary though realistic study objective for both models: the definition of erosion control measures at strategic locations at the catchment scale; the identification of target areas for the definition of control measures strategies at the regional scale. Two catchments were selected to test the sensitivity of LISEM to the spatial detail contained in soil maps: one catchment with relatively little contrast in soil texture, dominated by loess-derived soil (south of the Alsace), and one catchment with strongly contrasted soils at the limit between the Alsatian piedmont and the loess-covered hills of the Kochersberg. LISEM was run for both catchments using different soil maps ranging in scale from 1/25 000 to 1/100 000 to derive soil related input parameters. The comparison of the output differences was used to quantify the map scale impact on the quality of the model output. The sensitivity of MESALES was tested on the Haut-Rhin county for which two soil maps are available for comparison: 1/50 000 and 1/100 000. The order of resulting target areas (communes) was compared to evaluate the error induced by using the coarser soil data at 1/100 000. Results shows that both models are sensitive to the soil map scale used for model data input. A low sensitivity was found for the catchment with relatively homogeneous soil textures and the use of 1/100 000 soil maps seems allowed. The results for the catchment with strong soil texture variations showed significant differences depending on soil map scale on 75% of the catchment area. Here, the use of 1/100 000 soil map will indeed lead to wrong erosion diagnostics and will hamper the definition of a sound erosion control strategy. The regional scale model MESALES proved to be very sensitive to soil information. The two soil related model parameters (crusting sensitivity, and soil erodibility) reacted very often in the same direction therewith amplifying the change in the final erosion hazard class. The 1/100 000 soil map yielded different results on 40% of the sloping area compared to the 1/50 000 map. Significant differences in the order of target areas were found as well. The present study shows that the degree of sensitivity of the model output to soil map scale is rather variable and depends partly on the spatial variability of soil texture within the study area. Soil (textural) diversity needs to be accounted for to assure a fruitful use of soil erosion models. In some situations this might imply that additional soil data need to be collected in the field to refine the available soil map.

Islands of biogeodiversity in arid lands on a polygons map study: Detecting scale invariance patterns from natural resources maps.

PubMed

Ibáñez, J J; Pérez-Gómez, R; Brevik, Eric C; Cerdà, A

2016-12-15

Many maps (geology, hydrology, soil, vegetation, etc.) are created to inventory natural resources. Each of these resources is mapped using a unique set of criteria, including scales and taxonomies. Past research indicates that comparing results of related maps (e.g., soil and geology maps) may aid in identifying mapping deficiencies. Therefore, this study was undertaken in Almeria Province, Spain to (i) compare the underlying map structures of soil and vegetation maps and (ii) investigate if a vegetation map can provide useful soil information that was not shown on a soil map. Soil and vegetation maps were imported into ArcGIS 10.1 for spatial analysis, and results then exported to Microsoft Excel worksheets for statistical analyses to evaluate fits to linear and power law regression models. Vegetative units were grouped according to the driving forces that determined their presence or absence: (i) climatophilous (ii) lithologic-climate; and (iii) edaphophylous. The rank abundance plots for both the soil and vegetation maps conformed to Willis or Hollow Curves, meaning the underlying structures of both maps were the same. Edaphophylous map units, which represent 58.5% of the vegetation units in the study area, did not show a good correlation with the soil map. Further investigation revealed that 87% of the edaphohygrophilous units were found in ramblas, ephemeral riverbeds that are not typically classified and mapped as soils in modern systems, even though they meet the definition of soil given by the most commonly used and most modern soil taxonomic systems. Furthermore, these edaphophylous map units tend to be islands of biodiversity that are threatened by anthropogenic activity in the region. Therefore, this study revealed areas that need to be revisited and studied pedologically. The vegetation mapped in these areas and the soils that support it are key components of the earth's critical zone that must be studied, understood, and preserved. Copyright © 2016 Elsevier B.V. All rights reserved.

Soil mapping and processes models to support climate change mitigation and adaptation strategies: a review

NASA Astrophysics Data System (ADS)

Muñoz-Rojas, Miriam; Pereira, Paulo; Brevik, Eric; Cerda, Artemi; Jordan, Antonio

2017-04-01

As agreed in Paris in December 2015, global average temperature is to be limited to "well below 2 °C above pre-industrial levels" and efforts will be made to "limit the temperature increase to 1.5 °C above pre-industrial levels. Thus, reducing greenhouse gas emissions (GHG) in all sectors becomes critical and appropriate sustainable land management practices need to be taken (Pereira et al., 2017). Mitigation strategies focus on reducing the rate and magnitude of climate change by reducing its causes. Complementary to mitigation, adaptation strategies aim to minimise impacts and maximize the benefits of new opportunities. The adoption of both practices will require developing system models to integrate and extrapolate anticipated climate changes such as global climate models (GCMs) and regional climate models (RCMs). Furthermore, integrating climate models driven by socio-economic scenarios in soil process models has allowed the investigation of potential changes and threats in soil characteristics and functions in future climate scenarios. One of the options with largest potential for climate change mitigation is sequestering carbon in soils. Therefore, the development of new methods and the use of existing tools for soil carbon monitoring and accounting have therefore become critical in a global change context. For example, soil C maps can help identify potential areas where management practices that promote C sequestration will be productive and guide the formulation of policies for climate change mitigation and adaptation strategies. Despite extensive efforts to compile soil information and map soil C, many uncertainties remain in the determination of soil C stocks, and the reliability of these estimates depends upon the quality and resolution of the spatial datasets used for its calculation. Thus, better estimates of soil C pools and dynamics are needed to advance understanding of the C balance and the potential of soils for climate change mitigation. Here, we discuss the most recent advances on the application of soil mapping and modeling to support climate change mitigation and adaptation strategies; and These strategies are a key component of the implementation of sustainable land management policies need to be integrated are critical to. The objective of this work is to present a review about the advantages of soil mapping and process modeling for sustainable land management. Muñoz-Rojas, M., Pereira, P., Brevic, E., Cerda, A., Jordan, A. (2017) Soil mapping and processes models for sustainable land management applied to modern challenges. In: Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B. (Eds.) Soil mapping and process modelling for sustainable land use management (Elsevier Publishing House) ISBN: 9780128052006

Photovoltaic Module Soiling Map | Photovoltaic Research | NREL

Science.gov Websites

proposed in: M. Deceglie, L. Micheli, and M. Muller, "Quantifying soiling loss directly from PV yield described in: L. Micheli and M. Muller, "An investigation of the key parameters for predicting PV : M. Muller, L. Micheli, and A.A. Martinez-Morales, "A Method to Extract Soiling Loss Data from

Using Vegetation Maps to Provide Information on Soil Distribution

NASA Astrophysics Data System (ADS)

José Ibáñez, Juan; Pérez-Gómez, Rufino; Brevik, Eric C.; Cerdà, Artemi

2016-04-01

Many different types of maps (geology, hydrology, soil, vegetation, etc.) are created to inventory natural resources. Each of these resources is mapped using a unique set of criteria, including scales and taxonomies. Past research has indicated that comparing the results of different but related maps (e.g., soil and geology maps) may aid in identifying deficiencies in those maps. Therefore, this study was undertaken in the Almería Province (Andalusia, Spain) to (i) compare the underlying map structures of soil and vegetation maps and (ii) to investigate if a vegetation map can provide useful soil information that was not shown on a soil map. To accomplish this soil and vegetation maps were imported into ArcGIS 10.1 for spatial analysis. Results of the spatial analysis were exported to Microsoft Excel worksheets for statistical analyses to evaluate fits to linear and power law regression models. Vegetative units were grouped according to the driving forces that determined their presence or absence (P/A): (i) climatophilous (climate is the only determinant of P/A) (ii); lithologic-climate (climate and parent material determine PNV P/A); and (iii) edaphophylous (soil features determine PNV P/A). The rank abundance plots for both the soil and vegetation maps conformed to Willis or Hollow Curves, meaning the underlying structures of both maps were the same. Edaphophylous map units, which represent 58.5% of the vegetation units in the study area, did not show a good correlation with the soil map. Further investigation revealed that 87% of the edaphohygrophylous units (which demand more soil water than is supplied by other soil types in the surrounding landscape) were found in ramblas, ephemeral riverbeds that are not typically classified and mapped as soils in modern systems, even though they meet the definition of soil given by the most commonly used and most modern soil taxonomic systems. Furthermore, these edaphophylous map units tend to be islands of biodiversity that are threatened by anthropogenic activity in the region. Therefore, this study revealed areas in Almería Province that need to be revisited and studied pedologically. The vegetation mapped in these areas and the soils that support it are key components of the earth's critical zone that must be studied, understood, and preserved.

Temporal changes of spatial soil moisture patterns: controlling factors explained with a multidisciplinary approach

NASA Astrophysics Data System (ADS)

Martini, Edoardo; Wollschläger, Ute; Kögler, Simon; Behrens, Thorsten; Dietrich, Peter; Reinstorf, Frido; Schmidt, Karsten; Weiler, Markus; Werban, Ulrike; Zacharias, Steffen

2016-04-01

Characterizing the spatial patterns of soil moisture is critical for hydrological and meteorological models, as soil moisture is a key variable that controls matter and energy fluxes and soil-vegetation-atmosphere exchange processes. Deriving detailed process understanding at the hillslope scale is not trivial, because of the temporal variability of local soil moisture dynamics. Nevertheless, it remains a challenge to provide adequate information on the temporal variability of soil moisture and its controlling factors. Recent advances in wireless sensor technology allow monitoring of soil moisture dynamics with high temporal resolution at varying scales. In addition, mobile geophysical methods such as electromagnetic induction (EMI) have been widely used for mapping soil water content at the field scale with high spatial resolution, as being related to soil apparent electrical conductivity (ECa). The objective of this study was to characterize the spatial and temporal pattern of soil moisture at the hillslope scale and to infer the controlling hydrological processes, integrating well established and innovative sensing techniques, as well as new statistical methods. We combined soil hydrological and pedological expertise with geophysical measurements and methods from digital soil mapping for designing a wireless soil moisture monitoring network. For a hillslope site within the Schäfertal catchment (Central Germany), soil water dynamics were observed during 14 months, and soil ECa was mapped on seven occasions whithin this period of time using an EM38-DD device. Using the Spearman rank correlation coefficient, we described the temporal persistence of a dry and a wet characteristic state of soil moisture as well as the switching mechanisms, inferring the local properties that control the observed spatial patterns and the hydrological processes driving the transitions. Based on this, we evaluated the use of EMI for mapping the spatial pattern of soil moisture under different hydrologic conditions and the factors controlling the temporal variability of the ECa-soil moisture relationship. The approach provided valuable insight into the time-varying contribution of local and nonlocal factors to the characteristic spatial patterns of soil moisture and the transition mechanisms. The spatial organization of soil moisture was controlled by different processes in different soil horizons, and the topsoil's moisture did not mirror processes that take place within the soil profile. Results show that, for the Schäfertal hillslope site which is presumed to be representative for non-intensively managed soils with moderate clay content, local soil properties (e.g., soil texture and porosity) are the major control on the spatial pattern of ECa. In contrast, the ECa-soil moisture relationship is small and varies over time indicating that ECa is not a good proxy for soil moisture estimation at the investigated site.Occasionally observed stronger correlations between ECa and soil moisture may be explained by background dependencies of ECa to other state variables such as pore water electrical conductivity. The results will help to improve conceptual understanding for hydrological model studies at similar or smaller scales, and to transfer observation concepts and process understanding to larger or less instrumented sites, as well as to constrain the use of EMI-based ECa data for hydrological applications.

Prediction of iron oxide contents using diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Marques, José, Jr.; Arantes Camargo, Livia

2015-04-01

Determining soil iron oxides using conventional analysis is relatively unfeasible when large areas are mapped, with the aim of characterizing spatial variability. Diffuse reflectance spectroscopy (DRS) is rapid, less expensive, non-destructive and sometimes more accurate than conventional analysis. Furthermore, this technique allows the simultaneous characterization of many soil attributes with agronomic and environmental relevance. This study aims to assess the DRS capability to predict iron oxides content -hematite and goethite - , characterizing their spatial variability in soils of Brazil. Soil samples collected from an 800-hectare area were scanned in the visible and near-infrared spectral range. Moreover, chemometric calibration was obtained through partial least-squares regression (PLSR). Then, spatial distribution maps of the attributes were constructed using predicted values from calibrated models through geostatistical methods. The studied area presented soils with varied contents of iron oxides as examples for the Oxisols and Entisols. In the spectra of each soil is observed that the reflectance decreases with the content of iron oxides present in the soil. In soils with a high content of iron oxides can be observed more pronounced concavities between 380 and 1100 nm which are characteristic of the presence of these oxides. In soils with higher reflectance it were observed concavity characteristics due to the presence of kaolinite, in agreement with the low iron contents of those soils. The best accuracy of prediction models [residual prediction deviation (RPD) = 1.7] was obtained for goethite within the visible region (380-800 nm), and for hematite (RPD = 2.0) within the visible near infrared (380-2300 nm). The maps of goethite and hematite predicted showed the spatial distribution pattern similar to the maps of clay and iron extracted by dithionite-citrate-bicarbonate, being consistent with the iron oxide contents of soils present in the study area. These results confirm the value of DRS in the mapping of iron oxides in large areas at detailed scale.

The importance of magnetic methods for soil mapping and process modelling. Case study in Ukraine

NASA Astrophysics Data System (ADS)

Menshov, Oleksandr; Pereira, Paulo; Kruglov, Oleksandr; Sukhorada, Anatoliy

2016-04-01

The correct planning of agriculture areas is fundamental for a sustainable future in Ukraine. After the recent political problems in Ukraine, new challenges emerged regarding sustainability questions. At the same time the soil mapping and modelling are intensively developing all over the world (Pereira et al., 2015; Brevik et al., in press). Magnetic susceptibility (MS) methods are low cost and accurate for the developing maps of agricultural areas, fundamental for Ukrain's economy.This allow to colleact a great amount of soil data, usefull for a better understading of the spatial distribution of soil properties. Recently, this method have been applied in other works in Ukraine and elsewhere (Jordanova et al., 2011; Menshov et al., 2015). The objective of this work is to study the spatial distribution of MS and humus content on the topsoils (0-5 cm) in two different areas. The first is located in Poltava region and the second in Kharkiv region. The results showed that MS depends of soil type, topography and anthropogenic influence. For the interpretation of MS spatial distribution in top soil we consider the frequency and time after the last tillage, tilth depth, fertilizing, and the puddling regarding the vehicle model. On average the soil MS of the top soil of these two cases is about 30-70×10-8 m3/kg. In Poltava region not disturbed soil has on average MS values of 40-50×10-8 m3/kg, for Kharkiv region 50-60×10-8 m3/kg. The tilled soil of Poltava region has on average an MS of 60×10-8 m3/kg, and 70×10-8 m3/kg in Kharkiv region. MS is higher in non-tilled soils than in the tilled ones. The correlation between MS and soil humus content is very high ( up to 0.90) in both cases. Breivik, E., Baumgarten, A., Calzolari, C., Miller, B., Pereira, P., Kabala, C., Jordán, A. Soil mapping, classification, and modelling: history and future directions. Geoderma (in press), doi:10.1016/j.geoderma.2015.05.017 Jordanova D., Jordanova N., Atanasova A., Tsacheva T., Petrov P., (2011). Soil tillage erosion by using magnetism of soils - a case study from Bulgaria. Environ. Monit. Assess, 183, 381-394. Menshov O. Pereira P., Kruglov O., (2015). Spatial variability of soil magnetic susceptibility in an agricultural field located in Eastern Ukraine. Geophysical Research Abstracts, 17, EGU2015-578-2. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. (2015) Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, 26, 180-192. DOI: 10.1002/ldr.2195

Soil magnetic susceptibility: A quantitative proxy of soil drainage for use in ecological restoration

USGS Publications Warehouse

Grimley, D.A.; Wang, J.-S.; Liebert, D.A.; Dawson, J.O.

2008-01-01

Flooded, saturated, or poorly drained soils are commonly anaerobic, leading to microbially induced magnetite/maghemite dissolution and decreased soil magnetic susceptibility (MS). Thus, MS is considerably higher in well-drained soils (MS typically 40-80 ?? 10-5 standard international [SI]) compared to poorly drained soils (MS typically 10-25 ?? 10-5 SI) in Illinois, other soil-forming factors being equal. Following calibration to standard soil probings, MS values can be used to rapidly and precisely delineate hydric from nonhydric soils in areas with relatively uniform parent material. Furthermore, soil MS has a moderate to strong association with individual tree species' distribution across soil moisture regimes, correlating inversely with independently reported rankings of a tree species' flood tolerance. Soil MS mapping can thus provide a simple, rapid, and quantitative means for precisely guiding reforestation with respect to plant species' adaptations to soil drainage classes. For instance, in native woodlands of east-central Illinois, Quercus alba , Prunus serotina, and Liriodendron tulipifera predominantly occur in moderately well-drained soils (MS 40-60 ?? 10-5 SI), whereas Acer saccharinum, Carya laciniosa, and Fraxinus pennsylvanica predominantly occur in poorly drained soils (MS <20 ?? 10-5 SI). Using a similar method, an MS contour map was used to guide restoration of mesic, wet mesic, and wet prairie species to pre-settlement distributions at Meadowbrook Park (Urbana, IL, U.S.A.). Through use of soil MS maps calibrated to soil drainage class and native vegetation occurrence, restoration efforts can be conducted more successfully and species distributions more accurately reconstructed at the microecosystem level. ?? 2008 Society for Ecological Restoration International.

The threat of soil salinity: A European scale review.

PubMed

Daliakopoulos, I N; Tsanis, I K; Koutroulis, A; Kourgialas, N N; Varouchakis, A E; Karatzas, G P; Ritsema, C J

2016-12-15

Soil salinisation is one of the major soil degradation threats occurring in Europe. The effects of salinisation can be observed in numerous vital ecological and non-ecological soil functions. Drivers of salinisation can be detected both in the natural and man-made environment, with climate and the foreseen climate change also playing an important role. This review outlines the state of the art concerning drivers and pressures, key indicators as well as monitoring, modeling and mapping methods for soil salinity. Furthermore, an overview of the effect of salinisation on soil functions and the respective mechanism is presented. Finally, the state of salinisation in Europe is presented according to the most recent literature and a synthesis of consistent datasets. We conclude that future research in the field of soil salinisation should be focused on among others carbon dynamics of saline soil, further exploration of remote sensing of soil properties and the harmonization and enrichment of soil salinity maps across Europe within a general context of a soil threat monitoring system to support policies and strategies for the protection of European soils. Copyright © 2016 Elsevier B.V. All rights reserved.

Combining Soil Databases for Topsoil Organic Carbon Mapping in Europe.

PubMed

Aksoy, Ece; Yigini, Yusuf; Montanarella, Luca

2016-01-01

Accuracy in assessing the distribution of soil organic carbon (SOC) is an important issue because of playing key roles in the functions of both natural ecosystems and agricultural systems. There are several studies in the literature with the aim of finding the best method to assess and map the distribution of SOC content for Europe. Therefore this study aims searching for another aspect of this issue by looking to the performances of using aggregated soil samples coming from different studies and land-uses. The total number of the soil samples in this study was 23,835 and they're collected from the "Land Use/Cover Area frame Statistical Survey" (LUCAS) Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and "Soil Transformations in European Catchments" (SoilTrEC) Project (samples from local soil data coming from six different critical zone observatories (CZOs) in Europe). Moreover, 15 spatial indicators (slope, aspect, elevation, compound topographic index (CTI), CORINE land-cover classification, parent material, texture, world reference base (WRB) soil classification, geological formations, annual average temperature, min-max temperature, total precipitation and average precipitation (for years 1960-1990 and 2000-2010)) were used as auxiliary variables in this prediction. One of the most popular geostatistical techniques, Regression-Kriging (RK), was applied to build the model and assess the distribution of SOC. This study showed that, even though RK method was appropriate for successful SOC mapping, using combined databases was not helpful to increase the statistical significance of the method results for assessing the SOC distribution. According to our results; SOC variation was mainly affected by elevation, slope, CTI, average temperature, average and total precipitation, texture, WRB and CORINE variables for Europe scale in our model. Moreover, the highest average SOC contents were found in the wetland areas; agricultural areas have much lower soil organic carbon content than forest and semi natural areas; Ireland, Sweden and Finland has the highest SOC, on the contrary, Portugal, Poland, Hungary, Spain, Italy have the lowest values with the average 3%.

Combining Soil Databases for Topsoil Organic Carbon Mapping in Europe

PubMed Central

Aksoy, Ece

2016-01-01

Accuracy in assessing the distribution of soil organic carbon (SOC) is an important issue because of playing key roles in the functions of both natural ecosystems and agricultural systems. There are several studies in the literature with the aim of finding the best method to assess and map the distribution of SOC content for Europe. Therefore this study aims searching for another aspect of this issue by looking to the performances of using aggregated soil samples coming from different studies and land-uses. The total number of the soil samples in this study was 23,835 and they’re collected from the “Land Use/Cover Area frame Statistical Survey” (LUCAS) Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and “Soil Transformations in European Catchments” (SoilTrEC) Project (samples from local soil data coming from six different critical zone observatories (CZOs) in Europe). Moreover, 15 spatial indicators (slope, aspect, elevation, compound topographic index (CTI), CORINE land-cover classification, parent material, texture, world reference base (WRB) soil classification, geological formations, annual average temperature, min-max temperature, total precipitation and average precipitation (for years 1960–1990 and 2000–2010)) were used as auxiliary variables in this prediction. One of the most popular geostatistical techniques, Regression-Kriging (RK), was applied to build the model and assess the distribution of SOC. This study showed that, even though RK method was appropriate for successful SOC mapping, using combined databases was not helpful to increase the statistical significance of the method results for assessing the SOC distribution. According to our results; SOC variation was mainly affected by elevation, slope, CTI, average temperature, average and total precipitation, texture, WRB and CORINE variables for Europe scale in our model. Moreover, the highest average SOC contents were found in the wetland areas; agricultural areas have much lower soil organic carbon content than forest and semi natural areas; Ireland, Sweden and Finland has the highest SOC, on the contrary, Portugal, Poland, Hungary, Spain, Italy have the lowest values with the average 3%. PMID:27011357

Mapping soil erosion risk in Serra de Grândola (Portugal)

NASA Astrophysics Data System (ADS)

Neto Paixão, H. M.; Granja Martins, F. M.; Zavala, L. M.; Jordán, A.; Bellinfante, N.

2012-04-01

Geomorphological processes can pose environmental risks to people and economical activities. Information and a better knowledge of the genesis of these processes is important for environmental planning, since it allows to model, quantify and classify risks, what can mitigate the threats. The objective of this research is to assess the soil erosion risk in Serra de Grândola, which is a north-south oriented mountain ridge with an altitude of 383 m, located in southwest of Alentejo (southern Portugal). The study area is 675 km2, including the councils of Grândola, Santiago do Cacém and Sines. The process for mapping of erosive status was based on the guidelines for measuring and mapping the processes of erosion of coastal areas of the Mediterranean proposed by PAP/RAC (1997), developed and later modified by other authors in different areas. This method is based on the application of a geographic information system that integrates different types of spatial information inserted into a digital terrain model and in their derivative models. Erosive status are classified using information from soil erodibility, slope, land use and vegetation cover. The rainfall erosivity map was obtained using the modified Fournier index, calculated from the mean monthly rainfall, as recorded in 30 meteorological stations with influence in the study area. Finally, the soil erosion risk map was designed by ovelaying the erosive status map and the rainfall erosivity map.

Participatory GIS for Soil Conservation in Phewa Watershed of Nepal

NASA Astrophysics Data System (ADS)

Bhandari, K. P.

2012-07-01

Participatory Geographic Information Systems (PGIS) can integrate participatory methodologies with geo-spatial technologies for the representation of characteristic of particular place. Over the last decade, researchers use this method to integrate the local knowledge of community within a GIS and Society conceptual framework. Participatory GIS are tailored to answer specific geographic questions at the local level and their modes of implementation vary considerably across space, ranging from field-based, qualitative approaches to more complex web-based applications. These broad ranges of techniques, PGIS are becoming an effective methodology for incorporating community local knowledge into complex spatial decision-making processes. The objective of this study is to reduce the soil erosion by formulating the general rule for the soil conservation by participation of the stakeholders. The poster was prepared by satellite image, topographic map and Arc GIS software including the local knowledge. The data were collected from the focus group discussion and the individual questionnaire for incorporate the local knowledge and use it to find the risk map on the basis of economic, social and manageable physical factors for the sensitivity analysis. The soil erosion risk map is prepared by the physical factors Rainfall-runoff erosivity, Soil erodibility, Slope length, Slope steepness, Cover-management, Conservation practice using RUSLE model. After the comparison and discussion among stakeholders, researcher and export group, and the soil erosion risk map showed that socioeconomic, social and manageable physical factors management can reduce the soil erosion. The study showed that the preparation of the poster GIS map and implement this in the watershed area could reduce the soil erosion in the study area compared to the existing national policy.

A comparison between probability and information measures of uncertainty in a simulated soil map and the economic value of imperfect soil information.

NASA Astrophysics Data System (ADS)

Lark, R. Murray

2014-05-01

Conventionally the uncertainty of a conventional soil map has been expressed in terms of the mean purity of its map units: the probability that the soil profile class examined at a site would be found to correspond to the eponymous class of the simple map unit that is delineated there (Burrough et al, 1971). This measure of uncertainty has an intuitive meaning and is used for quality control in soil survey contracts (Western, 1978). However, it may be of limited value to the manager or policy maker who wants to decide whether the map provides a basis for decision making, and whether the cost of producing a better map would be justified. In this study I extend a published analysis of the economic implications of uncertainty in a soil map (Giasson et al., 2000). A decision analysis was developed to assess the economic value of imperfect soil map information for agricultural land use planning. Random error matrices for the soil map units were then generated, subject to constraints which ensure consistency with fixed frequencies of the different soil classes. For each error matrix the mean map unit purity was computed, and the value of the implied imperfect soil information was computed by the decision analysis. An alternative measure of the uncertainty in a soil map was considered. This is the mean soil map information which is the difference between the information content of a soil observation, at a random location in the region, and the information content of a soil observation given that the map unit is known. I examined the relationship between the value of imperfect soil information and the purity and information measures of map uncertainty. In both cases there was considerable variation in the economic value of possible maps with fixed values of the uncertainty measure. However, the correlation was somewhat stronger with the information measure, and there was a clear upper bound on the value of an imperfect soil map when the mean information takes some particular value. This suggests that the information measure may be a useful one for general communication of the value of soil and similar thematic data. Burrough, P.A., Beckett, P.H.T., Jarvis, M.G., 1971. The relation between cost and utility in soil survey. J. Soil Sci. 22, 359-394. Giasson, E., van Es, C, van Wambeke, A., Bryant, R.B. 2000. Assessing the economic value of soil information using decision analysis techniques. Soil Science 165, 971-978 Western, S., 1978. Soil survey contracts and quality control. Oxford Univ. Press, Oxford.

Noninvasive methods for dynamic mapping of microbial populations across the landscape

NASA Astrophysics Data System (ADS)

Meredith, L. K.; Sengupta, A.; Troch, P. A.; Volkmann, T. H. M.

2017-12-01

Soil microorganisms drive key ecosystem processes, and yet characterizing their distribution and activity in soil has been notoriously difficult. This is due, in part, to the heterogeneous nature of their response to changing environmental and nutrient conditions across time and space. These dynamics are challenging to constrain in both natural and experimental systems because of sampling difficulty and constraints. For example, soil microbial sampling at the Landscape Evolution Observatory (LEO) infrastructure in Biosphere 2 is limited in efforts to minimize soil disruption to the long term experiment that aims to characterize the interacting biological, hydrological, and geochemical processes driving soil evolution. In this and other systems, new methods are needed to monitor soil microbial communities and their genetic potential over time. In this study, we take advantage of the well-defined boundary conditions on hydrological flow at LEO to develop a new method to nondestructively characterize in situ microbial populations. In our approach, we sample microbes from the seepage flow at the base of each of three replicate LEO hillslopes and use hydrological models to `map back' in situ microbial populations. Over the course of a 3-month periodic rainfall experiment we collected samples from the LEO outflow for DNA and extraction and microbial community composition analysis. These data will be used to describe changes in microbial community composition over the course of the experiment. In addition, we will use hydrological flow models to identify the changing source region of discharge water over the course of periodic rainfall pulses, thereby mapping back microbial populations onto their geographic origin in the slope. These predictions of in situ microbial populations will be ground-truthed against those derived from destructive soil sampling at the beginning and end of the rainfall experiment. Our results will show the suitability of this method for long-term, non-destructive monitoring of the microbial communities that contribute to soil evolution in this large-scale model system. Furthermore, this method may be useful for other study systems with limitations to destructive sampling including other model infrastructures and natural landscapes.

The application of the piecewise linear approximation to the spectral neighborhood of soil line for the analysis of the quality of normalization of remote sensing materials

NASA Astrophysics Data System (ADS)

Kulyanitsa, A. L.; Rukhovich, A. D.; Rukhovich, D. D.; Koroleva, P. V.; Rukhovich, D. I.; Simakova, M. S.

2017-04-01

The concept of soil line can be to describe the temporal distribution of spectral characteristics of the bare soil surface. In this case, the soil line can be referred to as the multi-temporal soil line, or simply temporal soil line (TSL). In order to create TSL for 8000 regular lattice points for the territory of three regions of Tula oblast, we used 34 Landsat images obtained in the period from 1985 to 2014 after their certain transformation. As Landsat images are the matrices of the values of spectral brightness, this transformation is the normalization of matrices. There are several methods of normalization that move, rotate, and scale the spectral plane. In our study, we applied the method of piecewise linear approximation to the spectral neighborhood of soil line in order to assess the quality of normalization mathematically. This approach allowed us to range normalization methods according to their quality as follows: classic normalization > successive application of the turn and shift > successive application of the atmospheric correction and shift > atmospheric correction > shift > turn > raw data. The normalized data allowed us to create the maps of the distribution of a and b coefficients of the TSL. The map of b coefficient is characterized by the high correlation with the ground-truth data obtained from 1899 soil pits described during the soil surveys performed by the local institute for land management (GIPROZEM).

Seeing the soil through the net: an eye-opener on the soil map of the Flemish region (Belgium)

NASA Astrophysics Data System (ADS)

Dondeyne, Stefaan; Vanierschot, Laura; Langohr, Roger; Van Ranst, Eric; Deckers, Jozef; Oorts, Katrien

2017-04-01

A systematic soil survey of Belgium was conducted from 1948 to 1991. Field surveys were done at the detailed scale of 1:5000 with the final maps published at a 1:20,000 scale. The legend of these detailed soil maps (scale 1:20,000) has been converted to the 3rd edition of the international soil classification system 'World Reference Base for Soil Resources' (WRB). Over the last years, the government of the Flemish region made great efforts to make these maps, along with other environmental data, available to the general audience through the internet. The soil maps are widely used and consulted by researchers, teachers, land-use planners, environmental consultancy agencies and archaeologists. The maps can be downloaded and consulted in the viewer 'Visual Soil Explorer' ('Bodemverkenner'). To increase the legibility of the maps, we assembled a collection of photographs from soil profiles representing 923 soil types and 413 photos of related landscape settings. By clicking on a specific location in the 'Visual Soil Explorer', pictures of the corresponding soil type and landscape appear in a pop-up window, with a brief explanation about the soil properties. The collection of photographs of soil profiles cover almost 80% of the total area of the Flemish region, and include the 100 most common soil types. Our own teaching experience shows that these information layers are particular valuable for teaching soil geography and earth sciences in general. Overall, such visual information layers should contribute to a better interpretation of the soil maps and legacy soil data by serving as an eye-opener on the soil map to the wider community.

Soil erosion assessment using the Universal Soil Loss Equation (USLE) in a GIS framework: A case study of Zacatecas, México

NASA Astrophysics Data System (ADS)

Betanzos Arroyo, L. I.; Prol Ledesma, R. M.; da Silva Pinto da Rocha, F. J. P.

2014-12-01

The Universal Soil Loss Equation (USLE), which is considered to be a contemporary approach in soil loss assessment, was used to assess soil erosion hazard in the Zacatecas mining district. The purpose of this study is to produce erosion susceptibility maps for an area that is polluted with mining tailings which are susceptible to erosion and can disperse the particles that contain heavy metals and other toxic elements. USLE method is based in the estimation of soil loss per unit area and takes into account specific parameters such as precipitation data, topography, soil erodibility, erosivity and runoff. The R-factor (rainfall erosivity) was calculated from monthly and annual precipitation data. The K-factor (soil erodibility) was estimated using soil maps available from the CONABIO at a scale of 1:250000. The LS-factor (slope length and steepness) was determined from a 30-m digital elevation model. A raster-based Geographic Information System (GIS) was used to interactively calculate soil loss and map erosion hazard. The results show that estimated erosion rates ranged from 0 to 4770.48 t/ha year. Maximum proportion of the total area of the Zacatecas mining district have nil to very extremely slight erosion severity. Small areas in the central and south part of the study area shows the critical condition requiring sustainable land management.

Farmer data sourcing. The case study of the spatial soil information maps in South Tyrol.

NASA Astrophysics Data System (ADS)

Della Chiesa, Stefano; Niedrist, Georg; Thalheimer, Martin; Hafner, Hansjörg; La Cecilia, Daniele

2017-04-01

Nord-Italian region South Tyrol is Europe's largest apple growing area exporting ca. 15% in Europe and 2% worldwide. Vineyards represent ca. 1% of Italian production. In order to deliver high quality food, most of the farmers in South Tyrol follow sustainable farming practices. One of the key practice is the sustainable soil management, where farmers collect regularly (each 5 years) soil samples and send for analyses to improve cultivation management, yield and finally profitability. However, such data generally remain inaccessible. On this regard, in South Tyrol, private interests and the public administration have established a long tradition of collaboration with the local farming industry. This has granted to the collection of large spatial and temporal database of soil analyses along all the cultivated areas. Thanks to this best practice, information on soil properties are centralized and geocoded. The large dataset consist mainly in soil information of texture, humus content, pH and microelements availability such as, K, Mg, Bor, Mn, Cu Zn. This data was finally spatialized by mean of geostatistical methods and several high-resolution digital maps were created. In this contribution, we present the best practice where farmers data source soil information in South Tyrol. Show the capability of a large spatial-temporal geocoded soil dataset to reproduce detailed digital soil property maps and to assess long-term changes in soil properties. Finally, implication and potential application are discussed.

Quantification of soil mapping by digital analysis of LANDSAT data. [Clinton County, Indiana

NASA Technical Reports Server (NTRS)

Kirschner, F. R.; Kaminsky, S. A.; Hinzel, E. J.; Sinclair, H. R.; Weismiller, R. A.

1977-01-01

Soil survey mapping units are designed such that the dominant soil represents the major proportion of the unit. At times, soil mapping delineations do not adequately represent conditions as stated in the mapping unit descriptions. Digital analysis of LANDSAT multispectral scanner (MSS) data provides a means of accurately describing and quantifying soil mapping unit composition. Digital analysis of LANDSAT MSS data collected on 9 June 1973 was used to prepare a spectral soil map for a 430-hectare area in Clinton County, Indiana. Fifteen spectral classes were defined, representing 12 soil and 3 vegetation classes. The 12 soil classes were grouped into 4 moisture regimes based upon their spectral responses; the 3 vegetation classes were grouped into one all-inclusive class.

High-resolution, real-time mapping of surface soil moisture at the field scale using ground penetrating radar

NASA Astrophysics Data System (ADS)

Lambot, S.; Minet, J.; Slob, E.; Vereecken, H.; Vanclooster, M.

2008-12-01

Measuring soil surface water content is essential in hydrology and agriculture as this variable controls important key processes of the hydrological cycle such as infiltration, runoff, evaporation, and energy exchanges between the earth and the atmosphere. We present a ground-penetrating radar (GPR) method for automated, high-resolution, real-time mapping of soil surface dielectric permittivity and correlated water content at the field scale. Field scale characterization and monitoring is not only necessary for field scale management applications, but also for unravelling upscaling issues in hydrology and bridging the scale gap between local measurements and remote sensing. In particular, such methods are necessary to validate and improve remote sensing data products. The radar system consists of a vector network analyzer combined with an off-ground, ultra-wideband monostatic horn antenna, thereby setting up a continuous-wave steeped-frequency GPR. Radar signal analysis is based on three-dimensional electromagnetic inverse modelling. The forward model accounts for all antenna effects, antenna-soil interactions, and wave propagation in three-dimensional multilayered media. A fast procedure was developed to evaluate the involved Green's function, resulting from a singular, complex integral. Radar data inversion is focused on the surface reflection in the time domain. The method presents considerable advantages compared to the current surface characterization methods using GPR, namely, the ground wave and common reflection methods. Theoretical analyses were performed, dealing with the effects of electric conductivity on the surface reflection when non-negligible, and on near-surface layering, which may lead to unrealistic values for the surface dielectric permittivity if not properly accounted for. Inversion strategies are proposed. In particular the combination of GPR with electromagnetic induction data appears to be promising to deal with highly conductive soils. Finally, we present laboratory and field results where the GPR measurements are compared to ground-truth gravimetric and time domain reflectometry data. An example of high resolution surface soil moisture map is presented and discussed. The proposed method appears to be an appropriate solution in any applications where soil surface water content must be known at the field scale.

Prediction of Vehicle Mobility on Large-Scale Soft-Soil Terrain Maps Using Physics-Based Simulation

DTIC Science & Technology

2016-08-04

soil type. The modeling approach is based on (i) a seamless integration of multibody dynamics and discrete element method (DEM) solvers, and (ii...ensure that the vehicle follows a desired path. The soil is modeled as a Discrete Element Model (DEM) with a general cohesive material model that is

High resolution regional soil carbon mapping in Madagascar : towards easy to update maps

NASA Astrophysics Data System (ADS)

Grinand, Clovis; Dessay, Nadine; Razafimbelo, Tantely; Razakamanarivo, Herintsitoaina; Albrecht, Alain; Vaudry, Romuald; Tiberghien, Matthieu; Rasamoelina, Maminiaina; Bernoux, Martial

2013-04-01

The soil organic carbon plays an important role in climate change regulation through carbon emissions and sequestration due to land use changes, notably tropical deforestation. Monitoring soil carbon emissions from shifting-cultivation requires to evaluate the amount of carbon stored at plot scale with a sufficient level of accuracy to be able to detect changes. The objective of this work was to map soil carbon stocks (30 cm and 100 cm depths) for different land use at regional scale using high resolution satellite dataset. The Andohahela National Parc and its surroundings (South-Est Madagascar) - a region with the largest deforestation rate in the country - was selected as a pilot area for the development of the methodology. A three steps approach was set up: (i) carbon inventory using mid infra-red spectroscopy and stock calculation, (ii) spatial data processing and (iii) modeling and mapping. Soil spectroscopy was successfully used for measuring organic carbon in this region. The results show that Random Forest was the inference model that produced the best estimates on calibration and validation datasets. By using a simple and robust method, we estimated uncertainty levels of of 35% and 43% for 30-cm and 100-cm carbon maps respectively. The approach developed in this study was based on open data and open source software that can be easily replicated to other regions and for other time periods using updated satellite images.

Soil Fertility Evaluation and Land Management of Dryland Farming at Tegallalang Sub-District, Gianyar Regency, Bali, Indonesia

NASA Astrophysics Data System (ADS)

Sardiana, I. K.; Susila, D.; Supadma, A. A.; Saifulloh, M.

2017-12-01

The landuse of Tegallalang Subdistrict is dominated by dryland farming. The practice of cultivation on agricultural dryland that ignores the carrying capacity of the environment can lead to land degradation that makes the land vulnerable to the deterioration of soil fertility. Soil fertility evaluation and land management of dryland farming in Tegallalang Sub-district, Gianyar Regency were aimed at (1) identifying the soil fertility and it’s respective limiting factors, (2) mapping the soil fertility using Geographic Information Systems (GIS) and (3) developing land management for dryland farming in Tegallalang Sub-district. This research implementing explora-tory method which followed by laboratory analysis. Soil samples were taken on each homogene-ous land units which developed by overlay of slope, soil type, and land use maps. The following soil fertility were measured, such as CEC, base saturation, P2O5, K- Total and C-Organic. The values of soil fertility were mapping using QGIS 2.18.7 and refer to land management evaluation. The results showed that the soil fertility in the research area considered high, and low level. The High soil fertility presents on land units at the flat to undulating slope with different land management systems (fertilizer, without fertilizer, soil tillage and without soil tillage). The low soil fertility includes land units that present on steep slope, and without land managements. The limiting factors of soil fertility were texture, C-Organic, CEC, P2O5, and K- total. It was recommended to applying organic fertilizer, Phonska, and dolomite on the farming area.

Definition of spectrally separable classes for soil survey research

NASA Technical Reports Server (NTRS)

Cipra, J. E.; Swain, P. H.; Gill, J. H.; Baumgardner, M. F.; Kristof, S. J.

1972-01-01

A procedure is outlined for defining spectral classes such that the differences between classes can be quantified. It also facilitates determination of a number of classes such that the classes are spectrally discriminable. This is accomplished by partitioning the data into many classes and then combining similar spectral classes on the basis of appropriate criteria. Multispectral data were collected over a 12-mile flightline in White County, Indiana, in connection with the 1971 Corn Blight Watch Experiment. Data were collected in May by the University of Michigan airborne scanning spectrometer at an altitude of 5000 feet. Spectral maps resulting from the analysis were compared to existing soil surveys of the National Cooperative Soil Survey. The method should help determine the extent to which spectral properties of soil surfaces can be associated with morphologic and topographic differences of interest to soil surveyors engaged in operational soil mapping.

Digital soil map of the Ussuri River basin

NASA Astrophysics Data System (ADS)

Bugaets, A. N.; Pschenichnikova, N. F.; Tereshkina, A. A.; Krasnopeev, S. M.; Gartsman, B. I.; Golodnaya, O. M.; Oznobikhin, V. I.

2017-08-01

On the basis of digital soil, topographic, and geological maps; raster topography model; forestry materials; and literature data, the digital soil map of the Ussuri River basin (24400 km2) was created on a scale of 1: 100000. To digitize the initial paper-based maps and analyze the results, an ESRI ArcGIS Desktop (ArcEditor) v.10.1 (http://www.esri.com) and an open-code SAGA GIS v.2.3 (System for Automated Geoscientific Analyses, http://www.saga-gis.org) were used. The spatial distribution of soil areas on the obtained digital soil map is in agreement with modern cartographic data and the SRTM digital elevation model (SRTM DEM). The regional soil classification developed by G.I. Ivanov was used in the legend to the soil map. The names of soil units were also correlated with the names suggested in the modern Russian soil classification system. The major soil units on the map are at the soil subtypes that reflect the entire vertical spectrum of soils in the south of the Far East of Russia (Primorye region). These are mountainous tundra soils, podzolic soils, brown taiga soils, mountainous brown forest soils, bleached brown soils, meadow-brown soils, meadow gley soils, and floodplain soils). With the help of the spatial analysis function of GIS, the comparison of the particular characteristics of the soil cover with numerical characteristics of the topography, geological composition of catchments, and vegetation cover was performed.

Soil-geographical regionalization as a basis for digital soil mapping: Karelia case study

NASA Astrophysics Data System (ADS)

Krasilnikov, P.; Sidorova, V.; Dubrovina, I.

2010-12-01

Recent development of digital soil mapping (DSM) allowed improving significantly the quality of soil maps. We tried to make a set of empirical models for the territory of Karelia, a republic at the North-East of the European territory of Russian Federation. This territory was selected for the pilot study for DSM for two reasons. First, the soils of the region are mainly monogenetic; thus, the effect of paleogeographic environment on recent soils is reduced. Second, the territory was poorly mapped because of low agricultural development: only 1.8% of the total area of the republic is used for agriculture and has large-scale soil maps. The rest of the territory has only small-scale soil maps, compiled basing on the general geographic concepts rather than on field surveys. Thus, the only solution for soil inventory was the predictive digital mapping. The absence of large-scaled soil maps did not allow data mining from previous soil surveys, and only empirical models could be applied. For regionalization purposes, we accepted the division into Northern and Southern Karelia, proposed in the general scheme of soil regionalization of Russia; boundaries between the regions were somewhat modified. Within each region, we specified from 15 (Northern Karelia) to 32 (Southern Karelia) individual soilscapes and proposed soil-topographic and soil-lithological relationships for every soilscape. Further field verification is needed to adjust the models.

Aquifer sensitivity to pesticide leaching: Testing a soils and hydrogeologic index method

USGS Publications Warehouse

Mehnert, E.; Keefer, D.A.; Dey, W.S.; Wehrmann, H.A.; Wilson, S.D.; Ray, C.

2005-01-01

For years, researchers have sought index and other methods to predict aquifer sensitivity and vulnerability to nonpoint pesticide contamination. In 1995, an index method and map were developed to define aquifer sensitivity to pesticide leaching based on a combination of soil and hydrogeologic factors. The soil factor incorporated three soil properties: hydraulic conductivity, amount of organic matter within individual soil layers, and drainage class. These properties were obtained from a digital soil association map. The hydrogeologic factor was depth to uppermost aquifer material. To test this index method, a shallow ground water monitoring well network was designed, installed, and sampled in Illinois. The monitoring wells had a median depth of 7.6 m and were located adjacent to corn and soybean fields where the only known sources of pesticides were those used in normal agricultural production. From September 1998 through February 2001, 159 monitoring wells were sampled for 14 pesticides but no pesticide metabolites. Samples were collected and analyzed to assess the distribution of pesticide occurrence across three units of aquifer sensitivity. Pesticides were detected in 18% of all samples and nearly uniformly from samples from the three units of aquifer sensitivity. The new index method did not predict pesticide occurrence because occurrence was not dependent on the combined soil and hydrogeologic factors. However, pesticide occurrence was dependent on the tested hydrogeologic factor and was three times higher in areas where the depth to the uppermost aquifer was <6 m than in areas where the depth to the uppermost aquifer was 6 to <15 m. Copyright ?? 2005 National Ground Water Association.

Soil magnetic susceptibility mapping as a pollution and provenance tool: an example from southern New Zealand

NASA Astrophysics Data System (ADS)

Martin, A. P.; Ohneiser, C.; Turnbull, R. E.; Strong, D. T.; Demler, S.

2018-02-01

The presence or absence, degree and variation of heavy metal contamination in New Zealand soils is a matter of ongoing debate as it affects soil quality, agriculture and human health. In many instances, however, the soil heavy metal concentration data do not exist to answer these questions and the debate is ongoing. To address this, magnetic susceptibility (a common proxy for heavy metal contamination) values were measured in topsoil (0-30 cm) and subsoil (50-70 cm) at grid sites spaced at 8 km intervals across ca. 20 000 km2 of southern New Zealand. Samples were measured for both mass- and volume-specific magnetic susceptibility, with results being strongly, positively correlated. Three different methods of determining anomalies were applied to the data including the topsoil-subsoil difference method, Tukey boxplot method and geoaccumulation index method, with each method filtering out progressively more anomalies. Additional soil magnetic (hysteresis, isothermal remanence and thermomagnetic) measurements were made on a select subset of samples from anomalous sites. Magnetite is the dominant remanence carrying mineral, and magnetic susceptibility is governed by that minerals concentration in soils, rather than mineral type. All except two anomalous sites have a dominant geogenic source (cf. anthropogenic). By proxy, heavy metal contamination in southern New Zealand soils is minimal, making them relatively pristine. The provenance of the magnetic minerals in the anomalous sites can be traced back to likely sources in outcrops of igneous rocks within the same catchment, terrane or rock type: a distance of <100 km but frequently <1 km. Soil provenance is a key step when mapping element or isotopic distribution, vectoring to mineralization or studying soil for agricultural suitability, water quality or environmental regulation. Measuring soil magnetic susceptibility is a useful, quick and inexpensive tool that usefully supplements soil geochemical data.

Cartographic modeling of heterogeneous landscape for footprint analysis of Eddy Covariance Measurements (Central Forest and Central Chernozem reserves, Russia)

NASA Astrophysics Data System (ADS)

Kozlov, Daniil

2014-05-01

The topographical, soil and vegetation maps of FLUXNET study areas are widely used for interpretation of eddy covariance measurements, for calibration of biogeochemical models and for making regional assessments of carbon balance. The poster presents methodological problems and results of ecosystem mapping using GIS, remote sensing, statistical and field methods on the example of two RusFluxNet sites in the Central Forest (33° E, 56°30'N) and Central Chernozem (36°10' E, 51°36'N) reserves. In the Central Forest reserve tacheometric measurements were used for topographical and peat surveys of bogged sphagnum spruce forest of 20-hectare area. Its common borders and its areas affected by windfall were determined. The supplies and spatial distribution of organic matter were obtained. The datasets of groundwater monitoring measurements on ten wells were compared with each other and the analysis of spatial and temporal groundwater variability was performed. The map of typical ecosystems of the reserve and its surroundings was created on the basis of analysis of multi-temporal Landsat images. In the Central Chernozem reserve the GNSS topographical survey was used for flux tower footprint mapping (22 ha). The features of microrelief predetermine development of different soils within the footprint. Close relationship between soil (73 drilling site) and terrain attributes (DEM with 2.5 m) allowed to build maps of soils and soil properties: carbon content, bulk density, upper boundary of secondary carbonates. Position for chamber-based soil respiration measurements was defined on the basis of these maps. The detailed geodetic and soil surveys of virgin lands and plowland were performed in order to estimate the effect of agrogenic processes such as dehumification, compaction and erosion on soils during the whole period of agricultural use of Central Chernozem reserve area and around. The choice of analogous soils was based on the similarity of their position within the landscape as judged from the terrain attributes of the DEM. The dynamics of soil cover during the last 50 years was estimated on the basis of repetitive detailed surveys of the five key plots conducted in 1963, 1984 and 2013. All results of this study and map analysis conclusions are presented in the poster.

Visualizing Soil Landscapes on Mobile Devices

NASA Astrophysics Data System (ADS)

Schulze, Darrell; Lindbo, David

2016-04-01

The Integrating Spatial Educational Experiences (Isee) project utilizes the most detailed US soil survey data to create thematic maps of soil properties that are then combined with a highly optimized hillshade basemap for display. The Isee app, currently available for the iPad platform from the Apple App Store, allows the cached maps to be zoomed and panned quickly to any location down to a scale of 1:18,000. Maps currently available for the states of Indiana, Illinois, Kentucky, Ohio, Texas, West Virginia, and Wisconsin include, Dominant Soil Parent Materials, Natural Soil Drainage Classes, Limiting Layers, Surface Soil Colors, and Acid Subsoils. Other thematic maps will be added in the future. The ability to zoom, pan, and change maps quickly allows the user to see and understand soil landscape relationships that are not often apparent using static maps, while the ability to access the maps conveniently in the field allows the user to see how soil landscape features on the maps appear in the field.

Can next-generation soil data products improve soil moisture modelling at the continental scale? An assessment using a new microclimate package for the R programming environment

NASA Astrophysics Data System (ADS)

Kearney, Michael R.; Maino, James L.

2018-06-01

Accurate models of soil moisture are vital for solving core problems in meteorology, hydrology, agriculture and ecology. The capacity for soil moisture modelling is growing rapidly with the development of high-resolution, continent-scale gridded weather and soil data together with advances in modelling methods. In particular, the GlobalSoilMap.net initiative represents next-generation, depth-specific gridded soil products that may substantially increase soil moisture modelling capacity. Here we present an implementation of Campbell's infiltration and redistribution model within the NicheMapR microclimate modelling package for the R environment, and use it to assess the predictive power provided by the GlobalSoilMap.net product Soil and Landscape Grid of Australia (SLGA, ∼100 m) as well as the coarser resolution global product SoilGrids (SG, ∼250 m). Predictions were tested in detail against 3 years of root-zone (3-75 cm) soil moisture observation data from 35 monitoring sites within the OzNet project in Australia, with additional tests of the finalised modelling approach against cosmic-ray neutron (CosmOz, 0-50 cm, 9 sites from 2011 to 2017) and satellite (ASCAT, 0-2 cm, continent-wide from 2007 to 2009) observations. The model was forced by daily 0.05° (∼5 km) gridded meteorological data. The NicheMapR system predicted soil moisture to within experimental error for all data sets. Using the SLGA or the SG soil database, the OzNet soil moisture could be predicted with a root mean square error (rmse) of ∼0.075 m3 m-3 and a correlation coefficient (r) of 0.65 consistently through the soil profile without any parameter tuning. Soil moisture predictions based on the SLGA and SG datasets were ≈ 17% closer to the observations than when using a chloropleth-derived soil data set (Digital Atlas of Australian Soils), with the greatest improvements occurring for deeper layers. The CosmOz observations were predicted with similar accuracy (r = 0.76 and rmse of ∼0.085 m3 m-3). Comparisons at the continental scale to 0-2 cm satellite data (ASCAT) showed that the SLGA/SG datasets increased model fit over simulations using the DAAS soil properties (r ∼ 0.63 &rmse 15% vs. r 0.48 &rmse 18%, respectively). Overall, our results demonstrate the advantages of using GlobalSoilMap.net products in combination with gridded weather data for modelling soil moisture at fine spatial and temporal resolution at the continental scale.

Assessment of potential soil degradation on agricultural land in the czech republic.

PubMed

Šarapatka, Bořivoj; Bednář, Marek

2015-01-01

Many attempts have been made worldwide to develop methods to identify the areas most threatened by soil degradation. Some soils in afflicted areas may be irreversibly degraded and thus have very little resilience (the ability to restore themselves). For the purpose of assessing the current state of soil degradation in the Czech Republic (CZ) we have developed an overall indicator of land vulnerability to the threat of soil degradation on the basis of individual factors that contribute to soil degradation and are monitored on a long-term basis in various research worksites in the CZ. Individual degradation factors were divided into two groups: chemical and physical degradation. On the basis of principal component analysis, individual degradation factors were assigned a specific weight of influence. With the use of a GIS, the input factors of degradation were combined to create maps of chemical and physical soil degradation, and consequently a map of overall degradation-threatened soils for the CZ, along with a map of areas differentiated according to the prevailing type of degradation. Results showed that, at present, the most important degradation factor in the CZ is water erosion, followed by loss of organic matter. Statistical analysis showed that approximately 51% of agricultural land is moderately threatened in the CZ. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Horizontal and vertical variability of soil properties in a trace element contaminated area

NASA Astrophysics Data System (ADS)

Burgos, Pilar; Madejón, Engracia; Pérez-de-Mora, Alfredo; Cabrera, Francisco

2008-02-01

The spatial distribution of some soil chemical properties and trace element contents of a plot affected by the Aznalcóllar mine spill were investigated using statistical and geostatistical methods to assess the extent of soil contamination. Total and EDTA-extractable soil trace element concentrations and total S content showed great variability and high coefficients of variation in the three examined depths. Soil in the plot was found to be significantly contaminated by As, Cd, Cu, Pb and Zn within a wide range of pH. Total trace element concentrations at all depths (0-60 cm) were much higher than background values of non-affected soil, indicating that despite the clean-up operations, the concentration of trace elements in the experimental plot was still high. The spatial distribution of the different variables was estimated by kriging to design contour maps. These maps allowed the identification of specific zones with high metal concentrations and low pH values corresponding to spots of residual sludge. Moreover, kriged maps showed distinct spatial distribution and hence different behaviour for the elements considered. This information may be applied to optimise remediation strategies in highly and moderately contaminated areas.

Interpretation of ERTS-MSS images of a Savanna area in eastern Colombia

NASA Technical Reports Server (NTRS)

Elberson, G. W. W.

1973-01-01

The application of ERTS-1 imagery for extrapolating existing soil maps into unmapped areas of the Llanos Orientales of Colombia, South America is discussed. Interpretations of ERTS-1 data were made according to conventional photointerpretation techniques. Most units delineated in the existing reconnaissance soil map at a scale of 1:250,000 could be recognized and delineated in the ERTS image. The methods of interpretation are described and the results obtained for specific areas are analyzed.

DOSoReMI.hu: collection of countrywide DSM products partly according to GSM.net specifications, partly driven by specific user demands

NASA Astrophysics Data System (ADS)

Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor; Illés, Gábor; Bakacsi, Zsófia; Szabó, József

2017-04-01

Due to former soil surveys and mapping activities significant amount of soil information has accumulated in Hungary. In traditional soil mapping the creation of a new map was troublesome and laborious. As a consequence, robust maps were elaborated and rather the demands were fitted to the available map products. Until recently spatial soil information demands have been serviced with the available datasets either in their actual form or after certain specific and often enforced, thematic and spatial inference. Considerable imperfection may occur in the accuracy and reliability of the map products, since there might be significant discrepancies between the available data and the expected information. The DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project was started intentionally for the renewal of the national soil spatial infrastructure in Hungary. During our activities we have significantly extended the potential, how soil information requirements could be satisfied. Soil property, soil type as well as functional soil maps were targeted. The set of the applied digital soil mapping techniques has been gradually broadened incorporating and eventually integrating geostatistical, data mining and GIS tools. Soil property maps have been compiled partly according to GSM.net specifications, partly by slightly or more strictly changing some of their predefined parameters (depth intervals, pixel size, property etc.) according to the specific demands on the final products. The elaborated primary maps were further processed, since even DOSoReMI.hu intended to take steps for the regionalization of higher level soil information (processes, functions, and services) involving crop models in the spatial modelling. The framework of DOSoReMI.hu also provides opportunity for the elaboration of goal specific soil maps, with the prescription of the parameters (thematic, resolution, accuracy, reliability etc.) characterizing the map product. As a result, unique digital soil map products (in a more general meaning) were elaborated regionalizing specific soil (related) features, which were never mapped before, even nationally with high ( 1 ha) spatial resolution. Based upon the collected experiences, the full range of GSM.net products were also targeted. The web publishing of the results was also elaborated creating a proper WMS environment. Our paper will present the resulted national maps furthermore some conclusions drawn from the experiences.] Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA) under Grant K105167 and AGRARKLÍMA.2 VKSZ_12-1-2013-0034.

Magnetic properties of soils in boreal regions. Case study from Ukraine

NASA Astrophysics Data System (ADS)

Menshov, Oleksandr; Kruglov, Oleksandr; Sukhorada, Anatoliy

2014-05-01

The investigation of soil magnetism is a part of the general soil researching for solving soil science and agronomy tasks. Soils are rather magnetic and sometimes they are the main near-surface object, which generates local magnetic anomalies. Soils have been studied within the main soil-climatic zones of Ukraine: Polesie, Forest Steppe, Steppe, Dry Steppe, Crimean and Carpathian mountains. The investigated soils types are: soddy-podsolic, gray forest, chestnut, chernozems leached, typical, ordinary, southern, and meadow, turf, bog soils, brawn and mountains soils. A part of Ukraine soils are from boreal regions. Among them are chernozems of Polesie soil-climatic zone. This territory was under influence of ice age. Another part of Ukraine boreal region is Carpathian maintains with special type of climate, landscapes and soils. The comprehensive analyze of Ukraine soils from the boreal territories and other parts is presented. Soil magnetism increases from North to South in the transition between the soil-climatic zones of Ukraine. The most magnetic are ordinary and south chernozems. The least magnetic are soddy-podzolic, meadaw and bog soils. The maximal values of the magnetic parameters are fixed in the watersheds, plateaus of the landscapes, minimal values are fixed in the floods, ravines, bor terraces. Magnetic susceptibility mapping is useful for agricultural mapping of lands, investigation of erosion, soil fertility, the necessity for mineral and organic fertilizers. Magnetic methods of investigations are high speed, effective and low-cost. Moreover, the magnetic methods a very important if the dangerous soil processes could not be fixed with visual image. In the same time, these hazards effect on the conditioning and the productivity of agricultural land. We have marked the decreasing of the magnetic susceptibility values within the risk of erosion sections of the catena.

Irrigation salinity hazard assessment and risk mapping in the lower Macintyre Valley, Australia.

PubMed

Huang, Jingyi; Prochazka, Melissa J; Triantafilis, John

2016-05-01

In the Murray-Darling Basin of Australia, secondary soil salinization occurs due to excessive deep drainage and the presence of shallow saline water tables. In order to understand the cause and best management, soil and vadose zone information is necessary. This type of information has been generated in the Toobeah district but owing to the state border an inconsistent methodology was used. This has led to much confusion from stakeholders who are unable to understand the ambiguity of the results in terms of final overall risk of salinization. In this research, a digital soil mapping method that employs various ancillary data is presented. Firstly, an electromagnetic induction survey using a Geonics EM34 and EM38 was used to characterise soil and vadose zone stratigraphy. From the apparent electrical conductivity (ECa) collected, soil sampling locations were selected and with laboratory analysis carried out to determine average (2-12m) clay and EC of a saturated soil-paste extract (ECe). EM34 ECa, land surface parameters derived from a digital elevation model and measured soil data were used to establish multiple linear regression models, which allowed for mapping of various hazard factors, including clay and ECe. EM38 ECa data were calibrated to deep drainage obtained from Salt and Leaching Fraction (SaLF) modelling of soil data. Expert knowledge and indicator kriging were used to determine critical values where the salinity hazard factors were likely to contribute to a shallow saline water table (i.e., clay ≤35%; ECe>2.5dS/m, and deep drainage >100mm/year). This information was combined to produce an overall salinity risk map for the Toobeah district using indicator kriging. The risk map shows potential salinization areas and where detailed information is required and where targeted research can be conducted to monitor soil conditions and water table heights and determine best management strategies. Copyright © 2016 Elsevier B.V. All rights reserved.

Spatially Resolved Carbon Isotope and Elemental Analyses of the Root-Rhizosphere-Soil System to Understand Below-ground Nutrient Interactions

NASA Astrophysics Data System (ADS)

Denis, E. H.; Ilhardt, P.; Tucker, A. E.; Huggett, N. L.; Rosnow, J. J.; Krogstad, E. J.; Moran, J.

2017-12-01

The intimate relationships between plant roots, rhizosphere, and soil are fostered by the release of organic compounds from the plant (through various forms of rhizodeposition) into soil and the simultaneous harvesting and delivery of inorganic nutrients from the soil to the plant. This project's main goal is to better understand the spatial controls on bi-directional nutrient exchange through the rhizosphere and how they impact overall plant health and productivity. Here, we present methods being developed to 1) spatially track the release and migration of plant-derived organics into the rhizosphere and soil and 2) map the local inorganic geochemical microenvironments within and surrounding the rhizosphere. Our studies focused on switchgrass microcosms containing soil from field plots at the Kellogg Biological Station (Hickory Corners, Michigan), which have been cropped with switchgrass for nearly a decade. We used a 13CO2 tracer to label our samples for both one and two diel cycles and tracked subsequent movement of labeled organic carbon using spatially specific δ13C analysis (with 50 µm resolution). The laser ablation-isotope ratio mass spectrometry (LA-IRMS) approach allowed us to map the extent of 13C-label migration into roots, rhizosphere, and surrounding soil. Preliminary results show the expected decrease of organic exudates with distance from a root and that finer roots (<0.1 mm) incorporated more 13C-label than thicker roots, which likely correlates to specific root growth rates. We are adapting both laser induced breakdown spectroscopy (LIBS) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to spatially map inorganic nutrient content in the exact same samples used for LA-IRMS analysis. Both of these methods provide rapid surface mapping of a wide range of elements (with high dynamic range) at 150 μm spatial resolution. Preliminary results show that, based on elemental content, we can distinguish between roots, rhizosphere, soil, and specific types of mineral grains within soil. Integrating spatially resolved analysis of photosynthate distribution with local geochemical microenvironments may reveal key properties of nutrient exchange hotspots that help direct overall plant health and productivity.

