Spatial Dependence of Physical Attributes and Mechanical Properties of Ultisol in a Sugarcane Field.

PubMed

Tavares, Uilka Elisa; Rolim, Mário Monteiro; de Oliveira, Veronildo Souza; Pedrosa, Elvira Maria Regis; Siqueira, Glécio Machado; Magalhães, Adriana Guedes

2015-01-01

This study investigates the effect of conventional tillage and application of the monoculture of sugar cane on soil health. Variables like density, moisture, texture, consistency limits, and preconsolidation stress were taken as indicators of soil quality. The measurements were made at a 120 × 120 m field cropped with sugar cane under conventional tillage. The objective of this work was to characterize the soil and to study the spatial dependence of the physical and mechanical attributes. Then, undisturbed soil samples were collected to measure bulk density, moisture content and preconsolidation stress and disturbed soil samples for classification of soil texture, and consistency limits. The soil texture indicated that soil can be characterized as sandy clay soil and a sandy clay loam soil, and the consistency limits indicated that the soil presents an inorganic low plasticity clay. The preconsolidation tests tillage in soil moisture content around 19% should be avoided or should be chosen a management of soil with lighter vehicles in this moisture content, to avoid risk of compaction. Using geostatistical techniques mapping was possible to identify areas of greatest conservation soil and greater disturbance of the ground.

Spatial Dependence of Physical Attributes and Mechanical Properties of Ultisol in a Sugarcane Field

PubMed Central

Tavares, Uilka Elisa; Monteiro Rolim, Mário; Souza de Oliveira, Veronildo; Maria Regis Pedrosa, Elvira; Siqueira, Glécio Machado; Guedes Magalhães, Adriana

2015-01-01

This study investigates the effect of conventional tillage and application of the monoculture of sugar cane on soil health. Variables like density, moisture, texture, consistency limits, and preconsolidation stress were taken as indicators of soil quality. The measurements were made at a 120 × 120 m field cropped with sugar cane under conventional tillage. The objective of this work was to characterize the soil and to study the spatial dependence of the physical and mechanical attributes. Then, undisturbed soil samples were collected to measure bulk density, moisture content and preconsolidation stress and disturbed soil samples for classification of soil texture, and consistency limits. The soil texture indicated that soil can be characterized as sandy clay soil and a sandy clay loam soil, and the consistency limits indicated that the soil presents an inorganic low plasticity clay. The preconsolidation tests tillage in soil moisture content around 19% should be avoided or should be chosen a management of soil with lighter vehicles in this moisture content, to avoid risk of compaction. Using geostatistical techniques mapping was possible to identify areas of greatest conservation soil and greater disturbance of the ground. PMID:26167528

Is the textural classification built on sand?

USDA-ARS?s Scientific Manuscript database

In 1967, the Committee of the Soil Science Society of America noted that the current system of particle size boundaries arose due to geographic accident. The committee noted that there is “no narrowly defineable natural particle size boundaries that would be equally significant in all soil materials...

An overview of impact of subsurface drainage project studies on salinity management in developing countries

NASA Astrophysics Data System (ADS)

Tiwari, Priyanka; Goel, Arun

2017-05-01

Subsurface drainage has been used for more than a century to keep water table at a desired level of salinity and waterlogging control. This paper has been focused on the impact assessment of pilot studies in India and some other countries from 1969 to 2014 . This review article may prove quite useful in deciding the installation of subsurface drainage project depending on main design parameters, such as drain depth and drain spacing, installation area and type of used outlet. A number of pilot studies have been taken up in past to solve the problems of soil salinity and waterlogging in India. The general guidelines that arise on the behalf of this review paper are to adapt drain depth >1.2 m and spacing depending on soil texture classification, i.e., 100-150 m for light-textured soils, 50-100 m for medium-textured soils and 30-50 m heavy-textured soils, for better result obtained from the problem areas in Indian soil and climatic conditions. An attempt has been made in the manner of literature survey to highlight the salient features of these studies, and it is hopeful to go a long way in selecting design parameters for subsurface drainage problems in the future with similar soil, water table and climatic conditions.

Uncertainties and Solutions Related to Use of WRB (2007) in the Boreo-nemoral zone, Case of Latvia

NASA Astrophysics Data System (ADS)

Kasparinskis, Raimonds; Nikodemus, Olgerts; Rolavs, Nauris

2014-05-01

Relatively high diversity of soils groups according to the WRB (2007) classification is observed in forest ecosystems in the boreo-nemoral zone in Latvia. This is due to the geological genesis of area and environmental conditions (Kasparinskis, Nikodemus, 2012), as well as historical land use and management (Nikodemus et al., 2013). Due to the relatively young soils, Albic, Spodic and Cambic horizons are relatively weakly expressed in many cases. Relatively well developed Albic horizons occur in sandy forest soils, but unusually well expressed Spodic features are observed. In some cases there is a Cambic horizon, however location of Cambisols in the WRB (2007) soil classification sequence does not provide an opportunity to classify these soils as Cambisols, but they are classified as Arenosols. This sequence does not reflect the logical sheme of soil development, and therefore raises the question about location of Podzols, Arenosols and Cambisols in the sequence of WRB (2007) soil classification. Soils with two parent materials (abrupt textural change) are relatively common in Latvia, where conceptually on the small scale mapping results in classification as the soil group Planosols, but in many cases there is occurrence of Fluvic materials, as parent material in the upper part of the soil profile is formed by Baltic Ice lake sandy sediments - this leads to question about the location of Fluvisols and Planosols in the sequence of the WRB (2007) soil classification. Soil research has found cases, where a relatively well developed Spodic horizon was established as the result of ground water table depth in areas of abrupt textural change. In this case the profile corresponds to the soil group of Podzols, however in some cases - Gleysols not Planosols due to a high ground water table. Therefore there is a need for discussion also about the location of Podzols and Planosols in the sequence of the WRB (2007) soil classification. The above mentioned questions raise problems related to unambiguous determination of soil groups. Soil classification must be very precise by reflecting relationships of soil forming processes. In the development of international soil classification it is advisable to pay more attention on ecological processes. This study was supported by the European Social Fund No. 2013/0020/1DP/1.1.1.2.0/13/APIA/VIAA/066. References: IUSS Working Group, 2007. World Reference Base for Soil Resources 2006, first update 2007. World Soil Resources Reports 103. FAO, Rome. 103-116. Kasparinskis R., Nikodemus O. 2012. Influence of environmental factors on the spatial distribution and diversity of forest soil in Latvia. Estonian Journal of Earth Sciences. 61(1): 48-64. Nikodemus O., Kasparinskis R., Kukuls I. 2013. Influence of Afforestation on Soil Genesis, Morphology and Properties in Glacial Till Deposits. Archives of Agronomy and Soil Science. 59(3): 449-465.

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.

Mapping soil texture classes and optimization of the result by accuracy assessment

NASA Astrophysics Data System (ADS)

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

2014-05-01

There are increasing demands nowadays on spatial soil information in order to support environmental related and land use management decisions. The GlobalSoilMap.net (GSM) project aims to make a new digital soil map of the world using state-of-the-art and emerging technologies for soil mapping and predicting soil properties at fine resolution. Sand, silt and clay are among the mandatory GSM soil properties. Furthermore, soil texture class information is input data of significant agro-meteorological and hydrological models. Our present work aims to compare and evaluate different digital soil mapping methods and variables for producing the most accurate spatial prediction of texture classes in Hungary. In addition to the Hungarian Soil Information and Monitoring System as our basic data, digital elevation model and its derived components, geological database, and physical property maps of the Digital Kreybig Soil Information System have been applied as auxiliary elements. Two approaches have been applied for the mapping process. At first the sand, silt and clay rasters have been computed independently using regression kriging (RK). From these rasters, according to the USDA categories, we have compiled the texture class map. Different combinations of reference and training soil data and auxiliary covariables have resulted several different maps. However, these results consequentially include the uncertainty factor of the three kriged rasters. Therefore we have suited data mining methods as the other approach of digital soil mapping. By working out of classification trees and random forests we have got directly the texture class maps. In this way the various results can be compared to the RK maps. The performance of the different methods and data has been examined by testing the accuracy of the geostatistically computed and the directly classified results. We have used the GSM methodology to assess the most predictive and accurate way for getting the best among the several result maps. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Global digital data sets of soil type, soil texture, surface slope and other properties: Documentation of archived data tape

NASA Technical Reports Server (NTRS)

Staub, B.; Rosenzweig, C.; Rind, D.

1987-01-01

The file structure and coding of four soils data sets derived from the Zobler (1986) world soil file is described. The data were digitized on a one-degree square grid. They are suitable for large-area studies such as climate research with general circulation models, as well as in forestry, agriculture, soils, and hydrology. The first file is a data set of codes for soil unit, land-ice, or water, for all the one-degree square cells on Earth. The second file is a data set of codes for texture, land-ice, or water, for the same soil units. The third file is a data set of codes for slope, land-ice, or water for the same units. The fourth file is the SOILWRLD data set, containing information on soil properties of land cells of both Matthews' and Food and Agriculture Organization (FAO) sources. The fourth file reconciles land-classification differences between the two and has missing data filled in.

NDVI and Panchromatic Image Correlation Using Texture Analysis

DTIC Science & Technology

2010-03-01

6 Figure 5. Spectral reflectance of vegetation and soil from 0.4 to 1.1 mm (From Perry...should help the classification methods to be able to classify kelp. Figure 5. Spectral reflectance of vegetation and soil from 0.4 to 1.1 mm...1988). Image processing software for imaging spectrometry analysis. Remote Sensing of Enviroment , 24: 201–210. Perry, C., & Lautenschlager, L. F

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.

Ch'ol nomenclature for soil classification in the ejido Oxolotán, Tacotalpa, Tabasco, México.

PubMed

Sánchez-Hernández, Rufo; Méndez-De la Cruz, Lucero; Palma-López, David J; Bautista-Zuñiga, Francisco

2018-05-30

The traditional ecological knowledge of land of the Ch'ol originary people from southeast Mexico forms part of their cultural identity; it is local and holistic and implies an integrated physical and spiritual worldview that contributes to improve their living conditions. We analyzed the nomenclature for soil classification used in the Mexican state of Tabasco by the Ch'ol farmers with the objective of contributing to the knowledge of the Maya soil classification. A map of the study area was generated from the digital database of parcels in the ejido Oxolotán in the municipality of Tacotalpa, to which a geopedological map was overlaid in order to obtain modeled topographic profiles (Zavala-Cruz et al., Ecosistemas y Recursos Agropecuarios 3:161-171, 2016). In each modeled profile, a soil profile was made and classified according to IUSS Working Group WRB (181, 2014) in order to generate a map of soil groups, which was used to survey the study area with the participation of 245 local Ch'ol farmers for establishing an ethnopedological soil classification (Ortiz et al.: 62, 1990). In addition, we organized a participatory workshop with 35 people to know details of the names of the soils and their indicators of fertility and workability, from which we selected 15 participants for field trips and description of soil profiles. The color, texture, and stoniness are attributes important in the Ch'ol nomenclature, although the names do not completely reflect the visible characteristic of the soil surface. On the other hand, the mere presence of stones is sufficient to name a land class, while according to IUSS Working Group WRB (181, 2014), a certain amount and distribution of stones in the soil profiles is necessary to be taken into consideration in the name. Perception of soil quality by local farmers considers the compaction or hardness of the cultivable soil layer, because of which black or sandy soils are perceived as better for cultivation of banana, or as secondary vegetation in fallow. Red, yellow, or brown soils are seen as of less quality and are only used for establishing grasslands, while maize is cultivated in all soil classes. Farmers provided the Ch'ol nomenclature, perceived problems, and uses of each class of soil. Translation of Ch'ol soil names and comparison with descriptions of soil profiles revealed that the Ch'ol soil nomenclature takes into account the soil profile, given it is based on characteristics of both surface and subsurface horizons including color of soil matrix and mottles, stoniness, texture, and vegetation.

What makes up marginal lands and how can it be defined and classified?

NASA Astrophysics Data System (ADS)

Ivanina, Vadym

2017-04-01

Definitions of marginal lands are often not explicit. The term "marginal" is not supported by either a precise definition or research to determine which lands fall into this category. To identify marginal lands terminology/methodology is used which varies between physical characteristics and the current land use of a site as basic perspective. The term 'Marginal' is most commonly followed by 'degraded' lands, and other widely used terms such as 'abandoned', 'idle', 'pasture', 'surplus agricultural land', 'Conservation Reserve Programme' (CRP)', 'barren and carbon-poor land', etc. Some terms are used synonymously. To the category of "marginal" lands are predominantly included lands which are excluded from cultivation due to economic infeasibility or physical restriction for growing conventional crops. Such sites may still have potential to be used for alternative agricultural practice, e.g. bioenergy feedstock production. The existing categorizing of marginal lands does not allow evaluating soil fertility potential or to define type and level of constrains for growing crops as the reason of a low practical value with regards to land use planning. A new marginal land classification has to be established and developed. This classification should be built on criteria of soil biophysical properties, ecologic, environment and climate handicaps for growing crops, be easy in use and of high practical value. The SEEMLA consortium made steps to build such a marginal land classification which is based on direct criteria depicting soil properties and constrains, and defining their productivity potential. By this classification marginal lands are divided into eleven categories: shallow rooting, low fertility, stony texture, sandy texture, clay texture, salinic, sodicic, acidic, overwet, eroded, and contaminated. The basis of this classification was taken criteria modified after and adapted from Regulation EU (1305)2013. To define an area of marginal lands with climate and economic limitations, SEEMLA established and implemented the term "area of land marginality" with a broader on marginal lands. This term includes marginal lands themselves, evaluation of climate constrains and economic efficiency to grow crops. This approach allows to define, categorize and classify marginal land by direct indicators of soil biophysical properties, ecologic and environment constrains, and provides additional evaluation of lands marginality with regards to suitability for growing crops based on climate criteria.

Potential impacts of robust surface roughness indexes on DTM-based segmentation

NASA Astrophysics Data System (ADS)

Trevisani, Sebastiano; Rocca, Michele

2017-04-01

In this study, we explore the impact of robust surface texture indexes based on MAD (median absolute differences), implemented by Trevisani and Rocca (2015), in the unsupervised morphological segmentation of an alpine basin. The area was already object of a geomorphometric analysis, consisting in the roughness-based segmentation of the landscape (Trevisani et al. 2012); the roughness indexes were calculated on a high resolution DTM derived by means of airborne Lidar using the variogram as estimator. The calculated roughness indexes have been then used for the fuzzy clustering (Odeh et al., 1992; Burrough et al., 2000) of the basin, revealing the high informative geomorphometric content of the roughness-based indexes. However, the fuzzy clustering revealed a high fuzziness and a high degree of mixing between textural classes; this was ascribed both to the morphological complexity of the basin and to the high sensitivity of variogram to non-stationarity and signal-noise. Accordingly, we explore how the new implemented roughness indexes based on MAD affect the morphological segmentation of the studied basin. References Burrough, P.A., Van Gaans, P.F.M., MacMillan, R.A., 2000. High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems 113, 37-52. Odeh, I.O.A., McBratney, A.B., Chittleborough, D.J., 1992. Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Sciences Society of America Journal 56, 505-516. Trevisani, S., Cavalli, M. & Marchi, L. 2012, "Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin", Geomorphology, vol. 161-162, pp. 26-39. Trevisani, S. & Rocca, M. 2015, "MAD: Robust image texture analysis for applications in high resolution geomorphometry", Computers and Geosciences, vol. 81, pp. 78-92.

New best estimates for radionuclide solid-liquid distribution coefficients in soils. Part 2: naturally occurring radionuclides.

PubMed

Vandenhove, H; Gil-García, C; Rigol, A; Vidal, M

2009-09-01

Predicting the transfer of radionuclides in the environment for normal release, accidental, disposal or remediation scenarios in order to assess exposure requires the availability of an important number of generic parameter values. One of the key parameters in environmental assessment is the solid liquid distribution coefficient, K(d), which is used to predict radionuclide-soil interaction and subsequent radionuclide transport in the soil column. This article presents a review of K(d) values for uranium, radium, lead, polonium and thorium based on an extensive literature survey, including recent publications. The K(d) estimates were presented per soil groups defined by their texture and organic matter content (Sand, Loam, Clay and Organic), although the texture class seemed not to significantly affect K(d). Where relevant, other K(d) classification systems are proposed and correlations with soil parameters are highlighted. The K(d) values obtained in this compilation are compared with earlier review data.

Determining and representing width of soil boundaries using electrical conductivity and MultiGrid

NASA Astrophysics Data System (ADS)

Greve, Mogens Humlekrog; Greve, Mette Balslev

2004-07-01

In classical soil mapping, map unit boundaries are considered crisp even though all experienced survey personnel are aware of the fact, that soil boundaries really are transition zones of varying width. However, classification of transition zone width on site is difficult in a practical survey. The objective of this study is to present a method for determining soil boundary width and a way of representing continuous soil boundaries in GIS. A survey was performed using the non-contact conductivity meter EM38 from Geonics Inc., which measures the bulk Soil Electromagnetic Conductivity (SEC). The EM38 provides an opportunity to classify the width of transition zones in an unbiased manner. By calculating the spatial rate of change in the interpolated EM38 map across the crisp map unit delineations from a classical soil mapping, a measure of transition zone width can be extracted. The map unit delineations are represented as transition zones in a GIS through a concept of multiple grid layers, a MultiGrid. Each layer corresponds to a soil type and the values in a layer represent the percentage of that soil type in each cell. As a test, the subsoil texture was mapped at the Vindum field in Denmark using both the classical mapping method with crisp representation of the boundaries and the new map with MultiGrid and continuous boundaries. These maps were then compared to an independent reference map of subsoil texture. The improvement of the prediction of subsoil texture, using continuous boundaries instead of crisp, was in the case of the Vindum field, 15%.

Multi-Scale Soil Moisture Monitoring and Modeling at ARS Watersheds for NASA's Soil Moisture Active Passive (SMAP) Calibration/Validation Mission

NASA Astrophysics Data System (ADS)

Coopersmith, E. J.; Cosh, M. H.

2014-12-01

NASA's SMAP satellite, launched in November of 2014, produces estimates of average volumetric soil moisture at 3, 9, and 36-kilometer scales. The calibration and validation process of these estimates requires the generation of an identically-scaled soil moisture product from existing in-situ networks. This can be achieved via the integration of NLDAS precipitation data to perform calibration of models at each ­in-situ gauge. In turn, these models and the gauges' volumetric estimations are used to generate soil moisture estimates at a 500m scale throughout a given test watershed by leveraging, at each location, the gauge-calibrated models deemed most appropriate in terms of proximity, calibration efficacy, soil-textural similarity, and topography. Four ARS watersheds, located in Iowa, Oklahoma, Georgia, and Arizona are employed to demonstrate the utility of this approach. The South Fork watershed in Iowa represents the simplest case - the soil textures and topography are relative constants and the variability of soil moisture is simply tied to the spatial variability of precipitation. The Little Washita watershed in Oklahoma adds soil textural variability (but remains topographically simple), while the Little River watershed in Georgia incorporates topographic classification. Finally, the Walnut Gulch watershed in Arizona adds a dense precipitation network to be employed for even finer-scale modeling estimates. Results suggest RMSE values at or below the 4% volumetric standard adopted for the SMAP mission are attainable over the desired spatial scales via this integration of modeling efforts and existing in-situ networks.

Identification and classification of structural soil conservation measures based on very high resolution stereo satellite data.

PubMed

Eckert, Sandra; Tesfay Ghebremicael, Selamawit; Hurni, Hans; Kohler, Thomas

2017-05-15

Land degradation affects large areas of land around the globe, with grave consequences for those living off the land. Major efforts are being made to implement soil and water conservation measures that counteract soil erosion and help secure vital ecosystem services. However, where and to what extent such measures have been implemented is often not well documented. Knowledge about this could help to identify areas where soil and water conservation measures are successfully supporting sustainable land management, as well as areas requiring urgent rehabilitation of conservation structures such as terraces and bunds. This study explores the potential of the latest satellite-based remote sensing technology for use in assessing and monitoring the extent of existing soil and water conservation structures. We used a set of very high resolution stereo Geoeye-1 satellite data, from which we derived a detailed digital surface model as well as a set of other spectral, terrain, texture, and filtered information layers. We developed and applied an object-based classification approach, working on two segmentation levels. On the coarser level, the aim was to delimit certain landscape zones. Information about these landscape zones is useful in distinguishing different types of soil and water conservation structures, as each zone contains certain specific types of structures. On the finer level, the goal was to extract and identify different types of linear soil and water conservation structures. The classification rules were based mainly on spectral, textural, shape, and topographic properties, and included object relationships. This approach enabled us to identify and separate from other classes the majority (78.5%) of terraces and bunds, as well as most hillside terraces (81.25%). Omission and commission errors are similar to those obtained by the few existing studies focusing on the same research objective but using different types of remotely sensed data. Based on our results, we estimate that the construction of the conservation structures in our study area in Eritrea required over 300,000 person-days of work, which underlines the huge efforts involved in soil and water conservation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fusion method of SAR and optical images for urban object extraction

NASA Astrophysics Data System (ADS)

Jia, Yonghong; Blum, Rick S.; Li, Fangfang

2007-11-01

A new image fusion method of SAR, Panchromatic (Pan) and multispectral (MS) data is proposed. First of all, SAR texture is extracted by ratioing the despeckled SAR image to its low pass approximation, and is used to modulate high pass details extracted from the available Pan image by means of the á trous wavelet decomposition. Then, high pass details modulated with the texture is applied to obtain the fusion product by HPFM (High pass Filter-based Modulation) fusion method. A set of image data including co-registered Landsat TM, ENVISAT SAR and SPOT Pan is used for the experiment. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, and the proposed approach is effective for image interpret and classification.

Wavelet-based image analysis system for soil texture analysis

NASA Astrophysics Data System (ADS)

Sun, Yun; Long, Zhiling; Jang, Ping-Rey; Plodinec, M. John

2003-05-01

Soil texture is defined as the relative proportion of clay, silt and sand found in a given soil sample. It is an important physical property of soil that affects such phenomena as plant growth and agricultural fertility. Traditional methods used to determine soil texture are either time consuming (hydrometer), or subjective and experience-demanding (field tactile evaluation). Considering that textural patterns observed at soil surfaces are uniquely associated with soil textures, we propose an innovative approach to soil texture analysis, in which wavelet frames-based features representing texture contents of soil images are extracted and categorized by applying a maximum likelihood criterion. The soil texture analysis system has been tested successfully with an accuracy of 91% in classifying soil samples into one of three general categories of soil textures. In comparison with the common methods, this wavelet-based image analysis approach is convenient, efficient, fast, and objective.

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.

Visual soil evaluation - future research requirements

NASA Astrophysics Data System (ADS)

Emmet-Booth, Jeremy; Forristal, Dermot; Fenton, Owen; Ball, Bruce; Holden, Nick

2017-04-01

A review of Visual Soil Evaluation (VSE) techniques (Emmet-Booth et al., 2016) highlighted their established utility for soil quality assessment, though some limitations were identified; (1) The examination of aggregate size, visible intra-porosity and shape forms a key assessment criterion in almost all methods, thus limiting evaluation to structural form. The addition of criteria that holistically examine structure may be desirable. For example, structural stability can be indicated using dispersion tests or examining soil surface crusting, while the assessment of soil colour may indirectly indicate soil organic matter content, a contributor to stability. Organic matter assessment may also indicate structural resilience, along with rooting, earthworm numbers or shrinkage cracking. (2) Soil texture may influence results or impeded method deployment. Modification of procedures to account for extreme texture variation is desirable. For example, evidence of compaction in sandy or single grain soils greatly differs to that in clayey soils. Some procedures incorporate separate classification systems or adjust deployment based on texture. (3) Research into impacts of soil moisture content on VSE evaluation criteria is required. Criteria such as rupture resistance and shape may be affected by moisture content. It is generally recommended that methods are deployed on moist soils and quantification of influences of moisture variation on results is necessary. (4) Robust sampling strategies for method deployment are required. Dealing with spatial variation differs between methods, but where methods can be deployed over large areas, clear instruction on sampling is required. Additionally, as emphasis has been placed on the agricultural production of soil, so the ability of VSE for exploring structural quality in terms of carbon storage, water purification and biodiversity support also requires research. References Emmet-Booth, J.P., Forristal. P.D., Fenton, O., Ball, B.C. & Holden, N.M. 2016. A review of visual soil evaluation techniques for soil structure. Soil Use and Management, 32, 623-634.

Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement

NASA Astrophysics Data System (ADS)

Bangs, Corey F.; Kruse, Fred A.; Olsen, Chris R.

2013-05-01

Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture feature data on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were used from the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) portions of the electromagnetic spectrum. Haralick texture features (contrast, entropy, and correlation) were extracted from the average gray-level image for each of the four spectral ranges studied. A maximum likelihood classifier was trained using a set of ground truth regions of interest (ROIs) and applied separately to the spectral data, texture data, and a fused dataset containing both. Classification accuracy was measured by comparison of results to a separate verification set of test ROIs. Analysis indicates that the spectral range (source of the gray-level image) used to extract the texture feature data has a significant effect on the classification accuracy. This result applies to texture-only classifications as well as the classification of integrated spectral data and texture feature data sets. Overall classification improvement for the integrated data sets was near 1%. Individual improvement for integrated spectral and texture classification of the "Urban" class showed approximately 9% accuracy increase over spectral-only classification. Texture-only classification accuracy was highest for the "Dirt Path" class at approximately 92% for the spectral range from 947 to 1343nm. This research demonstrates the effectiveness of texture feature data for more accurate analysis of hyperspectral data and the importance of selecting the correct spectral range to be used for the gray-level image source to extract these features.

Sensitivity of Photosynthetic Gas Exchange and Growth of Lodgepole Pine to Climate Variability Depends on the Age of Pleistocene Glacial Surfaces

NASA Astrophysics Data System (ADS)

Osborn, B.; Chapple, W.; Ewers, B. E.; Williams, D. G.

2014-12-01

The interaction between soil conditions and climate variability plays a central role in the ecohydrological functions of montane conifer forests. Although soil moisture availability to trees is largely dependent on climate, the depth and texture of soil exerts a key secondary influence. Multiple Pleistocene glacial events have shaped the landscape of the central Rocky Mountains creating a patchwork of soils differing in age and textural classification. This mosaic of soil conditions impacts hydrological properties, and montane conifer forests potentially respond to climate variability quite differently depending on the age of glacial till and soil development. We hypothesized that the age of glacial till and associated soil textural changes exert strong control on growth and photosynthetic gas exchange of lodgepole pine. We examined physiological and growth responses of lodgepole pine to interannual variation in maximum annual snow water equivalence (SWEmax) of montane snowpack and growing season air temperature (Tair) and vapor pressure deficit (VPD) across a chronosequence of Pleistocene glacial tills ranging in age from 700k to 12k years. Soil textural differences across the glacial tills illustrate the varying degrees of weathering with the most well developed soils with highest clay content on the oldest till surfaces. We show that sensitivity of growth and carbon isotope discrimination, an integrated measure of canopy gas exchange properties, to interannual variation SWEmax , Tair and VPD is greatest on young till surfaces, whereas trees on old glacial tills with well-developed soils are mostly insensitive to these interannual climate fluctuations. Tree-ring widths were most sensitive to changes in SWEmax on young glacial tills (p < 0.01), and less sensitive on the oldest till (p < 0.05). Tair correlates strongly with δ13C values on the oldest and youngest tills sites, but shows no significant relationship on the middle aged glacial till. It is clear that growth and photosynthetic gas exchange parameters are sensitive to glacial till surfaces, which is evident by the different responses to SWEmax and Tair across sites.