Permeability of soils in Mississippi

USGS Publications Warehouse

O'Hara, Charles G.

1994-01-01

The permeability of soils in Mississippi was determined and mapped using a geographic information system (GIS). Soil permeabilities in Mississippi were determined to range in value from nearly 0.0 to values exceeding 5.0 inches per hour. The U.S. Soil Conservation Service's State Soil Geographic Data Base (STATSGO) was used as the primary source of data for the determination of area-weighted soil permeability. STATSGO provides soil layer properties that are spatially referenced to mapped areas. These mapped areas are referred to as polygons in the GIS. The polygons arc boundaries of soils mapped as a group and are given unique Map Unit Identifiers (MUIDs). The data describing the physical characteristics of the soils within each polygon are stored in a tabular data base format and are referred to as attributes. The U.S. Soil Conservation Service developed STATSGO to be primarily used as a guide for regional resource planning, management, and monitoring. STATSGO was designed so that soil information could be extracted from properties tables at the layer level, combined by component, and statistically expanded to cover the entire map unit. The results of this study provide a mapped value for permeability which is representative of the vertical permeability of soils in that area. The resultant permeability map provides a representative vertical soil permeability for a given area sufficient for county, multi- county, and area planning, and will be used as the soil permeability data component in the evaluation of the susceptibility of major aquifers to contami- nation in Mississippi.

Time-lapse monitoring of soil water content using electromagnetic conductivity imaging

USDA-ARS?s Scientific Manuscript database

The volumetric soil water content (VWC) is fundamental to agriculture. Unfortunately, the universally accepted thermogravimetric method is labour intensive and time-consuming to use for field-scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatio-...

Fingerprinting: Modelling and mapping physical top soil properties with the Mole

NASA Astrophysics Data System (ADS)

Loonstra, Eddie; van Egmond, Fenny

2010-05-01

The Mole is a passive gamma ray soil sensor system. It is designed for the mobile collection of radioactive energy stemming from soil. As the system is passive, it only measures energy that reaches the surface of soil. In general, this energy comes from upto 30 to 40 cm deep, which can be considered topsoil. The gathered energy spectra are logged every second, are processed with the method of Full Spectrum Analysis. This method uses all available spectral data and processes it with a Chi square optimalisation using a set of standard spectra into individual nuclide point data. A standard spectrum is the measured full spectrum of a specific detector derived when exposed to 1 Bq/kg of a nuclide. With this method the outcome of the surveys become quantitative.The outcome of a field survey with the Mole results in a data file containing point information of position, Total Counts and the decay products of 232Th, 238U, 40K and 137Cs. Five elements are therefor available for the modelling of soil properties. There are several ways for the modelling of soil properties with sensor derived gamma ray data. The Mole generates ratio scale output. For modelling a quantitative deterministic approach is used based on sample locations. This process is called fingerprinting. Fingerprinting is a comparison of the concentration of the radioactive trace elements and the lab results (pH, clay content, etc.) by regression analysis. This results in a mathematical formula describing the relationship between a dependent and independent property. The results of the sensor readings are interpolated into a nuclide map with GIS software. With the derived formula a soil property map is composed. The principle of fingerprinting can be applied on large geographical areas for physical soil properties such as clay, loam or sand (50 micron), grain size and organic matter. Collected sample data of previous field surveys within the same region can be used for the prediction of soil properties elsewhere when adding a relatively small number of new calibration samples. For this purpose stratification of data is necessary. All radioactive trace elements play a part in the fingerprinting process for the mapping of physical soil properties. Clay content is best predicted with 232Th. It has a general R2 of 0.75 up to 0,9. The correlation is positive and basically linear. The variation of loam (or sand) content is very well described by 232Th or the combination of 232Th and 238U. It has a comparable R2 to clay. Grain size can be well modelled with 40K, probably due to the fact that this nuclide is positively correlated with matter. 40K is therefor negatively correlated to grain size. The R2 is good: 0,7 to 0,8 on average. The combination of 40K and 137Cs is generally applied for modelling organic matter content with a quality comparable with that of grain size models. Finally, Total Counts turns out to be a very useful parameter for the identification of different types of parent material and of unnatural or non-parent material. Passive gamma ray soil sensors as the Mole are very suitable for high resolution mapping of physical soil properties. The FSA method has the advantage that data from previous surveys becomes applicable in the fingerprinting procedure of new fields. Being able to model the physical soil properties with gamma ray sensors opens the possibility to run pedotransfer function models for a particular survey.

Concepts of soil mapping as a basis for the assessment of soil functions

NASA Astrophysics Data System (ADS)

Baumgarten, Andreas

2014-05-01

Soil mapping systems in Europe have been designed mainly as a tool for the description of soil characteristics from a morphogenetic viewpoint. Contrasting to the American or FAO system, the soil development has been in the main focus of European systems. Nevertheless , recent developments in soil science stress the importance of the functions of soils with respect to the ecosystems. As soil mapping systems usually offer a sound and extensive database, the deduction of soil functions from "classic" mapping parameters can be used for local and regional assessments. According to the used pedo-transfer functions and mapping systems, tailored approaches can be chosen for different applications. In Austria, a system mainly for spatial planning purposes has been developed that will be presented and illustrated by means of best practice examples.

Using a spatial and tabular database to generate statistics from terrain and spectral data for soil surveys

USGS Publications Warehouse

Horvath , E.A.; Fosnight, E.A.; Klingebiel, A.A.; Moore, D.G.; Stone, J.E.; Reybold, W.U.; Petersen, G.W.

1987-01-01

A methodology has been developed to create a spatial database by referencing digital elevation, Landsat multispectral scanner data, and digitized soil premap delineations of a number of adjacent 7.5-min quadrangle areas to a 30-m Universal Transverse Mercator projection. Slope and aspect transformations are calculated from elevation data and grouped according to field office specifications. An unsupervised classification is performed on a brightness and greenness transformation of the spectral data. The resulting spectral, slope, and aspect maps of each of the 7.5-min quadrangle areas are then plotted and submitted to the field office to be incorporated into the soil premapping stages of a soil survey. A tabular database is created from spatial data by generating descriptive statistics for each data layer within each soil premap delineation. The tabular data base is then entered into a data base management system to be accessed by the field office personnel during the soil survey and to be used for subsequent resource management decisions.Large amounts of data are collected and archived during resource inventories for public land management. Often these data are stored as stacks of maps or folders in a file system in someone's office, with the maps in a variety of formats, scales, and with various standards of accuracy depending on their purpose. This system of information storage and retrieval is cumbersome at best when several categories of information are needed simultaneously for analysis or as input to resource management models. Computers now provide the resource scientist with the opportunity to design increasingly complex models that require even more categories of resource-related information, thus compounding the problem.Recently there has been much emphasis on the use of geographic information systems (GIS) as an alternative method for map data archives and as a resource management tool. Considerable effort has been devoted to the generation of tabular databases, such as the U.S. Department of Agriculture's SCS/S015 (Soil Survey Staff, 1983), to archive the large amounts of information that are collected in conjunction with mapping of natural resources in an easily retrievable manner.During the past 4 years the U.S. Geological Survey's EROS Data Center, in a cooperative effort with the Bureau of Land Management (BLM) and the Soil Conservation Service (SCS), developed a procedure that uses spatial and tabular databases to generate elevation, slope, aspect, and spectral map products that can be used during soil premapping. The procedure results in tabular data, residing in a database management system, that are indexed to the final soil delineations and help quantify soil map unit composition.The procedure was developed and tested on soil surveys on over 600 000 ha in Wyoming, Nevada, and Idaho. A transfer of technology from the EROS Data Center to the BLM will enable the Denver BLM Service Center to use this procedure in soil survey operations on BLM lands. Also underway is a cooperative effort between the EROS Data Center and SCS to define and evaluate maps that can be produced as derivatives of digital elevation data for 7.5-min quadrangle areas, such as those used during the premapping stage of the soil surveys mentioned above, the idea being to make such products routinely available.The procedure emphasizes the applications of digital elevation and spectral data to order-three soil surveys on rangelands, and will:Incorporate digital terrain and spectral data into a spatial database for soil surveys.Provide hardcopy products (that can be generated from digital elevation model and spectral data) that are useful during the soil pre-mapping process.Incorporate soil premaps into a spatial database that can be accessed during the soil survey process along with terrain and spectral data.Summarize useful quantitative information for soil mapping and for making interpretations for resource management.

Retrieval and Mapping of Heavy Metal Concentration in Soil Using Time Series Landsat 8 Imagery

NASA Astrophysics Data System (ADS)

Fang, Y.; Xu, L.; Peng, J.; Wang, H.; Wong, A.; Clausi, D. A.

2018-04-01

Heavy metal pollution is a critical global environmental problem which has always been a concern. Traditional approach to obtain heavy metal concentration relying on field sampling and lab testing is expensive and time consuming. Although many related studies use spectrometers data to build relational model between heavy metal concentration and spectra information, and then use the model to perform prediction using the hyperspectral imagery, this manner can hardly quickly and accurately map soil metal concentration of an area due to the discrepancies between spectrometers data and remote sensing imagery. Taking the advantage of easy accessibility of Landsat 8 data, this study utilizes Landsat 8 imagery to retrieve soil Cu concentration and mapping its distribution in the study area. To enlarge the spectral information for more accurate retrieval and mapping, 11 single date Landsat 8 imagery from 2013-2017 are selected to form a time series imagery. Three regression methods, partial least square regression (PLSR), artificial neural network (ANN) and support vector regression (SVR) are used to model construction. By comparing these models unbiasedly, the best model are selected to mapping Cu concentration distribution. The produced distribution map shows a good spatial autocorrelation and consistency with the mining area locations.

Improvements on mapping soil liquefaction at a regional scale

NASA Astrophysics Data System (ADS)

Zhu, Jing

Earthquake induced soil liquefaction is an important secondary hazard during earthquakes and can lead to significant damage to infrastructure. Mapping liquefaction hazard is important in both planning for earthquake events and guiding relief efforts by positioning resources once the events have occurred. This dissertation addresses two aspects of liquefaction hazard mapping at a regional scale including 1) predictive liquefaction hazard mapping and 2) post-liquefaction cataloging. First, current predictive hazard liquefaction mapping relies on detailed geologic maps and geotechnical data, which are not always available in at-risk regions. This dissertation improves the predictive liquefaction hazard mapping by the development and validation of geospatial liquefaction models (Chapter 2 and 3) that predict liquefaction extent and are appropriate for global application. The geospatial liquefaction models are developed using logistic regression from a liquefaction database consisting of the data from 27 earthquake events from six countries. The model that performs best over the entire dataset includes peak ground velocity (PGV), VS30, distance to river, distance to coast, and precipitation. The model that performs best over the noncoastal dataset includes PGV, VS30, water table depth, distance to water body, and precipitation. Second, post-earthquake liquefaction cataloging historically relies on field investigation that is often limited by time and expense, and therefore results in limited and incomplete liquefaction inventories. This dissertation improves the post-earthquake cataloging by the development and validation of a remote sensing-based method that can be quickly applied over a broad region after an earthquake and provide a detailed map of liquefaction surface effects (Chapter 4). Our method uses the optical satellite images before and after an earthquake event from the WorldView-2 satellite with 2 m spatial resolution and eight spectral bands. Our method uses the changes of spectral variables that are sensitive to surface moisture and soil characteristics paired with a supervised classification.

3D imaging of soil apparent electrical conductivity from VERIS data using a 1D spatially constrained inversion algorithm

NASA Astrophysics Data System (ADS)

Jesús Moral García, Francisco; Rebollo Castillo, Francisco Javier; Monteiro Santos, Fernando

2016-04-01

Maps of apparent electrical conductivity of the soil are commonly used in precision agriculture to indirectly characterize some important properties like salinity, water, and clay content. Traditionally, these studies are made through an empirical relationship between apparent electrical conductivity and properties measured in soil samples collected at a few locations in the experimental area and at a few selected depths. Recently, some authors have used not the apparent conductivity values but the soil bulk conductivity (in 2D or 3D) calculated from measured apparent electrical conductivity through the application of an inversion method. All the published works used data collected with electromagnetic (EM) instruments. We present a new software to invert the apparent electrical conductivity data collected with VERIS 3100 and 3150 (or the more recent version with three pairs of electrodes) using the 1D spatially constrained inversion method (1D SCI). The software allows the calculation of the distribution of the bulk electrical conductivity in the survey area till a depth of 1 m. The algorithm is applied to experimental data and correlations with clay and water content have been established using soil samples collected at some boreholes. Keywords: Digital soil mapping; inversion modelling; VERIS; soil apparent electrical conductivity.

Exploring the potential offered by legacy soil databases for ecosystem services mapping of Central African soils

NASA Astrophysics Data System (ADS)

Verdoodt, Ann; Baert, Geert; Van Ranst, Eric

2014-05-01

Central African soil resources are characterised by a large variability, ranging from stony, shallow or sandy soils with poor life-sustaining capabilities to highly weathered soils that recycle and support large amounts of biomass. Socio-economic drivers within this largely rural region foster inappropriate land use and management, threaten soil quality and finally culminate into a declining soil productivity and increasing food insecurity. For the development of sustainable land use strategies targeting development planning and natural hazard mitigation, decision makers often rely on legacy soil maps and soil profile databases. Recent development cooperation financed projects led to the design of soil information systems for Rwanda, D.R. Congo, and (ongoing) Burundi. A major challenge is to exploit these existing soil databases and convert them into soil inference systems through an optimal combination of digital soil mapping techniques, land evaluation tools, and biogeochemical models. This presentation aims at (1) highlighting some key characteristics of typical Central African soils, (2) assessing the positional, geographic and semantic quality of the soil information systems, and (3) revealing its potential impacts on the use of these datasets for thematic mapping of soil ecosystem services (e.g. organic carbon storage, pH buffering capacity). Soil map quality is assessed considering positional and semantic quality, as well as geographic completeness. Descriptive statistics, decision tree classification and linear regression techniques are used to mine the soil profile databases. Geo-matching as well as class-matching approaches are considered when developing thematic maps. Variability in inherent as well as dynamic soil properties within the soil taxonomic units is highlighted. It is hypothesized that within-unit variation in soil properties highly affects the use and interpretation of thematic maps for ecosystem services mapping. Results will mainly be based on analyses done in Rwanda, but can be complemented with ongoing research results or prospects for Burundi.

Satellite-derived land covers for runoff estimation using SCS-CN method in Chen-You-Lan Watershed, Taiwan

NASA Astrophysics Data System (ADS)

Zhang, Wen-Yan; Lin, Chao-Yuan

2017-04-01

The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.

One perspective on spatial variability in geologic mapping

USGS Publications Warehouse

Markewich, H.W.; Cooper, S.C.

1991-01-01

This paper discusses some of the differences between geologic mapping and soil mapping, and how the resultant maps are interpreted. The role of spatial variability in geologic mapping is addressed only indirectly because in geologic mapping there have been few attempts at quantification of spatial differences. This is largely because geologic maps deal with temporal as well as spatial variability and consider time, age, and origin, as well as composition and geometry. Both soil scientists and geologists use spatial variability to delineate mappable units; however, the classification systems from which these mappable units are defined differ greatly. Mappable soil units are derived from systematic, well-defined, highly structured sets of taxonomic criteria; whereas mappable geologic units are based on a more arbitrary heirarchy of categories that integrate many features without strict values or definitions. Soil taxonomy is a sorting tool used to reduce heterogeneity between soil units. Thus at the series level, soils in any one series are relatively homogeneous because their range of properties is small and well-defined. Soil maps show the distribution of soils on the land surface. Within a map area, soils, which are often less than 2 m thick, show a direct correlation to topography and to active surface processes as well as to parent material.

Aesthetic Cartography

NASA Astrophysics Data System (ADS)

Toland, Alexandra

2017-04-01

Art theorists, Carole Gray and Heather Delday (2010) pose the question, "What might be known through creative practice that could not be known by any other means?" As a visual artist with a doctorate degree in environmental planning from the TU-Berlin's Department of Soil Protection, I have long considered this question in my work and over the years contributed an active voice to discussions on research, education, and public engagement with soil and art and soil and culture in Germany and around the world. After presenting many other examples of artists' work at international scientific symposia, I would like to present examples of some of my own artistic practice with soil mapping and soil protection issues at the 2017 EGU. In combining methods of visual art, landscape analysis, and soil mapping, I have developed a practice called Aesthetic Cartography that employs sculptural techniques, object-making, installation and performance, printing and graphic design, as well as site analysis, data mining, and map reading and interpretation. Given my background in participatory planning practices, I also integrate small-group dialogic processes in the creation and implementation of my works. The projects making up the body of works in Aesthetic Cartography are mainly focused on urban issues, including: soil sealing, inner-city watershed management, creative brownfield use, rubble substrates and leachates, foraging and urban agriculture, and envisioning sustainable cities of the future. In the session SSS1.4 - Soil, Art, and Culture I will summarize project goals, materials and methods, venues and public contexts, elements of collaboration and participation, as well as target audiences involved in several projects of the Aesthetic Cartography series. The aim of the presentation is not to give a comprehensive answer to Gray and Delday's question above, but rather to share personal insights from a professional practice that merges artistic and scientific approaches to soil protection and soil communication.

Binational digital soils map of the Ambos Nogales watershed, southern Arizona and northern Sonora, Mexico

USGS Publications Warehouse

Norman, Laura

2004-01-01

We have prepared a digital map of soil parameters for the international Ambos Nogales watershed to use as input for selected soils-erosion models. The Ambos Nogales watershed in southern Arizona and northern Sonora, Mexico, contains the Nogales wash, a tributary of the Upper Santa Cruz River. The watershed covers an area of 235 km2, just under half of which is in Mexico. Preliminary investigations of potential erosion revealed a discrepancy in soils data and mapping across the United States-Mexican border due to issues including different mapping resolutions, incompatible formatting, and varying nomenclature and classification systems. To prepare a digital soils map appropriate for input to a soils-erosion model, the historical analog soils maps for Nogales, Ariz., were scanned and merged with the larger-scale digital soils data available for Nogales, Sonora, Mexico using a geographic information system.

Soil depth mapping using seismic surface waves: Evaluation on eroded loess covered hillslopes

NASA Astrophysics Data System (ADS)

Bernardie, Severine; Samyn, Kevin; Cerdan, Olivier; Grandjean, Gilles

2010-05-01

The purposes of the multidisciplinary DIGISOIL project are the integration and improvement of in situ and proximal technologies for the assessment of soil properties and soil degradation indicators. Foreseen developments concern sensor technologies, data processing and their integration to applications of (digital) soil mapping (DSM). Among available techniques, the seismic one is, in this study, particularly tested for characterising soil vulnerability to erosion. The spectral analysis of surface waves (SASW) method is an in situ seismic technique used for evaluation of the stiffnesses (G) and associated depth in layered systems. A profile of Rayleigh wave velocity versus frequency, i.e., the dispersion curve, is calculated from each recorded seismogram before to be inverted to obtain the vertical profile of shear wave velocity Vs. Then, the soil stiffness can easily be calculated from the shear velocity if the material density is estimated, and the soil stiffness as a function of depth can be obtained. This last information can be a good indicator to identify the soil bedrock limit. SASW measurements adapted to soil characterisation is proposed in the DIGISOIL project, as it produces in an easy and quick way a 2D map of the soil. This system was tested for the digital mapping of the depth of loamy material in a catchment of the European loess belt. The validation of this methodology has been performed with the realisation of several acquisitions along the seismic profiles: - Several boreholes were drilled until the bedrock, permitting to get the geological features of the soil and the depth of the bedrock; - Several laboratory measurements of various parameters were done on samples taken from the boreholes at various depths, such as dry density, solid density, and water content; - Dynamic penetration tests were also conducted along the seismic profile, until the bedrock is attained. Some empirical correlations between the parameters measured with laboratory tests, the qc obtained from the dynamic penetration tests and the Vs acquired from the SASW measurements permit to assess the accuracy of the procedure and to evaluate its limitations. The depth to bedrock determined by this procedure can then be combined with the soil erosion susceptibility to produce a risk map. This methodology will help to target measures within areas that show a reduced soil depth associated with a high soil erosion susceptibility.

Effectiveness of Vegetation Index Transformation for Land Use Identifying and Mapping in the Area of Oil palm Plantation based on SPOT-6 Imagery (Case Study: PT.Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu)

NASA Astrophysics Data System (ADS)

Setyowati, H. A.; S, S. H. Murti B.; Sukentyas, E. S.

2016-11-01

The reflection of land surface, atmosphere and vegetation conditions affect the reflectance value of the object is recorded on remote sensing image so that it can affect the outcome of information extraction from remote sensing imagery one multispectral classification. This study aims to assess the ability of the transformation of generic vegetation index (Wide Dynamic Range Vegetation Index), the vegetation index transformation that is capable reducing the influence of the atmosphere (Atmospherically Resistant Vegetation Index), and the transformation of vegetation index that is capable of reducing the influence of the background soil (Second Modified Soil Adjusted Vegetation Index) for the identification and mapping of land use in the oil palm plantation area based on SPOT-6 archived on June 13, 2013 from LAPAN. The study area selected oil palm plantations PT. Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu, Riau Province. The method is using the transformation of the vegetation index ARVI, MSAVI2, and WDRVI. Sample selection method used was stratified random sampling. The test method used mapping accuracy of the confusion matrix. The results showed that the best transformation of the vegetation index for the identification and mapping of land use in the plantation area is ARVI transformation with a total of accuracy is 96%. Accuracy of mapping land use settlements 100%, replanting 82.35%, 81.25% young oil palm, old oil palm 99.46%, 100% bush, body of water 100%, and 100% bare-soil.

Application of remote sensing to estimating soil erosion potential

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Kiefer, R. W.

1980-01-01

A variety of remote sensing data sources and interpretation techniques has been tested in a 6136 hectare watershed with agricultural, forest and urban land cover to determine the relative utility of alternative aerial photographic data sources for gathering the desired land use/land cover data. The principal photographic data sources are high altitude 9 x 9 inch color infrared photos at 1:120,000 and 1:60,000 and multi-date medium altitude color and color infrared photos at 1:60,000. Principal data for estimating soil erosion potential include precipitation, soil, slope, crop, crop practice, and land use/land cover data derived from topographic maps, soil maps, and remote sensing. A computer-based geographic information system organized on a one-hectare grid cell basis is used to store and quantify the information collected using different data sources and interpretation techniques. Research results are compared with traditional Universal Soil Loss Equation field survey methods.

Method for the Preparation of Hazard Map in Urban Area Using Soil Depth and Groundwater Level

NASA Astrophysics Data System (ADS)

Kim, Sung-Wook; Choi, Eun-Kyeong; Cho, Jin Woo; Lee, Ju-Hyoung

2017-04-01

The hazard maps for predicting collapse on natural slopes consists of a combination of topographic, hydrological, and geological factors. Topographic factors are extracted from DEM, including aspect, slope, curvature, and topographic index. Hydrological factors, such as distance to drainage, drainage density, stream-power index, and wetness index are most important factors for slope instability. However, most of the urban areas are located on the plains and it is difficult to apply the hazard map using the topography and hydrological factors. In order to evaluate the risk of collapse of flat and low slope areas, soil depth and groundwater level data were collected and used as a factor for interpretation. In addition, the reliability of the hazard map was compared with the disaster history of the study area (Gangnam-gu and Yeouido district). In the disaster map of the disaster prevention agency, the urban area was mostly classified as the stable area and did not reflect the collapse history. Soil depth, drainage conditions and groundwater level obtained from boreholes were added as input data of hazard map, and disaster vulnerability increased at the location where the actual collapse points. In the study area where damage occurred, the moderate and low grades of the vulnerability of previous hazard map were 12% and 88%, respectively. While, the improved map showed 2% high grade, moderate grade 29%, low grade 66% and very low grade 2%. These results were similar to actual damage. Keywords: hazard map, urban area, soil depth, ground water level Acknowledgement This research was supported by a Grant from a Strategic Research Project (Horizontal Drilling and Stabilization Technologies for Urban Search and Rescue (US&R) Operation) funded by the Korea Institute of Civil Engineering and Building Technology.

Assessing soil erosion risk using RUSLE through a GIS open source desktop and web application.

PubMed

Duarte, L; Teodoro, A C; Gonçalves, J A; Soares, D; Cunha, M

2016-06-01

Soil erosion is a serious environmental problem. An estimation of the expected soil loss by water-caused erosion can be calculated considering the Revised Universal Soil Loss Equation (RUSLE). Geographical Information Systems (GIS) provide different tools to create categorical maps of soil erosion risk which help to study the risk assessment of soil loss. The objective of this study was to develop a GIS open source application (in QGIS), using the RUSLE methodology for estimating erosion rate at the watershed scale (desktop application) and provide the same application via web access (web application). The applications developed allow one to generate all the maps necessary to evaluate the soil erosion risk. Several libraries and algorithms from SEXTANTE were used to develop these applications. These applications were tested in Montalegre municipality (Portugal). The maps involved in RUSLE method-soil erosivity factor, soil erodibility factor, topographic factor, cover management factor, and support practices-were created. The estimated mean value of the soil loss obtained was 220 ton km(-2) year(-1) ranged from 0.27 to 1283 ton km(-2) year(-1). The results indicated that most of the study area (80 %) is characterized by very low soil erosion level (<321 ton km(-2) year(-1)) and in 4 % of the studied area the soil erosion was higher than 962 ton km(-2) year(-1). It was also concluded that areas with high slope values and bare soil are related with high level of erosion and the higher the P and C values, the higher the soil erosion percentage. The RUSLE web and the desktop application are freely available.

In situ silicone tube microextraction: a new method for undisturbed sampling of root-exuded thiophenes from marigold (Tagetes erecta L.) in soil.

PubMed

Mohney, Brian K; Matz, Tricia; Lamoreaux, Jessica; Wilcox, David S; Gimsing, Anne Louise; Mayer, Philipp; Weidenhamer, Jeffrey D

2009-11-01

The difficulties of monitoring allelochemical concentrations in soil and their dynamics over time have been a major barrier to testing hypotheses of allelopathic effects. Here, we evaluate three diffusive sampling strategies that employ polydimethylsiloxane (PDMS) sorbents to map the spatial distribution and temporal dynamics of root-exuded thiophenes from the African marigold, Tagetes erecta. Solid phase root zone extraction (SPRE) probes constructed by inserting stainless steel wire into PDMS tubing were used to monitor thiophene concentrations at various depths beneath marigolds growing in PVC pipes. PDMS sheets were used to map the distribution of thiophenes beneath marigolds grown in thin glass boxes. Concentrations of the two major marigold thiophenes measured by these two methods were extremely variable in both space and time. Dissection and analysis of roots indicated that distribution of thiophenes in marigold roots also was quite variable. A third approach used 1 m lengths of PDMS microtubing placed in marigold soil for repeated sampling of soil without disturbance of the roots. The two ends of the tubing remained out of the soil so that solvent could be washed through the tubing to collect samples for HPLC analysis. Unlike the other two methods, initial experiments with this approach show more uniformity of response, and suggest that soil concentrations of marigold thiophenes are affected greatly even by minimal disturbance of the soil. Silicone tube microextraction gave a linear response for alpha-terthienyl when maintained in soils spiked with 0-10 ppm of this thiophene. This method, which is experimentally simple and uses inexpensive materials, should be broadly applicable to the measurement of non-polar root exudates, and thus provides a means to test hypotheses about the role of root exudates in plant-plant and other interactions.

Detection of anomalous crop condition and soil variability mapping using a 26 year Landsat record and the Palmer crop moisture index

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Tagestad, J. D.; Downs, J. L.; Murray, C. J.

2015-07-01

Cost-effective and reliable vegetation monitoring methods are needed for applications ranging from traditional agronomic mapping, to verifying the safety of geologic injection activities. A particular challenge is defining baseline crop conditions and subsequent anomalies from long term imagery records (Landsat) in the face of large spatiotemporal variability. We develop a new method for defining baseline crop response (near peak growth) using the normalized difference vegetation index (NDVI) from 26 years (1986-2011) of Landsat data for 400 km2 surrounding a planned geologic carbon sequestration site near Jacksonville, Illinois. The normal score transform (yNDVI) was applied on a field by field basis to accentuate spatial patterns and level differences due to planting times. We tested crop type and soil moisture (Palmer crop moisture index (CMI)) as predictors of expected crop condition. Spatial patterns in yNDVI were similar between corn and soybeans - the two major crops. Linear regressions between yNDVI and the cumulative CMI (CCMI) exposed complex interactions between crop condition, field location (topography and soils), and annual moisture. Wet toposequence positions (depressions) were negatively correlated to CCMI and dry positions (crests) positively correlated. However, only 21% of the landscape showed a statistically significant (p < 0.05) linear relationship. To map anomalous crop conditions, we defined a tolerance interval based on yNDVI statistics. Tested on an independent image (2013), 63 of 1483 possible fields showed unusual crop condition. While the method is not directly suitable for crop health assessment, the spatial patterns in correlation between yNDVI and CCMI have potential applications for pest damage detection and edaphological soil mapping, especially in the developing world.

Application of multispectral remote sensing to soil survey research in Indiana

NASA Technical Reports Server (NTRS)

Zachary, A. L.; Cipra, J. E.; Diderickson, R. I.; Kristof, S. J.; Baumgardner, M. F.

1972-01-01

Computer-implemented mappings based on spectral properties of bare soil surfaces were compared with mapping units of interest to soil surveyors. Some soil types could be differentiated by their spectral properties. In other cases, soils with similar surface colors and textures could not be distinguished spectrally. The spectral maps seemed useful for delineating boundaries between soils in many cases.

An approach for using soil surveys to guide the placement of water quality buffers

Treesearch

M.G. Dosskey; M.J. Helmers; D.E. Eisenhauer

2006-01-01

Vegetative buffers may function better for filtering agricultural runoff in some locations than in others because of intrinsic characteristics of the land on which they are placed. The objective of this study was to develop a method based on soil survey attributes that can be used to compare soil map units for how effectively a buffer installed in them could remove...

Combining Neutron and Magnetic Resonance Imaging to Study the Interaction of Plant Roots and Soil

NASA Astrophysics Data System (ADS)

Oswald, Sascha E.; Tötzke, Christian; Haber-Pohlmeier, Sabina; Pohlmeier, Andreas; Kaestner, Anders P.; Lehmann, Eberhard

The soil in direct vicinity of the roots, the root-soil interface or so called rhizosphere, is heavily modified by the activity of roots, compared to bulk soil, e.g. in respect to microbiology and soil chemistry. It has turned out that the root-soil interface, though small in size, also plays a decisive role in the hydraulics controlling the water flow from bulk soil into the roots. A promising approach for the non-invasive investigation of water dynamics, water flow and solute transport is the combination of the two imaging techniques magnetic resonance imaging (MRI) and neutron imaging (NI). Both methods are complementary, because NI maps the total proton density, possibly amplified by NI tracers, which usually corresponds to total water content, and is able to detect changes and spatial patterns with high resolution. On the other side, nuclear magnetic resonance relaxation times reflect the interaction between fluid and matrix, while also a mapping of proton spin density and thus water content is possible. Therefore MRI is able to classify different water pools via their relaxation times additionally to the water distribution inside soil as a porous medium. We have started such combined measurements with the approach to use the same samples and perform tomography with each imaging method at different location and short-term sample transfer.

High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia.

PubMed

Wang, Bin; Waters, Cathy; Orgill, Susan; Gray, Jonathan; Cowie, Annette; Clark, Anthony; Liu, De Li

2018-07-15

Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are central in understanding the global carbon cycle and informing related land management decisions. However, mapping SOC stocks in semi-arid rangelands is challenging due to the lack of data and poor spatial coverage. The use of remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide more reliable and cost-effective estimates of SOC compared with field-based, direct measurement. Despite this potential, the role of remote sensing data in improving the knowledge of soil information in semi-arid rangelands has not been fully explored. This study firstly investigated the use of high spatial resolution satellite data (seasonal fractional cover data; SFC) together with elevation, lithology, climatic data and observed soil data to map the spatial distribution of SOC at two soil depths (0-5cm and 0-30cm) in semi-arid rangelands of eastern Australia. Overall, model performance statistics showed that random forest (RF) and boosted regression trees (BRT) models performed better than support vector machine (SVM). The models obtained moderate results with R 2 of 0.32 for SOC stock at 0-5cm and 0.44 at 0-30cm, RMSE of 3.51MgCha -1 at 0-5cm and 9.16MgCha -1 at 0-30cm without considering SFC covariates. In contrast, by including SFC, the model accuracy for predicting SOC stock improved by 7.4-12.7% at 0-5cm, and by 2.8-5.9% at 0-30cm, highlighting the importance of including SFC to enhance the performance of the three modelling techniques. Furthermore, our models produced a more accurate and higher resolution digital SOC stock map compared with other available mapping products for the region. The data and high-resolution maps from this study can be used for future soil carbon assessment and monitoring. Copyright © 2018 Elsevier B.V. All rights reserved.

Mapping soil deformation around plant roots using in vivo 4D X-ray Computed Tomography and Digital Volume Correlation.

PubMed

Keyes, S D; Gillard, F; Soper, N; Mavrogordato, M N; Sinclair, I; Roose, T

2016-06-14

The mechanical impedance of soils inhibits the growth of plant roots, often being the most significant physical limitation to root system development. Non-invasive imaging techniques have recently been used to investigate the development of root system architecture over time, but the relationship with soil deformation is usually neglected. Correlative mapping approaches parameterised using 2D and 3D image data have recently gained prominence for quantifying physical deformation in composite materials including fibre-reinforced polymers and trabecular bone. Digital Image Correlation (DIC) and Digital Volume Correlation (DVC) are computational techniques which use the inherent material texture of surfaces and volumes, captured using imaging techniques, to map full-field deformation components in samples during physical loading. Here we develop an experimental assay and methodology for four-dimensional, in vivo X-ray Computed Tomography (XCT) and apply a Digital Volume Correlation (DVC) approach to the data to quantify deformation. The method is validated for a field-derived soil under conditions of uniaxial compression, and a calibration study is used to quantify thresholds of displacement and strain measurement. The validated and calibrated approach is then demonstrated for an in vivo test case in which an extending maize root in field-derived soil was imaged hourly using XCT over a growth period of 19h. This allowed full-field soil deformation data and 3D root tip dynamics to be quantified in parallel for the first time. This fusion of methods paves the way for comparative studies of contrasting soils and plant genotypes, improving our understanding of the fundamental mechanical processes which influence root system development. Copyright © 2016 Elsevier Ltd. All rights reserved.

Estimation of soil clay and organic matter using two quantitative methods (PLSR and MARS) based on reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Nawar, Said; Buddenbaum, Henning; Hill, Joachim

2014-05-01

A rapid and inexpensive soil analytical technique is needed for soil quality assessment and accurate mapping. This study investigated a method for improved estimation of soil clay (SC) and organic matter (OM) using reflectance spectroscopy. Seventy soil samples were collected from Sinai peninsula in Egypt to estimate the soil clay and organic matter relative to the soil spectra. Soil samples were scanned with an Analytical Spectral Devices (ASD) spectrometer (350-2500 nm). Three spectral formats were used in the calibration models derived from the spectra and the soil properties: (1) original reflectance spectra (OR), (2) first-derivative spectra smoothened using the Savitzky-Golay technique (FD-SG) and (3) continuum-removed reflectance (CR). Partial least-squares regression (PLSR) models using the CR of the 400-2500 nm spectral region resulted in R2 = 0.76 and 0.57, and RPD = 2.1 and 1.5 for estimating SC and OM, respectively, indicating better performance than that obtained using OR and SG. The multivariate adaptive regression splines (MARS) calibration model with the CR spectra resulted in an improved performance (R2 = 0.89 and 0.83, RPD = 3.1 and 2.4) for estimating SC and OM, respectively. The results show that the MARS models have a great potential for estimating SC and OM compared with PLSR models. The results obtained in this study have potential value in the field of soil spectroscopy because they can be applied directly to the mapping of soil properties using remote sensing imagery in arid environment conditions. Key Words: soil clay, organic matter, PLSR, MARS, reflectance spectroscopy.

High resolution digital soil mapping as a future instrument for developing sustainable landuse strategies

NASA Astrophysics Data System (ADS)

Gries, Philipp; Funke, Lisa-Marie; Baumann, Frank; Schmidt, Karsten; Behrens, Thorsten; Scholten, Thomas

2016-04-01

Climate change, increase in population and intensification of land use pose a great challenge for sustainable handling of soils. Intelligent landuse systems are able to minimize and/or avoid soil erosion and loss of soil fertility. A successful application of such systems requires area-wide soil information with high resolution. Containing three consecutive steps, the project INE-2-H („innovative sustainable landuse") at the University of Tuebingen is about creating high-resolution soil information using Digital Soil Mapping (DSM) techniques to develop sustainable landuse strategies. Input data includes soil data from fieldwork (texture and carbon content), the official digital soil and geological map (1:50.000) as well as a wide selection of local, complex and combined terrain parameters. First, soil maps have been created using the DSM approach and Random Forest (RF). Due to high resolution (10x10 m pixels), those maps show a more detailed spatial variability of soil information compared to the official maps used. Root mean square errors (RMSE) of the modelled maps vary from 2.11 % to 6.87 % and the coefficients of determination (R²) go from 0.42 to 0.68. Second, soil erosion potentials have been estimated according to the Universal Soil Loss Equation (USLE). Long-term average annual soil loss ranges from 0.56 to 24.23 [t/ha/a]. Third, combining high-resolution erosion potentials with expert-knowledge of local farmers will result in a landuse system adapted to local conditions. This system will include sustainable strategies reducing soil erosion and conserving soil fertility.

Selenium in soils of the Lower Wasatch Formation, Campbell County, Wyoming: Geochemistry, distribution, and environmental hazards

NASA Technical Reports Server (NTRS)

Kolm, K. E.

1975-01-01

The author has identified the following significant results. Seleniferous Shingle series soils and sandstone outcrops of section 27, T 48 N, R 71 W, Wyoming are mapped on aerial photography by their association with Astragalus bisulcatus. Chemical leachate analyses and atomic absorption methods reveal all studied Samsil and Shingle soils to contain acid, base, and water soluble selenium compounds, and that water extractions showed varied concentration behavior due to soil pH. Acid-soluble selenium is found to be associated with smectite. Statistical analyses confirm that A. bisulcatus presence has a weak influence on soil-lens organic selenium concentration, and determine the importance of other geobotanical factors for convertor presence. Environmental procedures of high selenium lens burial, convertor plant eradication, and revegetated site monitoring are recommended. Usage of density analysis and photographic enlargement are used to successfully produce both a control area and a Campbell County, Wyoming regional map of A. bisulcatus supportive soils and outcrops using Skylab photography.

`VIS/NIR mapping of TOC and extent of organic soils in the Nørre Å valley

NASA Astrophysics Data System (ADS)

Knadel, M.; Greve, M. H.; Thomsen, A.

2009-04-01

Organic soils represent a substantial pool of carbon in Denmark. The need for carbon stock assessment calls for more rapid and effective mapping methods to be developed. The aim of this study was to compare traditional soil mapping with maps produced from the results of a mobile VIS/NIR system and to evaluate the ability to estimate TOC and map the area of organic soils. The Veris mobile VIS/NIR spectroscopy system was compared to traditional manual sampling. The system is developed for in-situ near surface measurements of soil carbon content. It measures diffuse reflectance in the 350 nm-2200 nm region. The system consists of two spectrophotometers mounted on a toolbar and pulled by a tractor. Optical measurements are made through a sapphire window at the bottom of the shank. The shank was pulled at a depth of 5-7 cm at a speed of 4-5 km/hr. 20-25 spectra per second with 8 nm resolution were acquired by the spectrometers. Measurements were made on 10-12 m spaced transects. The system also acquired soil electrical conductivity (EC) for two soil depths: shallow EC-SH (0- 31 cm) and deep conductivity EC-DP (0- 91 cm). The conductivity was recorded together with GPS coordinates and spectral data for further construction of the calibration models. Two maps of organic soils in the Nørre Å valley (Central Jutland) were generated: (i) based on a conventional 25 m grid with 162 sampling points and laboratory analysis of TOC, (ii) based on in-situ VIS/NIR measurements supported by chemometrics. Before regression analysis, spectral information was compressed by calculating principal components. The outliers were determined by a mahalanobis distance equation and removed. Clustering using a fuzzy c- means algorithm was conducted. Within each cluster a location with the minimal spatial variability was selected. A map of 15 representative sample locations was proposed. The interpolation of the spectra into a single spectrum was performed using a Gaussian kernel weighting function. Spectra obtained near a sampled location were averaged. The collected spectra were correlated to TOC of the 15 representative samples using multivariate regression techniques (Unscrambler 9.7; Camo ASA, Oslo, Norway). Two types of calibrations were performed: using only spectra and using spectra together with the auxiliary data (EC-SH and EC-DP). These calibration equations were computed using PLS regression, segmented cross-validation method on centred data (using the raw spectral data, log 1/R). Six different spectra pre-treatments were conducted: (1) only spectra, (2) Savitsky-Golay smoothing over 11 wavelength points and transformation to a (3) 1'st and (4) 2'nd Savitzky and Golay derivative algorithm with a derivative interval of 21 wavelength points, (5) with or (6) without smoothing. The best treatment was considered to be the one with the lowest Root Mean Square Error of Prediction (RMSEP), the highest r2 between the VIS/NIR-predicted and measured values in the calibration model and the lowest mean deviation of predicted TOC values. The best calibration model was obtained with the mathematical pre-treatment's including smoothing, calculating the 2'nd derivative and outlier removal. The two TOC maps were compared after interpolation using kriging. They showed a similar pattern in the TOC distribution. Despite the unfavourable field conditions the VIS/NIR system performed well in both low and high TOC areas. Water content in places exceeding field capacity in the lower parts of the investigated field did not seriously degrade measurements. The present study represents the first attempt to apply the mobile Veris VIS/NIR system to the mapping of TOC of peat soils in Denmark. The result from this study show that a mobile VIS/NIR system can be applied to cost effective TOC mapping of mineral and organic soils with highly varying water content. Key words: VIS/NIR spectroscopy, organic soils, TOC

The History of Soil Mapping and Classification in Europe: The role of the European Commission

NASA Astrophysics Data System (ADS)

Montanarella, Luca

2014-05-01

Early systematic soil mapping in Europe dates back to the early times of soil science in the 19th Century and was developed at National scales mostly for taxation purposes. National soil classification systems emerged out of the various scientific communities active at that time in leading countries like Germany, Austria, France, Belgium, United Kingdom and many others. Different scientific communities were leading in the various countries, in some cases stemming from geological sciences, in others as a branch of agricultural sciences. Soil classification for the purpose of ranking soils for their capacity to be agriculturally productive emerged as the main priority, allowing in some countries for very detailed and accurate soil maps at 1:5,000 scale and larger. Detailed mapping was mainly driven by taxation purposes in the early times but evolved in several countries also as a planning and management tool for farms and local administrations. The need for pan-European soil mapping and classification efforts emerged only after World War II in the early 1950's under the auspices of FAO with the aim to compile a common European soil map as a contribution to the global soil mapping efforts of FAO at that time. These efforts evolved over the next decades, with the support of the European Commission, towards the establishment of a permanent network of National soil survey institutions (the European Soil Bureau Network). With the introduction of digital soil mapping technologies, the new European Soil Information System (EUSIS) was established, incorporating data at multiple scales for the EU member states and bordering countries. In more recent years, the formal establishment of the European Soil Data Centre (ESDAC) hosted by the European Commission, together with a formal legal framework for soil mapping and soil classification provided by the INSPIRE directive and the related standardization and harmonization efforts, has led to the operational development of advanced digital soil mapping techniques supporting the contribution of Europe to a common global soil information system under the coordination of the Global Soil Partnership (GSP) of FAO. Further information: http://eusoils.jrc.ec.europa.eu/ References: Mark G Kibblewhite, Ladislav Miko, Luca Montanarella, Legal frameworks for soil protection: current development and technical information requirements, Current Opinion in Environmental Sustainability, Volume 4, Issue 5, November 2012, Pages 573-577. Luca Montanarella, Ronald Vargas, Global governance of soil resources as a necessary condition for sustainable development, Current Opinion in Environmental Sustainability, Volume 4, Issue 5, November 2012, Pages 559-564.

Remote sensing of soils, land forms, and land use in the northern great plains in preparation for ERTS applications

NASA Technical Reports Server (NTRS)

Frazee, C. J.; Westin, F. C.; Gropper, J.; Myers, V. I.

1972-01-01

Research to determine the optimum time or season for obtaining imagery to identify and map soil limitations was conducted in the proposed Oahe irrigation project area in South Dakota. The optimum time for securing photographs or imagery is when the soil surface patterns are most apparent. For cultivated areas similar to the study area, May is the optimum time. The fields are cultivated or the planted crop has not yet masked soil surface features. Soil limitations in 59 percent of the field of the flight line could be mapped using the above criteria. The remaining fields cannot be mapped because the vegetation or growing crops do not express features related to soil differences. This suggests that imagery from more than one year is necessary to map completely the soil limitations of Oahe area by remote sensing techniques. Imagery from the other times studied is not suitable for identifying and mapping soil limitations of Oahe area by remote sensing techniques. Imagery from the other times studied is not suitable for identifying and mapping soil limitations because the vegetative cover masked the soil surface and does not reflect soil differences.

GPRsurvey as a part of land-use planning in Levi, Finnish Lapland

NASA Astrophysics Data System (ADS)

Kupila, Juho

2010-05-01

The need for detailed information regarding overlying soil layers in townplanning areas has become an important issue, especially in certain areas of Finnish Lapland where the lack of usable soil materials is obvious. Use of ground penetrating radar (GPR) is a fast and cost-effective method of determining the structure of subsurface layers and quantity of soil material above the bedrock surface. This environmental project was carried out by the Geological Survey of Finland together with local enterprises, environmental authorities and an EU structural fund. One of the goals of the project was to use GPR to determine the thickness of soil layers and the differences in material above the bedrock level in certain target areas of the project. The study area is located in the municipality of Kittilä, in the center of the Levi ski resort. The study area (total size of 28 hectares) and surroundings are under fast townplanning and there are, for example, plans for a hotel, apartments and underground garages and service routes, thus it is very important to determine the volume of quarrying. As well, the quality and quantity of existing soil is valid data for the reuse of materials and upcoming construction. One drilling program has already been executed in the area (11 boreholes), so GPR profiles were planned based on this drilling data, soil mapping data and data collected from the townplanning map of the area. According to these earlier drillings and soil mapping, most of the soil in the study area was morainic, so the antenna for the GPR-survey was set at 100 MHz. The positioning method used in this project was VRS-GPS (Virtual Reference Station Global Positioning System), which is a very accurate positioning system to use. Accuracy can be as good as a few centimeters. After the GPR-survey, secondary drilling program was carried out according to the GPR-profiles, thus the total amount of collected data from the planning area was 23 boreholes and 3500 meters of GPR-profiles. In the second phase of the project, all the collected data was used as a reference to build a 3D-model of the planning area. Interpreted GPR-profiles, surface soil map and borehole data formed a database from which an exact model of the study area subsurface was created using GISsoftware. Acquired results show the feasibility of this method to help local actors and authorities in planning and constructing of the area, in present and upcoming projects.

Taxonomic classification of soils using digital information from LANDSAT data. Huayllamarca and eucaliptus areas. M.S. Thesis - Bolivia Univ.

NASA Technical Reports Server (NTRS)

Quiroga, S. Q.

1977-01-01

The applicability of LANDSAT digital information to soil mapping is described. A compilation of all cartographic information and bibliography of the study area is made. LANDSAT MSS images on a scale of 1:250,000 are interpreted and a physiographic map with legend is prepared. The study area is inspected and a selection of the sample areas is made. A digital map of the different soil units is produced and the computer mapping units are checked against the soil units encountered in the field. The soil boundaries obtained by automatic mapping were not substantially changed by field work. The accuracy of the automatic mapping is rather high.

L-band Soil Moisture Mapping using Small UnManned Aerial Systems

NASA Astrophysics Data System (ADS)

Dai, E.

2015-12-01

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 promises to provide global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions as low as 5 km for some products. However, there exists a need for measurements of soil moisture on smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters (i.e., the height of the platform) .Compared with various other proposed methods of validation based on either situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed (~km scale) coverage at very high spatial resolution (~15 m) suitable for scaling scale studies, and at comparatively low operator cost. The LDCR on Tempest unit can supply the soil moisture mapping with different resolution which is of order the Tempest altitude.

Automated mapping of impervious surfaces in urban and suburban areas: Linear spectral unmixing of high spatial resolution imagery

NASA Astrophysics Data System (ADS)

Yang, Jian; He, Yuhong

2017-02-01

Quantifying impervious surfaces in urban and suburban areas is a key step toward a sustainable urban planning and management strategy. With the availability of fine-scale remote sensing imagery, automated mapping of impervious surfaces has attracted growing attention. However, the vast majority of existing studies have selected pixel-based and object-based methods for impervious surface mapping, with few adopting sub-pixel analysis of high spatial resolution imagery. This research makes use of a vegetation-bright impervious-dark impervious linear spectral mixture model to characterize urban and suburban surface components. A WorldView-3 image acquired on May 9th, 2015 is analyzed for its potential in automated unmixing of meaningful surface materials for two urban subsets and one suburban subset in Toronto, ON, Canada. Given the wide distribution of shadows in urban areas, the linear spectral unmixing is implemented in non-shadowed and shadowed areas separately for the two urban subsets. The results indicate that the accuracy of impervious surface mapping in suburban areas reaches up to 86.99%, much higher than the accuracies in urban areas (80.03% and 79.67%). Despite its merits in mapping accuracy and automation, the application of our proposed vegetation-bright impervious-dark impervious model to map impervious surfaces is limited due to the absence of soil component. To further extend the operational transferability of our proposed method, especially for the areas where plenty of bare soils exist during urbanization or reclamation, it is still of great necessity to mask out bare soils by automated classification prior to the implementation of linear spectral unmixing.

Mapping soil water content on golf course greens with GPR

USDA-ARS?s Scientific Manuscript database

Ground-penetrating radar (GPR) can be an effective and efficient method for high-resolution mapping of volumetric water content in the sand layer directly beneath the ground surface at a golf course green. This information could potentially be very useful to golf course superintendents for determi...

Using digital soil maps to infer edaphic affinities of plant species in Amazonia: Problems and prospects.

PubMed

Moulatlet, Gabriel Massaine; Zuquim, Gabriela; Figueiredo, Fernando Oliveira Gouvêa; Lehtonen, Samuli; Emilio, Thaise; Ruokolainen, Kalle; Tuomisto, Hanna

2017-10-01

Amazonia combines semi-continental size with difficult access, so both current ranges of species and their ability to cope with environmental change have to be inferred from sparse field data. Although efficient techniques for modeling species distributions on the basis of a small number of species occurrences exist, their success depends on the availability of relevant environmental data layers. Soil data are important in this context, because soil properties have been found to determine plant occurrence patterns in Amazonian lowlands at all spatial scales. Here we evaluate the potential for this purpose of three digital soil maps that are freely available online: SOTERLAC, HWSD, and SoilGrids. We first tested how well they reflect local soil cation concentration as documented with 1,500 widely distributed soil samples. We found that measured soil cation concentration differed by up to two orders of magnitude between sites mapped into the same soil class. The best map-based predictor of local soil cation concentration was obtained with a regression model combining soil classes from HWSD with cation exchange capacity (CEC) from SoilGrids. Next, we evaluated to what degree the known edaphic affinities of thirteen plant species (as documented with field data from 1,200 of the soil sample sites) can be inferred from the soil maps. The species segregated clearly along the soil cation concentration gradient in the field, but only partially along the model-estimated cation concentration gradient, and hardly at all along the mapped CEC gradient. The main problems reducing the predictive ability of the soil maps were insufficient spatial resolution and/or georeferencing errors combined with thematic inaccuracy and absence of the most relevant edaphic variables. Addressing these problems would provide better models of the edaphic environment for ecological studies in Amazonia.

Neighborhood size of training data influences soil map disaggregation

USDA-ARS?s Scientific Manuscript database

Soil class mapping relies on the ability of sample locations to represent portions of the landscape with similar soil types; however, most digital soil mapping (DSM) approaches intersect sample locations with one raster pixel per covariate layer regardless of pixel size. This approach does not take ...

SoilGrids250m: Global gridded soil information based on machine learning.

PubMed

Hengl, Tomislav; Mendes de Jesus, Jorge; Heuvelink, Gerard B M; Ruiperez Gonzalez, Maria; Kilibarda, Milan; Blagotić, Aleksandar; Shangguan, Wei; Wright, Marvin N; Geng, Xiaoyuan; Bauer-Marschallinger, Bernhard; Guevara, Mario Antonio; Vargas, Rodrigo; MacMillan, Robert A; Batjes, Niels H; Leenaars, Johan G B; Ribeiro, Eloi; Wheeler, Ichsani; Mantel, Stephan; Kempen, Bas

2017-01-01

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

Enhancing SMAP Soil Moisture Retrievals via Superresolution Techniques

NASA Astrophysics Data System (ADS)

Beale, K. D.; Ebtehaj, A. M.; Romberg, J. K.; Bras, R. L.

2017-12-01

Soil moisture is a key state variable that modulates land-atmosphere interactions and its high-resolution global scale estimates are essential for improved weather forecasting, drought prediction, crop management, and the safety of troop mobility. Currently, NASA's Soil Moisture Active/Passive (SMAP) satellite provides a global picture of soil moisture variability at a resolution of 36 km, which is prohibitive for some hydrologic applications. The goal of this research is to enhance the resolution of SMAP passive microwave retrievals by a factor of 2 to 4 using modern superresolution techniques that rely on the knowledge of high-resolution land surface models. In this work, we explore several super-resolution techniques including an empirical dictionary method, a learned dictionary method, and a three-layer convolutional neural network. Using a year of global high-resolution land surface model simulations as training set, we found that we are able to produce high-resolution soil moisture maps that outperform the original low-resolution observations both qualitatively and quantitatively. In particular, on a patch-by-patch basis we are able to produce estimates of high-resolution soil moisture maps that improve on the original low-resolution patches by on average 6% in terms of mean-squared error, and 14% in terms of the structural similarity index.

Assimilation of optical and radar remote sensing data in 3D mapping of soil properties over large areas.

PubMed

Poggio, Laura; Gimona, Alessandro

2017-02-01

Soil is very important for many land functions. To achieve sustainability it is important to understand how soils vary over space in the landscape. Remote sensing data can be instrumental in mapping and spatial modelling of soil properties, resources and their variability. The aims of this study were to compare satellite sensors (MODIS, Landsat, Sentinel-1 and Sentinel-2) with varying spatial, temporal and spectral resolutions for Digital Soil Mapping (DSM) of a set of soil properties in Scotland, evaluate the potential benefits of adding Sentinel-1 data to DSM models, select the most suited mix of sensors for DSM to map the considered set of soil properties and validate the results of topsoil (2D) and whole profile (3D) models. The results showed that the use of a mixture of sensors proved more effective to model and map soil properties than single sensors. The use of radar Sentinel-1 data proved useful for all soil properties, improving the prediction capability of models with only optical bands. The use of MODIS time series provided stronger relationships than the use of temporal snapshots. The results showed good validation statistics with a RMSE below 20% of the range for all considered soil properties. The RMSE improved from previous studies including only MODIS sensor and using a coarser prediction grid. The performance of the models was similar to previous studies at regional, national or continental scale. A mix of optical and radar data proved useful to map soil properties along the profile. The produced maps of soil properties describing both lateral and vertical variability, with associated uncertainty, are important for further modelling and management of soil resources and ecosystem services. Coupled with further data the soil properties maps could be used to assess soil functions and therefore conditions and suitability of soils for a range of purposes. Copyright © 2016 Elsevier B.V. All rights reserved.

LiDAR-derived topographic indices to inform sampling and mapping of soil moisture at the plot to field scale

NASA Astrophysics Data System (ADS)

Kaleita, A. L.

2013-12-01

Identifying field-scale soil moisture patterns, and quantifying their impact on hydrology and nutrient flux, is currently limited by the time and resources required to do sufficient monitoring. A small number of monitoring locations or occasions may not be sufficient to capture the true spatial and temporal dynamics of these patterns. While process models can help to fill in data gaps, it is often difficult if not impossible to effectively parameterize them at the field and sub-field scale. Thus, empirical methods that can optimize sampling and mapping of soil moisture by using a minimal amount of readily available data may be of significant value. LiDAR is one source of such readily available data. Various topographic indices, including relative elevation, land slope, curvature, and slope aspect are known to influence soil moisture patterns, though the exact nature of that relationship appears to vary from study to study. The objective of this study was to use these data to identify critical sampling locations for mapping soil moisture, and to upscale point measurements at those locations to both a single field-average value, and to a high-resolution pattern map for the field. This study analyzed in-situ soil moisture measurements from the working agricultural field in Story County, Iowa. Theta probe soil moisture measurement values were taken every 50 meters on a 300 x 250 meter grid (~18 acres) during the summer growing seasons of 2004, 2005, 2007, and 2008. The elevation in the field varies by approximately 5 meters and the grid covers six different soil types and a variety of different landscape positions throughout the field. We used self-organizing maps (SOMs) and K-means clustering algorithms to split apart the field study area into distinct categories of similarly-characterized locations. We then used the SOM and clustering metrics to identify locations within each group that were representative of the behavior of that group of locations. We developed a weighted upscaling process to estimate a whole-field average soil moisture content from these few critical samples, and we compared the results to those obtained through the more traditional 'temporal stability' approach. The cluster-based approach was as good as and often better than the temporal stability approach, with the significant advantage that the former does not require any initial period of exhaustive soil moisture monitoring, whereas the latter does. A second objective was to use the classification results of the landscape data to interpolate these sparse critical sampling point data over the whole field. Using what we term 'feature-space interpolation' we were able to re-create a high-resolution soil moisture map for the field using only three measurements, by giving locations with similar landscape characteristics similar soil moisture values. The results showed a small but significant statistical improvement over traditional distance-based interpolation methods, and the resulting patterns also had stronger correlation with end-of-season yield, suggesting this approach may have valuable applications in production agriculture decision-making and assessment.

STATEWIDE MAPPING OF FLORIDA SOIL RADON POTENTIALS VOLUME 2. APPENDICES A-P

EPA Science Inventory

The report gives results of a statewide mapping of Florida soil radon potentials. Statewide maps identify Florida Regions with different levels of soil radon potential. The maps provide scientific estimates of regional radon potentials that can serve as a basis for implementing r...

STATEWIDE MAPPING OF FLORIDA SOIL RADON POTENTIALS VOLUME 1. TECHNICAL REPORT

EPA Science Inventory

The report gives results of a statewide mapping of Florida soil radon potentials. Statewide maps identify Florida Regions with different levels of soil radon potential. The maps provide scientific estimates of regional radon potentials that can serve as a basis for implementing r...

Studying of Forests Potentials for Introducing of Mediterranean Industrial Woody Species to Desertification Combating

NASA Astrophysics Data System (ADS)

Mahdavi Najafabadi, R.; Khajeddin, S. J.; Sofyanian, A. R.; Karimzadeh, H. R.; Rezaei, M.

2009-04-01

Most of arid and semiarid parts of the world suffer from great lack of forest land. Therefore taking a good care of these forest lands quantity and quality and control of renewable natural resources is very important. Zagroass forests are located in semiarid parts of Iran. The main purpose of this research is to determine the potential habitat of forest olive for Chaharmahal va Bakhtiary using GIS. This province has a total area of 1653300 hectars. The main steps of this project are as follows: collecting data and maps, digitizing topographic maps with scale of 1:25000, and developing maps of slope, elevation levels, aspect, climatic classification. Regretion analysis was performed on the climatic data and the gradian equations were developed with a high R2 value. Using these equations the following maps were developed. For the whole province: isothermal, isoheytal, abs. max isothermal, relative humidity relative humidity of dry months. Soil maps were also digitized and the information system suitable for this study was developed. Using this bank the following layers were made: land units, soil depth, two soil textures, EC, pH, CaCo3. The following layers were made using digitized data, land use hydraulic network, lake and marsh land. Considering ecological needs of olive and extracting them from all diferent layers using boolean method. The layers showing suitable locations for planting olive(olea europea) was made. One of these maps includes all types of soils suitable for planting olive and the other excludes silty clay loam soils which are not so suitable. The total area achived was 9500 hectars in the whole province and the area excluding silty clay loam soils was determined to be 900 hectars. Using RS information and GIS technology in these types of projects can increase accuracy specialy including some more layers is recommended.

Estimation of Surface Runoff in the Jucar River Basin from Rainfall Data and SMOS Soil Moisture

NASA Astrophysics Data System (ADS)

Garcia Leal, Julio A.; Estrela, Teodoro; Fidalgo, Arancha; Gabaldo, Onofre; Gonzalez Robles, Maura; Herrera Daza, Eddy; Khodayar, Samiro; Lopez-Baeza, Ernesto

2013-04-01

Surface runoff is the water that flows after soil is infiltrated to full capacity and excess water from rain, meltwater, or other sources flows over the land. When the soil is saturated and the depression storage filled, and rain continues to fall, the rainfall will immediately produce surface runoff. The Soil Conservation Service Curve Number (SCS-CN) method is widely used for determining the approximate direct runoff volume for a given rainfall event in a particular area. The advantage of the method is its simplicity and widespread inclusion in existing computer models. It was originally developed by the US Department of Agriculture, Soil Conservation Service, and documented in detail in the National Engineering Handbook, Sect. 4: Hydrology (NEH-4) (USDA-SCS, 1985). Although the SCS-CN method was originally developed in the United States and mainly for the evaluation of storm runoff in small agricultural watersheds, it soon evolved well beyond its original objective and was adopted for various land uses and became an integral part of more complex, long-term, simulation models. The basic assumption of the SCS-CN method is that, for a single storm, the ratio of actual soil retention after runoff begins to potential maximum retention is equal to the ratio of direct runoff to available rainfall. This relationship, after algebraic manipulation and inclusion of simplifying assumptions, results in the following equation given in USDA-SCS (1985): (P--0,2S)2 Q = (P + 0,8S) where Q is the average runoff (mm), P the effective precipitation (mm) and S is potential maximum retention (mm) after the rainfall event. The study has been applied to the Jucar River Basin area, East of Spain. A selection of recent significant rainfall events has been made corresponding to the periods around 22nd November, 2011 and 28-29 September and 10 October, 2012, from Jucar River Basin Authority rain gauge data. Potential maximum retention values for each point have been assumed as the first SMOS soil moisture values available at the closest DGG node immediately after saturation produced by the rain. The results are shown as maps of precipitation and soil moisture obtained using a V4 integration method between a linear and nearest neighbour methods. Surface runoff maps are consequently obtained using the SCS-CN equation given earlier. These results have also been compared to COSMO-CLM model simulations for the same periods. It is envisaged to obtain precipitation maps from MSG-SEVIRI data.