Interactions between soil texture, water, and nutrients control patterns of biocrusts abundance and structure

NASA Astrophysics Data System (ADS)

Young, Kristina; Bowker, Matthew; Reed, Sasha; Howell, Armin

2017-04-01

Heterogeneity in the abiotic environment structures biotic communities by controlling niche space and parameters. This has been widely observed and demonstrated in vascular plant and other aboveground communities. While soil organisms are presumably also strongly influenced by the physical and chemical dimensions of the edaphic environment, there are fewer studies linking the development, structure, productivity or function of surface soil communities to specific edaphic gradients. Here, we use biological soil crusts (biocrusts) as a model system to determine mechanisms regulating community structure of soil organisms. We chose soil texture to serve as an edaphic gradient because of soil texture's influence over biocrust distribution on a landscape level. We experimentally manipulated texture in constructed soil, and simultaneously manipulated two main outcomes of texture, water and nutrient availability, to determine the mechanism underlying texture's influence on biocrust abundance and structure. We grew biocrust communities from a field-sourced inoculum on four different soil textures, sieved from the same parent soil material, manipulating watering levels and nutrient additions across soil textures in a full-factorial design over a 5-month period of time. We measured abundance and structure of biocrusts over time, and measured two metrics of function, N2 fixation rates and soil stabilization, at the conclusion of the experiment. Our results showed finer soil textures resulted in faster biocrust community development and dominance by mosses, whereas coarser textures grew more slowly and had biocrust communities dominated by cyanobacteria and lichen. Additionally, coarser textured soils contained cyanobacterial filaments significantly deeper into the soil profile than fine textured soils. N2-fixation values increased with increasing moss cover and decreased with increasing cyanobacterial cover, however, the rate of change depended on soil texture and water amount. Soil shear resistance was highest on finer textured soil with the highest watering treatment, whereas compression resistance was highest on the coarsest textured soils with the highest watering amounts. Nutrient addition did not influence total cover or biocrust function, but did decrease lichen cover. Taken together, these results suggest that interactions between soil texture, water, and to a lesser degree nutrients, create predictable patterns in biocrust assemblage and offers a mechanistic understanding of edaphic controls over biocrust abundance and structure. These insights add to our increasing understanding of how edaphic gradients structure soil communities.

Classification of andisol soil on robusta coffee plantation in Silima Pungga - Pungga District

NASA Astrophysics Data System (ADS)

Marbun, P.; Nasution, Z.; Hanum, H.; Karim, A.

2018-02-01

The survey study aims to classify the Inceptisol soil on Robusta coffee plantation in Silima Pugga-Pungga District, from Order level to Sub Group level. The study was conducted on location of sample soil profiles which were determined based on Soil Map Unit (SMU) with the main Andisol Order, i.e. SMU 12, SMU 15 and SMU 17 of 18 existing SMU. The soil profiles were described to determine the morphological characteristics of the soil, while the physical and chemical properties were done by laboratory analysis. The soil samples were taken from each horizon in each profile and analyzed in the laboratory in the form of soil texture, bulk density, pH H2O, pH KCl, pH NaF, C-organic, exchangeable bases (Ca2+, Mg2+, K+, Na+), ZPC (zero point charge), base saturation, cation exchange capasity (CEC), P-retention, Al-Oxalate (Al-O) and Si-Oxalate (Si-O). The results showed that the classification of Andisol soil based on Soil Taxonomy only has one Sub Group namely Typic Hapludand. It is expected that the results of this study can provide information for more appropriate land management in order to increase the production of Robusta coffee plant in Silima Pungga-Pungga Sub district.

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.

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

Effect of soil texture on the microwave emission from soils

NASA Technical Reports Server (NTRS)

Schmugge, T. J.

1980-01-01

The intensity brightness temperature of the microwave emission from the soil is determined primarily by its dielectric properties. The large difference between the dielectric constant of water and that of dry soil produces a strong dependence of the soil's dielectric constant on its moisture content. This dependence is effected by the texture of the soil because the water molecules close to the particle surface are tightly bound and do not contribute significantly to the dielectric properties. Since this surface area is a function of the particle size distribution (soil texture), being larger for clay soils with small particles, and smaller for sandy soils with larger particles; the dielectric properties will depend on soil texture. Laboratory measurements of the dielectric constant for soils are summarized. The dependence of the microwave emission on texture is demonstrated by measurements of brightness temperature from an aircraft platform for a wide range of soil textures. It is concluded that the effect of soil texture differences on the observed values can be normalized by expressing the soil moisture values as a percent field capacity for the soil.

Modified centroid for estimating sand, silt, and clay from soil texture class

USDA-ARS?s Scientific Manuscript database

Models that require inputs of soil particle size commonly use soil texture class for input; however, texture classes do not represent the continuum of soil size fractions. Soil texture class and clay percentage are collected as a standard practice for many land management agencies (e.g., NRCS, BLM, ...

Orientation selectivity based structure for texture classification

NASA Astrophysics Data System (ADS)

Wu, Jinjian; Lin, Weisi; Shi, Guangming; Zhang, Yazhong; Lu, Liu

2014-10-01

Local structure, e.g., local binary pattern (LBP), is widely used in texture classification. However, LBP is too sensitive to disturbance. In this paper, we introduce a novel structure for texture classification. Researches on cognitive neuroscience indicate that the primary visual cortex presents remarkable orientation selectivity for visual information extraction. Inspired by this, we investigate the orientation similarities among neighbor pixels, and propose an orientation selectivity based pattern for local structure description. Experimental results on texture classification demonstrate that the proposed structure descriptor is quite robust to disturbance.

Natural Processes Influencing Terrain Attributes. Report 1. Prediction of Residual Soil Texture in Humid Temperate Climates of the Federal Republic of Germany and Selected Analogous Portions of the United States-Pilot Study,

DTIC Science & Technology

1982-06-01

use of such commercial products . TTnv Ioa1 fi Ar 4 SECURITY CLASSIFICATION OF THIS PAGE (When Data _ _ne__D_ REPORT DOCUMENTATION PAGE BEFORE INSTCTIONS...in terrain are subject to modification by cultural processes indigenous to the area of study. Two areas, in close proximity and each the product of...residual soil is a reflec- tion of the combined weathering products of all of the rocks. At present, there appears to be no adequate solution to this

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.

The Effects of Soil Texture on the Ability of Human Remains Detection Dogs to Detect Buried Human Remains.

PubMed

Alexander, Michael B; Hodges, Theresa K; Wescott, Daniel J; Aitkenhead-Peterson, Jacqueline A

2016-05-01

Despite technological advances, human remains detection (HRD) dogs still remain one of the best tools for locating clandestine graves. However, soil texture may affect the escape of decomposition gases and therefore the effectiveness of HDR dogs. Six nationally credentialed HRD dogs (three HRD only and three cross-trained) were evaluated on novel buried human remains in contrasting soils, a clayey and a sandy soil. Search time and accuracy were compared for the clayey soil and sandy soil to assess odor location difficulty. Sandy soil (p < 0.001) yielded significantly faster trained response times, but no significant differences were found in performance accuracy between soil textures or training method. Results indicate soil texture may be significant factor in odor detection difficulty. Prior knowledge of soil texture and moisture may be useful for search management and planning. Appropriate adjustments to search segment sizes, sweep widths and search time allotment depending on soil texture may optimize successful detection. © 2016 American Academy of Forensic Sciences.

Assessing quality of citizen scientists’ soil texture estimates to evaluate land potential

USDA-ARS?s Scientific Manuscript database

Texture influences nearly all soil processes and is often the most measured parameter in soil science. Estimating soil texture is a universal and fundamental practice applied by resource scientists to classify and understand the behavior and management of soil systems. While trained soil scientist c...

A research of selected textural features for detection of asbestos-cement roofing sheets using orthoimages

NASA Astrophysics Data System (ADS)

Książek, Judyta

2015-10-01

At present, there has been a great interest in the development of texture based image classification methods in many different areas. This study presents the results of research carried out to assess the usefulness of selected textural features for detection of asbestos-cement roofs in orthophotomap classification. Two different orthophotomaps of southern Poland (with ground resolution: 5 cm and 25 cm) were used. On both orthoimages representative samples for two classes: asbestos-cement roofing sheets and other roofing materials were selected. Estimation of texture analysis usefulness was conducted using machine learning methods based on decision trees (C5.0 algorithm). For this purpose, various sets of texture parameters were calculated in MaZda software. During the calculation of decision trees different numbers of texture parameters groups were considered. In order to obtain the best settings for decision trees models cross-validation was performed. Decision trees models with the lowest mean classification error were selected. The accuracy of the classification was held based on validation data sets, which were not used for the classification learning. For 5 cm ground resolution samples, the lowest mean classification error was 15.6%. The lowest mean classification error in the case of 25 cm ground resolution was 20.0%. The obtained results confirm potential usefulness of the texture parameter image processing for detection of asbestos-cement roofing sheets. In order to improve the accuracy another extended study should be considered in which additional textural features as well as spectral characteristics should be analyzed.

A neural network detection model of spilled oil based on the texture analysis of SAR image

NASA Astrophysics Data System (ADS)

An, Jubai; Zhu, Lisong

2006-01-01

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

Theory of Image Analysis and Recognition.

DTIC Science & Technology

1983-01-24

Stanley M. Dunn, "Texture Classification with Change Point Statistics," TR- 1082 , July 1981. 97. R. Chellappa, "Synthesis of Textures Using Simultane...July 1981. 96. Stanley M. Dunn, "Texture Classification with Change Point Statistics," TR- 1082 , July 1981. * 97. R. Chellappa, "Synthesis of Textures

Evaluating the importance of characterizing soil structure and horizons in parameterizing a hydrologic process model

USGS Publications Warehouse

Mirus, Benjamin B.

2015-01-01

Incorporating the influence of soil structure and horizons into parameterizations of distributed surface water/groundwater models remains a challenge. Often, only a single soil unit is employed, and soil-hydraulic properties are assigned based on textural classification, without evaluating the potential impact of these simplifications. This study uses a distributed physics-based model to assess the influence of soil horizons and structure on effective parameterization. This paper tests the viability of two established and widely used hydrogeologic methods for simulating runoff and variably saturated flow through layered soils: (1) accounting for vertical heterogeneity by combining hydrostratigraphic units with contrasting hydraulic properties into homogeneous, anisotropic units and (2) use of established pedotransfer functions based on soil texture alone to estimate water retention and conductivity, without accounting for the influence of pedon structures and hysteresis. The viability of this latter method for capturing the seasonal transition from runoff-dominated to evapotranspiration-dominated regimes is also tested here. For cases tested here, event-based simulations using simplified vertical heterogeneity did not capture the state-dependent anisotropy and complex combinations of runoff generation mechanisms resulting from permeability contrasts in layered hillslopes with complex topography. Continuous simulations using pedotransfer functions that do not account for the influence of soil structure and hysteresis generally over-predicted runoff, leading to propagation of substantial water balance errors. Analysis suggests that identifying a dominant hydropedological unit provides the most acceptable simplification of subsurface layering and that modified pedotransfer functions with steeper soil-water retention curves might adequately capture the influence of soil structure and hysteresis on hydrologic response in headwater catchments.

Soil Texture Estimates: A Tool to Compare Texture-by-Feel and Lab Data

ERIC Educational Resources Information Center

Franzmeier, D.P.; Owens, P.R.

2008-01-01

Soil texture is a fundamental soil property that impacts agricultural and engineering land-use. Comparing texture estimates-by-feel to laboratory-known values to calibrate fingers is a common practice. As educators, it is difficult to assess this field skill consistently and fairly. The instructor may give full credit for the correct texture class…

Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor

PubMed Central

Pham, Tuyen Danh; Park, Young Ho; Kwon, Seung Yong; Nguyen, Dat Tien; Vokhidov, Husan; Park, Kang Ryoung; Jeong, Dae Sik; Yoon, Sungsoo

2015-01-01

In general, dirty banknotes that have creases or soiled surfaces should be replaced by new banknotes, whereas clean banknotes should be recirculated. Therefore, the accurate classification of banknote fitness when sorting paper currency is an important and challenging task. Most previous research has focused on sensors that used visible, infrared, and ultraviolet light. Furthermore, there was little previous research on the fitness classification for Indian paper currency. Therefore, we propose a new method for classifying the fitness of Indian banknotes, with a one-dimensional line image sensor that uses only visible light. The fitness of banknotes is usually determined by various factors such as soiling, creases, and tears, etc. although we just consider banknote soiling in our research. This research is novel in the following four ways: first, there has been little research conducted on fitness classification for the Indian Rupee using visible-light images. Second, the classification is conducted based on the features extracted from the regions of interest (ROIs), which contain little texture. Third, 1-level discrete wavelet transformation (DWT) is used to extract the features for discriminating between fit and unfit banknotes. Fourth, the optimal DWT features that represent the fitness and unfitness of banknotes are selected based on linear regression analysis with ground-truth data measured by densitometer. In addition, the selected features are used as the inputs to a support vector machine (SVM) for the final classification of banknote fitness. Experimental results showed that our method outperforms other methods. PMID:26343654

Use of feature extraction techniques for the texture and context information in ERTS imagery: Spectral and textural processing of ERTS imagery. [classification of Kansas land use

NASA Technical Reports Server (NTRS)

Haralick, R. H. (Principal Investigator); Bosley, R. J.

1974-01-01

The author has identified the following significant results. A procedure was developed to extract cross-band textural features from ERTS MSS imagery. Evolving from a single image texture extraction procedure which uses spatial dependence matrices to measure relative co-occurrence of nearest neighbor grey tones, the cross-band texture procedure uses the distribution of neighboring grey tone N-tuple differences to measure the spatial interrelationships, or co-occurrences, of the grey tone N-tuples present in a texture pattern. In both procedures, texture is characterized in such a way as to be invariant under linear grey tone transformations. However, the cross-band procedure complements the single image procedure by extracting texture information and spectral information contained in ERTS multi-images. Classification experiments show that when used alone, without spectral processing, the cross-band texture procedure extracts more information than the single image texture analysis. Results show an improvement in average correct classification from 86.2% to 88.8% for ERTS image no. 1021-16333 with the cross-band texture procedure. However, when used together with spectral features, the single image texture plus spectral features perform better than the cross-band texture plus spectral features, with an average correct classification of 93.8% and 91.6%, respectively.

Influence of soil texture, moisture, and surface cracks on the performance of a root-feeding flea beetle, Longitarsus bethae (Coleoptera: Chrysomelidae), a biological control agent for Lantana camara (Verbenaceae).

PubMed

Simelane, David O

2007-06-01

Laboratory studies were conducted to determine the influence of soil texture, moisture and surface cracks on adult preference and survival of the root-feeding flea beetle, Longitarsus bethae Savini and Escalona (Coleoptera: Chrysomelidae), a natural enemy of the weed, Lantana camara L. (Verbenaceae). Adult feeding, oviposition preference, and survival of the immature stages of L. bethae were examined at four soil textures (clayey, silty loam, sandy loam, and sandy soil), three soil moisture levels (low, moderate, and high), and two soil surface conditions (with or without surface cracks). Both soil texture and moisture had no influence on leaf feeding and colonization by adult L. bethae. Soil texture had a significant influence on oviposition, with adults preferring to lay on clayey and sandy soils to silty or sandy loam soils. However, survival to adulthood was significantly higher in clayey soils than in other soil textures. There was a tendency for females to deposit more eggs at greater depth in both clayey and sandy soils than in other soil textures. Although oviposition preference and depth of oviposition were not influenced by soil moisture, survival in moderately moist soils was significantly higher than in other moisture levels. Development of immature stages in high soil moisture levels was significantly slower than in other soil moisture levels. There were no variations in the body size of beetles that emerged from different soil textures and moisture levels. Females laid almost three times more eggs on cracked than on noncracked soils. It is predicted that clayey and moderately moist soils will favor the survival of L. bethae, and under these conditions, damage to the roots is likely to be high. This information will aid in the selection of suitable release sites where L. bethae would be most likely to become established.

Nitrogen enrichment in runoff sediments as affected by soil texture in Beijing mountain area.

PubMed

Yang, Yang; Ye, Zhihan; Liu, Baoyuan; Zeng, Xianqin; Fu, Suhua; Lu, Bingjun

2014-02-01

Enrichment ratio (ER) is widely used in nonpoint source pollution models to estimate the nutrient loss associated with soil erosion. The objective of this study was to determine the ER of total nitrogen (ERN) in the sediments eroded from the typical soils with varying soil textures in Beijing mountain area. Each of the four soils was packed into a 40 by 30 by 15 cm soil pan and received 40-min simulated rainfalls at the intensity of 90 mm h(-1) on five slopes. ERN for most sediments were above unity, indicating the common occurrence of nitrogen enrichment accompanied with soil erosion in Beijing mountain area. Soil texture was not the only factor that influenced N enrichment in this experiment since the ERN for the two fine-textured soils were not always lower. Soil properties such as soil structure might exert a more important influence in some circumstances. The selective erosion of clay particles was the main reason for N enrichment, as implied by the significant positive correlation between the ER of total nitrogen and clay fraction in eroded sediments. Significant regression equations between ERN and sediment yield were obtained for two pairs of soils, which were artificially categorized by soil texture. The one for fine-textured soils had greater intercept and more negative slope. Thus, the initially higher ERN would be lower than that for the other two soils with coarser texture once the sediment yield exceeded 629 kg ha(-1).

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

PubMed

Li, Guiying; Lu, Dengsheng; Moran, Emilio; Hetrick, Scott

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

PubMed Central

LI, GUIYING; LU, DENGSHENG; MORAN, EMILIO; HETRICK, SCOTT

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms – maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes. PMID:22368311

Cloud field classification based on textural features

NASA Technical Reports Server (NTRS)

Sengupta, Sailes Kumar

1989-01-01

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes of features. Preliminary results based on the GLDV textural features alone look promising.

Hygrothermal Material Properties for Soils in Building Science

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

Kehrer, Manfred; Pallin, Simon B.

2017-01-01

Hygrothermal performance of soils coupled to buildings is complicated because of the dearth of information on soil properties. However they are important when numerical simulation of coupled heat and moisture transport for below-grade building components are performed as their temperature and moisture content has an influence on the durability of the below-grade building component. Soils can be classified by soil texture. According to the Unified Soil Classification System (USCA), 12 different soils can be defined on the basis of three soil components: clay, sand, and silt. This study shows how existing material properties for typical American soils can be transferredmore » and used for the calculation of the coupled heat and moisture transport of building components in contact with soil. Furthermore a thermal validation with field measurements under known boundary conditions is part of this study, too. Field measurements for soil temperature and moisture content for two specified soils are carried out right now under known boundary conditions. As these field measurements are not finished yet, the full hygrothermal validation is still missing« less

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).

Growth patterns of red pine on fine-textured soils.

Treesearch

David H. Alban; Donald H. Prettyman; Gary J. Brand

1987-01-01

Compares growth of 28- to 49-year-old red pine plantations on sandy and fine-textured soils. Red pine growing on these two contrasting soils did not differ in bole form, live crown ratio, or mortality, and tree growth predicted by models (STEMS and REDPINE) developed from trees growing on sandy soils worked equally well for trees growing on fine-textured soils.

Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

PubMed

Al Ajmi, Eiman; Forghani, Behzad; Reinhold, Caroline; Bayat, Maryam; Forghani, Reza

2018-06-01

There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

Extraction of texture features with a multiresolution neural network

NASA Astrophysics Data System (ADS)

Lepage, Richard; Laurendeau, Denis; Gagnon, Roger A.

1992-09-01

Texture is an important surface characteristic. Many industrial materials such as wood, textile, or paper are best characterized by their texture. Detection of defaults occurring on such materials or classification for quality control anD matching can be carried out through careful texture analysis. A system for the classification of pieces of wood used in the furniture industry is proposed. This paper is concerned with a neural network implementation of the features extraction and classification components of the proposed system. Texture appears differently depending at which spatial scale it is observed. A complete description of a texture thus implies an analysis at several spatial scales. We propose a compact pyramidal representation of the input image for multiresolution analysis. The feature extraction system is implemented on a multilayer artificial neural network. Each level of the pyramid, which is a representation of the input image at a given spatial resolution scale, is mapped into a layer of the neural network. A full resolution texture image is input at the base of the pyramid and a representation of the texture image at multiple resolutions is generated by the feedforward pyramid structure of the neural network. The receptive field of each neuron at a given pyramid level is preprogrammed as a discrete Gaussian low-pass filter. Meaningful characteristics of the textured image must be extracted if a good resolving power of the classifier must be achieved. Local dominant orientation is the principal feature which is extracted from the textured image. Local edge orientation is computed with a Sobel mask at four orientation angles (multiple of (pi) /4). The resulting intrinsic image, that is, the local dominant orientation image, is fed to the texture classification neural network. The classification network is a three-layer feedforward back-propagation neural network.

Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

NASA Astrophysics Data System (ADS)

Bianconi, Francesco; Bello-Cerezo, Raquel; Napoletano, Paolo

2018-01-01

Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images.

Effects of Soil Texture on Belowground Carbon and Nutrient Storage in a Lowland Amazonian Forest Ecosystem.

Treesearch

Whendee L. Silver; Jason Neff; Megan McGroddy; Ed Veldkamp; Michael Keller; Raimundo Cosme

2000-01-01

Soil texture plays a key role in belowground C storage in forest ecosystems and strongly influences nutrient availability and retention, particularly in highly weathered soils. We used field data and the Century ecosystem model to explore the role of soil texture in belowground C storage, nutrient pool sizes, and N fluxes in highly weathered soils in an Amazonian...

Classification of cloud fields based on textural characteristics

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Chen, D. W.

1987-01-01

The present study reexamines the applicability of texture-based features for automatic cloud classification using very high spatial resolution (57 m) Landsat multispectral scanner digital data. It is concluded that cloud classification can be accomplished using only a single visible channel.

[Distribution of soil organic carbon, total nitrogen, total phosphorus and water stable aggregates of cropland with different soil textures on the Loess Plateau, Northwest China].

PubMed

Ge, Nan Nan; Shi, Yun; Yang, Xian Long; Zhang, Qing Yin; Li, Xue Zhang; Jia, Xiao Xu; Shao, Ming An; Wei, Xiao Rong

2017-05-18

In this study, combined with field investigation and laboratory analyses, we assessed the distribution of soil organic carbon, nitrogen, phosphorous contents and their stoichiometric ratios, and the distribution of soil water stable aggregates along a soil texture gradient in the cropland of the Loess Plateau to understand the effect of soil texture and the regulation of soil aggregates on soil fertility in cropland. The results showed that, with the change from fine soils to coarse soils along the texture gradient (loam clay→ clay loam→ sandy loam), the contents of macroaggregates, organic carbon, nitrogen, phosphorous and their stoichiometric ratios decreased, while pH value and microaggregates content showed an opposite changing pattern. The contents of macroaggregates, organic carbon, nitrogen, phosphorous, and C/P and N/P were significantly increased, but pH value and microaggregates content were significantly decreased with increasing the soil clay content. Furthermore, the contents of organic carbon, nitrogen, phosphorous, and C/P and N/P increased with the increase of macroaggregates content. These results indicated that soil fertility in croplands at a regional scale was mainly determined by soil texture, and was regulated by soil macroaggregates.

Texture classification using autoregressive filtering

NASA Technical Reports Server (NTRS)

Lawton, W. M.; Lee, M.

1984-01-01

A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.

Comparison of germination and seed vigor of sunflower in two contaminated soils of different texture

NASA Astrophysics Data System (ADS)

Zhao, Xin; Han, Jaemaro; Lee, Jong Keun; Kim, Jae Young

2014-05-01

Phytoremediation as an emerging low-cost and ecologically friendly alternative to the conventional soil remediation technologies has gained a great deal of attention and into lots of research. As a kind of the methods that use of green plants to remediate heavy metals contaminated soils, the early growth status of plant seeds in the contaminated environmental directly affects the effect of phytoremediation. Germination test in the water (aqueous solution of heavy metal) is generally used for assessing heavy metal phytotoxicity and possibility of plant growth, but there is a limit. Because soil is commonly main target of phytoremediation, not the water. The bioavailability of heavy metals in the soil also depends on the texture. So soil texture is an important factor of phytoremediation effect. Sunflower is the representative species which have good tolerance to various heavy metals; furthermore, the seeds of sunflower can be used as the raw-material for producing bio-diesel. The objectives of this research were to investigate germination rate of sunflowers in various heavy metal contaminated soils and to compare the seedling vigor index (SVI) of sunflower in two contaminated soils of different texture. Sunflower (Helianthusannuus L.) seeds were obtained from a commercial market. In order to prove the soil texture effect on heavy metal contaminated soil, germination tests in soil were conducted with two different types of soil texture (i.e., loam soil and sandy loam soil) classified by soil textural triangle (defined by USDA) including representative soil texture of Korea. Germination tests in soil were conducted using KS I ISO 11260-1 (2005) for reference that sunflower seeds were incubated for 7 days in dark at 25 ± 1 Celsius degree. The target heavy metals are Nickel (Ni) and Zinc (Zn). The Ni and Zn concentrations were 0, 10, 50, 100, 200, 300, 500 mg-Ni/kg-dry soil, and 0, 10, 50, 100, 300, 500, 900 mg-Zn/kg-dry soil, respectively. After germination test for 7 days, germination rate of sunflower was calculated, and shoot and root lengths were also measured. According to the results of germination tests, the seeds germination rates were reduced with increasing heavy metal concentrations in both loam soil and sandy loam soil. The SVI values in loam soil in more than in sandy loam soil. Keywords: phytoremediation, sunflower, soil texture, germination test ACKNOWLEDGEMENT This work is supported by the Korea Ministry of the Environment as 'The GAIA (Geo-Advanced Innovative Action) Project'.

Documentation of procedures for textural/spatial pattern recognition techniques

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Bryant, W. F.

1976-01-01

A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features.

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.

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.

A Case Study of Petroleum Degradation in Different Soil Textural Classes.

PubMed

Kogbara, Reginald B; Ayotamuno, Josiah M; Worlu, Daniel C; Fubara-Manuel, Isoteim

2016-01-01

Patents have been granted for a number of techniques for petroleum biodegradation including use of micro-organisms for degradation of hydrocarbon-based substances and for hydrocarbon degradation in oil reservoirs, but there is a dearth of information on hydrocarbon degradation in different soil textures. Hence, this work investigated the effects of different soil textures on degradation of petroleum hydrocarbons during a six-week period. Five soil textural classes commonly found in Port Harcourt metropolis, Nigeria, namely sand, loamy sand, sandy loam, silty clay and clay, were employed. The soils were contaminated with the same amount of crude oil and then remediated by biostimulation. Selected soil properties were monitored over time. Bacterial numbers declined significantly in the fine soil textures after petroleum contamination, but were either unaffected or increased significantly in the coarser soil textures. Hydrocarbon losses ranged from 42% - 99%; the sandy loam had the highest, while the clay soil had the least total hydrocarbon content (THC) reduction. The total heterotrophic bacterial (THB) counts generally corroborated the THC results. Fold increase in bacterial numbers due to remediation treatment decreased with increasing clay content. The results suggest that higher sand than clay content of soil favours faster hydrocarbon degradation. Hydrocarbon degradation efficiency increased with silt content among soil groupings such as fine and coarse soils but not necessarily with increasing silt content of soil. Thus, there seems to be cut-off sand and clay contents in soil at which the effect of the silt content becomes significant.

Automated classification of articular cartilage surfaces based on surface texture.

PubMed

Stachowiak, G P; Stachowiak, G W; Podsiadlo, P

2006-11-01

In this study the automated classification system previously developed by the authors was used to classify articular cartilage surfaces with different degrees of wear. This automated system classifies surfaces based on their texture. Plug samples of sheep cartilage (pins) were run on stainless steel discs under various conditions using a pin-on-disc tribometer. Testing conditions were specifically designed to produce different severities of cartilage damage due to wear. Environmental scanning electron microscope (SEM) (ESEM) images of cartilage surfaces, that formed a database for pattern recognition analysis, were acquired. The ESEM images of cartilage were divided into five groups (classes), each class representing different wear conditions or wear severity. Each class was first examined and assessed visually. Next, the automated classification system (pattern recognition) was applied to all classes. The results of the automated surface texture classification were compared to those based on visual assessment of surface morphology. It was shown that the texture-based automated classification system was an efficient and accurate method of distinguishing between various cartilage surfaces generated under different wear conditions. It appears that the texture-based classification method has potential to become a useful tool in medical diagnostics.

Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses

NASA Astrophysics Data System (ADS)

Agüera, Francisco; Aguilar, Fernando J.; Aguilar, Manuel A.

The area occupied by plastic-covered greenhouses has undergone rapid growth in recent years, currently exceeding 500,000 ha worldwide. Due to the vast amount of input (water, fertilisers, fuel, etc.) required, and output of different agricultural wastes (vegetable, plastic, chemical, etc.), the environmental impact of this type of production system can be serious if not accompanied by sound and sustainable territorial planning. For this, the new generation of satellites which provide very high resolution imagery, such as QuickBird and IKONOS can be useful. In this study, one QuickBird and one IKONOS satellite image have been used to cover the same area under similar circumstances. The aim of this work was an exhaustive comparison of QuickBird vs. IKONOS images in land-cover detection. In terms of plastic greenhouse mapping, comparative tests were designed and implemented, each with separate objectives. Firstly, the Maximum Likelihood Classification (MLC) was applied using five different approaches combining R, G, B, NIR, and panchromatic bands. The combinations of the bands used, significantly influenced some of the indexes used to classify quality in this work. Furthermore, the quality classification of the QuickBird image was higher in all cases than that of the IKONOS image. Secondly, texture features derived from the panchromatic images at different window sizes and with different grey levels were added as a fifth band to the R, G, B, NIR images to carry out the MLC. The inclusion of texture information in the classification did not improve the classification quality. For classifications with texture information, the best accuracies were found in both images for mean and angular second moment texture parameters. The optimum window size in these texture parameters was 3×3 for IK images, while for QB images it depended on the quality index studied, but the optimum window size was around 15×15. With regard to the grey level, the optimum was 128. Thus, the optimum texture parameter depended on the main objective of the image classification. If the main classification goal is to minimize the number of pixels wrongly classified, the mean texture parameter should be used, whereas if the main classification goal is to minimize the unclassified pixels the angular second moment texture parameter should be used. On the whole, both QuickBird and IKONOS images offered promising results in classifying plastic greenhouses.

Land use classification using texture information in ERTS-A MSS imagery

NASA Technical Reports Server (NTRS)

Haralick, R. M. (Principal Investigator); Shanmugam, K. S.; Bosley, R.

1973-01-01

The author has identified the following significant results. Preliminary digital analysis of ERTS-1 MSS imagery reveals that the textural features of the imagery are very useful for land use classification. A procedure for extracting the textural features of ERTS-1 imagery is presented and the results of a land use classification scheme based on the textural features are also presented. The land use classification algorithm using textural features was tested on a 5100 square mile area covered by part of an ERTS-1 MSS band 5 image over the California coastline. The image covering this area was blocked into 648 subimages of size 8.9 square miles each. Based on a color composite of the image set, a total of 7 land use categories were identified. These land use categories are: coastal forest, woodlands, annual grasslands, urban areas, large irrigated fields, small irrigated fields, and water. The automatic classifier was trained to identify the land use categories using only the textural characteristics of the subimages; 75 percent of the subimages were assigned correct identifications. Since texture and spectral features provide completely different kinds of information, a significant increase in identification accuracy will take place when both features are used together.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

Novel chromatin texture features for the classification of pap smears

NASA Astrophysics Data System (ADS)

Bejnordi, Babak E.; Moshavegh, Ramin; Sujathan, K.; Malm, Patrik; Bengtsson, Ewert; Mehnert, Andrew

2013-03-01

This paper presents a set of novel structural texture features for quantifying nuclear chromatin patterns in cells on a conventional Pap smear. The features are derived from an initial segmentation of the chromatin into bloblike texture primitives. The results of a comprehensive feature selection experiment, including the set of proposed structural texture features and a range of different cytology features drawn from the literature, show that two of the four top ranking features are structural texture features. They also show that a combination of structural and conventional features yields a classification performance of 0.954±0.019 (AUC±SE) for the discrimination of normal (NILM) and abnormal (LSIL and HSIL) slides. The results of a second classification experiment, using only normal-appearing cells from both normal and abnormal slides, demonstrates that a single structural texture feature measuring chromatin margination yields a classification performance of 0.815±0.019. Overall the results demonstrate the efficacy of the proposed structural approach and that it is possible to detect malignancy associated changes (MACs) in Papanicoloau stain.

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

A modelling approach to evaluate the long-term effect of soil texture on spring wheat productivity under a rain-fed condition.

PubMed

He, Yong; Hou, Lingling; Wang, Hong; Hu, Kelin; McConkey, Brian

2014-07-30

Soil surface texture is an important environmental factor that influences crop productivity because of its direct effect on soil water and complex interactions with other environmental factors. Using 30-year data, an agricultural system model (DSSAT-CERES-Wheat) was calibrated and validated. After validation, the modelled yield and water use (WU) of spring wheat (Triticum aestivum L.) from two soil textures (silt loam and clay) under rain-fed condition were analyzed. Regression analysis showed that wheat grown in silt loam soil is more sensitive to WU than wheat grown in clay soil, indicating that the wheat grown in clay soil has higher drought tolerance than that grown in silt loam. Yield variation can be explained by WU other than by precipitation use (PU). These results demonstrated that the DSSAT-CERES-Wheat model can be used to evaluate the WU of different soil textures and assess the feasibility of wheat production under various conditions. These outcomes can improve our understanding of the long-term effect of soil texture on spring wheat productivity in rain-fed condition.

Field-Scale Evaluation of Infiltration Parameters From Soil Texture for Hydrologic Analysis

NASA Astrophysics Data System (ADS)

Springer, Everett P.; Cundy, Terrance W.

1987-02-01

Recent interest in predicting soil hydraulic properties from simple physical properties such as texture has major implications in the parameterization of physically based models of surface runoff. This study was undertaken to (1) compare, on a field scale, soil hydraulic parameters predicted from texture to those derived from field measurements and (2) compare simulated overland flow response using these two parameter sets. The parameters for the Green-Ampt infiltration equation were obtained from field measurements and using texture-based predictors for two agricultural fields, which were mapped as single soil units. Results of the analyses were that (1) the mean and variance of the field-based parameters were not preserved by the texture-based estimates, (2) spatial and cross correlations between parameters were induced by the texture-based estimation procedures, (3) the overland flow simulations using texture-based parameters were significantly different than those from field-based parameters, and (4) simulations using field-measured hydraulic conductivities and texture-based storage parameters were very close to simulations using only field-based parameters.

Space Object Classification Using Fused Features of Time Series Data

NASA Astrophysics Data System (ADS)

Jia, B.; Pham, K. D.; Blasch, E.; Shen, D.; Wang, Z.; Chen, G.

In this paper, a fused feature vector consisting of raw time series and texture feature information is proposed for space object classification. The time series data includes historical orbit trajectories and asteroid light curves. The texture feature is derived from recurrence plots using Gabor filters for both unsupervised learning and supervised learning algorithms. The simulation results show that the classification algorithms using the fused feature vector achieve better performance than those using raw time series or texture features only.

Standardizing texture and facies codes for a process-based classification of clastic sediment and rock

USGS Publications Warehouse

Farrell, K.M.; Harris, W.B.; Mallinson, D.J.; Culver, S.J.; Riggs, S.R.; Pierson, J.; ,; Lautier, J.C.

2012-01-01

Proposed here is a universally applicable, texturally based classification of clastic sediment that is independent from composition, cementation, and geologic environment, is closely allied to process sedimentology, and applies to all compartments in the source-to-sink system. The classification is contingent on defining the term "clastic" so that it is independent from composition or origin and includes any particles or grains that are subject to erosion, transportation, and deposition. Modifications to Folk's (1980) texturally based classification that include applying new assumptions and defining a broader array of textural fields are proposed to accommodate this. The revised ternary diagrams include additional textural fields that better define poorly sorted and coarse-grained deposits, so that all end members (gravel, sand, and mud size fractions) are included in textural codes. Revised textural fields, or classes, are based on a strict adherence to volumetric estimates of percentages of gravel, sand, and mud size grain populations, which by definition must sum to 100%. The new classification ensures that descriptors are applied consistently to all end members in the ternary diagram (gravel, sand, and mud) according to several rules, and that none of the end members are ignored. These modifications provide bases for standardizing vertical displays of texture in graphic logs, lithofacies codes, and their derivatives- hydrofacies. Hydrofacies codes are nondirectional permeability indicators that predict aquifer or reservoir potential. Folk's (1980) ternary diagram for fine-grained clastic sediments (sand, silt, and clay size fractions) is also revised to preserve consistency with the revised diagram for gravel, sand, and mud. Standardizing texture ensures that the principles of process sedimentology are consistently applied to compositionally variable rock sequences, such as mixed carbonate-siliciclastic ramp settings, and the extreme ends of depositional systems.

A reexamination of soil textural effects on microwave emission and backscattering

NASA Technical Reports Server (NTRS)

Dobson, M. C.; Kouyate, F.; Ulaby, F. T.

1984-01-01

Microwave frequency measurements of moist soil dielectric properties are noted to challenge the validity of percent-of-field-capacity as a moisture indicator that is independent of soil texture in terms of microwave sensitivity. In arriving at this view, gravimetric, volumetric, and percent-of-field-capacity were tested for their ability to reduce dielectric behavior divergence between soil textures at 1.4 and 5.0 GHz. The most congruent dielectric behavior between soil textures is found to occur when soil moisture is expressed on a volumetric basis that is proportional to the number of water dipoles/unit volume. An inadequate characterization of soil bulk density in the field, combined with the dependency of bulk density on water retention at field capacity, offers the most plausible explanation for the earlier conclusions.

A signature dissimilarity measure for trabecular bone texture in knee radiographs

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

Woloszynski, T.; Podsiadlo, P.; Stachowiak, G. W.

Purpose: The purpose of this study is to develop a dissimilarity measure for the classification of trabecular bone (TB) texture in knee radiographs. Problems associated with the traditional extraction and selection of texture features and with the invariance to imaging conditions such as image size, anisotropy, noise, blur, exposure, magnification, and projection angle were addressed. Methods: In the method developed, called a signature dissimilarity measure (SDM), a sum of earth mover's distances calculated for roughness and orientation signatures is used to quantify dissimilarities between textures. Scale-space theory was used to ensure scale and rotation invariance. The effects of image size,more » anisotropy, noise, and blur on the SDM developed were studied using computer generated fractal texture images. The invariance of the measure to image exposure, magnification, and projection angle was studied using x-ray images of human tibia head. For the studies, Mann-Whitney tests with significance level of 0.01 were used. A comparison study between the performances of a SDM based classification system and other two systems in the classification of Brodatz textures and the detection of knee osteoarthritis (OA) were conducted. The other systems are based on weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM) and local binary patterns (LBP). Results: Results obtained indicate that the SDM developed is invariant to image exposure (2.5-30 mA s), magnification (x1.00-x1.35), noise associated with film graininess and quantum mottle (<25%), blur generated by a sharp film screen, and image size (>64x64 pixels). However, the measure is sensitive to changes in projection angle (>5 deg.), image anisotropy (>30 deg.), and blur generated by a regular film screen. For the classification of Brodatz textures, the SDM based system produced comparable results to the LBP system. For the detection of knee OA, the SDM based system achieved 78.8% classification accuracy and outperformed the WND-CHARM system (64.2%). Conclusions: The SDM is well suited for the classification of TB texture images in knee OA detection and may be useful for the texture classification of medical images in general.« less

An evaluation of several different classification schemes - Their parameters and performance. [maximum likelihood decision for crop identification

NASA Technical Reports Server (NTRS)

Scholz, D.; Fuhs, N.; Hixson, M.

1979-01-01

The overall objective of this study was to apply and evaluate several of the currently available classification schemes for crop identification. The approaches examined were: (1) a per point Gaussian maximum likelihood classifier, (2) a per point sum of normal densities classifier, (3) a per point linear classifier, (4) a per point Gaussian maximum likelihood decision tree classifier, and (5) a texture sensitive per field Gaussian maximum likelihood classifier. Three agricultural data sets were used in the study: areas from Fayette County, Illinois, and Pottawattamie and Shelby Counties in Iowa. The segments were located in two distinct regions of the Corn Belt to sample variability in soils, climate, and agricultural practices.

How much fertilizer nitrogen does sugarcane need?

USDA-ARS?s Scientific Manuscript database

Nitrogen rate recommendations for sugarcane in Louisiana take into account crop age (plant cane or stubble) and soil texture (light or heavy). Recommended rates in the 1950s ranged from 40 pounds N/A for plant cane on light-textured soil to 100 pounds of N/A for stubble cane on heavy-textured soil a...

A Comparison of Land Surface Model Soil Hydraulic Properties Estimated by Inverse Modeling and Pedotransfer Functions

NASA Technical Reports Server (NTRS)

Gutmann, Ethan D.; Small, Eric E.

2007-01-01

Soil hydraulic properties (SHPs) regulate the movement of water in the soil. This in turn plays an important role in the water and energy cycles at the land surface. At present, SHPS are commonly defined by a simple pedotransfer function from soil texture class, but SHPs vary more within a texture class than between classes. To examine the impact of using soil texture class to predict SHPS, we run the Noah land surface model for a wide variety of measured SHPs. We find that across a range of vegetation cover (5 - 80% cover) and climates (250 - 900 mm mean annual precipitation), soil texture class only explains 5% of the variance expected from the real distribution of SHPs. We then show that modifying SHPs can drastically improve model performance. We compare two methods of estimating SHPs: (1) inverse method, and (2) soil texture class. Compared to texture class, inverse modeling reduces errors between measured and modeled latent heat flux from 88 to 28 w/m(exp 2). Additionally we find that with increasing vegetation cover the importance of SHPs decreases and that the van Genuchten m parameter becomes less important, while the saturated conductivity becomes more important.

Texture operator for snow particle classification into snowflake and graupel

NASA Astrophysics Data System (ADS)

Nurzyńska, Karolina; Kubo, Mamoru; Muramoto, Ken-ichiro

2012-11-01

In order to improve the estimation of precipitation, the coefficients of Z-R relation should be determined for each snow type. Therefore, it is necessary to identify the type of falling snow. Consequently, this research addresses a problem of snow particle classification into snowflake and graupel in an automatic manner (as these types are the most common in the study region). Having correctly classified precipitation events, it is believed that it will be possible to estimate the related parameters accurately. The automatic classification system presented here describes the images with texture operators. Some of them are well-known from the literature: first order features, co-occurrence matrix, grey-tone difference matrix, run length matrix, and local binary pattern, but also a novel approach to design simple local statistic operators is introduced. In this work the following texture operators are defined: mean histogram, min-max histogram, and mean-variance histogram. Moreover, building a feature vector, which is based on the structure created in many from mentioned algorithms is also suggested. For classification, the k-nearest neighbourhood classifier was applied. The results showed that it is possible to achieve correct classification accuracy above 80% by most of the techniques. The best result of 86.06%, was achieved for operator built from a structure achieved in the middle stage of the co-occurrence matrix calculation. Next, it was noticed that describing an image with two texture operators does not improve the classification results considerably. In the best case the correct classification efficiency was 87.89% for a pair of texture operators created from local binary pattern and structure build in a middle stage of grey-tone difference matrix calculation. This also suggests that the information gathered by each texture operator is redundant. Therefore, the principal component analysis was applied in order to remove the unnecessary information and additionally reduce the length of the feature vectors. The improvement of the correct classification efficiency for up to 100% is possible for methods: min-max histogram, texture operator built from structure achieved in a middle stage of co-occurrence matrix calculation, texture operator built from a structure achieved in a middle stage of grey-tone difference matrix creation, and texture operator based on a histogram, when the feature vector stores 99% of initial information.

Breast density characterization using texton distributions.

PubMed

Petroudi, Styliani; Brady, Michael

2011-01-01

Breast density has been shown to be one of the most significant risks for developing breast cancer, with women with dense breasts at four to six times higher risk. The Breast Imaging Reporting and Data System (BI-RADS) has a four class classification scheme that describes the different breast densities. However, there is great inter and intra observer variability among clinicians in reporting a mammogram's density class. This work presents a novel texture classification method and its application for the development of a completely automated breast density classification system. The new method represents the mammogram using textons, which can be thought of as the building blocks of texture under the operational definition of Leung and Malik as clustered filter responses. The new proposed method characterizes the mammographic appearance of the different density patterns by evaluating the texton spatial dependence matrix (TDSM) in the breast region's corresponding texton map. The TSDM is a texture model that captures both statistical and structural texture characteristics. The normalized TSDM matrices are evaluated for mammograms from the different density classes and corresponding texture models are established. Classification is achieved using a chi-square distance measure. The fully automated TSDM breast density classification method is quantitatively evaluated on mammograms from all density classes from the Oxford Mammogram Database. The incorporation of texton spatial dependencies allows for classification accuracy reaching over 82%. The breast density classification accuracy is better using texton TSDM compared to simple texton histograms.

Riverbank restoration in the southern United States: The effects of soil texture and moisture regime on survival and growth of willow posts

Treesearch

S. Reza Pezeshki; Steven D. Schaff; F. Douglas Shields

2000-01-01

Field studies were conducted to quantify the relationship between soil conditions and growth of black willow posts planted for riverbank erosion control along Harland Creek (HC) and Twentymile Creek (TC) sites in Mississippi. Both sites had a wide range of soil texture and moisture regimes. Soil texture, water level, redox potential (Eh), and willow survival and growth...

Texture classification of vegetation cover in high altitude wetlands zone

NASA Astrophysics Data System (ADS)

Wentao, Zou; Bingfang, Wu; Hongbo, Ju; Hua, Liu

2014-03-01

The aim of this study was to investigate the utility of datasets composed of texture measures and other features for the classification of vegetation cover, specifically wetlands. QUEST decision tree classifier was applied to a SPOT-5 image sub-scene covering the typical wetlands area in Three River Sources region in Qinghai province, China. The dataset used for the classification comprised of: (1) spectral data and the components of principal component analysis; (2) texture measures derived from pixel basis; (3) DEM and other ancillary data covering the research area. Image textures is an important characteristic of remote sensing images; it can represent spatial variations with spectral brightness in digital numbers. When the spectral information is not enough to separate the different land covers, the texture information can be used to increase the classification accuracy. The texture measures used in this study were calculated from GLCM (Gray level Co-occurrence Matrix); eight frequently used measures were chosen to conduct the classification procedure. The results showed that variance, mean and entropy calculated by GLCM with a 9*9 size window were effective in distinguishing different vegetation types in wetlands zone. The overall accuracy of this method was 84.19% and the Kappa coefficient was 0.8261. The result indicated that the introduction of texture measures can improve the overall accuracy by 12.05% and the overall kappa coefficient by 0.1407 compared with the result using spectral and ancillary data.

Rock classification based on resistivity patterns in electrical borehole wall images

NASA Astrophysics Data System (ADS)

Linek, Margarete; Jungmann, Matthias; Berlage, Thomas; Pechnig, Renate; Clauser, Christoph

2007-06-01

Electrical borehole wall images represent grey-level-coded micro-resistivity measurements at the borehole wall. Different scientific methods have been implemented to transform image data into quantitative log curves. We introduce a pattern recognition technique applying texture analysis, which uses second-order statistics based on studying the occurrence of pixel pairs. We calculate so-called Haralick texture features such as contrast, energy, entropy and homogeneity. The supervised classification method is used for assigning characteristic texture features to different rock classes and assessing the discriminative power of these image features. We use classifiers obtained from training intervals to characterize the entire image data set recovered in ODP hole 1203A. This yields a synthetic lithology profile based on computed texture data. We show that Haralick features accurately classify 89.9% of the training intervals. We obtained misclassification for vesicular basaltic rocks. Hence, further image analysis tools are used to improve the classification reliability. We decompose the 2D image signal by the application of wavelet transformation in order to enhance image objects horizontally, diagonally and vertically. The resulting filtered images are used for further texture analysis. This combined classification based on Haralick features and wavelet transformation improved our classification up to a level of 98%. The application of wavelet transformation increases the consistency between standard logging profiles and texture-derived lithology. Texture analysis of borehole wall images offers the potential to facilitate objective analysis of multiple boreholes with the same lithology.

Identification of Soil Properties and Organophosphate Residues From Agricultural Land in Wanasari Sub-District, Brebes, Indonesia

NASA Astrophysics Data System (ADS)

Joko, Tri; Anggoro, Sutrisno; Sunoko, Henna Rya; Rachmawati, Savitri

2018-02-01

Organophosphates have been used to eradicate pests and prevent losses from harvest failures caused by pest attack. It is undeniable that the organophosphate persist in soil. This study aims to identify the organophosphate residue and soil properties include pH, soil texture, and permeability. The soil samples were taken from cropland in 10 villages, Wanasari sub-district, Brebes, Indonesia. Organophosphate residue determined by gas chromatography using Flame Photometric Detector. Soil texture was determined by soil texture triangle from NRCS USDA, and the permeability value was determined by falling head method. The mean value of chlorpyrifos, profenofos, diazinon were 0.0078; 0.0388; 0.2271 mg/l respectively. The soil texture varies from clay, silt clay, loam, silt loam, and silt clay loam with permeability value at 10-7 with the soil pH value between 6.4 - 8.1. The results showed that organophosphate residues found in the soil and its potential affect the soil fertility decline. We recommend to conduct routine soil quality analysis to prevent soil damage in the agricultural environment.

Water repellency of two forest soils after biochar addition

Treesearch

D. S. Page-Dumroese; P. R. Robichaud; R. E. Brown; J. M. Tirocke

2015-01-01

Practical application of black carbon (biochar) to improve forest soil may be limited because biochar is hydrophobic. In a laboratory, we tested the water repellency of biochar application (mixed or surface applied) to two forest soils of varying texture (a granitic coarse-textured Inceptisol and an ash cap fine-textured Andisol) at four different application rates (0...

Estimating spatially distributed soil texture using time series of thermal remote sensing - a case study in central Europe

NASA Astrophysics Data System (ADS)

Müller, Benjamin; Bernhardt, Matthias; Jackisch, Conrad; Schulz, Karsten

2016-09-01

For understanding water and solute transport processes, knowledge about the respective hydraulic properties is necessary. Commonly, hydraulic parameters are estimated via pedo-transfer functions using soil texture data to avoid cost-intensive measurements of hydraulic parameters in the laboratory. Therefore, current soil texture information is only available at a coarse spatial resolution of 250 to 1000 m. Here, a method is presented to derive high-resolution (15 m) spatial topsoil texture patterns for the meso-scale Attert catchment (Luxembourg, 288 km2) from 28 images of ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal remote sensing. A principle component analysis of the images reveals the most dominant thermal patterns (principle components, PCs) that are related to 212 fractional soil texture samples. Within a multiple linear regression framework, distributed soil texture information is estimated and related uncertainties are assessed. An overall root mean squared error (RMSE) of 12.7 percentage points (pp) lies well within and even below the range of recent studies on soil texture estimation, while requiring sparser sample setups and a less diverse set of basic spatial input. This approach will improve the generation of spatially distributed topsoil maps, particularly for hydrologic modeling purposes, and will expand the usage of thermal remote sensing products.

Classification and recognition of texture collagen obtaining by multiphoton microscope with neural network analysis

NASA Astrophysics Data System (ADS)

Wu, Shulian; Peng, Yuanyuan; Hu, Liangjun; Zhang, Xiaoman; Li, Hui

2016-01-01

Second harmonic generation microscopy (SHGM) was used to monitor the process of chronological aging skin in vivo. The collagen structures of mice model with different ages were obtained using SHGM. Then, texture feature with contrast, correlation and entropy were extracted and analysed using the grey level co-occurrence matrix. At last, the neural network tool of Matlab was applied to train the texture of collagen in different statues during the aging process. And the simulation of mice collagen texture was carried out. The results indicated that the classification accuracy reach 85%. Results demonstrated that the proposed approach effectively detected the target object in the collagen texture image during the chronological aging process and the analysis tool based on neural network applied the skin of classification and feature extraction method is feasible.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

PubMed Central

Zhang, Xin; Cui, Jintian; Wang, Weisheng; Lin, Chao

2017-01-01

To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. PMID:28640181

Uncertainty in Pedotransfer Functions from Soil Survey Data

NASA Astrophysics Data System (ADS)

Pachepsky, Y. A.; Rawls, W. J.

2002-05-01

Pedotransfer functions (PTFs) are empirical relationships between hard-to-get soil parameters, i.e. hydraulic properties, and more easily obtainable basic soil properties, such as texture. Use of PTFs in large-scale projects and pilot studies relies on data of soil survey that provides soil basic data as a categorical information. Unlike numerical variables, categorical data cannot be directly used in statistical regressions or neural networks to develop PTFs. Objectives of this work were (a) to find and test techniques to develop PTFs for soil water retention and saturated hydraulic conductivity with soil categorical data as inputs, (b) to evaluate sources of uncertainty in results of such PTFs and to research opportunities of mitigating the uncertainty. We used a subset of about 12,000 samples from the US National Soil characterization database to estimate water retention, and the data set for circa 1000 hydraulic conductivity measurements done in the US. Regression trees and polynomial neural networks based on dummy coding were the techniques tried for the PTF development. The jackknife validation was used to prevent the over-parameterization. Both techniques were equally efficient in developing PTFs, but regression trees gave much more transparent results. Textural class was the leading predictor with RMSE values of about 6.5 and 4.1 vol.% for water retention at -33 and -1500 kPa, respectively. The RMSE values decreased 10% when the laboratory textural analysis was used to establish the textural class. Textural class in the field was determined correctly only in 41% of all cases. To mitigate this source of error, we added slopes, position on the slope classes, and land surface shape classes to the list of PTF inputs. Regression trees generated topotextural groups that encompassed several textural classes. Using topographic variables and soil horizon appeared to be the way to make up for errors made in field determination of texture. Adding field descriptors of soil structure to the field-determined textural class gave similar results. No large improvement was achieved probably because textural class, topographic descriptors and structure descriptors were correlated predictors in many cases. Both median values and uncertainty of the saturated hydraulic conductivity had a power-law decrease as clay content increased. Defining two classes of bulk density helped to estimate hydraulic conductivity within textural classes. We conclude that categorical field soil survey data can be used in PTF-based estimating soil water retention and saturated hydraulic conductivity with quantified uncertainty

SOIL Geo-Wiki: A tool for improving soil information

NASA Astrophysics Data System (ADS)

Skalský, Rastislav; Balkovic, Juraj; Fritz, Steffen; See, Linda; van der Velde, Marijn; Obersteiner, Michael

2014-05-01

Crowdsourcing is increasingly being used as a way of collecting data for scientific research, e.g. species identification, classification of galaxies and unravelling of protein structures. The WorldSoilProfiles.org database at ISRIC is a global collection of soil profiles, which have been 'crowdsourced' from experts. This system, however, requires contributors to have a priori knowledge about soils. Yet many soil parameters can be observed in the field without specific knowledge or equipment such as stone content, soil depth or color. By crowdsourcing this information over thousands of locations, the uncertainty in current soil datasets could be radically reduced, particularly in areas currently without information or where multiple interpretations are possible from different existing soil maps. Improved information on soils could benefit many research fields and applications. Better soil data could enhance assessments of soil ecosystem services (e.g. soil carbon storage) and facilitate improved process-based ecosystem modeling from local to global scales. Geo-Wiki is a crowdsourcing tool that was developed at IIASA for land cover validation using satellite imagery. Several branches are now available focused on specific aspects of land cover validation, e.g. validating cropland extent or urbanized areas. Geo-Wiki Pictures is a smart phone application for collecting land cover related information on the ground. The extension of Geo-Wiki to a mobile environment provides a tool for experts in land cover validation but is also a way of reaching the general public in the validation of land cover. Here we propose a Soil Geo-Wiki tool that builds on the existing functionality of the Geo-Wiki application, which will be largely designed for the collection and sharing of soil information. Two distinct applications are envisaged: an expert-oriented application mainly for scientific purposes, which will use soil science related language (e.g. WRB or any other global reference soil classification system) and allow experts to upload and share scientifically rigorous soil data; and an application oriented towards the general public, which will be more focused on describing well observed, individual soil properties using simplified classification keys. The latter application will avoid the use of soil science related terminology and focus on the most useful soil parameters such as soil surface features, stone content, soil texture, soil plasticity, calcium carbonate presence, soil color, soil pH, soil repellency, and soil depth. Collection of soil and landscape pictures will also be supported in Soil Geo-Wiki to allow for comprehensive data collection while simultaneously allowing for quality checking by experts.

Combining multiple features for color texture classification

NASA Astrophysics Data System (ADS)

Cusano, Claudio; Napoletano, Paolo; Schettini, Raimondo

2016-11-01

The analysis of color and texture has a long history in image analysis and computer vision. These two properties are often considered as independent, even though they are strongly related in images of natural objects and materials. Correlation between color and texture information is especially relevant in the case of variable illumination, a condition that has a crucial impact on the effectiveness of most visual descriptors. We propose an ensemble of hand-crafted image descriptors designed to capture different aspects of color textures. We show that the use of these descriptors in a multiple classifiers framework makes it possible to achieve a very high classification accuracy in classifying texture images acquired under different lighting conditions. A powerful alternative to hand-crafted descriptors is represented by features obtained with deep learning methods. We also show how the proposed combining strategy hand-crafted and convolutional neural networks features can be used together to further improve the classification accuracy. Experimental results on a food database (raw food texture) demonstrate the effectiveness of the proposed strategy.

Cloud cover analysis with Arctic Advanced Very High Resolution Radiometer data. II - Classification with spectral and textural measures

NASA Technical Reports Server (NTRS)

Key, J.

1990-01-01

The spectral and textural characteristics of polar clouds and surfaces for a 7-day summer series of AVHRR data in two Arctic locations are examined, and the results used in the development of a cloud classification procedure for polar satellite data. Since spatial coherence and texture sensitivity tests indicate that a joint spectral-textural analysis based on the same cell size is inappropriate, cloud detection with AVHRR data and surface identification with passive microwave data are first done on the pixel level as described by Key and Barry (1989). Next, cloud patterns within 250-sq-km regions are described, then the spectral and local textural characteristics of cloud patterns in the image are determined and each cloud pixel is classified by statistical methods. Results indicate that both spectral and textural features can be utilized in the classification of cloudy pixels, although spectral features are most useful for the discrimination between cloud classes.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073

A Geostatistical Toolset for Reconstructing Louisiana's Coastal Stratigraphy using Subsurface Boring and Cone Penetrometer Test Data

NASA Astrophysics Data System (ADS)

Li, A.; Tsai, F. T. C.; Jafari, N.; Chen, Q. J.; Bentley, S. J.

2017-12-01

A vast area of river deltaic wetlands stretches across southern Louisiana coast. The wetlands are suffering from a high rate of land loss, which increasingly threats coastal community and energy infrastructure. A regional stratigraphic framework of the delta plain is now imperative to answer scientific questions (such as how the delta plain grows and decays?) and to provide information to coastal protection and restoration projects (such as marsh creation and construction of levees and floodwalls). Through years, subsurface investigations in Louisiana have been conducted by state and federal agencies (Louisiana Department of Natural Resources, United States Geological Survey, United States Army Corps of Engineers, etc.), research institutes (Louisiana Geological Survey, LSU Coastal Studies Institute, etc.), engineering firms, and oil-gas companies. This has resulted in the availability of various types of data, including geological, geotechnical, and geophysical data. However, it is challenging to integrate different types of data and construct three-dimensional stratigraphy models in regional scale. In this study, a set of geostatistical methods were used to tackle this problem. An ordinary kriging method was used to regionalize continuous data, such as grain size, water content, liquid limit, plasticity index, and cone penetrometer tests (CPTs). Indicator kriging and multiple indicator kriging methods were used to regionalize categorized data, such as soil classification. A compositional kriging method was used to regionalize compositional data, such as soil composition (fractions of sand, silt and clay). Stratigraphy models were constructed for three cases in the coastal zone: (1) Inner Harbor Navigation Canal (IHNC) area: soil classification and soil behavior type (SBT) stratigraphies were constructed using ordinary kriging; (2) Middle Barataria Bay area: a soil classification stratigraphy was constructed using multiple indicator kriging; (3) Lower Barataria Bay and Lower Breton Sound areas: a soil texture stratigraphy was constructed using soil compositional data and compositional kriging. Cross sections were extracted from the three-dimensional stratigraphy models to reveal spatial distributions of different stratigraphic features.

Iris Image Classification Based on Hierarchical Visual Codebook.

PubMed

Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

2014-06-01

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

Responses of plant available water and forest productivity to variably layered coarse textured soils

NASA Astrophysics Data System (ADS)

Huang, Mingbin; Barbour, Lee; Elshorbagy, Amin; Si, Bing; Zettl, Julie

2010-05-01

Reforestation is a primary end use for reconstructed soils following oil sands mining in northern Alberta, Canada. Limited soil water conditions strongly restrict plant growth. Previous research has shown that layering of sandy soils can produce enhanced water availability for plant growth; however, the effect of gradation on these enhancements is not well defined. The objective of this study was to evaluate the effect of soil texture (gradation and layering) on plant available water and consequently on forest productivity for reclaimed coarse textured soils. A previously validated system dynamics (SD) model of soil moisture dynamics was coupled with ecophysiological and biogeochemical processes model, Biome-BGC-SD, to simulate forest dynamics for different soil profiles. These profiles included contrasting 50 cm textural layers of finer sand overlying coarser sand in which the sand layers had either a well graded or uniform soil texture. These profiles were compared to uniform profiles of the same sands. Three tree species of jack pine (Pinus banksiana Lamb.), white spruce (Picea glauce Voss.), and trembling aspen (Populus tremuloides Michx.) were simulated using a 50 year climatic data base from northern Alberta. Available water holding capacity (AWHC) was used to identify soil moisture regime, and leaf area index (LAI) and net primary production (NPP) were used as indices of forest productivity. Published physiological parameters were used in the Biome-BGC-SD model. Relative productivity was assessed by comparing model predictions to the measured above-ground biomass dynamics for the three tree species, and was then used to study the responses of forest leaf area index and potential productivity to AWHC on different soil profiles. Simulated results indicated soil layering could significantly increase AWHC in the 1-m profile for coarse textured soils. This enhanced AWHC could result in an increase in forest LAI and NPP. The increased extent varied with soil textures and vegetative types. The simulated results showed that the presence of 50 cm of coarser graded sand overlying 50 cm of finer graded sand is the most effective reclaimed prescription to increase AWHC and forest productivity among the studied soil profiles.

Termite infestation associated with type of soil in pulau pinang, malaysia (isoptera: rhinotermitidae).

PubMed

Majid, Abdul Hafiz Ab; Ahmad, Abu Hassan

2013-12-01

Nine soil samples from nine buildings infested with Coptotermes gestroi in Pulau Pinang, Malaysia, were tested for the type of soil texture. The soil texture analysis procedures used the hydrometer method. Four of nine buildings (44%) yielded loamy sand-type soil, whereas five of nine buildings (56%) contained sandy loam-type soil.

Termite Infestation Associated with Type of Soil in Pulau Pinang, Malaysia (Isoptera: Rhinotermitidae)

PubMed Central

Majid, Abdul Hafiz Ab; Ahmad, Abu Hassan

2013-01-01

Nine soil samples from nine buildings infested with Coptotermes gestroi in Pulau Pinang, Malaysia, were tested for the type of soil texture. The soil texture analysis procedures used the hydrometer method. Four of nine buildings (44%) yielded loamy sand-type soil, whereas five of nine buildings (56%) contained sandy loam-type soil. PMID:24575252

Use of large-scale multi-configuration EMI measurements to characterize heterogeneous subsurface structures and their impact on crop productivity

NASA Astrophysics Data System (ADS)

Brogi, Cosimo; Huisman, Johan Alexander; Kaufmann, Manuela Sarah; von Hebel, Christian; van der Kruk, Jan; Vereecken, Harry

2017-04-01

Soil subsurface structures can play a key role in crop performance, especially during water stress periods. Geophysical techniques like electromagnetic induction EMI have been shown to be able of providing information about dominant shallow subsurface features. However, previous work with EMI has typically not reached beyond the field scale. The objective of this study is to use large-scale multi-configuration EMI to characterize patterns of soil structural organization (layering and texture) and the associated impact on crop vegetation at the km2 scale. For this, we carried out an intensive measurement campaign and collected high spatial resolution multi-configuration EMI data on an agricultural area of approx. 1 km2 (102 ha) near Selhausen (North Rhine-Westphalia, Germany) with a maximum depth of investigation of around 2.5 m. We measured using two EMI instruments simultaneously with a total of nine coil configurations. The instruments were placed inside polyethylene sleds that were pulled by an all-terrain-vehicle along parallel lines with a spacing of 2 to 2.5 m. The driving speed was between 5 and 7 km h-1 and we used a 0.2 Hz sampling frequency to obtain an in-line resolution of approximately 0.3 m. The survey area consists of almost 50 different fields managed in different way. The EMI measurements were collected between April and December 2016 within a few days after the harvest of each field. After data acquisition, EMI data were automatically filtered, temperature corrected, and interpolated onto a common grid. The resulting EMI maps allowed us to identify three main areas with different subsurface heterogeneities. The differences between these areas are likely related to the late quaternary geological history (Pleistocene and Holocene) of the area that resulted in spatially variable soil texture and layering, which has a strong impact on spatio-temporal soil water content variability. The high resolution surveys also allowed us to identify small scale geomorphological structures as well as anthropogenic activities such as soil management and drainage networks carried out in the last 150 years. To identify areas with similar subsurface structures with high spatial resolution, we applied multiband image classification using the nine coil configurations as bands of a single image. We compared both supervised and unsupervised classification and obtained promising preliminary results showing a good degree of conformity between EMI supervised classification maps and observed patterns in plant productivity.

Soil texture drives responses of soil respiration to precipitation pulses in the sonoran desert: Implications for climate change

USGS Publications Warehouse

Cable, J.M.; Ogle, K.; Williams, D.G.; Weltzin, J.F.; Huxman, T. E.

2008-01-01

Climate change predictions for the desert southwestern U.S. are for shifts in precipitation patterns. The impacts of climate change may be significant, because desert soil processes are strongly controlled by precipitation inputs ('pulses') via their effect on soil water availability. This study examined the response of soil respiration-an important biological process that affects soil carbon (C) storage-to variation in pulses representative of climate change scenarios for the Sonoran Desert. Because deserts are mosaics of different plant cover types and soil textures-which create patchiness in soil respiration-we examined how these landscape characteristics interact to affect the response of soil respiration to pulses. Pulses were applied to experimental plots of bare and vegetated soil on contrasting soil textures typical of Sonoran Desert grasslands. The data were analyzed within a Bayesian framework to: (1) determine pulse size and antecedent moisture (soil moisture prior to the pulse) effects on soil respiration, (2) quantify soil texture (coarse vs. fine) and cover type (bare vs. vegetated) effects on the response of soil respiration and its components (plant vs. microbial) to pulses, and (3) explore the relationship between long-term variation in pulse regimes and seasonal soil respiration. Regarding objective (1), larger pulses resulted in higher respiration rates, particularly from vegetated fine-textured soil, and dry antecedent conditions amplified respiration responses to pulses (wet antecedent conditions dampened the pulse response). Regarding (2), autotrophic (plant) activity was a significant source (???60%) of respiration and was more sensitive to pulses on coarse- versus fine-textured soils. The sensitivity of heterotrophic (microbial) respiration to pulses was highly dependent on antecedent soil water. Regarding (3), seasonal soil respiration was predicted to increase with both growing season precipitation and mean pulse size (but only for pulses between 7 and 25 mm). Thus, the heterogeneity of the desert landscape and the timing or the number of medium-sized pulses is expected to significantly impact desert soil C loss with climate change. ?? 2008 Springer Science+Business Media, LLC.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon.

PubMed

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E; Moran, Emilio

2008-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

PubMed Central

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E.; Moran, Emilio

2009-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. PMID:19789716

Relationships between Soil compaction and harvest season, soil texture, and landscape position for aspen forests

Treesearch

Randy Kolka; Aaron Steber; Ken Brooks; Charles H. Perry; Matt Powers

2012-01-01

Although a number of harvesting studies have assessed compaction, no study has considered the interacting relationships of harvest season, soil texture, and landscape position on soil bulk density and surface soil strength for harvests in the western Lake States. In 2005, we measured bulk density and surface soil strength in recent clearcuts of predominantly aspen...

Time-frequency feature representation using multi-resolution texture analysis and acoustic activity detector for real-life speech emotion recognition.

PubMed

Wang, Kun-Ching

2015-01-14

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

Pet fur color and texture classification

NASA Astrophysics Data System (ADS)

Yen, Jonathan; Mukherjee, Debarghar; Lim, SukHwan; Tretter, Daniel

2007-01-01

Object segmentation is important in image analysis for imaging tasks such as image rendering and image retrieval. Pet owners have been known to be quite vocal about how important it is to render their pets perfectly. We present here an algorithm for pet (mammal) fur color classification and an algorithm for pet (animal) fur texture classification. Per fur color classification can be applied as a necessary condition for identifying the regions in an image that may contain pets much like the skin tone classification for human flesh detection. As a result of the evolution, fur coloration of all mammals is caused by a natural organic pigment called Melanin and Melanin has only very limited color ranges. We have conducted a statistical analysis and concluded that mammal fur colors can be only in levels of gray or in two colors after the proper color quantization. This pet fur color classification algorithm has been applied for peteye detection. We also present here an algorithm for animal fur texture classification using the recently developed multi-resolution directional sub-band Contourlet transform. The experimental results are very promising as these transforms can identify regions of an image that may contain fur of mammals, scale of reptiles and feather of birds, etc. Combining the color and texture classification, one can have a set of strong classifiers for identifying possible animals in an image.

Comparison of model microbial allocation parameters in soils of varying texture

NASA Astrophysics Data System (ADS)

Hagerty, S. B.; Slessarev, E.; Schimel, J.

2017-12-01

The soil microbial community decomposes the majority of carbon (C) inputs to the soil. However, not all of this C is respired—rather, a substantial portion of the carbon processed by microbes may remain stored in the soil. The balance between C storage and respiration is controlled by microbial turnover rates and C allocation strategies. These microbial community properties may depend on soil texture, which has the potential to influence both the nature and the fate of microbial necromass and extracellular products. To evaluate the role of texture on microbial turnover and C allocation, we sampled four soils from the University of California's Hastings Reserve that varied in texture (one silt loam, two sandy loam, and on clay soil), but support similar grassland plant communities. We added 14C- glucose to the soil and measured the concentration of the label in the carbon dioxide (CO2), microbial biomass, and extractable C pools over 7 weeks. The labeled biomass turned over the slowest in the clay soil; the concentration of labeled biomass was more than 1.5 times the concentration of the other soils after 8 weeks. The clay soil also had the lowest mineralization rate of the label, and mineralization slowed after two weeks. In contrast, in the sandier soils mineralization rates were higher and did not plateau until 5 weeks into the incubation period. We fit the 14C data to a microbial allocation model and estimated microbial parameters; assimilation efficiency, exudation, and biomass specific respiration and turnover for each soil. We compare these parameters across the soil texture gradient to assess the extent to which models may need to account for variability in microbial C allocation across soils of different texture. Our results suggest that microbial C turns over more slowly in high-clay soils than in sandy soils, and that C lost from microbial biomass is retained at higher rates in high-clay soils. Accounting for these differences in microbial allocation and carbon stabilization could improve model representations of C cycling across a range of soil types.

Please Don't Move-Evaluating Motion Artifact From Peripheral Quantitative Computed Tomography Scans Using Textural Features.

PubMed

Rantalainen, Timo; Chivers, Paola; Beck, Belinda R; Robertson, Sam; Hart, Nicolas H; Nimphius, Sophia; Weeks, Benjamin K; McIntyre, Fleur; Hands, Beth; Siafarikas, Aris

Most imaging methods, including peripheral quantitative computed tomography (pQCT), are susceptible to motion artifacts particularly in fidgety pediatric populations. Methods currently used to address motion artifact include manual screening (visual inspection) and objective assessments of the scans. However, previously reported objective methods either cannot be applied on the reconstructed image or have not been tested for distal bone sites. Therefore, the purpose of the present study was to develop and validate motion artifact classifiers to quantify motion artifact in pQCT scans. Whether textural features could provide adequate motion artifact classification performance in 2 adolescent datasets with pQCT scans from tibial and radial diaphyses and epiphyses was tested. The first dataset was split into training (66% of sample) and validation (33% of sample) datasets. Visual classification was used as the ground truth. Moderate to substantial classification performance (J48 classifier, kappa coefficients from 0.57 to 0.80) was observed in the validation dataset with the novel texture-based classifier. In applying the same classifier to the second cross-sectional dataset, a slight-to-fair (κ = 0.01-0.39) classification performance was observed. Overall, this novel textural analysis-based classifier provided a moderate-to-substantial classification of motion artifact when the classifier was specifically trained for the measurement device and population. Classification based on textural features may be used to prescreen obviously acceptable and unacceptable scans, with a subsequent human-operated visual classification of any remaining scans. Copyright © 2017 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

The Evaluation of Basal Respiration for Various Soil Textures in Ecologically Sensitive Area

NASA Astrophysics Data System (ADS)

Huličová, P.; Kotorová, D.; Fazekašová, D.; Hynšt, J.

2017-10-01

The present contribution was focused on monitoring changes in the soil basal respiration in different textures of soil in the dry polder Beša. The research was conducted between 2012 and 2014 on soil type Fluvisol locations on three soil textures: clay - loam soil, clayey soil and clay soil in three soil depths. The basal respiration (BR) has been determine by soil CO2 production measuring from incubated soil samples in serum bottles in laboratory condition. Release Co2 has been analysed by gas chromatography. Content of clay particles were in the range 52.18 % to 81.31%, indicating the high difference between the minimum and maximum content. By using of multiple LSD-test we recorded statistically significant impact of clay on basal respiration. Results confirm the values of basal respiration with the depth of the soil profile decreased.

Saturated hydraulic conductivity of US soils grouped according to textural class and bulk density

USDA-ARS?s Scientific Manuscript database

Importance of the saturated hydraulic conductivity as soil hydraulic property led to the development of multiple pedotransfer functions for estimating it. One approach to estimating Ksat was using textural classes rather than specific textural fraction contents as pedotransfer inputs. The objective...

Saturated hydraulic conductivity of US soils grouped according textural class and bulk density

USDA-ARS?s Scientific Manuscript database

Importance of the saturated hydraulic conductivity as soil hydraulic property led to the development of multiple pedotransfer functions for estimating it. One approach to estimating Ksat was using textural classes rather than specific textural fraction contents as pedotransfer inputs. The objective...

PM10 emission efficiency for agricultural soils: Comparing a wind tunnel, a dust generator, and the open-air plot

NASA Astrophysics Data System (ADS)

Avecilla, Fernando; Panebianco, Juan E.; Mendez, Mariano J.; Buschiazzo, Daniel E.

2018-06-01

The PM10 emission efficiency of soils has been determined through different methods. Although these methods imply important physical differences, their outputs have never been compared. In the present study the PM10 emission efficiency was determined for soils through a wide range of textures, using three typical methodologies: a rotary-chamber dust generator (EDG), a laboratory wind tunnel on a prepared soil bed, and field measurements on an experimental plot. Statistically significant linear correlation was found (p < 0.05) between the PM10 emission efficiency obtained from the EDG and wind tunnel experiments. A significant linear correlation (p < 0.05) was also found between the PM10 emission efficiency determined both with the wind tunnel and the EDG, and a soil texture index (%sand + %silt)/(%clay + %organic matter) that reflects the effect of texture on the cohesion of the aggregates. Soils with higher sand content showed proportionally less emission efficiency than fine-textured, aggregated soils. This indicated that both methodologies were able to detect similar trends regarding the correlation between the soil texture and the PM10 emission. The trends attributed to soil texture were also verified for two contrasting soils under field conditions. However, differing conditions during the laboratory-scale and the field-scale experiments produced significant differences in the magnitude of the emission efficiency values. The causes of these differences are discussed within the paper. Despite these differences, the results suggest that standardized laboratory and wind tunnel procedures are promissory methods, which could be calibrated in the future to obtain results comparable to field values, essentially through adjusting the simulation time. However, more studies are needed to extrapolate correctly these values to field-scale conditions.

A new Downscaling Approach for SMAP, SMOS and ASCAT by predicting sub-grid Soil Moisture Variability based on Soil Texture

NASA Astrophysics Data System (ADS)

Montzka, C.; Rötzer, K.; Bogena, H. R.; Vereecken, H.

2017-12-01

Improving the coarse spatial resolution of global soil moisture products from SMOS, SMAP and ASCAT is currently an up-to-date topic. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. A method has been developed that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. Here, we predict for each SMOS, SMAP and ASCAT grid cell the sub-grid soil moisture variability based on the SoilGrids1km data set. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean. The resulting data set provides important information for downscaling coarse soil moisture observations of the SMOS, SMAP and ASCAT missions. Downscaling SMAP data by a field capacity proxy indicates adequate accuracy of the sub-grid soil moisture patterns.

Numerical analysis of groundwater recharge through stony soils using limited data

NASA Astrophysics Data System (ADS)

Hendrickx, J. M. H.; Khan, A. S.; Bannink, M. H.; Birch, D.; Kidd, C.

1991-10-01

This study evaluates groundwater recharge on an alluvial fan in Quetta Valley (Baluchistan, Pakistan), through deep stony soils with limited data of soil texture, soil profile descriptions, water-table depths and meteorological variables. From the soil profile descriptions, a representative profile was constructed with typical soil layers. Next, the texture of each layer was compared with textures of soils with known soil physical characteristics; it is assumed that soils from the same textural class have similar water retention and hydraulic conductivity curves. Finally, the water retention and hydraulic conductivity curves were transformed to account for the volume of stones in each layer; this varied between 0 and 60 vol. %. These data were used in a transient finite difference model and in a steady-state analytical solution to evaluate the travel time of the recharge water and the maximum annual recharge volume. Travel times proved to be less sensitive to differences in soil physical characteristics than to differences in annual infiltration rates. Therefore, estimation of soil physical characteristics from soil texture data alone appears justified for this study. Estimated travel times on the alluvial fan in the Quetta Valley vary between 1.6 years, through a soil profile of 25 m with an infiltration rate of 120 cm year -1, to 18.3 years through a soil profile of 100 m with an infiltration rate of 40 cm year -1. When the infiltration rate of the soil exceeds 40 cm day -1, the infiltration process proceeds so fast that evaporation losses are small. If the depth of ponding at the start of infiltration is more than 1 m, at least 90% of the applied recharge water will reach the water table, providing that the ponding area is bare of vegetation.

Fast Image Texture Classification Using Decision Trees

NASA Technical Reports Server (NTRS)

Thompson, David R.

2011-01-01

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Remote sensing of physiographic soil units of Bennett County, South Dakota

NASA Technical Reports Server (NTRS)

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

1973-01-01

A study was conducted in Bennett County, South Dakota, to establish a rangeland test site for evaluating the usefulness of ERTS data for mapping soil resources in rangeland areas. Photographic imagery obtained in October, 1970, was analyzed to determine which type of imagery is best for mapping drainage and land use patterns. Imagery of scales ranging from 1:1,000,000 to 1.20,000 was used to delineate soil-vegetative physiographic units. The photo characteristics used to define physiographic units were texture, drainage pattern, tone pattern, land use pattern and tone. These units will be used as test data for evaluating ERTS data. The physiographic units were categorized into a land classification system. The various categories which were delineated at the different scales of imagery were designed to be useful for different levels of land use planning. The land systems are adequate only for planning of large areas for general uses. The lowest category separated was the facet. The facets have a definite soil composition and represent different soil landscapes. These units are thought to be useful for providing natural resource information needed for local planning.

Texture and color features for tile classification

NASA Astrophysics Data System (ADS)

Baldrich, Ramon; Vanrell, Maria; Villanueva, Juan J.

1999-09-01

In this paper we present the results of a preliminary computer vision system to classify the production of a ceramic tile industry. We focus on the classification of a specific type of tiles whose production can be affected by external factors, such as humidity, temperature, origin of clays and pigments. Variations on these uncontrolled factors provoke small differences in the color and the texture of the tiles that force to classify all the production. A constant and non- subjective classification would allow avoiding devolution from customers and unnecessary stock fragmentation. The aim of this work is to simulate the human behavior on this classification task by extracting a set of features from tile images. These features are induced by definitions from experts. To compute them we need to mix color and texture information and to define global and local measures. In this work, we do not seek a general texture-color representation, we only deal with textures formed by non-oriented colored-blobs randomly distributed. New samples are classified using Discriminant Analysis functions derived from known class tile samples. The last part of the paper is devoted to explain the correction of acquired images in order to avoid time and geometry illumination changes.

Texture analysis based on the Hermite transform for image classification and segmentation

NASA Astrophysics Data System (ADS)

Estudillo-Romero, Alfonso; Escalante-Ramirez, Boris; Savage-Carmona, Jesus

2012-06-01

Texture analysis has become an important task in image processing because it is used as a preprocessing stage in different research areas including medical image analysis, industrial inspection, segmentation of remote sensed imaginary, multimedia indexing and retrieval. In order to extract visual texture features a texture image analysis technique is presented based on the Hermite transform. Psychovisual evidence suggests that the Gaussian derivatives fit the receptive field profiles of mammalian visual systems. The Hermite transform describes locally basic texture features in terms of Gaussian derivatives. Multiresolution combined with several analysis orders provides detection of patterns that characterizes every texture class. The analysis of the local maximum energy direction and steering of the transformation coefficients increase the method robustness against the texture orientation. This method presents an advantage over classical filter bank design because in the latter a fixed number of orientations for the analysis has to be selected. During the training stage, a subset of the Hermite analysis filters is chosen in order to improve the inter-class separability, reduce dimensionality of the feature vectors and computational cost during the classification stage. We exhaustively evaluated the correct classification rate of real randomly selected training and testing texture subsets using several kinds of common used texture features. A comparison between different distance measurements is also presented. Results of the unsupervised real texture segmentation using this approach and comparison with previous approaches showed the benefits of our proposal.