Principles of soil mapping of a megalopolis with St. Petersburg as an example

NASA Astrophysics Data System (ADS)

Aparin, B. F.; Sukhacheva, E. Yu.

2014-07-01

For the first time, a soil map of St. Petersburg has been developed on a scale of 1 : 50000 using MicroStation V8i software. The legend to this map contains more than 60 mapping units. The classification of urban soils and information on the soil cover patterns are principally new elements of this legend. New concepts of the urbanized soil space and urbopedocombinations have been suggested for soil mapping of urban territories. The typification of urbopedocombinations in St. Petersburg has been performed on the basis of data on the geometry and composition of the polygons of soils and nonsoil formations. The ratio between the areas of soils and nonsoil formations and their spatial distribution patterns have been used to distinguish between six types of the urbanized soil space. The principles of classification of the soils of urban territories have been specified, and a separate order of pedo-allochthonous soils has been suggested for inclusion into the Classification and Diagnostic System of Russian Soils (2004). Six types of pedo-allochthonous soils have been distinguished on the basis of data on their humus and organic horizons and the character of the underlying mineral substrate.

Soil property maps of Africa at 250 m resolution

NASA Astrophysics Data System (ADS)

Kempen, Bas; Hengl, Tomislav; Heuvelink, Gerard B. M.; Leenaars, Johan G. B.; Walsh, Markus G.; MacMillan, Robert A.; Mendes de Jesus, Jorge S.; Shepherd, Keith; Sila, Andrew; Desta, Lulseged T.; Tondoh, Jérôme E.

2015-04-01

Vast areas of arable land in sub-Saharan Africa suffer from low soil fertility and physical soil constraints, and significant amounts of nutrients are lost yearly due to unsustainable soil management practices. At the same time it is expected that agriculture in Africa must intensify to meet the growing demand for food and fiber the next decades. Protection and sustainable management of Africa's soil resources is crucial to achieve this. In this context, comprehensive, accurate and up-to-date soil information is an essential input to any agricultural or environmental management or policy and decision-making model. In Africa, detailed soil information has been fragmented and limited to specific zones of interest for decades. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. AfSIS builds on recent advances in digital soil mapping, infrared spectroscopy, remote sensing, (geo)statistics, and integrated soil fertility management to improve the way soils are evaluated, mapped, and monitored. Over the period 2008-2014, the AfSIS project has compiled two soil profile data sets (about 28,000 unique locations): the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site (new soil samples) database -- the two data sets represent the most comprehensive soil sample database of the African continent to date. In addition a large set of high-resolution environmental data layers (covariates) was assembled. The point data were used in the AfSIS project to generate a set of maps of key soil properties for the African continent at 250 m spatial resolution: sand, silt and clay fractions, bulk density, organic carbon, total nitrogen, pH, cation-exchange capacity, exchangeable bases (Ca, K, Mg, Na), exchangeable acidity, and Al content. These properties were mapped for six depth intervals up to 2 m: 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Random forests modelling was used to relate the soil profile observations to a set covariates, that included global soil class and property maps, MODIS imagery and a DEM, in a 3D mapping framework. The model residuals were interpolated by 3D kriging, after which the kriging predictions were added to the random forests predictions to obtain the soil property predictions. The model predictions were validated with 5-fold cross-validation. The random forests models explained between 37% (exch. Na) and 85% (Al content) of the variation in the data. Results also show that globally predicted soil classes help improve continental scale mapping of the soil nutrients and are often among the most important predictors. We conclude that the first mapping results look promising. We used an automated modelling framework that enables re-computing the maps as new data becomes arrives, hereby gradually improving the maps. We showed that global maps of soil classes and properties produced with models that were predominantly calibrated on areas with plentiful observations can be used to improve the accuracy of predictions in regions with less plentiful data, such as Africa.

An overview on the history of pedology and soil mapping in Italy

NASA Astrophysics Data System (ADS)

Calzolari, C.

2012-04-01

In Italy, the word pedology (pedologia) was introduced in a text book as synonym of soil science for the first time in 1904 by Vinassa de Regny. In the literature, the term cohabitates with the words agrology (agrologia), agro-geology (agro-geologia), agricultural geognostic (geognostica agraria), geopedology (geo-pedologia) used in different historical moments by differently rooted soil scientists. When early pedologists started with systematic studies of soils, their characteristics and geography, they were strongly influenced by their cultural background, mainly geology and agro-chemistry. Along the time, the soil concept evolved, as did the concept of pedology, and this is somehow witnessed by the use of different Italian words with reference to soil: suolo, terreno, terra. Differently from agro-chemists, early pedologists based the soil study on the field description of soil profile. This was firstly based on the vertical differentiation between humus rich layers and "inactive" layers and later on, as long as the discipline evolved, on the presence of genetic horizons. The first complete soil map of Italy is dated 1928. Its Author, the geologist De Angelis d'Ossat, was the president of the organising committee of the 1924 International Soil Conference of Rome, where the International Society of Soil Science was founded. The map was based on the geological map of Italy, drafted in scale 1:1,000,000 after the creation of the Kingdom of Italy in 1861. The internal disputes within the Geological Society, together with the scarce interest of most of geologists for soil, did not facilitate the birth of a central soil survey. Soil mapping was mainly conducted by universities and research institutes, and we had to wait until 1953 for a new soil map (scale 1:3,125,000) at national level to be realised by Paolo Principi, based on literature data. In 1966 a new 1:1,000,000 soil map of Italy was eventually published by a national committee, led by Fiorenzo Mancini. This was based on literature data and on field surveys, and the mapping units limits, based on geomorphology, are still the basis of the most updated European 1:1,000,000 soil map. At the end of the 80ies of the past century, soil survey and mapping were taken over by the Italian regional administrations, which set up regional soil surveys working in co-ordination among them and with the research institutions.

Soils of Walker Branch Watershed

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lietzke, D.A.

1994-03-01

The soil survey of Walker Branch Watershed (WBW) utilized the most up-to-date knowledge of soils, geology, and geohydrology in building the soils data base needed to reinterpret past research and to begin new research in the watershed. The soils of WBW were also compared with soils mapped elsewhere along Chestnut Ridge on the Oak Ridge Reservation to (1) establish whether knowledge obtained elsewhere could be used within the watershed, (2) determine whether there were any soils restricted to the watershed, and (3) evaluate geologic formation lateral variability. Soils, surficial geology, and geomorphology were mapped at a scale of 1:1,200 usingmore » a paper base map having 2-ft contour intervals. Most of the contours seemed to reasonably represent actual landform configurations, except for dense wooded areas. For example, the very large dolines or sinkholes were shown on the contour base map, but numerous smaller ones were not. In addition, small drainageways and gullies were often not shown. These often small but important features were located approximately as soil mapping progressed.« less

Digital modelling of landscape and soil in a mountainous region: A neuro-fuzzy approach

NASA Astrophysics Data System (ADS)

Viloria, Jesús A.; Viloria-Botello, Alvaro; Pineda, María Corina; Valera, Angel

2016-01-01

Research on genetic relationships between soil and landforms has largely improved soil mapping. Recent technological advances have created innovative methods for modelling the spatial soil variation from digital elevation models (DEMs) and remote sensors. This generates new opportunities for the application of geomorphology to soil mapping. This study applied a method based on artificial neural networks and fuzzy clustering to recognize digital classes of land surfaces in a mountainous area in north-central Venezuela. The spatial variation of the fuzzy memberships exposed the areas where each class predominates, while the class centres helped to recognize the topographic attributes and vegetation cover of each class. The obtained classes of terrain revealed the structure of the land surface, which showed regional differences in climate, vegetation, and topography and landscape stability. The land-surface classes were subdivided on the basis of the geological substratum to produce landscape classes that additionally considered the influence of soil parent material. These classes were used as a framework for soil sampling. A redundancy analysis confirmed that changes of landscape classes explained the variation in soil properties (p = 0.01), and a Kruskal-Wallis test showed significant differences (p = 0.01) in clay, hydraulic conductivity, soil organic carbon, base saturation, and exchangeable Ca and Mg between classes. Thus, the produced landscape classes correspond to three-dimensional bodies that differ in soil conditions. Some changes of land-surface classes coincide with abrupt boundaries in the landscape, such as ridges and thalwegs. However, as the model is continuous, it disclosed the remaining variation between those boundaries.

Mapping gullies, dunes, lava fields, and landslides via surface roughness

NASA Astrophysics Data System (ADS)

Korzeniowska, Karolina; Pfeifer, Norbert; Landtwing, Stephan

2018-01-01

Gully erosion is a widespread and significant process involved in soil and land degradation. Mapping gullies helps to quantify past, and anticipate future, soil losses. Digital terrain models offer promising data for automatically detecting and mapping gullies especially in vegetated areas, although methods vary widely measures of local terrain roughness are the most varied and debated among these methods. Rarely do studies test the performance of roughness metrics for mapping gullies, limiting their applicability to small training areas. To this end, we systematically explored how local terrain roughness derived from high-resolution Light Detection And Ranging (LiDAR) data can aid in the unsupervised detection of gullies over a large area. We also tested expanding this method for other landforms diagnostic of similarly abrupt land-surface changes, including lava fields, dunes, and landslides, as well as investigating the influence of different roughness thresholds, resolutions of kernels, and input data resolution, and comparing our method with previously published roughness algorithms. Our results show that total curvature is a suitable metric for recognising analysed gullies and lava fields from LiDAR data, with comparable success to that of more sophisticated roughness metrics. Tested dunes or landslides remain difficult to distinguish from the surrounding landscape, partly because they are not easily defined in terms of their topographic signature.

The History of Electromagnetic Induction Techniques in Soil Survey

NASA Astrophysics Data System (ADS)

Brevik, Eric C.; Doolittle, Jim

2014-05-01

Electromagnetic induction (EMI) has been used to characterize the spatial variability of soil properties since the late 1970s. Initially used to assess soil salinity, the use of EMI in soil studies has expanded to include: mapping soil types; characterizing soil water content and flow patterns; assessing variations in soil texture, compaction, organic matter content, and pH; and determining the depth to subsurface horizons, stratigraphic layers or bedrock, among other uses. In all cases the soil property being investigated must influence soil apparent electrical conductivity (ECa) either directly or indirectly for EMI techniques to be effective. An increasing number and diversity of EMI sensors have been developed in response to users' needs and the availability of allied technologies, which have greatly improved the functionality of these tools. EMI investigations provide several benefits for soil studies. The large amount of georeferenced data that can be rapidly and inexpensively collected with EMI provides more complete characterization of the spatial variations in soil properties than traditional sampling techniques. In addition, compared to traditional soil survey methods, EMI can more effectively characterize diffuse soil boundaries and identify included areas of dissimilar soils within mapped soil units, giving soil scientists greater confidence when collecting spatial soil information. EMI techniques do have limitations; results are site-specific and can vary depending on the complex interactions among multiple and variable soil properties. Despite this, EMI techniques are increasingly being used to investigate the spatial variability of soil properties at field and landscape scales.

Using remote sensing and GIS techniques to estimate discharge and recharge fluxes for the Death Valley regional groundwater flow system, USA

USGS Publications Warehouse

D'Agnese, F. A.; Faunt, C.C.; Turner, A.K.; ,

1996-01-01

The recharge and discharge components of the Death Valley regional groundwater flow system were defined by techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were used to calculate discharge volumes for these area. An empirical method of groundwater recharge estimation was modified to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.The recharge and discharge components of the Death Valley regional groundwater flow system were defined by remote sensing and GIS techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. This map provided a basis for subsequent evapotranspiration and infiltration estimations. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were then used to calculate discharge volumes for these areas. A previously used empirical method of groundwater recharge estimation was modified by GIS methods to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.

Mapping Spatial Variability of Soil Salinity in a Coastal Paddy Field Based on Electromagnetic Sensors

PubMed Central

Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi

2015-01-01

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles. PMID:26020969

Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

PubMed

Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi

2015-01-01

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

Mapping fire effects on ash and soil properties. Current knowledge and future perspectives.

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Cerda, Artemi; Strielko, Irina

2014-05-01

Fire has heterogeneous impacts on ash and soil properties, depending on severity, topography of the burned area, type of soil and vegetation affected, and meteorological conditions during and post-fire. The heterogeneous impacts of fire and the complex topography of wildland environments impose the challenge of understand fire effects at diverse scales in space and time. Mapping is fundamental to identify the impacts of fire on ash and soil properties because allow us to recognize the degree of the fire impact, vulnerable areas, soil protection and distribution of ash and soil nutrients, important to landscape recuperation. Several methodologies have been used to map fire impacts on ash soil properties. Burn severity maps are very useful to understand the immediate and long-term impacts of fire on the ecosystems (Wagtendonk et al., 2004; Kokaly et al., 2007). These studies normally are carried out with remote sensing techniques and study large burned areas. On a large scale it is very important to detect the most vulnerable areas (e.g. with risk of runoff increase, flooding, erosion, sedimentation and debris flow) and propose -if necessary- immediate rehabilitation measures. Post-fire rehabilitation measures can be extremely costly. Thus the identification of the most affected areas will reduce the erosion risks and soil degradation (Miller and Yool, 2002; Robichaud et al., 2007; Robichaud, 2009), as the consequent economical, social and ecological impacts. Recently, the United States Department of Agriculture created a field guide to map post-fire burn severity, based on remote sensing and Geographical Information Systems (GIS) technologies. The map produced should reflect the effects of fire on soil properties, and identify areas where fire was more severe (Parsons et al. 2010). Remote sensing studies have made attempts to estimate soil and ash properties after the fire, as hydrophobicity (Lewis et al., 2008), water infiltration (Finnley and Glenn, 2010), forest floor consumption (Lewis et al., 2011), ash cover (Robichaud et al., 2007) and other aspects related with soil as the vegetation factors that affect post-fire erosion risk (Fox et al., 2008). Field studies had also indented to estimate and map the impacts of fire in soil properties. Contrary to remote sensing studies, the mapping of fire effects on ash and soil properties in the field is specially carried out at small scale (e.g. slope or plot). The small scale resolution studies are important because identify small patterns that are normally ignored by remote sensing studies, but fundamental to understand the post-fire evolution of the burned areas. One of the important aspects of the small scale studies of fire effect on ash and soil properties is the great spatial variability, showing that the impact of fire is extremely heterogeneous in space and time (Outeiro et al., 2008; Pereira et al. in press). The small scale mapping of fire effects on soil properties normally is carried out using Geostatistical methods or using deterministic interpolation methods (Robichaud and Miller, 1999; Pereira et al., 2013). Several reports were published on the spatial distribution and mapping of ash and duff thickness (Robichaud and Miller, 1999; Pereira et al., 2013; Pereira et al. in press), fire severity (Pereira et al., 2014), ash chemical characteristics as total nitrogen (Pereira et al., 2010a), and ash extractable elements (Pereira et al., 2010b). Also, previous works mapped fire effects on soil temperature (Gimeno-Garcia et al., 2004), soil hydrophobicity (Woods et al., 2007), total nitrogen (Hirobe et al., 2003), phosphorous (Rodriguez et al., 2009) and major cations (Outeiro et al., 2008). It is important to integrate remote sensing and field based works of fire effects on ash and soil properties in order to have a better validation of the models predicted. The aim of this work is present the current knowledge about mapping fire effects in ash and soil properties at diverse scales and the future perspectives. References Finley, C.D., Glenn, N.F. (2010) Fire and vegetation type effects on soil hydrophobicity and infiltration in the sagebrussh-steppe: II. Hyperspectral analysis. Journal of Arid Environments, 74: 660-666. Fox, D.A., Maselli, F., Carrega, P. (2008) Using SPOT images and field sampling to map burn severity and vegetation factors affecting post-fire erosion risk. Catena, 75: 326-335. Gimeno-Garcia. E., Andreu., V., Rubio, J.L. (2004) Spatial patterns of soil temperatures during experiemntal fires. Geoderma, 118: 17-34. Hirobe, M., Tokushi, N., Wachrinrat, C., Takeda, H. (2003) Fire history influences on the spatial heterogeneity of soil nitrogen transformations in three adjacent stands in a dry tropical forest in Thailand. Plant and Soil, 249: 309-318. Kokaly, R.F., Rockwell, B.W., Haire, S.L., King, T.V.V. (2007) Characterization of post fire surface cover, soils, and burn severity at the Cerro Grande fire, New Mexico, using hyperspectral and multispectral remote sensing. Remote Sensing of the Environment, 106: 305-325. Lewis, S.A., Hudak, A.T., Ottmar, R.D., Robichaud, P.R., Lentile, L.B., Hood, S.M., Cronan, J.B., Morgan, P. (2012) Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of Alaska. International Journal of Wildland Fire, 20: 255-271. Lewis, S.A., Robichaud, P.R., Frazier, B.E., Wu, J.Q., Laes, D.Y.M. (2008) Using hyperspectral imagery to predict post-wildfire soil repellency. Geomorphology, 98, 192-205. Miller, J.D., Yool, S. (2002) Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data. Remote Sensing of the Environment, 82: 481-496. Outeiro, L., Aspero, F., Ubeda, X. (2008) Geostatistical methods to study spatial variability of soil cation after a prescribed fire and rainfall. Catena, 74: 310-320. Parsons, A., Robichaud, P.R., Lewis, S.A., Napper, C., Clark, J.T. (2010) Field guide for mapping post-fire soil burn severity. Gen. Tech. Rep. RMRS-GTR-243. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 49 p. Pereira, P. Úbeda X., Martin D A (2010b) Mapping wildfire effects on Ca2+ and Mg2+ released from ash. A microplot analysis, EGU General Assembly 2010, Geophysical Research Abstracts, 12,EGU 2010 - 30 Vienna. ISSN: 1607-7962. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, (In Press), DOI: 10.1002/ldr.2195 Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J., Jordan, A. Burguet, M. (2013) Spatial models for monitoring the spatio-temporal evolution of ashes after fire - a case study of a burnt grassland in Lithuania, Solid Earth, 4: 153-165. Pereira, P., Úbeda, X., Baltrenaite, E. (2010a) Mapping Total Nitrogen in ash after a Wildfire, a microplot analysis, Ekologija, 56 (3-4), 144-152. Pereira, P., Cerda, A., Ubeda, X., Mataix-Solera, J., Martin, D.A., Jordan, A., Martin, D.A., Mierauskas, P., Arcenegui, V., Zavala, L. (2014) Do fire severity effects change with the time?, What ash tell us, Flamma, 5: 23-27. Robichaud, P.R. (2009) Post-fire stabilization and rehabilitation. In: Cerda, A., Robichaud, P. (eds) Fire Effects on Soils and Restoration Strategies, Science Publishers, 299-320. Robichaud, P.R., Lewis, S.A., Laes, D.Y.M., Hudak, A.T., Kokaly, R.F., Zamudio, J.Z. (2007) Post-fire burn severity mapping with hyperspectral image unmixing. Remote Sensing of the Environment, 108: 467-480. Robichaud, P.R., Miller, S.M. (1999) Spatial interpolation and simulation of post-burn duff thickness after prescribed fire. International Journal of Wildland Fire, 9: 137-143. Rodriguez, A., Duran, J., Fernandez-Palacios, J.M., Gallardo, A. (2009) Short-term wildfire effects on the spatial pattern and scale of labile organic-N and inorganic-N and P pools. Forest Ecology and Management, 257: 739-746. Wagtendonk, J.W., Root, R.R., Key, C.H. (2004) Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sensing of the Environment, 92: 397-408. Woods, S.W., Birkas, A., Ahl, R. (2007) Spatial variability of soil hydrophobicity after wildfires in Montana and Colorado. Geomorphology, 86: 465-479.

Digital soils survey map of the Patagonia Mountains, Arizona

USGS Publications Warehouse

Norman, Laura; Wissler, Craig; Guertin, D. Phillip; Gray, Floyd

2002-01-01

The ‘Soil Survey of Santa Cruz and Parts of Cochise and Pima Counties, Arizona,' a product of the USDA’s Soil Conservation Service and the Forest Service in cooperation with the Arizona Agricultural Experiment Station, released in 1979, was created according to the site conditions in 1971, when soil scientists identified soils types on aerial photographs. The scale at which these maps were published is 1:20,000. These soil maps were automated for incorporation into the hydrologic modeling within a GIS. The aerial photos onto which the soils units were drawn had not been orthoganalized, and contained distortion. A total of 15 maps composed the study area. These maps were scanned into TIFF format using an 8-bit black and white drum scanner at 100 dpi. The images were imported into ERDAS IMAGINE and the white borders were removed through subset decollaring processes. Five CD-ROM’s containing Digital Orthophoto Quarter Quads (DOQQ’s) were used to register and rectify the scanned soils maps. Polygonal data was then attributed according to the datasets.

Geochemical and mineralogical maps for soils of the conterminous United States

USGS Publications Warehouse

Smith, David B.; Cannon, William F.; Woodruff, Laurel G.; Solano, Federico; Ellefsen, Karl J.

2014-01-01

The U.S. Geological Survey began sampling in 2007 for a low-density (1 site per 1,600 square kilometers, 4,857 sites) geochemical and mineralogical survey of soils in the conterminous United States as part of the North American Soil Geochemical Landscapes Project. The sampling protocol for the national-scale survey included, at each site, a sample from a depth of 0 to 5 centimeters, a composite of the soil A horizon, and a deeper sample from the soil C horizon or, if the top of the C horizon was at a depth greater than 1 meter, a sample from a depth of approximately 80–100 centimeters. The <2-millimeter fraction of each sample was analyzed for a suite of 45 major and trace elements by methods that yield the total or near-total elemental content. The major mineralogical components in the samples from the soil A and C horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. Sampling in the conterminous United States was completed in 2010, with chemical and mineralogical analyses completed in May 2013. The resulting data set provides an estimate of the abundance and spatial distribution of chemical elements and minerals in soils of the conterminous United States and represents a baseline for soil geochemistry and mineralogy against which future changes may be recognized and quantified. This report releases geochemical and mineralogical maps along with a histogram, boxplot, and empirical cumulative distribution function plot for each element or mineral.

Application of remote sensing technology to land evaluation, planning utilization of land resources, and assessment of wildlife areas in eastern South Dakota

NASA Technical Reports Server (NTRS)

1975-01-01

A soils map for land evaluation in Potter County (Eastern South Dakota) was developed to demonstrate the use of remote sensing technology in the area of diverse parent materials and topography. General land use and soils maps have also been developed for land planning LANDSAT, RB-57 imagery, and USGS photographs are being evaluated for making soils and land use maps. LANDSAT fulfilled the requirements for general land use and a general soils map. RB-57 imagery supplemented by large scale black and white stereo coverage was required to provide the detail needed for the final soils map for land evaluation. Color infrared prints excelled black and white coverage for this soil mapping effort. An identification and classification key for wetland types in the Lake Dakota Plain was developed for June 1975 using color infrared imagery. Wetland types in the region are now being mapped via remote sensing techniques to provide a current inventory for development of mitigation measures.

The Dokuchaev hypothesis as a basis for predictive digital soil mapping (on the 125th anniversary of its publication)

NASA Astrophysics Data System (ADS)

Florinsky, I. V.

2012-04-01

Predictive digital soil mapping is widely used in soil science. Its objective is the prediction of the spatial distribution of soil taxonomic units and quantitative soil properties via the analysis of spatially distributed quantitative characteristics of soil-forming factors. Western pedometrists stress the scientific priority and principal importance of Hans Jenny's book (1941) for the emergence and development of predictive soil mapping. In this paper, we demonstrate that Vasily Dokuchaev explicitly defined the central idea and statement of the problem of contemporary predictive soil mapping in the year 1886. Then, we reconstruct the history of the soil formation equation from 1899 to 1941. We argue that Jenny adopted the soil formation equation from Sergey Zakharov, who published it in a well-known fundamental textbook in 1927. It is encouraging that this issue was clarified in 2011, the anniversary year for publications of Dokuchaev and Jenny.

A Comparison of Fuzzy Models in Similarity Assessment of Misregistered Area Class Maps

NASA Astrophysics Data System (ADS)

Brown, Scott

Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggregation, and the epsilon band. The fifth method used conventional crisp classification. I applied these methods to a case study map and simulated data in two sets: a test set with misregistration error, and a control set with equivalent uniform random error. For all five methods, I used raw accuracy or the kappa statistic to measure similarity. Rough-set epsilon bands report the most similarity increase in test maps relative to control data. Conversely, the fuzzy inference model reports a decrease in test map similarity.

Urban cover mapping using digital, high-resolution aerial imagery

Treesearch

Soojeong Myeong; David J. Nowak; Paul F. Hopkins; Robert H. Brock

2003-01-01

High-spatial resolution digital color-infrared aerial imagery of Syracuse, NY was analyzed to test methods for developing land cover classifications for an urban area. Five cover types were mapped: tree/shrub, grass/herbaceous, bare soil, water and impervious surface. Challenges in high-spatial resolution imagery such as shadow effect and similarity in spectral...

Design of synthetic soil images using the Truncated Multifractal method

NASA Astrophysics Data System (ADS)

Sotoca, Juan J. Martin; Saa-Requejo, Antonio; López de Herrera, Juan; Grau, Juan B.

2017-04-01

The use of synthetic images in soils is an increasingly used resource when comparing different segmentation methods. This type of images can simulate features of the real soil images. We can find examples of 2D and 3D synthetic soil images in the studies by Zhang (2001), Schlüter et al. (2010) and Wang et al. (2011). The aim of this presentation is to show an improved version of the Truncated Multifractal method (TMM) which was initially introduced by Martín-Sotoca et al. (2016a, 2016b). The TMM is able to construct a 3D synthetic soil image that is composed of a known air-filled pore space and a background space, which includes, as a novelty, a pebble space. The pebble space simulates the pebbles or granules of high intensity that typically appear in computed tomography (CT) soil images. The TMM can simulate the two main characteristics of the CT soil images: the scaling nature of the pore space and the low contrast at the solid/pore interface with non-bimodal greyscale value histograms. In this presentation we introduce some new components which improve the similitude between real and synthetic CT soil images. REFERENCES Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2016a). New segmentation method based on fractal properties using singularity maps. Geoderma, doi: 10.1016/j.geoderma.2016.09.005 Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B., Tarquis, A.M. (2016b). Local 3D segmentation of soil pore space based on fractal properties using singularity maps. Geoderma, doi: 10.1016/j.geoderma.2016.11.029 Schlüter, S., Weller, U., Vogel, H.J., (2010). Thresholding of X-ray microtomography images of soil using gradient masks. Comput. Geosci. 36, 1246-1251 Wang, W., Kravchenko, A.N., Smucker, A.J.M., Rivers, M.L. (2011). Comparison of image segmentation methods in simulated 2D and 3D microtomographic images of soil aggregates. Geoderma, 162, 231-241 Zhang, Y.J. (2001). A review of recent evaluation methods for image segmentation: International symposium on signal processing and its applications. Kuala Lumpur, Malaysia, 13-16, pp. 148-151

A study of the utilization of ERTS-1 data from the Wabash River Basin. [crop identification, water resources, urban land use, soil mapping, and atmospheric modeling

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The most significant results were obtained in the water resources research, urban land use mapping, and soil association mapping projects. ERTS-1 data was used to classify water bodies to determine acreages and high agreement was obtained with USGS figures. Quantitative evaluation was achieved of urban land use classifications from ERTS-1 data and an overall test accuracy of 90.3% was observed. ERTS-1 data classifications of soil test sites were compared with soil association maps scaled to match the computer produced map and good agreement was observed. In some cases the ERTS-1 results proved to be more accurate than the soil association map.

Assessment of the postagrogenic transformation of soddy-podzolic soils: Cartographic and analytic support

NASA Astrophysics Data System (ADS)

Sorokina, N. P.; Kozlov, D. N.; Kuznetsova, I. V.

2013-10-01

The results of experimental studies of the postagrogenic transformation of loamy soddy-podzolic soils on the southern slope of the Klin-Dmitrov Moraine Ridge are discussed. A chronosequence of soils (arable soils (cropland)-soils under fallow with meadow vegetation-soils under secondary forests of different ages-soils under a conventionally initial native forest) was examined, and the stages of the postagrogenic transformation of the automorphic soddy-podzolic soils were identified. The differentiation of the former plow horizon into the A1 and A1A2 horizons (according to the differences in the humus content, texture, and acidity) served as the major criterion of the soil transformation. A stage of textural differentiation with clay depletion from the uppermost layer was identified in the soils of the 20- to 60-year-old fallows. The specificity of the postagrogenic transformation of the soils on the slopes was demonstrated. From the methodological point of view, it was important to differentiate between the chronosequences of automorphic and semihydromorphic soils of the leveled interfluves and the soils of the slopes. For this purpose, a series of maps reflecting the history of the land use and the soil cover pattern was analyzed. The cartographic model included the attribute data of the soil surveys, the cartographic sources (a series of historical maps of the land use, topographic maps, remote sensing data, and a digital elevation model), and two base maps: (a) the integral map of the land use and (b) the map of the soil combinations with the separation of the zonal automorphic, semihydromorphic, and erosional soil combinations. This scheme served as a matrix for the organization and analysis of the already available and new materials.

Predicting and mapping soil available water capacity in Korea.

PubMed

Hong, Suk Young; Minasny, Budiman; Han, Kyung Hwa; Kim, Yihyun; Lee, Kyungdo

2013-01-01

The knowledge on the spatial distribution of soil available water capacity at a regional or national extent is essential, as soil water capacity is a component of the water and energy balances in the terrestrial ecosystem. It controls the evapotranspiration rate, and has a major impact on climate. This paper demonstrates a protocol for mapping soil available water capacity in South Korea at a fine scale using data available from surveys. The procedures combined digital soil mapping technology with the available soil map of 1:25,000. We used the modal profile data from the Taxonomical Classification of Korean Soils. The data consist of profile description along with physical and chemical analysis for the modal profiles of the 380 soil series. However not all soil samples have measured bulk density and water content at -10 and -1500 kPa. Thus they need to be predicted using pedotransfer functions. Furthermore, water content at -10 kPa was measured using ground samples. Thus a correction factor is derived to take into account the effect of bulk density. Results showed that Andisols has the highest mean water storage capacity, followed by Entisols and Inceptisols which have loamy texture. The lowest water retention is Entisols which are dominated by sandy materials. Profile available water capacity to a depth of 1 m was calculated and mapped for Korea. The western part of the country shows higher available water capacity than the eastern part which is mountainous and has shallower soils. The highest water storage capacity soils are the Ultisols and Alfisols (mean of 206 and 205 mm, respectively). Validation of the maps showed promising results. The map produced can be used as an indication of soil physical quality of Korean soils.

Predicting active-layer soil thickness using topographic variables at a small watershed scale

PubMed Central

Li, Aidi; Tan, Xing; Wu, Wei; Liu, Hongbin; Zhu, Jie

2017-01-01

Knowledge about the spatial distribution of active-layer (AL) soil thickness is indispensable for ecological modeling, precision agriculture, and land resource management. However, it is difficult to obtain the details on AL soil thickness by using conventional soil survey method. In this research, the objective is to investigate the possibility and accuracy of mapping the spatial distribution of AL soil thickness through random forest (RF) model by using terrain variables at a small watershed scale. A total of 1113 soil samples collected from the slope fields were randomly divided into calibration (770 soil samples) and validation (343 soil samples) sets. Seven terrain variables including elevation, aspect, relative slope position, valley depth, flow path length, slope height, and topographic wetness index were derived from a digital elevation map (30 m). The RF model was compared with multiple linear regression (MLR), geographically weighted regression (GWR) and support vector machines (SVM) approaches based on the validation set. Model performance was evaluated by precision criteria of mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Comparative results showed that RF outperformed MLR, GWR and SVM models. The RF gave better values of ME (0.39 cm), MAE (7.09 cm), and RMSE (10.85 cm) and higher R2 (62%). The sensitivity analysis demonstrated that the DEM had less uncertainty than the AL soil thickness. The outcome of the RF model indicated that elevation, flow path length and valley depth were the most important factors affecting the AL soil thickness variability across the watershed. These results demonstrated the RF model is a promising method for predicting spatial distribution of AL soil thickness using terrain parameters. PMID:28877196

Spatial variability of soil carbon, pH, available phosphorous and potassium in organic farm located in Mediterranean Croatia

NASA Astrophysics Data System (ADS)

Bogunović, Igor; Pereira, Paulo; Šeput, Miranda

2016-04-01

Soil organic carbon (SOC), pH, available phosphorus (P), and potassium (K) are some of the most important factors to soil fertility. These soil parameters are highly variable in space and time, with implications to crop production. The aim of this work is study the spatial variability of SOC, pH, P and K in an organic farm located in river Rasa valley (Croatia). A regular grid (100 x 100 m) was designed and 182 samples were collected on Silty Clay Loam soil. P, K and SOC showed moderate heterogeneity with coefficient of variation (CV) of 21.6%, 32.8% and 51.9%, respectively. Soil pH record low spatial variability with CV of 1.5%. Soil pH, P and SOC did not follow normal distribution. Only after a Box-Cox transformation, data respected the normality requirements. Directional exponential models were the best fitted and used to describe spatial autocorrelation. Soil pH, P and SOC showed strong spatial dependence with nugget to sill ratio with 13.78%, 0.00% and 20.29%, respectively. Only K recorded moderate spatial dependence. Semivariogram ranges indicate that future sampling interval could be 150 - 200 m in order to reduce sampling costs. Fourteen different interpolation models for mapping soil properties were tested. The method with lowest Root Mean Square Error was the most appropriated to map the variable. The results showed that radial basis function models (Spline with Tension and Completely Regularized Spline) for P and K were the best predictors, while Thin Plate Spline and inverse distance weighting models were the least accurate. The best interpolator for pH and SOC was the local polynomial with the power of 1, while the least accurate were Thin Plate Spline. According to soil nutrient maps investigated area record very rich supply with K while P supply was insufficient on largest part of area. Soil pH maps showed mostly neutral reaction while individual parts of alkaline soil indicate the possibility of penetration of seawater and salt accumulation in the soil profile. Future research should focus on spatial patterns on soil pH, electrical conductivity and sodium adsorption ratio. Keywords: geostatistics, semivariogram, interpolation models, soil chemical properties

Design of a Horizontal Penetrometer for Measuring On-the-Go Soil Resistance

PubMed Central

Topakci, Mehmet; Unal, Ilker; Canakci, Murad; Celik, Huseyin Kursat; Karayel, Davut

2010-01-01

Soil compaction is one of the main negative factors that limits plant growth and crop yield. Therefore, it is important to determine the soil resistance level and map it for the field to find solutions for the negative effects of the compaction. Nowadays, high powered communication technology and computers help us on this issue within the approach of precision agriculture applications. This study is focused on the design of a penetrometer, which can make instantaneous soil resistance measurements in the soil horizontally and data acquisition software based on the GPS (Global Positioning System). The penetrometer was designed using commercial 3D parametric solid modelling design software. The data acquisition software was developed in Microsoft Visual Basic.NET programming language. After the design of the system, manufacturing and assembly of the system was completed and then a field experiment was carried out. According to the data from GPS and penetration resistance values which are collected in Microsoft SQL Server database, a Kriging method by ArcGIS was used and soil resistance was mapped in the field for a soil depth of 40 cm. During operation, no faults, either in mechanical and software parts, were seen. As a result, soil resistance values of 0.2 MPa and 3 MPa were obtained as minimum and maximum values, respectively. In conclusion, the experimental results showed that the designed system works quite well in the field and the horizontal penetrometer is a practical tool for providing on-line soil resistance measurements. This study contributes to further research for the development of on-line soil resistance measurements and mapping within the precision agriculture applications. PMID:22163410

Design of a horizontal penetrometer for measuring on-the-go soil resistance.

PubMed

Topakci, Mehmet; Unal, Ilker; Canakci, Murad; Celik, Huseyin Kursat; Karayel, Davut

2010-01-01

Soil compaction is one of the main negative factors that limits plant growth and crop yield. Therefore, it is important to determine the soil resistance level and map it for the field to find solutions for the negative effects of the compaction. Nowadays, high powered communication technology and computers help us on this issue within the approach of precision agriculture applications. This study is focused on the design of a penetrometer, which can make instantaneous soil resistance measurements in the soil horizontally and data acquisition software based on the GPS (Global Positioning System). The penetrometer was designed using commercial 3D parametric solid modelling design software. The data acquisition software was developed in Microsoft Visual Basic.NET programming language. After the design of the system, manufacturing and assembly of the system was completed and then a field experiment was carried out. According to the data from GPS and penetration resistance values which are collected in Microsoft SQL Server database, a Kriging method by ArcGIS was used and soil resistance was mapped in the field for a soil depth of 40 cm. During operation, no faults, either in mechanical and software parts, were seen. As a result, soil resistance values of 0.2 MPa and 3 MPa were obtained as minimum and maximum values, respectively. In conclusion, the experimental results showed that the designed system works quite well in the field and the horizontal penetrometer is a practical tool for providing on-line soil resistance measurements. This study contributes to further research for the development of on-line soil resistance measurements and mapping within the precision agriculture applications.

Spatial distribution of pH and organic matter in urban soils and its implications on site-specific land uses in Xuzhou, China.

PubMed

Mao, Yingming; Sang, Shuxun; Liu, Shiqi; Jia, Jinlong

2014-05-01

The spatial variation of soil pH and soil organic matter (SOM) in the urban area of Xuzhou, China, was investigated in this study. Conventional statistics, geostatistics, and a geographical information system (GIS) were used to produce spatial distribution maps and to provide information about land use types. A total of 172 soil samples were collected based on grid method in the study area. Soil pH ranged from 6.47 to 8.48, with an average of 7.62. SOM content was very variable, ranging from 3.51 g/kg to 17.12 g/kg, with an average of 8.26 g/kg. Soil pH followed a normal distribution, while SOM followed a log-normal distribution. The results of semi-variograms indicated that soil pH and SOM had strong (21%) and moderate (44%) spatial dependence, respectively. The variogram model was spherical for soil pH and exponential for SOM. The spatial distribution maps were achieved using kriging interpolation. The high pH and high SOM tended to occur in the mixed forest land cover areas such as those in the southwestern part of the urban area, while the low values were found in the eastern and the northern parts, probably due to the effect of industrial and human activities. In the central urban area, the soil pH was low, but the SOM content was high, which is mainly attributed to the disturbance of regional resident activities and urban transportation. Furthermore, anthropogenic organic particles are possible sources of organic matter after entering the soil ecosystem in urban areas. These maps provide useful information for urban planning and environmental management. Copyright © 2014 Académie des sciences. Published by Elsevier SAS. All rights reserved.

The application of remote sensing technology to the solution of problems in the management of resources in Indiana

NASA Technical Reports Server (NTRS)

Weismiller, R. A.; Mroczynski, R. P. (Principal Investigator)

1978-01-01

The author has identified the following significant results. Of the sampling techniques considered, a combination soil mapping and area sampling offered the most practical method for gathering soils data. Using the dot grid count, a relative percentage composition of soils can be calculated for each spectral class. From these percentages, a legend describing the dominant soils and inclusions can be developed. Interval drainage class seemed to be correlated with magnitude. For every parent material area, the more poorly drained soils had a lower magnitude of reflectance. Soil spectral classes seemed to be predominantly one internal drainage class.

Postfire soil burn severity mapping with hyperspectral image unmixing

USGS Publications Warehouse

Robichaud, P.R.; Lewis, S.A.; Laes, D.Y.M.; Hudak, A.T.; Kokaly, R.F.; Zamudio, J.A.

2007-01-01

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r2 = 0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat-derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r2 = 0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes. ?? 2006 Elsevier Inc. All rights reserved.

Integrating depth functions and hyper-scale terrain analysis for 3D soil organic carbon modeling in agricultural fields at regional scale

NASA Astrophysics Data System (ADS)

Ramirez-Lopez, L.; van Wesemael, B.; Stevens, A.; Doetterl, S.; Van Oost, K.; Behrens, T.; Schmidt, K.

2012-04-01

Soil Organic Carbon (SOC) represents a key component in the global C cycle and has an important influence on the global CO2 fluxes between terrestrial biosphere and atmosphere. In the context of agricultural landscapes, SOC inventories are important since soil management practices have a strong influence on CO2 fluxes and SOC stocks. However, there is lack of accurate and cost-effective methods for producing high spatial resolution of SOC information. In this respect, our work is focused on the development of a three dimensional modeling approach for SOC monitoring in agricultural fields. The study area comprises ~420 km2 and includes 4 of the 5 agro-geological regions of the Grand-Duchy of Luxembourg. The soil dataset consist of 172 profiles (1033 samples) which were not sampled specifically for this study. This dataset is a combination of profile samples collected in previous soil surveys and soil profiles sampled for other research purposes. The proposed strategy comprises two main steps. In the first step the SOC distribution within each profile (vertical distribution) is modeled. Depth functions for are fitted in order to summarize the information content in the profile. By using these functions the SOC can be interpolated at any depth within the profiles. The second step involves the use of contextual terrain (ConMap) features (Behrens et al., 2010). These features are based on the differences in elevation between a given point location in the landscape and its circular neighbourhoods at a given set of different radius. One of the main advantages of this approach is that it allows the integration of several spatial scales (eg. local and regional) for soil spatial analysis. In this work the ConMap features are derived from a digital elevation model of the area and are used as predictors for spatial modeling of the parameters of the depth functions fitted in the previous step. In this poster we present some preliminary results in which we analyze: i. The use of different depth functions, ii. The use of different machine learning approaches for modeling the parameters of the fitted depth functions using the ConMap features and iii. The influence of different spatial scales on the SOC profile distribution variability. Keywords: 3D modeling, Digital soil mapping, Depth functions, Terrain analysis. Reference Behrens, T., K. Schmidt, K., Zhu, A.X. Scholten, T. 2010. The ConMap approach for terrain-based digital soil mapping. European Journal of Soil Science, v. 61, p.133-143.

The pedological heritage of the Dolomites (Northern Italy): Features, distribution and evolution of the soils, with some implications for land management

NASA Astrophysics Data System (ADS)

Zilioli, Diana Maria; Bini, Claudio; Wahsha, Mohammad; Ciotoli, Giancarlo

2011-12-01

Since 1997, the Department of Environmental Sciences of Ca' Foscari University of Venice has undertaken numerous research projects aimed at deepening understanding of pedogenic processes in the Dolomites, and at highlighting the fundamental contribution that soil science can give to the conservation of natural resources and achieve sustainable management of mountain ecosystems. A total of several hundred profiles have been described, analyzed and mapped. This paper reports the results from the analysis of pedo-environmental characters of profiles developed from different parent materials, at altitudes between 1300 m and 2900 m and in different conditions of slope, exposure and vegetation cover. Soil forming factors, landforms and land surfaces have been interpreted to understand the soil-landscape in the mapped areas and to develop a qualitative model of soil geography into the Dolomites scenery. The application of land evaluation methods in some of the investigated territories that are subjected to intensive tourist fluxes revealed some criticisms. Collected results also highlighted the high environmental heterogeneity of soils of the Dolomites.

LANDSAT-1 data, its use in a soil survey program

NASA Technical Reports Server (NTRS)

Westin, F. C.; Frazee, C. J.

1975-01-01

The following applications of LANDSAT imagery were investigated: assistance in recognizing soil survey boundaries, low intensity soil surveys, and preparation of a base map for publishing thematic soils maps. The following characteristics of LANDSAT imagery were tested as they apply to the recognition of soil boundaries in South Dakota and western Minnesota: synoptic views due to the large areas covered, near-orthography and lack of distortion, flexibility of selecting the proper season, data recording in four parts of the spectrum, and the use of computer compatible tapes. A low intensity soil survey of Pennington County, South Dakota was completed in 1974. Low intensity inexpensive soil surveys can provide the data needed to evaluate agricultural land for the remaining counties until detailed soil surveys are completed. In using LANDSAT imagery as a base map for publishing thematic soil maps, the first step was to prepare a mosaic with 20 LANDSAT scenes from several late spring passes in 1973.

ERTS-1 MSS imagery: Its use in delineating soil associations and as a base map for publishing soils information. [South Dakota

NASA Technical Reports Server (NTRS)

Westin, F. C.

1974-01-01

ERTS 1 imagery is a useful tool in the identification and refinement of soil association areas and an excellent base map upon which soil association information can be published. Prints of bands 5 and 7 were found to be most useful to help delineate major soil and vegetation areas. After delineating major soil areas, over 4800 land sale prices covering a period of 1967-72 were located in the soil areas and averaged. The soil association then were described as soil association value areas and published on a 1:1,000,000 scale ERTS mosaic of South Dakota constructed using negative prints of band 7. The map is intended for use by state and county revenue officers, by individual buyers and sellers of land and lending institutions, and as a reference map by those planning road routes and cable lines and pipelines.

Detailed deposition density maps constructed by large-scale soil sampling for gamma-ray emitting radioactive nuclides from the Fukushima Dai-ichi Nuclear Power Plant accident.

PubMed

Saito, Kimiaki; Tanihata, Isao; Fujiwara, Mamoru; Saito, Takashi; Shimoura, Susumu; Otsuka, Takaharu; Onda, Yuichi; Hoshi, Masaharu; Ikeuchi, Yoshihiro; Takahashi, Fumiaki; Kinouchi, Nobuyuki; Saegusa, Jun; Seki, Akiyuki; Takemiya, Hiroshi; Shibata, Tokushi

2015-01-01

Soil deposition density maps of gamma-ray emitting radioactive nuclides from the Fukushima Dai-ichi Nuclear Power Plant (NPP) accident were constructed on the basis of results from large-scale soil sampling. In total 10,915 soil samples were collected at 2168 locations. Gamma rays emitted from the samples were measured by Ge detectors and analyzed using a reliable unified method. The determined radioactivity was corrected to that of June 14, 2011 by considering the intrinsic decay constant of each nuclide. Finally the deposition maps were created for (134)Cs, (137)Cs, (131)I, (129m)Te and (110m)Ag. The radioactivity ratio of (134)Cs-(137)Cs was almost constant at 0.91 regardless of the locations of soil sampling. The radioactivity ratios of (131)I and (129m)Te-(137)Cs were relatively high in the regions south of the Fukushima NPP site. Effective doses for 50 y after the accident were evaluated for external and inhalation exposures due to the observed radioactive nuclides. The radiation doses from radioactive cesium were found to be much higher than those from the other radioactive nuclides. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Peculiarities of changes in the soil cover of landscapes adjacent to a megalopolis

NASA Astrophysics Data System (ADS)

Lazareva, Margarita; Aparin, Boris; Sukhacheva, Elena

2017-04-01

The progressive growth of cities has a significant impact on the soil cover of territories adjacent to the same. Megalopolises are centers of anthropogenic impact on the soils. Generally, forms and intensity of the urban impact on the soil cover weaken with increasing distance from the city's boundaries. In this respect, ample opportunities for the analysis of urban impact on the adjacent territories are provided by the study of the soil cover in the Leningrad Region (the LR). Saint Petersburg is a major European megalopolis, which is the administrative center of the LR. The time period of Saint Petersburg's impact on the environment does not exceed 300 years, which allows us to identify very clearly the character and areas of its impact on the soil cover. Over the past decades, there have been significant changes in the soils and the soil cover of the LR. In a large territory, there appeared new anthropogenic soils and soil cover organization forms, having no natural analogues, with a dramatic increase in the surface area of degraded soils. To access the current state of soil cover, to identify the role of anthropogenic factors of changes in this state; to carry out land reclamation, remediation and rehabilitation measures; to perform land cadastral valuation etc., we need an information resource containing data on the current state of soils and soil cover in the LR, the key element of which should be a map. We carried out mapping and created a 1:200 000 digital soil map (DSM) for the LR's territories. Diagnostics of soil contours were performed using traditionally drawn-up (paper) maps of soils and soil-formation factors; satellite images (Google, Yandex); data of remote sensing (Spot 5, Landsat 7,8); digital maps of main soil-formation factors (topographical ones, etc.). The digital soil map of the LR has been created in the geographic information system - QGIS. The map clarifies the contours of natural soils and soil combinations, and shows, for the first time, the contours of: - non-soil formations; - soils of the initial soil formation; - soils of agricultural lands within their existing boundaries; - soils and soil combinations that are specific for human settlements and horticultural land plots; - fallow lands; - anthropogenically disturbed soils. During the analysis of the created digital medium-scale soil map, we identified some changes in the soil cover of the territories adjacent to Saint Petersburg. Virtually in all the landscapes, we found a large number of soil cover structures, the components of which, along with natural soils, are anthropogenically disturbed soils, anthropogenic soils and non-soil formations. We revealed that the human impact on the soil cover is manifested within the range that varies from insignificant changes in soil parameters to radical transformations of the soil profile, complete destruction of soil and "creation" of new soil forms and soil cover organization forms. We have developed a typology of anthropogenically changed and anthropogenically created soil cover structures, taking into consideration the types of the economic impact on and the quality of environmental functions performed by the soils.

Use of slope, aspect, and elevation maps derived from digital elevation model data in making soil surveys

USGS Publications Warehouse

Klingebiel, A.A.; Horvath, E.H.; Moore, D.G.; Reybold, W.U.

1987-01-01

Maps showing different classes of slope, aspect, and elevation were developed from U.S. Geological Survey digital elevation model data. The classes were displayed on clear Mylar at 1:24 000-scale and registered with topographic maps and orthophotos. The maps were used with aerial photographs, topographic maps, and other resource data to determine their value in making order-three soil surveys. They were tested on over 600 000 ha in Wyoming, Idaho, and Nevada under various climatic and topographic conditions. Field evaluations showed that the maps developed from digital elevation model data were accurate, except for slope class maps where slopes were <4%. The maps were useful to soil scientists, especially where (i) class boundaries coincided with soil changes, landform delineations, land use and management separations, and vegetation changes, and (ii) rough terrain and dense vegetation made it difficult to traverse the area. In hot, arid areas of sparse vegetation, the relationship of slope classes to kinds of soil and vegetation was less significant.

Quantification and site-specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete.

PubMed

Karydas, Christos G; Sekuloska, Tijana; Silleos, Georgios N

2009-02-01

Due to inappropriate agricultural management practices, soil erosion is becoming one of the most dangerous forms of soil degradation in many olive farming areas in the Mediterranean region, leading to significant decrease of soil fertility and yield. In order to prevent further soil degradation, proper measures are necessary to be locally implemented. In this perspective, an increase in the spatial accuracy of remote sensing datasets and advanced image analysis are significant tools necessary and efficient for mapping soil erosion risk on a fine scale. In this study, the Revised Universal Soil Loss Equation (RUSLE) was implemented in the spatial domain using GIS, while a very high resolution satellite image, namely a QuickBird image, was used for deriving cover management (C) and support practice (P) factors, in order to map the risk of soil erosion in Kolymvari, a typical olive farming area in the island of Crete, Greece. The results comprised a risk map of soil erosion when P factor was taken uniform (conventional approach) and a risk map when P factor was quantified site-specifically using object-oriented image analysis. The results showed that the QuickBird image was necessary in order to achieve site-specificity of the P factor and therefore to support fine scale mapping of soil erosion risk in an olive cultivation area, such as the one of Kolymvari in Crete. Increasing the accuracy of the QB image classification will further improve the resulted soil erosion mapping.

Soil mapping and processes modelling for sustainable land management: a review

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Brevik, Eric; Muñoz-Rojas, Miriam; Miller, Bradley; Smetanova, Anna; Depellegrin, Daniel; Misiune, Ieva; Novara, Agata; Cerda, Artemi

2017-04-01

Soil maps and models are fundamental for a correct and sustainable land management (Pereira et al., 2017). They are an important in the assessment of the territory and implementation of sustainable measures in urban areas, agriculture, forests, ecosystem services, among others. Soil maps represent an important basis for the evaluation and restoration of degraded areas, an important issue for our society, as consequence of climate change and the increasing pressure of humans on the ecosystems (Brevik et al. 2016; Depellegrin et al., 2016). The understanding of soil spatial variability and the phenomena that influence this dynamic is crucial to the implementation of sustainable practices that prevent degradation, and decrease the economic costs of soil restoration. In this context, soil maps and models are important to identify areas affected by degradation and optimize the resources available to restore them. Overall, soil data alone or integrated with data from other sciences, is an important part of sustainable land management. This information is extremely important land managers and decision maker's implements sustainable land management policies. The objective of this work is to present a review about the advantages of soil mapping and process modeling for sustainable land management. References Brevik, E., Calzolari, C., Miller, B., Pereira, P., Kabala, C., Baumgarten, A., Jordán, A. (2016) Historical perspectives and future needs in soil mapping, classification and pedological modelling, Geoderma, 264, Part B, 256-274. Depellegrin, D.A., Pereira, P., Misiune, I., Egarter-Vigl, L. (2016) Mapping Ecosystem Services in Lithuania. International Journal of Sustainable Development and World Ecology, 23, 441-455. Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B., Smetanova, A., Depellegrin, D., Misiune, I., Novara, A., Cerda, A. (2017) Soil mapping and process modelling for sustainable land management. In: Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B. (Eds.) Soil mapping and process modelling for sustainable land use management (Elsevier Publishing House) ISBN: 9780128052006

Estimating the Pollution Risk of Cadmium in Soil Using a Composite Soil Environmental Quality Standard

PubMed Central

Huang, Biao; Zhao, Yongcun

2014-01-01

Estimating standard-exceeding probabilities of toxic metals in soil is crucial for environmental evaluation. Because soil pH and land use types have strong effects on the bioavailability of trace metals in soil, they were taken into account by some environmental protection agencies in making composite soil environmental quality standards (SEQSs) that contain multiple metal thresholds under different pH and land use conditions. This study proposed a method for estimating the standard-exceeding probability map of soil cadmium using a composite SEQS. The spatial variability and uncertainty of soil pH and site-specific land use type were incorporated through simulated realizations by sequential Gaussian simulation. A case study was conducted using a sample data set from a 150 km2 area in Wuhan City and the composite SEQS for cadmium, recently set by the State Environmental Protection Administration of China. The method may be useful for evaluating the pollution risks of trace metals in soil with composite SEQSs. PMID:24672364

Quantifying soil burn severity for hydrologic modeling to assess post-fire effects on sediment delivery

NASA Astrophysics Data System (ADS)

Dobre, Mariana; Brooks, Erin; Lew, Roger; Kolden, Crystal; Quinn, Dylan; Elliot, William; Robichaud, Pete

2017-04-01

Soil erosion is a secondary fire effect with great implications for many ecosystem resources. Depending on the burn severity, topography, and the weather immediately after the fire, soil erosion can impact municipal water supplies, degrade water quality, and reduce reservoirs' storage capacity. Scientists and managers use field and remotely sensed data to quickly assess post-fire burn severity in ecologically-sensitive areas. From these assessments, mitigation activities are implemented to minimize post-fire flood and soil erosion and to facilitate post-fire vegetation recovery. Alternatively, land managers can use fire behavior and spread models (e.g. FlamMap, FARSITE, FOFEM, or CONSUME) to identify sensitive areas a priori, and apply strategies such as fuel reduction treatments to proactively minimize the risk of wildfire spread and increased burn severity. There is a growing interest in linking fire behavior and spread models with hydrology-based soil erosion models to provide site-specific assessment of mitigation treatments on post-fire runoff and erosion. The challenge remains, however, that many burn severity mapping and modeling products quantify vegetation loss rather than measuring soil burn severity. Wildfire burn severity is spatially heterogeneous and depends on the pre-fire vegetation cover, fuel load, topography, and weather. Severities also differ depending on the variable of interest (e.g. soil, vegetation). In the United States, Burned Area Reflectance Classification (BARC) maps, derived from Landsat satellite images, are used as an initial burn severity assessment. BARC maps are classified from either a Normalized Burn Ratio (NBR) or differenced Normalized Burned Ratio (dNBR) scene into four classes (Unburned, Low, Moderate, and High severity). The development of soil burn severity maps requires further manual field validation efforts to transform the BARC maps into a product more applicable for post-fire soil rehabilitation activities. Alternative spectral indices and modeled output approaches may prove better predictors of soil burn severity and hydrologic effects, but these have not yet been assessed in a model framework. In this project we compare field-verified soil burn severity maps to satellite-derived and modeled burn severity maps. We quantify the extent to which there are systematic differences in these mapping products. We then use the Water Erosion Prediction Project (WEPP) hydrologic soil erosion model to assess sediment delivery from these fires using the predicted and observed soil burn severity maps. Finally, we discuss differences in observed and predicted soil burn severity maps and application to watersheds in the Pacific Northwest to estimate post-fire sediment delivery.

Global soil-climate-biome diagram: linking soil properties to climate and biota

NASA Astrophysics Data System (ADS)

Zhao, X.; Yang, Y.; Fang, J.

2017-12-01

As a critical component of the Earth system, soils interact strongly with both climate and biota and provide fundamental ecosystem services that maintain food, climate, and human security. Despite significant progress in digital soil mapping techniques and the rapidly growing quantity of observed soil information, quantitative linkages between soil properties, climate and biota at the global scale remain unclear. By compiling a large global soil database, we mapped seven major soil properties (bulk density [BD]; sand, silt and clay fractions; soil pH; soil organic carbon [SOC] density [SOCD]; and soil total nitrogen [STN] density [STND]) based on machine learning algorithms (regional random forest [RF] model) and quantitatively assessed the linkage between soil properties, climate and biota at the global scale. Our results demonstrated a global soil-climate-biome diagram, which improves our understanding of the strong correspondence between soils, climate and biomes. Soil pH decreased with greater mean annual precipitation (MAP) and lower mean annual temperature (MAT), and the critical MAP for the transition from alkaline to acidic soil pH decreased with decreasing MAT. Specifically, the critical MAP ranged from 400-500 mm when the MAT exceeded 10 °C but could decrease to 50-100 mm when the MAT was approximately 0 °C. SOCD and STND were tightly linked; both increased in accordance with lower MAT and higher MAP across terrestrial biomes. Global stocks of SOC and STN were estimated to be 788 ± 39.4 Pg (1015 g, or billion tons) and 63 ± 3.3 Pg in the upper 30-cm soil layer, respectively, but these values increased to 1654 ± 94.5 Pg and 133 ± 7.8 Pg in the upper 100-cm soil layer, respectively. These results reveal quantitative linkages between soil properties, climate and biota at the global scale, suggesting co-evolution of the soil, climate and biota under conditions of global environmental change.

Mapping the Indonesian territory, based on pollution, social demography and geographical data, using self organizing feature map

NASA Astrophysics Data System (ADS)

Hernawati, Kuswari; Insani, Nur; Bambang S. H., M.; Nur Hadi, W.; Sahid

2017-08-01

This research aims to mapping the 33 (thirty-three) provinces in Indonesia, based on the data on air, water and soil pollution, as well as social demography and geography data, into a clustered model. The method used in this study was unsupervised method that combines the basic concept of Kohonen or Self-Organizing Feature Maps (SOFM). The method is done by providing the design parameters for the model based on data related directly/ indirectly to pollution, which are the demographic and social data, pollution levels of air, water and soil, as well as the geographical situation of each province. The parameters used consists of 19 features/characteristics, including the human development index, the number of vehicles, the availability of the plant's water absorption and flood prevention, as well as geographic and demographic situation. The data used were secondary data from the Central Statistics Agency (BPS), Indonesia. The data are mapped into SOFM from a high-dimensional vector space into two-dimensional vector space according to the closeness of location in term of Euclidean distance. The resulting outputs are represented in clustered grouping. Thirty-three provinces are grouped into five clusters, where each cluster has different features/characteristics and level of pollution. The result can used to help the efforts on prevention and resolution of pollution problems on each cluster in an effective and efficient way.

Hungarian contribution to the Global Soil Organic Carbon Map (GSOC17) using advanced machine learning algorithms and geostatistics

NASA Astrophysics Data System (ADS)

Szatmári, Gábor; Laborczi, Annamária; Takács, Katalin; Pásztor, László

2017-04-01

The knowledge about soil organic carbon (SOC) baselines and changes, and the detection of vulnerable hot spots for SOC losses and gains under climate change and changed land management is still fairly limited. Thus Global Soil Partnership (GSP) has been requested to develop a global SOC mapping campaign by 2017. GSPs concept builds on official national data sets, therefore, a bottom-up (country-driven) approach is pursued. The elaborated Hungarian methodology suits the general specifications of GSOC17 provided by GSP. The input data for GSOC17@HU mapping approach has involved legacy soil data bases, as well as proper environmental covariates related to the main soil forming factors, such as climate, organisms, relief and parent material. Nowadays, digital soil mapping (DSM) highly relies on the assumption that soil properties of interest can be modelled as a sum of a deterministic and stochastic component, which can be treated and modelled separately. We also adopted this assumption in our methodology. In practice, multiple regression techniques are commonly used to model the deterministic part. However, this global (and usually linear) models commonly oversimplify the often complex and non-linear relationship, which has a crucial effect on the resulted soil maps. Thus, we integrated machine learning algorithms (namely random forest and quantile regression forest) in the elaborated methodology, supposing then to be more suitable for the problem in hand. This approach has enable us to model the GSOC17 soil properties in that complex and non-linear forms as the soil itself. Furthermore, it has enable us to model and assess the uncertainty of the results, which is highly relevant in decision making. The applied methodology has used geostatistical approach to model the stochastic part of the spatial variability of the soil properties of interest. We created GSOC17@HU map with 1 km grid resolution according to the GSPs specifications. The map contributes to the GSPs GSOC17 proposals, as well as to the development of global soil information system under GSP Pillar 4 on soil data and information. However, we elaborated our adherent code (created in R software environment) in such a way that it can be improved, specified and applied for further uses. Hence, it opens the door to create countrywide map(s) with higher grid resolution for SOC (or other soil related properties) using the advanced methodology, as well as to contribute and support the SOC (or other soil) related country level decision making. Our paper will present the soil mapping methodology itself, the resulted GSOC17@HU map, some of our conclusions drawn from the experiences and their effects on the further uses. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Estimating of Soil Texture Using Landsat Imagery: a Case Study in Thatta Tehsil, Sindh

NASA Astrophysics Data System (ADS)

Khalil, Zahid

2016-07-01

Soil texture is considered as an important environment factor for agricultural growth. It is the most essential part for soil classification in large scale. Today the precise soil information in large scale is of great demand from various stakeholders including soil scientists, environmental managers, land use planners and traditional agricultural users. With the increasing demand of soil properties in fine scale spatial resolution made the traditional laboratory methods inadequate. In addition the costs of soil analysis with precision agriculture systems are more expensive than traditional methods. In this regard, the application of geo-spatial techniques can be used as an alternative for examining soil analysis. This study aims to examine the ability of Geo-spatial techniques in identifying the spatial patterns of soil attributes in fine scale. Around 28 samples of soil were collected from the different areas of Thatta Tehsil, Sindh, Pakistan for analyzing soil texture. An Ordinary Least Square (OLS) regression analysis was used to relate the reflectance values of Landsat8 OLI imagery with the soil variables. The analysis showed there was a significant relationship (p<0.05) of band 2 and 5 with silt% (R2 = 0.52), and band 4 and 6 with clay% (R2 =0.40). The equation derived from OLS analysis was then used for the whole study area for deriving soil attributes. The USDA textural classification triangle was implementing for the derivation of soil texture map in GIS environment. The outcome revealed that the 'sandy loam' was in great quantity followed by loam, sandy clay loam and clay loam. The outcome shows that the Geo-spatial techniques could be used efficiently for mapping soil texture of a larger area in fine scale. This technology helped in decreasing cost, time and increase detailed information by reducing field work to a considerable level.

Evaluation of automated global mapping of Reference Soil Groups of WRB2015

NASA Astrophysics Data System (ADS)

Mantel, Stephan; Caspari, Thomas; Kempen, Bas; Schad, Peter; Eberhardt, Einar; Ruiperez Gonzalez, Maria

2017-04-01

SoilGrids is an automated system that provides global predictions for standard numeric soil properties at seven standard depths down to 200 cm, currently at spatial resolutions of 1km and 250m. In addition, the system provides predictions of depth to bedrock and distribution of soil classes based on WRB and USDA Soil Taxonomy (ST). In SoilGrids250m(1), soil classes (WRB, version 2006) consist of the RSG and the first prefix qualifier, whereas in SoilGrids1km(2), the soil class was assessed at RSG level. Automated mapping of World Reference Base (WRB) Reference Soil Groups (RSGs) at a global level has great advantages. Maps can be updated in a short time span with relatively little effort when new data become available. To translate soil names of older versions of FAO/WRB and national classification systems of the source data into names according to WRB 2006, correlation tables are used in SoilGrids. Soil properties and classes are predicted independently from each other. This means that the combinations of soil properties for the same cells or soil property-soil class combinations do not necessarily yield logical combinations when the map layers are studied jointly. The model prediction procedure is robust and probably has a low source of error in the prediction of RSGs. It seems that the quality of the original soil classification in the data and the use of correlation tables are the largest sources of error in mapping the RSG distribution patterns. Predicted patterns of dominant RSGs were evaluated in selected areas and sources of error were identified. Suggestions are made for improvement of WRB2015 RSG distribution predictions in SoilGrids. Keywords: Automated global mapping; World Reference Base for Soil Resources; Data evaluation; Data quality assurance References 1 Hengl T, de Jesus JM, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, et al. (2016) SoilGrids250m: global gridded soil information based on Machine Learning. Earth System Science Data (ESSD), in review. 2 Hengl T, de Jesus JM, MacMillan RA, Batjes NH, Heuvelink GBM, et al. (2014) SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE 9(8): e105992. doi:10.1371/journal.pone.0105992

Development of an NDIR CO2 Sensor-Based System for Assessing Soil Toxicity Using Substrate-Induced Respiration

PubMed Central

Kaur, Jasmeen; Adamchuk, Viacheslav I.; Whalen, Joann K.; Ismail, Ashraf A.

2015-01-01

The eco-toxicological indicators used to evaluate soil quality complement the physico-chemical criteria employed in contaminated site remediation, but their cost, time, sophisticated analytical methods and in-situ inapplicability pose a major challenge to rapidly detect and map the extent of soil contamination. This paper describes a sensor-based approach for measuring potential (substrate-induced) microbial respiration in diesel-contaminated and non-contaminated soil and hence, indirectly evaluates their microbial activity. A simple CO2 sensing system was developed using an inexpensive non-dispersive infrared (NDIR) CO2 sensor and was successfully deployed to differentiate the control and diesel-contaminated soils in terms of CO2 emission after glucose addition. Also, the sensor system distinguished glucose-induced CO2 emission from sterile and control soil samples (p ≤ 0.0001). Significant effects of diesel contamination (p ≤ 0.0001) and soil type (p ≤ 0.0001) on glucose-induced CO2 emission were also found. The developed sensing system can provide in-situ evaluation of soil microbial activity, an indicator of soil quality. The system can be a promising tool for the initial screening of contaminated environmental sites to create high spatial density maps at a relatively low cost. PMID:25730479

Guide for evaluating sweetgum sites

Treesearch

W. M. Broadfoot; Roger M. Krinard

1959-01-01

Studies at the Stonevill Research Center1 have established 3 practical methods of estimating the ability of Midsouth soils to grow sweetgum (liquidambar styraciflua). The methods were developed from data collected from 104 sweetgum plots in the area mapped on the opposite page.

Digital soil mapping for the support of delineation of Areas Facing Natural Constraints defined by common European biophysical criteria

NASA Astrophysics Data System (ADS)

Pásztor, László; Bakacsi, Zsófia; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor; Tóth, Tibor; Szabó, József

2016-04-01

One of the main objectives of the EU's Common Agricultural Policy is to encourage maintaining agricultural production in Areas Facing Natural Constraints (ANC) in order to sustain agricultural production and use natural resources, in such a way to secure both stable production and income to farmers and to protect the environment. ANC assignment has both ecological and severe economical aspects. Recently the delimitation of ANCs is suggested to be carried out by using common biophysical diagnostic criteria on low soil productivity and poor climate conditions all over Europe. The criterion system was elaborated and has been repeatedly upgraded by JRC. The operational implementation is under member state competence. This process requires application of available soil databases and proper thematic and spatial inference methods. In our paper we present the inferences applied for the latest identification and delineation of areas with low soil productivity in Hungary according to JRC biophysical criteria related to soil: limited soil drainage, texture and stoniness (coarse texture, heavy clay, vertic properties), shallow rooting depth, chemical properties (salinity, sodicity, low pH). The compilation of target specific maps were based on the available legacy and recently collected data. In the present work three different data sources were used. The most relevant available data were queried from the datasets for each mapped criterion for either direct application or for the compilation a suitable, synthetic (non-measured) parameter. In some cases the values of the target variable originated from only one, in other cases from more databases. The reference dataset used in the mapping process was set up after substantial statistical analysis and filtering. It consisted of the values of the target variable attributed to the finally selected georeferenced locations. For spatial inference regression kriging was applied. Accuracy assessment was carried out by Leave One Out Cross Validation (LOOCV). In some cases the DSM product directly provided the delineation result by simple querying, in other cases further interpretation of the map was necessary. As the result of our work not only spatial fulfilment of the European biophysical criteria was assessed and provided for decision makers, but unique digital soil map products were elaborated regionalizing specific soil features, which were never mapped before, even nationally with 1 ha spatial resolution. Acknowledgement: Our work was supported by the "European Fund for Agricultural and Rural Development: Europe investing in rural areas" with the support of the European Union and Hungary and by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Soil moisture mapping at Bubnow Wetland using L-band radiometer (ELBARA III)

NASA Astrophysics Data System (ADS)

Łukowski, Mateusz; Schwank, Mike; Szlązak, Radosław; Wiesmann, Andreas; Marczewski, Wojciech; Usowicz, Bogusław; Usowicz, Jerzy; Rojek, Edyta; Werner, Charles

2016-04-01

The study of soil moisture is a scientific challenge. Not only because of large diversity of soils and differences in their water content, but also due to the difficulty of measuring, especially in large scale. On this field of interest several methods to determine the content of water in soil exists. The basic and referential is gravimetric method, which is accurate, but suitable only for small spatial scales and time-consuming. Indirect methods are faster, but need to be validated, for example those based on dielectric properties of materials (e.g. time domain reflectometry - TDR) or made from distance (remote), like brightness temperature measurements. Remote sensing of soil moisture can be performed locally (from towers, drones, planes etc.) or globally (satellites). These techniques can complement and help to verify different models and assumptions. In our studies, we applied spatial statistics to local soil moisture mapping using ELBARA III (ESA L-band radiometer, 1.4 GHz) mounted on tower (6.5 meter height). Our measurements were carried out in natural Bubnow Wetland, near Polesie National Park (Eastern Poland), during spring time. This test-site had been selected because it is representative for one of the biggest wetlands in Europe (1400 km2), called "Western Polesie", localized in Ukraine, Poland and Belarus. We have investigated Bubnow for almost decade, using meteorological and soil moisture stations, conducting campaigns of hand-held measurements and collecting soil samples. Now, due to the possibility of rotation at different incidence angles (as in previous ELBARA systems) and the new azimuth tracking capabilities, we obtained brightness temperature data not only at different distances from the tower, but also around it, in footprints containing different vegetation and soil types. During experiment we collected data at area about 450 m2 by rotating ELBARA's antenna 5-175° in horizontal and 30-70° in vertical plane. This type of approach allows combining multiple independent measurements (performed nearly simultaneously) to one consistent soil moisture map. Spatial statistics helps with correcting blind spots or distortions causes by assembly elements, especially on corners of ELBARA's tower. Moreover, using this technique we can observe distribution of soil moisture with time dependency. In order to validate our data, the results were compared with measurements obtained by means of the TDR method. The presented approach enables better understanding the soil moisture spatial distribution over a particular local area of interests, before extending soil water assessments on larger areas. The work was partially funded under two ESA projects: 1) "ELBARA_PD (Penetration Depth)" No. 4000107897/13/NL/KML, funded by the Government of Poland through an ESA-PECS contract (Plan for European Cooperating States) 2) "Technical Support for the fabrication and deployment of the radiometer ELBARA-III in Bubnow, Poland" No. 4000113360/15/NL/FF/gp

Digital soil mapping as a basis for climatically oriented agriculture a thematic on the territory of the national crop testing fields of the Republic of Tatarstan, Russia

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Giniyatullin, K. G.; Ryazanov, S. S.

2018-01-01

The concept of climate-optimized agriculture (COA) of the UN FAO implies the transformation of agriculture techniques in conditions of changing climate. It is important to implement a timely transition to the concept of COA and sustainable development of soil resources, accurate digital maps of spatial distribution of soils and soil properties are needed. Digital mapping of soil humus content was carried out on the territory of the national crop testing fields (NCTF) of the Republic of Tatarstan (Russian Federation) and the accuracy of the maps obtained was estimated.

Ecological effects of the Hayman Fire - Part 3: Soil properties, erosion, and implications for rehabilitation and aquatic ecosystems

Treesearch

Jan E. Cipra; Eugene F. Kelly; Lee MacDonald; John Norman

2003-01-01

This team was asked to address three questions regarding soil properties, erosion and sedimentation, and how aquatic and terrestrial ecosystems have responded or could respond to various land management options. We have used soil survey maps, burn severity maps, and digital elevation model (DEM) maps as primary map data. We used our own field measurements and...

The comparative evaluation of ERTS-1 imagery for resource inventory in land use planning. [Oregon

NASA Technical Reports Server (NTRS)

Simonson, G. H. (Principal Investigator); Paine, D. P.; Lawrence, R. D.; Pyott, W. T.; Herzog, J. H.; Murray, R. J.; Norgren, J. A.; Cornwell, J. A.; Rogers, R. A.

1973-01-01

The author has identified the following significant results. Multidiscipline team interpretation and mapping of resources for Crook County is nearly complete on 1:250,000 scale enlargements of ERTS-1 imagery. Maps of geology, landforms, soils and vegetation-land use are being interpreted to show limitations, suitabilities and geologic hazards for land use planning. Mapping of lineaments and structures from ERTS-1 imagery has shown a number of features not previously mapped in Oregon. A timber inventory of Ochoco National Forest has been made. Inventory of forest clear-cutting practices has been successfully demonstrated with ERTS-1 color composites. Soil tonal differences in fallow fields shown on ERTS-1 correspond with major soil boundaries in loess-mantled terrain. A digital classification system used for discriminating natural vegetation and geologic materials classes has been successful in separation of most major classes around Newberry Cauldera, Mt. Washington and Big Summit Prairie. Computer routines are available for correction of scanner data variations; and for matching scales and coordinates between digital and photographic imagery. Methods of Diazo film color printing of computer classifications and elevation-slope perspective plots with computer are being developed.

Using Multispectral and Elevation Data to Predict Soil Properties for a Better Management of Fertilizers at Field Scale

NASA Astrophysics Data System (ADS)

Drouin, Ariane; Michaud, Aubert; Sylvain, Jean-Daniel; N'Dayegamiye, Adrien; Gasser, Marc-Olivier; Nolin, Michel; Perron, Isabelle; Grenon, Lucie; Beaudin, Isabelle; Desjardins, Jacques; Côté, Noémi

2013-04-01

This project aims at developing and validating an operational integrated management and localized approach at field scale using remote sensing data. It is realized in order to support the competitiveness of agricultural businesses, to ensure soil productivity in the long term and prevent diffuse contamination of surface waters. Our intention is to help agrienvironmental advisors and farmers in the consideration of spatial variability of soil properties in the management of fields. The proposed approach of soil properties recognition is based on the combination of elevation data and multispectral satellite imagery (Landsat) within statistical models. The method is based on the use of the largest possible number of satellite images to cover the widest range of soil moisture variability. Several spectral indices are calculated for each image (normalized brightness index, soil color index, organic matter index, etc.). The assignation of soils is based on a calibration procedure making use of the spatial soil database available in Canada. It includes soil profile point data associated to a database containing the information collected in the field. Three soil properties are predicted and mapped: A horizon texture, B horizon texture and drainage class. All the spectral indices, elevation data and soil data are combined in a discriminant analysis that produces discriminant functions. These are then used to produce maps of soil properties. In addition, from mapping soil properties, management zones are delineated within the field. The delineation of management zones with relatively similar soil properties is created to enable farmers to manage their fertilizers by taking greater account of their soils. This localized or precision management aims to adjust the application of fertilizer according to the real needs of soils and to reduce costs for farmers and the exports of nutrients to the stream. Mapping of soil properties will be validated in three agricultural regions in Quebec through an experimental field protocol (spatial sampling by management zones). Soils will be sampled, but crop yields under different nitrogen rates will also be assessed. Specifically, in each of the management areas defined, five different doses of nitrogen were applied (0, 50, 100, 150, 200 kg N / ha) on corn fields. In fall, the corn is harvested to assess differences in yields between the management areas and also in terms of doses of nitrogen. Ultimately, on the basis of well-established management areas, showing contrasting soil properties, the farmer will be able to ensure optimal correction of soil acidity, nitrogen fertilization, richness of soil in P and K, and improve soil drainage and physical properties. Environmentally, the principles of integrated and localized management carries significant benefits, particularly in terms of reduction of diffuse nutrient pollution.

Mapping soil salinity and a fresh-water intrusion in three-dimensions using a quasi-3d joint-inversion of DUALEM-421S and EM34 data

NASA Astrophysics Data System (ADS)

Zare, Ehsan; Huang, Jingyi; Koganti, Triven; Triantafilis, John

2017-04-01

In order to understand the drivers of topsoil salinization, the distribution and movement of salt in accordance with groundwater need mapping. In this study, we described a method to map the distribution of soil salinity, as measured by the electrical conductivity of a saturated soil-paste extract (ECe), and in 3-dimensions around a water storage reservoir in an irrigated field near Bourke, New South Wales, Australia. A quasi-3d electromagnetic conductivity image (EMCI) or model of the true electrical conductivity (sigma) was developed using 133 apparent electrical conductivity (ECa) measurements collected on a 50 m grid and using various coil arrays of DUALEM-421S and EM34 instruments. For the DUALEM-421S we considered ECa in horizontal coplanar (i.e., 1 mPcon, 2 mPcon and 4 mPcon) and vertical coplanar (i.e., 1 mHcon, 2 mHcon and 4 mHcon) arrays. For the EM34, three measurements in the horizontal mode (i.e., EM34-10H, EM34-20H and EM34-40H) were considered. We estimated σ using a quasi-3d joint-inversion algorithm (EM4Soil). The best correlation (R2 = 0.92) between σ and measured soil ECe was identified when a forward modelling (FS), inversion algorithm (S2) and damping factor (lambda = 0.2) were used and using both DUALEM-421 and EM34 data; but not including the 4 m coil arrays of the DUALEM-421S. A linear regression calibration model was used to predict ECe in 3-dimensions beneath the study field. The predicted ECe was consistent with previous studies and revealed the distribution of ECe and helped to infer a freshwater intrusion from a water storage reservoir at depth and as a function of its proximity to near-surface prior stream channels and buried paleochannels. It was concluded that this method can be applied elsewhere to map the soil salinity and water movement and provide guidance for improved land management.|

Mapping soil salinity and a fresh-water intrusion in three-dimensions using a quasi-3d joint-inversion of DUALEM-421S and EM34 data.

PubMed

Huang, J; Koganti, T; Santos, F A Monteiro; Triantafilis, J

2017-01-15

In order to understand the drivers of topsoil salinization, the distribution and movement of salt in accordance with groundwater need mapping. In this study, we described a method to map the distribution of soil salinity, as measured by the electrical conductivity of a saturated soil-paste extract (EC e ), and in 3-dimensions around a water storage reservoir in an irrigated field near Bourke, New South Wales, Australia. A quasi-3d electromagnetic conductivity image (EMCI) or model of the true electrical conductivity (σ) was developed using 133 apparent electrical conductivity (EC a ) measurements collected on a 50m grid and using various coil arrays of DUALEM-421S and EM34 instruments. For the DUALEM-421S we considered EC a in horizontal coplanar (i.e., 1mPcon, 2mPcon and 4mPcon) and vertical coplanar (i.e., 1mHcon, 2mHcon and 4mHcon) arrays. For the EM34, three measurements in the horizontal mode (i.e., EM34-10H, EM34-20H and EM34-40H) were considered. We estimated σ using a quasi-3d joint-inversion algorithm (EM4Soil). The best correlation (R 2 =0.92) between σ and measured soil EC e was identified when a forward modelling (FS), inversion algorithm (S2) and damping factor (λ=0.2) were used and using both DUALEM-421 and EM34 data; but not including the 4m coil arrays of the DUALEM-421S. A linear regression calibration model was used to predict EC e in 3-dimensions beneath the study field. The predicted EC e was consistent with previous studies and revealed the distribution of EC e and helped to infer a freshwater intrusion from a water storage reservoir at depth and as a function of its proximity to near-surface prior stream channels and buried paleochannels. It was concluded that this method can be applied elsewhere to map the soil salinity and water movement and provide guidance for improved land management. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

NASA Astrophysics Data System (ADS)

Baatz, Roland; Hendricks Franssen, Harrie-Jan; Han, Xujun; Hoar, Tim; Reemt Bogena, Heye; Vereecken, Harry

2017-05-01

In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm-3 for the assimilation period and 0.05 cm3 cm-3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm-3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

Oxygen-17 anomaly in soil nitrate: A new precipitation proxy for desert landscapes

NASA Astrophysics Data System (ADS)

Wang, Fan; Ge, Wensheng; Luo, Hao; Seo, Ji-Hye; Michalski, Greg

2016-03-01

The nitrogen cycle in desert soil ecosystems is particularly sensitive to changes in precipitation, even of relatively small magnitude and short duration, because it is already under water stress. This suggests that desert soils may have preserved past evidence of small variations in continental precipitation. We have measured nitrate (NO3-) concentrations in soils from the Atacama (Chile), Kumtag (China), Mojave (US), and Thar (India) deserts, and stable nitrogen and oxygen isotope (15N, 17O, and 18O) abundances of the soil NO3-. 17O anomalies (Δ17O), the deviations from the mass-independent isotopic fractionation, were detected in soil NO3- from almost all sites of these four deserts. There was a strong negative correlation between the mean annual precipitation (MAP) and soil NO3- Δ17O values (Δ

Highly spatially- and seasonally-resolved predictive contamination maps for persistent organic pollutants: development and validation.

PubMed

Ballabio, Cristiano; Guazzoni, Niccoló; Comolli, Roberto; Tremolada, Paolo

2013-08-01

A reliable spatial assessment of the POPs contamination in soils is essential for burden studies and flux evaluations. Soil characteristics and properties vary enormously even within small spatial scale and over time; therefore soil capacity of accumulating POPs varies greatly. In order to include this very high spatial and temporal variability, models can be used for assessing soil accumulation capacity in a specific time and space and, from it, the spatial distribution and temporal trends of POPs concentrations. In this work, predictive contamination maps of the accumulation capacity of soils were developed at a space resolution of 1×1m with a time frame of one day, in a study area located in the central Alps. Physical algorithms for temperature and organic carbon estimation along the soil profile and across the year were fitted to estimate the horizontal, vertical and seasonal distribution of the contamination potential for PCBs in soil (Ksa maps). The resulting maps were cross-validated with an independent set of PCB contamination data, showing very good agreement (e.g. for CB-153, R(2)=0.80, p-value≤2.2·10(-06)). Slopes of the regression between predicted Ksa and experimental concentrations were used to map the soil contamination for the whole area, taking into account soil characteristics and temperature conditions. These maps offer the opportunity to evaluate burden (concentration maps) and fluxes (emission maps) with highly resolved temporal and spatial detail. In addition, in order to explain the observed low autumn PCB concentrations in soil related to the high Ksa values of this period, a dynamic model of seasonal variation of soil concentrations was developed basing on rate parameters fitted on measured concentrations. The model was able to describe, at least partially, the observed different behavior between the quite rapid discharge phase in summer and the slow recharge phase in autumn. Copyright © 2013 Elsevier B.V. All rights reserved.

Thermal inertia mapping of below ground objects and voids

NASA Astrophysics Data System (ADS)

Del Grande, Nancy K.; Ascough, Brian M.; Rumpf, Richard L.

2013-05-01

Thermal inertia (effusivity) contrast marks the borders of naturally heated below ground object and void sites. The Dual Infrared Effusivity Computed Tomography (DIRECT) method, patent pending, detects and locates the presence of enhanced heat flows from below ground object and void sites at a given area. DIRECT maps view contrasting surface temperature differences between sites with normal soil and sites with soil disturbed by subsurface, hollow or semi-empty object voids (or air gaps) at varying depths. DIRECT utilizes an empirical database created to optimize the scheduling of daily airborne thermal surveys to view and characterize unseen object and void types, depths and volumes in "blind" areas.

A guide for the use of digital elevation model data for making soil surveys

USGS Publications Warehouse

Klingebiel, A.A.; Horvath, Emil H.; Reybold, William U.; Moore, D.G.; Fosnight, E.A.; Loveland, Thomas R.

1988-01-01

The intent of this publication is twofold: (1) to serve as a user guide for soil scientists and others interested in learning about the value and use of digital elevation model (DEM) data in making soil surveys and (2) to provide documentation of the Soil Landscape Analysis Project (SLAP). This publication provides a step-by-step guide on how digital slope-class maps are adjusted to topographic maps and orthophotoquads to obtain accurate slope-class maps, and how these derivative maps can be used as a base for soil survey premaps. In addition, guidance is given on the use of aspect-class maps and other resource data in making pre-maps. The value and use of tabular summaries are discussed. Examples of the use of DEM products by the authors and by selected field soil scientists are also given. Additional information on SLAP procedures may be obtained from USDA, Soil Conservation Service, Soil Survey Division, P.O. Box 2890, Washington, D.C. 20013, and from references (Horvath and others, 1987; Horvath and others, 1983; Klingebiel and others, 1987; and Young, 1987) listed in this publication. The slope and aspect products and the procedures for using these products have evolved during 5 years of cooperative research with the USDA, Soil Conservation Service and Forest Service, and the USDI, Bureau of Land Management.

Generation of kth-order random toposequences

NASA Astrophysics Data System (ADS)

Odgers, Nathan P.; McBratney, Alex. B.; Minasny, Budiman

2008-05-01

The model presented in this paper derives toposequences from a digital elevation model (DEM). It is written in ArcInfo Macro Language (AML). The toposequences are called kth-order random toposequences, because they take a random path uphill to the top of a hill and downhill to a stream or valley bottom from a randomly selected seed point, and they are located in a streamshed of order k according to a particular stream-ordering system. We define a kth-order streamshed as the area of land that drains directly to a stream segment of stream order k. The model attempts to optimise the spatial configuration of a set of derived toposequences iteratively by using simulated annealing to maximise the total sum of distances between each toposequence hilltop in the set. The user is able to select the order, k, of the derived toposequences. Toposequences are useful for determining soil sampling locations for use in collecting soil data for digital soil mapping applications. Sampling locations can be allocated according to equal elevation or equal-distance intervals along the length of the toposequence, for example. We demonstrate the use of this model for a study area in the Hunter Valley of New South Wales, Australia. Of the 64 toposequences derived, 32 were first-order random toposequences according to Strahler's stream-ordering system, and 32 were second-order random toposequences. The model that we present in this paper is an efficient method for sampling soil along soil toposequences. The soils along a toposequence are related to each other by the topography they are found in, so soil data collected by this method is useful for establishing soil-landscape rules for the preparation of digital soil maps.

Comparison Between Two Methods for Estimating the Vertical Scale of Fluctuation for Modeling Random Geotechnical Problems

NASA Astrophysics Data System (ADS)

Pieczyńska-Kozłowska, Joanna M.

2015-12-01

The design process in geotechnical engineering requires the most accurate mapping of soil. The difficulty lies in the spatial variability of soil parameters, which has been a site of investigation of many researches for many years. This study analyses the soil-modeling problem by suggesting two effective methods of acquiring information for modeling that consists of variability from cone penetration test (CPT). The first method has been used in geotechnical engineering, but the second one has not been associated with geotechnics so far. Both methods are applied to a case study in which the parameters of changes are estimated. The knowledge of the variability of parameters allows in a long term more effective estimation, for example, bearing capacity probability of failure.

A semester-long soil mapping project for an undergraduate pedology course

NASA Astrophysics Data System (ADS)

Brown, David J.

2015-04-01

Most students taking a pedology course will never work as soil mappers. But many will use soil maps at some point in their careers. At Montana State University, students spent 3 "lab" hours a week, complementing two lectures a week, in the field learning how to study soils literally from the ground up. The only prerequisites for enrollment were completion of an introductory soil science class and 3rd year standing at the university. The area to be mapped, just a km from campus, included a steep mountain backslope, and a complex footslope-toeslope area with diverse soils. Students were divided into teams of 3-4, with approximately 40 students altogether split over two sections that overlapped in the field by one hour. In the first lab session, groups completed a very basic description of just one soil profile. In subsequent weeks, they rotated through multiple pits excavated in a small area, and expanded their soil profile descriptions and interpretations. As students developed proficiency, they were assigned more dispersed locations to study, working for the most part independently as I hiked between pits. Throughout this process, every pit was geolocated using a GPS unit, and every profile description was copied and retained in a designated class file. Student groups delineated map units using stereo air photography, then used these delineations to guide the selection of their final locations to describe. At the end of the course, groups used all of the combined and georeferenced profile descriptions to construct a soil map of the study area complete with map unit descriptions. Most students struggled to make sense of the substantial variability within their map units, but through this struggle -- and their semester of field work -- they gained an appreciation for the value and limitations of a soil map that could not be obtained from even the most entertaining lecture. Both the class and particularly the field sessions received consistently high student reviews during the four years I had students map soils at Montanta State University.

Mapping specific soil functions based on digital soil property maps

NASA Astrophysics Data System (ADS)

Pásztor, László; Fodor, Nándor; Farkas-Iványi, Kinga; Szabó, József; Bakacsi, Zsófia; Koós, Sándor

2016-04-01

Quantification of soil functions and services is a great challenge in itself even if the spatial relevance is supposed to be identified and regionalized. Proxies and indicators are widely used in ecosystem service mapping. Soil services could also be approximated by elementary soil features. One solution is the association of soil types with services as basic principle. Soil property maps however provide quantified spatial information, which could be utilized more versatilely for the spatial inference of soil functions and services. In the frame of the activities referred as "Digital, Optimized, Soil Related Maps and Information in Hungary" (DOSoReMI.hu) numerous soil property maps have been compiled so far with proper DSM techniques partly according to GSM.net specifications, partly by slightly or more strictly changing some of its predefined parameters (depth intervals, pixel size, property etc.). The elaborated maps have been further utilized, since even DOSoReMI.hu was intended to take steps toward the regionalization of higher level soil information (secondary properties, functions, services). In the meantime the recently started AGRAGIS project requested spatial soil related information in order to estimate agri-environmental related impacts of climate change and support the associated vulnerability assessment. One of the most vulnerable services of soils in the context of climate change is their provisioning service. In our work it was approximated by productivity, which was estimated by a sequential scenario based crop modelling. It took into consideration long term (50 years) time series of both measured and predicted climatic parameters as well as accounted for the potential differences in agricultural practice and crop production. The flexible parametrization and multiple results of modelling was then applied for the spatial assessment of sensitivity, vulnerability, exposure and adaptive capacity of soils in the context of the forecasted changes in climatic conditions in the Carpathian Basin. In addition to soil fertility, degradation risk due to N-leaching was also assessed by the model runs by taking into account the movement of nitrate in the profile during the simulated periods. Our paper will present the resulted national maps and some conclusions drawn from the experiences. Acknowledgement: Our work was supported by Iceland, Liechtenstein and Norway through the EEA Grants and the REC (Project No: EEA C12-12) and the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Understanding controls of hydrologic processes across two headwater monolithological catchments using model-data synthesis

NASA Astrophysics Data System (ADS)

Xiao, D.; Shi, Y.; Hoagland, B.; Del Vecchio, J.; Russo, T. A.; DiBiase, R. A.; Li, L.

2017-12-01

How do watershed hydrologic processes differ in catchments derived from different lithology? This study compares two first order, deciduous forest watersheds in Pennsylvania, a sandstone watershed, Garner Run (GR, 1.34 km2), and a shale-derived watershed, Shale Hills (SH, 0.08 km2). Both watersheds are simulated using a combination of national datasets and field measurements, and a physics-based land surface hydrologic model, Flux-PIHM. We aim to evaluate the effects of lithology on watershed hydrology and assess if we can simulate a new watershed without intensive measurements, i.e., directly use calibration information from one watershed (SH) to reproduce hydrologic dynamics of another watershed (GR). Without any calibration, the model at GR based on national datasets and calibration inforamtion from SH cannot capture some discharge peaks or the baseflow during dry periods. The model prediction agrees well with the GR field discharge and soil moisture after calibrating the soil hydraulic parameters using the uncertainty based Hornberger-Spear-Young algorithm and the Latin Hypercube Sampling method. Agreeing with the field observation and national datasets, the difference in parameter values shows that the sandstone watershed has a larger averaged soil pore diameter, greater water storage created by porosity, lower water retention ability, and greater preferential flow. The water budget calculation shows that the riparian zone and the colluvial valley serves as buffer zones that stores water at GR. Using the same procedure, we compared Flux-PIHM simulations with and without a field measured surface boulder map at GR. When the boulder map is used, the prediction of areal averaged soil moisture is improved, without performing extra calibration. When calibrated separately, the cases with or without boulder map yield different calibration values, but their hydrologic predictions are similar, showing equifinality. The calibrated soil hydraulic parameter values in the with boulder map case is more physically plausible than the without boulder map case. We switched the topography and soil properties between GR and SH, and results indicate that the hydrologic processes are more sensitive to changes in domain topography than to changes in the soil properties.

Texture mapping via optimal mass transport.

PubMed

Dominitz, Ayelet; Tannenbaum, Allen

2010-01-01

In this paper, we present a novel method for texture mapping of closed surfaces. Our method is based on the technique of optimal mass transport (also known as the "earth-mover's metric"). This is a classical problem that concerns determining the optimal way, in the sense of minimal transportation cost, of moving a pile of soil from one site to another. In our context, the resulting mapping is area preserving and minimizes angle distortion in the optimal mass sense. Indeed, we first begin with an angle-preserving mapping (which may greatly distort area) and then correct it using the mass transport procedure derived via a certain gradient flow. In order to obtain fast convergence to the optimal mapping, we incorporate a multiresolution scheme into our flow. We also use ideas from discrete exterior calculus in our computations.

SoilGrids250m: Global gridded soil information based on machine learning

PubMed Central

Mendes de Jesus, Jorge; Heuvelink, Gerard B. M.; Ruiperez Gonzalez, Maria; Kilibarda, Milan; Blagotić, Aleksandar; Shangguan, Wei; Wright, Marvin N.; Geng, Xiaoyuan; Bauer-Marschallinger, Bernhard; Guevara, Mario Antonio; Vargas, Rodrigo; MacMillan, Robert A.; Batjes, Niels H.; Leenaars, Johan G. B.; Ribeiro, Eloi; Wheeler, Ichsani; Mantel, Stephan; Kempen, Bas

2017-01-01

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10–fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License. PMID:28207752

On-the-go mapping of soil mechanical resistance using a linear depth effect model.

USDA-ARS?s Scientific Manuscript database

An instrumented blade sensor was developed to map soil mechanical resistance as well as its change with depth. The sensor has become a part of the Integrated Soil Physical Properties Mapping System (ISPPMS), which also includes an optical and a capacitor-based sensor. The instrumented blade of the...

7 CFR 12.31 - On-site wetland identification criteria.

Code of Federal Regulations, 2011 CFR

2011-01-01

... CONSERVATION Wetland Conservation § 12.31 On-site wetland identification criteria. (a) Hydric soils. (1) NRCS shall identify hydric soils through the use of published soil maps which reflect soil surveys completed by NRCS or through the use of on-site reviews. If a published soil map is unavailable for a given...

7 CFR 12.31 - On-site wetland identification criteria.

Code of Federal Regulations, 2014 CFR

2014-01-01

... CONSERVATION Wetland Conservation § 12.31 On-site wetland identification criteria. (a) Hydric soils. (1) NRCS shall identify hydric soils through the use of published soil maps which reflect soil surveys completed by NRCS or through the use of on-site reviews. If a published soil map is unavailable for a given...

7 CFR 12.31 - On-site wetland identification criteria.

Code of Federal Regulations, 2010 CFR

2010-01-01

... CONSERVATION Wetland Conservation § 12.31 On-site wetland identification criteria. (a) Hydric soils. (1) NRCS shall identify hydric soils through the use of published soil maps which reflect soil surveys completed by NRCS or through the use of on-site reviews. If a published soil map is unavailable for a given...

7 CFR 12.31 - On-site wetland identification criteria.

Code of Federal Regulations, 2013 CFR

2013-01-01

... CONSERVATION Wetland Conservation § 12.31 On-site wetland identification criteria. (a) Hydric soils. (1) NRCS shall identify hydric soils through the use of published soil maps which reflect soil surveys completed by NRCS or through the use of on-site reviews. If a published soil map is unavailable for a given...

7 CFR 12.31 - On-site wetland identification criteria.

Code of Federal Regulations, 2012 CFR

2012-01-01

... CONSERVATION Wetland Conservation § 12.31 On-site wetland identification criteria. (a) Hydric soils. (1) NRCS shall identify hydric soils through the use of published soil maps which reflect soil surveys completed by NRCS or through the use of on-site reviews. If a published soil map is unavailable for a given...

Application of mobile gamma-ray spectrometry for soil mapping

NASA Astrophysics Data System (ADS)

Werban, Ulrike; Lein, Claudia; Pohle, Marco; Dietrich, Peter

2017-04-01

Gamma-ray measurements have a long tradition for geological surveys and deposit exploration using airborne and borehole logging systems. For these applications, the relationships between the measured physical parameter - the concentration of natural gamma emitters 40K, 238U and 232Th - and geological origin or sedimentary developments are well described. Thus, Gamma-ray spectrometry seems a useful tool for carrying out spatial mapping of physical parameters related to soil properties. The isotope concentration in soils depends on different soil parameters (e.g. geochemical composition, grain size fractions), which are a result of source rock properties and processes during soil geneses. There is a rising interest in the method for application in Digital Soil Mapping or as input data for environmental, ecological or hydrological modelling, e.g. as indicator for clay content. However, the gamma-ray measurement is influenced by endogenous factors and processes like soil moisture variation, erosion and deposition of material or cultivation. We will present results from a time series of car borne gamma-ray measurements to observe heterogeneity of soil on a floodplain area in Central Germany. The study area is characterised by high variations in grain size distribution and occurrence of flooding events. For the survey, we used a 4 l NaI(Tl) detector with GPS connection mounted on a sledge, which is towed across the field sites by a four-wheel-vehicle. The comparison of data from different dates shows similar structures with small variation between the data ranges and shape of structures. We will present our experiences concerning the application of gamma-ray measurements under variable field conditions and their impacts on data quality.

The distribution of selected elements and minerals in soil of the conterminous United States

USGS Publications Warehouse

Woodruff, Laurel G.; Cannon, William F.; Smith, David; Solano, Federico

2015-01-01

In 2007, the U.S. Geological Survey initiated a low-density (1 site per 1600 km2, 4857 sites) geochemical and mineralogical survey of soil of the conterminous United States as part of the North American Soil Geochemical Landscapes Project. Three soil samples were collected, if possible, from each site; (1) a sample from a depth of 0 to 5 cm, (2) a composite of the soil A-horizon, and (3) a deeper sample from the soil C-horizon or, if the top of the C-horizon was at a depth greater than 100 cm, from a depth of approximately 80–100 cm. The < 2 mm fraction of each sample was analysed for a suite of 45 major and trace elements following near-total multi-acid digestion. The major mineralogical components in samples from the soil A- and C-horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. Sampling ended in 2010 and chemical and mineralogical analyses were completed in May 2013. Maps of the conterminous United States showing predicted element and mineral concentrations were interpolated from actual soil data for each soil sample type by an inverse distance weighted (IDW) technique using ArcGIS software. Regional- and national-scale map patterns for selected elements and minerals apparent in interpolated maps are described here in the context of soil-forming factors and possible human inputs. These patterns can be related to (1) soil parent materials, for example, in the distribution of quartz, (2) climate impacts, for example, in the distribution of feldspar and kaolinite, (3) soil age, for example, in the distribution of carbonate in young glacial deposits, and (4) possible anthropogenic loading of phosphorus (P) and lead (Pb) to surface soil. This new geochemical and mineralogical data set for the conterminous United States represents a major step forward from prior national-scale soil geochemistry data and provides a robust soil data framework for the United States now and into the future.

Spatial disaggregation of complex soil map units at regional scale based on soil-landscape relationships

NASA Astrophysics Data System (ADS)

Vincent, Sébastien; Lemercier, Blandine; Berthier, Lionel; Walter, Christian

2015-04-01

Accurate soil information over large extent is essential to manage agronomical and environmental issues. Where it exists, information on soil is often sparse or available at coarser resolution than required. Typically, the spatial distribution of soil at regional scale is represented as a set of polygons defining soil map units (SMU), each one describing several soil types not spatially delineated, and a semantic database describing these objects. Delineation of soil types within SMU, ie spatial disaggregation of SMU allows improved soil information's accuracy using legacy data. The aim of this study was to predict soil types by spatial disaggregation of SMU through a decision tree approach, considering expert knowledge on soil-landscape relationships embedded in soil databases. The DSMART (Disaggregation and Harmonization of Soil Map Units Through resampled Classification Trees) algorithm developed by Odgers et al. (2014) was used. It requires soil information, environmental covariates, and calibration samples, to build then extrapolate decision trees. To assign a soil type to a particular spatial position, a weighed random allocation approach is applied: each soil type in the SMU is weighted according to its assumed proportion of occurrence in the SMU. Thus soil-landscape relationships are not considered in the current version of DSMART. Expert rules on soil distribution considering the relief, parent material and wetlands location were proposed to drive the procedure of allocation of soil type to sampled positions, in order to integrate the soil-landscape relationships. Semantic information about spatial organization of soil types within SMU and exhaustive landscape descriptors were used. In the eastern part of Brittany (NW France), 171 soil types were described; their relative area in the SMU were estimated, geomorphological and geological contexts were recorded. The model predicted 144 soil types. An external validation was performed by comparing predicted with effectively observed soil types derived from available soil maps at scale of 1:25.000 or 1:50.000. Overall accuracies were 63.1% and 36.2%, respectively considering or not the adjacent pixels. The introduction of expert rules based on soil-landscape relationships to allocate soil types to calibration samples enhanced dramatically the results in comparison with a simple weighted random allocation procedure. It also enabled the production of a comprehensive soil map, retrieving expected spatial organization of soils. Estimation of soil properties for various depths is planned using disaggregated soil types, according to the GlobalSoilmap.net specifications. Odgers, N.P., Sun, W., McBratney, A.B., Minasny, B., Clifford, D., 2014. Disaggregating and harmonising soil map units through resampled classification trees. Geoderma 214, 91-100.

Digital Soil Mapping - A platform for enhancing soil learning

NASA Astrophysics Data System (ADS)

Owens, Phillip; Libohova, Zamir; Monger, Curtis; Lindbo, David; Schmidt, Axel

2017-04-01

The expansion of digital infrastructure and tools has generated massive data and information as well as a need for reliable processing and accurate interpretations. Digital Soil Mapping is no exception in that it has provided opportunities for professionals and the public to interact at field and training/workshop levels in order to better understand soils and their benefits. USDA-NRCS National Cooperative Soil Survey regularly conducts training and workshops for soil scientists and other professionals in the US and internationally. A combination of field experiences with workshops conducted in a class environment offers ideal conditions for enhancing soil learning experiences. Examples from US, Haiti and Central America show that Digital Soil Mapping (DSM) tools are very effective for understanding and visualizing soils and their functioning at different scales.

Optimal mapping of site-specific multivariate soil properties.

PubMed

Burrough, P A; Swindell, J

1997-01-01

This paper demonstrates how geostatistics and fuzzy k-means classification can be used together to improve our practical understanding of crop yield-site response. Two aspects of soil are important for precision farming: (a) sensible classes for a given crop, and (b) their spatial variation. Local site classifications are more sensitive than general taxonomies and can be provided by the method of fuzzy k-means to transform a multivariate data set with i attributes measured at n sites into k overlapping classes; each site has a membership value mk for each class in the range 0-1. Soil variation is of interest when conditions vary over patches manageable by agricultural machinery. The spatial variation of each of the k classes can be analysed by computing the variograms of mk over the n sites. Memberships for each of the k classes can be mapped by ordinary kriging. Areas of class dominance and the transition zones between them can be identified by an inter-class confusion index; reducing the zones to boundaries gives crisp maps of dominant soil groups that can be used to guide precision farming equipment. Automation of the procedure is straightforward given sufficient data. Time variations in soil properties can be automatically incorporated in the computation of membership values. The procedures are illustrated with multi-year crop yield data collected from a 5 ha demonstration field at the Royal Agricultural College in Cirencester, UK.

Assessment of Potential Location of High Arsenic Contamination Using Fuzzy Overlay and Spatial Anisotropy Approach in Iron Mine Surrounding Area

PubMed Central

Wirojanagud, Wanpen; Srisatit, Thares

2014-01-01

Fuzzy overlay approach on three raster maps including land slope, soil type, and distance to stream can be used to identify the most potential locations of high arsenic contamination in soils. Verification of high arsenic contamination was made by collection samples and analysis of arsenic content and interpolation surface by spatial anisotropic method. A total of 51 soil samples were collected at the potential contaminated location clarified by fuzzy overlay approach. At each location, soil samples were taken at the depth of 0.00-1.00 m from the surface ground level. Interpolation surface of the analysed arsenic content using spatial anisotropic would verify the potential arsenic contamination location obtained from fuzzy overlay outputs. Both outputs of the spatial surface anisotropic and the fuzzy overlay mapping were significantly spatially conformed. Three contaminated areas with arsenic concentrations of 7.19 ± 2.86, 6.60 ± 3.04, and 4.90 ± 2.67 mg/kg exceeded the arsenic content of 3.9 mg/kg, the maximum concentration level (MCL) for agricultural soils as designated by Office of National Environment Board of Thailand. It is concluded that fuzzy overlay mapping could be employed for identification of potential contamination area with the verification by surface anisotropic approach including intensive sampling and analysis of the substances of interest. PMID:25110751

Modelling Soil Erosion in the Densu River Basin Using RUSLE and GIS Tools.

PubMed

Ashiagbori, G; Forkuo, E K; Laari, P; Aabeyir, R

2014-07-01

Soil erosion involves detachment and transport of soil particles from top soil layers, degrading soil quality and reducing the productivity of affected lands. Soil eroded from the upland catchment causes depletion of fertile agricultural land and the resulting sediment deposited at the river networks creates river morphological change and reservoir sedimentation problems. However, land managers and policy makers are more interested in the spatial distribution of soil erosion risk than in absolute values of soil erosion loss. The aim of this paper is to model the spatial distribution of soil erosion in Densu River Basin of Ghana using RUSLE and GIS tools and to use the model to explore the relationship between erosion susceptibility, slope and land use/land cover (LULC) in the Basin. The rainfall map, digital elevation model, soil type map, and land cover map, were input data in the soil erosion model developed. This model was then categorized into four different erosion risk classes. The developed soil erosion map was then overlaid with the slope and LULC maps of the study area to explore their effects on erosion susceptibility of the soil in the Densu River Basin. The Model, predicted 88% of the basin as low erosion risk and 6% as moderate erosion risk, 3% as high erosion risk and 3% as severe risk. The high and severe erosion areas were distributed mainly within the areas of high slope gradient and also sections of the moderate forest LULC class. Also, the areas within the moderate forest LULC class found to have high erosion risk, had an intersecting high erodibility soil group.

SEARCH AND MAPPING OF THE OLD BURIED TAILINGS WITH RADIOACTIVE WASTES AT THE URBAN TERRITORY.

PubMed

Molchanov, O I; Soroka, Y N; Podrezov, A A; Soroka, M N

2017-11-01

The article presents results of investigation on search and mapping of the old buried tailings with radioactive wastes on the territory of Kamianske City. For solving the problem used complex of methods. These methods are as follows: soil-gas 222Rn measurement and measurement of 222Rn flux density from the ground surface, gamma-radiation survey, prospecting drilling, gamma-ray logging and laboratory analysis of radionuclides. The leading method in this complex was the method of soil-gas 222Rn measurement. Using this method location of the tailings has been precisely defined. The tailings boundaries have been contoured in the plan. Other methods permitted to define such parameters as thickness of the wastes, their volume (~330 000 m3), radionuclide and chemical composition. It was found that radioactive residues occur at a depth from 2 to 11 m and contain in its composition 226Ra, 210Pb and 210Po in the range from 8370 to 37 270 Bq kg-1. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An ERTS multispectral scanner experiment for mapping iron compounds

NASA Technical Reports Server (NTRS)

Vincent, R. K. (Principal Investigator)

1972-01-01

There are no author-identified significant results in this report. An experimental plan for enhancing spectral features related to the chemical composition of geological targets in ERTS multispectral scanner data is described. The experiment is designed to produce visible-reflective infrared ratio images from ERTS-1 data. Iron compounds are promising remote sensing targets because they display prominent spectral features in the visible-reflective infrared wavelength region and are geologically significant. The region selected for this ERTS experiment is the southern end of the Wind River Range in Wyoming. If this method proves successful it should prove useful for regional geologic mapping, mineralogical exploration, and soil mapping. It may also be helpful to ERTS users in scientific disciplines other than geology, especially to those concerned with targets composed of mixtures of live vegetation and soil or rock.

High Resolution Mapping of Soils and Landforms for the Desert Renewable Energy Conservation Plan (DRECP)

NASA Technical Reports Server (NTRS)

Potter, Christopher S.; Li, Shuang

2014-01-01

The Desert Renewable Energy Conservation Plan (DRECP), a major component of California's renewable energy planning efforts, is intended to provide effective protection and conservation of desert ecosystems, while allowing for the sensible development of renewable energy projects. This NASA mapping report was developed to support the DRECP and the Bureau of Land Management (BLM). We outline in this document remote sensing image processing methods to deliver new maps of biological soils crusts, sand dune movements, desert pavements, and sub-surface water sources across the DRECP area. We focused data processing first on the largely unmapped areas most likely to be used for energy developments, such as those within Renewable Energy Study Areas (RESA) and Solar Energy Zones (SEZs). We used imagery (multispectral and radar) mainly from the years 2009-2011.

Application of Satellite SAR Imagery in Mapping the Active Layer of Arctic Permafrost

NASA Technical Reports Server (NTRS)

Li, Shu-Sun; Romanovsky, V.; Lovick, Joe; Wang, Z.; Peterson, Rorik

2003-01-01

A method of mapping the active layer of Arctic permafrost using a combination of conventional synthetic aperture radar (SAR) backscatter and more sophisticated interferometric SAR (INSAR) techniques is proposed. The proposed research is based on the sensitivity of radar backscatter to the freeze and thaw status of the surface soil, and the sensitivity of INSAR techniques to centimeter- to sub-centimeter-level surface differential deformation. The former capability of SAR is investigated for deriving the timing and duration of the thaw period for surface soil of the active layer over permafrost. The latter is investigated for the feasibility of quantitative measurement of frost heaving and thaw settlement of the active layer during the freezing and thawing processes. The resulting knowledge contributes to remote sensing mapping of the active layer dynamics and Arctic land surface hydrology.

The informativeness of coefficients a and b of the soil line for the analysis of remote sensing materials

NASA Astrophysics Data System (ADS)

Rukhovich, D. I.; Rukhovich, A. D.; Rukhovich, D. D.; Simakova, M. S.; Kulyanitsa, A. L.; Bryzzhev, A. V.; Koroleva, P. V.

2016-08-01

The coefficients of the soil line are often taken into account in calculations of vegetation indices. These coefficients are usually calculated for the entire satellite image, or are taken as constants without any calculations. In both cases, the informativeness of these coefficients is low and insufficient for the needs of soil mapping. In our study, we calculated soil line coefficients at 8000 lattice points for the territory of Plavsk, Arsen'evsk, and Chern districts of Tula oblast on the basis of 34 Landsat 5, 7, and 8 images obtained in 1985-2014. In order to distinguish between the soil line calculated for a given image and the soil line calculated for lattice points on the basis of dozens of multitemporal images, we suggest that the latter can be referred to as the temporal soil line. The temporal soil line is described by a classical equation: NIR = RED a + b, where a is its slope relative to the horizontal axis (RED), and b is the Y-axis (NIR) intercept. Both coefficients were used to create soil maps. The verification of the maps was performed with the use of data on 1985 soil pits. The informativeness of these coefficients appeared to be sufficient for delineation of eight groups of soils of different taxonomic levels: soddy moderately podzolic soils, soddy slightly podzolic soils, soddy-podzolic soils, light gray forest soils, gray forest soils, dark gray forest soils, podzolized chernozems, and leached chernozems. The b coefficient proved to be more informative, as it allowed us to create the soil map precisely on its basis. In order to create the soil map on the basis of the a coefficient, we had to apply some threshold values of the b coefficient. The bare soil on each of Landsat scenes was separated with the help of the mask of agricultural fields and the notion of the spectral neighborhood of soil line (SNSL).

Seismic Microzonation of Breginjski Kot (NW Slovenia) Based on Detailed Engineering Geological Mapping

PubMed Central

2013-01-01

Breginjski kot is among the most endangered seismic zones in Slovenia with the seismic hazard assessed to intensity IX MSK and the design ground acceleration of 0.250 g, both for 500-year return period. The most destructive was the 1976 Friuli Mw = 6.4 earthquake which had maximum intensity VIII-IX. Since the previous microzonation of the area was based solely on the basic geological map and did not include supplementary field research, we have performed a new soil classification of the area. First, a detailed engineering geological mapping in scale 1 : 5.000 was conducted. Mapped units were described in detail and some of them interpreted anew. Stiff sites are composed of hard to medium-hard rocks which were subjected to erosion mainly evoked by glacial and postglacial age. At that time a prominent topography was formed and different types of sediments were deposited in valleys by mass flows. A distinction between sediments and weathered rocks, their exact position, and thickness are of significant importance for microzonation. On the basis of geological mapping, a soil classification was carried out according to the Medvedev method (intensity increments) and the Eurocode 8 standard (soil factors) and two microzonation maps were prepared. The bulk of the studied area is covered by soft sediments and nine out of ten settlements are situated on them. The microzonation clearly points out the dependence of damage distribution in the case of 1976 Friuli earthquake to local site effects. PMID:24453884

Quantification of map similarity to magnetic pre-screening for heavy metal pollution assessment in top soil

NASA Astrophysics Data System (ADS)

Cao, L.; Appel, E.; Roesler, W.; Ojha, G.

2013-12-01

From numerous published results, the link between magnetic concentration and heavy metal (HM) concentrations is well established. However, bivariate correlation analysis does not imply causality, and if there are extreme values, which often appear in magnetic data, they can lead to seemingly excellent correlation. It seems clear that site selection for chemical sampling based on magnetic pre-screening can deliver a superior result for outlining HM pollution, but this conclusion has only been drawn from qualitative evaluation so far. In this study, we use map similarity comparison techniques to demonstrate the usefulness of a combined magnetic-chemical approach quantitatively. We chose available data around the 'Schwarze Pumpe', a large coal burning power plant complex located in eastern Germany. The site of 'Schwarze Pumpe' is suitable for a demonstration study as soil in its surrounding is heavy fly-ash polluted, the magnetic natural background is very low, and magnetic investigations can be done in undisturbed forest soil. Magnetic susceptibility (MS) of top soil was measured by a Bartington MS2D surface sensor at 180 locations and by a SM400 downhole device in ~0.5m deep vertical sections at 90 locations. Cores from the 90 downhole sites were also studied for HM analysis. From these results 85 sites could be used to determine a spatial distribution map of HM contents reflecting the 'True' situation of pollution. Different sets comprising 30 sites were chosen by arbitrarily selection from the above 85 sample sites (we refer to four such maps here: S1-4). Additionally, we determined a 'Targeted' map from 30 sites selected on the basis of the pre-screening MS results. The map comparison process is as follows: (1) categorization of all absolute values into five classes by the Natural Breaks classification method; (2) use Delaunay triangulation for connecting the sample locations in the x-y plane; (3) determination of a distribution map of triangular planes with classified values as the Z coordinate; (4) calculation of normal vectors for each individual triangular plane; (5)transformation to the TINs into raster data assigning the same normal vectors to all grid-points which are inside the same TIN; (6) calculation of the root-mean-square of angles between normal vectors of two maps at the same grid points. Additionally, we applied the kappa statistics method to assess map similarities, and moreover developed a Fuzzy set approach. Combining both methods using indices of Khisto, Klocation, Kappa, Kfuzzy obtains a broad comparison system, which allows determining the degree of similarity and also the spatial distribution of similarity between two maps. The results indicate that the similarity between the 'Targeted' and 'True' distribution map is higher than that between 'S1-4' and the 'True' map. It manifests that magnetic pre-screening can provide a reliable basis for targeted selection of chemical sampling sites demonstrating the superior efficiency of a combined magnetic-chemical site assessment in comparison to a traditional chemical-only approach.

Making US Soil Taxonomy more scientifically applicable to environmental and food security issues.

NASA Astrophysics Data System (ADS)

Monger, Curtis; Lindbo, David L.; Wysocki, Doug; Schoeneberger, Phil; Libohova, Zamir

2017-04-01

US Department of Agriculture began mapping soils in the 1890s on a county-by-county basis until most of the conterminous United States was mapped by the late 1930s. This first-generation mapping was followed by a second-generation that re-mapped the US beginning in the 1940s. Soil classification during these periods evolved into the current system of Soil Taxonomy which is based on (1) soil features as natural phenomena and on (2) soil properties important for agriculture and other land uses. While this system has enabled communication among soil surveyors, the scientific applicability of Soil Taxonomy to address environmental and food security issues has been under-utilized. In particular, little effort has been exerted to understand how soil taxa interact and function together as larger units—as soil systems. Thus, much soil-geomorphic understanding that could be applied to process-based modeling remains unexploited. The challenge for soil taxonomists in the United States and elsewhere is to expand their expertise and work with modelers to explore how soil taxa are linked to each other, how they influence water, nutrient, and pollutant flow through the landscape, how they interact with ecology, and how they change with human land use.

Bank Erosion Vulnerability Zonation (BEVZ) -A Proposed Method of Preparing Bank Erosion Zonation and Its Application on the River Haora, Tripura, India

NASA Astrophysics Data System (ADS)

Bandyopadhyay, Shreya; de, Sunil Kumar

2014-05-01

In the present paper an attempt has been made to propose RS-GIS based method for erosion vulnerability zonation for the entire river based on simple techniques that requires very less field investigation. This method consist of 8 parameters, such as, rainfall erosivity, lithological factor, bank slope, meander index, river gradient, soil erosivity, vegetation cover and anthropogenic impact. Meteorological data, GSI maps, LISS III (30m resolution), SRTM DEM (56m resolution) and Google Images have been used to determine rainfall erosivity, lithological factor, bank slope, meander index, river gradient, vegetation cover and anthropogenic impact; Soil map of the NBSSLP, India has been used for assessing Soil Erosivity index. By integrating the individual values of those six parameters (the 1st two parameters are remained constant for this particular study area) a bank erosion vulnerability zonation map of the River Haora, Tripura, India (23°37' - 23°53'N and 91°15'-91°37'E) has been prepared. The values have been compared with the existing BEHI-NBS method of 60 spots and also with field data of 30 cross sections (covering the 60 spots) taken along 51 km stretch of the river in Indian Territory and found that the estimated values are matching with the existing method as well as with field data. The whole stretch has been divided into 5 hazard zones, i.e. Very High, High, Moderate, Low and Very Low Hazard Zones and they are covering 5.66 km, 16.81 km, 40.82km, 29.67 km and 9.04 km respectively. KEY WORDS: Bank erosion, Bank Erosion Hazard Index (BEHI), Near Bank Stress (NBS), Erosivity, Bank Erosion Vulnerability Zonation.

Analysis of the suitability of various soil groups and types of climate for avocado growing in the state of Michoacán, Mexico

NASA Astrophysics Data System (ADS)

Dubrovina, I. A.; Bautista, F.

2014-05-01

Avocado is the largest cash crop exported by Mexico, and the state of Michoacán is its largest producer. For the further development of avocado plantations, the optimal edaphic and bioclimatic conditions for this crop should be determined. We performed a review of the literature to find out the requirements of the avocado for soil and climatic conditions and analyzed the maps, soil databases, and data from local weather stations in the studied region for developing scales of suitability of soils and climates for avocado growing. To verify these scales, a method of data mining was applied; a decision tree developed by this method confirmed the high accuracy and adequacy of the suggested grouping.

On the soil moisture estimate at basin scale in Mediterranean basins with the ASAR sensor: the Mulargia basin case study

NASA Astrophysics Data System (ADS)

Fois, Laura; Montaldo, Nicola

2017-04-01

Soil moisture plays a key role in water and energy exchanges between soil, vegetation and atmosphere. For water resources planning and managementthesoil moistureneeds to be accurately and spatially monitored, specially where the risk of desertification is high, such as Mediterranean basins. In this sense active remote sensors are very attractive for soil moisture monitoring. But Mediterranean basinsaretypicallycharacterized by strong topography and high spatial variability of physiographic properties, and only high spatial resolution sensorsare potentially able to monitor the strong soil moisture spatial variability.In this regard the Envisat ASAR (Advanced Synthetic Aperture Radar) sensor offers the attractive opportunity ofsoil moisture mapping at fine spatial and temporal resolutions(up to 30 m, every 30 days). We test the ASAR sensor for soil moisture estimate in an interesting Sardinian case study, the Mulargia basin withan area of about 70 sq.km. The position of the Sardinia island in the center of the western Mediterranean Sea basin, its low urbanization and human activity make Sardinia a perfect reference laboratory for Mediterranean hydrologic studies. The Mulargia basin is a typical Mediterranean basinin water-limited conditions, and is an experimental basin from 2003. For soil moisture mapping23 satellite ASAR imagery at single and dual polarization were acquired for the 2003-2004period.Satellite observationsmay bevalidated through spatially distributed soil moisture ground-truth data, collected over the whole basin using the TDR technique and the gravimetric method, in days with available radar images. The results show that ASAR sensor observations can be successfully used for soil moisture mapping at different seasons, both wet and dry, but an accurate calibration with field data is necessary. We detect a strong relationship between the soil moisture spatial variability and the physiographic properties of the basin, such as soil water storage capacity, deep and texture of soils, type and density of vegetation, and topographic parameters. Finally we demonstrate that the high resolution ASAR imagery are an attractive tool for estimating surface soil moisture at basin scale, offering a unique opportunity for monitoring the soil moisture spatial variability in typical Mediterranean basins.

Factors affecting paddy soil arsenic concentration in Bangladesh: prediction and uncertainty of geostatistical risk mapping.

PubMed

Ahmed, Zia U; Panaullah, Golam M; DeGloria, Stephen D; Duxbury, John M

2011-12-15

Knowledge of the spatial correlation of soil arsenic (As) concentrations with environmental variables is needed to assess the nature and extent of the risk of As contamination from irrigation water in Bangladesh. We analyzed 263 paired groundwater and paddy soil samples covering highland (HL) and medium highland-1 (MHL-1) land types for geostatistical mapping of soil As and delineation of As contaminated areas in Tala Upazilla, Satkhira district. We also collected 74 non-rice soil samples to assess the baseline concentration of soil As for this area. The mean soil As concentrations (mg/kg) for different land types under rice and non-rice crops were: rice-MHL-1 (21.2)>rice-HL (14.1)>non-rice-MHL-1 (11.9)>non-rice-HL (7.2). Multiple regression analyses showed that irrigation water As, Fe, land elevation and years of tubewell operation are the important factors affecting the concentrations of As in HL paddy soils. Only years of tubewell operation affected As concentration in the MHL-1 paddy soils. Quantitatively similar increases in soil As above the estimated baseline-As concentration were observed for rice soils on HL and MHL-1 after 6-8 years of groundwater irrigation, implying strong retention of As added in irrigation water in both land types. Application of single geostatistical methods with secondary variables such as regression kriging (RK) and ordinary co-kriging (OCK) gave little improvement in prediction of soil As over ordinary kriging (OK). Comparing single prediction methods, kriging within strata (KWS), the combination of RK for HL and OCK for MHL-1, gave more accurate soil As predictions and showed the lowest misclassification of declaring a location "contaminated" with respect to 14.8 mg As/kg, the highest value obtained for the baseline soil As concentration. Prediction of soil As buildup over time indicated that 75% or the soils cropped to rice would contain at least 30 mg/L As by the year 2020. Copyright © 2011 Elsevier B.V. All rights reserved.

Elaboration of a framework for the compilation of countrywide, digital maps for the satisfaction of recent demands on spatial, soil related information in Hungary

NASA Astrophysics Data System (ADS)

Pásztor, László; Dobos, Endre; Szabó, József; Bakacsi, Zsófia; Laborczi, Annamária

2013-04-01

There is a heap of evidences that demands on soil related information have been significant worldwide and it is still increasing. Soil maps were typically used for long time to satisfy these demands. By the spread of GI technology, spatial soil information systems (SSIS) and digital soil mapping (DSM) took the role of traditional soil maps. Due to the relatively high costs of data collection, new conventional soil surveys and inventories are getting less and less frequent, which fact valorises legacy soil information and the systems which are serving the their digitally processed version. The existing data contain a wealth of information that can be exploited by proper methodology. Not only the degree of current needs for soil information has changed but also its nature. Traditionally the agricultural functions of soils were focussed on, which was also reflected in the methodology of data collection and mapping. Recently the multifunctionality of soils is getting to gain more and more ground; consequently information related to additional functions of soils becomes identically important. The new types of information requirements however cannot be fulfilled generally with new data collections at least not on such a level as it was done in the frame of traditional soil surveys. Soil monitoring systems have been established for the collection of recent information on the various elements of the DPSIR (Driving Forces-Pressures-State-Impacts-Responses) framework, but the primary goal of these systems has not been mapping by all means. And definitely this is the case concerning the two recently working Hungarian soil monitoring systems. In Hungary, presently soil data requirements are fulfilled with the recently available datasets either by their direct usage or after certain specific and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable imperfection as for the accuracy and reliability of the delivered products. Since, similarly to the great majority of the world, large-scale, comprehensive new surveys cannot be expected in the near future, the actually available legacy data should be relied on. With a recently started project we would like to significantly extend the potential, how countrywide soil information requirements could be satisfied. In the frame of our project we plan the execution of spatial and thematic data mining of significant amount of soil related information available in the form of legacy soil data as well as digital databases and spatial soil information systems. In the course of the analyses we will lean on auxiliary, spatial data themes related to environmental elements. Based on the established relationships we will convert and integrate the specific data sets for the regionalization of the various, derived soil parameters. By the aid of GIS and geostatistical tools we will carry out the spatial extension of certain pedological variables featuring the (including degradation) state, processes or functions of soils. We plan to compile digital soil maps which fulfil optimally the national and international demands from points of view of thematic, spatial and temporal accuracy. The targeted spatial resolution of the proposed countrywide, digital, thematic soil property and function maps is at least 1:50.000 (approx. 50-100 meter raster). Our stressful objective is the definite solution of the regionalization of the information collected in the frame of two recent, contemporary, national, systematic soil data collection (not designed for mapping purpose) on the recent state of soils, in order to produce countrywide maps for the spatial inventory of certain soil properties, processes and functions with sufficient accuracy and reliability.