Soil texture and climatc conditions for biocrust growth limitation: a meta analysis

NASA Astrophysics Data System (ADS)

Fischer, Thomas; Subbotina, Mariia

2015-04-01

Along with afforestation, attempts have been made to combat desertification by managing soil crusts, and is has been reported that recovery rates of biocrusts are dependent on many factors, including the type, severity, and extent of disturbance; structure of the vascular plant community; conditions of adjoining substrates; availability of inoculation material; and climate during and after disturbance (Belnap & Eldridge 2001). Because biological soil crusts are known to be more stable on and to prefer fine substrates (Belnap 2001), the question arises as to how successful crust management practices can be applied to coarser soil. In previous studies we observed similar crust biomasses on finer soils under arid and on coarser soils under temperate conditions. We hypothesized that the higher water holding capacity of finer substrates would favor crust development, and that the amount of silt and clay in the substrate that is required for enhanced crust development would vary with changes in climatic conditions. In a global meta study, climatic and soil texture threshold values promoting BSC growth were derived. While examining literature sources, it became evident that the amount of studies to be incorporated into this meta analysis was reversely related to the amount of common environmental parameters they share. We selected annual mean precipitaion, mean temperature and the amount of silt and clay as driving variables for crust growth. Response variable was the "relative crust biomass", which was computed per literature source as the ratio between each individual crust biomass value of the given study to the study maximum value reported. We distinguished lichen, green algal, cyanobacterial and moss crusts. To quantify threshold conditions at which crust biomass responded to differences in texture and climate, we (I) determined correlations between bioclimatic variables, (II) calculated linear models to determine the effect of typical climatic variables with soil clay content and with study site as a random effect. (III) Threshold values of texture and climatc effects were identified using a regression tree. Three mean annual temperature classes for texture dependent BSC growth limitation were identified: (1) <9 °C with a threshold value of 25% silt and clay (limited growth on coarser soils), (2) 9-19 °C, where texture did have no influence on relative crust biomass, and (3) >19 °C at soils with <4 or >17% silt and clay. Because biocrust development is limited under certain climatic and soil texture conditions, it is suggested to consider soil texture for biocrust rehabilitation purposes and in biogeochemical modeling of cryptogamic ground covers. References Belnap, J. & Eldridge, D. 2001. Disturbance and Recovery of Biological Soil Crusts. In: Belnap, J. & Lange, O. (eds.) Biological Soil Crusts: Structure, Function, and Management, Springer, Berlin. Belnap, J. 2001. Biological Soil Crusts and Wind Erosion. In: Belnap, J. & Lange, O. (eds.) Fischer, T., Subbotina, M. 2014. Climatic and soil texture threshold values for cryptogamic cover development: a meta analysis. Biologia 69/11:1520-1530,

Aggregate stability and water retention near saturation characteristics as affected by soil texture, aggregate size and polyacrylamide application

USDA-ARS?s Scientific Manuscript database

Understanding the effects of soil intrinsic properties and extrinsic conditions on aggregate stability is essential for the development of effective soil and water conservation practices. Our objective was to evaluate the combined role of soil texture, aggregate size and application of a stabilizing...

Fine gravel controls hydrologic and erodibility responses to trampling disturbance for coarse-textured soils with weak cyanobacterial crusts.

USDA-ARS?s Scientific Manuscript database

We compared short-term effects of lug-soled boot trampling disturbance on water infiltration and soil erodibility on coarse-textured soils covered by a mixture of fine gravel and coarse sand over weak cyanobacterially-dominated biological soil crusts. Trampling significantly reduced final infiltrati...

Vulnerability of tropical forest ecosystems and forest dependent communities to droughts.

PubMed

Vogt, D J; Vogt, K A; Gmur, S J; Scullion, J J; Suntana, A S; Daryanto, S; Sigurðardóttir, R

2016-01-01

Energy captured by and flowing through a forest ecosystem can be indexed by its total Net Primary Productivity (NPP). This forest NPP can also be a reflection of its sensitivity to, and its ability to adapt to, any climate change while also being harvested by humans. However detecting and identifying the vulnerability of forest and human ecosystems to climate change requires information on whether these coupled social and ecological systems are able to maintain functionality while responding to environmental variability. To better understand what parameters might be representative of environmental variability, we compiled a metadata analysis of 96 tropical forest sites. We found that three soil textural classes (i.e., sand, sandy loam and clay) had significant but different relationships between NPP and precipitation levels. Therefore, assessing the vulnerability of forests and forest dependent communities to drought was carried out using data from those sites that had one of those three soil textural classes. For example, forests growing on soil textures of sand and clay had NPP levels decreasing as precipitation levels increased, in contrast to those forest sites that had sandy loam soils where NPP levels increased. Also, forests growing on sandy loam soil textures appeared better adapted to grow at lower precipitation levels compared to the sand and clay textured soils. In fact in our tropical database the lowest precipitation level found for the sandy loam soils was 821 mm yr(-1) compared to sand at 1739 mm yr(-1) and clay at 1771 mm yr(-1). Soil texture also determined the level of NPP reached by a forest, i.e., forest growing on sandy loam and clay reached low-medium NPP levels while higher NPP levels (i.e., medium, high) were found on sand-textured soils. Intermediate precipitation levels (>1800-3000 mm yr(-1)) were needed to grow forests at the medium and high NPP levels. Low thresholds of NPP were identified at both low (∼750 mm) and high precipitation (>3500 mm) levels. By combining data on the ratios of precipitation to the amount of biomass produced in a year with how much less precipitation input occurs during a drought year, it is possible to estimate whether productivity levels are sufficient to support forest growth and forest dependent communities following a drought. In this study, the ratios of annual precipitation inputs required to produce 1 Mg ha(-1) yr(-1) biomass by soil texture class varied across the three soil textural classes. By using a conservative estimate of 20% of productivity collected or harvested by people and 30% precipitation reduction level as triggering a drought, it was possible to estimate a potential loss of annual productivity due to a drought. In this study, the total NPP unavailable due to drought and harvest by forest dependent communities per year was 10.2 Mg ha(-1) yr(-1) for the sandy textured soils (64% of NPP still available), 8.4 Mg ha(-1) yr(-1) for the sandy loam textured soils (60% available) and 12.7 Mg ha(-1) yr(-1) for the clay textured soils (29% available). Forests growing on clay textured soils would be most vulnerable to drought triggered reductions in productivity so NPP levels would be inadequate to maintain ecosystem functions and would potentially cause a forest-to-savanna shift. Further, these forests would not be able to provide sufficient NPP to satisfy the requirements of forest dependent communities. By predicting the productivity responses of different tropical forest ecosystems to changes in precipitation patterns coupled with edaphic data, it could be possible to spatially identify where tropical forests are most vulnerable to climate change impacts and where mitigation efforts should be concentrated. Copyright © 2015 Elsevier Inc. All rights reserved.

Boosting CNN performance for lung texture classification using connected filtering

NASA Astrophysics Data System (ADS)

Tarando, Sebastián. Roberto; Fetita, Catalin; Kim, Young-Wouk; Cho, Hyoun; Brillet, Pierre-Yves

2018-02-01

Infiltrative lung diseases describe a large group of irreversible lung disorders requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. This paper presents an original image pre-processing framework based on locally connected filtering applied in multiresolution, which helps improving the learning process and boost the performance of CNN for lung texture classification. By removing the dense vascular network from images used by the CNN for lung classification, locally connected filters provide a better discrimination between different lung patterns and help regularizing the classification output. The approach was tested in a preliminary evaluation on a 10 patient database of various lung pathologies, showing an increase of 10% in true positive rate (on average for all the cases) with respect to the state of the art cascade of CNNs for this task.

Method: automatic segmentation of mitochondria utilizing patch classification, contour pair classification, and automatically seeded level sets

PubMed Central

2012-01-01

Background While progress has been made to develop automatic segmentation techniques for mitochondria, there remains a need for more accurate and robust techniques to delineate mitochondria in serial blockface scanning electron microscopic data. Previously developed texture based methods are limited for solving this problem because texture alone is often not sufficient to identify mitochondria. This paper presents a new three-step method, the Cytoseg process, for automated segmentation of mitochondria contained in 3D electron microscopic volumes generated through serial block face scanning electron microscopic imaging. The method consists of three steps. The first is a random forest patch classification step operating directly on 2D image patches. The second step consists of contour-pair classification. At the final step, we introduce a method to automatically seed a level set operation with output from previous steps. Results We report accuracy of the Cytoseg process on three types of tissue and compare it to a previous method based on Radon-Like Features. At step 1, we show that the patch classifier identifies mitochondria texture but creates many false positive pixels. At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, our results show that use of contour pair classification and level set operations improve segmentation accuracy beyond patch classification alone. We show that the Cytoseg process performs well compared to another modern technique based on Radon-Like Features. Conclusions We demonstrated that texture based methods for mitochondria segmentation can be enhanced with multiple steps that form an image processing pipeline. While we used a random-forest based patch classifier to recognize texture, it would be possible to replace this with other texture identifiers, and we plan to explore this in future work. PMID:22321695

Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

PubMed Central

Wang, Kun-Ching

2015-01-01

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech. PMID:25594590

3D texture analysis for classification of second harmonic generation images of human ovarian cancer

NASA Astrophysics Data System (ADS)

Wen, Bruce; Campbell, Kirby R.; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A.; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin. W.; Campagnola, Paul J.

2016-10-01

Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.

Soil Conditions Affect Growth of Hardwoods in Shelterbelts

Treesearch

Willard H. Carmean

1976-01-01

Large growth differences were found for hardwoods in shelterbelts on three contrasting soils of western Minnesota. Fiver years after planting, height growth was outstanding for green ash and Russian olive planted on a moderately fine-textured, somewhat poorly drained soil. Growth was much poorer on coarse-textured or shallow soils. Size of planting stock was not...

A procedure for classifying textural facies in gravel‐bed rivers

USGS Publications Warehouse

Buffington, John M.; Montgomery, David R.

1999-01-01

Textural patches (i.e., grain‐size facies) are commonly observed in gravel‐bed channels and are of significance for both physical and biological processes at subreach scales. We present a general framework for classifying textural patches that allows modification for particular study goals, while maintaining a basic degree of standardization. Textures are classified using a two‐tier system of ternary diagrams that identifies the relative abundance of major size classes and subcategories of the dominant size. An iterative procedure of visual identification and quantitative grain‐size measurement is used. A field test of our classification indicates that it affords reasonable statistical discrimination of median grain size and variance of bed‐surface textures. We also explore the compromise between classification simplicity and accuracy. We find that statistically meaningful textural discrimination requires use of both tiers of our classification. Furthermore, we find that simplified variants of the two‐tier scheme are less accurate but may be more practical for field studies which do not require a high level of textural discrimination or detailed description of grain‐size distributions. Facies maps provide a natural template for stratifying other physical and biological measurements and produce a retrievable and versatile database that can be used as a component of channel monitoring efforts.

Fertility and acidity status of latossolos (oxisols) under pasture in the Brazilian Cerrado.

PubMed

Vendrame, Pedro R S; Brito, Osmar R; Guimarães, Maria F; Martins, Eder S; Becquer, Thierry

2010-12-01

The Cerrado region, with over 50 million hectares of cultivated pasture, provides 55% of Brazilian beef production. Previous investigations have shown that about 70-80% of this pasture is affected by some kind of degradation, leading to low productivity. However, until now, few surveys have been carried out on a regional scale. The aim of the present work is both to assess the fertility and acidity levels of Cerrado soils under pasture and compare the variability of the soils characteristics on a regional scale. Two soil depths were sampled in different places within the studied area: (1) a surface horizon (0.0-0.2 m) in order to evaluate its fertility and acidity status for pasture, and (2) a subsurface horizon (0.6-0.8 m), used for classification. Most of soils had levels of nutrients below the reference values for adequate pasture development. Whatever the texture, about 90% of soils had low or very low availability of phosphorus. Only 7 to 14% of soils had low pH, high exchangeable aluminum, and aluminum saturation above the critical acidity level. Except for nitrogen, no significant difference was found between Latossolos Vermelhos and Latossolos Vermelho-Amarelos.

Interactions between soil texture and placement of dairy slurry application: I. Flow characteristics and leaching of nonreactive components.

PubMed

Glaesner, Nadia; Kjaergaard, Charlotte; Rubaek, Gitte H; Magid, Jakob

2011-01-01

Land application of manure can exacerbate nutrient and contaminant transfers to the aquatic environment. This study examined the effect of injecting a dairy cattle (Bostaurus L.) manure slurry on mobilization and leaching of dissolved, nonreactive slurry components across a range of agricultural soils. We compared leaching of slurry-applied bromide through intact soil columns (20 cm diam., 20 cm high) of differing textures following surface application or injection of slurry. The volumetric fraction of soil pores >30 microm ranged from 43% in a loamy sand to 28% in a sandy loam and 15% in a loam-textured soil. Smaller active flow volumes and higher proportions of preferential flow were observed with increasing soil clay content. Injection of slurry in the loam soil significantly enhanced diffusion of applied bromide into the large fraction of small pores compared with surface application. The resulting physical protection against leaching of bromide was reflected by 60.2% of the bromide tracer was recovered in the effluent after injection, compared with 80.6% recovery after surface application. No effect of slurry injection was observed in the loamy sand and sandy loam soils. Our findings point to soil texture as an important factor influencing leaching of dissolved, nonreactive slurry components in soils amended with manure slurry.

Soil texture analysis revisited: Removal of organic matter matters more than ever

PubMed Central

Schjønning, Per; Watts, Christopher W.; Christensen, Bent T.; Munkholm, Lars J.

2017-01-01

Exact estimates of soil clay (<2 μm) and silt (2–20 μm) contents are crucial as these size fractions impact key soil functions, and as pedotransfer concepts based on clay and silt contents are becoming increasingly abundant. We examined the effect of removing soil organic matter (SOM) by H2O2 before soil dispersion and determination of clay and silt. Soil samples with gradients in SOM were retrieved from three long-term field experiments each with uniform soil mineralogy and texture. For soils with less than 2 g C 100 g-1 minerals, clay estimates were little affected by SOM. Above this threshold, underestimation of clay increased dramatically with increasing SOM content. Silt contents were systematically overestimated when SOM was not removed; no lower SOM threshold was found for silt, but the overestimation was more pronounced for finer textured soils. When exact estimates of soil particles <20 μm are needed, SOM should always be removed before soil dispersion. PMID:28542416

Soil texture analysis revisited: Removal of organic matter matters more than ever.

PubMed

Jensen, Johannes Lund; Schjønning, Per; Watts, Christopher W; Christensen, Bent T; Munkholm, Lars J

2017-01-01

Exact estimates of soil clay (<2 μm) and silt (2-20 μm) contents are crucial as these size fractions impact key soil functions, and as pedotransfer concepts based on clay and silt contents are becoming increasingly abundant. We examined the effect of removing soil organic matter (SOM) by H2O2 before soil dispersion and determination of clay and silt. Soil samples with gradients in SOM were retrieved from three long-term field experiments each with uniform soil mineralogy and texture. For soils with less than 2 g C 100 g-1 minerals, clay estimates were little affected by SOM. Above this threshold, underestimation of clay increased dramatically with increasing SOM content. Silt contents were systematically overestimated when SOM was not removed; no lower SOM threshold was found for silt, but the overestimation was more pronounced for finer textured soils. When exact estimates of soil particles <20 μm are needed, SOM should always be removed before soil dispersion.

Root system morphology of Oregon white oak on a glacial outwash soil.

Treesearch

Warren D. Devine; Constance A. Harrington

2005-01-01

Oregon white oak is reportedly a deeply rooted species, but its rooting habit on coarse-textured soils is undocumented. In the Puget Trough of western Washington, Oregon white oak grows in coarse-textured glacial outwash soils on lowland sites. Our objective was to quantify the gross root system morphology of Oregon white oak in these soils, thereby improving our...

Do soil tests help forecast nitrogen response in first-year corn following alfalfa on fine-textured soils?

USDA-ARS?s Scientific Manuscript database

Improved methods of predicting grain yield response to fertilizer N for first-year corn (Zea mays L.) following alfalfa (Medicago sativa L.) on fine-textured soils are needed. Data from 21 site-years in the North Central Region were used to (i) determine how Illinois soil nitrogen test (ISNT) and pr...

Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis.

PubMed

Pu, Hongbin; Sun, Da-Wen; Ma, Ji; Cheng, Jun-Hu

2015-01-01

The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat. Copyright © 2014 Elsevier Ltd. All rights reserved.

Texture as a basis for acoustic classification of substrate in the nearshore region

NASA Astrophysics Data System (ADS)

Dennison, A.; Wattrus, N. J.

2016-12-01

Segmentation and classification of substrate type from two locations in Lake Superior, are predicted using multivariate statistical processing of textural measures derived from shallow-water, high-resolution multibeam bathymetric data. During a multibeam sonar survey, both bathymetric and backscatter data are collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on substrate type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. Preliminary results from an analysis of bathymetric data and ground-truth samples collected from the Amnicon River, Superior, Wisconsin, and the Lester River, Duluth, Minnesota, demonstrate the ability to process and develop a novel classification scheme of the bottom type in two geomorphologically distinct areas.

Infiltration Processes and Flow Velocities Across the Landscape: When and Where is Macropore Flow Relevant?

NASA Astrophysics Data System (ADS)

Demand, D.; Blume, T.; Weiler, M.

2017-12-01

Preferential flow in macropores significantly affects the distributions of water and solutes in soil and many studies showed its relevance worldwide. Although some models include this process as a second pore domain, little is known about the spatial patterns and temporal dynamics. For example, while flow in the matrix is usually modeled and parameterized based on soil texture, an influence of texture on non-capillary flow for a given land-use class is poorly understood. To investigate the temporal and spatial dynamics on preferential flow we used a four-year soil moisture dataset from the mesoscale Attert catchment (288 km²) in Luxembourg. This dataset contains time series from 126 soil profiles in different textures and two land-use classes (forest, grassland). The soil moisture probes were installed in 10, 30 and 50 cm depth and measured in a 5-minute temporal resolution. Events were defined by a soil moisture increase higher than the instrument noise after a precipitation sum of more than 1 mm. Precipitation was measured next to the profiles so that each location could be associated to its unique precipitation characteristics. For every event and profile the soil moisture reaction was classified in sequential (ordered by depth) and non-sequential response. A non-sequential soil moisture reaction was used as an indicator of preferential flow. For sequential flow, the velocity was determined by the first reaction between two vertically adjacent sensors. The sensor reaction and wetting front velocity was analyzed in the context of precipitation characteristics and initial soil water content. Grassland sites showed a lower proportion of non-sequential flow than forest sites. For forest, non-sequential response is dependent on texture, rainfall intensity and initial water content. This is less distinct for the grassland sites. Furthermore, sequential reactions show higher flow velocities at sites, which also have high percentage of non-sequential response. In contrast, grassland sites show a more homogenous wetting front independent of soil texture. Compared against common modelling approaches of soil water flow, measured velocities show clear evidence of preferential flow, especially for forest soils. The analysis also shows that vegetation can alter the soil properties above the textural properties alone.

CO2 dinamics and priming effect of different Hungarian soils based on laboratory incubation experiment

NASA Astrophysics Data System (ADS)

Zacháry, Dóra; Szalai, Zoltán; Filep, Tibor; Kovács, József; Jakab, Gergely

2017-04-01

Soil processes are particularly important in terms of global carbon cycle, as soils globally contain approximately 2000 Gt carbon, which is higher than the carbon stock of the atmosphere and the terrestrial ecosystem together. Therefore small alterations in the soils' carbon sequestration potential can generate rapid and significant changes in the atmosphere carbon concentration. Soil texture is one of the most important soil parameters which plays a significant role in soil carbon sequestration. Fine textured soils generally considered containing more microbial biomass, and having a lower rate of biomass turnover and organic matter decomposition than coarse textured soils. In spite of this, several recent studies have shown contradicting trends. Our aim was to investigate the influence of the basic soil properties (texture, pH, organic matter content, etc.) on the biological and physicochemical processes determining the soil CO2 emission. Thirteen Hungarian soil samples (depth of 0-20 cm) were incubated during six months. The samples are mainly high clay and organic matter content forest soils, but two forest soils developed on sand were also collected. The soils are derived from C3 forests and C3 croplands from different sites of Hungary. C4 maize residues were added to the soils in order to get natural 13C enrichment for stable isotope measurement purposes and for quantifying the priming effect caused by the crop residue addition. The temperature (20°C) and humidity (70% field capacity) conditions were kept constant in an incubator. The soil respiration was measured at specified intervals (on day 3, 8, 15, 30, 51, 79, 107, 135 and 163) and trapped in 2M NaOH and quantified by titration with 1M HCl. Our first results based on the cumulative CO2 respiration values show positive priming for all type of soils. Results confirm the statement that in certain cases fine textured soils release more CO2. To determine which soil properties influence the most the soil CO2 emission, the linking among the mathematical model parameters and the soil properties would be useful. G. Jakab was supported by the János Bolyai scholarship of the HAS, which is kindly acknowledge.

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

NASA Astrophysics Data System (ADS)

Zhang, Yan; Wirkert, Sebastian J.; Iszatt, Justin; Kenngott, Hannes; Wagner, Martin; Mayer, Benjamin; Stock, Christian; Clancy, Neil T.; Elson, Daniel S.; Maier-Hein, Lena

2016-03-01

Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

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

Sahiner, B.; Chan, H.P.; Petrick, N.

1996-10-01

The authors investigated the classification of regions of interest (ROI`s) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI`s using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequentlymore » used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROI`s containing biopsy-proven masses and 504 ROI`s containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.« less

Use of Large-Scale Multi-Configuration EMI Measurements to Characterize Subsurface Structures of the Vadose Zone.

NASA Astrophysics Data System (ADS)

Huisman, J. A.; Brogi, C.; Pätzold, S.; Weihermueller, L.; von Hebel, C.; Van Der Kruk, J.; Vereecken, H.

2017-12-01

Subsurface structures of the vadose zone can play a key role in crop yield potential, especially during water stress periods. Geophysical techniques like electromagnetic induction EMI can provide information about dominant shallow subsurface features. However, previous studies with EMI have typically not reached beyond the field scale. We used high-resolution large-scale multi-configuration EMI measurements to characterize patterns of soil structural organization (layering and texture) and their impact on crop productivity at the km2 scale. We collected EMI data on an agricultural area of 1 km2 (102 ha) near Selhausen (NRW, Germany). The area consists of 51 agricultural fields cropped in rotation. Therefore, measurements were collected between April and December 2016, preferably within few days after the harvest. EMI data were automatically filtered, temperature corrected, and interpolated onto a common grid of 1 m resolution. Inspecting the ECa maps, we identified three main sub-areas with different subsurface heterogeneity. We also identified small-scale geomorphological structures as well as anthropogenic activities such as soil management and buried drainage networks. To identify areas with similar subsurface structures, we applied image classification techniques. We fused ECa maps obtained with different coil distances in a multiband image and applied supervised and unsupervised classification methodologies. Both showed good results in reconstructing observed patterns in plant productivity and the subsurface structures associated with them. However, the supervised methodology proved more efficient in classifying the whole study area. In a second step, we selected hundred locations within the study area and obtained a soil profile description with type, depth, and thickness of the soil horizons. Using this ground truth data it was possible to assign a typical soil profile to each of the main classes obtained from the classification. The proposed methodology was effective in producing a high resolution subsurface model in a large and complex study area that extends well beyond the field scale.

Soil Texture and Cultivar Effects on Rice (Oryza sativa, L.) Grain Yield, Yield Components and Water Productivity in Three Water Regimes.

PubMed

Dou, Fugen; Soriano, Junel; Tabien, Rodante E; Chen, Kun

2016-01-01

The objective of this study was to determine the effects of water regime/soil condition (continuous flooding, saturated, and aerobic), cultivar ('Cocodrie' and 'Rondo'), and soil texture (clay and sandy loam) on rice grain yield, yield components and water productivity using a greenhouse trial. Rice grain yield was significantly affected by soil texture and the interaction between water regime and cultivar. Significantly higher yield was obtained in continuous flooding than in aerobic and saturated soil conditions but the latter treatments were comparable to each other. For Rondo, its grain yield has decreased with soil water regimes in the order of continuous flooding, saturated and aerobic treatments. The rice grain yield in clay soil was 46% higher than in sandy loam soil averaged across cultivar and water regime. Compared to aerobic condition, saturated and continuous flooding treatments had greater panicle numbers. In addition, panicle number in clay soil was 25% higher than in sandy loam soil. The spikelet number of Cocodrie was 29% greater than that of Rondo, indicating that rice cultivar had greater effect on spikelet number than soil type and water management. Water productivity was significantly affected by the interaction of water regime and cultivar. Compared to sandy loam soil, clay soil was 25% higher in water productivity. Our results indicated that cultivar selection and soil texture are important factors in deciding what water management option to practice.

Quantification of Reflection Patterns in Ground-Penetrating Radar Data

NASA Astrophysics Data System (ADS)

Moysey, S.; Knight, R. J.; Jol, H. M.; Allen-King, R. M.; Gaylord, D. R.

2005-12-01

Radar facies analysis provides a way of interpreting the large-scale structure of the subsurface from ground-penetrating radar (GPR) data. Radar facies are often distinguished from each other by the presence of patterns, such as flat-lying, dipping, or chaotic reflections, in different regions of a radar image. When these patterns can be associated with radar facies in a repeated and predictable manner we refer to them as `radar textures'. While it is often possible to qualitatively differentiate between radar textures visually, pattern recognition tools, like neural networks, require a quantitative measure to discriminate between them. We investigate whether currently available tools, such as instantaneous attributes or metrics adapted from standard texture analysis techniques, can be used to improve the classification of radar facies. To this end, we use a neural network to perform cross-validation tests that assess the efficacy of different textural measures for classifying radar facies in GPR data collected from the William River delta, Saskatchewan, Canada. We found that the highest classification accuracies (>93%) were obtained for measures of texture that preserve information about the spatial arrangement of reflections in the radar image, e.g., spatial covariance. Lower accuracy (87%) was obtained for classifications based directly on windows of amplitude data extracted from the radar image. Measures that did not account for the spatial arrangement of reflections in the image, e.g., instantaneous attributes and amplitude variance, yielded classification accuracies of less than 65%. Optimal classifications were obtained for textural measures that extracted sufficient information from the radar data to discriminate between radar facies but were insensitive to other facies specific characteristics. For example, the rotationally invariant Fourier-Mellin transform delivered better classification results than the spatial covariance because dip angle of the reflections, but not dip direction, was an important discriminator between radar facies at the William River delta. To extend the use of radar texture beyond the identification of radar facies to sedimentary facies we are investigating how sedimentary features are encoded in GPR data at Borden, Ontario, Canada. At this site, we have collected extensive sedimentary and hydrologic data over the area imaged by GPR. Analysis of this data coupled with synthetic modeling of the radar signal has allowed us to develop insight into the generation of radar texture in complex geologic environments.

[An object-based information extraction technology for dominant tree species group types].

PubMed

Tian, Tian; Fan, Wen-yi; Lu, Wei; Xiao, Xiang

2015-06-01

Information extraction for dominant tree group types is difficult in remote sensing image classification, howevers, the object-oriented classification method using high spatial resolution remote sensing data is a new method to realize the accurate type information extraction. In this paper, taking the Jiangle Forest Farm in Fujian Province as the research area, based on the Quickbird image data in 2013, the object-oriented method was adopted to identify the farmland, shrub-herbaceous plant, young afforested land, Pinus massoniana, Cunninghamia lanceolata and broad-leave tree types. Three types of classification factors including spectral, texture, and different vegetation indices were used to establish a class hierarchy. According to the different levels, membership functions and the decision tree classification rules were adopted. The results showed that the method based on the object-oriented method by using texture, spectrum and the vegetation indices achieved the classification accuracy of 91.3%, which was increased by 5.7% compared with that by only using the texture and spectrum.

Soil Texture Mediates the Response of Tree Cover to Rainfall Intensity in African Savannas

NASA Astrophysics Data System (ADS)

Case, M. F.; Staver, A. C.

2017-12-01

Global circulation models predict widespread shifts in the frequency and intensity of rainfall, even where mean annual rainfall does not change. Resulting changes in soil moisture dynamics could have major consequences for plant communities and ecosystems, but the direction of potential vegetation responses can be challenging to predict. In tropical savannas, where tree and grasses coexist, contradictory lines of evidence have suggested that tree cover could respond either positively or negatively to less frequent, more intense rainfall. Here, we analyzed remote sensing data and continental-scale soils maps to examine whether soil texture or fire could explain heterogeneous responses of savanna tree cover to intra-annual rainfall variability across sub-Saharan Africa. We find that tree cover generally increases with mean wet-season rainfall, decreases with mean wet-season rainfall intensity, and decreases with fire frequency. However, soil sand content mediates these relationships: the response to rainfall intensity switches qualitatively depending on soil texture, such that tree cover decreases dramatically with less frequent, more intense rainfall on clay soils but increases with rainfall intensity on sandy soils in semi-arid savannas. We propose potential ecohydrological mechanisms for this heterogeneous response, and emphasize that predictions of savanna vegetation responses to global change should account for interactions between soil texture and changing rainfall patterns.