Historical Perspectives and Future Needs in the Development of the Soil Series Concept

NASA Astrophysics Data System (ADS)

Beaudette, Dylan E.; Brevik, Eric C.; Indorante, Samuel J.

2016-04-01

The soil series concept is an ever-evolving understanding of soil profile observations, their connection to the landscape, and functional limits on the range in characteristics that affect management. Historically, the soil series has played a pivotal role in the development of soil-landscape theory, modern soil survey methods, and concise delivery of soils information to the end-user-- in other words, soil series is the palette from which soil survey reports are crafted. Over the last 20 years the soil series has received considerable criticism as a means of soil information organization (soil survey development) and delivery (end-user application of soil survey data), with increasing pressure (internal and external) to retire the soil series. We propose that a modern re-examination of soil series information could help address several of the long-standing critiques of soil survey: consistency across survey vintage and political divisions and more robust estimates of soil properties and associated uncertainty. A new library of soil series data would include classic narratives describing morphology and management, quantitative descriptions of soil properties and their ranges, graphical depiction of the relationships between associated soil series, block diagrams illustrating soil-landscape models, maps of series distribution, and a probabilistic representation of a "typical" soil profile. These data would be derived from re-correlation of existing morphologic and characterization data informed by modern statistical methods and regional expertise.

Spatial mapping of lead, arsenic, iron, and polycyclic aromatic hydrocarbon soil contamination in Sydney, Nova Scotia: community impact from the coke ovens and steel plant.

PubMed

Lambert, Timothy W; Boehmer, Jennifer; Feltham, Jason; Guyn, Lindsay; Shahid, Rizwan

2011-01-01

This paper presents spatial maps of the arsenic, lead, and polycyclic aromatic hydrocarbon (PAH) soil contamination in Sydney, Nova Scotia, Canada. The spatial maps were designed to create exposure cohorts to help understand the observed increase in health effects. To assess whether contamination can be a proxy for exposures, the following hypothesis was tested: residential soils were impacted by the coke oven and steel plant industrial complex. The spatial map showed contaminants are centered on the industrial facility, significantly correlated, and exceed Canadian health risk-based soil quality guidelines. Core samples taken at 5-cm intervals suggest a consistent deposition over time. The concentrations in Sydney significantly exceed background Sydney soil concentrations, and are significantly elevated compared with North Sydney, an adjacent industrial community. The contaminant spatial maps will also be useful for developing cohorts of exposure and guiding risk management decisions.

Landslides of Palestinian Region

NASA Astrophysics Data System (ADS)

Alwahsh, H.

2013-12-01

Natural disasters are extreme sudden events caused by environmental and natural actors that take away the lives of many thousands of people each year and damage large amount of properties. They strike anywhere on earth, often without any warning. A risk maps of natural disaster are very useful to identify the places that might be adversely affected in the event of natural disaster. The earthquakes are one of natural disaster that have the greatest hazards and will cause loss of life and properties due to damaging the structures of building, dams, bridges. In addition, it will affect local geology and soil conditions. The site effects play an important role in earthquake risk because of its amplification or damping simulation. Another parameter in developing risk map is landslide, which is also one of the most important topics in site effect hazards. Palestine region has been suffering landslide hazards because of the topographical and geological conditions of this region. Most Palestine consists of mountainous area, which has great steep slopes and the type of soil is mainly grayish to yellowish silty clay (Marl Soil). Due to the above mentioned factors many landslides have been occurred from Negev south to the northern borders of Palestine. An example of huge and destruction landslide in a Palestine authority is the landslide in the White Mountain area in the city of Nablus, which occurred in 1997. The geotechnical and geophysical investigation as well as slope stability analysis should be considered in making landslide maps that are necessary to develop risk levels of the natural disaster. Landslides occurred in slopes that are created naturally or by human beings. Failure of soil mass occurs, and hence landslide of soil mass happen due to sliding of soil mass along a plane or curved surface. In general, the slopes become unstable when the shear stresses (driving force) generated in the soil mass exceed the available shearing resistance on the rupture surface. There are many factors which affect directly or indirectly the slope stability, the stability of a slope depends on the geometry and soil engineering properties which include geological, topography, climate, hydrologic conditions, weather and land use (human effects). There are many things that can be used to mitigate landslides disaster. The most important one is the control of the landslides by establishing landslide maps. Other methods such as geometrical, hydrological, mechanical and chemical methods would also be effective in mitigate landslides. Recently, due to the development of the technology in all aspects, a safe and economical design for slopes can be achieved easily.

Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks.

PubMed

Lavado Contador, J F; Maneta, M; Schnabel, S

2006-10-01

The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.

Engineering geological mapping in Wallonia (Belgium) : present state and recent computerized approach

NASA Astrophysics Data System (ADS)

Delvoie, S.; Radu, J.-P.; Ruthy, I.; Charlier, R.

2012-04-01

An engineering geological map can be defined as a geological map with a generalized representation of all the components of a geological environment which are strongly required for spatial planning, design, construction and maintenance of civil engineering buildings. In Wallonia (Belgium) 24 engineering geological maps have been developed between the 70s and the 90s at 1/5,000 or 1/10,000 scale covering some areas of the most industrialized and urbanized cities (Liège, Charleroi and Mons). They were based on soil and subsoil data point (boring, drilling, penetration test, geophysical test, outcrop…). Some displayed data present the depth (with isoheights) or the thickness (with isopachs) of the different subsoil layers up to about 50 m depth. Information about geomechanical properties of each subsoil layer, useful for engineers and urban planners, is also synthesized. However, these maps were built up only on paper and progressively needed to be updated with new soil and subsoil data. The Public Service of Wallonia and the University of Liège have recently initiated a study to evaluate the feasibility to develop engineering geological mapping with a computerized approach. Numerous and various data (about soil and subsoil) are stored into a georelational database (the geotechnical database - using Access, Microsoft®). All the data are geographically referenced. The database is linked to a GIS project (using ArcGIS, ESRI®). Both the database and GIS project consist of a powerful tool for spatial data management and analysis. This approach involves a methodology using interpolation methods to update the previous maps and to extent the coverage to new areas. The location (x, y, z) of each subsoil layer is then computed from data point. The geomechanical data of these layers are synthesized in an explanatory booklet joined to maps.

Covariate selection with iterative principal component analysis for predicting physical

USDA-ARS?s Scientific Manuscript database

Local and regional soil data can be improved by coupling new digital soil mapping techniques with high resolution remote sensing products to quantify both spatial and absolute variation of soil properties. The objective of this research was to advance data-driven digital soil mapping techniques for ...

Combining µXANES and µXRD mapping to analyse the heterogeneity in calcium carbonate granules excreted by the earthworm Lumbricus terrestris

PubMed Central

Brinza, Loredana; Schofield, Paul F.; Hodson, Mark E.; Weller, Sophie; Ignatyev, Konstantin; Geraki, Kalotina; Quinn, Paul D.; Mosselmans, J. Frederick W.

2014-01-01

The use of fluorescence full spectral micro-X-ray absorption near-edge structure (µXANES) mapping is becoming more widespread in the hard energy regime. This experimental method using the Ca K-edge combined with micro-X-ray diffraction (µXRD) mapping of the same sample has been enabled on beamline I18 at Diamond Light Source. This combined approach has been used to probe both long- and short-range order in calcium carbonate granules produced by the earthworm Lumbricus terrestris. In granules produced by earthworms cultured in a control artificial soil, calcite and vaterite are observed in the granules. However, granules produced by earthworms cultivated in the same artificial soil amended with 500 p.p.m. Mg also contain an aragonite. The two techniques, µXRD and µXANES, probe different sample volumes but there is good agreement in the phase maps produced. PMID:24365942

Application of remote sensing technology to land evaluation, planning utilization of land resources, and assessment of westland habitat in eastern South Dakota, parts 1 and 2

NASA Technical Reports Server (NTRS)

Myers, V. I. (Principal Investigator); Cox, T. L.; Best, R. G.

1976-01-01

The author has identified the following significant results. LANDSAT fulfilled the requirements for general soils and land use information. RB-57 imagery was required to provide the information and detail needed for mapping soils for land evaluation. Soils maps for land evaluation were provided on clear mylar at the scale of the county highway map to aid users in locating mapping units. Resulting mapped data were computer processed to provided a series of interpretive maps (land value, limitations to development, etc.) and area summaries for the users.

Preliminary soil-slip susceptibility maps, southwestern California

USGS Publications Warehouse

Morton, Douglas M.; Alvarez, Rachel M.; Campbell, Russell H.; Digital preparation by Bovard, Kelly R.; Brown, D.T.; Corriea, K.M.; Lesser, J.N.

2003-01-01

This group of maps shows relative susceptibility of hill slopes to the initiation sites of rainfall-triggered soil slip-debris flows in southwestern California. As such, the maps offer a partial answer to one part of the three parts necessary to predict the soil-slip/debris-flow process. A complete prediction of the process would include assessments of “where”, “when”, and “how big”. These maps empirically show part of the “where” of prediction (i.e., relative susceptibility to sites of initiation of the soil slips) but do not attempt to show the extent of run out of the resultant debris flows. Some information pertinent to “when” the process might begin is developed. “When” is determined mostly by dynamic factors such as rainfall rate and duration, for which local variations are not amenable to long-term prediction. “When” information is not provided on the maps but is described later in this narrative. The prediction of “how big” is addressed indirectly by restricting the maps to a single type of landslide process—soil slip-debris flows. The susceptibility maps were created through an iterative process from two kinds of information. First, locations of sites of past soil slips were obtained from inventory maps of past events. Aerial photographs, taken during six rainy seasons that produced abundant soil slips, were used as the basis for soil slip-debris flow inventory. Second, digital elevation models (DEM) of the areas that were inventoried were used to analyze the spatial characteristics of soil slip locations. These data were supplemented by observations made on the ground. Certain physical attributes of the locations of the soil-slip debris flows were found to be important and others were not. The most important attribute was the mapped bedrock formation at the site of initiation of the soil slip. However, because the soil slips occur in surficial materials overlying the bedrocks units, the bedrock formation can only serve as a surrogate for the susceptibility of the overlying surficial materials. The maps of susceptibility were created from those physical attributes learned to be important from the inventories. The multiple inventories allow a model to be created from one set of inventory data and evaluated with others. The resultant maps of relative susceptibility represent the best estimate generated from available inventory and DEM data. Slope and aspect values used in the susceptibility analysis were 10-meter DEM cells at a scale of 1:24,000. For most of the area 10-meter DEMs were available; for those quadrangles that have only 30-meter DEMs, the 30-meter DEMS were resampled to 10-meters to maintain resolution of 10-meter cells. Geologic unit values used in the susceptibility analysis were five-meter cells. For convenience, the soil slip susceptibility values are assembled on 1:100,000-scale bases. Any area of the 1:100,000-scale maps can be transferred to 1:24,000-scale base without any loss of accuracy. Figure 32 is an example of part of a 1:100,000-scale susceptibility map transferred back to a 1:24,000-scale quadrangle.

Soils of Walker Branch Watershed

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lietzke, D.A.

1994-01-01

The soil survey of Walker Branch Watershed (WBW) utilized the most up-to-date knowledge of soils, geology, and geohydrology in building the soils data base needed to reinterpret past research and to begin new research in the watershed. The soils of WBW were also compared with soils mapped elsewhere along Chestnut Ridge on the Oak Ridge Reservation to (1) establish whether knowledge obtained elsewhere could be used within the watershed, (2) determine whether there were any soils restricted to the watershed, and (3) evaluate geologic formation lateral variability. Soils, surficial geology, and geomorphology were mapped at a scale of 1:1200 usingmore » a paper base map having 2-ft contour intervals. Most of the contours seemed to reasonably represent actual landform configurations, except for dense wooded areas. For example, the very large dolines or sinkholes were shown on the contour base map, but numerous smaller ones were not. In addition, small drainageways and gullies were often not shown. These often small but important features were located approximately as soil mapping progressed. WBW is underlain by dolostones of the Knox Group, but only a very small part of the surface area contains outcroppings of rock and most outcrops were located in the lower part. Soil mapping revealed the presence of both ancient alluvium and ancient colluvium deposits, not recognized in previous soil surveys, that have been preserved in high-elevation stable portions of present-day landforms. An erosional geomorphic process of topographic inversion requiring several millions of years within the Pleistocene is necessary to bring about the degree of inversion that is expressed in the watershed. Indeed, some of these ancient alluvial and colluvial remnants may date back into the Tertiary. Also evident in the watershed, and preserved in the broad, nearly level bottoms of dolines, are multiple deposits of silty material either devoid or nearly devoid of coarse fragments. Recent research indicates that most of this silty material is the result of slope wash processed during the Holocene Age. Residual soils of the watershed were related to the underlying geologic formations by their morphology and types of chert. Colluvial soils were identified and mapped whenever the colluvium thickness exceeded 20 in. (50 cm). Except for the ancient colluvial soils (colluvium without a present-day source area), colluvial soils were not separated according to their geologic age, but stacked colluvial deposits are located in low footslope landforms. Colluvial soils in the watershed were identified and mapped according to their morphologic properties that would influence the perching and subsurface movement of water. Alluvial soils were restricted to present floodplains, low fan terraces, and low fan deltas. Nearly all alluvial soils contained very young surficial sediments derived from slopewash resulting from land clearing and subsequent agricultural activities.« less

Multi criteria evaluation for universal soil loss equation based on geographic information system

NASA Astrophysics Data System (ADS)

Purwaamijaya, I. M.

2018-05-01

The purpose of this research were to produce(l) a conceptual, functional model designed and implementation for universal soil loss equation (usle), (2) standard operational procedure for multi criteria evaluation of universal soil loss equation (usle) using geographic information system, (3) overlay land cover, slope, soil and rain fall layers to gain universal soil loss equation (usle) using multi criteria evaluation, (4) thematic map of universal soil loss equation (usle) in watershed, (5) attribute table of universal soil loss equation (usle) in watershed. Descriptive and formal correlation methods are used for this research. Cikapundung Watershed, Bandung, West Java, Indonesia was study location. This research was conducted on January 2016 to May 2016. A spatial analysis is used to superimposed land cover, slope, soil and rain layers become universal soil loss equation (usle). Multi criteria evaluation for universal soil loss equation (usle) using geographic information system could be used for conservation program.

Local soil classification and crop suitability: Implications for the historical land use and soil management in Monti di Trapani (Sicily)

NASA Astrophysics Data System (ADS)

Garcia-Vila, Margarita; Corselli, Rocco; Bonet, María Teresa; Lopapa, Giuseppe; Pillitteri, Valentina; Fereres, Elias

2017-04-01

In the past, the lack of technologies (e.g. synthetic fertilizers) to overcome biophysical limitations has played a central role in land use planning. Thus, landscape management and agronomic practices are reactions to local knowledge and perceptions on natural resources, particularly soil. In the framework of the European research project MEMOLA (FP7), the role of local farmers knowledge and perceptions on soil for the historical land use through the spatial distribution of crops and the various management practices have been assessed in three different areas of Monti di Trapani region (Sicily). The identification of the soil classification systems of farmers and the criteria on which it is based, linked to the evaluation of the farmers' ability to identify and map the different soil types, was a key step. Nevertheless, beyond the comparison of the ethnopedological classification approach versus standard soil classification systems, the study also aims at understanding local soil management and land use decisions. The applied methodology was based on an interdisciplinary approach, combining soil science methods and participatory appraisal tools, particularly: i) semi-structured interviews; ii) soil sampling and analysis; iii) discussion groups; and iv) a workshop with local edafologists and agronomists. A rich local glossary of terms associated with the soil conditions and an own soil classification system have been identified in the region. Also, a detailed soil map, including process of soil degradation and soil capability, has been generated. This traditional soil knowledge has conditioned the management and the spatial distribution of the crops, and therefore the configuration of the landscape, until the 1990s. Acknowledgements This work has been funded by the European Union project MEMOLA (Grant agreement no: 613265).

Assessment of runoff response to landscape changes in the San Pedro subbasin (Nayarit, Mexico) using remote sensing data and GIS.

PubMed

Hernández-Guzmán, Rafael; Ruiz-Luna, Arturo; Berlanga-Robles, César Alejandro

2008-10-01

Results on runoff estimates as a response to land-use and land-cover changes are presented. We used remote sensing and GIS techniques with rainfall time-series data, spatial ancillary information, and the curve-number method (NRCS-CN) to assess the runoff response in the San Pedro subbasin. Thematic maps with eight land-cover classes derived from satellite imagery classification (1973, 1990, and 2000) and hydrologic soil-group maps were used as the input for the runoff calculation. About 20% to 25% of the subbasin landscape has changed since 1973, mainly as consequence of the growth of agriculture. Forest is the main cover, although further analyses indicate that forest is degrading from good to poor conditions when evaluated as a function of the spectral response. Soils with low infiltration rates, classified as the hydrological soil-group "C", were dominant in the area (52%). The overlaying of all the hydrological soil groups with the land-use map produced a total of 43 hydro-group and land-use categories for which runoff was calculated using the curve-number method. Estimates of total runoff volumes (26 x 10(6) m3) were similar for the three dates analyzed in spite of landscape changes, but there were temporal variations among the hydro-group and land-use categories as a consequence. Changes are causing the rise of covers with high runoff potential and the increase of runoff depth is expected, but it can be reversed by different management of subbasin hydro-groups and land-use units.

The creation of digital thematic soil maps at the regional level (with the map of soil carbon pools in the Usa River basin as an example)

NASA Astrophysics Data System (ADS)

Pastukhov, A. V.; Kaverin, D. A.; Shchanov, V. M.

2016-09-01

A digital map of soil carbon pools was created for the forest-tundra ecotone in the Usa River basin with the use of ERDAS Imagine 2014 and ArcGIS 10.2 software. Supervised classification and thematic interpretation of satellite images and digital terrain models with the use of a georeferenced database on soil profiles were applied. Expert assessment of the natural diversity and representativeness of random samples for different soil groups was performed, and the minimal necessary size of the statistical sample was determined.

Mapping soil features from multispectral scanner data

NASA Technical Reports Server (NTRS)

Kristof, S. J.; Zachary, A. L.

1974-01-01

In being able to identify quickly gross variations in soil features, the computer-aided classification of multispectral scanner data can be an effective aid to soil surveying. Variations in soil tone are easily seen as well as variations in features related to soil tone, e.g., drainage patterns and organic matter content. Changes in surface texture also affect the reflectance properties of soils. Inasmuch as conventional soil classes are based on both surface and subsurface soil characteristics, the technique described here can be expected only to augment and not replace traditional soil mapping.

Analysis of Ricefield Land Damage in Denpasar City, Bali, Indonesia

NASA Astrophysics Data System (ADS)

Suyarto, R.; Wiyanti; Dibia, I. N.

2018-02-01

Soil as a natural resource, living area, environmental media, and factors of production including biomass production that supports human life and other living beings must be preserved, on the other hand, uncontrolled biomass production activities can cause soil damage, ultimately can threaten the survival of humans and other living things. Therefore, in order to control soil damage, first must inventories the soil condition data and its damage which then visualised in soil damage potential and soil damage status. The activities of the study are the preparation of a map of the initial soil conditions and the delineation of potentially land degradation distribution. Mapping results are used as work maps for verification on the field to take soil samples and create soil damage status. In general, Denpasar City have soil damage potential at very low, low until medium rate. Soil damage status in Denpasar City generally is low damage of bulk volume, total porosity, soil permeability and electrolyte conductivity which beyond limitation thresholds.

Remote sensing as a tool for estimating soil erosion potential

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Morgan, K. M.; Kiefer, R. W.

1979-01-01

The Universal Soil Loss Equation is a frequently used methodology for estimating soil erosion potential. The Universal Soil Loss Equation requires a variety of types of geographic information (e.g. topographic slope, soil erodibility, land use, crop type, and soil conservation practice) in order to function. This information is traditionally gathered from topographic maps, soil surveys, field surveys, and interviews with farmers. Remote sensing data sources and interpretation techniques provide an alternative method for collecting information regarding land use, crop type, and soil conservation practice. Airphoto interpretation techniques and medium altitude, multi-date color and color infrared positive transparencies (70mm) were utilized in this study to determine their effectiveness for gathering the desired land use/land cover data. Successful results were obtained within the test site, a 6136 hectare watershed in Dane County, Wisconsin.

Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: the case study of Denmark.

PubMed

Bou Kheir, Rania; Greve, Mogens H; Bøcher, Peder K; Greve, Mette B; Larsen, René; McCloy, Keith

2010-05-01

Soil organic carbon (SOC) is one of the most important carbon stocks globally and has large potential to affect global climate. Distribution patterns of SOC in Denmark constitute a nation-wide baseline for studies on soil carbon changes (with respect to Kyoto protocol). This paper predicts and maps the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature, plan curvature, profile curvature, flow accumulation, specific catchment area, tangent slope, tangent curvature, steady-state wetness index, Normalized Difference Vegetation Index (NDVI), Normalized Difference Wetness Index (NDWI) and Soil Color Index (SCI) were generated to statistically explain SOC field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME) and the lowest number of nodes (N) as well are: (i) the tree (T1) combining all of the parameters (ME=29.5%; N=54); (ii) the tree (T2) based on the parent material, soil type and landscape type (ME=31.5%; N=14); and (iii) the tree (T3) constructed using parent material, soil type, landscape type, elevation, tangent slope and SCI (ME=30%; N=39). The produced SOC maps at 1:50,000 cartographic scale using these trees are highly matching with coincidence values equal to 90.5% (Map T1/Map T2), 95% (Map T1/Map T3) and 91% (Map T2/Map T3). The overall accuracies of these maps once compared with field observations were estimated to be 69.54% (Map T1), 68.87% (Map T2) and 69.41% (Map T3). The proposed tree models are relatively simple, and may be also applied to other areas. Copyright 2010 Elsevier Ltd. All rights reserved.

Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP–AES and Portable XRF Instruments: A Comparative Study

PubMed Central

Lee, Hyeongyu; Choi, Yosoon; Suh, Jangwon; Lee, Seung-Ho

2016-01-01

Understanding spatial variation of potentially toxic trace elements (PTEs) in soil is necessary to identify the proper measures for preventing soil contamination at both operating and abandoned mining areas. Many studies have been conducted worldwide to explore the spatial variation of PTEs and to create soil contamination maps using geostatistical methods. However, they generally depend only on inductively coupled plasma atomic emission spectrometry (ICP–AES) analysis data, therefore such studies are limited by insufficient input data owing to the disadvantages of ICP–AES analysis such as its costly operation and lengthy period required for analysis. To overcome this limitation, this study used both ICP–AES and portable X-ray fluorescence (PXRF) analysis data, with relatively low accuracy, for mapping copper and lead concentrations at a section of the Busan abandoned mine in Korea and compared the prediction performances of four different approaches: the application of ordinary kriging to ICP–AES analysis data, PXRF analysis data, both ICP–AES and transformed PXRF analysis data by considering the correlation between the ICP–AES and PXRF analysis data, and co-kriging to both the ICP–AES (primary variable) and PXRF analysis data (secondary variable). Their results were compared using an independent validation data set. The results obtained in this case study showed that the application of ordinary kriging to both ICP–AES and transformed PXRF analysis data is the most accurate approach when considers the spatial distribution of copper and lead contaminants in the soil and the estimation errors at 11 sampling points for validation. Therefore, when generating soil contamination maps for an abandoned mine, it is beneficial to use the proposed approach that incorporates the advantageous aspects of both ICP–AES and PXRF analysis data. PMID:27043594

Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP-AES and Portable XRF Instruments: A Comparative Study.

PubMed

Lee, Hyeongyu; Choi, Yosoon; Suh, Jangwon; Lee, Seung-Ho

2016-03-30

Understanding spatial variation of potentially toxic trace elements (PTEs) in soil is necessary to identify the proper measures for preventing soil contamination at both operating and abandoned mining areas. Many studies have been conducted worldwide to explore the spatial variation of PTEs and to create soil contamination maps using geostatistical methods. However, they generally depend only on inductively coupled plasma atomic emission spectrometry (ICP-AES) analysis data, therefore such studies are limited by insufficient input data owing to the disadvantages of ICP-AES analysis such as its costly operation and lengthy period required for analysis. To overcome this limitation, this study used both ICP-AES and portable X-ray fluorescence (PXRF) analysis data, with relatively low accuracy, for mapping copper and lead concentrations at a section of the Busan abandoned mine in Korea and compared the prediction performances of four different approaches: the application of ordinary kriging to ICP-AES analysis data, PXRF analysis data, both ICP-AES and transformed PXRF analysis data by considering the correlation between the ICP-AES and PXRF analysis data, and co-kriging to both the ICP-AES (primary variable) and PXRF analysis data (secondary variable). Their results were compared using an independent validation data set. The results obtained in this case study showed that the application of ordinary kriging to both ICP-AES and transformed PXRF analysis data is the most accurate approach when considers the spatial distribution of copper and lead contaminants in the soil and the estimation errors at 11 sampling points for validation. Therefore, when generating soil contamination maps for an abandoned mine, it is beneficial to use the proposed approach that incorporates the advantageous aspects of both ICP-AES and PXRF analysis data.

Taxonomic classification of world map units in crop producing areas of Argentina and Brazil with representative US soil series and major land resource areas in which they occur

NASA Technical Reports Server (NTRS)

Huckle, H. F. (Principal Investigator)

1980-01-01

The most probable current U.S. taxonomic classification of the soils estimated to dominate world soil map units (WSM)) in selected crop producing states of Argentina and Brazil are presented. Representative U.S. soil series the units are given. The map units occurring in each state are listed with areal extent and major U.S. land resource areas in which similar soils most probably occur. Soil series sampled in LARS Technical Report 111579 and major land resource areas in which they occur with corresponding similar WSM units at the taxonomic subgroup levels are given.

Soil resources and potential for agricultural development in Bahr El Jebel in southern Sudan, Jonglei Canal project area

NASA Technical Reports Server (NTRS)

Myers, V. I.; Moore, D. G.; Abdel-Hady, M. A.; Abdel-Samie, A. G.; Elshazly, E. M. (Principal Investigator); Youvis, H.; Worcester, B. K.; Klingebiel, A. A.; Elshazly, M. M.; Hamad, M. A.

1978-01-01

The author has identified the following significant results. Fourteen LANDSAT scenes were used to produce mosaics of the 167, 474 sq km study area. These were black and white MSS 7 images and false color composite images. Five major soil-landscape units were delineated on the mosaics, and these were subdivided into a total of 40 soil mapping units. Aerial reconnaissance was useful in defining boundaries between mapping units and in estimating the proportion of the various soils which composed each mapping unit. Ground surveying permitted first-hand observation of major soils and sampling for quantitative laboratory analysis. Soil interpretations were made, including properties, potentials, and limitations.

Spatial analysis and hazard assessment on soil total nitrogen in the middle subtropical zone of China

NASA Astrophysics Data System (ADS)

Lu, Peng; Lin, Wenpeng; Niu, Zheng; Su, Yirong; Wu, Jinshui

2006-10-01

Nitrogen (N) is one of the main factors affecting environmental pollution. In recent years, non-point source pollution and water body eutrophication have become increasing concerns for both scientists and the policy-makers. In order to assess the environmental hazard of soil total N pollution, a typical ecological unit was selected as the experimental site. This paper showed that Box-Cox transformation achieved normality in the data set, and dampened the effect of outliers. The best theoretical model of soil total N was a Gaussian model. Spatial variability of soil total N at NE60° and NE150° directions showed that it had a strip anisotropic structure. The ordinary kriging estimate of soil total N concentration was mapped. The spatial distribution pattern of soil total N in the direction of NE150° displayed a strip-shaped structure. Kriging standard deviations (KSD) provided valuable information that will increase the accuracy of total N mapping. The probability kriging method is useful to assess the hazard of N pollution by providing the conditional probability of N concentration exceeding the threshold value, where we found soil total N>2.0g/kg. The probability distribution of soil total N will be helpful to conduct hazard assessment, optimal fertilization, and develop management practices to control the non-point sources of N pollution.

Mapping soil heterogeneity using RapidEye satellite images

NASA Astrophysics Data System (ADS)

Piccard, Isabelle; Eerens, Herman; Dong, Qinghan; Gobin, Anne; Goffart, Jean-Pierre; Curnel, Yannick; Planchon, Viviane

2016-04-01

In the frame of BELCAM, a project funded by the Belgian Science Policy Office (BELSPO), researchers from UCL, ULg, CRA-W and VITO aim to set up a collaborative system to develop and deliver relevant information for agricultural monitoring in Belgium. The main objective is to develop remote sensing methods and processing chains able to ingest crowd sourcing data, provided by farmers or associated partners, and to deliver in return relevant and up-to-date information for crop monitoring at the field and district level based on Sentinel-1 and -2 satellite imagery. One of the developments within BELCAM concerns an automatic procedure to detect soil heterogeneity within a parcel using optical high resolution images. Such heterogeneity maps can be used to adjust farming practices according to the detected heterogeneity. This heterogeneity may for instance be caused by differences in mineral composition of the soil, organic matter content, soil moisture or soil texture. Local differences in plant growth may be indicative for differences in soil characteristics. As such remote sensing derived vegetation indices may be used to reveal soil heterogeneity. VITO started to delineate homogeneous zones within parcels by analyzing a series of RapidEye images acquired in 2015 (as a precursor for Sentinel-2). Both unsupervised classification (ISODATA, K-means) and segmentation techniques were tested. Heterogeneity maps were generated from images acquired at different moments during the season (13 May, 30 June, 17 July, 31 August, 11 September and 1 November 2015). Tests were performed using blue, green, red, red edge and NIR reflectances separately and using derived indices such as NDVI, fAPAR, CIrededge, NDRE2. The results for selected winter wheat, maize and potato fields were evaluated together with experts from the collaborating agricultural research centers. For a few fields UAV images and/or yield measurements were available for comparison.

An approach for land suitability evaluation using geostatistics, remote sensing, and geographic information system in arid and semiarid ecosystems.

PubMed

Emadi, Mostafa; Baghernejad, Majid; Pakparvar, Mojtaba; Kowsar, Sayyed Ahang

2010-05-01

This study was undertaken to incorporate geostatistics, remote sensing, and geographic information system (GIS) technologies to improve the qualitative land suitability assessment in arid and semiarid ecosystems of Arsanjan plain, southern Iran. The primary data were obtained from 85 soil samples collected from tree depths (0-30, 30-60, and 60-90 cm); the secondary information was acquired from the remotely sensed data from the linear imaging self-scanner (LISS-III) receiver of the IRS-P6 satellite. Ordinary kriging and simple kriging with varying local means (SKVLM) methods were used to identify the spatial dependency of soil important parameters. It was observed that using the data collected from the spectral values of band 1 of the LISS-III receiver as the secondary variable applying the SKVLM method resulted in the lowest mean square error for mapping the pH and electrical conductivity (ECe) in the 0-30-cm depth. On the other hand, the ordinary kriging method resulted in a reliable accuracy for the other soil properties with moderate to strong spatial dependency in the study area for interpolation in the unstamped points. The parametric land suitability evaluation method was applied on the density points (150 x 150 m(2)) instead of applying on the limited representative profiles conventionally, which were obtained by the kriging or SKVLM methods. Overlaying the information layers of the data was used with the GIS for preparing the final land suitability evaluation. Therefore, changes in land characteristics could be identified in the same soil uniform mapping units over a very short distance. In general, this new method can easily present the squares and limitation factors of the different land suitability classes with considerable accuracy in arbitrary land indices.

Soil erodibility mapping using the RUSLE model to prioritize erosion control in the Wadi Sahouat basin, North-West of Algeria.

PubMed

Toubal, Abderrezak Kamel; Achite, Mohammed; Ouillon, Sylvain; Dehni, Abdelatif

2018-03-12

Soil losses must be quantified over watersheds in order to set up protection measures against erosion. The main objective of this paper is to quantify and to map soil losses in the Wadi Sahouat basin (2140 km 2 ) in the north-west of Algeria, using the Revised Universal Soil Loss Equation (RUSLE) model assisted by a Geographic Information System (GIS) and remote sensing. The Model Builder of the GIS allowed the automation of the different operations for establishing thematic layers of the model parameters: the erosivity factor (R), the erodibility factor (K), the topographic factor (LS), the crop management factor (C), and the conservation support practice factor (P). The average annual soil loss rate in the Wadi Sahouat basin ranges from 0 to 255 t ha -1  year -1 , maximum values being observed over steep slopes of more than 25% and between 600 and 1000 m elevations. 3.4% of the basin is classified as highly susceptible to erosion, 4.9% with a medium risk, and 91.6% at a low risk. Google Earth reveals a clear conformity with the degree of zones to erosion sensitivity. Based on the soil loss map, 32 sub-basins were classified into three categories by priority of intervention: high, moderate, and low. This priority is available to sustain a management plan against sediment filling of the Ouizert dam at the basin outlet. The method enhancing the RUSLE model and confrontation with Google Earth can be easily adapted to other watersheds.

Operational Mapping of Soil Moisture Using Synthetic Aperture Radar Data: Application to the Touch Basin (France)

PubMed Central

Baghdadi, Nicolas; Aubert, Maelle; Cerdan, Olivier; Franchistéguy, Laurent; Viel, Christian; Martin, Eric; Zribi, Mehrez; Desprats, Jean François

2007-01-01

Soil moisture is a key parameter in different environmental applications, such as hydrology and natural risk assessment. In this paper, surface soil moisture mapping was carried out over a basin in France using satellite synthetic aperture radar (SAR) images acquired in 2006 and 2007 by C-band (5.3 GHz) sensors. The comparison between soil moisture estimated from SAR data and in situ measurements shows good agreement, with a mapping accuracy better than 3%. This result shows that the monitoring of soil moisture from SAR images is possible in operational phase. Moreover, moistures simulated by the operational Météo-France ISBA soil-vegetation-atmosphere transfer model in the SIM-Safran-ISBA-Modcou chain were compared to radar moisture estimates to validate its pertinence. The difference between ISBA simulations and radar estimates fluctuates between 0.4 and 10% (RMSE). The comparison between ISBA and gravimetric measurements of the 12 March 2007 shows a RMSE of about 6%. Generally, these results are very encouraging. Results show also that the soil moisture estimated from SAR images is not correlated with the textural units defined in the European Soil Geographical Database (SGDBE) at 1:1000000 scale. However, dependence was observed between texture maps and ISBA moisture. This dependence is induced by the use of the texture map as an input parameter in the ISBA model. Even if this parameter is very important for soil moisture estimations, radar results shown that the textural map scale at 1:1000000 is not appropriate to differentiate moistures zones. PMID:28903238

Use of USLE/GIS methodology for predicting soil loss in a semiarid agricultural watershed.

PubMed

Erdogan, Emrah H; Erpul, Günay; Bayramin, Ilhami

2007-08-01

The Universal Soil Loss Equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices in agricultural watersheds by the effective integration of the GIS-based procedures to estimate the factor values in a grid cell basis. This study was performed in the Kazan Watershed located in the central Anatolia, Turkey, to predict soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Rain erosivity (R), soil erodibility (K), and cover management factor (C) values of the model were calculated from erosivity map, soil map, and land use map of Turkey, respectively. R values were site-specifically corrected using DEM and climatic data. The topographical and hydrological effects on the soil loss were characterized by LS factor evaluated by the flow accumulation tool using DEM and watershed delineation techniques. From resulting soil loss map of the watershed, the magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed. This could be very useful for deciding restoration practices to control the soil erosion of the sites to be severely influenced.

An Establishment of Rainfall-induced Soil Erosion Index for the Slope Land in Watershed

NASA Astrophysics Data System (ADS)

Tsai, Kuang-Jung; Chen, Yie-Ruey; Hsieh, Shun-Chieh; Shu, Chia-Chun; Chen, Ying-Hui

2014-05-01

With more and more concentrated extreme rainfall events as a result of climate change, in Taiwan, mass cover soil erosion occurred frequently and led to sediment related disasters in high intensity precipiton region during typhoons or torrential rain storms. These disasters cause a severely lost to the property, public construction and even the casualty of the resident in the affected areas. Therefore, we collected soil losses by using field investigation data from the upstream of watershed where near speific rivers to explore the soil erosion caused by heavy rainfall under different natural environment. Soil losses induced by rainfall and runoff were obtained from the long-term soil depth measurement of erosion plots, which were established in the field, used to estimate the total volume of soil erosion. Furthermore, the soil erosion index was obtained by referring to natural environment of erosion test plots and the Universal Soil Loss Equation (USLE). All data collected from field were used to compare with the one obtained from laboratory test recommended by the Technical Regulation for Soil and Water Conservation in Taiwan. With MATLAB as a modeling platform, evaluation model for soil erodibility factors was obtained by golden section search method, considering factors contributing to the soil erosion; such as degree of slope, soil texture, slope aspect, the distance far away from water system, topography elevation, and normalized difference vegetation index (NDVI). The distribution map of soil erosion index was developed by this project and used to estimate the rainfall-induced soil losses from erosion plots have been established in the study area since 2008. All results indicated that soil erodibility increases with accumulated rainfall amount regardless of soil characteristics measured in the field. Under the same accumulated rainfall amount, the volume of soil erosion also increases with the degree of slope and soil permeability, but decreases with the shear strength of top soil within 30 cm and the coverage of vegetation. The slope plays more important role than the soil permeability on soil erosion. However, soil losses are not proportional to the hardness of top soil or subsurface soil. The empirical formula integrated with soil erosion index map for evaluating soil erodibility obtained from optimal numerical search method can be used to estimate the soil losses induced by rainfall and runoff erosion on slope land in Taiwan. Keywords: Erosion Test Plot, Soil Erosion, Optimal Numerical Search, Universal Soil Loss Equation.

Regional-scale Assessment of Soil Salinity in the Red River Valley Using Multi-year MODIS EVI

USDA-ARS?s Scientific Manuscript database

The ability to inventory and map soil salinity at regional scales remains a significant challenge to soil, environmental, and natural resource scientists. Previous attempts to use satellite or aerial imagery to assess and map soil salinity have resulted in limited success due, in part, to the inabi...

Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability

USDA-ARS?s Scientific Manuscript database

Indices derived from remotely-sensed imagery are commonly used to predict soil properties with digital soil mapping (DSM) techniques. The use of images from single dates or a small number of dates is most common for DSM; however, selection of the appropriate images is complicated by temporal variabi...

Considering the spatial-scale factor when modelling sustainable land management.

NASA Astrophysics Data System (ADS)

Bouma, Johan

2015-04-01

Considering the spatial-scale factor when modelling sustainable land management. J.Bouma Em.prof. soil science, Wageningen University, Netherlands. Modelling soil-plant processes is a necessity when exploring future effects of climate change and innovative soil management on agricultural productivity. Soil data are needed to run models and traditional soil maps and the associated databases (based on various soil Taxonomies ), have widely been applied to provide such data obtained at "representative" points in the field. Pedotransferfunctions (PTF)are used to feed simulation models, statistically relating soil survey data ( obtained at a given point in the landscape) to physical parameters for simulation, thus providing a link with soil functionality. Soil science has a basic problem: their object of study is invisible. Only point data are obtained by augering or in pits. Only occasionally roadcuts provide a better view. Extrapolating point to area data is essential for all applications and presents a basic problem for soil science, because mapping units on soil maps, named for a given soil type,may also contain other soil types and quantitative information about the composition of soil map units is usually not available. For detailed work at farm level ( 1:5000-1:10000), an alternative procedure is proposed. Based on a geostatistical analysis, onsite soil observations are made in a grid pattern with spacings based on a geostatistical analysis. Multi-year simulations are made for each point of the functional properties that are relevant for the case being studied, such as the moisture supply capacity, nitrate leaching etc. under standardized boundary conditions to allow comparisons. Functional spatial units are derived next by aggregating functional point data. These units, which have successfully functioned as the basis for precision agriculture, do not necessarily correspond with Taxonomic units but when they do the Taxonomic names should be noted . At lower landscape and watershed scale ( 1:25.000 -1:50000) digital soil mapping can provide soil data for small grids that can be used for modeling, again through pedotransferfunctions. There is a risk, however, that digital mapping results in an isolated series of projects that don't increase the knowledge base on soil functionality, e.g.linking Taxonomic names ( such as soil series) to functionality, allowing predictions of soil behavior at new sites where certain soil series occur. We therefore suggest that aside from collecting 13 soil characteristics for each grid, as occurs in digital soil mapping, also the Taxonomic name of the representative soil in the grid is recorded. At spatial scales of 1:50000 and smaller, use of Taxonomic names becomes ever more attractive because at such small scales relations between soil types and landscape features become more pronounced. But in all cases, selection of procedures should not be science-based but based on the type of questions being asked including their level of generalization. These questions are quite different at the different spatial-scale levels and so should be the procedures.

Development of a national geodatabase (Greece) for soil surveys and land evaluation using space technology and GIS

NASA Astrophysics Data System (ADS)

Bilas, George; Dionysiou, Nina; Karapetsas, Nikolaos; Silleos, Nikolaos; Kosmas, Konstantinos; Misopollinos, Nikolaos

2016-04-01

This project was funded by OPEKEPE, Ministry of Agricultural Development and Food, Greece and involves development of a national geodatabase and a WebGIS that encompass soil data of all the agricultural areas of Greece in order to supply the country with a multi-purpose master plan for agricultural land management. The area mapped covered more than 385,000 ha divided in more than 9.000 Soil Mapping Units (SMUs) based on physiographic analysis, field work and photo interpretation of satellite images. The field work included description and sampling in three depths (0-30, 30-60 and >60 cm) of 2,000 soil profiles and 8,000 augers (sampling 0-30 and >30 cm). In total more than 22,000 soil samples were collected and analyzed for determining main soil properties associated with soil classification and soil evaluation. Additionally the project included (1) integration of all data in the Soil Geodatabase, (2) finalization of SMUs, (3) development of a Master Plan for Agricultural Land Management and (4) development and operational testing of the Web Portal for e-information and e-services. The integrated system is expected, after being fully operational, to provide important electronic services and benefits to farmers, private sector and governmental organizations. An e-book with the soil maps of Greece was also provided including 570 sheets with data description and legends. The Master Plan for Agricultural Land Management includes soil quality maps for 30 agricultural crops, together with maps showing soil degradation risks, such as erosion, desertification, salinity and nitrates, thus providing the tools for soil conservation and sustainable land management.

Monitoring restoration impacts to endemic plant communities in soil inclusions of arid environments

USGS Publications Warehouse

Louhaichi, Mounir; Pyke, David A.; Shaff, Scott E.; Johnson, Douglas E.

2013-01-01

Soil inclusions are small patches of soil with different properties than the surrounding, dominant soil. In arid areas of western North America, soil inclusions called slickspot soils are saltier than adjacent soil and support different types of native vegetation. Traditional sagebrush restoration efforts, such as using drills to plant seeds or herbicides to control invasive vegetation, may damage sensitive slickspot soil and supporting vegetation. USGS scientists David Pyke and Scott Shaff and collaborators monitored slickspot size and cover of endangered slickspot peppergrass for two years to see if they were affected by the application of the herbicide glyphosate or by a minimum-till drill in the Snake River Plain, ID. The researchers examined the use of aerial photographs versus on-the-ground measurements and concluded that slickspot sizes were not affected by these treatments. Remote sensing using aerial photographs proved a useful method for mapping slickspot soils.

Impervious surface mapping with Quickbird imagery

PubMed Central

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio

2010-01-01

This research selects two study areas with different urban developments, sizes, and spatial patterns to explore the suitable methods for mapping impervious surface distribution using Quickbird imagery. The selected methods include per-pixel based supervised classification, segmentation-based classification, and a hybrid method. A comparative analysis of the results indicates that per-pixel based supervised classification produces a large number of “salt-and-pepper” pixels, and segmentation based methods can significantly reduce this problem. However, neither method can effectively solve the spectral confusion of impervious surfaces with water/wetland and bare soils and the impacts of shadows. In order to accurately map impervious surface distribution from Quickbird images, manual editing is necessary and may be the only way to extract impervious surfaces from the confused land covers and the shadow problem. This research indicates that the hybrid method consisting of thresholding techniques, unsupervised classification and limited manual editing provides the best performance. PMID:21643434

Map the Permafrost and its Affected Soils and Vegetation on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zhao, L.; Sheng, Y.; Pang, Q.; Zou, D.; Wang, Z.; Li, W.; Wu, X.; Yue, G.; Fang, H.; Zhao, Y.

2015-12-01

Great amount of literatures had been published to deal with the actual distribution and changes of permafrost on the Tibetan Plateau (TP) on the basis of observed ground temperature dataset along Qinghai-Xizang Highway and/or Railway (QXH/R) during the last several decades. But there is very limited data available in the eastern part of the QXH/R and almost no observation in the western part of QXH/R not only for the observed permafrost data, but also for the dataset on ground surface conditions, such as soil and vegetation, which are used as model parameters, initial variables, or benchmark data sets for calibration, validation, and comparison in various Earth System Models (ESMs). To evaluate the status of permafrost and its environmental conditions, such as the distribution and thermal state of permafrost, soil and vegetation on the TP, detailed investigation on permafrost were conducted in 5 regions with different climatic and geologic conditions over the whole plateau from 2009 to 2013, and more than 100 ground temperatures (GTs) monitoring boreholes were drilled and equipped with thermistors, of which 10 sites were equipped with automatic meteorological stations. Geophysical prospecting methods, such as ground penetrating radar (GPR) and electromagnetic prospecting, were used in the same time to detect the permafrost distribution and thicknesses. The monitoring data revealed that the thermal state of permafrost was well correlated with elevation, and regulated by annual precipitation, local geological, geomorphological and hydrological conditions through heat exchanges between ground and atmosphere. Different models, including GTs statistical model, Common Land Surface Model (CoLM), Noah land surface model and TTOP models, were used to map the permafrost in 5 selected regions and the whole TP, while the investigated and monitored data were used as calibration and validation for all models. Finally, we compiled the permafrost map of the TP, soil and vegetation map within the permafrost regions on the TP. We also compiled the soil organic carbon density map of permafrost affected soils on the TP. An overview on permafrost thickness, GTs, ice content was statistically summarized based on investigation data.

Multisensor on-the-go mapping of readily dispersible clay, particle size and soil organic matter

NASA Astrophysics Data System (ADS)

Debaene, Guillaume; Niedźwiecki, Jacek; Papierowska, Ewa

2016-04-01

Particle size fractions affect strongly the physical and chemical properties of soil. Readily dispersible clay (RDC) is the part of the clay fraction in soils that is easily or potentially dispersible in water when small amounts of mechanical energy are applied to soil. The amount of RDC in the soil is of significant importance for agriculture and environment because clay dispersion is a cause of poor soil stability in water which in turn contributes to soil erodibility, mud flows, and cementation. To obtain a detailed map of soil texture, many samples are needed. Moreover, RDC determination is time consuming. The use of a mobile visible and near-infrared (VIS-NIR) platform is proposed here to map those soil properties and obtain the first detailed map of RDC at field level. Soil properties prediction was based on calibration model developed with 10 representative samples selected by a fuzzy logic algorithm. Calibration samples were analysed for soil texture (clay, silt and sand), RDC and soil organic carbon (SOC) using conventional wet chemistry analysis. Moreover, the Veris mobile sensor platform is also collecting electrical conductivity (EC) data (deep and shallow), and soil temperature. These auxiliary data were combined with VIS-NIR measurement (data fusion) to improve prediction results. EC maps were also produced to help understanding RDC data. The resulting maps were visually compared with an orthophotography of the field taken at the beginning of the plant growing season. Models were developed with partial least square regression (PLSR) and support vector machine regression (SVMR). There were no significant differences between calibration using PLSR or SVMR. Nevertheless, the best models were obtained with PLSR and standard normal variate (SNV) pretreatment and the fusion with deep EC data (e.g. for RDC and clay content: RMSECV = 0,35% and R2 = 0,71; RMSECV = 0,32% and R2 = 0,73 respectively). The best models were used to predict soil properties from the field spectra collected with the VIS-NIR platform. Maps of soil properties were generated using natural neighbour (NN) interpolation. Calibration results were satisfactory for all soil properties and allowed for the generation of detailed maps. The spatial variability of RDC was in accordance with the field orthophotography. Areas of high RDC content were corresponding to area of bad plant development. Soil texture has been correctly predicted by VIS-NIR spectroscopy (laboratory or on-the-go) before. However, readily dispersible clay (an important parameter for soil stability) has never been investigated before. This study introduces the possibility of using VIS-NIR for predicting readily dispersible clay at field level. The results obtained could be used in preventing soil erosion. Acknowledgement: This research was financed by a National Science Centre grant (NCN - Poland) with decision number UMO-2012/07/B/ST10/04387

Soil sail content estimation in the yellow river delta with satellite hyperspectral data

USGS Publications Warehouse

Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

2008-01-01

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

Integrated terrain mapping with digital Landsat images in Queensland, Australia

USGS Publications Warehouse

Robinove, Charles Joseph

1979-01-01

Mapping with Landsat images usually is done by selecting single types of features, such as soils, vegetation, or rocks, and creating visually interpreted or digitally classified maps of each feature. Individual maps can then be overlaid on or combined with other maps to characterize the terrain. Integrated terrain mapping combines several terrain features into each map unit which, in many cases, is more directly related to uses of the land and to methods of land management than the single features alone. Terrain brightness, as measured by the multispectral scanners in Landsat 1 and 2, represents an integration of reflectance from the terrain features within the scanner's instantaneous field of view and is therefore more correlatable with integrated terrain units than with differentiated ones, such as rocks, soils, and vegetation. A test of the feasibilty of the technique of mapping integrated terrain units was conducted in a part of southwestern Queensland, Australia, in cooperation with scientists of the Queensland Department of Primary Industries. The primary purpose was to test the use of digital classification techniques to create a 'land systems map' usable for grazing land management. A recently published map of 'land systems' in the area (made by aerial photograph interpretation and ground surveys), which are integrated terrain units composed of vegetation, soil, topography, and geomorphic features, was used as a basis for comparison with digitally classified Landsat multispectral images. The land systems, in turn, each have a specific grazing capacity for cattle (expressed in beasts per km 2 ) which is estimated following analysis of both research results and property carrying capacities. Landsat images, in computer-compatible tape form, were first contrast-stretched to increase their visual interpretability, and digitally classified by the parallelepiped method into distinct spectral classes to determine their correspondence to the land systems classes and to areally smaller, but readily recognizable, 'land units.' Many land systems appeared as distinct spectral classes or as acceptably homogeneous combinations of several spectral classes. The digitally classified map corresponded to the general geographic patterns of many of the land systems. Statistical correlation of the digitally classified map and the published map was not possible because the published map showed only land systems whereas the digitally classified map showed some land units as well as systems. The general correspondence of spectral classes to the integrated terrain units means that the digital mapping of the units may precede fieldwork and act as a guide to field sampling and detailed terrain unit description as well as measuring of the location, area, and extent of each unit. Extension of the Landsat mapping and classification technique to other arid and semi-arid regions of the world may be feasible.

Combined spatial prediction of schistosomiasis and soil-transmitted helminthiasis in Sierra Leone: a tool for integrated disease control.

PubMed

Hodges, Mary H; Soares Magalhães, Ricardo J; Paye, Jusufu; Koroma, Joseph B; Sonnie, Mustapha; Clements, Archie; Zhang, Yaobi

2012-01-01

A national mapping of Schistosoma haematobium was conducted in Sierra Leone before the mass drug administration (MDA) with praziquantel. Together with the separate mapping of S. mansoni and soil-transmitted helminths, the national control programme was able to plan the MDA strategies according to the World Health Organization guidelines for preventive chemotherapy for these diseases. A total of 52 sites/schools were selected according to prior knowledge of S. haematobium endemicity taking into account a good spatial coverage within each district, and a total of 2293 children aged 9-14 years were examined. Spatial analysis showed that S. haematobium is heterogeneously distributed in the country with significant spatial clustering in the central and eastern regions of the country, most prevalent in Bo (24.6% and 8.79 eggs/10 ml), Koinadugu (20.4% and 3.53 eggs/10 ml) and Kono (25.3% and 7.91 eggs/10 ml) districts. By combining this map with the previously reported maps on intestinal schistosomiasis using a simple probabilistic model, the combined schistosomiasis prevalence map highlights the presence of high-risk communities in an extensive area in the northeastern half of the country. By further combining the hookworm prevalence map, the at-risk population of school-age children requiring integrated schistosomiasis/soil-transmitted helminth treatment regimens according to the coendemicity was estimated. The first comprehensive national mapping of urogenital schistosomiasis in Sierra Leone was conducted. Using a new method for calculating the combined prevalence of schistosomiasis using estimates from two separate surveys, we provided a robust coendemicity mapping for overall urogenital and intestinal schistosomiasis. We also produced a coendemicity map of schistosomiasis and hookworm. These coendemicity maps can be used to guide the decision making for MDA strategies in combination with the local knowledge and programme needs.

A Field and Laboratory Based Assessment of the Potential of High Frequency Ground Penetrating Radar (HFGPR) to Evaluate the Presence and Spatial Variabilty of Hydrophobic Soil Layers

NASA Astrophysics Data System (ADS)

Weirich, F. H.; Neumann, W.; Campbell, D.

2017-12-01

The presence of fire related hydrophobic (water repellant) soil layers in a wide range of environmental settings can result in greatly increased rates of storm runoff and erosion. In many situations this can contribute to the generation of debris and/or hyperconcentrated flows. While the role of hydrophobic soils in greatly increasing sediment production in such situations is known, the ability to predict the volume of sediment that will be generated by specific storm events has been limited, in part, by limits on the ability to assess the characteristics of hydrophobic soil layers. At present, the most widely accepted method of assessing the presence, strength, extent and persistence of hydrophobic soil layers requires the performance of an in situ water drop penetration test (WDPT). This approach, while effective on a local site, is labor and time intensive and can be difficult to employ on a watershed or even slope wide basis. As part of a wider research effort to develop more effective methods of evaluating the characteristics of hydrophobic soils a combined field and laboratory based program has been undertaken to evaluate the capability of higher frequency ground penetrating radar (HFGPR) to detect and map out the spatial extent, strength, and persistence of hydrophobic soil layers. This has involved the testing of HFGPR systems at several field site in burnt watersheds in Southern California as well as a program of laboratory tests on samples of fire impacted soils collected from the same watersheds. The field tests were undertaken on sites ranging from a location that had burnt a few weeks earlier to locations where over 5 years had passed since a burn took place. Laboratory samples of soils were taken from the same range of sites and used in the laboratory tests. In parallel with the HFGPR testing WDPT's were used to confirm the findings of the HFGPR approach. Both the field and laboratory results indicate that the use of HFGPR, under appropriate soil moisture conditions, is capable of mapping out the presence, spatial extent, and persistence of hydrophobic soil layers. Layers at depth ranging from 1-6 cm were successfully mapped. The persistence of layers on some sites 5 years after a burn were also able to be measured using this approach. Work to further refine both the approach and its limitations is ongoing.

Preduction of Vehicle Mobility on Large-Scale Soft-Soil Terrain Maps Using Physics-Based Simulation

DTIC Science & Technology

2016-08-02

PREDICTION OF VEHICLE MOBILITY ON LARGE-SCALE SOFT- SOIL TERRAIN MAPS USING PHYSICS-BASED SIMULATION Tamer M. Wasfy, Paramsothy Jayakumar, Dave...NRMM • Objectives • Soft Soils • Review of Physics-Based Soil Models • MBD/DEM Modeling Formulation – Joint & Contact Constraints – DEM Cohesive... Soil Model • Cone Penetrometer Experiment • Vehicle- Soil Model • Vehicle Mobility DOE Procedure • Simulation Results • Concluding Remarks 2UNCLASSIFIED

Modelling and mapping the topsoil organic carbon content for Tanzania

NASA Astrophysics Data System (ADS)

Kempen, Bas; Kaaya, Abel; Ngonyani Mhaiki, Consolatha; Kiluvia, Shani; Ruiperez-Gonzalez, Maria; Batjes, Niels; Dalsgaard, Soren

2014-05-01

Soil organic carbon (SOC), held in soil organic matter, is a key indicator of soil health and plays an important role in the global carbon cycle. The soil can act as a net source or sink of carbon depending on land use and management. Deforestation and forest degradation lead to the release of vast amounts of carbon from the soil in the form of greenhouse gasses, especially in tropical countries. Tanzania has a high deforestation rate: it is estimated that the country loses 1.1% of its total forested area annually. During 2010-2013 Tanzania has been a pilot country under the UN-REDD programme. This programme has supported Tanzania in its initial efforts towards reducing greenhouse gas emission from forest degradation and deforestation and towards preserving soil carbon stocks. Formulation and implementation of the national REDD strategy requires detailed information on the five carbon pools among these the SOC pool. The spatial distribution of SOC contents and stocks was not available for Tanzania. The initial aim of this research, was therefore to develop high-resolution maps of the SOC content for the country. The mapping exercise was carried out in a collaborative effort with four Tanzanian institutes and data from the Africa Soil Information Service initiative (AfSIS). The mapping exercise was provided with over 3200 field observations on SOC from four sources; this is the most comprehensive soil dataset collected in Tanzania so far. The main source of soil samples was the National Forest Monitoring and Assessment (NAFORMA). The carbon maps were generated by means of digital soil mapping using regression-kriging. Maps at 250 m spatial resolution were developed for four depth layers: 0-10 cm, 10-20 cm, 20-30 cm, and 0-30 cm. A total of 37 environmental GIS data layers were prepared for use as covariates in the regression model. These included vegetation indices, terrain parameters, surface temperature, spectral reflectances, a land cover map and a small-scale Soil and Terrain (SOTER) map. Prediction uncertainty was quantified by the 90% prediction interval and the predictions were validated by cross-validation. The SOTER map proved to be the best predictor of SOC content, followed by the terrain parameters, mid-infrared reflectance, surface temperature, several vegetation indices, and the land cover map. The maps show that the SOC content decreases with depth, which is typically observed in soils. For the 0-10 cm layer the average predicted SOC content is 1.31%, for the 10-20 cm layer this is 0.93%, for the 20-30cm layer 0.72%, and for the 0-30cm layer 1.00%. The mean absolute error of the 0-10cm layer was 0.54%, that of the 10-20cm layer 0.38%, that of the 20-30cm layer 0.31%, and that of the 0-30cm layer 0.34%. The R2-value of the 0-10 cm layer was 0.47, that of the 10-20cm layer 0.49, that of the 20-30cm layer 0.44, and that of the 0-30cm layer 0.59. The next step will be the development of maps of SOC stock and key properties that are of interest for soil fertility management such as pH and the textural fractions.

Characterization of the spatial variability of soil available zinc at various sampling densities using grouped soil type information.

PubMed

Song, Xiao-Dong; Zhang, Gan-Lin; Liu, Feng; Li, De-Cheng; Zhao, Yu-Guo

2016-11-01

The influence of anthropogenic activities and natural processes involved high uncertainties to the spatial variation modeling of soil available zinc (AZn) in plain river network regions. Four datasets with different sampling densities were split over the Qiaocheng district of Bozhou City, China. The difference of AZn concentrations regarding soil types was analyzed by the principal component analysis (PCA). Since the stationarity was not indicated and effective ranges of four datasets were larger than the sampling extent (about 400 m), two investigation tools, namely F3 test and stationarity index (SI), were employed to test the local non-stationarity. Geographically weighted regression (GWR) technique was performed to describe the spatial heterogeneity of AZn concentrations under the non-stationarity assumption. GWR based on grouped soil type information (GWRG for short) was proposed so as to benefit the local modeling of soil AZn within each soil-landscape unit. For reference, the multiple linear regression (MLR) model, a global regression technique, was also employed and incorporated the same predictors as in the GWR models. Validation results based on 100 times realization demonstrated that GWRG outperformed MLR and can produce similar or better accuracy than the GWR approach. Nevertheless, GWRG can generate better soil maps than GWR for limit soil data. Two-sample t test of produced soil maps also confirmed significantly different means. Variogram analysis of the model residuals exhibited weak spatial correlation, rejecting the use of hybrid kriging techniques. As a heuristically statistical method, the GWRG was beneficial in this study and potentially for other soil properties.

Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US

DOE PAGES

Kaleita, Amy L.; Schott, Linda R.; Hargreaves, Sarah K.; ...

2017-07-07

Soil microbial communities are structured by biogeochemical processes that occur at many different spatial scales, which makes soil sampling difficult. Because soil microbial communities are important in nutrient cycling and soil fertility, it is important to understand how microbial communities function within the heterogeneous soil landscape. In this study, a self-organizing map was used to determine whether landscape data can be used to characterize the distribution of microbial biomass and activity in order to provide an improved understanding of soil microbial community function. Points within a row crop field in south-central Iowa were clustered via a self-organizing map using sixmore » landscape properties into three separate landscape clusters. Twelve sampling locations per cluster were chosen for a total of 36 locations. After the soil samples were collected, the samples were then analysed for various metabolic indicators, such as nitrogen and carbon mineralization, extractable organic carbon, microbial biomass, etc. It was found that sampling locations located in the potholes and toe slope positions had significantly greater microbial biomass nitrogen and carbon, total carbon, total nitrogen and extractable organic carbon than the other two landscape position clusters, while locations located on the upslope did not differ significantly from the other landscape clusters. However, factors such as nitrate, ammonia, and nitrogen and carbon mineralization did not differ significantly across the landscape. Altogether, this research demonstrates the effectiveness of a terrain-based clustering method for guiding soil sampling of microbial communities.« less

Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaleita, Amy L.; Schott, Linda R.; Hargreaves, Sarah K.