Lung texture classification using bag of visual words

NASA Astrophysics Data System (ADS)

Asherov, Marina; Diamant, Idit; Greenspan, Hayit

2014-03-01

Interstitial lung diseases (ILD) refer to a group of more than 150 parenchymal lung disorders. High-Resolution Computed Tomography (HRCT) is the most essential imaging modality of ILD diagnosis. Nonetheless, classification of various lung tissue patterns caused by ILD is still regarded as a challenging task. The current study focuses on the classification of five most common categories of lung tissues of ILD in HRCT images: normal, emphysema, ground glass, fibrosis and micronodules. The objective of the research is to classify an expert-given annotated region of interest (AROI) using a bag of visual words (BoVW) framework. The images are divided into small patches and a collection of representative patches are defined as visual words. This procedure, termed dictionary construction, is performed for each individual lung texture category. The assumption is that different lung textures are represented by a different visual word distribution. The classification is performed using an SVM classifier with histogram intersection kernel. In the experiments, we use a dataset of 1018 AROIs from 95 patients. Classification using a leave-one-patient-out cross validation (LOPO CV) is used. Current classification accuracy obtained is close to 80%.

An application to pulmonary emphysema classification based on model of texton learning by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

2012-03-01

We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

[Influence of a new phosphoramide urease inhibitor on urea-N transformation in different texture soil].

PubMed

Zhou, Xuan; Wu, Liang Huan; Dai, Feng

2016-12-01

Addition of urease inhibitors is one of the important measures to increase nitrogen (N) use efficiency of crop, due to retardant of urea hydrolysis and reduction of ammonia volatilization loss. An incubation experiment was conducted to investigate the urease inhibition effect of a new phosphoramide urease inhibitor, NPPT (N-(n-propyl) thiophosphoric triamide) in different texture soils under dark condition at 25 ℃, and NBPT (N-(n-butyl) thiophosphoric triamide) was obtained to compare the inhibition effect on urease in different soil textures by different dosages of urea adding. Results showed that the effective reaction time of urea was less than 9 d in the loamy and clay soil. Addition of inhibitors for retardation of urea hydrolysis was more than 3 d. In sandy soil, urea decomposition was relatively slow, and adding inhibitor significantly inhibited soil urease acti-vity, and reduced NH 4 + -N content. During the incubation time, the inhibition effect of high dosage urea in the soil was better than that of low dosage. At day 6, the urease inhibition rate of NBPT and NPPT (N 250 mg·kg -1 ) were 56.3% and 53.0% in sandy soil, 0.04% and 0.3% in loamy soil, 4.1% and 6.2% in clay soil; the urease inhibition rate of NBPT and NPPT (N 500 mg·kg -1 ) were 59.4% and 65.8% in sandy soil, 14.5% and 15.1% in loamy soil, 49.1% and 48.1% in clay soil. The urease inhibition effects in different texture soil were in order of sandy soil > clay soil> loamy soil. The soil NH 4 + -N content by different inhibitors during incubation time increased at first and then decreased, while soil NO 3 - -N content and apparent nitrification rate both showed rising trends. Compared with urea treatment, addition of urease inhibitors (NBPT and NPPT) significantly increased urea-N left in the soil and reduced NH 4 + -N content. In short, new urease inhibitor NPPT in different texture is an effective urease inhibitor.

Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

PubMed Central

Moran, Emilio Federico.

2010-01-01

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance. PMID:21643433

Revamping of entisol soil physical characteristics with compost treatment

NASA Astrophysics Data System (ADS)

Sumono; Loka, S. P.; Nasution, D. L. S.

2018-02-01

Physical characteristic of Entisol soil is an important factor for the growth of plant. The aim of this research was to know the effect of compost application on physical characteristics of Entisol soil. The research method used was experimental method with 6 (six) treatments and 3 replications of which K1 = 10 kg Entisol soil without compost, K2 = 9 Kg Entisol soil with 1 kg compost, K3 = 8 kg Entisol soil with 2 kg compost, K4 = 7 kg Entisol soilwith3 kg compost, K5 = 6 kg Entisol soil with 4 kg compost and K6 = 5 kg Entisol soil with 5 kg compost. The observed parameters were soil texture, soil organic matter, soil thickness, porosity, soil pore size, soil permeability and water availability. The results showed that the Entisol soil texture was loamy sand texture, the value of soil organic matter ranged from 0.74% to 4.69%, soil thickness ranged from 13.83 to 20.16 cm, porosity ranged from16% to 37%, soil pore size ranged from 2.859 to 5.493 µm, permeability ranged from 1.24 to 5.64 cm/hour and water availability ranged from 6.67% to 9.12% by each treatment.

Singular spectrum decomposition of Bouligand-Minkowski fractal descriptors: an application to the classification of texture Images

NASA Astrophysics Data System (ADS)

Florindo, João. Batista

2018-04-01

This work proposes the use of Singular Spectrum Analysis (SSA) for the classification of texture images, more specifically, to enhance the performance of the Bouligand-Minkowski fractal descriptors in this task. Fractal descriptors are known to be a powerful approach to model and particularly identify complex patterns in natural images. Nevertheless, the multiscale analysis involved in those descriptors makes them highly correlated. Although other attempts to address this point was proposed in the literature, none of them investigated the relation between the fractal correlation and the well-established analysis employed in time series. And SSA is one of the most powerful techniques for this purpose. The proposed method was employed for the classification of benchmark texture images and the results were compared with other state-of-the-art classifiers, confirming the potential of this analysis in image classification.

Classification of interstitial lung disease patterns with topological texture features

NASA Astrophysics Data System (ADS)

Huber, Markus B.; Nagarajan, Mahesh; Leinsinger, Gerda; Ray, Lawrence A.; Wismüller, Axel

2010-03-01

Topological texture features were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honey-combing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction. The best classification results were obtained by the MF features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced topological texture features can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Invasion Potential of Two Tropical Physalis Species in Arid and Semi-Arid Climates: Effect of Water-Salinity Stress and Soil Types on Growth and Fecundity.

PubMed

Ozaslan, Cumali; Farooq, Shahid; Onen, Huseyin; Bukun, Bekir; Ozcan, Selcuk; Gunal, Hikmet

2016-01-01

Invasive plants are recognized for their impressive abilities to withstand adverse environmental conditions however, all invaders do not express the similar abilities. Therefore, survival, growth, nutrient uptake and fecundity of two co-occurring, invasive Physalis species were tested under water and salinity stresses, and different soil textures in the current study. Five different water stress levels (100, 75, 50, 25, and 12.5% pot water contents), four different soil salinity levels (0, 3, 6, and 12 dSm-1) and four different soil textures (67% clay, 50% clay, silt clay loam and sandy loam) were included in three different pot experiments. Both weeds survived under all levels of water stress except 12.5% water contents and on all soil types however, behaved differently under increasing salinity. The weeds responded similarly to salinity up till 3 dSm-1 whereas, P. philadelphica survived for longer time than P. angulata under remaining salinity regimes. Water and salinity stress hampered the growth and fecundity of both weeds while, soil textures had slight effect. Both weeds preferred clay textured soils for better growth and nutrient uptake however, interactive effect of weeds and soil textures was non-significant. P. angulata accumulated higher K and Na while P. philadelphica accrued more Ca and Mg as well as maintained better K/Na ratio. P. angulata accumulated more Na and P under salinity stress while, P. philadelphica accrued higher K and Mg, and maintained higher K/Na ratio. Collectively, highest nutrient accumulation was observed under stress free conditions and on clay textured soils. P. philadelphica exhibited higher reproductive output under all experimental conditions than P. angulata. It is predicted that P. philadelphica will be more problematic under optimal water supply and high salinity while P. angulata can better adapt water limited environments. The results indicate that both weeds have considerable potential to further expand their ranges in semi-arid regions of Turkey.

Invasion Potential of Two Tropical Physalis Species in Arid and Semi-Arid Climates: Effect of Water-Salinity Stress and Soil Types on Growth and Fecundity

PubMed Central

Ozaslan, Cumali; Bukun, Bekir; Ozcan, Selcuk

2016-01-01

Invasive plants are recognized for their impressive abilities to withstand adverse environmental conditions however, all invaders do not express the similar abilities. Therefore, survival, growth, nutrient uptake and fecundity of two co-occurring, invasive Physalis species were tested under water and salinity stresses, and different soil textures in the current study. Five different water stress levels (100, 75, 50, 25, and 12.5% pot water contents), four different soil salinity levels (0, 3, 6, and 12 dSm-1) and four different soil textures (67% clay, 50% clay, silt clay loam and sandy loam) were included in three different pot experiments. Both weeds survived under all levels of water stress except 12.5% water contents and on all soil types however, behaved differently under increasing salinity. The weeds responded similarly to salinity up till 3 dSm-1 whereas, P. philadelphica survived for longer time than P. angulata under remaining salinity regimes. Water and salinity stress hampered the growth and fecundity of both weeds while, soil textures had slight effect. Both weeds preferred clay textured soils for better growth and nutrient uptake however, interactive effect of weeds and soil textures was non-significant. P. angulata accumulated higher K and Na while P. philadelphica accrued more Ca and Mg as well as maintained better K/Na ratio. P. angulata accumulated more Na and P under salinity stress while, P. philadelphica accrued higher K and Mg, and maintained higher K/Na ratio. Collectively, highest nutrient accumulation was observed under stress free conditions and on clay textured soils. P. philadelphica exhibited higher reproductive output under all experimental conditions than P. angulata. It is predicted that P. philadelphica will be more problematic under optimal water supply and high salinity while P. angulata can better adapt water limited environments. The results indicate that both weeds have considerable potential to further expand their ranges in semi-arid regions of Turkey. PMID:27741269

Distinguishing the Biomass Allocation Variance Resulting from Ontogenetic Drift or Acclimation to Soil Texture

PubMed Central

Xie, Jiangbo; Tang, Lisong; Wang, Zhongyuan; Xu, Guiqing; Li, Yan

2012-01-01

In resource-poor environments, adjustment in plant biomass allocation implies a complex interplay between environmental signals and plant development rather than a delay in plant development alone. To understand how environmental factors influence biomass allocation or the developing phenotype, it is necessary to distinguish the biomass allocations resulting from environmental gradients or ontogenetic drift. Here, we compared the development trajectories of cotton plants (Gossypium herbaceum L.), which were grown in two contrasting soil textures during a 60-d period. Those results distinguished the biomass allocation pattern resulting from ontogenetic drift and the response to soil texture. The soil texture significantly changed the biomass allocation to leaves and roots, but not to stems. Soil texture also significantly changed the development trajectories of leaf and root traits, but did not change the scaling relationship between basal stem diameter and plant height. Results of nested ANOVAs of consecutive plant-size categories in both soil textures showed that soil gradients explained an average of 63.64–70.49% of the variation of biomass allocation to leaves and roots. Ontogenetic drift explained 77.47% of the variation in biomass allocation to stems. The results suggested that the environmental factors governed the biomass allocation to roots and leaves, and ontogenetic drift governed the biomass allocation to stems. The results demonstrated that biomass allocation to metabolically active organs (e.g., roots and leaves) was mainly governed by environmental factors, and that biomass allocation to metabolically non-active organs (e.g., stems) was mainly governed by ontogenetic drift. We concluded that differentiating the causes of development trajectories of plant traits was important to the understanding of plant response to environmental gradients. PMID:22911802

Development and validation of a method to estimate the potential wind erosion risk in Germany

NASA Astrophysics Data System (ADS)

Funk, Roger; Deumlich, Detlef; Völker, Lidia

2017-04-01

The introduction of the Cross Compliance (CC) regulations for soil protection resulted in the demand for the classification of the the wind erosion risk on agricultural areas in Germany nationwide. A spatial highly resolved method was needed based on uniform data sets and validation principles, which provides a fair and equivalent procedure for all affected farmers. A GIS-procedure was developed, which derives the site specific wind erosion risk from the main influencing factors: soil texture, wind velocity, wind direction and landscape structure following the German standard DIN 19706. The procedure enables different approaches in the Federal States and comparable classification results. Here, we present the approach of the Federal State of Brandenburg. In the first step a complete soil data map was composed in a grid size of 10 x 10 m. Data were taken from 1.) the Soil quality Appraisal (scale 1:10.000), 2.) the Medium-scale Soil Mapping (MMK, 1:25.000), 3.) extrapolating the MMK, 4.) new Soil quality Appraisal (new areas after coal-mining). Based on the texture and carbon content the wind erosion susceptibility was divided in 6 classes. This map was combined with data of the annual average wind velocity resulting in an increase of the risk classes for wind velocities > 5 ms-1 and a decrease for < 3 ms-1. The sheltering effect of landscape structure is regarded by allocating a height to each landscape element, corresponding to the described features in the digital "Biotope and Land Use Map". The "hill shade" procedure of ArcGIS was used to set virtual shadows behind the landscape elements for eight directions. The relative frequency of wind from each direction was used as a weighting factor and multiplied with the numerical values of the shadowed cells. Depending on the distance to the landscape element the shadowing effect was combined with the risk classes. The results show that the wind erosion risk is obviously reduced by integrating landscape structures into the risk assessment. After the renewed classification for the entire Federal State, about 60% of the area in the highest, and 40% in the medium risk classes changed into lower classes. The area of the highest potential risk class decreased from 40% to 17% in relation to the total area. A validation of this approach was made by data of the Digital Surface Model (DSM, first pulse) from laser scanning of an area of 144 km2 with a spatial resolution of 1 x 1 m. It could be shown that the allocated height values of the landscape elements were correct in 75% per cent, too low in 15% and too high in 11% off all cases. The current landscape element map of the Federal State of Brandenburg will be replaced, when the DSM is available for the entire area in the near future.

Loss of surface horizon of an irrigated soil detected by radiometric images of normalized difference vegetation index.

NASA Astrophysics Data System (ADS)

Fabian Sallesses, Leonardo; Aparicio, Virginia Carolina; Costa, Jose Luis

2017-04-01

The use of the soil in the Humid Pampa of Argentina has changed since the mid-1990s from agricultural-livestock production (that included pastures with direct grazing) to a purely agricultural production. Also, in recent years the area under irrigation by central pivot has been increased to 150%. The waters used for irrigation are sodium carbonates. The combination of irrigation and rain increases the sodium absorption ratio of soil (SARs), consequently raising the clay dispersion and reducing infiltration. This implies an increased risk of soil loss. A reduction in the development of white clover crop (Trifolium repens L.) was observed at an irrigation plot during 2015 campaign. The clover was planted in order to reduce the impact of two maize (Zea mays L.) campaigns under irrigation, which had increased soil SAR and deteriorated soil structure. SPOT-5 radiometric normalized difference vegetation index (NDVI) images were used to determine two zones of high and low production. In each zone, four random points were selected for further geo-referenced field sampling. Two geo-referenced measures of effective depth and surface soil sampling were carried out in each point. Texture of soil samples was determined by Pipette Method of Sedimentation Analysis. Data exploratory analysis showed that low production zone had a media effective depth = 80 cm and silty clay loam texture, while high production zone had a media effective depth > 140 cm and silt loam texture. The texture class of the low production zone did not correspond to prior soil studies carried out by the INTA (National Institute of Agricultural Technology), which showed that those soil textures were silt loam at surface and silty clay loam at sub-surface. The loss of the A horizon is proposed as a possible explanation, but further research is required. Besides, the need of a soil cartography actualization, which integrates new satellite imaging technologies and geo-referenced measurements with soil sensors is emphasized. Key words: soil use change, satellite images, erosion.

Multisensor multiresolution data fusion for improvement in classification

NASA Astrophysics Data System (ADS)

Rubeena, V.; Tiwari, K. C.

2016-04-01

The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification.

Dissipation of terbuthylazine, metolachlor and mesotrione in soils with contrasting texture

NASA Astrophysics Data System (ADS)

Carretta, Laura; Cardinali, Alessandra; Zanin, Giuseppe; Masin, Roberta

2017-04-01

Herbicides play an important role in the crops production, but their use may result in residues with undesirable effects on the environment. The determination of the herbicide dissipation rate in agricultural soil is an important issue for monitoring their environmental fate. As soil composition is one of the factors affecting herbicide persistence, this study aimed to evaluate the dissipation of three herbicides, terbuthylazine (TERB), metolachlor (METO) and mesotrione (MESO) in soils with contrasting texture. The field trial was conducted at the Padua University Experimental Farm (45.3° N, 12.0° E) in the Po Valley, north-east Italy in 2012. The persistence of three herbicides has been studied in three diverse soil textures (clay, sand and loam soil) at two different depths (0-5 and 5-15 cm). A randomized complete block design was used for this experiment with six plots (2 m × 2 m) for each of 3 treatments. TERB, METO and MESO were applied in May on maize as a formulated product (Lumax®) with hand-held field plot sprayer at a dose of 3.5 L/ha. Soil organic carbon content was the highest in clay texture (1.10%) followed by loam soil (0.67%) and sandy soil (0.24%). The soil was sampled with a soil auger before herbicides treatment, and soon after treatment soil samples were taken to assess initial concentration, then at increasing times from spraying to evaluate field dissipation kinetics (t50). The dissipation of the herbicides in the treated plots was followed for nearly 2 months after their application. The herbicides were analysed by liquid chromatography-mass spectrometry. The dissipation of TERB, METO and MESO could be described by a pseudo first order kinetics. Within the herbicides, TERB showed the highest t50, followed by METO and MESO. Considering the tested soil, the highest t50 value was found for clay soil texture for TERB and METO, whereas for MESO there was no difference among different soils. Significant differences were found within the 2 soil depths for TERB and MESO only in sandy soil, for METO only in loam soil. In detail, considering the average of both depths, TERB and METO degraded slowly in clay soil (22 days and 16 days respectively) followed by loam soil (14 days and 7 days) and sandy soil (12 days and 5 days). On the other hand, MESO did not show significant differences (ranging from about 4 days in clay soil to 5 days in loam soil). These results suggest that soil texture have a large influence on the dissipation of TERB and METO, whereas no influence was observed on MESO.

Soil erosion model predictions using parent material/soil texture-based parameters compared to using site-specific parameters

Treesearch

R. B. Foltz; W. J. Elliot; N. S. Wagenbrenner

2011-01-01

Forested areas disturbed by access roads produce large amounts of sediment. One method to predict erosion and, hence, manage forest roads is the use of physically based soil erosion models. A perceived advantage of a physically based model is that it can be parameterized at one location and applied at another location with similar soil texture or geological parent...

Texture-Based Automated Lithological Classification Using Aeromagenetic Anomaly Images

USGS Publications Warehouse

Shankar, Vivek

2009-01-01

This report consists of a thesis submitted to the faculty of the Department of Electrical and Computer Engineering, in partial fulfillment of the requirements for the degree of Master of Science, Graduate College, The University of Arizona, 2004 Aeromagnetic anomaly images are geophysical prospecting tools frequently used in the exploration of metalliferous minerals and hydrocarbons. The amplitude and texture content of these images provide a wealth of information to geophysicists who attempt to delineate the nature of the Earth's upper crust. These images prove to be extremely useful in remote areas and locations where the minerals of interest are concealed by basin fill. Typically, geophysicists compile a suite of aeromagnetic anomaly images, derived from amplitude and texture measurement operations, in order to obtain a qualitative interpretation of the lithological (rock) structure. Texture measures have proven to be especially capable of capturing the magnetic anomaly signature of unique lithological units. We performed a quantitative study to explore the possibility of using texture measures as input to a machine vision system in order to achieve automated classification of lithological units. This work demonstrated a significant improvement in classification accuracy over random guessing based on a priori probabilities. Additionally, a quantitative comparison between the performances of five classes of texture measures in their ability to discriminate lithological units was achieved.

A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data

PubMed Central

Qadri, Salman; Khan, Dost Muhammad; Ahmad, Farooq; Qadri, Syed Furqan; Babar, Masroor Ellahi; Shahid, Muhammad; Ul-Rehman, Muzammil; Razzaq, Abdul; Shah Muhammad, Syed; Fahad, Muhammad; Ahmad, Sarfraz; Pervez, Muhammad Tariq; Naveed, Nasir; Aslam, Naeem; Jamil, Mutiullah; Rehmani, Ejaz Ahmad; Ahmad, Nazir; Akhtar Khan, Naeem

2016-01-01

The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared) while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI). Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class). By implementing a cross validation method (80-20), we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively. PMID:27376088

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.

The Science of Soil Textures

ERIC Educational Resources Information Center

Bigham, Gary

2010-01-01

Off-road motorcycle racing and ATV riding. Gardening and fishing. What do these high-adrenaline and slower-paced pastimes have in common? Each requires soil, and the texture of that soil has an effect on all of them. In the inquiry-based lessons described here, students work both in the field or laboratory and in the classroom to collect soil…

Utilizing management zones for Rotylenchulus reniformis in Cotton: Effects on nematode levels, crop damage, and Pasteuria sp.

USDA-ARS?s Scientific Manuscript database

The effect of soil texture on nematode levels is the primary basis for site-specific nematode management. Management zones (MZs) are sampled independently and decisions are based on those sample results. MZs based on soil electrical conductivity (EC, a proxy for soil texture) have not been tested ...

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

Root uptake of 137Cs by natural and semi-natural grasses as a function of texture and moisture of soils.

PubMed

Grytsyuk, N; Arapis, G; Davydchuk, V

2006-01-01

This work studies the dependence of 137Cs root uptake on the structure of landscape, especially on texture and moisture of soils, under natural conditions, on abandoned radiopolluted lands in Northern Ukraine. Researches were carried out on a wide range of landscape conditions, at various levels of 137Cs contamination (from 20 up to 5000 kBqm(-2)), with different types of soils (approx. 20 soil varieties), which differ in texture, granulometric composition, degrees of gleyization and water regime, and anthropogenic transformation. The results showed that transfer factor (TF) values of 137Cs differ 50 times for the natural grassy coenoses and 8 times for the semi-natural ones. The lowest 137Cs TF values were measured in the herbages of dry meadows at automorphous loamy soils, while the highest were observed in wetland meadows at organic soils. Finally, the correlation between 137Cs TF values and granulometric composition of soil was determined for both automorphic and hydromorphic mineral soils.

Texture classification of normal tissues in computed tomography using Gabor filters

NASA Astrophysics Data System (ADS)

Dettori, Lucia; Bashir, Alia; Hasemann, Julie

2007-03-01

The research presented in this article is aimed at developing an automated imaging system for classification of normal tissues in medical images obtained from Computed Tomography (CT) scans. Texture features based on a bank of Gabor filters are used to classify the following tissues of interests: liver, spleen, kidney, aorta, trabecular bone, lung, muscle, IP fat, and SQ fat. The approach consists of three steps: convolution of the regions of interest with a bank of 32 Gabor filters (4 frequencies and 8 orientations), extraction of two Gabor texture features per filter (mean and standard deviation), and creation of a Classification and Regression Tree-based classifier that automatically identifies the various tissues. The data set used consists of approximately 1000 DIACOM images from normal chest and abdominal CT scans of five patients. The regions of interest were labeled by expert radiologists. Optimal trees were generated using two techniques: 10-fold cross-validation and splitting of the data set into a training and a testing set. In both cases, perfect classification rules were obtained provided enough images were available for training (~65%). All performance measures (sensitivity, specificity, precision, and accuracy) for all regions of interest were at 100%. This significantly improves previous results that used Wavelet, Ridgelet, and Curvelet texture features, yielding accuracy values in the 85%-98% range The Gabor filters' ability to isolate features at different frequencies and orientations allows for a multi-resolution analysis of texture essential when dealing with, at times, very subtle differences in the texture of tissues in CT scans.

Estimating local scaling properties for the classification of interstitial lung disease patterns

NASA Astrophysics Data System (ADS)

Huber, Markus B.; Nagarajan, Mahesh B.; Leinsinger, Gerda; Ray, Lawrence A.; Wismueller, Axel

2011-03-01

Local scaling properties of texture regions were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honeycombing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and the estimation of local scaling properties with Scaling Index Method (SIM). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions including the Bonferroni correction. The best classification results were obtained by the set of SIM features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers with the highest accuracy (94.1%, 93.7%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced texture features using local scaling properties can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Wood texture classification by fuzzy neural networks

NASA Astrophysics Data System (ADS)

Gonzaga, Adilson; de Franca, Celso A.; Frere, Annie F.

1999-03-01

The majority of scientific papers focusing on wood classification for pencil manufacturing take into account defects and visual appearance. Traditional methodologies are base don texture analysis by co-occurrence matrix, by image modeling, or by tonal measures over the plate surface. In this work, we propose to classify plates of wood without biological defects like insect holes, nodes, and cracks, by analyzing their texture. By this methodology we divide the plate image in several rectangular windows or local areas and reduce the number of gray levels. From each local area, we compute the histogram of difference sand extract texture features, given them as input to a Local Neuro-Fuzzy Network. Those features are from the histogram of differences instead of the image pixels due to their better performance and illumination independence. Among several features like media, contrast, second moment, entropy, and IDN, the last three ones have showed better results for network training. Each LNN output is taken as input to a Partial Neuro-Fuzzy Network (PNFN) classifying a pencil region on the plate. At last, the outputs from the PNFN are taken as input to a Global Fuzzy Logic doing the plate classification. Each pencil classification within the plate is done taking into account each quality index.

Learnings from Opportunistic Wetlands: The Role of Substrate and Landscape Position on Reconstructed Landforms in a Sub-humid Climate

NASA Astrophysics Data System (ADS)

Little-Devito, M.; Chasmer, L.; Devito, K.; Kettridge, N.; Lukenbach, M. C.; Mendoza, C. A.

2017-12-01

Wetlands are important features in large-scale reclamation projects, and are integral to sustaining landscape eco-hydrological function and meeting reclamation goals. Despite a sub-humid climate, opportunistic wetlands are appearing on reconstructed landforms, and present an opportunity to understand the requirements for wetland construction, relative wetland succession, and their role in functioning landscapes. The relative importance and relationship between local and landscape-scale factors in determining initial wetland formation, as well as the relative occurrence and wetland type found on newly reclaimed landscapes was studied using both field and active (LiDAR) remote sensing methods. A random transect survey approach was used to characterize vegetation communities, soil and hydrologic characteristics, and local and landscape physiographic position across reconstructed landforms. Transects were also used to validate a broader area LiDAR-based classification. Preliminary findings suggest a higher frequency of opportunistic wetlands than anticipated. Soil texture of constructed landforms was important in determining the significance of local and landscape factors. On fine-textured constructed landforms, regardless of landscape position, wetlands formed on flat areas and in shallow depressions where soils had low water storage that promoted frequent surface saturation. Wetlands were less frequent on coarse-textured landforms and their location was controlled by landscape-scale factors, being restricted to the toes of slopes and areas intersecting the groundwater table. Wetlands found across all material types were predominantly Salix sp. and Carex sp. swamps with Typha sp. marsh complexes. This may indicate a potential initial phase of wetland succession and paludification in the Boreal Plains. These findings have important implications for understanding general wetland development, the initial phase of wetland paludification, and will aid the development of a geomorphic framework to better inform wetland construction and promote sustainable forest-wetland complexes similar to those found in natural landscapes.

Speech-Language and Nutritional Sciences in hospital environment: analysis of terminology of food consistencies classification.