Soil microbial communities are structured by biogeochemical processes that occur at many different spatial scales, which makes soil sampling difficult. Because soil microbial communities are important in nutrient cycling and soil fertility, it is important to understand how microbial communities function within the heterogeneous soil landscape. In this study, a self-organizing map was used to determine whether landscape data can be used to characterize the distribution of microbial biomass and activity in order to provide an improved understanding of soil microbial community function. Points within a row crop field in south-central Iowa were clustered via a self-organizing map using sixmore » landscape properties into three separate landscape clusters. Twelve sampling locations per cluster were chosen for a total of 36 locations. After the soil samples were collected, the samples were then analysed for various metabolic indicators, such as nitrogen and carbon mineralization, extractable organic carbon, microbial biomass, etc. It was found that sampling locations located in the potholes and toe slope positions had significantly greater microbial biomass nitrogen and carbon, total carbon, total nitrogen and extractable organic carbon than the other two landscape position clusters, while locations located on the upslope did not differ significantly from the other landscape clusters. However, factors such as nitrate, ammonia, and nitrogen and carbon mineralization did not differ significantly across the landscape. Altogether, this research demonstrates the effectiveness of a terrain-based clustering method for guiding soil sampling of microbial communities.« less

Using remote sensing and GIS techniques to estimate discharge and recharge. fluxes for the Death Valley regional groundwater flow system, USA

USGS Publications Warehouse

D'Agnese, F. A.; Faunt, C.C.; Keith, Turner A.

1996-01-01

The recharge and discharge components of the Death Valley regional groundwater flow system were defined by remote sensing and GIS techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. This map provided a basis for subsequent evapotranspiration and infiltration estimations. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were then used to calculate discharge volumes for these areas. A previously used empirical method of groundwater recharge estimation was modified by GIS methods to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.

Predictive spatial modelling for mapping soil salinity at continental scale

NASA Astrophysics Data System (ADS)

Bui, Elisabeth; Wilford, John; de Caritat, Patrice

2017-04-01

Soil salinity is a serious limitation to agriculture and one of the main causes of land degradation. Soil is considered saline if its electrical conductivity (EC) is > 4 dS/m. Maps of saline soil distribution are essential for appropriate land development. Previous attempts to map soil salinity over extensive areas have relied on satellite imagery, aerial electromagnetic (EM) and/or proximally sensed EM data; other environmental (climate, topographic, geologic or soil) datasets are generally not used. Having successfully modelled and mapped calcium carbonate distribution over the 0-80 cm depth in Australian soils using machine learning with point samples from the National Geochemical Survey of Australia (NGSA), we took a similar approach to map soil salinity at 90-m resolution over the continent. The input data were the EC1:5 measurements on the < 2mm fraction at 1315 georeferenced points across the continent at two depth intervals (TOS, 0-10 cm, and BOS, 60-80 cm) (see http://www.ga.gov.au/energy/projects/national-geochemical-survey/atlas.html) were log-transformed and combined with values for climate, elevation and terrain attributes, soil and lithology classes, geophysics, and MODIS vegetation indices extracted at the same locations which were used as predictors in decision tree models. The machine learning software 'Cubist' (www.rulequest.com) was used as the inference engine for the modelling, a 90:10 training:test set data split was used to validate results, and 100 randomly sampled trees were built using the training data. The results were good with an average internal correlation (r) of 0.88 between predicted and measured logEC1:5 (training data), an average external correlation of 0.48 (test subset), and a Lin's concordance correlation coefficient (which evaluates the 1:1 fit) of 0.61. Therefore, the rules derived were mapped and the mean prediction for each 90-m pixel was used for the final logEC1:5 map. This is the most detailed picture of soil salinity over Australia since the 2001 National Land and Water Resources Audit and is generally consistent with it. Our map will be useful as a baseline salinity map circa 2008, when the NGSA samples were collected, for future State of the Environment reports.

Soil Moisture Retrieval Using Convolutional Neural Networks: Application to Passive Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Hu, Z.; Xu, L.; Yu, B.

2018-04-01

A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.

Plant-based plume-scale mapping of tritium contamination in desert soils

USGS Publications Warehouse

Andraski, Brian J.; Stonestrom, David A.; Michel, R.L.; Halford, K.J.; Radyk, J.C.

2005-01-01

Plant-based techniques were tested for field-scale evaluation of tritium contamination adjacent to a low-level radioactive waste (LLRW) facility in the Amargosa Desert, Nevada. Objectives were to (i) characterize and map the spatial variability of tritium in plant water, (ii) develop empirical relations to predict and map subsurface contamination from plant-water concentrations, and (iii) gain insight into tritium migration pathways and processes. Plant sampling [creosote bush, Larrea tridentata (Sessé & Moc. ex DC.) Coville] required one-fifth the time of soil water vapor sampling. Plant concentrations were spatially correlated to a separation distance of 380 m; measurement uncertainty accounted for <0.1% of the total variability in the data. Regression equations based on plant tritium explained 96 and 90% of the variation in root-zone and sub-root-zone soil water vapor concentrations, respectively. The equations were combined with kriged plant-water concentrations to map subsurface contamination. Mapping showed preferential lateral movement of tritium through a dry, coarse-textured layer beneath the root zone, with concurrent upward movement through the root zone. Analysis of subsurface fluxes along a transect perpendicular to the LLRW facility showed that upward diffusive-vapor transport dominates other transport modes beneath native vegetation. Downward advective-liquid transport dominates at one endpoint of the transect, beneath a devegetated road immediately adjacent to the facility. To our knowledge, this study is the first to document large-scale subsurface vapor-phase tritium migration from a LLRW facility. Plant-based methods provide a noninvasive, cost-effective approach to mapping subsurface tritium migration in desert areas.

A Brief History of the use of Electromagnetic Induction Techniques in Soil Survey

NASA Astrophysics Data System (ADS)

Brevik, Eric C.; Doolittle, James

2017-04-01

Electromagnetic induction (EMI) has been used to characterize the spatial variability of soil properties since the late 1970s. Initially used to assess soil salinity, the use of EMI in soil studies has expanded to include: mapping soil types; characterizing soil water content and flow patterns; assessing variations in soil texture, compaction, organic matter content, and pH; and determining the depth to subsurface horizons, stratigraphic layers or bedrock, among other uses. In all cases the soil property being investigated must influence soil apparent electrical conductivity (ECa) either directly or indirectly for EMI techniques to be effective. An increasing number and diversity of EMI sensors have been developed in response to users' needs and the availability of allied technologies, which have greatly improved the functionality of these tools and increased the amount and types of data that can be gathered with a single pass. EMI investigations provide several benefits for soil studies. The large amount of georeferenced data that can be rapidly and inexpensively collected with EMI provides more complete characterization of the spatial variations in soil properties than traditional sampling techniques. In addition, compared to traditional soil survey methods, EMI can more effectively characterize diffuse soil boundaries and identify included areas of dissimilar soils within mapped soil units, giving soil scientists greater confidence when collecting spatial soil information. EMI techniques do have limitations; results are site-specific and can vary depending on the complex interactions among multiple and variable soil properties. Despite this, EMI techniques are increasingly being used to investigate the spatial variability of soil properties at field and landscape scales. The future should witness a greater use of multiple-frequency and multiple-coil EMI sensors and integration with other sensors to assess the spatial variability of soil properties. Data analysis will be improved with advanced processing and presentation systems and more sophisticated geostatistical modeling algorithms will be developed and used to interpolate EMI data, improve the resolution of subsurface features, and assess soil properties.

Soil and plant contamination with Mycobacterium avium subsp. paratuberculosis after exposure to naturally contaminated mouflon feces.

PubMed

Pribylova, Radka; Slana, Iva; Kaevska, Marija; Lamka, Jiri; Babak, Vladimir; Jandak, Jiri; Pavlik, Ivo

2011-05-01

The aim of this study was to demonstrate the persistence of Mycobacterium avium subsp. paratuberculosis (MAP) in soil and colonization of different plant parts after deliberate exposure to mouflon feces naturally contaminated with different amounts of MAP. Samples of aerial parts of plants, their roots, and the soil below the roots were collected after 15 weeks and examined using IS900 real-time quantitative PCR (qPCR) and cultivation. Although the presence of viable MAP cells was not demonstrated, almost all samples were found to be positive using qPCR. MAP IS900 was not only found in the upper green parts, but also in the roots and soil samples (from 1.00 × 10(0) to 6.43 × 10(3)). The level of soil and plant contamination was influenced mainly by moisture, clay content, and the depth from which the samples were collected, rather than by the initial concentration of MAP in the feces at the beginning of the experiment.

Vegetation types on acid soils of Micronesia

Treesearch

Marjorie C. Falanruw; Thomas G.. Cole; Craig D. Whitesell

1987-01-01

The soils and vegetation of the Caroline high islands, Federated States of Micronesia, are being mapped by the U.S. Department of Agriculture's Forest Service and Soil Conservation Service. By the end of 1987, vegetation maps and reports on Kosrae, Pohnpei, Yap, four Truk Islands, and Palau are expected to be available. To compare soil types with vegetation types...

Mapping of hydropedologic spatial patterns in a steep headwater catchment

Treesearch

Cody P. Gillin; Scott W. Bailey; Kevin J. McGuire; John P. Gannon

2015-01-01

A hydropedologic approach can be used to describe soil units affected by distinct hydrologic regimes. We used field observations of soil morphology and geospatial information technology to map the distribution of five hydropedologic soil units across a 42-ha forested headwater catchment. Soils were described and characterized at 172 locations within Watershed 3, the...

Vulnerability of North American Boreal Peatlands to Interactions between Climate, Hydrology, and Wildland Fires

NASA Astrophysics Data System (ADS)

Bourgeau-Chavez, L. L.; Jenkins, L. K.; Kasischke, E. S.; Turetsky, M.; Benscoter, B.; Banda, E. J.; Boren, E. J.; Endres, S. L.; Billmire, M.

2013-12-01

North American boreal peatland sites of Alaska, Alberta Canada, and the southern limit of the boreal ecoregion (Michigan's Upper Peninsula) are the focus of an ongoing project to better understand the fire weather, hydrology, and climatic controls on boreal peatland fires. The overall goal of the research project is to reduce uncertainties of the role of northern high latitude ecosystems in the global carbon cycle and to improve carbon emission estimates from boreal fires. Boreal peatlands store tremendous reservoirs of soil carbon that are likely to become increasingly vulnerable to fire as climate change lowers water tables and exposes C-rich peat to burning. Increasing fire activity in peatlands could cause these ecosystems to become net sources of C to the atmosphere, which is likely to have large influences on atmospheric carbon concentrations through positive feedbacks that enhance climate warming. Remote sensing is key to monitoring, understanding and quantifying changes occurring in boreal peatlands. Remote sensing methods are being developed to: 1) map and classify peatland cover types; 2) characterize seasonal and inter-annual variations in the moisture content of surface peat (fuel) layers; 3) map the extent and seasonal timing of fires in peatlands; and 4) discriminate different levels of fuel consumption/burn severity in peat fires. A hybrid radar and optical infrared methodology has been developed to map peatland types (bog vs. fen) and level of biomass (open herbaceous, shrubby, forested). This methodology relies on multi-season data to detect phenological changes in hydrology which characterize the different ecosystem types. Landsat data are being used to discriminate burn severity classes in the peatland types using standard dNBR methods as well as individual bands. Cross referencing the peatland maps and burn severity maps will allow for assessment of the distribution of upland and peatland ecosystems affected by fire and quantitative analysis of emissions. Radar imagery from multiple platforms (L-band PALSAR, C-band ERS-2, Envisat, and Radarsat-2) is being used to develop soil moisture extraction algorithms to monitor changes (drying - wetting) through time and to develop a standard method for soil moisture assessment. Using data from the 1990s (ERS-1 and 2) through the present (Radarsat-2) will allow for determination of patterns of wetting and drying across the landscape. All the remote sensing analysis is supported with field work which has been coordinated with that of Canadian scientists. Field collection includes vegetation and hydrology data to validate peatland distribution maps, collection of water table depths and peat moisture content data to aid in algorithm development for radar organic soil moisture retrieval, and characterization of variations in depth of burning and carbon consumption during peatland fires to use in burn severity mapping and fire emissions modeling.

Mapping iron oxides and the color of Australian soil using visible-near-infrared reflectance spectra

NASA Astrophysics Data System (ADS)

Viscarra Rossel, R. A.; Bui, E. N.; de Caritat, P.; McKenzie, N. J.

2010-12-01

Iron (Fe) oxide mineralogy in most Australian soils is poorly characterized, even though Fe oxides play an important role in soil function. Fe oxides reflect the conditions of pH, redox potential, moisture, and temperature in the soil environment. The strong pigmenting effect of Fe oxides gives most soils their color, which is largely a reflection of the soil's Fe mineralogy. Visible-near-infrared (vis-NIR) spectroscopy can be used to identify and measure the abundance of certain Fe oxides in soil, and the visible range can be used to derive tristimuli soil color information. The aims of this paper are (1) to measure the abundance of hematite and goethite in Australian soils from their vis-NIR spectra, (2) to compare these results to measurements of soil color, and (3) to describe the spatial variability of hematite, goethite, and soil color and map their distribution across Australia. We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer with a wavelength range of 350-2500 nm. We determined the Fe oxide abundance for each sample using the diagnostic absorption features of hematite (near 880 nm) and goethite (near 920 nm) and derived a normalized iron oxide difference index (NIODI) to better discriminate between them. The NIODI was generalized across Australia with its spatial uncertainty using sequential indicator simulation, which resulted in a map of the probability of the occurrence of hematite and goethite. We also derived soil RGB color from the spectra and mapped its distribution and uncertainty across the country using sequential Gaussian simulations. The simulated RGB color values were made into a composite true color image and were also converted to Munsell hue, value, and chroma. These color maps were compared to the map of the NIODI, and both were used to interpret our results. The work presented here was validated by randomly splitting the data into training and test data sets, as well as by comparing our results to existing studies on the distribution of Fe oxides in Australian soils.

Matching soil grid unit resolutions with polygon unit scales for DNDC modelling of regional SOC pool

NASA Astrophysics Data System (ADS)

Zhang, H. D.; Yu, D. S.; Ni, Y. L.; Zhang, L. M.; Shi, X. Z.

2015-03-01

Matching soil grid unit resolution with polygon unit map scale is important to minimize uncertainty of regional soil organic carbon (SOC) pool simulation as their strong influences on the uncertainty. A series of soil grid units at varying cell sizes were derived from soil polygon units at the six map scales of 1:50 000 (C5), 1:200 000 (D2), 1:500 000 (P5), 1:1 000 000 (N1), 1:4 000 000 (N4) and 1:14 000 000 (N14), respectively, in the Tai lake region of China. Both format soil units were used for regional SOC pool simulation with DeNitrification-DeComposition (DNDC) process-based model, which runs span the time period 1982 to 2000 at the six map scales, respectively. Four indices, soil type number (STN) and area (AREA), average SOC density (ASOCD) and total SOC stocks (SOCS) of surface paddy soils simulated with the DNDC, were attributed from all these soil polygon and grid units, respectively. Subjecting to the four index values (IV) from the parent polygon units, the variation of an index value (VIV, %) from the grid units was used to assess its dataset accuracy and redundancy, which reflects uncertainty in the simulation of SOC. Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pool, matching with soil polygon units map scales, respectively. With the optimal raster resolution the soil grid units dataset can hold the same accuracy as its parent polygon units dataset without any redundancy, when VIV < 1% of all the four indices was assumed as criteria to the assessment. An quadratic curve regression model y = -8.0 × 10-6x2 + 0.228x + 0.211 (R2 = 0.9994, p < 0.05) was revealed, which describes the relationship between optimal soil grid unit resolution (y, km) and soil polygon unit map scale (1:x). The knowledge may serve for grid partitioning of regions focused on the investigation and simulation of SOC pool dynamics at certain map scale.

Use of mobile gammaspectrometry for estimation of texture at regional scale

NASA Astrophysics Data System (ADS)

Dierke, C.; Werban, U.; Dietrich, P.

2012-04-01

In the last years gamma-ray measurements from air and ground were increasingly used for spatial mapping of physical soil parameters. Many applications of gamma-ray measurements for soil characterisation and in digital soil mapping (DSM) are known from Australia or single once from Northern America. During the last years there are attempts to use that method in Europe as well. The measured isotope concentration of the gamma emitter 40K, 238U and 232Th in soils depends on different soil parameters, which are the result of composition and properties of parent rock and processes during soil geneses under different climatic conditions. Grain size distribution, type of clay minerals and organic matter are soil parameters which influence directly the gamma-ray concentration. From former studies we know, that there are site specific relationships at the field scale between gamma-ray measurements and soil properties. One of the target soil properties in DSM is for e.g. the spatial distribution of texture at the landscape scale. Thus there is a need of more regional understanding of gamma-ray concentration and soil properties with regard to the complex geology of Europe. We did systematic measurements at different field sites across Europe to investigate the relationship between the concentrations of gamma radiant and grain size. The areas are characterised by different pedogenesis and varying clay content. For the measurement we used a mobile 4l Na(I) detector with GPS connection, which is mounted on a sledge and can be towed across the agricultural used plane. Additionally we selected points for soil sampling and analysis of soil texture. For the interpretation we used the single nuclide concentration as well as the ratios. The results show site specific relationships dependent from source material. At soils developed from alluvial sediments the K/Th ratio is an indicator for clay content at regional scale. At soils developed from loess sediments Th can be used do discriminate between fine (clay + silt) and coarse (sand) fraction. This knowledge will led to a more conceptual understanding of gamma-ray measurements at regional scale. These activities are done within the iSOIL project. iSOIL- Interactions between soil related sciences - Linking geophysics, soil science and digital soil mapping is a Collaborative Project (Grant Agreement number 211386) co-funded by the Research DG of the European Commission within the RTD activities of the FP7 Thematic Priority Environment; iSOIL is one member of the SOIL TECHNOLOGY CLUSTER of Research Projects funded by the EC.

Machine learning for predicting soil classes in three semi-arid landscapes

USGS Publications Warehouse

Brungard, Colby W.; Boettinger, Janis L.; Duniway, Michael C.; Wills, Skye A.; Edwards, Thomas C.

2015-01-01

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.

Soil zymography - A novel technique for mapping enzyme activity in the rhizosphere

NASA Astrophysics Data System (ADS)

Spohn, Marie

2014-05-01

The effect plant roots on microbial activity in soil at the millimeter scale is poorly understood. One reason for this is that spatially explicit methods for the study of microbial activity in soil are limited. Here we present a quantitative in situ technique for mapping the distribution of exoenzymes in soil along with some results about the effects of roots on exoenzyme activity in soil. In the first study we showed that both acid and alkaline phosphatase activity were up to 5.4-times larger in the rhizosphere of Lupinus albus than in the bulk soil. While acid phosphatase activity (produced by roots and microorganisms) was closely associated with roots, alkaline phosphatase activity (produced only by microorganisms) was more widely distributed, leading to a 2.5-times larger area of activity of alkaline than of acid phosphatase. These results indicate a spatial differentiation of different ecophysiological groups of organic phosphorus mineralizing organisms in the rhizosphere which might alleviate a potential competition for phosphorus between them. In a second study cellulase, chitinase and phosphatase activities were analyzed in the presence of living Lupinus polyphyllus roots and dead/dying roots (in the same soils 10, 20 and 30 days after cutting the L. polyphyllus shoots). The activity of all three enzymes was 9.0 to 13.9-times higher at the living roots compared to the bulk soil. Microhotspots of cellulase, chitinase and phosphatase activity in the soil were found up to 60 mm away from the living roots. 10 days after shoot cutting, the areas of high activities of cellulase and phosphatase activity were extend up to 55 mm away from the next root, while the extension of the area of chitinase activity did not change significantly. At the root, cellulase and chitinase activity increased first at the root tips after shoot cutting and showed maximal activity 20 days after shoot cutting. The number and activity of microhotspots of chitinase activity was maximal 10 days after shoot cutting and decreased thereafter. In conclusion, the study showed that fresh root detritus stimulates enzyme activities much stronger than living roots, probably because of the high pulse input of C and N from dying roots compared to slow continuous release of rhizodeposits. Taken together, soil zymography is a very promising novel technique to gain insights the effects of roots on the spatial and temporal dynamic of exoenzyme activity in soil. References Spohn, M., Carminati, A., Kuzyakov, Y. (2013). Zymography - A novel in situ method for mapping distribution of enzyme activity in soil. Soil Biology and Biochemistry 58, 275-280. Spohn, M., Kuzyakov, Y. (2013): Distribution of microbial- and root- derived phosphatase activities in the rhizosphere depending on P availability and C allocation - Coupling soil zymography with 14C imaging. Soil Biology and Biochemistry 67, 106-113. Spohn, M., Kuzyakov, Y. (accepted): Spatial and temporal dynamics of hotspots of enzyme activity as affected by living and dead roots - A soil zymography analysis. Plant and Soil

Accounting for pH heterogeneity and variability in modelling human health risks from cadmium in contaminated land.

PubMed

Gay, J Rebecca; Korre, Anna

2009-07-01

The authors have previously published a methodology which combines quantitative probabilistic human health risk assessment and spatial statistical methods (geostatistics) to produce an assessment, incorporating uncertainty, of risks to human health from exposure to contaminated land. The model assumes a constant soil to plant concentration factor (CF(veg)) when calculating intake of contaminants. This model is modified here to enhance its use in a situation where CF(veg) varies according to soil pH, as is the case for cadmium. The original methodology uses sequential indicator simulation (SIS) to map soil concentration estimates for one contaminant across a site. A real, age-stratified population is mapped across the contaminated area, and intake of soil contaminants by individuals is calculated probabilistically using an adaptation of the Contaminated Land Exposure Assessment (CLEA) model. The proposed improvement involves not only the geostatistical estimation of the contaminant concentration, but also that of soil pH, which in turn leads to a variable CF(veg) estimate which influences the human intake results. The results presented demonstrate that taking pH into account can influence the outcome of the risk assessment greatly. It is proposed that a similar adaptation could be used for other combinations of soil variables which influence CF(veg).

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

NASA Astrophysics Data System (ADS)

Carranza, Coleen D. U.; van der Ploeg, Martine J.; Torfs, Paul J. J. F.

2018-04-01

Recent advances in radar remote sensing popularized the mapping of surface soil moisture at different spatial scales. Surface soil moisture measurements are used in combination with hydrological models to determine subsurface soil moisture values. However, variability of soil moisture across the soil column is important for estimating depth-integrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface soil moisture. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface soil moisture conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled soil moisture values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.

Mapping of Soil-Ecological Conditions of a Medium-Size Industrial City (Birobidzhan City, Jewish Autonomous Oblast, FarEast of Russia as an Example)

NASA Astrophysics Data System (ADS)

Kalmanova, V. B.; Matiushkina, L. A.

2018-01-01

The authors analyze soil relations with other elements of the city ecosystem (the position in the landscape, soil-forming rocks and lithology, vegetation and its state) to develop the legend and map of soils in the City of Birobidzhan (scale 1:25 000). The focus of study is the morphological structure of urban soils with different degree of disturbance of these relations under the impact of technical effects, economic and recreational activities of the city population. The soil cover structure is composed of four large ecological groups of soils: natural untransformed, natural with a disturbed surface, anthropogenic soils and technogenic surface formations. Using cartometry of the mapped soil contours the authors created the scheme of soil-ecological city zoning, which in a general way depicts the state of soil ecological functions in the city as well as identified zones of soils with preserved, partially and fully distured ecological functions and zones of local geochemical anomalies at the initial formation stage (environmental risk zones).

Using Remote Sensing, Geomorphology, and Soils to Map Episodic Streams in Drylands

NASA Astrophysics Data System (ADS)

Thibodeaux-Yost, S. N. S.

2016-12-01

Millions of acres of public land in the California deserts are currently being evaluated and permitted for the construction of large-scale renewable energy projects. The absence of a standard method for identifying episodic streams in arid and semi-arid (dryland) regions is a source of conflict between project developers and the government agencies responsible for conserving natural resources and permitting renewable energy projects. There is a need for a consistent, efficient, and cost-effective dryland stream delineation protocol that accurately reflects the extent and distribution of active watercourses. This thesis evaluates the stream delineation method and results used by the developer for the proposed Ridgecrest Solar Power Project on the El Paso Fan, Ridgecrest, Kern County, California. This evaluation is then compared and contrasted with results achieved using remote sensing, geomorphology, soils, and GIS analysis to identify stream presence on the site. This study's results identified 105 acres of watercourse, a value 10 times greater than that originally identified by the project developer. In addition, the applied methods provide an ecohydrologic base map to better inform project siting and potential project impact mitigation opportunities. This study concludes that remote sensing, geomorphology, and dryland soils can be used to accurately and efficiently identify episodic stream activity and the extent of watercourses in dryland environments.

Reduction of Topographic Effect for Curve Number Estimated from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Zhang, Wen-Yan; Lin, Chao-Yuan

2016-04-01

The Soil Conservation Service Curve Number (SCS-CN) method is commonly used in hydrology to estimate direct runoff volume. The CN is the empirical parameter which corresponding to land use/land cover, hydrologic soil group and antecedent soil moisture condition. In large watersheds with complex topography, satellite remote sensing is the appropriate approach to acquire the land use change information. However, the topographic effect have been usually found in the remotely sensed imageries and resulted in land use classification. This research selected summer and winter scenes of Landsat-5 TM during 2008 to classified land use in Chen-You-Lan Watershed, Taiwan. The b-correction, the empirical topographic correction method, was applied to Landsat-5 TM data. Land use were categorized using K-mean classification into 4 groups i.e. forest, grassland, agriculture and river. Accuracy assessment of image classification was performed with national land use map. The results showed that after topographic correction, the overall accuracy of classification was increased from 68.0% to 74.5%. The average CN estimated from remotely sensed imagery decreased from 48.69 to 45.35 where the average CN estimated from national LULC map was 44.11. Therefore, the topographic correction method was recommended to normalize the topographic effect from the satellite remote sensing data before estimating the CN.

Description of a user-oriented geographic information system - The resource analysis program

NASA Technical Reports Server (NTRS)

Tilmann, S. E.; Mokma, D. L.

1980-01-01

This paper describes the Resource Analysis Program, an applied geographic information system. Several applications are presented which utilized soil, and other natural resource data, to develop integrated maps and data analyses. These applications demonstrate the methods of analysis and the philosophy of approach used in the mapping system. The applications are evaluated in reference to four major needs of a functional mapping system: data capture, data libraries, data analysis, and mapping and data display. These four criteria are then used to describe an effort to develop the next generation of applied mapping systems. This approach uses inexpensive microcomputers for field applications and should prove to be a viable entry point for users heretofore unable or unwilling to venture into applied computer mapping.

Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

NASA Astrophysics Data System (ADS)

Wang, W.; Rinke, A.; Moore, J. C.; Cui, X.; Ji, D.; Li, Q.; Zhang, N.; Wang, C.; Zhang, S.; Lawrence, D. M.; McGuire, A. D.; Zhang, W.; Delire, C.; Koven, C.; Saito, K.; MacDougall, A.; Burke, E.; Decharme, B.

2015-03-01

We perform a land surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies between 6 modern stand-alone land surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by 5 different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99-135 x 104 km2) between the two diagnostic methods based on air temperature which are also consistent with the best current observation-based estimate of actual permafrost area (101 x 104 km2). However the uncertainty (1-128 x 104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air temperature based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification and snow cover. Models are particularly poor at simulating permafrost distribution using definition that soil temperature remains at or below 0°C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in permafrost distribution can be made for the Tibetan Plateau.

Geochemical cartography as a tool for assessing the degree of soil contamination with heavy metals in Poland

NASA Astrophysics Data System (ADS)

Szymon Borkowski, Andrzej; Kwiatkowska-Malina, Jolanta

2016-04-01

Spatial disposition of chemical elements including heavy metals in the soil environment is a very important information during preparation of the thematic maps for the environmental protection and/or spatial planning. This knowledge is also essential for the earth's surface and soil's monitoring, designation of areas requiring improvement including remediation. The main source of anthropogenic pollution of soil with heavy metals are industry related to the mining coal and liquid fuels, mining and metallurgy, chemical industry, energy production, waste management, agriculture and transport. The geochemical maps as a kind of specific thematic maps made on the basis of datasets obtained from the Polish Geological Institute's resources allow to get to know the spatial distribution of different chemical elements including heavy metals in soil. The results of the research carried out by the Polish Geological Institute showed strong contamination in some regions in Poland mainly with arsenic, cadmium, lead and nickel. For this reason it was the point to prepare geochemical maps showing contamination of soil with heavy metals, and determine main sources of contamination and zones where heavy metals concentration was higher than acceptable contents. It was also presented a summary map of soil contamination with heavy metals. Additionally, location of highly contaminated zones was compiled with predominant in those areas types of arable soils and then results were thoroughly analyzed. This information can provide a base for further detailed studies on the soil contamination with heavy metals.

General soil map Lower Pantano wash area, Pima County, Arizona

NASA Technical Reports Server (NTRS)

Richardson, M. L.

1972-01-01

High altitude color photography was used to determine soil type variation over large areas at a contact print scale of 1:125,000. It was found that color variation and land form could be used as a basis for assigning seven soil mapping units to the area as depicted on stereoscopic pairs of the color photography. A unit is assigned by soil scientists on the basis of similarity of soil features in the area to predetermined physical and chemical characteristics of the same soil type.

A method for mapping topsoil field-saturated hydraulic conductivity in the Cévennes-Vivarais region using infiltration tests conducted with different techniques

NASA Astrophysics Data System (ADS)

Braud, Isabelle; Desprats, Jean-François; Ayral, Pierre-Alain; Bouvier, Christophe; Vandervaere, Jean-Pierre

2017-04-01

Topsoil field-saturated hydraulic conductivity, Kfs, is a parameter that controls the partition of rainfall between infiltration and runoff. It is a key parameter in most distributed hydrological models. However, there is a mismatch between the scale of local in situ measurements and the scale at which the parameter is required in models. Therefore it is necessary to design methods to regionally map this parameter at the model scale. The paper propose a method for mapping Kfs in the Cévennes-Vivarais region, south-east France, using more easily available GIS data: geology and land cover. The mapping is based on a data set gathering infiltration tests performed in the area or close to it for more than ten years. The data set is composed of infiltration tests performed using various techniques: Guelph permeameter, double ring and single ring infiltration tests, infiltrometers with multiple suctions. The different methods lead to different orders of magnitude for Kfs rendering the pooling of all the data challenging. Therefore, a method is first proposed to pool the data from the different infiltration methods, leading to a homogenized set of Kfs, based on an equivalent double ring/tension disk infiltration value. Statistical tests showed significant differences in distributions among different geologies and land covers. Thus those variables were retained as proxy for mapping Kfs at the regional scale. This map was compared to a map based on the Rawls and Brakensiek (RB) pedo-transfer function (Manus et al., 2009, Vannier et al., 2016), showing very different patterns between both maps. In addition, RB values did not fit observed values at the plot scale, highlighting that soil texture only is not a good predictor of Kfs. References Manus, C., Anquetin, S., Braud, I., Vandervaere, J.P., Viallet, P., Creutin, J.D., Gaume, E., 2009. A modelling approach to assess the hydrological response of small Mediterranean catchments to the variability of soil characteristics in a context of extreme events. Hydrology and Earth System Sciences, 13: 79-87. Vannier, O., Anquetin, S., Braud, I., 2016. Investigating the role of geology in the hydrological response of Mediterranean catchments prone to flash-floods: regional modelling study and process understanding. Journal of Hydrology, 541 Part A, 158-172.

Dependence of the cyclization of branched tetraethers (CBT) on soil moisture in the Chinese Loess Plateau and the adjacent areas: implications for palaeorainfall reconstructions

NASA Astrophysics Data System (ADS)

Wang, H.; Liu, W.; Zhang, C. L.

2014-06-01

Branched glycerol dialkyl glycerol tetraethers (bGDGTs) have been show promising for continental paleotemperature studies in loess-paleosol sequences (LPSs). Thus far, however, little is known about the effect of soil moisture on their distributions on the Chinese Loess Plateau (CLP). In this study, the relationships between environmental variables and the cyclization of bGDGTs (the so called CBT index) were investigated in a comprehensive set of surface soils in the CLP and its adjacent arid/semi-arid areas. We find that CBT correlates best with soil water content (SWC) or mean annual precipitation (MAP) for the total sample set. Particularly for the CLP soils, there is a significant positive relationship between CBT and MAP (CBT = -0.0021 · MAP + 1.7, n = 37, R2 = 0.87; MAP range: 210-680 mm). This indicates that CBT is mainly controlled by soil moisture in the alkalescent soils (pH > 7) in arid/semi-arid regions, where it is not sensitive to soil pH. Therefore, we suggest that CBT can potentially be used as a palaeorainfall proxy on the CLP. According to the preliminary CBT-MAP relationship for modern CLP soils, palaeorainfall history was reconstructed from three LPSs (Yuanbao, Lantian, and Mangshan) with published bGDGT data spanning the past 70 ka. The CBT-derived MAP records of the three sites consistently show precession-driven variations resembling the speleothem δ18O monsoon record, and are also in general accord with the fluctuations of the respective magnetic susceptibility (MS) record, supporting CBT as a reasonable proxy for palaeorainfall reconstruction in LPS studies. Moreover, the comparison of CBT-derived MAP and bGDGT-derived temperature may enable us to further assess the relative timing and magnitude of hydrological and thermal changes on the CLP, independent of chronology.

Topsoil organic carbon content of Europe, a new map based on a generalised additive model

NASA Astrophysics Data System (ADS)

de Brogniez, Delphine; Ballabio, Cristiano; Stevens, Antoine; Jones, Robert J. A.; Montanarella, Luca; van Wesemael, Bas

2014-05-01

There is an increasing demand for up-to-date spatially continuous organic carbon (OC) data for global environment and climatic modeling. Whilst the current map of topsoil organic carbon content for Europe (Jones et al., 2005) was produced by applying expert-knowledge based pedo-transfer rules on large soil mapping units, the aim of this study was to replace it by applying digital soil mapping techniques on the first European harmonised geo-referenced topsoil (0-20 cm) database, which arises from the LUCAS (land use/cover area frame statistical survey) survey. A generalized additive model (GAM) was calibrated on 85% of the dataset (ca. 17 000 soil samples) and a backward stepwise approach selected slope, land cover, temperature, net primary productivity, latitude and longitude as environmental covariates (500 m resolution). The validation of the model (applied on 15% of the dataset), gave an R2 of 0.27. We observed that most organic soils were under-predicted by the model and that soils of Scandinavia were also poorly predicted. The model showed an RMSE of 42 g kg-1 for mineral soils and of 287 g kg-1 for organic soils. The map of predicted OC content showed the lowest values in Mediterranean countries and in croplands across Europe, whereas highest OC content were predicted in wetlands, woodlands and in mountainous areas. The map of standard error of the OC model predictions showed high values in northern latitudes, wetlands, moors and heathlands, whereas low uncertainty was mostly found in croplands. A comparison of our results with the map of Jones et al. (2005) showed a general agreement on the prediction of mineral soils' OC content, most probably because the models use some common covariates, namely land cover and temperature. Our model however failed to predict values of OC content greater than 200 g kg-1, which we explain by the imposed unimodal distribution of our model, whose mean is tilted towards the majority of soils, which are mineral. Finally, average OC content predictions for each land cover class compared well between models, with our model always showing smaller standard deviations. We concluded that the chosen model and covariates are appropriate for the prediction of OC content in European mineral soils. We presented in this work the first map of topsoil OC content at European scale based on a harmonised soil dataset. The associated uncertainty map shall support the end-users in a careful use of the predictions.

Considerations for applying digital soil mapping to ecological sites

USDA-ARS?s Scientific Manuscript database

Recent advancements in the spatial prediction of soil properties are not currently being fully utilized for ecological studies. Linking digital soil mapping (DSM) with ecological sites (ES) has the potential to better land management decisions by improving spatial resolution and precision as well as...

Employing a portable X-Ray fluorescence (P-XRF) analyser and GIS to identify and map heavy metal pollution in soils of a traditional bonfire site

NASA Astrophysics Data System (ADS)

Dao, Ligang; Zhang, Chaosheng; Morrison, Liam

2010-05-01

Soils in the vicinity of bonfires are recipients of metal contaminants from burning of metal-containing materials. In order to better understand the impacts of bonfires on soils, a total of 218 surface soil samples were collected from a traditional bonfire site in Galway City, Ireland. Concentrations of Cu, Pb and Zn were determined using a portable X-ray Fluorescence (P-XRF) analyser. Strong variations were observed for these metals, and several samples contained elevated Zn concentrations which exceeded the intervention threshold of the Dutch criteria (720 mg kg-1). Spatial clusters and spatial outliers were detected using the local Moran's I index and were mapped using GIS. Two clear high value spatial clusters could be observed on the upper left side and centre part of the study area for Cu, Pb and Zn. Results of variogram analyses showed high nugget-sill-ratios for Cu, Pb and Zn, indicating strong spatial variation over short distances which could be resulted from anthropogenic activities. The spatial interpolation method of ordinary kriging was applied to produce the spatial interpolation maps for Cu, Pb and Zn, and the areas with elevated concentrations were in line with historical locations of the bonfires. The hazard maps showed small parts of the study area with Zn concentrations exceeding the Dutch intervention values. In order to prevent further contamination from bonfires, it is advised that tyres and other metal-containing wastes should not be burnt. The results in this study provide useful information for management of bonfires.

Mapping as a tool for predicting the risk of anthrax outbreaks in Northern Region of Ghana.

PubMed

Nsoh, Ayamdooh Evans; Kenu, Ernest; Forson, Eric Kofi; Afari, Edwin; Sackey, Samuel; Nyarko, Kofi Mensah; Yebuah, Nathaniel

2016-01-01

Anthrax is a febrile soil-born infectious disease that can affect all warm-blooded animals including man. Outbreaks of anthrax have been reported in northern region of Ghana but no concerted effort has been made to implement risk-based surveillance systems to document outbreaks so as to implement policies to address the disease. We generated predictive maps using soil pH, temperature and rainfall as predictor variables to identify hotspot areas for the outbreaks. A 10-year secondary data records on soil pH, temperature and rainfall were used to create climate-based risk maps using ArcGIS 10.2. The monthly mean values of rainfall and temperature for ten years were calculated and anthrax related evidence based constant raster values were created as weights for the three factors. All maps were generated using the Kriging interpolation method. There were 43 confirmed outbreaks. The deaths involved were 131 cattle, 44 sheep, 15 goats, 562 pigs with 6 human deaths and 22 developed cutaneous anthrax. We found three strata of well delineated distribution pattern indicating levels of risk due to suitability of area for anthrax spore survival. The likelihood of outbreaks occurrence and reoccurrence was higher in Strata I, Strata II and strata III respectively in descending order, due to the suitability of soil pH, temperature and rainfall for the survival and dispersal of B. anthracis spore. The eastern corridor of Northern region is a Hots spot area. Policy makers can develop risk based surveillance system and focus on this area to mitigate anthrax outbreaks and reoccurrence.

Soil and ecological sites of the Santa Rita Experimental Range

Treesearch

Donald J. Breckenfeld; Daniel Robinett

2003-01-01

A soil survey and rangeland resource inventory of the Santa Rita Experimental Range (SRER) was conducted by staff from the Tucson office of the Natural Resources Conservation Service (NRCS) during April and May of 1997. Thirty-two soils series and taxadjuncts were mapped on the SRER and delineated in 24 different mapping units. These soils all occur in an Aridic and...

Mapping soil magnetic susceptibility and mineralogy in Ukraine

NASA Astrophysics Data System (ADS)

Menshov, Oleksandr; Pereira, Paulo; Kruglov, Oleksandr; Sukhorada, Anatoliy

2017-04-01

Soil suatainable planning is fundamental for agricultural areas. Soil mapping and modeling are increasingly used in agricultural areas in the entire world (Brevik et al., 2016). They are beneficial to land managers, to reduce soil degradation, increase soil productivity and their restoration. Magnetic susceptibility (MS) methods are low cost and accurate for the developing maps of agricultural areas.. The objective of this work is to identify the minerals responsible for MS increase in soils from the two study areas in Poltava and Kharkiv region. The thermomagnetic analyses were conducted using the KLY-4 with an oven apparatus. The hysteresis parameters were measured with the Rotating Magnetometer at the Geophysical Centre Dourbes, Belgium. The results showed that all of samples from Kharkiv area and the majortity of the samples collected in Poltava area represent the pseudo single domain (PSD) zone particles in Day plot. According to Hanesch et al. (2006), the transformation of goethite, ferrihydrite or hematite to a stronger ferrimagnetic phase like magnetite or maghemite is common in strongly magnetic soils with high values of organic carbon content. In our case of thermomagnetic study, the first peak on the heating curve near 260 ˚C indicates the presence of ferrihydrite which gradually transforms into maghemite (Jordanova et al., 2013). A further decrease in the MS identified on the heating curve may be related to the transformation of the maghemite to hematite. A second MS peak on the heating curve near 530 ˚C and the ultimate loss of magnetic susceptibility near 580 ˚C were caused by the reduction of hematite to magnetite. The shape of the thermomagnetic curves suggests the presence of single domain (SD) particles at room temperature and their transformation to a superparamagnetic (SP) state under heating. Magnetic mineralogical analyses suggest the presence of highly magnetic minerals like magnetite and maghemite as well as slightly magnetic goethite, ferrihydrite, and hematite. Pseudosingle-domain, single-domain, and superparamagnetic grains of pedogenic origin dominate in the chernozem soils of the Kharkiv and Poltava region. References Brevik, E. C., Calzolari, C., Miller, B. A., Pereira, P., Kabala, C., Baumgarten, A., Jordán, A.: Soil mapping, classification, and pedologic modeling: history and future directions, Geoderma, 264, 256-274, 2016. Hanesch, M., Stanjek, H., Petersen, N.: Thermomagnetic measurements of soil iron minerals: the role of organic carbon, Geophysical Journal International, 165, 1, 53-61, 2006. Jordanova, D., Jordanova, N., Werban, U.: Environmental significance of magnetic properties of Gley soils near Rosslau (Germany), Environ Earth Sci., 69, 1719-1732, 2013.

Soil Organic Carbon Estimation and Mapping Using "on-the-go" VisNIR Spectroscopy

NASA Astrophysics Data System (ADS)

Brown, D. J.; Bricklemyer, R. S.; Christy, C.

2007-12-01

Soil organic carbon (SOC) and other soil properties related to carbon sequestration (eg. soil clay content and mineralogy) vary spatially across landscapes. To cost effectively capture this variability, new technologies, such as Visible and Near Infrared (VisNIR) spectroscopy, have been applied to soils for rapid, accurate, and inexpensive estimation of SOC and other soil properties. For this study, we evaluated an "on the go" VisNIR sensor developed by Veris Technologies, Inc. (Salinas, KS) for mapping SOC, soil clay content and mineralogy. The Veris spectrometer spanned 350 to 2224 nm with 8 nm spectral resolution, and 25 spectra were integrated every 2 seconds resulting in 3 -5 m scanning distances on the ground. The unit was mounted to a mobile sensor platform pulled by a tractor, and scanned soils at an average depth of 10 cm through a quartz-sapphire window. We scanned eight 16.2 ha (40 ac) wheat fields in north central Montana (USA), with 15 m transect intervals. Using random sampling with spatial inhibition, 100 soil samples from 0-10 cm depths were extracted along scanned transects from each field and were analyzed for SOC. Neat, sieved (<2 mm) soil sample materials were also scanned in the lab using an Analytical Spectral Devices (ASD, Boulder, CO, USA) Fieldspec Pro FR spectroradiometer with a spectral range of 350-2500 and spectral resolution of 2-10 nm. The analyzed samples were used to calibrate and validate a number of partial least squares regression (PLSR) VisNIR models to compare on-the-go scanning vs. higher spectral resolution laboratory spectroscopy vs. standard SOC measurement methods.

Mapping and predicting sinkholes by integration of remote sensing and spectroscopy methods

NASA Astrophysics Data System (ADS)

Goldshleger, N.; Basson, U.; Azaria, I.

2013-08-01

The Dead Sea coastal area is exposed to the destructive process of sinkhole collapse. The increase in sinkhole activity in the last two decades has been substantial, resulting from the continuous decrease in the Dead Sea's level, with more than 1,000 sinkholes developing as a result of upper layer collapse. Large sinkholes can reach 25 m in diameter. They are concentrated mainly in clusters in several dozens of sites with different characteristics. In this research, methods for mapping, monitoring and predicting sinkholes were developed using active and passive remote-sensing methods: field spectrometer, geophysical ground penetration radar (GPR) and a frequency domain electromagnetic instrument (FDEM). The research was conducted in three stages: 1) literature review and data collection; 2) mapping regions abundant with sinkholes in various stages and regions vulnerable to sinkholes; 3) analyzing the data and translating it into cognitive and accessible scientific information. Field spectrometry enabled a comparison between the spectral signatures of soil samples collected near active or progressing sinkholes, and those collected in regions with no visual sign of sinkhole occurrence. FDEM and GPR investigations showed that electrical conductivity and soil moisture are higher in regions affected by sinkholes. Measurements taken at different time points over several seasons allowed monitoring the progress of an 'embryonic' sinkhole.

Ecogeochemical mapping of urban soils as a tool for indication of risk factors

NASA Astrophysics Data System (ADS)

Sahakyan, Lilit; Saghetalyan, Armen; Asmaryan, Shushanik

2010-05-01

Today, most global and local environmental issues are connected with the disturbance of natural equilibrium of chemical elements, which is manifested by two contrary but synchronous and interconnected geochemical processes: dispersion and concentration of chemical elements. The ecological consequence of those intensively running processes is pollution of environmental compartments. High intensity and multi-component character of pollution is common to urban ecosystems. In this respect emphasized should be mining centers representing biogeochemical provinces where the whole range of geochemical processes connected with socio-economic activities of the man reaches its maximum and high natural background of chemical elements is coupled with their man-made load. Ecogeochemical mapping of soils of mining regions and cities is one of major tools while assessing ecological state of the territory and indicating risk factors. When systemizing indices of geochemical pollution, the produced case specific maps coupled with ecogeochemical mapping techniques are territorial generalization of levels of pollution and levels of its danger. This allows indicating its spatial differentiation and finally ranging the city's territory by features of the defined level of ecological risk. Moreover, ecogeochemical mapping of soils allows indicating dominating pollutants, peculiarities of their distribution and major risk factors as well and thus revealing risk groups in the population. An alternative method of ecogeochemical mapping of urban soils which allows to notably reduce the process of pollution level assessment and identification of risk factor is that of remote sensing. Collation between spatially conjugated data of soil analyses and multi-zonal satellite images allows developing spectral characteristics (signatures) of pollution of the territory with heavy metals (HM) and development of appropriate assessment criteria which may be reflected as diverse case specific maps. This work considers the outcomes of application of ecogeochemical mapping of urban soils while revealing risk factors on a case of one of Armenia's mining centers - the city of Kajaran. It lies within the bounds of sulfide copper-molybdenum deposit, on which base a mining and dressing set of plants - a city-forming enterprise - operates. As established, the city's territory is polluted predominantly with major ore elements: Mo, Cu. At the same time locally indicated are anomalies of a series of elements found in the ore in insignificant concentrations: As, Hg, Cd. Proceeding from fact that soils are indicators of atmospheric pollution, investigated were HM contents in dust. As established, the dust of the quarry and tailing repositories contains high contents of Cu, Mo, Zn and also Hg, As, Cd. The assessment of farm crops cultivated on polluted soils indicated Mo, Cu, Pb, Ni, Cr, Zn, Hg excesses vs. MPC in potatoes, beans, beetroot and dill. Thus, the dust of the quarry and tailing repositories and farm crops has been defined as the major risk factors. Data on detailed above-surface investigations with clear spatial and temporal coordination were collated with multi-zonal satellite images (Landsat ETM +28m) of the territory. As a result spectral signatures have been obtained which allows differentiation of the territory by the value of summary pollution with HM.

Mapping Soil pH Buffering Capacity of Selected Fields

NASA Technical Reports Server (NTRS)

Weaver, A. R.; Kissel, D. E.; Chen, F.; West, L. T.; Adkins, W.; Rickman, D.; Luvall, J. C.

2003-01-01

Soil pH buffering capacity, since it varies spatially within crop production fields, may be used to define sampling zones to assess lime requirement, or for modeling changes in soil pH when acid forming fertilizers or manures are added to a field. Our objective was to develop a procedure to map this soil property. One hundred thirty six soil samples (0 to 15 cm depth) from three Georgia Coastal Plain fields were titrated with calcium hydroxide to characterize differences in pH buffering capacity of the soils. Since the relationship between soil pH and added calcium hydroxide was approximately linear for all samples up to pH 6.5, the slope values of these linear relationships for all soils were regressed on the organic C and clay contents of the 136 soil samples using multiple linear regression. The equation that fit the data best was b (slope of pH vs. lime added) = 0.00029 - 0.00003 * % clay + 0.00135 * % O/C, r(exp 2) = 0.68. This equation was applied within geographic information system (GIS) software to create maps of soil pH buffering capacity for the three fields. When the mapped values of the pH buffering capacity were compared with measured values for a total of 18 locations in the three fields, there was good general agreement. A regression of directly measured pH buffering capacities on mapped pH buffering capacities at the field locations for these samples gave an r(exp 2) of 0.88 with a slope of 1.04 for a group of soils that varied approximately tenfold in their pH buffering capacities.

Spectral mapping of soil organic matter

NASA Technical Reports Server (NTRS)

Kristof, S. J.; Baumgardner, M. F.; Johannsen, C. J.

1974-01-01

Multispectral remote sensing data were examined for use in the mapping of soil organic matter content. Computer-implemented pattern recognition techniques were used to analyze data collected in May 1969 and May 1970 by an airborne multispectral scanner over a 40-km flightline. Two fields within the flightline were selected for intensive study. Approximately 400 surface soil samples from these fields were obtained for organic matter analysis. The analytical data were used as training sets for computer-implemented analysis of the spectral data. It was found that within the geographical limitations included in this study, multispectral data and automatic data processing techniques could be used very effectively to delineate and map surface soils areas containing different levels of soil organic matter.

Mapping regional soil water erosion risk in the Brittany-Loire basin for water management agency

NASA Astrophysics Data System (ADS)

Degan, Francesca; Cerdan, Olivier; Salvador-Blanes, Sébastien; Gautier, Jean-Noël

2014-05-01

Soil water erosion is one of the main degradation processes that affect soils through the removal of soil particles from the surface. The impacts for environment and agricultural areas are diverse, such as water pollution, crop yield depression, organic matter loss and reduction in water storage capacity. There is therefore a strong need to produce maps at the regional scale to help environmental policy makers and soil and water management bodies to mitigate the effect of water and soil pollution. Our approach aims to model and map soil erosion risk at regional scale (155 000 km²) and high spatial resolution (50 m) in the Brittany - Loire basin. The factors responsible for soil erosion are different according to the spatial and time scales considered. The regional scale entails challenges about homogeneous data sets availability, spatial resolution of results, various erosion processes and agricultural practices. We chose to improve the MESALES model (Le Bissonnais et al., 2002) to map soil erosion risk, because it was developed specifically for water erosion in agricultural fields in temperate areas. The MESALES model consists in a decision tree which gives for each combination of factors the corresponding class of soil erosion risk. Four factors that determine soil erosion risk are considered: soils, land cover, climate and topography. The first main improvement of the model consists in using newly available datasets that are more accurate than the initial ones. The datasets used cover all the study area homogeneously. Soil dataset has a 1/1 000 000 scale and attributes such as texture, soil type, rock fragment and parent material are used. The climate dataset has a spatial resolution of 8 km and a temporal resolution of mm/day for 12 years. Elevation dataset has a spatial resolution of 50 m. Three different land cover datasets are used where the finest spatial resolution is 50 m over three years. Using these datasets, four erosion factors are characterized and quantified: the soil factors (soil sealing, erodibility and runoff), the rate of land cover over three years for each season and for 77 land use classes, the topographic factor (slope and drainage area) and the climate hazard (seasonal amount and rainfall erosivity). These modifications of the original MESALES model allow to better represent erosion risk for arable and bare land. We validated model results by stakeholder consultations and meetings over all the study area. The model has finally been modified taking into account validation results. Results are provided with a spatial resolution of 1 km, and then integrated into 2121 catchments. An erosion risk map for each season and an annual erosion risk map are produced. These new maps allow to organize in hierarchy 2121 catchments into three erosion risk classes. In the annual erosion risk map, 347 catchments have the highest erosion risk, which corresponds to 16 % of total Brittany-Loire basin area. Water management agency now uses these maps to identify priority areas and to plan specific preservation practices.

Distribution of soil organic carbon in the conterminous United States

USGS Publications Warehouse

Bliss, Norman B.; Waltman, Sharon; West, Larry T.; Neale, Anne; Mehaffey, Megan; Hartemink, Alfred E.; McSweeney, Kevin M.

2014-01-01

The U.S. Soil Survey Geographic (SSURGO) database provides detailed soil mapping for most of the conterminous United States (CONUS). These data have been used to formulate estimates of soil carbon stocks, and have been useful for environmental models, including plant productivity models, hydrologic models, and ecological models for studies of greenhouse gas exchange. The data were compiled by the U.S. Department of Agriculture Natural Resources Conservation Service (NRCS) from 1:24,000-scale or 1:12,000-scale maps. It was found that the total soil organic carbon stock in CONUS to 1 m depth is 57 Pg C and for the total profile is 73 Pg C, as estimated from SSURGO with data gaps filled from the 1:250,000-scale Digital General Soil Map. We explore the non-linear distribution of soil carbon on the landscape and with depth in the soil, and the implications for sampling strategies that result from the observed soil carbon variability.

Use of remote sensing technology for inventorying and planning utilization of land resources in South Dakota

NASA Technical Reports Server (NTRS)

1975-01-01

A project was undertaken in Meade County, South Dakota to provide (1) a general county-wide resource survey of land use and soils and (2) a detailed survey of land use for the environmentally sensitive area adjacent to the Black Hills. Imagery from LANDSAT-1 was visually interpreted to provide land use information and a general soils map. A detailed land use map for the Black Hills area was interpreted from RB-57 photographs and interpretations of soil characteristics were input into a computer data base and mapped. The detailed land use data were then used in conjunction with soil maps to provide information for the development of zoning ordinance maps and other land use planning in the Black Hills area. The use of photographs as base maps was also demonstrated. In addition, the use of airborne thermography to locate spoilage areas in sugar beet piles and to determine the apparent temperature of rooftops was evaluated.

Estimation of Annual Average Soil Loss, Based on Rusle Model in Kallar Watershed, Bhavani Basin, Tamil Nadu, India

NASA Astrophysics Data System (ADS)

Rahaman, S. Abdul; Aruchamy, S.; Jegankumar, R.; Ajeez, S. Abdul

2015-10-01

Soil erosion is a widespread environmental challenge faced in Kallar watershed nowadays. Erosion is defined as the movement of soil by water and wind, and it occurs in Kallar watershed under a wide range of land uses. Erosion by water can be dramatic during storm events, resulting in wash-outs and gullies. It can also be insidious, occurring as sheet and rill erosion during heavy rains. Most of the soil lost by water erosion is by the processes of sheet and rill erosion. Land degradation and subsequent soil erosion and sedimentation play a significant role in impairing water resources within sub watersheds, watersheds and basins. Using conventional methods to assess soil erosion risk is expensive and time consuming. A comprehensive methodology that integrates Remote sensing and Geographic Information Systems (GIS), coupled with the use of an empirical model (Revised Universal Soil Loss Equation- RUSLE) to assess risk, can identify and assess soil erosion potential and estimate the value of soil loss. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the study area. The final map of annual soil erosion shows a maximum soil loss of 398.58 t/ h-1/ y-1. Based on the result soil erosion was classified in to soil erosion severity map with five classes, very low, low, moderate, high and critical respectively. Further RUSLE factors has been broken into two categories, soil erosion susceptibility (A=RKLS), and soil erosion hazard (A=RKLSCP) have been computed. It is understood that functions of C and P are factors that can be controlled and thus can greatly reduce soil loss through management and conservational measures.

7 CFR 657.4 - NRCS responsibilities.

Code of Federal Regulations, 2014 CFR

2014-01-01

... inventories. (2) Identify the soil mapping units within the State that qualify as prime. In doing this, State Conservationists, in consultation with the cooperators of the National Cooperative Soil Survey, have the... the framework of this memorandum. (3) Prepare a statewide list of: (i) Soil mapping units that meet...

7 CFR 657.4 - NRCS responsibilities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... inventories. (2) Identify the soil mapping units within the State that qualify as prime. In doing this, State Conservationists, in consultation with the cooperators of the National Cooperative Soil Survey, have the... the framework of this memorandum. (3) Prepare a statewide list of: (i) Soil mapping units that meet...

7 CFR 657.4 - NRCS responsibilities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... inventories. (2) Identify the soil mapping units within the State that qualify as prime. In doing this, State Conservationists, in consultation with the cooperators of the National Cooperative Soil Survey, have the... the framework of this memorandum. (3) Prepare a statewide list of: (i) Soil mapping units that meet...

7 CFR 657.4 - NRCS responsibilities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... inventories. (2) Identify the soil mapping units within the State that qualify as prime. In doing this, State Conservationists, in consultation with the cooperators of the National Cooperative Soil Survey, have the... the framework of this memorandum. (3) Prepare a statewide list of: (i) Soil mapping units that meet...

7 CFR 657.4 - NRCS responsibilities.

Code of Federal Regulations, 2013 CFR

2013-01-01

... inventories. (2) Identify the soil mapping units within the State that qualify as prime. In doing this, State Conservationists, in consultation with the cooperators of the National Cooperative Soil Survey, have the... the framework of this memorandum. (3) Prepare a statewide list of: (i) Soil mapping units that meet...

Utilizing soil polypedons to improve model performance for digital soil mapping

USDA-ARS?s Scientific Manuscript database

Most digital soil mapping approaches that use point data to develop relationships with covariate data intersect sample locations with one raster pixel regardless of pixel size. Resulting models are subject to spurious values in covariate data which may limit model performance. An alternative approac...

Calibration of the DRASTIC ground water vulnerability mapping method

USGS Publications Warehouse

Rupert, M.G.

2001-01-01

Ground water vulnerability maps developed using the DRASTIC method have been produced in many parts of the world. Comparisons of those maps with actual ground water quality data have shown that the DRASTIC method is typically a poor predictor of ground water contamination. This study significantly improved the effectiveness of a modified DRASTIC ground water vulnerability map by calibrating the point rating schemes to actual ground water quality data by using nonparametric statistical techniques and a geographic information system. Calibration was performed by comparing data on nitrite plus nitrate as nitrogen (NO2 + NO3-N) concentrations in ground water to land-use, soils, and depth to first-encountered ground water data. These comparisons showed clear statistical differences between NO2 + NO3-N concentrations and the various categories. Ground water probability point ratings for NO2 + NO3-N contamination were developed from the results of these comparisons, and a probability map was produced. This ground water probability map was then correlated with an independent set of NO2 + NO3-N data to demonstrate its effectiveness in predicting elevated NO2 + NO3-N concentrations in ground water. This correlation demonstrated that the probability map was effective, but a vulnerability map produced with the uncalibrated DRASTIC method in the same area and using the same data layers was not effective. Considerable time and expense have been outlaid to develop ground water vulnerability maps with the DRASTIC method. This study demonstrates a cost-effective method to improve and verify the effectiveness of ground water vulnerability maps.

Infrared thermal remote sensing for soil salinity assessment on landscape scale

NASA Astrophysics Data System (ADS)

Ivushkin, Konstantin; Bartholomeus, Harm; Bregt, Arnold K.; Pulatov, Alim; Bui, Elisabeth N.; Wilford, John

2017-04-01

Soil salinity is considered as one of the most severe land degradation aspects. An increased soil salt level inhibits growth and development of crops. Therefore, up to date soil salinity information is vital for appropriate management practices and reclamation strategies. This information is required at increasing spatial and temporal resolution for appropriate management adaptations. Conventional soil sampling and associated laboratory analyses are slow, expensive, and often cannot deliver the temporal and spatial resolution required. The change of canopy temperature is one of the stress indicators in plants. Its behaviour in response to salt stress on individual plant level is well studied in laboratory and greenhouse experiments, but its potential for landscape scale studies using remote sensing techniques is not investigated yet. In our study, possibilities of satellite thermography for landscape scale soil salinity assessment of cropped areas were studied. The performance of satellite thermography is compared with other approaches that have been used before, like Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The study areas were Syrdarya province of Uzbekistan and four study areas in four Australian states namely, Western Australia, South Australia, Queensland and New South Wales. The diversity of the study areas allowed us to analyse behaviour of canopy temperature of different crops (wheat, cotton, barley) and different agriculture practices (rain fed and irrigated). MODIS and Landsat TM multiannual satellite images were used to measure canopy temperature. As ground truth for Uzbekistan study area we used a provincial soil salinity map. For the Australian study areas we used the EC map for the whole country. ANOVA was used to analyse relations between the soil salinity maps and canopy temperature, NDVI, EVI. Time series graphs were created to analyse the dynamics of the indicators during the growing season. The results showed significant relations between the soil salinity maps and canopy temperature. The amplitude of canopy temperature difference between salinity classes varies for different crops, but the trend of temperature increase under increased salinity is present in all cases. The calculated F-values were higher for canopy temperature than for all other compared indicators. The vegetation indices also showed significant differences, but F-values were lower compared to canopy temperature. Also the visual comparison of the soil salinity map and the canopy temperature map show similar spatial patterns. The NDVI and EVI maps look more random and noisy and patterns are less pronounced than for the canopy temperature map. The strongest relation between the soil salinity map and canopy temperature was usually observed at the end of a dry season and in the period of maximum crop development. Satellite thermography appeared to be a valuable approach to detect soil salinity under agricultural crops at landscape scale.

Estimation of regional differences in wind erosion sensitivity in Hungary

NASA Astrophysics Data System (ADS)

Mezősi, G.; Blanka, V.; Bata, T.; Kovács, F.; Meyer, B.

2015-01-01

In Hungary, wind erosion is one of the most serious natural hazards. Spatial and temporal variation in the factors that determine the location and intensity of wind erosion damage are not well known, nor are the regional and local sensitivities to erosion. Because of methodological challenges, no multi-factor, regional wind erosion sensitivity map is available for Hungary. The aim of this study was to develop a method to estimate the regional differences in wind erosion sensitivity and exposure in Hungary. Wind erosion sensitivity was modelled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available data sets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities.

Spectral signature selection for mapping unvegetated soils

NASA Technical Reports Server (NTRS)

May, G. A.; Petersen, G. W.

1975-01-01

Airborne multispectral scanner data covering the wavelength interval from 0.40-2.60 microns were collected at an altitude of 1000 m above the terrain in southeastern Pennsylvania. Uniform training areas were selected within three sites from this flightline. Soil samples were collected from each site and a procedure developed to allow assignment of scan line and element number from the multispectral scanner data to each sampling location. These soil samples were analyzed on a spectrophotometer and laboratory spectral signatures were derived. After correcting for solar radiation and atmospheric attenuation, the laboratory signatures were compared to the spectral signatures derived from these same soils using multispectral scanner data. Both signatures were used in supervised and unsupervised classification routines. Computer-generated maps using the laboratory and multispectral scanner derived signatures resulted in maps that were similar to maps resulting from field surveys. Approximately 90% agreement was obtained between classification maps produced using multispectral scanner derived signatures and laboratory derived signatures.

The Impacts of Heating Strategy on Soil Moisture Estimation Using Actively Heated Fiber Optics.

PubMed

Dong, Jianzhi; Agliata, Rosa; Steele-Dunne, Susan; Hoes, Olivier; Bogaard, Thom; Greco, Roberto; van de Giesen, Nick

2017-09-13

Several recent studies have highlighted the potential of Actively Heated Fiber Optics (AHFO) for high resolution soil moisture mapping. In AHFO, the soil moisture can be calculated from the cumulative temperature ( T cum ), the maximum temperature ( T max ), or the soil thermal conductivity determined from the cooling phase after heating ( λ ). This study investigates the performance of the T cum , T max and λ methods for different heating strategies, i.e., differences in the duration and input power of the applied heat pulse. The aim is to compare the three approaches and to determine which is best suited to field applications where the power supply is limited. Results show that increasing the input power of the heat pulses makes it easier to differentiate between dry and wet soil conditions, which leads to an improved accuracy. Results suggest that if the power supply is limited, the heating strength is insufficient for the λ method to yield accurate estimates. Generally, the T cum and T max methods have similar accuracy. If the input power is limited, increasing the heat pulse duration can improve the accuracy of the AHFO method for both of these techniques. In particular, extending the heating duration can significantly increase the sensitivity of T cum to soil moisture. Hence, the T cum method is recommended when the input power is limited. Finally, results also show that up to 50% of the cable temperature change during the heat pulse can be attributed to soil background temperature, i.e., soil temperature changed by the net solar radiation. A method is proposed to correct this background temperature change. Without correction, soil moisture information can be completely masked by the background temperature error.

The Impacts of Heating Strategy on Soil Moisture Estimation Using Actively Heated Fiber Optics

PubMed Central

Dong, Jianzhi; Agliata, Rosa; Steele-Dunne, Susan; Hoes, Olivier; Bogaard, Thom; Greco, Roberto; van de Giesen, Nick

2017-01-01

Several recent studies have highlighted the potential of Actively Heated Fiber Optics (AHFO) for high resolution soil moisture mapping. In AHFO, the soil moisture can be calculated from the cumulative temperature (Tcum), the maximum temperature (Tmax), or the soil thermal conductivity determined from the cooling phase after heating (λ). This study investigates the performance of the Tcum, Tmax and λ methods for different heating strategies, i.e., differences in the duration and input power of the applied heat pulse. The aim is to compare the three approaches and to determine which is best suited to field applications where the power supply is limited. Results show that increasing the input power of the heat pulses makes it easier to differentiate between dry and wet soil conditions, which leads to an improved accuracy. Results suggest that if the power supply is limited, the heating strength is insufficient for the λ method to yield accurate estimates. Generally, the Tcum and Tmax methods have similar accuracy. If the input power is limited, increasing the heat pulse duration can improve the accuracy of the AHFO method for both of these techniques. In particular, extending the heating duration can significantly increase the sensitivity of Tcum to soil moisture. Hence, the Tcum method is recommended when the input power is limited. Finally, results also show that up to 50% of the cable temperature change during the heat pulse can be attributed to soil background temperature, i.e., soil temperature changed by the net solar radiation. A method is proposed to correct this background temperature change. Without correction, soil moisture information can be completely masked by the background temperature error. PMID:28902141

Comparison of soil pollution concentrations determined using AAS and portable XRF techniques.

PubMed

Radu, Tanja; Diamond, Dermot

2009-11-15

Past mining activities in the area of Silvermines, Ireland, have resulted in heavily polluted soils. The possibility of spreading pollution to the surrounding areas through dust blow-offs poses a potential threat for the local communities. Conventional environmental soil and dust analysis techniques are very slow and laborious and consequently there is a need for fast and accurate analytical methods, which can provide real-time in situ pollution mapping. Laboratory-based aqua regia acid digestion of the soil samples collected in the area followed by the atomic absorption spectrophotometry (AAS) analysis confirmed very high pollution, especially by Pb, As, Cu, and Zn. In parallel, samples were analyzed using portable X-ray fluorescence radioisotope and miniature tube powered (XRF) NITON instruments and their performance was compared. Overall, the portable XRF instrument gave excellent correlation with the laboratory-based reference AAS method.

Distribution of Heavy Metal Pollution in Surface Soil Samples in China: A Graphical Review.

PubMed

Duan, Qiannan; Lee, Jianchao; Liu, Yansong; Chen, Han; Hu, Huanyu

2016-09-01

Soil pollution in China is one of most wide and severe in the world. Although environmental researchers are well aware of the acuteness of soil pollution in China, a precise and comprehensive mapping system of soil pollution has never been released. By compiling, integrating and processing nearly a decade of soil pollution data, we have created cornerstone maps that illustrate the distribution and concentration of cadmium, lead, zinc, arsenic, copper and chromium in surficial soil across the nation. These summarized maps and the integrated data provide precise geographic coordinates and heavy metal concentrations; they are also the first ones to provide such thorough and comprehensive details about heavy metal soil pollution in China. In this study, we focus on some of the most polluted areas to illustrate the severity of this pressing environmental problem and demonstrate that most developed and populous areas have been subjected to heavy metal pollution.

Evaluation of Soil Contamination Indices in a Mining Area of Jiangxi, China

PubMed Central

Wu, Jin; Teng, Yanguo; Lu, Sijin; Wang, Yeyao; Jiao, Xudong

2014-01-01

There is currently a wide variety of methods used to evaluate soil contamination. We present a discussion of the advantages and limitations of different soil contamination assessment methods. In this study, we analyzed seven trace elements (As, Cd, Cr, Cu, Hg, Pb, and Zn) that are indicators of soil contamination in Dexing, a city in China that is famous for its vast nonferrous mineral resources in China, using enrichment factor (EF), geoaccumulation index (Igeo), pollution index (PI), and principal component analysis (PCA). The three contamination indices and PCA were then mapped to understand the status and trends of soil contamination in this region. The entire study area is strongly enriched in Cd, Cu, Pb, and Zn, especially in areas near mine sites. As and Hg were also present in high concentrations in urban areas. Results indicated that Cr in this area originated from both anthropogenic and natural sources. PCA combined with Geographic Information System (GIS) was successfully used to discriminate between natural and anthropogenic trace metals. PMID:25397401

Estimating Surface Soil Moisture in a Mixed-Landscape using SMAP and MODIS/VIIRS Data

NASA Astrophysics Data System (ADS)

Tang, J.; Di, L.; Xiao, J.

2017-12-01

Soil moisture, a critical parameter of earth ecosystem linking land surface and atmosphere, has been widely applied in many application (Di, 1991; Njoku et al. 2003; Western 2002; Zhao et al. 2014; McColl et al. 2017) from regional to continental or even global scale. The advent of satellite-based remote sensing, particular in the last two decades, has proven successful for mapping the surface soil moisture (SSM) from space (Petropoulos et al. 2015; Kim et al. 2015; Molero et al. 2016). The current soil moisture products, however, is not able to fully characterize the spatial and temporal variability of soil moisture at mixed landscape types (Albergel et al. 2013; Zeng et al. 2015). In this research, we derived the SSM at 1-km spatial resolution by using sensor observation and high-level products from SMAP and MODIS/VIIRS as well as metrorological, landcover, and soil data. Specifically, we proposed a practicable method to produce the originally planned SMAP L3_SM_A with comparable quality by downscaling the SMAP L3_SM_P product through a proved method, the geographically weighted regression method at mixed landscape in southern New Hampshire. This estimated SSM was validated using the Soil Climate Analysis Network (SCAN) from Natural Resources Conservation Service (NRCS) of United States Department of Agriculture (USDA).

Alternate data sources for soil surveys on rangeland

USGS Publications Warehouse

Horvath, Emil H.; Klingebiel, A.A.; Moore, D.G.; Fosnight, E.A.

1983-01-01

the feasibility of using this approach for producing physiographic maps as an aid for mapping soils and range sites. The project is a cooperative investigation of the Earth Resources Observation Systems Data Center of the U.S. Geological Survey, the Soil Conservation Service, and the Bureau of Land Management.

Multi-scale soil salinity mapping and monitoring with proximal and remote sensing

USDA-ARS?s Scientific Manuscript database

This talk is part of a technical short course on “Soil mapping and process modelling at diverse scales”. In the talk, guidelines, special considerations, protocols, and strengths and limitations are presented for characterizing spatial and temporal variation in soil salinity at several spatial scale...

Digital Soil Mapping – A platform for enhancing soil learning.

USDA-ARS?s Scientific Manuscript database

The expansion of digital infrastructure and tools has generated massive data and information as well as a need for reliable processing and accurate interpretations. Digital Soil Mapping is no exception in that it has provided opportunities for professionals and the public to interact at field and tr...

Mapping Soil Organic Matter with Hyperspectral Imaging

NASA Astrophysics Data System (ADS)

Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel

2014-05-01

Soil organic matter (SOM) plays a central role for both food security and the global environment. Soil organic matter is the 'glue' that binds soil particles together, leading to positive effects on soil water and nutrient availability for plant growth and helping to counteract the effects of erosion, runoff, compaction and crusting. Hyperspectral measurements of samples of soil profiles have been conducted with the aim of mapping soil organic matter on a macroscopic scale (millimeters and centimeters). Two soil profiles have been selected from the same experimental site, one from a plot amended with biochar and another one from a control plot, with the specific objective to quantify and map the distribution of biochar in the amended profile. The soil profiles were of size (30 x 10 x 10) cm3 and were scanned with two pushbroomtype hyperspectral cameras, one which is sensitive in the visible wavelength region (400 - 1000 nm) and one in the near infrared region (1000 - 2500 nm). The images from the two detectors were merged together into one full dataset covering the whole wavelength region. Layers of 15 mm were removed from the 10 cm high sample such that a total of 7 hyperspectral images were obtained from the samples. Each layer was analyzed with multivariate statistical techniques in order to map the different components in the soil profile. Moreover, a 3-dimensional visalization of the components through the depth of the sample was also obtained by combining the hyperspectral images from all the layers. Mid-infrared spectroscopy of selected samples of the measured soil profiles was conducted in order to correlate the chemical constituents with the hyperspectral results. The results show that hyperspectral imaging is a fast, non-destructive technique, well suited to characterize soil profiles on a macroscopic scale and hence to map elements and different organic matter quality present in a complete pedon. As such, we were able to map and quantify biochar in our profile. Smaller interesting regions can also easily be selected from the hyperspectral images for more detailed study at microscopic scale.

Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard.

PubMed

Liu, Rui; Chen, Yun; Wu, Jianping; Gao, Lei; Barrett, Damian; Xu, Tingbao; Li, Xiaojuan; Li, Linyi; Huang, Chang; Yu, Jia

2017-04-01

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies. © 2016 Society for Risk Analysis.

Proposed method for hazard mapping of landslide propagation zone

NASA Astrophysics Data System (ADS)

Serbulea, Manole-Stelian; Gogu, Radu; Manoli, Daniel-Marcel; Gaitanaru, Dragos Stefan; Priceputu, Adrian; Andronic, Adrian; Anghel, Alexandra; Liviu Bugea, Adrian; Ungureanu, Constantin; Niculescu, Alexandru

2013-04-01

Sustainable development of communities situated in areas with landslide potential requires a fully understanding of the mechanisms that govern the triggering of the phenomenon as well as the propagation of the sliding mass, with catastrophic consequences on the nearby inhabitants and environment. Modern analysis methods for areas affected by the movement of the soil bodies are presented in this work, as well as a new procedure to assess the landslide hazard. Classical soil mechanics offer sufficient numeric models to assess the landslide triggering zone, such as Limit Equilibrium Methods (Fellenius, Janbu, Morgenstern-Price, Bishop, Spencer etc.), blocks model or progressive mobilization models, Lagrange-based finite element method etc. The computation methods for assessing the propagation zones are quite recent and have high computational requirements, thus not being sufficiently used in practice to confirm their feasibility. The proposed procedure aims to assess not only the landslide hazard factor, but also the affected areas, by means of simple mathematical operations. The method can easily be employed in GIS software, without requiring engineering training. The result is obtained by computing the first and second derivative of the digital terrain model (slope and curvature maps). Using the curvature maps, it is shown that one can assess the areas most likely to be affected by the propagation of the sliding masses. The procedure is first applied on a simple theoretical model and then used on a representative section of a high exposure area in Romania. The method is described by comparison with Romanian legislation for risk and vulnerability assessment, which specifies that the landslide hazard is to be assessed, using an average hazard factor Km, obtained from various other factors. Following the employed example, it is observed that using the Km factor there is an inconsistent distribution of the polygonal surfaces corresponding to different landslide potential. For small values of Km (0.00..0.10) the polygonal surfaces have reduced dimensions along the slopes belonging to main rivers. This can be corrected by including in the analysis the potential areas to be affected by soil instability. Finally, it is shown that the proposed procedure can be used to better assess these areas and to produce more reliable landslide hazard maps. This work was supported by a grant of the Romanian National Authority for Scientific Research, Program for research - Space Technology and Advanced Research - STAR, project number 30/2012.

Soils and the soil cover of the Valley of Geysers

NASA Astrophysics Data System (ADS)

Kostyuk, D. N.; Gennadiev, A. N.

2014-06-01

The results of field studies of the soil cover within the tourist part of the Valley of Geysers in Kamchatka performed in 2010 and 2011 are discussed. The morphology of soils, their genesis, and their dependence on the degree of hydrothermal impact are characterized; the soil cover patterns developing in the valley are analyzed. On the basis of the materials provided by the Kronotskii Biospheric Reserve and original field data, the soil map of the valley has been developed. The maps of vegetation conditions, soil temperature at the depth of 15 cm, and slopes of the surface have been used for this purpose together with satellite imagery and field descriptions of reference soil profiles. The legend to the soil map includes nine soil units and seven units of parent materials and their textures. Soil names are given according to the classification developed by I.L. Goldfarb (2005) for the soils of hydrothermal fields. The designation of soil horizons follows the new Classification and Diagnostic System of Russian Soils (2004). It is suggested that a new horizon—a thermometamorphic horizon TRM—can be introduced into this system by analogy with other metamorphic (transformed in situ) horizons distinguished in this system. This horizon is typical of the soils partly or completely transformed by hydrothermal impacts.

Mapping of available heavy metals in Catamarca (Argentina)

NASA Astrophysics Data System (ADS)

Roca, N.; Pazos, M. S.; Bech, J.

2009-04-01

Copper, iron, manganese and zinc are four essential elements for plant growth. Mapping heavy metal migration and distribution in soils is a preliminary step in assessing heavy metal availability in soils. However, data of qualitative and quantitative trace elements composition of soils of Argentina are scarce. Despite the small amounts required by plants, agricultural soils are usually deficient in one or more micronutrients, therefore, their concentration in plant tissues falls below the levels that allow optimal growth. Soil nature plays a fundamental role in the availability of micronutrients and their behaviour at a soil-plant level. The aim of this study is to determine the plant availability and areas of deficiency in agricultural soils with risk of salinization. The presented maps have been elaborated on the basis of the information provided by the monochromatic aerial photographs, scale 1:7000 and projected using the topographic information of the National Topographic Maps. Soils were sampled according to the spatial variation of soil types and land use. Sampling points were geo-referenced. Soil samples were analyzed at the laboratory for complete physicochemical and mineralogical characteristics. The percentage of organic matter is the determining factor in the presence and distribution of the available metals in the soils of the studied area, being the top horizon the one of greatest accumulation. CuDTPA, FeDPTA and MnDPTA are mobile within the profile, whereas ZnDPTA remains adsorbed without vertical displacement. ZnDTPA is the only available metal which also shows differences due to soil salinity and textural classes. However, soil geochemical conditions imply low extractability and a certain difficulty for micronutrient absorption by plants.

Variations in Soil Carbon and Nitrogen Stocks of Deep Profile Following Re-vegetation along Precipitation Gradient in the Loess Plateau of China

NASA Astrophysics Data System (ADS)

Tuo, D.; Gao, G.; Fu, B.

2017-12-01

Precipitation is one of the most important limit factor affect soil organic carbon (SOC) and total nitrogen (TN) following re-vegetation; however, the effect of precipitation on the C and N cycling in deep soils is poorly understood. This study was designed to measure SOC and TN stocks and C/N ratio to a depth of 300 cm following re-vegetation along a precipitation gradient (280 to 540 mm yr-1) on the Loess Plateau of China. The results showed that the relationship of soil C-N coupling after cropland abandoned was related to mean annual precipitation (MAP) and soil depth. SOC and TN stocks in the shallow layers of 0-100 cm were 3.8 and 0.41 kg m-2, respectively, and that in the deep layers of 100-300 cm can represent about 62.7-72.5% and 60.2-88.7% to a depth of 0-300 cm, respectively. Positive linearly relationships were obtained between MAP and SOC and TN stocks at most soil layers of 0-300 cm (p < 0.05). The relationships between the MAP and changes of SOC and TN stocks following short-term restoration were highly dependent on soil depth. Changes of SOC and TN stocks after re-vegetation in shallow soils (0-100 cm) were gaining at regional scale, but in deep soils (100-300 cm), which were losing at wetter sites (MAP > 400 mm). The initial soil C loss may be attributed to greater C decomposition and lower belowground C input. The change of C/N ratio had significantly negatively correlation with MAP in each soil depth, except for 0-20 cm, indicating the effect of soil N on C accumulation is higher at drier areas rather than wetter sites. Based on the investigated factors, precipitation, soil water and clay had a dominant control over the spatial distribution of SOC, TN and C/N ratio to a 300 cm soil depth. This information is helpful our understanding of the dynamics of soil C and N of deep soils following re-vegetation in the semiarid regions.

Application of Remote Sensing Data to Improve the Water and Soil Resource Management of Rwanda

NASA Astrophysics Data System (ADS)

Csorba, Ádám; Bukombe, Benjamin; Naramabuye, Francois Xavier; Szegi, Tamás; Vekerdy, Zoltán; Michéli, Erika

2017-04-01

The Rwandan agriculture strongly relies in the dry seasons on the water stored in artificial reservoirs of various sizes for irrigation purposes. Furthermore, the success of irrigation depends on a wide range of soil properties which directly affect the moisture regime of the growing medium. By integrating remote sensing and auxiliary data the objectives of our study are to monitor the water level fluctuation in the reservoirs, estimate the volume of water available for irrigation and to combine this information with soil property maps to support the decision making for sustainable irrigation water management in a study area in Southern Rwanda. For water level and volume estimation a series of Sentinel-1 (product type: GRD, acquisition mode: IW, polarizations HH and VH) data were obtained covering the study area and spanning over a period of two years. To map the extent of water bodies the Radar-Based Water Body Mapping module of the Water Observation and Information System (WOIS) was used. High-resolution optical data (Sentinel-2) were used for validation in cloud-free periods. To estimate the volume changes in the reservoirs, we combined the information derived from the water body mapping procedure and digital elevation models. For sustainable irrigation water management, digital soil property maps were developed by the application of wide range of environmental covariates related to soil forming factors. To develop covariates which represent the land use a time series analysis of the 2 years of Sentinel-1 data was performed. As auxiliary soil data, the ISRIC-WISE harmonized soil profile database was used. The developed digital soil mapping approach is integrated into a new WOIS workflow.

Regional-scale assessment of soil salinity in the Red River Valley using multi-year MODIS EVI and NDVI.

PubMed

Lobell, D B; Lesch, S M; Corwin, D L; Ulmer, M G; Anderson, K A; Potts, D J; Doolittle, J A; Matos, M R; Baltes, M J

2010-01-01

The ability to inventory and map soil salinity at regional scales remains a significant challenge to scientists concerned with the salinization of agricultural soils throughout the world. Previous attempts to use satellite or aerial imagery to assess soil salinity have found limited success in part because of the inability of methods to isolate the effects of soil salinity on vegetative growth from other factors. This study evaluated the use of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in conjunction with directed soil sampling to assess and map soil salinity at a regional scale (i.e., 10-10(5) km(2)) in a parsimonious manner. Correlations with three soil salinity ground truth datasets differing in scale were made in Kittson County within the Red River Valley (RRV) of North Dakota and Minnesota, an area where soil salinity assessment is a top priority for the Natural Resource Conservation Service (NRCS). Multi-year MODIS imagery was used to mitigate the influence of temporally dynamic factors such as weather, pests, disease, and management influences. The average of the MODIS enhanced vegetation index (EVI) for a 7-yr period exhibited a strong relationship with soil salinity in all three datasets, and outperformed the normalized difference vegetation index (NDVI). One-third to one-half of the spatial variability in soil salinity could be captured by measuring average MODIS EVI and whether the land qualified for the Conservation Reserve Program (a USDA program that sets aside marginally productive land based on conservation principles). The approach has the practical simplicity to allow broad application in areas where limited resources are available for salinity assessment.

Factors influencing in situ gamma-ray measurements

NASA Astrophysics Data System (ADS)

Loonstra, E. H.; van Egmond, F. M.

2009-04-01

Introduction In situ passive gamma-ray sensors are very well suitable for mapping physical soil properties. In order to make a qualitative sound soil map, high quality input parameters for calibration are required. This paper will focus on the factors that affect the output of in situ passive gamma-ray sensors, the primary source, soil, not taken into account. Factors The gamma-ray spectrum contains information of naturally occurring nuclides 40K, 238U and 232Th and man-made nuclides like 137Cs, as well as the total count rate. Factors that influence the concentration of these nuclides and the count rate can be classified in 3 categories. These are sensor design, environmental conditions and operational circumstances. Sensor design The main elements of an in situ gamma-ray sensor that influence the outcome and quality of the output are the crystal and the spectrum analysis method. Material and size of the crystal determine the energy resolution. Though widely used, NaI crystals are not the most efficient capturer of gamma radiation. Alternatives are BGO and CsI. BGO has a low peak resolution, which prohibits use in cases where man-made nuclides are subject of interest. The material is expensive and prone to temperature instability. CsI is robust compared to NaI and BGO. The density of CsI is higher than NaI, yielding better efficiency, especially for smaller crystal sizes. More volume results in higher energy efficiency. The reduction of the measured spectral information into concentration of radionuclides is mostly done using the Windows analysis method. In Windows, the activities of the nuclides are found by summing the intensities of the spectrum found in a certain interval surrounding a peak. A major flaw of the Windows method is the limited amount of spectral information that is incorporated into the analysis. Another weakness is the inherent use of ‘stripping factors' to account for contributions of radiation from nuclide A into the peak of nuclide B. This can be overcome using Full Spectrum Analysis (FSA). This method incorporates virtually all data present in the measured gamma spectrum. In FSA, a Chi-squared algorithm is used to fit a set of "Standard Spectra" to the measured spectrum. The uncertainty in the FSA method is at least a factor 2 lower compared to the Windows method. Environmental conditions Environmental conditions can influence the signal output and therefore the quality. In general, the density of the medium through which gamma-radiation travels determines the interaction of the radiation with matter and thus affects the sensor readings. Excluding soil as being the source; water is the most important external factor in this respect. The amount of water in soil will affect the signal. In general, energy loss occurs as water content in soil increases. As a result, the nuclide concentrations will be lower. Monte Carlo simulations show a difference of 16% in nuclide concentration for completely dry and fully saturated sandy soils. Another water related issue is rainfall. With rain radon gas, a product of 238U, will precipitate. This causes spectral noise effects. Snow and fog have the same effect to a minor degree. Another aspect is the openness of soil. From experience we know that the concentration of 40K differs if soil is tilled. Finally, on earth there is always radioactive noise present from the galaxy. The "Standard Spectra" used in the FSA method can take noise and geometric effects into account. Operational circumstances During a survey an operator should be aware of the effects of driving speed and measurement height. In general, a larger crystal has better energy efficiency and is therefore more suitable for high speed. E.g. a 70 x 150 mm CsI crystal provides qualitative satisfactory output for soil mapping up to 10 km/hr. Sample locations, however, are best measured during a longer period (3 to 5 minutes). The measurement height affects the measurement resolution; the lower the sensor, the smaller the measured area. In addition, Monte Carlo simulations show that the mass of the survey vehicle has to be taken into account. A large tractor e.g. can lead to spectral shape disruptions of 5 to 10%. Conclusion Several factors can effect the measured concentrations of radioactive nuclides. Potential users should take mentioned factors into account in respect to their needs when building and applying gamma-ray sensors. For purposes of soil mapping the use of FSA method is preferred, as it incorporates several disturbing factors. This allows the user to create a consistent dataset which will lead to a better understanding of the relation between nuclide concentrations and physical soil properties.

Land Ecology Essay I: The siren song of the finish line

USDA-ARS?s Scientific Manuscript database

As the National Cooperative Soils Survey nears the completion of initial mapping and description activities, the options for next steps are being considered. One option is to deploy new and emerging mapping technologies for existing and refined concepts of soil behavior to create more precise maps ...

Dynamic prescription maps for site-specific variable rate irrigation of cotton

USDA-ARS?s Scientific Manuscript database

A prescription map is a set of instructions that controls a variable rate irrigation (VRI) system. These maps, which may be based on prior yield, soil texture, topography, or soil electrical conductivity data, are often manually applied at the beginning of an irrigation season and remain static. The...

Microzonation Studies In District of Dikili, Izmir (Turkey) In The Context of Social Responsibility by Using GIS Tecniques

NASA Astrophysics Data System (ADS)

Karabulut, Savas; Cinku, Mualla; Tezel, Okan; Hisarli, Mumtaz; Ozcep, Ferhat; Tun, Muammer; Avdan, Ugur; Ozel, Oguz; Acikca, Ahmet; Aygordu, Ozan; Benli, Aral; Kesisyan, Arda; Yilmaz, Hakan; Varici, Cagri; Ozturkan, Hasan; Ozcan, Cuneyt; Kivrak, Ali

2015-04-01

Social Responsibility Projects (SRP) are important tools in contributing to the development of communities and applied educational science. Researchers dealing with engineering studies generally focus on technical specifications. However, when the subject depends on earthquake, engineers should be consider also social and educational components, besides the technical aspects. If scientific projects collaborated with municipalities of cities, it should be known that it will reach a wide range of people. Turkey is one of the most active region that experienced destructive earthquakes. The 1999 Marmara earthquake was responsible for the loose of more than 18.000 people. The destructive damage occurred on buildings that made on problematic soils. This however, is still the one of most important issues in Turkey which needs to be solved. Inspite of large earthquakes that occurred along the major segments of the North and East Anatolian Fault Zones due to the northwards excursion of Anatolia, the extensional regime in the Aegean region is also characterized by earthquakes that occurred with the movement of a number of strike slip and normal faults. The Dikili village within the Eastern Aegean extensional region experienced a large earthquake in 1939 (M: 6.8). The seismic activity is still characterised by high level and being detected. A lot of areas like the Kabakum village have been moved to its present location during this earthquake. The probability of an earthquake hazard in Dikili is considerably high level, today. Therefore, it is very important to predict the soil behaviour and engineering problems by using Geographic Information System (GIS) tools in this area. For this purpose we conducted a project with the collaboration of the Dikili Municipality in İzmir (Turkey) to determine the following issues: a) Possible disaster mitigation as a result of earthquake-soil-structure interaction, b) Geo-enginnering problems (i.e: soil liquefaction, soil settlement, soil bearing capacity, soil amplification), c) The basin structure and possible fault of the Dikili district, d) Risk analysis on cultivated areas due to salty water injection, e) The tectonic activity of the study area from Miocene to present. During this study a number of measurements were carried out to solve the problems defined above. These measurements include; microtremor single station (H/V) method according to Nakamura's technique, which is applied at 222 points. The results provide maps of soil fundamental frequency, soil amplification and soil sedimentary thickness by using developed amprical relationships. Spatial Autocorrelation Technique (SPAC) was carried out in 11 sites with Guralp CG-5 seismometer to predict the shear wave velocity-depth model towards the sismological bedrock. Multi-channel analysis of Surface Wave (MASW), Microtremor Array Method (MAM) and Seismic Refraction Method were applied at 121 sites with SARA-Doremi Seismograph. The soil liquefaction-induced settlements are determined in the frame of shallow soil engineering problems. Vertical Electrical Sounding (VES) was carried out to define the presence of saltly and drinkable and hot/cold underground water, the location of possible faults and the bedrock depth which was estimated with a Scientrex Saris Resistivity Equipment. To define the areas which are influenced by salty water, induced polarization (IP) method was applied at 34 sites. The basin structure and the probably faults of the study area were determined by applying gravity measurements on 248 points with a CG-5 Autogravity meter. Evaluation of the combined data is very important for producing microzonation maps. We therefore integrated all of the data into the GIS database and prepared large variety of maps.

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

PubMed Central

Zhang, Jialin; Li, Xiuhong; Yang, Rongjin; Liu, Qiang; Zhao, Long; Dou, Baocheng

2017-01-01

In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation. PMID:28617351

Digital soil mapping in assessment of land suitability for organic farming

NASA Astrophysics Data System (ADS)

Ghambashidze, Giorgi; Kentchiashvili, Naira; Tarkhnishvili, Maia; Jolokhava, Tamar; Meskhi, Tea

2017-04-01

Digital soil mapping (DSM) is a fast-developing sub discipline of soil science which gets more importance along with increased availability of spatial data. DSM is based on three main components: the input in the form of field and laboratory observational methods, the process used in terms of spatial and non-spatial soil inference systems, and the output in the form of spatial soil information systems, which includes outputs in the form of rasters of prediction along with the uncertainty of prediction. Georgia is one of the countries who are under the way of spatial data infrastructure development, which includes soil related spatial data also. Therefore, it is important to demonstrate the capacity of DSM technics for planning and decision making process, in which assessment of land suitability is a major interest for those willing to grow agricultural crops. In that term land suitability assessment for establishing organic farms is in high demand as market for organically produced commodities is still increasing. It is the first attempt in Georgia to use DSM to predict areas with potential for organic farming development. Current approach is based on risk assessment of soil pollution with toxic elements (As, Hg, Pb, Cd, Cr) and prediction of bio-availability of those elements to plants on example of the region of Western Georgia, where detailed soil survey was conducted and spatial database of soil was created. The results of the study show the advantages of DSM at early stage assessment and depending on availability and quality of the input data, it can achieve acceptable accuracy.

Soil anomaly mapping using a caesium magnetometer: Limits in the low magnetic amplitude case

NASA Astrophysics Data System (ADS)

Mathé, Vivien; Lévêque, François; Mathé, Pierre-Etienne; Chevallier, Claude; Pons, Yves

2006-03-01

Caesium magnetometers are new tools for soil property mapping with a decimetric resolution [Mathé, V., Lévêque, F., 2003. High resolution magnetic survey for soil monitoring: detection of drainage and soil tillage effects. Earth and Planetary Science Letters 212 (1-2), 241-251]. However, when the magnetic anomalies are only a few nanoteslas (nT), the geologic and pedogenic signal must first be isolated from magnetic disturbances for this method to be useful. This paper investigates the instrumental artifacts and environmental disturbances to adapt the survey protocol to slightly magnetic soils. Among the possible instrumental sources of disturbances listed and quantified, the most significant are: 1) The battery effect upon sensors 2 m away (classic protocol, about ± 0.15 nT) while increasing this distance up to 10 m cancelled it; 2) The noise level of magnetometers and sensors, which, according to tests on two magnetometers and three sensors, rarely and randomly exceeds 0.1 nT, but seems to increase with the electronic component age. Among the environmental disturbances, temporal variations such as diurnal variation or fluctuations linked to the moving of metallic masses play a major role, although the pseudogradient or base-station methods have commonly cancelled them. The efficiency of the latter is strongly dependent on the source nature. However, the ground currents and electromagnetic fields propagating in soils cause more problems. As a first step to better understand such disturbance sources, uncommon magnetic signal variations supposedly due to electromagnetic wave conversions and likely linked to the railway traffic are presented. Based on previous results, an adapted protocol using one magnetometer and two caesium sensors (0.3 and 1.6 m above the surface) is proposed to increase the signal / noise ratio. At first, to maintain an accurate horizontal and vertical location of the sensors, the latter are affixed to a wooden handcart running on plastic rails. Rails adapt to micro-topography, thereby decreasing strongly the soil-sensors distance variations. Anomalies due to topography rarely exceed 0.1 nT. Finally, a method to remove diurnal variations from high-resolution magnetic maps is proposed. Parallel profiles performed successively are adjusted by a cross-profile. Assuming that the temporal variations during each profile are negligible (less than 0.05 nT), this technique, contrary to the pseudogradient, preserves both the decimetric and the metric anomalies (gain of more than 1 nT).

Mars Aqueous Processing System

NASA Technical Reports Server (NTRS)

Berggren, Mark; Wilson, Cherie; Carrera, Stacy; Rose, Heather; Muscatello, Anthony; Kilgore, James; Zubrin, Robert

2012-01-01

The goal of the Mars Aqueous Processing System (MAPS) is to establish a flexible process that generates multiple products that are useful for human habitation. Selectively extracting useful components into an aqueous solution, and then sequentially recovering individual constituents, can obtain a suite of refined or semi-refined products. Similarities in the bulk composition (although not necessarily of the mineralogy) of Martian and Lunar soils potentially make MAPS widely applicable. Similar process steps can be conducted on both Mars and Lunar soils while tailoring the reaction extents and recoveries to the specifics of each location. The MAPS closed-loop process selectively extracts, and then recovers, constituents from soils using acids and bases. The emphasis on Mars involves the production of useful materials such as iron, silica, alumina, magnesia, and concrete with recovery of oxygen as a byproduct. On the Moon, similar chemistry is applied with emphasis on oxygen production. This innovation has been demonstrated to produce high-grade materials, such as metallic iron, aluminum oxide, magnesium oxide, and calcium oxide, from lunar and Martian soil simulants. Most of the target products exhibited purities of 80 to 90 percent or more, allowing direct use for many potential applications. Up to one-fourth of the feed soil mass was converted to metal, metal oxide, and oxygen products. The soil residue contained elevated silica content, allowing for potential additional refining and extraction for recovery of materials needed for photovoltaic, semiconductor, and glass applications. A high-grade iron oxide concentrate derived from lunar soil simulant was used to produce a metallic iron component using a novel, combined hydrogen reduction/metal sintering technique. The part was subsequently machined and found to be structurally sound. The behavior of the lunar-simulant-derived iron product was very similar to that produced using the same methods on a Michigan iron ore concentrate, which demonstrates that lunar-derived material can be used in a manner similar to conventional terrestrial iron. Metallic iron was also produced from the Mars soil simulant. The aluminum and magnesium oxide products produced by MAPS from lunar and Mars soil simulants exhibited excellent thermal stability, and were shown to be capable of use for refractory oxide structural materials, or insulation at temperatures far in excess of what could be achieved using unrefined soils. These materials exhibited the refractory characteristics needed to support iron casting and forming operations, as well as other thermal processing needs. Extraction residue samples contained up to 79 percent silica. Such samples were successfully fused into a glass that exhibited high light transmittance.

Topographic soil wetness index derived from combined Alaska-British Columbia datasets for the Gulf of Alaska region

NASA Astrophysics Data System (ADS)

D'Amore, D. V.; Biles, F. E.

2016-12-01

The flow of water is often highlighted as a priority in land management planning and assessments related to climate change. Improved measurement and modeling of soil moisture is required to develop predictive estimates for plant distributions, soil moisture, and snowpack, which all play important roles in ecosystem planning in the face of climate change. Drainage indexes are commonly derived from GIS tools with digital elevation models. Soil moisture classes derived from these tools are useful digital proxies for ecosystem functions associated with the concentration of water on the landscape. We developed a spatially explicit topographically derived soil wetness index (TWI) across the perhumid coastal temperate rainforest (PCTR) of Alaska and British Columbia. Developing applicable drainage indexes in complex terrain and across broad areas required careful application of the appropriate DEM, caution with artifacts in GIS covers and mapping realistic zones of wetlands with the indicator. The large spatial extent of the model has facilitated the mapping of forest habitat and the development of water table depth mapping in the region. A key element of the TWI is the merging of elevation datasets across the US-Canada border where major rivers transect the international boundary. The unified TWI allows for seemless mapping across the international border and unified ecological applications. A python program combined with the unified DEM allows end users to quickly apply the TWI to all areas of the PCTR. This common platform can facilitate model comparison and improvements to local soil moisture conditions, generation of streamflow, and ecological site conditions. In this presentation we highlight the application of the TWI for mapping risk factors related to forest decline and the development of a regional water table depth map. Improved soil moisture maps are critical for deriving spatial models of changes in soil moisture for both plant growth and streamflow across future climate conditions.

A regional approach to the environmental risk assessment in the Campania region

NASA Astrophysics Data System (ADS)

Minolfi, Giulia; Albanese, Stefano; Lima, Annamaria; De Vivo, Benedetto

2016-04-01

Environmental risk assessment and analysis has a crucial role for guaranteeing the safety of the population, especially in intensive urbanized and industrialized areas, such as the Campania region (Italy). In Italy, since 2006, the human health risk assessment has become mandatory for contaminated soil and waters at contaminated sites. While traditional risk assessment procedures are usually run at site specific level (brownfields), with this work we would like to introduce a freshly developed method to assess risks at regional level by means of GIS, considering the hazard due to the presence in the environment of a contaminated media, the land use variability and the actual distribution of the population. 3535 top soils were collected across the whole Campania region (Italy) with a sampling density of 1 sample/4 km2. Samples were analyzed at ACME Analytical Lab. Ltd (Vancouver, Canada), to determine the concentration of 52 elements, with a combined methods of ICP-MS and ICP-ES following an aqua regia digestion. After a detailed statistical data analysis and geochemical mapping, we reclassified the interpolated maps of some potentially toxic elements (Sb, As, Be, Cd, Co, Cr, Hg, Ni, Pb, Se, Sn, Tl, V, Zn), in accordance with the Italian environmental law (D.Lgs 152/2006), on the base of the trigger and action limits (CSC) for human safety established by this latter. The obtained maps were summed up in the GIS environment in order to get a cumulative map of the potential hazard for the topsoils of Campania region. Considering that environmental risk for the population is strongly influenced by the exposure pathways followed by contaminants to reach the human target, in the case of Campania region we evaluated as relevant pathways both the soil/dust and food ingestion. Furthermore to consider the influence of the land use in the onset of the risk, each land use type was associated with a specific value of a Land Use Risk Coefficient (LURC) which is also dependent on pathways. Maps of regional risk for soil/dust and food ingestion were obtained by using the product of the map of the potential hazard map and the respective maps of the LURC distribution. A final comparison of the regional distribution of risk and the demographic data, at the scale of municipality, was necessary to better assess in which regional municipality a relevant potential increase of risk could occur and to rank them in a list of environmental priorities. In such priority areas follow-up investigations, with higher density sampling, have been carried out, in order to further understand the relationships between pollution and pathologies in the areas.

TRENDS IN ENGINEERING GEOLOGIC AND RELATED MAPPING.

USGS Publications Warehouse

Varnes, David J.; Keaton, Jeffrey R.

1983-01-01

Progress is reviewed that has been made during the period 1972-1982 in producing medium- and small-scale engineering geologic maps with a variety of content. Improved methods to obtain and present information are evolving. Standards concerning text and map content, soil and rock classification, and map symbols have been proposed. Application of geomorphological techniques in terrain evaluation has increased, as has the use of aerial photography and other remote sensing. Computers are being used to store, analyze, retrieve, and print both text and map information. Development of offshore resources, especially petroleum, has led to marked improvement and growth in marine engineering geology and geotechnology. Coordinated planning for societal needs has required broader scope and increased complexity of both engineering geologic and environmental geologic studies. Refs.

An evaluation of the utility of ERTS-1 data for mapping and developing natural resources of Iran

NASA Technical Reports Server (NTRS)

Ebtehadj, K. (Principal Investigator)

1974-01-01

The author has identified the following significant results. Results are reported in structural mapping leading to tectonic interpretation; in surficial deposits mapping; in analysis of salt diapirism in southwest Iran; in updating and correcting existing hydrological maps; in monitoring fluctuations of water in some intermittent lakes; in the delineation of wetland areas and the study of fluvial suspended load of the head of the Persian Gulf in relation to the fishing industry; in exercises in soil mapping; in range and agricultural surveys and inventory using multistage sampling methods, and in the computer analysis of ERTS-1 digital tapes for urban land use. The completion of a 1:1,000,000 false color photomosaic of Iran is also discussed.

Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

USGS Publications Warehouse

Hively, W. Dean; McCarty, Gregory W.; Reeves, James B.; Lang, Megan W.; Oesterling, Robert A.; Delwiche, Stephen R.

2011-01-01

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

Abiotic versus biotic controls on soil nitrogen cycling in drylands along a 3200 km transect

NASA Astrophysics Data System (ADS)

Liu, Dongwei; Zhu, Weixing; Wang, Xiaobo; Pan, Yuepeng; Wang, Chao; Xi, Dan; Bai, Edith; Wang, Yuesi; Han, Xingguo; Fang, Yunting

2017-03-01

Nitrogen (N) cycling in drylands under changing climate is not well understood. Our understanding of N cycling over larger scales to date relies heavily on the measurement of bulk soil N, and the information about internal soil N transformations remains limited. The 15N natural abundance (δ15N) of ammonium and nitrate can serve as a proxy record for the N processes in soils. To better understand the patterns and mechanisms of N cycling in drylands, we collected soils along a 3200 km transect at about 100 km intervals in northern China, with mean annual precipitation (MAP) ranging from 36 to 436 mm. We analyzed N pools and δ15N of ammonium, dual isotopes (15N and 18O) of nitrate, and the microbial gene abundance associated with soil N transformations. We found that N status and its driving factors were different above and below a MAP threshold of 100 mm. In the arid zone with MAP below 100 mm, soil inorganic N accumulated, with a large fraction being of atmospheric origin, and ammonia volatilization was strong in soils with high pH. In addition, the abundance of microbial genes associated with soil N transformations was low. In the semiarid zone with MAP above 100 mm, soil inorganic N concentrations were low and were controlled mainly by biological processes (e.g., plant uptake and denitrification). The preference for soil ammonium over nitrate by the dominant plant species may enhance the possibility of soil nitrate losses via denitrification. Overall, our study suggests that a shift from abiotic to biotic controls on soil N biogeochemistry under global climate changes would greatly affect N losses, soil N availability, and other N transformation processes in these drylands in China.

Application of digital soil mapping in Argentina: An example using apparent soil electrical conductivity

NASA Astrophysics Data System (ADS)

Domenech, Marisa; Castro Franco, Mauricio; Costa, Jose Luis; Aparicio, Virginia

2017-04-01

Apparent soil electrical conductivity (ECa) has been used to capture soil data in several Argentinean Pampas locations. The aim of this study was to generate digital soil mapping on the basis of understanding the relation among ECa and soil properties in three farming fields of the southeast Buenos Aires province. We carried out a geostatistical analysis using ECa data obtained at two depths 0-30cm (ECa_30cm) and 0-90cm (ECa_90cm). Then, two zones derived from ECa measurements were delimited in each field. A soil-sampling scheme was applied in each zone using two depths: 0-30cm and 30-90cm. Texture, Organic Matter Content (OMC), cation-exchange capacity (CEC), pH, saturated paste electrical conductivity (ECe) and effective depth were analyzed. The relation between zones and soil properties were studied using nested factor ANOVA. Our results indicated that clay content and effective depth showed significant differences among ECa_30 zones in all fields. In Argentine Pampas, the presence of petrocalcic horizons limits the effective soil depth at field scale. These horizons vary in depth, structure, hardness and carbonates content. In addition, they influence the spatial pattern of clay content. The relation among other physical and chemical soil properties was not consistent. Two soil unit maps were delimited in each field. These results might support irrigation management due to clay content and effective depth would be controlling soil water storage. Our findings highlight the high accuracy use of soil sensors in developing digital soil mapping at field scale, irrigation management zones, precision agriculture and hydrological modeling in Pampas region conditions.

Applications of Skylab EREP photographs to mapping of landforms and environmental geology in the Great Plains and Midwest. [Illinois, Iowa, Kansas, Missouri, Nebraska, and South Dakota

NASA Technical Reports Server (NTRS)

Morrison, R. B. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The utility of Skylab 2 and 3 S-190A multispectral photos for environmental-geologic/geomorphic applications is being tested by using them to prepare 1:250,000-scale maps of geomorphic features, surficial geology, geologic linear features, and soil associations of large, representative parts of the Great Plains and Midwest. Parts of Nebraska, Iowa, Missouri, and South Dakota were mapped. The maps were prepared primarily by interpretation of the S-190A photos, supplemented by information from topographic, geologic, and soil maps and reports. The color band provides the greatest information on geology, soils, and geomorphology; its resolution also is the best of all the multispectral bands and permits maximum detail of mapping. The color-IR band shows well the differences in soil drainage and moisture, and vegetative types, but has only moderate resolution. The B/W-red band is superior for topographic detail and stream alinements. The B/W-infrared bands best show differences in soil moisture and drainage but have poor resolution, especially those from SL 2. The B/W-green band generally is so low contrast and degraded by haze as to be nearly useless. Where stereoscopic coverage is provided, interpretation and mapping are done most efficiently using a Kern PG-2 stereoplotter.

Preliminary investigation of Large Format Camera photography utility in soil mapping and related agricultural applications

NASA Technical Reports Server (NTRS)

Pelletier, R. E.; Hudnall, W. H.

1987-01-01

The use of Space Shuttle Large Format Camera (LFC) color, IR/color, and B&W images in large-scale soil mapping is discussed and illustrated with sample photographs from STS 41-6 (October 1984). Consideration is given to the characteristics of the film types used; the photographic scales available; geometric and stereoscopic factors; and image interpretation and classification for soil-type mapping (detecting both sharp and gradual boundaries), soil parent material topographic and hydrologic assessment, natural-resources inventory, crop-type identification, and stress analysis. It is suggested that LFC photography can play an important role, filling the gap between aerial and satellite remote sensing.

Use of LANDSAT data to define soil boundaries in Carroll County, Missouri

NASA Technical Reports Server (NTRS)

Davidson, S. E.

1981-01-01

Bands 4, 5 and 7 false color composite photographs were prepared using data from LANDSAT scenes acquired during April 1977 and April 1981 on computer compatible tapes, and these color composites were compared with band 7 black and white photographs prepared for the entire county. Delineations of soil boundaries at the soil association level were achieved using LANDSAT spectral reflectance data and slope maps for a portion of Carroll County, Missouri. Forty two spectral reflectance classes from April 1977 LANDSAT data were overlaid on digitized slope maps of nine USGS 7.5 minute series topographic quadrangle slope maps to achieve boundary delineations of the soil associations.

A potential global soils data base

NASA Technical Reports Server (NTRS)

Stoner, E. R.; Joyce, A. T.; Hogg, H. C.

1984-01-01

A general procedure is outlined for refining the existing world soil maps from the existing 1:1 million scale to 1:250,000 through the interpretation of Landsat MSS and TM images, and the use of a Geographic Information System to relate the soils maps to available information on climate, topography, geology, and vegetation.

Introduction to the special section ‘Applications of electromagnetic induction to digital soil mapping’

USDA-ARS?s Scientific Manuscript database

Use of electromagnetic induction (EMI) instruments has increased as a tool to map soils because it provides a means of locating suitable sampling sites that provide the basis for mapping the spatial variability of various soil properties either directly or indirectly measured with EMI, including sa...

Using IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies

NASA Technical Reports Server (NTRS)

Sullivan, Dana; Shaw, Joey; Rickman, Doug

2005-01-01

Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil- mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness and tillage on our ability to depict soil property variability. Soils consisted mostly of fine-loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20 ha grids to a depth of 15 cm and analyzed for % sand (0.05 - 2 mm), silt (0.002 -0.05 mm), clay (less than 0.002 mm), citrate dithionite extractable iron (Fe(sub d)) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones compared to field scale soil property variability.

Development of risk maps to minimize uranium exposures in the Navajo Churchrock mining district

PubMed Central

2009-01-01

Background Decades of improper disposal of uranium-mining wastes on the Navajo Nation has resulted in adverse human and ecological health impacts as well as socio-cultural problems. As the Navajo people become increasingly aware of the contamination problems, there is a need to develop a risk-communication strategy to properly inform tribal members of the extent and severity of the health risks. To be most effective, this strategy needs to blend accepted risk-communication techniques with Navajo perspectives such that the strategy can be used at the community level to inform culturally- and toxicologically-relevant decisions about land and water use as well as mine-waste remediation. Objective The objective of this study was to develop GIS-based thematic maps as communication tools to clearly identify high risk exposure areas and offer alternatives to minimize public and ecological health impacts. Methods Thematic maps were produced that incorporated data derived from environmental sampling and public health surveys. The maps show the location and quality of unregulated water resources and identify regulated water sources that could be used as alternatives. In addition, the maps show the location of contaminated soil and sediment areas in which disturbance of surface deposits should be avoided. Preliminary feedback was collected from an informal Navajo working group to assess the clarity and efficacy of this proposed communication method. Results The working group found the maps to be both clear and effective, and made suggestions for improvements, such as the addition of more map features. The working group predicted that once the maps are presented to the public, water hauling and soil use behaviors will change, and dialogue with chapter officials will be initiated to accelerate further risk reduction efforts. Implications Because risk communication is complicated by language barriers, lack of infrastructure, and historical mistrust of non-Navajo researchers, mapping provides an easily interpretable medium that can be objectively viewed by community members and decision makers to evaluate activities that affect toxicant exposures. PMID:19589163

Interpolated mapping and investigation of environmental radioactivity levels in soils and mushrooms in the Middle Black Sea Region of Turkey.

PubMed

Türkekul, İbrahim; Yeşilkanat, Cafer Mert; Ciriş, Ali; Kölemen, Uğur; Çevik, Uğur

2018-06-01

The activity concentration of natural ( 238 U, 232 Th, and 40 K) and artificial ( 137 Cs) radionuclides was determined in 50 samples (obtained from the same station) from various species of mushrooms and soil collected from the Middle Black Sea Region (Turkey). The activities of 238 U, 232 Th, 40 K, and 137 Cs were found as 84 ± 16, 45 ± 14, 570 ± 28, and 64 ± 6 Bq kg -1 (dry weight), respectively, in the mushroom samples and as 51 ± 6, 41 ± 6, 201 ± 11, and 44 ± 4 Bq kg -1 , respectively, in the soil samples for the entire area of study. The results of all radionuclide activity measurements, except those of 238 U and 232 Th in the mushroom samples, are consistent with previous studies. In the soil samples, the mean values of 238 U and 232 Th are above the world mean, and the activity mean of 40 K is below the world mean. Finally, the activity estimation was made with both the soil and mushroom samples for unmeasured points within the study area by using the ordinary kriging method. Radiological distribution maps were generated.

An integrated Landsat/ancillary data classification of desert rangeland

NASA Technical Reports Server (NTRS)

Price, K. P.; Ridd, M. K.; Merola, J. A.

1985-01-01

Range inventorying methods using Landsat MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, UT. The vegetation is predominately desert shrub and annual grasses, with same annual forbs. Three Landsat scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

An integrated LANDSAT/ancillary data classification of desert rangeland

NASA Technical Reports Server (NTRS)

Price, K. P.; Ridd, M. K.; Merola, J. A.

1984-01-01

Range inventorying methods using LANDSAT MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, Utah. The vegetation is predominately desert shrub and annual grasses, with some annual forbs. Three LANDSAT scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

A study of the utilization of ERTS-1 data from the Wabash River Basin

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The identification and area estimation of crops experiment tested the usefulness of ERTS data for crop survey and produced results indicating that crop statistics could be obtained from ERTS imagery. Soil association mapping results showed that strong relationships exist between ERTS data derived maps and conventional soil maps. Urban land use analysis experiment results indicate potential for accurate gross land use mapping. Water resources mapping demonstrated the feasibility of mapping water bodies using ERTS imagery.

Combination of Geophysical Methods to Support Urban Geological Mapping

NASA Astrophysics Data System (ADS)

Gabàs, A.; Macau, A.; Benjumea, B.; Bellmunt, F.; Figueras, S.; Vilà, M.

2014-07-01

Urban geological mapping is a key to assist management of new developed areas, conversion of current urban areas or assessment of urban geological hazards. Geophysics can have a pivotal role to yield subsurface information in urban areas provided that geophysical methods are capable of dealing with challenges related to these scenarios (e.g., low signal-to-noise ratio or special logistical arrangements). With this principal aim, a specific methodology is developed to characterize lithological changes, to image fault zones and to delineate basin geometry in the urban areas. The process uses the combination of passive and active techniques as complementary data: controlled source audio-magnetotelluric method (CSAMT), magnetotelluric method (MT), microtremor H/V analysis and ambient noise array measurements to overcome the limitations of traditional geophysical methodology. This study is focused in Girona and Salt surrounding areas (NE of Spain) where some uncertainties in subsurface knowledge (maps of bedrock depth and the isopach maps of thickness of quaternary sediments) need to be resolved to carry out the 1:5000 urban geological mapping. These parameters can be estimated using this proposed methodology. (1) Acoustic impedance contrast between Neogene sediments and Paleogene or Paleozoic bedrock is detected with microtremor H/V analysis that provides the soil resonance frequency. The minimum value obtained is 0.4 Hz in Salt city, and the maximum value is the 9.5 Hz in Girona city. The result of this first method is a fast scanner of the geometry of basement. (2) Ambient noise array constrains the bedrock depth using the measurements of shear-wave velocity of soft soil. (3) Finally, the electrical resistivity models contribute with a good description of lithological changes and fault imaging. The conductive materials (1-100 Ωm) are associated with Neogene Basin composed by unconsolidated detrital sediments; medium resistive materials (100-400 Ωm) correspond to Paleogene, and resistive materials (600-1,000 Ωm) are related with complex basement, granite of Paleozoic. The Neogene basin-basement boundary is constrained between surface and 500 m depth, approximately. The new geophysical methodology presented is an optimized and fast tool to refine geological mapping by adding 2D information to traditional geological data and improving the knowledge of subsoil.

Acid sulfate soils are an environmental hazard in Finland

NASA Astrophysics Data System (ADS)

Pihlaja, Jouni

2016-04-01

Acid sulfate soils (ASS) create significant threats to the environment on coastal regions of the Baltic Sea in Finland. The sediments were deposited during the ancient Litorina Sea phase of the Baltic Sea about 7500-4500 years ago. Finland has larger spatial extent of the ASS than any other European country. Mostly based on anthropogenic reasons (cultivation, trenching etc.) ASS deposits are currently being exposed to oxygen which leads to chemical reaction creating sulfuric acid. The acidic waters then dissolve metals form the soil. Acidic surface run off including the metals are then leached into the water bodies weakening the water quality and killing fish or vegetation. In constructed areas acidic waters may corrode building materials. Geological Survey of Finland (GTK) is mapping ASS deposits in Finland. The goal is to map a total of 5 million hectares of the potentially ASS affected region. It has been estimated that the problematic Litorina Sea deposits, which are situated 0-100 m above the recent Baltic Sea shoreline, cover 500 000 hectares area. There are several phases in mapping. The work begins at the office with gathering the existing data, interpreting airborne geophysical data and compiling a field working plan. In the field, quality of the soil is studied and in uncertain cases samples are taken to laboratory analyses. Also electrical conductivity and pH of soil and water are measured in the field. Laboratory methods include multielemental determinations with ICP-OES, analyses of grain size and humus content (LOI), and incubation. So far, approximately 60 % of the potential ASS affected regions in Finland are mapped. Over 15 000 sites have been studied in the field and 4000 laboratory analyses are done. The spatial database presented in the scale of 1: 250 000 can be viewed at the GTK's web pages (http://gtkdata.gtk.fi/hasu/index.html).

The Use of AIS Data for Identifying and Mapping Calcareous Soils in Western Nebraska

NASA Technical Reports Server (NTRS)

Samson, S. A.

1985-01-01

The identification of calcareous soils, through unique spectral responses of the vegetation to the chemical nature of calcareous soils, can improve the accuracy of delineating the boundaries of soil mapping units over conventional field techniques. The objective of this experiment is to evaluate the use of the Airborne Imaging Spectrometer (AIS) in the identification and delineation of calcareous soils in the western Sandhills of Nebraska. Based upon statistical differences found in separating the spectral curves below 1.3 microns, calcareous and non-calcareous soils may be identified by differences in species of vegetation. Additional work is needed to identify biogeochemical differences between the two soils.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

The Soil Series in Soil Classifications of the United States

NASA Astrophysics Data System (ADS)

Indorante, Samuel; Beaudette, Dylan; Brevik, Eric C.

2014-05-01

Organized national soil survey began in the United States in 1899, with soil types as the units being mapped. The soil series concept was introduced into the U.S. soil survey in 1903 as a way to relate soils being mapped in one area to the soils of other areas. The original concept of a soil series was all soil types formed in the same parent materials that were of the same geologic age. However, within about 15 years soil series became the primary units being mapped in U.S. soil survey. Soil types became subdivisions of soil series, with the subdivisions based on changes in texture. As the soil series became the primary mapping unit the concept of what a soil series was also changed. Instead of being based on parent materials and geologic age, the soil series of the 1920s was based on the morphology and composition of the soil profile. Another major change in the concept of soil series occurred when U.S. Soil Taxonomy was released in 1975. Under Soil Taxonomy, the soil series subdivisions were based on the uses the soils might be put to, particularly their agricultural uses (Simonson, 1997). While the concept of the soil series has changed over the years, the term soil series has been the longest-lived term in U.S. soil classification. It has appeared in every official classification system used by the U.S. soil survey (Brevik and Hartemink, 2013). The first classification system was put together by Milton Whitney in 1909 and had soil series at its second lowest level, with soil type at the lowest level. The second classification system used by the U.S. soil survey was developed by C.F. Marbut, H.H. Bennett, J.E. Lapham, and M.H. Lapham in 1913. It had soil series at the second highest level, with soil classes and soil types at more detailed levels. This was followed by another system in 1938 developed by M. Baldwin, C.E. Kellogg, and J. Thorp. In this system soil series were again at the second lowest level with soil types at the lowest level. The soil type concept was dropped and replaced by the soil phase in the 1950s in a modification of the 1938 Baldwin et al. classification (Simonson, 1997). When Soil Taxonomy was released in 1975, soil series became the most detailed (lowest) level of the classification system, and the only term maintained throughout all U.S. classifications to date. While the number of recognized soil series have increased steadily throughout the history of U.S. soil survey, there was a rapid increase in the recognition of new soil series following the introduction of Soil Taxonomy (Brevik and Hartemink, 2013). References Brevik, E.C., and A.E. Hartemink. 2013. Soil maps of the United States of America. Soil Science Society of America Journal 77:1117-1132. doi:10.2136/sssaj2012.0390. Simonson, R.W. 1997. Evolution of soil series and type concepts in the United States. Advances in Geoecology 29:79-108.

Magnetic mapping of distribution of wood ash used for fertilization of forest soil.

PubMed

Petrovský, Eduard; Remeš, Jiří; Kapička, Aleš; Podrázský, Vilém; Grison, Hana; Borůvka, Luboš

2018-06-01

The effect of wood-ash fertilization on forest soils has been assessed mainly through geochemical methods (e.g., content of soil organic matter or nutrients). However, a simple and fast method of determining the distribution of the ash and the extent of affected soil is missing. In this study we present the use of magnetic susceptibility, which is controlled by Fe-oxides, in comparing the fertilized soil in the forest plantation of pine and oak with intact forest soil. Spatial and vertical distribution of magnetic susceptibility was measured in an oak and pine plantation next to stems of young plants, where wood ash was applied as fertilizer. Pattern of the susceptibility distribution was compared with that in non-fertilized part of the plantation as well as with a spot of intact natural forest soil nearby. Our results show that the wood-ash samples contain significant amount of ferrimagnetic magnetite with susceptibility higher than that of typical forest soil. Clear differences were observed between magnetic susceptibility of furrows and ridges. Moreover, the dispersed ash remains practically on the surface, does not penetrate to deeper layers. Finally, our data suggest significant differences in surface values between the pine and oak plants. Based on this study we may conclude that magnetic susceptibility may represent a simple and approximate method of assessing the extent of soil affected by wood-ash. Copyright © 2018 Elsevier B.V. All rights reserved.

The application of ERTS imagery to the FAO/Unesco soil map of the world

NASA Technical Reports Server (NTRS)

Dudal, R. J.; Pecrot, A. J. (Principal Investigator)

1977-01-01

The author has identified the following significant results. It was concluded that direct identification and mapping of the various soil degradation forms and intensities from the color composite imager was generally difficult, if not impossible. The imagery, however, provided valuable information on some main environmental criteria which can be used in connection other available field data to assess actual soil degradation and estimate soil degradation hazards.

Spatial prediction of soil texture in region Centre (France) from summary data

NASA Astrophysics Data System (ADS)

Dobarco, Mercedes Roman; Saby, Nicolas; Paroissien, Jean-Baptiste; Orton, Tom G.

2015-04-01

Soil texture is a key controlling factor of important soil functions like water and nutrient holding capacity, retention of pollutants, drainage, soil biodiversity, and C cycling. High resolution soil texture maps enhance our understanding of the spatial distribution of soil properties and provide valuable information for decision making and crop management, environmental protection, and hydrological planning. We predicted the soil texture of agricultural topsoils in the Region Centre (France) combining regression and area-to-point kriging. Soil texture data was collected from the French soil-test database (BDAT), which is populated with soil analysis performed by farmers' demand. To protect the anonymity of the farms the data was treated by commune. In a first step, summary statistics of environmental covariates by commune were used to develop prediction models with Cubist, boosted regression trees, and random forests. In a second step the residuals of each individual observation were summarized by commune and kriged following the method developed by Orton et al. (2012). This approach allowed to include non-linear relationships among covariates and soil texture while accounting for the uncertainty on areal means in the area-to-point kriging step. Independent validation of the models was done using data from the systematic soil monitoring network of French soils. Future work will compare the performance of these models with a non-stationary variance geostatistical model using the most important covariates and summary statistics of texture data. The results will inform on whether the later and statistically more-challenging approach improves significantly texture predictions or whether the more simple area-to-point regression kriging can offer satisfactory results. The application of area-to-point regression kriging at national level using BDAT data has the potential to improve soil texture predictions for agricultural topsoils, especially when combined with existing maps (i.e., model ensemble).

Effective use of ERTS multisensor data in the Great Plains

NASA Technical Reports Server (NTRS)

Myers, V. I. (Principal Investigator)

1972-01-01

The author has identified the following significant results. One unique advantage of ERTS imagery for delineating soil associations is the large area that can be scanned with one photo. Although soil associations usually are published at scales of 1:500,000 or 1:1,000,000, the delineations are drawn on much larger scale maps covering small pieces of the scene and then pieced together. Alluvial areas are usually swollen out of proportion to other soil areas. ERTS imagery puts alluvial areas into their proper size. A second feature of ERTS imagery is that a soil association map constructed with its aid assures that the cartographic level of the associations is more nearly the same. Another advantage of ERTS imagery is that the actual shape and configuration of soil associations are apparent. Also with ERTS imagery significant new delineations may become apparent which were missed when constructing soil association maps from conventional large scale photos.