PubMed

Amaral, Ana Cláudia Fernandes; Rodrigues, Lívia Azevedo; Furlan, Renata Maria Moreira Moraes; Vicente, Laélia Cristina Caseiro; Motta, Andréa Rodrigues

2015-01-01

To verify if there is an agreement between speech-language pathologists and nutritionists about the classification of food textures used in hospitals and their opinions about the possible consequences of differences in this classification. This is a descriptive, cross-sectional study with 30 speech-language pathologists and 30 nutritionists who worked in 14 hospitals of public and/or private network in Belo Horizonte, Brazil. The professionals answered a questionnaire, prepared by the researchers, and classified five different foods, with and without theoretical direction. The data were analyzed using Fisher's exact and Z -tests to compare ratios with a 5% significance level. Both speech-language therapists (100%) and nutritionists (90%) perceive divergence in the classification and, 86.2% and 100% of them, respectively, believe that this difference may affect the patients' recovery. Aspiration risk was the most mentioned problem. For the general classification of food textures, most of the professionals (88.5%) suggested four to six terms. As to the terminology used in the classification of food presented without theoretical direction, the professionals cited 49 terms and agreed only in the solid and liquid classifications. With theoretical direction, the professionals also agreed in the classification of thick and thin paste. Both the professionals recognized divergences in the classification of food textures and the consequent risk of damage to patient's recovery. The use of theoretical direction increased the agreement between these professionals.

Investigation of quartz grain surface textures by atomic force microscopy for forensic analysis.

PubMed

Konopinski, D I; Hudziak, S; Morgan, R M; Bull, P A; Kenyon, A J

2012-11-30

This paper presents a study of quartz sand grain surface textures using atomic force microscopy (AFM) to image the surface. Until now scanning electron microscopy (SEM) has provided the primary technique used in the forensic surface texture analysis of quartz sand grains as a means of establishing the provenance of the grains for forensic reconstructions. The ability to independently corroborate the grain type classifications is desirable and provides additional weight to the findings of SEM analysis of the textures of quartz grains identified in forensic soil/sediment samples. AFM offers a quantitative means of analysis that complements SEM examination, and is a non-destructive technique that requires no sample preparation prior to scanning. It therefore has great potential to be used for forensic analysis where sample preservation is highly valuable. By taking quantitative topography scans, it is possible to produce 3D representations of microscopic surface textures and diagnostic features for examination. Furthermore, various empirical measures can be obtained from analysing the topography scans, including arithmetic average roughness, root-mean-square surface roughness, skewness, kurtosis, and multiple gaussian fits to height distributions. These empirical measures, combined with qualitative examination of the surfaces can help to discriminate between grain types and provide independent analysis that can corroborate the morphological grain typing based on the surface textures assigned using SEM. Furthermore, the findings from this study also demonstrate that quartz sand grain surfaces exhibit a statistically self-similar fractal nature that remains unchanged across scales. This indicates the potential for a further quantitative measure that could be utilised in the discrimination of quartz grains based on their provenance for forensic investigations. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Median Robust Extended Local Binary Pattern for Texture Classification.

PubMed

Liu, Li; Lao, Songyang; Fieguth, Paul W; Guo, Yulan; Wang, Xiaogang; Pietikäinen, Matti

2016-03-01

Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.

Spatial variation of corn canopy temperature as dependent upon soil texture and crop rooting characteristics

NASA Technical Reports Server (NTRS)

Choudhury, B. J.

1983-01-01

A soil plant atmosphere model for corn (Zea mays L.) together with the scaling theory for soil hydraulic heterogeneity are used to study the sensitivity of spatial variation of canopy temperature to field averaged soil texture and crop rooting characteristics. The soil plant atmosphere model explicitly solves a continuity equation for water flux resulting from root water uptake, changes in plant water storage and transpirational flux. Dynamical equations for root zone soil water potential and the plant water storage models the progressive drying of soil, and day time dehydration and night time hydration of the crop. The statistic of scaling parameter which describes the spatial variation of soil hydraulic conductivity and matric potential is assumed to be independent of soil texture class. The field averaged soil hydraulic characteristics are chosen to be representative of loamy sand and clay loam soils. Two rooting characteristics are chosen, one shallow and the other deep rooted. The simulation shows that the range of canopy temperatures in the clayey soil is less than 1K, but for the sandy soil the range is about 2.5 and 5.0 K, respectively, for the shallow and deep rooted crops.

Soil and agronomic factors associated with cadmium accumulations in kidneys of grazing sheep.

PubMed

Morcombe, P W; Petterson, D S; Ross, P J; Edwards, J R

1994-12-01

Mean concentration of cadmium (Cd) in kidneys of hogget sheep from 67 flocks grazing in the Agricultural Region of Western Australia was tested for association with soil, pastoral, climatic and nutritional factors. Hoggets grazing pastures on acidic soils and soils with a sandy-textured surface had higher Cd concentrations in kidneys than hoggets grazing pastures on more alkaline soils or soils with a clay-textured surface. Application of more than 100 kg of phosphatic fertiliser during the past 3 years to loamy soils was also associated with greater Cd concentration in kidneys of the grazing animals.

Potential of the Thermal Infrared Wavelength Region to predict semi-arid Soil Surface Properties for Remote Sensing Monitoring

NASA Astrophysics Data System (ADS)

Eisele, Andreas; Chabrillat, Sabine; Lau, Ian; Hecker, Christoph; Hewson, Robert; Carter, Dan; Wheaton, Buddy; Ong, Cindy; Cudahy, Thomas John; Kaufmann, Hermann

2014-05-01

Digital soil mapping with the means of passive remote sensing basically relies on the soils' spectral characteristics and an appropriate atmospheric window, where electromagnetic radiation transmits without significant attenuation. Traditionally the atmospheric window in the solar-reflective wavelength region (visible, VIS: 0.4 - 0.7 μm; near infrared, NIR: 0.7 - 1.1 μm; shortwave infrared, SWIR: 1.1 - 2.5 μm) has been used to quantify soil surface properties. However, spectral characteristics of semi-arid soils, typically have a coarse quartz rich texture and iron coatings that can limit the prediction of soil surface properties. In this study we investigated the potential of the atmospheric window in the thermal wavelength region (long wave infrared, LWIR: 8 - 14 μm) to predict soil surface properties such as the grain size distribution (texture) and the organic carbon content (SOC) for coarse-textured soils from the Australian wheat belt region. This region suffers soil loss due to wind erosion processes and large scale monitoring techniques, such as remote sensing, is urgently required to observe the dynamic changes of such soil properties. The coarse textured sandy soils of the investigated area require methods, which can measure the special spectral response of the quartz dominated mineralogy with iron oxide enriched grain coatings. By comparison, the spectroscopy using the solar-reflective region has limitations to discriminate such arid soil mineralogy and associated coatings. Such monitoring is important for observing potential desertification trends associated with coarsening of topsoil texture and reduction in SOC. In this laboratory study we identified the relevant LWIR wavelengths to predict these soil surface properties. The results showed the ability of multivariate analyses methods (PLSR) to predict these soil properties from the soil's spectral signature, where the texture parameters (clay and sand content) could be predicted well in the models using the LWIR-window (sand content: R2 = 0.84 and RMSECV = 1.09 %, and for clay content: R2 = 0.77 and RMSECV = 1.0 %, both with 3 factor models). In comparison, the quantification from the solar-reflective window showed its limitations in its relative complex PLSR models and a lower prediction accuracy (sand content: R2 = 0.69 and RMSECV = 1.5 % with 7 factors, and for clay content: R2 = 0.64 and RMSECV = 1.26 % with 9 factors). The prediction of the SOC content, on the other hand, showed minor disparity between the two atmospheric windows (LWIR: R2 = 0.73 and RMSECV = 0.1 % with 6 factors, VNIR-SWIR: R2 = 0.69 and RMSECV = 0.11 %, with 9 factors). The prospect of the LWIR for determining soil texture was demonstrated to be even more impressive when reduced to the spectral band specifications of airborne (TASI-600) and spaceborne (ASTER) sensors. The results demonstrate the high potential of the LWIR to detect and quantify soil surface properties in the future for a monitoring via LWIR hyperspectral remote sensing.

Consequences of using different soil texture determination methodologies for soil physical quality and unsaturated zone time lag estimates

NASA Astrophysics Data System (ADS)

Fenton, O.; Vero, S.; Ibrahim, T. G.; Murphy, P. N. C.; Sherriff, S. C.; Ó hUallacháin, D.

2015-11-01

Elucidation of when the loss of pollutants, below the rooting zone in agricultural landscapes, affects water quality is important when assessing the efficacy of mitigation measures. Investigation of this inherent time lag (tT) is divided into unsaturated (tu) and saturated (ts) components. The duration of these components relative to each other differs depending on soil characteristics and the landscape position. The present field study focuses on tu estimation in a scenario where the saturated zone is likely to constitute a higher proportion of tT. In such instances, or where only initial breakthrough (IBT) or centre of mass (COM) is of interest, utilisation of site and depth specific "simple" textural class or actual sand-silt-clay percentages to generate soil water characteristic curves with associated soil hydraulic parameters is acceptable. With the same data it is also possible to estimate a soil physical quality (S) parameter for each soil layer which can be used to infer many other physical, chemical and biological quality indicators. In this study, hand texturing in the field was used to determine textural classes of a soil profile. Laboratory methods, including hydrometer, pipette and laser diffraction methods were used to determine actual sand-silt-clay percentages of sections of the same soil profile. Results showed that in terms of S, hand texturing resulted in a lower index value (inferring a degraded soil) than that of pipette, hydrometer and laser equivalents. There was no difference between S index values determined using the pipette, hydrometer and laser diffraction methods. The difference between the three laboratory methods on both the IBT and COM stages of tu were negligible, and in this instance were unlikely to affect either groundwater monitoring decisions, or to be of consequence from a policy perspective. When tu estimates are made over the full depth of the vadose zone, which may extend to several metres, errors resulting from the use of hydraulic parameters generated from hand texture data will be resultantly greater, and may lead to flawed predictions regarding the achievability of water policy targets. For this reason laboratory analysis, regardless of method, should be preferred to simple field assessments.

Ground-based cloud classification by learning stable local binary patterns

NASA Astrophysics Data System (ADS)

Wang, Yu; Shi, Cunzhao; Wang, Chunheng; Xiao, Baihua

2018-07-01

Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set.

High-resolution land cover classification using low resolution global data

NASA Astrophysics Data System (ADS)

Carlotto, Mark J.

2013-05-01

A fusion approach is described that combines texture features from high-resolution panchromatic imagery with land cover statistics derived from co-registered low-resolution global databases to obtain high-resolution land cover maps. The method does not require training data or any human intervention. We use an MxN Gabor filter bank consisting of M=16 oriented bandpass filters (0-180°) at N resolutions (3-24 meters/pixel). The size range of these spatial filters is consistent with the typical scale of manmade objects and patterns of cultural activity in imagery. Clustering reduces the complexity of the data by combining pixels that have similar texture into clusters (regions). Texture classification assigns a vector of class likelihoods to each cluster based on its textural properties. Classification is unsupervised and accomplished using a bank of texture anomaly detectors. Class likelihoods are modulated by land cover statistics derived from lower resolution global data over the scene. Preliminary results from a number of Quickbird scenes show our approach is able to classify general land cover features such as roads, built up area, forests, open areas, and bodies of water over a wide range of scenes.

Mapping lava flow textures using three-dimensional measures of surface roughness

NASA Astrophysics Data System (ADS)

Mallonee, H. C.; Kobs-Nawotniak, S. E.; McGregor, M.; Hughes, S. S.; Neish, C.; Downs, M.; Delparte, D.; Lim, D. S. S.; Heldmann, J. L.

2016-12-01

Lava flow emplacement conditions are reflected in the surface textures of a lava flow; unravelling these conditions is crucial to understanding the eruptive history and characteristics of basaltic volcanoes. Mapping lava flow textures using visual imagery alone is an inherently subjective process, as these images generally lack the resolution needed to make these determinations. Our team has begun mapping lava flow textures using visual spectrum imagery, which is an inherently subjective process involving the challenge of identifying transitional textures such as rubbly and slabby pāhoehoe, as these textures are similar in appearance and defined qualitatively. This is particularly problematic for interpreting planetary lava flow textures, where we have more limited data. We present a tool to objectively classify lava flow textures based on quantitative measures of roughness, including the 2D Hurst exponent, RMS height, and 2D:3D surface area ratio. We collected aerial images at Craters of the Moon National Monument (COTM) using Unmanned Aerial Vehicles (UAVs) in 2015 and 2016 as part of the FINESSE (Field Investigations to Enable Solar System Science and Exploration) and BASALT (Biologic Analog Science Associated with Lava Terrains) research projects. The aerial images were stitched together to create Digital Terrain Models (DTMs) with resolutions on the order of centimeters. The DTMs were evaluated by the classification tool described above, with output compared against field assessment of the texture. Further, the DTMs were downsampled and reevaluated to assess the efficacy of the classification tool at data resolutions similar to current datasets from other planetary bodies. This tool allows objective classification of lava flow texture, which enables more accurate interpretations of flow characteristics. This work also gives context for interpretations of flows with comparatively low data resolutions, such as those on the Moon and Mars. Textural maps based on quantitative measures of roughness are a valuable asset for studies of lava flows on Earth and other planetary bodies.

A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification

PubMed Central

Xie, Jin; Zhang, Lei; You, Jane; Zhang, David; Qu, Xiaofeng

2012-01-01

Human hand back skin texture (HBST) is often consistent for a person and distinctive from person to person. In this paper, we study the HBST pattern recognition problem with applications to personal identification and gender classification. A specially designed system is developed to capture HBST images, and an HBST image database was established, which consists of 1,920 images from 80 persons (160 hands). An efficient texton learning based method is then presented to classify the HBST patterns. First, textons are learned in the space of filter bank responses from a set of training images using the l1 -minimization based sparse representation (SR) technique. Then, under the SR framework, we represent the feature vector at each pixel over the learned dictionary to construct a representation coefficient histogram. Finally, the coefficient histogram is used as skin texture feature for classification. Experiments on personal identification and gender classification are performed by using the established HBST database. The results show that HBST can be used to assist human identification and gender classification. PMID:23012512

Impact of land-use on carbon storage as dependent on soil texture: evidence from a desertified dryland using repeated paired sampling design.

PubMed

Ye, Xuehua; Tang, Shuangli; Cornwell, William K; Gao, Shuqin; Huang, Zhenying; Dong, Ming; Cornelissen, Johannes H C

2015-03-01

Desertification resulting from land-use affects large dryland areas around the world, accompanied by carbon loss. However it has been difficult to interpret different land-use contributions to carbon pools owing to confounding factors related to climate, topography, soil texture and other original soil properties. To avoid such confounding effects, a unique systematic and extensive repeated design of paired sampling plots of different land-use types was adopted on Ordos Plateau, N China. The sampling enabled to quantify the effects of the predominant land-use types on carbon storage as dependent on soil texture, and to define the most promising land-use choices for carbon storage, both in grassland on sandy soil and in desert grassland on brown calcareous soil. The results showed that (1) desertification control should be an effective measure to improve the carbon sequestration in sandy grassland, and shrub planting should be better than grass planting; (2) development of man-made grassland should be a good choice to solve the contradictions of ecology and economy in desert grassland; (3) grassland on sandy soil is more vulnerable to soil degradation than desert grassland on brown calcareous soil. The results may be useful for the selection of land-use types, aiming at desertification prevention in drylands. Follow-up studies should directly investigate the role of soil texture on the carbon storage dynamic caused by land-use change. Copyright © 2014 Elsevier Ltd. All rights reserved.

Influence of edaphic factors on the mineralization of neem oil coated urea in four Indian soils.

PubMed

Kumar, Rajesh; Devakumar, C; Kumar, Dinesh; Panneerselvam, P; Kakkar, Garima; Arivalagan, T

2008-11-12

The utility of neem (Azadirachta indica A Juss) oil coated urea as a value-added nitrogenous fertilizer has been now widely accepted by Indian farmers and the fertilizer industry. In the present study, the expeller grade (EG) and hexane-extracted (HE) neem oils, the two most common commercial grades, were used to prepare neem oil coated urea (NOCU) of various oil doses, for which mineralization rates were assessed in four soils at three incubation temperatures (20, 27, and 35 degrees C). Neem oil dose-dependent conservation of ammonium N was observed in NOCU treatments in all of the soils. However, a longer incubation period and a higher soil temperature caused depletion of ammonium N. Overall, the nitrification in NOCU treatment averaged 56.6% against 77.3% for prilled urea in four soils. NOCU prepared from EG neem oil was consistently superior to that derived from hexane-extracted oil. The performance of NOCUs was best in coarse-textured soil and poorest in sodic soil. The nitrification rate (NR) of the NOCUs in the soils followed the order sodic > fine-textured > medium-textured > coarse-textured. The influence of edaphic factors on NR of NOCUs has been highlighted. The utility of the present study in predicting the performance of NOCU in diverse Indian soils was highlighted through the use of algorithms for computation of the optimum neem oil dose that would cause maximum inhibition of nitrification in any soil.

Ice/water Classification of Sentinel-1 Images

NASA Astrophysics Data System (ADS)

Korosov, Anton; Zakhvatkina, Natalia; Muckenhuber, Stefan

2015-04-01

Sea Ice monitoring and classification relies heavily on synthetic aperture radar (SAR) imagery. These sensors record data either only at horizontal polarization (RADARSAT-1) or vertically polarized (ERS-1 and ERS-2) or at dual polarization (Radarsat-2, Sentinel-1). Many algorithms have been developed to discriminate sea ice types and open water using single polarization images. Ice type classification, however, is still ambiguous in some cases. Sea ice classification in single polarization SAR images has been attempted using various methods since the beginning of the ERS programme. The robust classification using only SAR images that can provide useful results under varying sea ice types and open water tend to be not generally applicable in operational regime. The new generation SAR satellites have capability to deliver images in several polarizations. This gives improved possibility to develop sea ice classification algorithms. In this study we use data from Sentinel-1 at dual-polarization, i.e. HH (horizontally transmitted and horizontally received) and HV (horizontally transmitted, vertically received). This mode assembles wide SAR image from several narrower SAR beams, resulting to an image of 500 x 500 km with 50 m resolution. A non-linear scheme for classification of Sentinel-1 data has been developed. The processing allows to identify three classes: ice, calm water and rough water at 1 km spatial resolution. The raw sigma0 data in HH and HV polarization are first corrected for thermal and random noise by extracting the background thermal noise level and smoothing the image with several filters. At the next step texture characteristics are computed in a moving window using a Gray Level Co-occurence Matrix (GLCM). A neural network is applied at the last step for processing array of the most informative texture characteristics and ice/water classification. The main results are: * the most informative texture characteristics to be used for sea ice classification were revealed; * the best set of parameters including the window size, number of levels of quantization of sigma0 values and co-occurence distance was found; * a support vector machine (SVM) was trained on results of visual classification of 30 Sentinel-1 images. Despite the general high accuracy of the neural network (95% of true positive classification) problems with classification of young newly formed ice and rough water arise due to the similar average backscatter and texture. Other methods of smoothing and computation of texture characteristics (e.g. computation of GLCM from a variable size window) is assessed. The developed scheme will be utilized in NRT processing of Sentinel-1 data at NERSC within the MyOcean2 project.

Soybean Photosynthesis and Yield as Influenced by Heterodera glycines, Soil Type and Irrigation.

PubMed

Koenning, S R; Barker, K R

1995-03-01

The effects of soil types and soil water matric pressure on the Heterodera glycines-Glycine max interaction were examined in microplots in 1988 and 1989. Reproduction of H. glycines was restricted in fine-textured soils as compared with coarse-textured ones. Final population densities of this pathogen in both years of the study were greater in nonirrigated soils than in irrigated soils. The net photosynthetic rate of soybean (per unit area of leaf) was suppressed only slightly or not at all in response to infection by H. glycines and other stresses. Relative soybean-yield suppression in response to H. glycines was not affected by water content in fine-textured soils, but slopes of the damage functions were steepest in sand, sandy loam, and muck soils at high water content (irrigated plots). Yield restriction of soybean in response to this pathogen under irrigation was equal to or greater than the yield suppression under dry conditions. Although yield potential may be elevated by irrigation when soil-water content is inadequate, supplemental irrigation cannot be used to circumvent nematode damage to soybean.

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

Relationships between physical-geographical factors and soil degradation on agricultural land.

PubMed

Bednář, Marek; Šarapatka, Bořivoj

2018-07-01

It is a well-known fact that soil degradation is dramatically increasing and currently threatens agricultural soils all around the world. The objective of this study was to reveal the possible connection between soil degradation and seven physical-geographical factors - slope steepness, altitude, elevation differences, rainfall, temperature, soil texture and solar radiation - in the form of threshold values (if these exist), where soil degradation begins and ends. The analysis involved the whole area of the Czech Republic which consists of 13,027 cadasters (78,866 km 2 ). The greatest total degradation threat occurs in areas with slope steepness >7 degrees, average annual temperature <5.9 °C, elevation differences >10.54, altitude >766 m a.s.l. Similarly, the results for water erosion, wind erosion, soil compaction, loss of organic matter, acidification and heavy metal contamination were processed. The results enable us to identify the relationships of different levels of threats which could consequently be used in various ways - for classification of threatened areas, for more effective implementation of anti-degradation measures, or purely for a better understanding of the role of physical geographical factors in soil degradation in the Czech Republic, and thus could increase the chances of reducing vulnerability to land degradation not only in the Czech Republic. Copyright © 2018 Elsevier Inc. All rights reserved.

24 CFR 3285.202 - Soil classifications and bearing capacity.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 5 2010-04-01 2010-04-01 false Soil classifications and bearing... Soil classifications and bearing capacity. The soil classification and bearing capacity of the soil must be determined before the foundation is constructed and anchored. The soil classification and...

24 CFR 3285.202 - Soil classifications and bearing capacity.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 5 2014-04-01 2014-04-01 false Soil classifications and bearing... Soil classifications and bearing capacity. The soil classification and bearing capacity of the soil must be determined before the foundation is constructed and anchored. The soil classification and...

24 CFR 3285.202 - Soil classifications and bearing capacity.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 5 2012-04-01 2012-04-01 false Soil classifications and bearing... Soil classifications and bearing capacity. The soil classification and bearing capacity of the soil must be determined before the foundation is constructed and anchored. The soil classification and...

24 CFR 3285.202 - Soil classifications and bearing capacity.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 5 2013-04-01 2013-04-01 false Soil classifications and bearing... Soil classifications and bearing capacity. The soil classification and bearing capacity of the soil must be determined before the foundation is constructed and anchored. The soil classification and...

24 CFR 3285.202 - Soil classifications and bearing capacity.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 5 2011-04-01 2011-04-01 false Soil classifications and bearing... Soil classifications and bearing capacity. The soil classification and bearing capacity of the soil must be determined before the foundation is constructed and anchored. The soil classification and...

Photodegradation of polycyclic aromatic hydrocarbons in soils under a climate change base scenario.

PubMed

Marquès, Montse; Mari, Montse; Audí-Miró, Carme; Sierra, Jordi; Soler, Albert; Nadal, Martí; Domingo, José L

2016-04-01

The photodegradation of polycyclic aromatic hydrocarbons (PAHs) in two typical Mediterranean soils, either coarse- or fine-textured, was here investigated. Soil samples, spiked with the 16 US EPA priority PAHs, were incubated in a climate chamber at stable conditions of temperature (20 °C) and light (9.6 W m(-2)) for 28 days, simulating a climate change base scenario. PAH concentrations in soils were analyzed throughout the experiment, and correlated with data obtained by means of Microtox(®) ecotoxicity test. Photodegradation was found to be dependent on exposure time, molecular weight of each hydrocarbon, and soil texture. Fine-textured soil was able to enhance sorption, being PAHs more photodegraded than in coarse-textured soil. According to the EC50 values reported by Microtox(®), a higher detoxification was observed in fine-textured soil, being correlated with the outcomes of the analytical study. Significant photodegradation rates were detected for a number of PAHs, namely phenanthrene, anthracene, benzo(a)pyrene, and indeno(123-cd)pyrene. Benzo(a)pyrene, commonly used as an indicator for PAH pollution, was completely removed after 7 days of light exposure. In addition to the PAH chemical analysis and the ecotoxicity tests, a hydrogen isotope analysis of benzo(a)pyrene was also carried out. The degradation of this specific compound was associated to a high enrichment in (2)H, obtaining a maximum δ(2)H isotopic shift of +232‰. This strong isotopic effect observed in benzo(a)pyrene suggests that compound-specific isotope analysis (CSIA) may be a powerful tool to monitor in situ degradation of PAHs. Moreover, hydrogen isotopes of benzo(a)pyrene evidenced a degradation process of unknown origin occurring in the darkness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Intelligent estimation of spatially distributed soil physical properties

USGS Publications Warehouse

Iwashita, F.; Friedel, M.J.; Ribeiro, G.F.; Fraser, Stephen J.

2012-01-01

Spatial analysis of soil samples is often times not possible when measurements are limited in number or clustered. To obviate potential problems, we propose a new approach based on the self-organizing map (SOM) technique. This approach exploits underlying nonlinear relation of the steady-state geomorphic concave-convex nature of hillslopes (from hilltop to bottom of the valley) to spatially limited soil textural data. The topographic features are extracted from Shuttle Radar Topographic Mission elevation data; whereas soil textural (clay, silt, and sand) and hydraulic data were collected in 29 spatially random locations (50 to 75. cm depth). In contrast to traditional principal component analysis, the SOM identifies relations among relief features, such as, slope, horizontal curvature and vertical curvature. Stochastic cross-validation indicates that the SOM is unbiased and provides a way to measure the magnitude of prediction uncertainty for all variables. The SOM cross-component plots of the soil texture reveals higher clay proportions at concave areas with convergent hydrological flux and lower proportions for convex areas with divergent flux. The sand ratio has an opposite pattern with higher values near the ridge and lower values near the valley. Silt has a trend similar to sand, although less pronounced. The relation between soil texture and concave-convex hillslope features reveals that subsurface weathering and transport is an important process that changed from loss-to-gain at the rectilinear hillslope point. These results illustrate that the SOM can be used to capture and predict nonlinear hillslope relations among relief, soil texture, and hydraulic conductivity data. ?? 2011 Elsevier B.V.

Development of type transfer functions for regional-scale nonpoint source groundwater vulnerability assessments

NASA Astrophysics Data System (ADS)

Stewart, Iris T.; Loague, Keith

2003-12-01

Groundwater vulnerability assessments of nonpoint source agrochemical contamination at regional scales are either qualitative in nature or require prohibitively costly computational efforts. By contrast, the type transfer function (TTF) modeling approach for vadose zone pesticide leaching presented here estimates solute concentrations at a depth of interest, only uses available soil survey, climatic, and irrigation information, and requires minimal computational cost for application. TTFs are soil texture based travel time probability density functions that describe a characteristic leaching behavior for soil profiles with similar soil hydraulic properties. Seven sets of TTFs, representing different levels of upscaling, were developed for six loam soil textural classes with the aid of simulated breakthrough curves from synthetic data sets. For each TTF set, TTFs were determined from a group or subgroup of breakthrough curves for each soil texture by identifying the effective parameters of the function that described the average leaching behavior of the group. The grouping of the breakthrough curves was based on the TTF index, a measure of the magnitude of the peak concentration, the peak arrival time, and the concentration spread. Comparison to process-based simulations show that the TTFs perform well with respect to mass balance, concentration magnitude, and the timing of concentration peaks. Sets of TTFs based on individual soil textures perform better for all the evaluation criteria than sets that span all textures. As prediction accuracy and computational cost increase with the number of TTFs in a set, the selection of a TTF set is determined by a given application.

Influence of Texture and Colour in Breast TMA Classification

PubMed Central

Fernández-Carrobles, M. Milagro; Bueno, Gloria; Déniz, Oscar; Salido, Jesús; García-Rojo, Marcial; González-López, Lucía

2015-01-01

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors. PMID:26513238

Noninvasive Classification of Hepatic Fibrosis Based on Texture Parameters From Double Contrast-Enhanced Magnetic Resonance Images

PubMed Central

Bahl, Gautam; Cruite, Irene; Wolfson, Tanya; Gamst, Anthony C.; Collins, Julie M.; Chavez, Alyssa D.; Barakat, Fatma; Hassanein, Tarek; Sirlin, Claude B.