Evaluation of freely available ancillary data used for detailed soil mapping in Brazil

NASA Astrophysics Data System (ADS)

Samuel-Rosa, Alessandro; Anjos, Lúcia; Vasques, Gustavo; Heuvelink, Gerard

2014-05-01

Brazil is one of the world's largest food producers, and is home of both largest rainforest and largest supply of renewable fresh water on Earth. However, it lacks detailed soil information in extensive areas of the country. The best soil map covering the entire country was published at a scale of 1:5,000,000. Termination of governmental support for systematic soil mapping in the 1980's made detailed soil mapping of the whole country a very difficult task to accomplish. Nowadays, due to new user-driven demands (e.g. precision agriculture), most detailed soil maps are produced for small size areas. Many of them rely on as is freely available ancillary data, although their accuracy is usually not reported or unknown. Results from a validation exercise that we performed using ground control points from a small hilly catchment (20 km²) in Southern Brazil (-53.7995ºE, -29.6355ºN) indicate that most freely available ancillary data needs some type of correction before use. Georeferenced and orthorectified RapidEye imagery (recently acquired by the Brazilian government) has a horizontal accuracy (root-mean-square error, RMSE) of 37 m, which is worse than the value published in the metadata (32 m). Like any remote sensing imagery, RapidEye imagery needs to be correctly registered before its use for soil mapping. Topographic maps produced by the Brazilian Army and derived geological maps (scale of 1:25,000) have a horizontal accuracy of 65 m, which is more than four times the maximum value allowed by Brazilian legislation (15 m). Worse results were found for geological maps derived from 1:50,000 topographic maps (RMSE = 147 m), for which the maximum allowed value is 30 m. In most cases positional errors are of systematic origin and can be easily corrected (e.g., affine transformation). ASTER GDEM has many holes and is very noisy, making it of little use in the studied area. TOPODATA, which is SRTM kriged from originally 3 to 1 arc-second by the Brazilian National Institute for Space Research, has a vertical accuracy of 19 m and is strongly affected by double-oblique stripes which were intensified by kriging. Many spurious sinks were created which are not easily corrected using either frequency filters or sink-filling algorithms. The exceptions are SRTM v4.1, which is the most vertically accurate DEM available (RMSE = 18.7 m), and Google Earth imagery compiled from various sources (positional accuracy of RMSE = 8 m). It is likely that most mapping efforts will continue to be employed in small size areas to fulfill local user-driven demands in the forthcoming years. Also, many new techniques and technologies will possibly be developed and employed for soil mapping. However, employing better quality ancillary data still is a challenge to be overcome to produce high-quality soil information to allow better decision making and land use policy in Brazil.

Predictable bacterial composition and hydrocarbon degradation in Arctic soils following diesel and nutrient disturbance.

PubMed

Bell, Terrence H; Yergeau, Etienne; Maynard, Christine; Juck, David; Whyte, Lyle G; Greer, Charles W

2013-06-01

Increased exploration and exploitation of resources in the Arctic is leading to a higher risk of petroleum contamination. A number of Arctic microorganisms can use petroleum for growth-supporting carbon and energy, but traditional approaches for stimulating these microorganisms (for example, nutrient addition) have varied in effectiveness between sites. Consistent environmental controls on microbial community response to disturbance from petroleum contaminants and nutrient amendments across Arctic soils have not been identified, nor is it known whether specific taxa are universally associated with efficient bioremediation. In this study, we contaminated 18 Arctic soils with diesel and treated subsamples of each with monoammonium phosphate (MAP), which has successfully stimulated degradation in some contaminated Arctic soils. Bacterial community composition of uncontaminated, diesel-contaminated and diesel+MAP soils was assessed through multiplexed 16S (ribosomal RNA) rRNA gene sequencing on an Ion Torrent Personal Genome Machine, while hydrocarbon degradation was measured by gas chromatography analysis. Diversity of 16S rRNA gene sequences was reduced by diesel, and more so by the combination of diesel and MAP. Actinobacteria dominated uncontaminated soils with <10% organic matter, while Proteobacteria dominated higher-organic matter soils, and this pattern was exaggerated following disturbance. Degradation with and without MAP was predictable by initial bacterial diversity and the abundance of specific assemblages of Betaproteobacteria, respectively. High Betaproteobacteria abundance was positively correlated with high diesel degradation in MAP-treated soils, suggesting this may be an important group to stimulate. The predictability with which bacterial communities respond to these disturbances suggests that costly and time-consuming contaminated site assessments may not be necessary in the future.

Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

NASA Astrophysics Data System (ADS)

Wang, Hui-Lin; An, Ru; You, Jia-jun; Wang, Ying; Chen, Yuehong; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballard, Jonathan Arthur

2017-10-01

Soil moisture plays an important role in the water cycle within the surface ecosystem, and it is the basic condition for the growth of plants. Currently, the spatial resolutions of most soil moisture data from remote sensing range from ten to several tens of km, while those observed in-situ and simulated for watershed hydrology, ecology, agriculture, weather, and drought research are generally <1 km. Therefore, the existing coarse-resolution remotely sensed soil moisture data need to be downscaled. This paper proposes a universal and multitemporal soil moisture downscaling method suitable for large areas. The datasets comprise land surface, brightness temperature, precipitation, and soil and topographic parameters from high-resolution data and active/passive microwave remotely sensed essential climate variable soil moisture (ECV_SM) data with a spatial resolution of 25 km. Using this method, a total of 288 soil moisture maps of 1-km resolution from the first 10-day period of January 2003 to the last 10-day period of December 2010 were derived. The in-situ observations were used to validate the downscaled ECV_SM. In general, the downscaled soil moisture values for different land cover and land use types are consistent with the in-situ observations. Mean square root error is reduced from 0.070 to 0.061 using 1970 in-situ time series observation data from 28 sites distributed over different land uses and land cover types. The performance was also assessed using the GDOWN metric, a measure of the overall performance of the downscaling methods based on the same dataset. It was positive in 71.429% of cases, indicating that the suggested method in the paper generally improves the representation of soil moisture at 1-km resolution.

Time-dependent landslide probability mapping

USGS Publications Warehouse

Campbell, Russell H.; Bernknopf, Richard L.; ,

1993-01-01

Case studies where time of failure is known for rainfall-triggered debris flows can be used to estimate the parameters of a hazard model in which the probability of failure is a function of time. As an example, a time-dependent function for the conditional probability of a soil slip is estimated from independent variables representing hillside morphology, approximations of material properties, and the duration and rate of rainfall. If probabilities are calculated in a GIS (geomorphic information system ) environment, the spatial distribution of the result for any given hour can be displayed on a map. Although the probability levels in this example are uncalibrated, the method offers a potential for evaluating different physical models and different earth-science variables by comparing the map distribution of predicted probabilities with inventory maps for different areas and different storms. If linked with spatial and temporal socio-economic variables, this method could be used for short-term risk assessment.

Mapping forest soil organic matter on New Jersey's coastal plain

Treesearch

Brian J. Clough; Edwin J. Green; Richard B. Lathrop

2012-01-01

Managing forest soil organic matter (SOM) stocks is a vital strategy for reducing the impact of anthropogenic carbon dioxide emissions. However, the SOM pool is highly variable, and developing accurate estimates to guide management decisions has remained a difficult task. We present the results of a spatial model designed to map soil organic matter for all forested...

Field guide for mapping post-fire soil burn severity

Treesearch

Annette Parson; Peter R. Robichaud; Sarah A. Lewis; Carolyn Napper; Jess T. Clark

2010-01-01

Following wildfires in the United States, the U.S. Department of Agriculture and U.S. Department of the Interior mobilize Burned Area Emergency Response (BAER) teams to assess immediate post-fire watershed conditions. BAER teams must determine threats from flooding, soil erosion, and instability. Developing a postfire soil burn severity map is an important first step...

Mapping Surface Soil Organic Carbon for Crop Fields with Remote Sensing

NASA Technical Reports Server (NTRS)

Chen, Feng; Kissel, David E.; West, Larry T.; Rickman, Doug; Luvall, J. C.; Adkins, Wayne

2004-01-01

The organic C concentration of surface soil can be used in agricultural fields to vary crop production inputs. Organic C is often highly spatially variable, so that maps of soil organic C can be used to vary crop production inputs using precision farming technology. The objective of this research was to demonstrate the feasibility of mapping soil organic C on three fields, using remotely sensed images of the fields with a bare surface. Enough soil samples covering the range in soil organic C must be taken from each field to develop a satisfactory relationship between soil organic C content and image reflectance values. The number of soil samples analyzed in the three fields varied from 22 to 26. The regression equations differed between fields, but gave highly significant relationships with R2 values of 0.93, 0.95, and 0.89 for the three fields. A comparison of predicted and measured values of soil organic C for an independent set of 2 soil samples taken on one of the fields gave highly satisfactory results, with a comparison equation of % organic C measured + 1.02% organic C predicted, with r2 = 0.87.

Lead exposure from soil in Peruvian mining towns: a national assessment supported by two contrasting examples

PubMed Central

Bravo, Carolina; Gil, Vladimir; Sherpa, Shaky; Jack, Darby

2012-01-01

Abstract Objective To estimate the population of Peru living in the vicinity of active or former mining operations that could be exposed to lead from contaminated soil. Methods Geographic coordinates were compiled for 113 active mines, 138 ore processing plants and 3 smelters, as well as 7743 former mining sites. The population living within 5 km of these sites was calculated from census data for 2000. In addition, the lead content of soil in the historic mining town of Cerro de Pasco and around a recent mine and ore processing plant near the city of Huaral was mapped in 2009 using a hand-held X-ray fluorescence analyser. Findings Spatial analysis indicated that 1.6 million people in Peru could be living within 5 km of an active or former mining operation. Two thirds of the population potentially exposed was accounted for by 29 clusters of mining operations, each with a population of over 10 000 each. These clusters included 112 active and 3438 former mining operations. Soil lead levels exceeded 1200 mg/kg, a reference standard for residential soil, in 35 of 74 sites tested in Cerro de Pasco but in only 4 of 47 sites tested around the newer operations near Huaral. Conclusion Soil contamination with lead is likely to be extensive in Peruvian mining towns but the level of contamination is spatially far from uniform. Childhood exposure by soil ingestion could be substantially reduced by mapping soil lead levels, making this information public and encouraging local communities to isolate contaminated areas from children. PMID:23284193

Modeling the reflectance of the lunar regolith by a new method combining Monte Carlo Ray tracing and Hapke's model with application to Chang'E-1 IIM data.

PubMed

Wong, Un-Hong; Wu, Yunzhao; Wong, Hon-Cheng; Liang, Yanyan; Tang, Zesheng

2014-01-01

In this paper, we model the reflectance of the lunar regolith by a new method combining Monte Carlo ray tracing and Hapke's model. The existing modeling methods exploit either a radiative transfer model or a geometric optical model. However, the measured data from an Interference Imaging spectrometer (IIM) on an orbiter were affected not only by the composition of minerals but also by the environmental factors. These factors cannot be well addressed by a single model alone. Our method implemented Monte Carlo ray tracing for simulating the large-scale effects such as the reflection of topography of the lunar soil and Hapke's model for calculating the reflection intensity of the internal scattering effects of particles of the lunar soil. Therefore, both the large-scale and microscale effects are considered in our method, providing a more accurate modeling of the reflectance of the lunar regolith. Simulation results using the Lunar Soil Characterization Consortium (LSCC) data and Chang'E-1 elevation map show that our method is effective and useful. We have also applied our method to Chang'E-1 IIM data for removing the influence of lunar topography to the reflectance of the lunar soil and to generate more realistic visualizations of the lunar surface.

Application of phyto-indication and radiocesium indicative methods for microrelief mapping

NASA Astrophysics Data System (ADS)

Panidi, E.; Trofimetz, L.; Sokolova, J.

2016-04-01

Remote sensing technologies are widely used for production of Digital Elevation Models (DEMs), and geomorphometry techniques are valuable tools for DEM analysis. One of the broadly used applications of these technologies and techniques is relief mapping. In the simplest case, we can identify relief structures using DEM analysis, and produce a map or map series to show the relief condition. However, traditional techniques might fail when used for mapping microrelief structures (structures below ten meters in size). In this case high microrelief dynamics lead to technological and conceptual difficulties. Moreover, erosion of microrelief structures cannot be detected at the initial evolution stage using DEM modelling and analysis only. In our study, we investigate the possibilities and specific techniques for allocation of erosion microrelief structures, and mapping techniques for the microrelief derivatives (e.g. quantitative parameters of microrelief). Our toolset includes the analysis of spatial redistribution of the soil pollutants and phyto-indication analysis, which complement the common DEM modelling and geomorphometric analysis. We use field surveys produced at the test area, which is arable territory with high erosion risks. Our main conclusion at the current stage is that the indicative methods (i.e. radiocesium and phyto-indication methods) are effective for allocation of the erosion microrelief structures. Also, these methods need to be formalized for convenient use.

Assessment of Three Flood Hazard Mapping Methods: A Case Study of Perlis

NASA Astrophysics Data System (ADS)

Azizat, Nazirah; Omar, Wan Mohd Sabki Wan

2018-03-01

Flood is a common natural disaster and also affect the all state in Malaysia. Regarding to Drainage and Irrigation Department (DID) in 2007, about 29, 270 km2 or 9 percent of region of the country is prone to flooding. Flood can be such devastating catastrophic which can effected to people, economy and environment. Flood hazard mapping can be used is an important part in flood assessment to define those high risk area prone to flooding. The purposes of this study are to prepare a flood hazard mapping in Perlis and to evaluate flood hazard using frequency ratio, statistical index and Poisson method. The six factors affecting the occurrence of flood including elevation, distance from the drainage network, rainfall, soil texture, geology and erosion were created using ArcGIS 10.1 software. Flood location map in this study has been generated based on flooded area in year 2010 from DID. These parameters and flood location map were analysed to prepare flood hazard mapping in representing the probability of flood area. The results of the analysis were verified using flood location data in year 2013, 2014, 2015. The comparison result showed statistical index method is better in prediction of flood area rather than frequency ratio and Poisson method.

Estimating soil water content from ground penetrating radar coarse root reflections

NASA Astrophysics Data System (ADS)

Liu, X.; Cui, X.; Chen, J.; Li, W.; Cao, X.

2016-12-01

Soil water content (SWC) is an indispensable variable for understanding the organization of natural ecosystems and biodiversity. Especially in semiarid and arid regions, soil moisture is the plants primary source of water and largely determine their strategies for growth and survival, such as root depth, distribution and competition between them. Ground penetrating radar (GPR), a kind of noninvasive geophysical technique, has been regarded as an accurate tool for measuring soil water content at intermediate scale in past decades. For soil water content estimation with surface GPR, fixed antenna offset reflection method has been considered to have potential to obtain average soil water content between land surface and reflectors, and provide high resolution and few measurement time. In this study, 900MHz surface GPR antenna was used to estimate SWC with fixed offset reflection method; plant coarse roots (with diameters greater than 5 mm) were regarded as reflectors; a kind of advanced GPR data interpretation method, HADA (hyperbola automatic detection algorithm), was introduced to automatically obtain average velocity by recognizing coarse root hyperbolic reflection signals on GPR radargrams during estimating SWC. In addition, a formula was deduced to determine interval average SWC between two roots at different depths as well. We examined the performance of proposed method on a dataset simulated under different scenarios. Results showed that HADA could provide a reasonable average velocity to estimate SWC without knowledge of root depth and interval average SWC also be determined. When the proposed method was applied to estimation of SWC on a real-field measurement dataset, a very small soil water content vertical variation gradient about 0.006 with depth was captured as well. Therefore, the proposed method could be used to estimate average soil water content from ground penetrating radar coarse root reflections and obtain interval average SWC between two roots at different depths. It is very promising for measuring root-zone-soil-moisture and mapping soil moisture distribution around a shrub or even in field plot scale.

Average variograms to guide soil sampling

NASA Astrophysics Data System (ADS)

Kerry, R.; Oliver, M. A.

2004-10-01

To manage land in a site-specific way for agriculture requires detailed maps of the variation in the soil properties of interest. To predict accurately for mapping, the interval at which the soil is sampled should relate to the scale of spatial variation. A variogram can be used to guide sampling in two ways. A sampling interval of less than half the range of spatial dependence can be used, or the variogram can be used with the kriging equations to determine an optimal sampling interval to achieve a given tolerable error. A variogram might not be available for the site, but if the variograms of several soil properties were available on a similar parent material and or particular topographic positions an average variogram could be calculated from these. Averages of the variogram ranges and standardized average variograms from four different parent materials in southern England were used to suggest suitable sampling intervals for future surveys in similar pedological settings based on half the variogram range. The standardized average variograms were also used to determine optimal sampling intervals using the kriging equations. Similar sampling intervals were suggested by each method and the maps of predictions based on data at different grid spacings were evaluated for the different parent materials. Variograms of loss on ignition (LOI) taken from the literature for other sites in southern England with similar parent materials had ranges close to the average for a given parent material showing the possible wider application of such averages to guide sampling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomas, J.M.; Callahan, C.A.; Cline, J.F.

Bioassays were used in a three-phase research project to assess the comparative sensitivity of test organisms to known chemicals, determine if the chemical components in field soil and water samples containing unknown contaminants could be inferred from our laboratory studies using known chemicals, and to investigate kriging (a relatively new statistical mapping technique) and bioassays as methods to define the areal extent of chemical contamination. The algal assay generally was most sensitive to samples of pure chemicals, soil elutriates and water from eight sites with known chemical contamination. Bioassays of nine samples of unknown chemical composition from the Rocky Mountainmore » Arsenal (RMA) site showed that a lettuce seed soil contact phytoassay was most sensitive. In general, our bioassays can be used to broadly identify toxic components of contaminated soil. Nearly pure compounds of insecticides and herbicides were less toxic in the sensitive bioassays than were the counterpart commercial formulations. This finding indicates that chemical analysis alone may fail to correctly rate the severity of environmental toxicity. Finally, we used the lettuce seed phytoassay and kriging techniques in a field study at RMA to demonstrate the feasibility of mapping contamination to aid in cleanup decisions. 25 references, 9 figures, 9 tables.« less

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

PubMed Central

Groenendyk, Derek G.; Ferré, Ty P.A.; Thorp, Kelly R.; Rice, Amy K.

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function. PMID:26121466

High-resolution Mapping of Permafrost and Soil Freeze/thaw Dynamics in the Tibetan Plateau Based on Multi-sensor Satellite Observations

NASA Astrophysics Data System (ADS)

Zhang, W.; Yi, Y.; Yang, K.; Kimball, J. S.

2016-12-01

The Tibetan Plateau (TP) is underlain by the world's largest extent of alpine permafrost ( 2.5×106 km2), dominated by sporadic and discontinuous permafrost with strong sensitivity to climate warming. Detailed permafrost distributions and patterns in most of the TP region are still unknown due to extremely sparse in-situ observations in this region characterized by heterogeneous land cover and large temporal dynamics in surface soil moisture conditions. Therefore, satellite-based temperature and moisture observations are essential for high-resolution mapping of permafrost distribution and soil active layer changes in the TP region. In this study, we quantify the TP regional permafrost distribution at 1-km resolution using a detailed satellite data-driven soil thermal process model (GIPL2). The soil thermal model is calibrated and validated using in-situ soil temperature/moisture observations from the CAMP/Tibet field campaign (9 sites: 0-300 cm soil depth sampling from 1997-2007), a multi-scale soil moisture and temperature monitoring network in the central TP (CTP-SMTMN, 57 sites: 5-40 cm, 2010-2014) and across the whole plateau (China Meteorology Administration, 98 sites: 0-320 cm, 2000-2015). Our preliminary results using the CAMP/Tibet and CTP-SMTMN network observations indicate strong controls of surface thermal and soil moisture conditions on soil freeze/thaw dynamics, which vary greatly with underlying topography, soil texture and vegetation cover. For regional mapping of soil freeze/thaw and permafrost dynamics, we use the most recent soil moisture retrievals from the NASA SMAP (Soil Moisture Active Passive) sensor to account for the effects of temporal soil moisture dynamics on soil thermal heat transfer, with surface thermal conditions defined by MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature records. Our study provides the first 1-km map of spatial patterns and recent changes of permafrost conditions in the TP.

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

PubMed

Groenendyk, Derek G; Ferré, Ty P A; Thorp, Kelly R; Rice, Amy K

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices

NASA Astrophysics Data System (ADS)

El Harti, Abderrazak; Lhissou, Rachid; Chokmani, Karem; Ouzemou, Jamal-eddine; Hassouna, Mohamed; Bachaoui, El Mostafa; El Ghmari, Abderrahmene

2016-08-01

Soil salinization is major environmental issue in irrigated agricultural production. Conventional methods for salinization monitoring are time and money consuming and limited by the high spatiotemporal variability of this phenomenon. This work aims to propose a spatiotemporal monitoring method of soil salinization in the Tadla plain in central Morocco using spectral indices derived from Thematic Mapper (TM) and Operational Land Imager (OLI) data. Six Landsat TM/OLI satellite images acquired during 13 years period (2000-2013) coupled with in-situ electrical conductivity (EC) measurements were used to develop the proposed method. After radiometric and atmospheric correction of TM/OLI images, a new soil salinity index (OLI-SI) is proposed for soil EC estimation. Validation shows that this index allowed a satisfactory EC estimation in the Tadla irrigated perimeter with coefficient of determination R2 varying from 0.55 to 0.77 and a Root Mean Square Error (RMSE) ranging between 1.02 dS/m and 2.35 dS/m. The times-series of salinity maps produced over the Tadla plain using the proposed method show that salinity is decreasing in intensity and progressively increasing in spatial extent, over the 2000-2013 period. This trend resulted in a decrease in agricultural activities in the southwestern part of the perimeter, located in the hydraulic downstream.

Assessment of soil compaction properties based on surface wave techniques

NASA Astrophysics Data System (ADS)

Jihan Syamimi Jafri, Nur; Rahim, Mohd Asri Ab; Zahid, Mohd Zulham Affandi Mohd; Faizah Bawadi, Nor; Munsif Ahmad, Muhammad; Faizal Mansor, Ahmad; Omar, Wan Mohd Sabki Wan

2018-03-01

Soil compaction plays an important role in every construction activities to reduce risks of any damage. Traditionally, methods of assessing compaction include field tests and invasive penetration tests for compacted areas have great limitations, which caused time-consuming in evaluating large areas. Thus, this study proposed the possibility of using non-invasive surface wave method like Multi-channel Analysis of Surface Wave (MASW) as a useful tool for assessing soil compaction. The aim of this study was to determine the shear wave velocity profiles and field density of compacted soils under varying compaction efforts by using MASW method. Pre and post compaction of MASW survey were conducted at Pauh Campus, UniMAP after applying rolling compaction with variation of passes (2, 6 and 10). Each seismic data was recorded by GEODE seismograph. Sand replacement test was conducted for each survey line to obtain the field density data. All seismic data were processed using SeisImager/SW software. The results show the shear wave velocity profiles increase with the number of passes from 0 to 6 passes, but decrease after 10 passes. This method could attract the interest of geotechnical community, as it can be an alternative tool to the standard test for assessing of soil compaction in the field operation.

High-Resolution Global Soil Moisture Map

NASA Image and Video Library

2015-05-19

High-resolution global soil moisture map from NASA SMAP combined radar and radiometer instruments, acquired between May 4 and May 11, 2015 during SMAP commissioning phase. The map has a resolution of 5.6 miles (9 kilometers). The data gap is due to turning the instruments on and off during testing. http://photojournal.jpl.nasa.gov/catalog/PIA19337

Complex conductivity of oil-contaminated clayey soils

NASA Astrophysics Data System (ADS)

Deng, Yaping; Shi, Xiaoqing; Revil, André; Wu, Jichun; Ghorbani, A.

2018-06-01

Spectral induced polarization (SIP) is considered as a promising tool in environmental investigations. However, few works have done regarding the electrical signature of oil contamination of clayey soils upon induced polarization. Laboratory column experiments plus one sandbox experiment are conducted in this study to investigate the performances of the SIP method in oil-contaminated soils. First, a total of 12 soils are investigated to reveal the influences of water and soil properties on the saturation dependence of the complex conductivity below 100 Hz. Results show that the magnitude of the complex conductivity consistently decreases with decreasing water saturation for all soils samples. The saturation n and quadrature conductivity p exponents tend to increase slightly with increasing water salinity when using a linear conductivity model. The saturation exponent increases marginally with the cation exchange capacity (CEC) and the specific surface area (Ssp) while the quadrature conductivity exponent exhibits a relatively stronger dependence on both CEC and Ssp. For the low CEC soil samples (normally ≤10 meq/100 g), the quadrature conductivity exponent p correlates well with the saturation exponent n using the relationship p = n-1. SIP method is further applied in a sandbox experiment to estimate the saturation distribution and total volume of the oil. Results demonstrate that the SIP method has a great potential for mapping the organic contaminant plume and quantifying the oil volume.

Determination Of Slope Instability Using Spatially Integrated Mapping Framework

NASA Astrophysics Data System (ADS)

Baharuddin, I. N. Z.; Omar, R. C.; Roslan, R.; Khalid, N. H. N.; Hanifah, M. I. M.

2016-11-01

The determination and identification of slope instability are often rely on data obtained from in-situ soil investigation work where it involves the logistic of machineries and manpower, thus these aspects may increase the cost especially for remote locations. Therefore a method, which is able to identify possible slope instability without frequent ground walkabout survey, is needed. This paper presents the method used in prediction of slope instability using spatial integrated mapping framework which applicable for remote areas such as tropical forest and natural hilly terrain. Spatial data such as geology, topography, land use map, slope angle and elevation were used in regional analysis during desktop study. Through this framework, the occurrence of slope instability was able to be identified and was validate using a confirmatory site- specific analysis.

Topsoil moisture mapping using geostatistical techniques under different Mediterranean climatic conditions.

PubMed

Martínez-Murillo, J F; Hueso-González, P; Ruiz-Sinoga, J D

2017-10-01

Soil mapping has been considered as an important factor in the widening of Soil Science and giving response to many different environmental questions. Geostatistical techniques, through kriging and co-kriging techniques, have made possible to improve the understanding of eco-geomorphologic variables, e.g., soil moisture. This study is focused on mapping of topsoil moisture using geostatistical techniques under different Mediterranean climatic conditions (humid, dry and semiarid) in three small watersheds and considering topography and soil properties as key factors. A Digital Elevation Model (DEM) with a resolution of 1×1m was derived from a topographical survey as well as soils were sampled to analyzed soil properties controlling topsoil moisture, which was measured during 4-years. Afterwards, some topography attributes were derived from the DEM, the soil properties analyzed in laboratory, and the topsoil moisture was modeled for the entire watersheds applying three geostatistical techniques: i) ordinary kriging; ii) co-kriging considering as co-variate topography attributes; and iii) co-kriging ta considering as co-variates topography attributes and gravel content. The results indicated topsoil moisture was more accurately mapped in the dry and semiarid watersheds when co-kriging procedure was performed. The study is a contribution to improve the efficiency and accuracy of studies about the Mediterranean eco-geomorphologic system and soil hydrology in field conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Prior-knowledge-based spectral mixture analysis for impervious surface mapping

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jinshui; He, Chunyang; Zhou, Yuyu

2014-01-03

In this study, we developed a prior-knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high- and low-density urban regions. First, an urban area was categorized into high- and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the Vegetation-Impervious model (V-I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the Vegetation-Impervious-Soil model (V-I-S) was used in an SMA analysis with four endmembers: high albedo, lowmore » albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0%, compared to 14.52% using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE = 6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high-density urban areas.« less

A study of the utilization of ERTS-1 data from the Wabash River Basin. [soil mapping, crop identification, water resources

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. In soil association mapping, computerized analysis of ERTS-1 MSS data has yielded images which will prove useful in the ongoing Cooperative Soil Survey program, involving the Soil Conservation Service of USDA and other state and local agencies. In the present mode of operation, a soil survey for a county may take up to 5 years to be completed. Results indicate that a great deal of soils information can be extracted from ERTS-1 data by computer analysis. This information is expected to be very valuable in the premapping conference phase of a soil survey, resulting in more efficient field operations during the actual mapping. In the earth surface features mapping effort it was found that temporal data improved the classification accuracy of forest classification in Tippecanoe County, Indiana. In water resources study a severe scanner look angle effect was observed in the aircraft scanner data of a test lake which was not present in ERTS-1 data of the same site. This effect was greatly accentuated by surface roughness caused by strong winds. Quantitative evaluation of urban features classification in ERTS-1 data was obtained. An 87.1% test accuracy was obtained for eight categories in Marion County, Indiana.

Modelling Multi Hazard Mapping in Semarang City Using GIS-Fuzzy Method

NASA Astrophysics Data System (ADS)

Nugraha, A. L.; Awaluddin, M.; Sasmito, B.

2018-02-01

One important aspect of disaster mitigation planning is hazard mapping. Hazard mapping can provide spatial information on the distribution of locations that are threatened by disaster. Semarang City as the capital of Central Java Province is one of the cities with high natural disaster intensity. Frequent natural disasters Semarang city is tidal flood, floods, landslides, and droughts. Therefore, Semarang City needs spatial information by doing multi hazard mapping to support disaster mitigation planning in Semarang City. Multi Hazards map modelling can be derived from parameters such as slope maps, rainfall, land use, and soil types. This modelling is done by using GIS method with scoring and overlay technique. However, the accuracy of modelling would be better if the GIS method is combined with Fuzzy Logic techniques to provide a good classification in determining disaster threats. The Fuzzy-GIS method will build a multi hazards map of Semarang city can deliver results with good accuracy and with appropriate threat class spread so as to provide disaster information for disaster mitigation planning of Semarang city. from the multi-hazard modelling using GIS-Fuzzy can be known type of membership that has a good accuracy is the type of membership Gauss with RMSE of 0.404 the smallest of the other membership and VAF value of 72.909% of the largest of the other membership.

Modeling of soil erosion and sediment transport in the East River Basin in southern China

USGS Publications Warehouse

Wu, Yping; Chen, Ji

2012-01-01

Soil erosion is a major global environmental problem that has caused many issues involving land degradation, sedimentation of waterways, ecological degradation, and nonpoint source pollution. Therefore, it is significant to understand the processes of soil erosion and sediment transport along rivers, and this can help identify the erosion prone areas and find potential measures to alleviate the environmental effects. In this study, we investigated soil erosion and identified the most seriously eroded areas in the East River Basin in southern China using a physically-based model, Soil and Water Assessment Tool (SWAT). We also introduced a classical sediment transport method (Zhang) into SWAT and compared it with the built-in Bagnold method in simulating sediment transport process along the river. The derived spatial soil erosion map and land use based erosion levels can explicitly illustrate the identification and prioritization of the critical soil erosion areas in this basin. Our results also indicate that erosion is quite sensitive to soil properties and slope. Comparison of Bagnold and Zhang methods shows that the latter can give an overall better performance especially in tracking the peak and low sediment concentrations along the river. We also found that the East River is mainly characterized by sediment deposition in most of the segments and at most times of a year. Overall, the results presented in this paper can provide decision support for watershed managers about where the best management practices (conservation measures) can be implemented effectively and at low cost. The methods we used in this study can also be of interest in sediment modeling for other basins worldwide.

Evaluation of the spatial variability of soil water content at the spatial resolution of SMAP data products : case studies in Italy and Morocco

NASA Astrophysics Data System (ADS)

Menenti, Massimo; Akdim, Nadia; Alfieri, Silvia Maria; Labbassi, Kamal; De Lorenzi, Francesca; Bonfante, Antonello; Basile, Angelo

2014-05-01

Frequent and contiguous observations of soil water content such as the ones to be provided by SMAP are potentially useful to improve distributed models of soil water balance. This requires matching of observations and model estimates provided both sample spatial patterns consistently. The spatial resolution of SMAP soil water content data products ranges from 3 km X 3 km to 40 km X 40 km. Even the highest spatial resolution may not be sufficient to capture the spatial variability due to terrain, soil properties and precipitation. We have evaluated the SMAP spatial resolution against spatial variability of soil water content in two Mediterranean landscapes: a hilly area dominated by vineyards and olive orchards in Central Italy and a large irrigation schemes (Doukkala) in Morocco. The "Valle Telesina" is a 20,000 ha complex landscape located in South Italy in the Campania region, which has a complex geology and geomorphology and it is characterised by an E-W elongated graben where the Calore river flows. The main crops are grapevine (6,448 ha) and olive (3,390 ha). Soil information was mainly derived from an existing soil map at 1:50 000 scale (Terribile et al., 1996). The area includes 47 SMUs (Soil Mapping Units) and about 60 soil typological units (STUs). (Bonfante et al., 2011). In Doukkala, the soil water retention and unsaturated capillary conductivity were estimated from grain size distribution of a number of samples (22 pilot points, each one sampled in 3 horizons of 20cm), and combined with a soil map. The land use classification was carried out using a NDVI time series at high spatial resolution (Landsat TM and SPOT HRV). We have calculated soil water content for each soil unit in each area in response to several climate cases generating daily maps of soil water content at different depths. To reproduce spatial sampling by SMAP we have filtered these spatial patterns by calculating box averages with grid sizes of 1 km X 1 km and 5 km X 5 km. We have repeated this procedure for soil water content in the 0 to 5 cm and 0 to 10 cm depths. For each case we have compared the variance of filtered soil water content with the expected accuracy of SMAP soil water content. The two areas are very different as regards morphology and soil formation. The Valle Telesina is characterized by a very significant variability of soil hydrological properties leading to complex patterns in soil water content. Contrariwise, the soil properties estimated for all soil mapping units in the Dhoukkala collapse into just two pairs of water retention and hydraulic conductivity characteristics, leading to smoother patterns of soil water content.

[Effects of soil data and map scale on assessment of total phosphorus storage in upland soils.

PubMed

Li, Heng Rong; Zhang, Li Ming; Li, Xiao di; Yu, Dong Sheng; Shi, Xue Zheng; Xing, Shi He; Chen, Han Yue

2016-06-01

Accurate assessment of total phosphorus storage in farmland soils is of great significance to sustainable agricultural and non-point source pollution control. However, previous studies haven't considered the estimation errors from mapping scales and various databases with different sources of soil profile data. In this study, a total of 393×10 4 hm 2 of upland in the 29 counties (or cities) of North Jiangsu was cited as a case for study. Analysis was performed of how the four sources of soil profile data, namely, "Soils of County", "Soils of Prefecture", "Soils of Province" and "Soils of China", and the six scales, i.e. 1:50000, 1:250000, 1:500000, 1:1000000, 1:4000000 and1:10000000, used in the 24 soil databases established for the four soil journals, affected assessment of soil total phosphorus. Compared with the most detailed 1:50000 soil database established with 983 upland soil profiles, relative deviation of the estimates of soil total phosphorus density (STPD) and soil total phosphorus storage (STPS) from the other soil databases varied from 4.8% to 48.9% and from 1.6% to 48.4%, respectively. The estimated STPD and STPS based on the 1:50000 database of "Soils of County" and most of the estimates based on the databases of each scale in "Soils of County" and "Soils of Prefecture" were different, with the significance levels of P＜0.001 or P＜0.05. Extremely significant differences (P＜0.001) existed between the estimates based on the 1:50000 database of "Soils of County" and the estimates based on the databases of each scale in "Soils of Province" and "Soils of China". This study demonstrated the significance of appropriate soil data sources and appropriate mapping scales in estimating STPS.

Does soil compaction increase floods? A review

NASA Astrophysics Data System (ADS)

Alaoui, Abdallah; Rogger, Magdalena; Peth, Stephan; Blöschl, Günter

2018-02-01

Europe has experienced a series of major floods in the past years which suggests that flood magnitudes may have increased. Land degradation due to soil compaction from crop farming or grazing intensification is one of the potential drivers of this increase. A literature review suggests that most of the experimental evidence was generated at plot and hillslope scales. At larger scales, most studies are based on models. There are three ways in which soil compaction affects floods at the catchment scale: (i) through an increase in the area affected by soil compaction; (ii) by exacerbating the effects of changes in rainfall, especially for highly degraded soils; and (iii) when soil compaction coincides with soils characterized by a fine texture and a low infiltration capacity. We suggest that future research should focus on better synthesising past research on soil compaction and runoff, tailored field experiments to obtain a mechanistic understanding of the coupled mechanical and hydraulic processes, new mapping methods of soil compaction that combine mechanical and remote sensing approaches, and an effort to bridge all disciplines relevant to soil compaction effects on floods.

Geochemical baseline studies of soil in Finland

NASA Astrophysics Data System (ADS)

Pihlaja, Jouni

2017-04-01

The soil element concentrations regionally vary a lot in Finland. Mostly this is caused by the different bedrock types, which are reflected in the soil qualities. Geological Survey of Finland (GTK) is carrying out geochemical baseline studies in Finland. In the previous phase, the research is focusing on urban areas and mine environments. The information can, for example, be used to determine the need for soil remediation, to assess environmental impacts or to measure the natural state of soil in industrial areas or mine districts. The field work is done by taking soil samples, typically at depth between 0-10 cm. Sampling sites are chosen to represent the most vulnerable areas when thinking of human impacts by possible toxic soil element contents: playgrounds, day-care centers, schools, parks and residential areas. In the mine districts the samples are taken from the areas locating outside the airborne dust effected areas. Element contents of the soil samples are then analyzed with ICP-AES and ICP-MS, Hg with CV-AAS. The results of the geochemical baseline studies are published in the Finnish national geochemical baseline database (TAPIR). The geochemical baseline map service is free for all users via internet browser. Through this map service it is possible to calculate regional soil baseline values using geochemical data stored in the map service database. Baseline data for 17 elements in total is provided in the map service and it can be viewed on the GTK's web pages (http://gtkdata.gtk.fi/Tapir/indexEN.html).

Magnetic properties of alluvial soils contaminated with lead, zinc and cadmium

NASA Astrophysics Data System (ADS)

Petrovský, E.; Kapička, A.; Jordanova, N.; Borůvka, L.

2001-09-01

Several proxy methods have been used recently to outline increased levels of pollution. One of them is based on measurements of the concentration of (ferri)magnetic minerals of anthropogenic origin. This method has been used recently in the mapping of both polluted and unpolluted areas. In order to validate this method, a more detailed study of links between magnetic parameters characterising the physical shape of magnetic minerals and concentrations of heavy metals is needed. In this study, we analysed the magnetic characteristics of alluvial soils, formed as a result of several breakdowns of wet deposit sink of ashes from a lead ore smelter. The soils were previously analysed for concentration of lead, zinc and cadmium. Our results show that in this case of a shared source of heavy metals and magnetic minerals, simple measurements of magnetic susceptibility discriminate well between polluted and clean areas. In addition, the concentration pattern agrees with the concentrations of the heavy metals studied in deeper soil layers that were not affected by post-depositional changes due to climate and remediation efforts.

Agricultural land cover mapping in the context of a geographically referenced digital information system. [Carroll, Macon, and Gentry Counties, Missouri

NASA Technical Reports Server (NTRS)

Stoner, E. R.

1982-01-01

The introduction of soil map information to the land cover mapping process can improve discrimination of land cover types and reduce confusion among crop types that may be caused by soil-specific management practices and background reflectance characteristics. Multiple dates of LANDSAT MSS digital were analyzed for three study areas in northern Missouri to produce cover types for major agricultural land cover classes. Digital data bases were then developed by adding ancillary data such as digitized soil and transportation network information to the LANDSAT-derived cover type map. Procedures were developed to manipulate the data base parameters to extract information applicable to user requirements. An agricultural information system combining such data can be used to determine the productive capacity of land to grow crops, fertilizer needs, chemical weed control rates, irrigation suitability, and trafficability of soil for planting.

Identifying soil landscape units at the district scale by numerically clustering remote and proximal sensed data

NASA Astrophysics Data System (ADS)

Zare, Ehsan; Huang, Jingyi; Triantafilis, John

2017-04-01

Identifying soil landscape units at a district scale is important as it allows for sustainable land-use management. However, given the large number of soil properties that need to be understood and mapped, cost-effective methods are required. In this study, we use a digital soil mapping (DSM) approach where remote and proximal sensed ancillary data collected across a farming district near Bourke, are numerical clustered (fuzzy k-means: FKM) to identify soil landscape units. The remote data was obtained from an air-borne gamma-ray spectrometer survey (i.e. potassium-K, uranium-U, thorium-Th and total counts-TC). Proximal sensed data was collected using an EM38 in the horizontal (EM38h) and vertical (EM38v) mode of operation. The FKM analysis (using Mahalanobis metric) of the kriged ancillary (i.e. common 100 m grid) data revealed a fuzziness exponent (phi) of 1.4 was suitable for further analysis and that k = 4 classes was smallest for the fuzziness performance index (FPI) and normalised classification entropy (NCE). Using laboratory measured physical (i.e. clay) and chemical (i.e. CEC, ECe and pH) properties we found k = 4 was minimized in terms of mean squared prediction error (i.e. 2p,C) when considering topsoil (0-0.3 m) clay (159.76), CEC (21.943), ECe (13.56) and pH (0.2296) and subsoil (0.9-1.2 m) clay (80.81), CEC (31.251) and ECe (16.66). These sigma2p,C are smaller than those calculated using the mapped soil landscape units identified using a traditional approach. Nevertheless, class 4A represents the Aeolian soil landscape (i.e. Nb4), while 4D, represents deep grey (CC19) self-mulching clays, and 4B and 4C yellow-grey (II1) self-mulching clays adjacent to the river and clay alluvial plain, respectively. The differences in clay and CEC reveal why 4B, 4C and 4D have been extensively developed for irrigated cotton production and also why the slightly less reactive 4B might be a source of deep drainage; evidenced by smaller topsoil (2.13 dS/m) and subsoil (3.76 dS/m) ECe. The research has implications for providing meaningful DSM of soil landscape units for farmers at districts scales where traditional methods are restrictive in terms of time and cost.

Mapping soil landscape as spatial continua: The Neural Network Approach

NASA Astrophysics Data System (ADS)

Zhu, A.-Xing

2000-03-01

A neural network approach was developed to populate a soil similarity model that was designed to represent soil landscape as spatial continua for hydroecological modeling at watersheds of mesoscale size. The approach employs multilayer feed forward neural networks. The input to the network was data on a set of soil formative environmental factors; the output from the network was a set of similarity values to a set of prescribed soil classes. The network was trained using a conjugate gradient algorithm in combination with a simulated annealing technique to learn the relationships between a set of prescribed soils and their environmental factors. Once trained, the network was used to compute for every location in an area the similarity values of the soil to the set of prescribed soil classes. The similarity values were then used to produce detailed soil spatial information. The approach also included a Geographic Information System procedure for selecting representative training and testing samples and a process of determining the network internal structure. The approach was applied to soil mapping in a watershed, the Lubrecht Experimental Forest, in western Montana. The case study showed that the soil spatial information derived using the neural network approach reveals much greater spatial detail and has a higher quality than that derived from the conventional soil map. Implications of this detailed soil spatial information for hydroecological modeling at the watershed scale are also discussed.

Regional modeling of wind erosion in the North West and South West of Iran

NASA Astrophysics Data System (ADS)

Mirmousavi, S. H.

2016-08-01

About two-thirds of the Iran's area is located in the arid and semiarid region. Lack of soil moisture and vegetation is poor in most areas can lead to soil erosion caused by wind. So that the annual suffered severe damage to large areas of rich soils. Modeling studies of wind erosion in Iran is very low and incomplete. Therefore, this study aimed to wind erosion modeling, taking into three factors: wind speed, vegetation and soil types have been done. Wind erosion sensitivity was modeled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available datasets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities.

Joint USGS/USEPA Pathogens in Soils Geographic ...

EPA Pesticide Factsheets

Online interactive maps In order to protect the environment from current and potential threats posed by uncontrolled intentional releases of hazardous substances, pollutants, and contaminants, the biothreat research community recognizes the needs to be able to detect threats in the appropriate matrices and also consider whether a detected constituent is naturally occurring or a contaminant associated with an accidental or purposeful release. Therefore, sensitive and specific methods for processing and analyzing environmental samples as well as methods to determine the existing risk to the public from endemic microorganisms are needed. Background data is also an important variable for assessing and managing the risks posed by a contaminated site. The EPA has collaborated with the USGS to analyze over 4800 soil samples collected during the USGS North American Soil Geochemical Landscapes Project for the presence of Bacillus anthracis and a subset of those samples for the presence of Yersinia pestis, and Francisella tularensis. EPA and USGS scientists correlated occurrences with geochemical constituents (> 40 major and trace elements), historical outbreak data, and climate data by creating online interactive maps using a Geographic Information Systems (GIS) platform. This on-going nationwide survey can be used as an investigative tool by animal and public health scientists and emergency responders determine the potential for disease outbreaks and persistenc

The potential of UAS imagery for soil mapping at the agricultural plot scale

NASA Astrophysics Data System (ADS)

Gilliot, Jean-Marc; Michelin, Joël; Becu, Maxime; Cissé, Moustapha; Hadjar, Dalila; Vaudour, Emmanuelle

2017-04-01

Soil mapping is expensive and time consuming. Airborne and satellite remote sensing data have already been used to predict some soil properties but now Unmanned Aerial Systems (UAS) allow to do many images acquisitions in various field conditions in favour of developing methods for better prediction models construction. This study propose an operational method for spatial prediction of soil properties (organic carbon, clay) at the scale of the agricultural plot by using UAS imagery. An agricultural plot of 28 ha, located in the western region of Paris France, was studied from March to May 2016. An area of 3.6 ha was delimited within the plot and a total of 16 flights were completed. The UAS platforms used were the eBee fixed wing provided by Sensefly® flying at an altitude from 60m to 130m and the iris+ 3DR® Quadcopter (from 30m to 100m). Two multispectral visible near-infrared cameras were used: the AirInov® MultiSPEC 4C® and the Micasense® RedEdge®. 42 ground control points (GCP) were sampled within the 3.6 ha plot. A centimetric Trimble Geo 7x DGPS was used to determine precise GCP positions. On each GCP the soil horizons were described and the top soil were sampled for standard physico-chemical analysis. Ground spectral measurements with a Spectral Evolution® SR-3500 spectroradiometer were made synchronously with the drone flights. 22 additional GCP were placed around the 3.6 ha area in order to realize a precise georeferencing. The multispectral mosaics were calculated using the Agisoft Photoscan® software and all mapping processings were done with the ESRI ArcGIS® 10.3 software. The soil properties were estimated by partial least squares regression (PLSR) between the laboratory analyses and the multispectral information of the UAS images, with the PLS package of the R software. The objective was to establish a model that would achieve an acceptable prediction quality using minimum number of points. For this, we tested 5 models with a decreasing number of calibration points: 20, 15, 10, 5 and 3 points. The remaining points were used to validate the models. The point positions were determined on the basis of a soil brightness index map calculated from the UAS image, in order to distribute the points in areas of contrasted brightness. Root Mean Squared Error Prediction (RMSEP) obtained by cross-validation were 1.6 g.kg-1 and 28 g.kg-1 for organic carbon and clay respectively, with 20 points. Results showed ability to obtain acceptable precision (2 g.kg-1 and 48 g.kg-1) with only 3 points. This work was supported by the SolFIT research network of the BASC LabEx (Laboratory of Excellence) and by the TOSCA-PLEIADES-CO project of the French Space Agency (CNES).

Visible-near infrared spectroscopy as a tool to improve mapping of soil properties

NASA Astrophysics Data System (ADS)

Evgrafova, Alevtina; Kühnel, Anna; Bogner, Christina; Haase, Ina; Shibistova, Olga; Guggenberger, Georg; Tananaev, Nikita; Sauheitl, Leopold; Spielvogel, Sandra

2017-04-01

Spectroscopic measurements, which are non-destructive, precise and rapid, can be used to predict soil properties and help estimate the spatial variability of soil properties at the pedon scale. These estimations are required for quantifying soil properties with higher precision, identifying the changes in soil properties and ecosystem response to climate change as well as increasing the estimation accuracy of soil-related models. Our objectives were to (i) predict soil properties for nested samples (n = 296) using the laboratory-based visible-near infrared (vis-NIR) spectra of air-dried (<2 mm) soil samples and values of measured soil properties for gridded samples (n = 174) as calibration and validation sets; (ii) estimate the precision and predictive accuracy of an empirical spectral model using (a) our own spectral library and (b) the global spectral library; (iii) support the global spectral library with obtained vis-NIR spectral data on permafrost-affected soils. The soil samples were collected from three permafrost-affected soil profiles underlain by permafrost at various depths between 23 cm to 57.5 cm below the surface (Cryosols) and one soil profile with no presence of permafrost within the upper 100 cm layer (Cambisol) in order to characterize the spatial distribution and variability of soil properties. The gridded soil samples (n = 174) were collected using an 80 cm wide grid with a mesh size of 10 cm on both axes. In addition, 300 nested soil samples were collected using a grid of 12 cm by 12 cm (25 samples per grid) from a hole of 1 cm in a diameter with a distance from the next sample of 1 cm. Due to a small amount of available soil material (< 1.5 g), 296 nested soil samples were analyzed only using vis-NIR spectroscopy. The air-dried mineral gridded soil samples (n = 174) were sieved through a 2-mm sieve and ground with an agate mortar prior to the elemental analysis. The soil organic carbon and total nitrogen concentrations (in %) were determined using a dry combustion method on the Vario EL cube analyzer (Elementar Analysensysteme GmbH, Germany). Inorganic C was removed from the mineral soil samples with pH values higher than 7 prior to the elemental analysis using the volatilization method (HCl, 6 hours). The pH of soil samples was measured in 0.01 M CaCl2 using a 1:2 soil:solution ratio. However, for soil sample with a high in organic matter content, a 1:10 ratio was applied. We also measured oxalate and dithionite extracted iron, aluminum and manganese oxides and hydroxides using inductively coupled plasma optical emission spectroscopy (Varian Vista MPX ICP-OES, Agilent Technologies, USA). We predicted the above-mentioned soil properties for all nested samples using partial least squares regression, which was performed using R program. We can conclude that vis-NIR spectroscopy can be used effectively in order to describe, estimate and further map the spatial patterns of soil properties using geostatistical methods. This research could also help to improve the global soil spectral library taking into account that only few previous applications of vis-NIR spectroscopy were conducted on permafrost-affected soils of Northern Siberia. Keywords: Visible-near infrared spectroscopy, vis-NIR, permafrost-affected soils, Siberia, partial least squares regression.

Soil functional types: surveying the biophysical dimensions of soil security

NASA Astrophysics Data System (ADS)

Cécillon, Lauric; Barré, Pierre

2015-04-01

Soil is a natural capital that can deliver key ecosystem services (ES) to humans through the realization of a series of soil processes controlling ecosystem functioning. Soil is also a diverse and endangered natural resource. A huge pedodiversity has been described at all scales, which is strongly altered by global change. The multidimensional concept soil security, encompassing biophysical, economic, social, policy and legal frameworks of soils has recently been proposed, recognizing the role of soils in global environmental sustainability challenges. The biophysical dimensions of soil security focus on the functionality of a given soil that can be viewed as the combination of its capability and its condition [1]. Indeed, all soils are not equal in term of functionality. They show different processes, provide different ES to humans and respond specifically to global change. Knowledge of soil functionality in space and time is thus a crucial step towards the achievement soil security. All soil classification systems incorporate some functional information, but soil taxonomy alone cannot fully describe the functioning, limitations, resistance and resilience of soils. Droogers and Bouma [2] introduced functional variants (phenoforms) for each soil type (genoform) so as to fit more closely to soil functionality. However, different genoforms can have the same functionality. As stated by McBratney and colleagues [1], there is a great need of an agreed methodology for defining the reference state of soil functionality. Here, we propose soil functional types (SFT) as a relevant classification system for the biophysical dimensions of soil security. Following the definition of plant functional types widely used in ecology, we define a soil functional type as "a set of soil taxons or phenoforms sharing similar processes (e.g. soil respiration), similar effects on ecosystem functioning (e.g. primary productivity) and similar responses to global change (land-use, management or climate) for a particular soil-provided ecosystem service (e.g. climate regulation)". One SFT can thus include several soil types having the same functionality for a particular soil-provided ES. Another consequence is that SFT maps for two different ES may not superimpose over the same area, since some soils may fall in the same SFT for a service and in different SFT for another one. Soil functional types could be assessed and monitored in space and time by a combination of soil functional traits that correspond to inherent and manageable properties of soils. Their metrology would involve either classic (pedological observations) or advanced (molecular ecology, spectrometry, geophysics) tools. SFT could be studied and mapped at all scales, depending on the purpose of the soil security assessment (e.g. global climate modeling, land planning and management, biodiversity conservation). Overall, research is needed to find a pathway from soil pedological maps to SFT maps which would yield important benefits towards the assessment and monitoring of soil security. Indeed, this methodology would allow (i) reducing the spatial uncertainty on the assessment of ES; (ii) identifying and mapping multifunctional soils, which may be the most important soil resource to preserve. References [1] McBratney et al., 2014. Geoderma 213:203-213. [2] Droogers P, Bouma J, 1997. SSSAJ 61:1704-1710.

Potentials of mathematical modeling and use of GIS in catchment management and the benefits for the Water Framework Directive fulfilling

NASA Astrophysics Data System (ADS)

Dostal, T.; Krasa, J.

2009-04-01

The EU Water Framework Directive (WFD) brings relatively strict demands concerning surface waters protection, soil protection and watershed management. Water quality and soil conservation are among the priorities of European environmental policy. The aims and corresponding limits are clearly and strictly formulated but the ways how to fulfill the task remain unspecified. Moreover the side effects and synergic effects are not considered. Therefore there is no recommended methodology for implementing the protection measures. At the Faculty of Civil Engineering (Czech Technical University in Prague) we deal with development and use of various methods routinely applicable in catchment management and engineering praxis. Mainly we focus on soil conservation, sediment transport assessment, retention capacity of landscape evaluation and flood prevention. Our contribution will present overview of applicable approaches and methods useful for the WFD implementation and for Watershed management strategy defining. Very important part of the problem is use of high precision data sources available for environmental modeling. Data in similar formats and precision (considering soil properties, land use and land cover, precipitation, etc.) exist throughout Europe, but the data availability for research is very limited. In spite of the INSPIRE Directive the European coordination here is low. Typical example can be found in Map of soil loss and sediment transport within Czech Republic. Methodically simple approach (using USLE - Wischmeier et al., 1978) was applied to whole Czech territory in coordination with GIS already in 2001 (Dostal et al.,2001). The map was consistently updated and in 2007 the LPIS database allowed us to estimate soil erosion rates in scale of individual parcels (Dostal et al., 2007). Each agricultural field block was assessed in 25m resolution raster (484 835 individual parcels, 35 301 km2). The data were then used for preparing Watershed management strategy - to estimate phosphorus loads from non-point sources and to define potential prevention measures in most endangered areas. The map is nowadays accessible for any Czech region at the internet as a WMS link. It can be easily downloaded from national metadata portal http://mis.cenia.cz using a key word „eroze"to search for the map. This map can be easily updated using high precision soil map (1:5000 scale) existing for the whole Czech territory. Unfortunately the soil map was not available for the recent assessment. Next example of application, generation of the map of rainfall-runoff conditions for sub catchments with area of ca 5 - 10 km2 can be mentioned. This Map classifies individual sub catchments according to their surface runoff production as response to causal rainfall event (Vrana et al, 2004). This material helps since 2004 for decision making related to state financial subsidy policy for flood control prevention in upper parts of the catchments. Related example are also Assessments of retention capacity of riverine floodplains or urban areas flood risk by surface runoff from agricultural land, which are recently processed for entire territory of The Czech Republic. One of the basic obstructions for wider implementation of simulation models and other mathematic-based tools in practice and especially for decision making support is relatively weak coordination within EU countries. There exist valid and relatively strict regulative on entire EU level on one hand, but the methods, which should be used to determine fixed values and limits are not specified properly. The approach within individual countries is very different regarding to both of methodologies recommended or accepted and input data availability for desired calculations and designs. The third problem is insufficient foreknowledge of important decision makers (local governments and state authorities) about current state of the art in mathematical modeling and GIS application in watershed and water quality management. The above mentioned calculations and mathematical simulations are still assumed mostly being a domain of science and it is not accepted that many analysis and models were already finished to the level of practical routine applicability. Another important problem is missing relation and cooperation between environmental field (where for instance Water Framework Directive is also assumed to be included) and economical and social fields. The regulative and limits are set up in the area of agriculture on one hand, to reach economic goals in food production, but on the other hand, side effects of those measures to water quality protection are not assumed and vice-versa, watershed management measures are mostly not assessed from point of view of the effects on a desired economic field. Already (Van Rompaey et al., 2000) examined the effect of conversion of arable land into peace (conversion mostly to grassland) in agreement with European agricultural policy of food overproduction prevention, on sediment transport into water reservoirs. Based on mathematical simulations and survey between farmers he approved that unimportant increasing of proportion of converted land in certain regions can significantly influence sediment transport and water quality in given catchment. Proposed presentation tends to invoke support of international and interdepartmental cooperation in given fields and to present various possibilities of application of mathematical modeling and GIS assisted analyses on the level of practical and routine applicability for watershed management in various scales. Acknowledgement: This paper has been worked out based on the results reached with support of the project "MSMT CR VZ CEZ MSM 6840770002 - Revitalization of water systems of the landscape and urban sites, significantly affected by anthropogenic changes". References • Dostal T., Krasa J., Vaska J., Vrana K., 2001. The map of soil erosion risk and sediment transport in the Czech Republic. VUV TGM, Czech Journal for Soil and Water Management, 1: p. 1-21 • Dostal, T. et. al., 2007. Metody a zpusoby predikce povrchoveho odtoku, eroznich a transportnich procesu v krajine (The methods of surface runoff, erosion and transport processes in a landscape prediction), annual research report from COST634 action, CTU Prague (in Czech) • Van Rompaey A. et al., 2000. The impact of land use policy on the soil erosion risk: a case study of central Belgium, Agriculture, Ecosystems and Environment, pp1 - 12 • Vrana et al., 2004. The map of rainfall-runoff conditions for Central Bohemia region (in Czech), CTU Prague, • Wischmeier,W.H., Smith, D.D., 1978. Predicting Rainfall Erosion Losses - a guide for conservation planning. Agricultural Handbook 537. US Department of Agriculture

Effects of long-term soil and crop management on soil hydraulic properties for claypan soils

USDA-ARS?s Scientific Manuscript database

Regional and national soil maps have been developed along with associated soil property databases to assist users in making land management decisions based on soil characteristics. These soil properties include average values from soil characterization for each soil series. In reality, these propert...

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy.

PubMed

Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas

2013-01-01

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(-1) for mineral soils and a root mean square error of 50 g C kg(-1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy

PubMed Central

Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas

2013-01-01

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg−1 for mineral soils and a root mean square error of 50 g C kg−1 for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation. PMID:23840459

Predictable bacterial composition and hydrocarbon degradation in Arctic soils following diesel and nutrient disturbance

PubMed Central

Bell, Terrence H; Yergeau, Etienne; Maynard, Christine; Juck, David; Whyte, Lyle G; Greer, Charles W

2013-01-01

Increased exploration and exploitation of resources in the Arctic is leading to a higher risk of petroleum contamination. A number of Arctic microorganisms can use petroleum for growth-supporting carbon and energy, but traditional approaches for stimulating these microorganisms (for example, nutrient addition) have varied in effectiveness between sites. Consistent environmental controls on microbial community response to disturbance from petroleum contaminants and nutrient amendments across Arctic soils have not been identified, nor is it known whether specific taxa are universally associated with efficient bioremediation. In this study, we contaminated 18 Arctic soils with diesel and treated subsamples of each with monoammonium phosphate (MAP), which has successfully stimulated degradation in some contaminated Arctic soils. Bacterial community composition of uncontaminated, diesel-contaminated and diesel+MAP soils was assessed through multiplexed 16S (ribosomal RNA) rRNA gene sequencing on an Ion Torrent Personal Genome Machine, while hydrocarbon degradation was measured by gas chromatography analysis. Diversity of 16S rRNA gene sequences was reduced by diesel, and more so by the combination of diesel and MAP. Actinobacteria dominated uncontaminated soils with <10% organic matter, while Proteobacteria dominated higher-organic matter soils, and this pattern was exaggerated following disturbance. Degradation with and without MAP was predictable by initial bacterial diversity and the abundance of specific assemblages of Betaproteobacteria, respectively. High Betaproteobacteria abundance was positively correlated with high diesel degradation in MAP-treated soils, suggesting this may be an important group to stimulate. The predictability with which bacterial communities respond to these disturbances suggests that costly and time-consuming contaminated site assessments may not be necessary in the future. PMID:23389106

Evaluation of Electromagnetic Induction to Characterize and Map Sodium-Affected Soils in the Northern Great Plains of the United States

NASA Astrophysics Data System (ADS)

Brevik, E. C.; Heilig, J.; Kempenich, J.; Doolittle, J.; Ulmer, M.

2012-04-01

Sodium-affected soils (SAS) cover over 4 million hectares in the Northern Great Plains of the United States. Improving the classification, interpretation, and mapping of SAS is a major goal of the United States Department of Agriculture-Natural Resource Conservation Service (USDA-NRCS) as Northern Great Plains soil surveys are updated. Apparent electrical conductivity (ECa) as measured with ground conductivity meters has shown promise for mapping SAS, however, this use of this geophysical tool needs additional evaluation. This study used an EM-38 MK2-2 meter (Geonics Limited, Mississauga, Ontario), a Trimble AgGPS 114 L-band DGPS (Trimble, Sunnyvale, CA) and the RTmap38MK2 program (Geomar Software, Inc., Mississauga, Ontario) on an Allegro CX field computer (Juniper Systems, North Logan, UT) to collect, observe, and interpret ECa data in the field. The ECa map generated on-site was then used to guide collection of soil samples for soil characterization and to evaluate the influence of soil properties in SAS on ECa as measured with the EM-38MK2-2. Stochastic models contained in the ESAP software package were used to estimate the SAR and salinity levels from the measured ECa data in 30 cm depth intervals to a depth of 90 cm and for the bulk soil (0 to 90 cm). This technique showed promise, with meaningful spatial patterns apparent in the ECa data. However, many of the stochastic models used for salinity and SAR for individual depth intervals and for the bulk soil had low R-squared values. At both sites, significant variability in soil clay and water contents along with a small number of soil samples taken to calibrate the ECa values to soil properties likely contributed to these low R-squared values.

7 CFR 12.22 - Highly erodible field determination criteria.

Code of Federal Regulations, 2014 CFR

2014-01-01

... percent or more of the total field acreage is identified as soil map units which are highly erodible; or (2) 50 or more acres in such field are identified as soil map units which are highly erodible. (b...

7 CFR 12.22 - Highly erodible field determination criteria.

Code of Federal Regulations, 2013 CFR

2013-01-01

... percent or more of the total field acreage is identified as soil map units which are highly erodible; or (2) 50 or more acres in such field are identified as soil map units which are highly erodible. (b...

7 CFR 12.22 - Highly erodible field determination criteria.

Code of Federal Regulations, 2011 CFR

2011-01-01

... percent or more of the total field acreage is identified as soil map units which are highly erodible; or (2) 50 or more acres in such field are identified as soil map units which are highly erodible. (b...

7 CFR 12.22 - Highly erodible field determination criteria.

Code of Federal Regulations, 2012 CFR

2012-01-01

... percent or more of the total field acreage is identified as soil map units which are highly erodible; or (2) 50 or more acres in such field are identified as soil map units which are highly erodible. (b...

On Campus Activity Guide. Environmental Education.

ERIC Educational Resources Information Center

Pinellas County School Board, Clearwater, FL.

Descriptions of about 100 secondary-level activities that can be done on the school grounds are presented. Among the lessons included are a study of life in sidewalk cracks, methods of estimating animal populations, soil testing, constructing and using triangulation instruments to map the school area, and creative writing exercises. Although most…