2016-01-01

Purpose To demonstrate a proof of concept that quantitative texture feature analysis of double contrast-enhanced magnetic resonance imaging (MRI) can classify fibrosis noninvasively, using histology as a reference standard. Materials and Methods A Health Insurance Portability and Accountability Act (HIPAA)-compliant Institutional Review Board (IRB)-approved retrospective study of 68 patients with diffuse liver disease was performed at a tertiary liver center. All patients underwent double contrast-enhanced MRI, with histopathology-based staging of fibrosis obtained within 12 months of imaging. The MaZda software program was used to compute 279 texture parameters for each image. A statistical regularization technique, generalized linear model (GLM)-path, was used to develop a model based on texture features for dichotomous classification of fibrosis category (F ≤2 vs. F ≥3) of the 68 patients, with histology as the reference standard. The model's performance was assessed and cross-validated. There was no additional validation performed on an independent cohort. Results Cross-validated sensitivity, specificity, and total accuracy of the texture feature model in classifying fibrosis were 91.9%, 83.9%, and 88.2%, respectively. Conclusion This study shows proof of concept that accurate, noninvasive classification of liver fibrosis is possible by applying quantitative texture analysis to double contrast-enhanced MRI. Further studies are needed in independent cohorts of subjects. PMID:22851409

Soil respiration in the cold desert environment of the Colorado Plateau (USA): Abiotic regulators and thresholds

USGS Publications Warehouse

Fernandez, D.P.; Neff, J.C.; Belnap, J.; Reynolds, R.L.

2006-01-01

Decomposition is central to understanding ecosystem carbon exchange and nutrient-release processes. Unlike mesic ecosystems, which have been extensively studied, xeric landscapes have received little attention; as a result, abiotic soil-respiration regulatory processes are poorly understood in xeric environments. To provide a more complete and quantitative understanding about how abiotic factors influence soil respiration in xeric ecosystems, we conducted soil- respiration and decomposition-cloth measurements in the cold desert of southeast Utah. Our study evaluated when and to what extent soil texture, moisture, temperature, organic carbon, and nitrogen influence soil respiration and examined whether the inverse-texture hypothesis applies to decomposition. Within our study site, the effect of texture on moisture, as described by the inverse texture hypothesis, was evident, but its effect on decomposition was not. Our results show temperature and moisture to be the dominant abiotic controls of soil respiration. Specifically, temporal offsets in temperature and moisture conditions appear to have a strong control on soil respiration, with the highest fluxes occurring in spring when temperature and moisture were favorable. These temporal offsets resulted in decomposition rates that were controlled by soil moisture and temperature thresholds. The highest fluxes of CO2 occurred when soil temperature was between 10 and 16??C and volumetric soil moisture was greater than 10%. Decomposition-cloth results, which integrate decomposition processes across several months, support the soil-respiration results and further illustrate the seasonal patterns of high respiration rates during spring and low rates during summer and fall. Results from this study suggest that the parameters used to predict soil respiration in mesic ecosystems likely do not apply in cold-desert environments. ?? Springer 2006.

Gender classification system in uncontrolled environments

NASA Astrophysics Data System (ADS)

Zeng, Pingping; Zhang, Yu-Jin; Duan, Fei

2011-01-01

Most face analysis systems available today perform mainly on restricted databases of images in terms of size, age, illumination. In addition, it is frequently assumed that all images are frontal and unconcealed. Actually, in a non-guided real-time supervision, the face pictures taken may often be partially covered and with head rotation less or more. In this paper, a special system supposed to be used in real-time surveillance with un-calibrated camera and non-guided photography is described. It mainly consists of five parts: face detection, non-face filtering, best-angle face selection, texture normalization, and gender classification. Emphases are focused on non-face filtering and best-angle face selection parts as well as texture normalization. Best-angle faces are figured out by PCA reconstruction, which equals to an implicit face alignment and results in a huge increase of the accuracy for gender classification. Dynamic skin model and a masked PCA reconstruction algorithm are applied to filter out faces detected in error. In order to fully include facial-texture and shape-outline features, a hybrid feature that is a combination of Gabor wavelet and PHoG (pyramid histogram of gradients) was proposed to equitable inner texture and outer contour. Comparative study on the effects of different non-face filtering and texture masking methods in the context of gender classification by SVM is reported through experiments on a set of UT (a company name) face images, a large number of internet images and CAS (Chinese Academy of Sciences) face database. Some encouraging results are obtained.

Response of Microbial Soil Carbon Mineralization Rates to Oxygen Limitations

NASA Astrophysics Data System (ADS)

Keiluweit, M.; Denney, A.; Nico, P. S.; Fendorf, S. E.

2014-12-01

The rate of soil organic matter (SOM) mineralization is known to be controlled by climatic factors as well as molecular structure, mineral-organic associations, and physical protection. What remains elusive is to what extent oxygen (O2) limitations impact overall rates of microbial SOM mineralization (oxidation) in soils. Even within upland soils that are aerobic in bulk, factors limiting O2 diffusion such as texture and soil moisture can result in an abundance of anaerobic microsites in the interior of soil aggregates. Variation in ensuing anaerobic respiration pathways can further impact SOM mineralization rates. Using a combination of (first) aggregate model systems and (second) manipulations of intact field samples, we show how limitations on diffusion and carbon bioavailability interact to impose anaerobic conditions and associated respiration constraints on SOM mineralization rates. In model aggregates, we examined how particle size (soil texture) and amount of dissolved organic carbon (bioavailable carbon) affect O2 availability and distribution. Monitoring electron acceptor profiles (O2, NO3-, Mn and Fe) and SOM transformations (dissolved, particulate, mineral-associated pools) across the resulting redox gradients, we then determined the distribution of operative microbial metabolisms and their cumulative impact on SOM mineralization rates. Our results show that anaerobic conditions decrease SOM mineralization rates overall, but those are partially offset by the concurrent increases in SOM bioavailability due to transformations of protective mineral phases. In intact soil aggregates collected from soils varying in texture and SOM content, we mapped the spatial distribution of anaerobic microsites. Optode imaging, microsensor profiling and 3D tomography revealed that soil texture regulates overall O2 availability in aggregate interiors, while particulate SOM in biopores appears to control the fine-scale distribution of anaerobic microsites. Collectively, our results suggest that texture and particulate organic matter content are useful predictors for the impact of O2 limitations on SOM mineralization rates.

Classification of Weed Species Using Artificial Neural Networks Based on Color Leaf Texture Feature

NASA Astrophysics Data System (ADS)

Li, Zhichen; An, Qiu; Ji, Changying

The potential impact of herbicide utilization compel people to use new method of weed control. Selective herbicide application is optimal method to reduce herbicide usage while maintain weed control. The key of selective herbicide is how to discriminate weed exactly. The HIS color co-occurrence method (CCM) texture analysis techniques was used to extract four texture parameters: Angular second moment (ASM), Entropy(E), Inertia quadrature (IQ), and Inverse difference moment or local homogeneity (IDM).The weed species selected for studying were Arthraxon hispidus, Digitaria sanguinalis, Petunia, Cyperus, Alternanthera Philoxeroides and Corchoropsis psilocarpa. The software of neuroshell2 was used for designing the structure of the neural network, training and test the data. It was found that the 8-40-1 artificial neural network provided the best classification performance and was capable of classification accuracies of 78%.

Collagen morphology and texture analysis: from statistics to classification

PubMed Central

Mostaço-Guidolin, Leila B.; Ko, Alex C.-T.; Wang, Fei; Xiang, Bo; Hewko, Mark; Tian, Ganghong; Major, Arkady; Shiomi, Masashi; Sowa, Michael G.

2013-01-01

In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage. PMID:23846580

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

PubMed

Ortiz-Ramón, Rafael; Larroza, Andrés; Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

2018-05-14

To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

Critical soil bulk density for soybean growth in Oxisols

NASA Astrophysics Data System (ADS)

Keisuke Sato, Michel; Veras de Lima, Herdjania; Oliveira, Pedro Daniel de; Rodrigues, Sueli

2015-10-01

The aim of this study was to evaluate the critical soil bulk density from the soil penetration resistance measurements for soybean root growth in Brazilian Amazon Oxisols. The experiment was carried out in a greenhouse using disturbed soil samples collected from the northwest of Para characterized by different texture. The treatments consisted of a range of soil bulk densities for each soil textural class. Three pots were used for soybean growth of and two for the soil penetration resistance curve. From the fitted model, the critical soil bulk density was determined considering the penetration resistance values of 2 and 3 MPa. After sixty days, plants were cut and root length, dry mass of root, and dry mass of shoots were determined. At higher bulk densities, the increase in soil water content decreased the penetration resistance, allowing unrestricted growth of soybean roots. Regardless of soil texture, the penetration resistance of 2 and 3 MPa had a slight effect on root growth in soil moisture at field capacity and a reduction of 50% in the soybean root growth was achieved at critical soil bulk density of 1.82, 1.75, 1.51, and 1.45 Mg m-3 for the sandy loam, sandy clay loam, clayey, and very clayey soil.

Texture Feature Extraction and Classification for Iris Diagnosis

NASA Astrophysics Data System (ADS)

Ma, Lin; Li, Naimin

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use.

Textural Analysis and Substrate Classification in the Nearshore Region of Lake Superior Using High-Resolution Multibeam Bathymetry

NASA Astrophysics Data System (ADS)

Dennison, Andrew G.

Classification of the seafloor substrate can be done with a variety of methods. These methods include Visual (dives, drop cameras); mechanical (cores, grab samples); acoustic (statistical analysis of echosounder returns). Acoustic methods offer a more powerful and efficient means of collecting useful information about the bottom type. Due to the nature of an acoustic survey, larger areas can be sampled, and by combining the collected data with visual and mechanical survey methods provide greater confidence in the classification of a mapped region. During a multibeam sonar survey, both bathymetric and backscatter data is collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on bottom type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, i.e a muddy area from a rocky area, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing of high-resolution multibeam data can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. The development of a new classification method is described here. It is based upon the analysis of textural features in conjunction with ground truth sampling. The processing and classification result of two geologically distinct areas in nearshore regions of Lake Superior; off the Lester River,MN and Amnicon River, WI are presented here, using the Minnesota Supercomputer Institute's Mesabi computing cluster for initial processing. Processed data is then calibrated using ground truth samples to conduct an accuracy assessment of the surveyed areas. From analysis of high-resolution bathymetry data collected at both survey sites is was possible to successfully calculate a series of measures that describe textural information about the lake floor. Further processing suggests that the features calculated capture a significant amount of statistical information about the lake floor terrain as well. Two sources of error, an anomalous heave and refraction error significantly deteriorated the quality of the processed data and resulting validate results. Ground truth samples used to validate the classification methods utilized for both survey sites, however, resulted in accuracy values ranging from 5 -30 percent at the Amnicon River, and between 60-70 percent for the Lester River. The final results suggest that this new processing methodology does adequately capture textural information about the lake floor and does provide an acceptable classification in the absence of significant data quality issues.

14C tebuconazole degradation in Colombian soils.

PubMed

Mosquera, C S; Martínez, M J; Guerrero, J A

2010-01-01

Tebuconazole is a fungicide used on onion crops (Allium Fistulosum L) in Colombia. Persistence of pesticides in soils is characterized by the half-life (DT50), which is influenced by their chemical structure, the physical and chemical properties of the soil and the previous soil history. Based on its structural and chemical properties, tebuconazole should be expected to be relatively persistent in soils. Laboratory incubation studies were conducted to evaluate persistence and bond residues of 14C tebuconazole in three soils, two inceptisol (I) and one histosol (H). Textural classifications were: loam (101), loamy sand (102) and loam (H03), respectively. Data obtained followed a first-order degradation kinetics (R2 > or = 0.899) with DT50 values between 158 and 198 days. The production of 14CO2 from the 14C-ring-labelled test chemicals was very low and increased slightly during 63 days in all cases. The methanol extractable 14C-residues were higher than aqueous ones and both decreased over incubation time for the three soils. The formation of bound 14C-residues increased with time and final values were 11.3; 5.55 and 7.87% for 101, 102 and H03 respectively. Soil 101 showed the lowest mineralization rate and the highest bound residues formation, which might be explained by the clay fraction content. In contrast, an inverse behavior was found for soils 102 and H03, these results might be explained by the higher soil organic carbon content.

Hyperspectral image classification based on local binary patterns and PCANet

NASA Astrophysics Data System (ADS)

Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

2018-04-01

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

Interactions between soil texture and placement of dairy slurry application: II. Leaching of phosphorus forms.

PubMed

Glaesner, Nadia; Kjaergaard, Charlotte; Rubaek, Gitte H; Magid, Jakob

2011-01-01

Managing phosphorus (P) losses in soil leachate folllowing land application of manure is key to curbing eutrophication in many regions. We compared P leaching from columns of variably textured, intact soils (20 cm diam., 20 cm high) subjected to surface application or injection of dairy cattle (Bos taurus L.) manure slurry. Surface application of slurry increased P leaching losses relative to baseline losses, but losses declined with increasing active flow volume. After elution of one pore volume, leaching averaged 0.54 kg P ha(-1) from the loam, 0.38 kg P ha(-1) from the sandy loam, and 0.22 kg P ha(-1) from the loamy sand following surface application. Injection decreased leaching of all P forms compared with surface application by an average of 0.26 kg P ha(-1) in loam and 0.23 kg P ha(-1) in sandy loam, but only by 0.03 kg P ha(-1) in loamy sand. Lower leaching losses were attributed to physical retention of particulate P and dissolved organic P, caused by placing slurry away from active flow paths in the fine-textured soil columns, as well as to chemical retention of dissolved inorganic P, caused by better contact between slurry P and soil adsorption sites. Dissolved organic P was less retained in soil after slurry application than other P forms. On these soils with low to intermediate P status, slurry injection lowered P leaching losses from clay-rich soil, but not from the sandy soils, highlighting the importance of soil texture in manageing P losses following slurry application.

Texture classification using non-Euclidean Minkowski dilation

NASA Astrophysics Data System (ADS)

Florindo, Joao B.; Bruno, Odemir M.

2018-03-01

This study presents a new method to extract meaningful descriptors of gray-scale texture images using Minkowski morphological dilation based on the Lp metric. The proposed approach is motivated by the success previously achieved by Bouligand-Minkowski fractal descriptors on texture classification. In essence, such descriptors are directly derived from the morphological dilation of a three-dimensional representation of the gray-level pixels using the classical Euclidean metric. In this way, we generalize the dilation for different values of p in the Lp metric (Euclidean is a particular case when p = 2) and obtain the descriptors from the cumulated distribution of the distance transform computed over the texture image. The proposed method is compared to other state-of-the-art approaches (such as local binary patterns and textons for example) in the classification of two benchmark data sets (UIUC and Outex). The proposed descriptors outperformed all the other approaches in terms of rate of images correctly classified. The interesting results suggest the potential of these descriptors in this type of task, with a wide range of possible applications to real-world problems.

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

Parametric classification of handvein patterns based on texture features

NASA Astrophysics Data System (ADS)

Al Mahafzah, Harbi; Imran, Mohammad; Supreetha Gowda H., D.

2018-04-01

In this paper, we have developed Biometric recognition system adopting hand based modality Handvein,which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor, LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kernel options. The feature level fusion is carried out which increased the performance level.

Soil moisture data as a constraint for groundwater recharge estimation

NASA Astrophysics Data System (ADS)

Mathias, Simon A.; Sorensen, James P. R.; Butler, Adrian P.

2017-09-01

Estimating groundwater recharge rates is important for water resource management studies. Modeling approaches to forecast groundwater recharge typically require observed historic data to assist calibration. It is generally not possible to observe groundwater recharge rates directly. Therefore, in the past, much effort has been invested to record soil moisture content (SMC) data, which can be used in a water balance calculation to estimate groundwater recharge. In this context, SMC data is measured at different depths and then typically integrated with respect to depth to obtain a single set of aggregated SMC values, which are used as an estimate of the total water stored within a given soil profile. This article seeks to investigate the value of such aggregated SMC data for conditioning groundwater recharge models in this respect. A simple modeling approach is adopted, which utilizes an emulation of Richards' equation in conjunction with a soil texture pedotransfer function. The only unknown parameters are soil texture. Monte Carlo simulation is performed for four different SMC monitoring sites. The model is used to estimate both aggregated SMC and groundwater recharge. The impact of conditioning the model to the aggregated SMC data is then explored in terms of its ability to reduce the uncertainty associated with recharge estimation. Whilst uncertainty in soil texture can lead to significant uncertainty in groundwater recharge estimation, it is found that aggregated SMC is virtually insensitive to soil texture.

Climate and soil properties limit the positive effects of land use reversion on carbon storage in Eastern Australia

NASA Astrophysics Data System (ADS)

Rabbi, S. M. F.; Tighe, Matthew; Delgado-Baquerizo, Manuel; Cowie, Annette; Robertson, Fiona; Dalal, Ram; Page, Kathryn; Crawford, Doug; Wilson, Brian R.; Schwenke, Graeme; McLeod, Malem; Badgery, Warwick; Dang, Yash P.; Bell, Mike; O'Leary, Garry; Liu, De Li; Baldock, Jeff

2015-12-01

Australia’s “Direct Action” climate change policy relies on purchasing greenhouse gas abatement from projects undertaking approved abatement activities. Management of soil organic carbon (SOC) in agricultural soils is an approved activity, based on the expectation that land use change can deliver significant changes in SOC. However, there are concerns that climate, topography and soil texture will limit changes in SOC stocks. This work analyses data from 1482 sites surveyed across the major agricultural regions of Eastern Australia to determine the relative importance of land use vs. other drivers of SOC. Variation in land use explained only 1.4% of the total variation in SOC, with aridity and soil texture the main regulators of SOC stock under different land uses. Results suggest the greatest potential for increasing SOC stocks in Eastern Australian agricultural regions lies in converting from cropping to pasture on heavy textured soils in the humid regions.

Climate and soil properties limit the positive effects of land use reversion on carbon storage in Eastern Australia

PubMed Central

Rabbi, S.M.F.; Tighe, Matthew; Delgado-Baquerizo, Manuel; Cowie, Annette; Robertson, Fiona; Dalal, Ram; Page, Kathryn; Crawford, Doug; Wilson, Brian R.; Schwenke, Graeme; Mcleod, Malem; Badgery, Warwick; Dang, Yash P.; Bell, Mike; O’Leary, Garry; Liu, De Li; Baldock, Jeff

2015-01-01

Australia’s “Direct Action” climate change policy relies on purchasing greenhouse gas abatement from projects undertaking approved abatement activities. Management of soil organic carbon (SOC) in agricultural soils is an approved activity, based on the expectation that land use change can deliver significant changes in SOC. However, there are concerns that climate, topography and soil texture will limit changes in SOC stocks. This work analyses data from 1482 sites surveyed across the major agricultural regions of Eastern Australia to determine the relative importance of land use vs. other drivers of SOC. Variation in land use explained only 1.4% of the total variation in SOC, with aridity and soil texture the main regulators of SOC stock under different land uses. Results suggest the greatest potential for increasing SOC stocks in Eastern Australian agricultural regions lies in converting from cropping to pasture on heavy textured soils in the humid regions. PMID:26639009

Consequences of using different soil texture determination methodologies for soil physical quality and unsaturated zone time lag estimates.

PubMed

Fenton, O; Vero, S; Ibrahim, T G; Murphy, P N C; Sherriff, S C; Ó hUallacháin, D

2015-11-01

Elucidation of when the loss of pollutants, below the rooting zone in agricultural landscapes, affects water quality is important when assessing the efficacy of mitigation measures. Investigation of this inherent time lag (t(T)) is divided into unsaturated (t(u)) and saturated (t(s)) components. The duration of these components relative to each other differs depending on soil characteristics and the landscape position. The present field study focuses on tu estimation in a scenario where the saturated zone is likely to constitute a higher proportion of t(T). In such instances, or where only initial breakthrough (IBT) or centre of mass (COM) is of interest, utilisation of site and depth specific "simple" textural class or actual sand-silt-clay percentages to generate soil water characteristic curves with associated soil hydraulic parameters is acceptable. With the same data it is also possible to estimate a soil physical quality (S) parameter for each soil layer which can be used to infer many other physical, chemical and biological quality indicators. In this study, hand texturing in the field was used to determine textural classes of a soil profile. Laboratory methods, including hydrometer, pipette and laser diffraction methods were used to determine actual sand-silt-clay percentages of sections of the same soil profile. Results showed that in terms of S, hand texturing resulted in a lower index value (inferring a degraded soil) than that of pipette, hydrometer and laser equivalents. There was no difference between S index values determined using the pipette, hydrometer and laser diffraction methods. The difference between the three laboratory methods on both the IBT and COM stages of t(u) were negligible, and in this instance were unlikely to affect either groundwater monitoring decisions, or to be of consequence from a policy perspective. When t(u) estimates are made over the full depth of the vadose zone, which may extend to several metres, errors resulting from the use of hydraulic parameters generated from hand texture data will be resultantly greater, and may lead to flawed predictions regarding the achievability of water policy targets. For this reason laboratory analysis, regardless of method, should be preferred to simple field assessments. Copyright © 2015 Elsevier B.V. All rights reserved.

The effect of soil texture on the degradation of textiles associated with buried bodies.

PubMed

Lowe, A C; Beresford, D V; Carter, D O; Gaspari, F; O'Brien, R C; Stuart, B H; Forbes, S L

2013-09-10

There are many factors which affect the rate of decomposition in a grave site including; the depth of burial, climatic conditions, physical conditions of the soil (e.g. texture, pH, moisture), and method of burial (e.g. clothing, wrappings). Clothing is often studied as a factor that can slow the rate of soft tissue decomposition. In contrast, the effect of soft tissue decomposition on the rate of textile degradation is usually reported as anecdotal evidence rather than being studied under controlled conditions. The majority of studies in this area have focused on the degradation of textiles buried directly in soil. The purpose of this study was to investigate the effect of soil texture on the degradation and/or preservation of textile materials associated with buried bodies. The study involved the burial of clothed domestic pig carcasses and control clothing in contrasting soil textures (silty clay loam, fine sand and fine sandy loam) at three field sites in southern Ontario, Canada. Graves were exhumed after 2, 12 and 14 months burial to observe the degree of degradation for both natural and synthetic textiles. Recovered textile samples were chemically analyzed using infrared (IR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) to investigate the lipid decomposition by-products retained in the textiles. The findings of this study demonstrate that natural textile in contact with a buried decomposing body will be preserved for longer periods of time when compared to the same textile buried directly in soil and not in contact with a body. The soil texture did not visually impact the degree of degradation or preservation. Furthermore, the natural-synthetic textile blend was resistant to degradation, regardless of soil texture, contact with the body or time since deposition. Chemical analysis of the textiles using GC-MS correctly identified a lipid degradation profile consistent with the degree of soft tissue decomposition. Such information may be important for estimating time since deposition in instances where only grave goods and associated materials are recovered from a burial site. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Dielectric constants of soils at microwave frequencies-2

NASA Technical Reports Server (NTRS)

Wang, J.; Schmugge, T.; Williams, D.

1978-01-01

The dielectric constants of several soil samples were measured at frequencies of 5 and 19 GHz using the infinite transmission line method. The results of these measurements are presented and discussed with respect to soil types and texture structures. A comparison is made with other measurements at 1.4 GHz. At all three frequencies, the dependence of dielectric constant on soil moisture can be approximated by two straight lines. At low moisture, the slope is less than at high moisture level. The intersection of the two lines is believed to be a function of soil texture.

Climate, soil texture, and soil types affect the contributions of fine-fraction-stabilized carbon to total soil organic carbon in different land uses across China.

PubMed

Cai, Andong; Feng, Wenting; Zhang, Wenju; Xu, Minggang

2016-05-01

Mineral-associated organic carbon (MOC), that is stabilized by fine soil particles (i.e., silt plus clay, <53 μm), is important for soil organic carbon (SOC) persistence and sequestration, due to its large contribution to total SOC (TSOC) and long turnover time. Our objectives were to investigate how climate, soil type, soil texture, and agricultural managements affect MOC contributions to TSOC in China. We created a dataset from 103 published papers, including 1106 data points pairing MOC and TSOC across three major land use types: cropland, grassland, and forest. Overall, the MOC/TSOC ratio ranged from 0.27 to 0.80 and varied significantly among soil groups in cropland, grassland, and forest. Croplands and forest exhibited significantly higher median MOC/TSOC ratios than in grassland. Moreover, forest and grassland soils in temperate regions had higher MOC/TSOC ratios than in subtropical regions. Furthermore, the MOC/TSOC ratio was much higher in ultisol, compared with the other soil types. Both the MOC content and MOC/TSOC ratio were positively correlated with the amount of fine fraction (silt plus clay) in soil, highlighting the importance of soil texture in stabilizing organic carbon across various climate zones. In cropland, different fertilization practices and land uses (e.g., upland, paddy, and upland-paddy rotation) significantly altered MOC/TSOC ratios, but not in cropping systems (e.g., mono- and double-cropping) characterized by climatic differences. This study demonstrates that the MOC/TSOC ratio is mainly driven by soil texture, soil types, and related climate and land uses, and thus the variations in MOC/TSOC ratios should be taken into account when quantitatively estimating soil C sequestration potential of silt plus clay particles on a large scale. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Remote sensing-based characterization of land management and biophysical factors in grassland

NASA Astrophysics Data System (ADS)

Ramspott, Matthew E.

Land use and management are important factors influencing ecosystem functions, including the cycling of carbon (C) in plant/soil systems. Information about land use and management, needed to prioritize conservation efforts in managed grasslands of the Central Great Plains, can be obtained using remote sensing techniques, but this process is complex in grasslands because of the subtle class differences, large within-class variability, and complex seasonal changes in canopy spectral characteristics. In this study, time-series of remotely sensed data were used to derive vegetation index (VI) and image texture measures. The utility of these measures for classification of five managed grassland types was assessed using ANOVA and stepwise discriminant analysis methods. Image texture was found to improve the accuracy of classification by ˜13% over the use of VI alone. The optimal timing of data acquisition for classification with VI was found to be in April/May and in October; optimal timing for acquisition of texture was in June. Remotely sensed VI have been commonly used to model photosynthetic capacity and net primary production in ecosystems. Since VI theoretically assume canopy conditions of uniform geometry and greenness, seasonally variable management-induced changes in the grassland canopy can potentially influence the VI response and therefore the strength and stability of the model. This study examined the seasonal and inter-annual stability of the relationship between VI and photosynthetic capacity under both idealized and realized conditions. With regression analysis, the relationship between VI and field-measured estimates of photosynthetic capacity was established and evaluated. This work identified two types of management activity strongly influencing the stability of this relationship: (1) Conservation management, in which the vegetation is neither hayed nor grazed, results in accumulation of senescent canopy material and leads to lower than expected VI response; (2) Heavy grazing management leads to elevated levels of forb (non-grass species) cover in the canopy coupled with low photosynthetic capacity and high levels of bare ground, resulting in higher than expected VI response. When sites exhibiting these characteristics were removed, the relationship between VI and photosynthetic capacity was found to be stable seasonally and between years.

A framework for global terrain classification using 250-m DEMs to predict geohazards

NASA Astrophysics Data System (ADS)

Iwahashi, J.; Matsuoka, M.; Yong, A.

2016-12-01

Geomorphology is key for identifying factors that control geohazards induced by landslides, liquefaction, and ground shaking. To systematically identify landforms that affect these hazards, Iwahashi and Pike (2007; IP07) introduced an automated terrain classification scheme using 1-km-scale Shuttle Radar Topography Mission (SRTM) digital elevation models (DEMs). The IP07 classes describe 16 categories of terrain types and were used as a proxy for predicting ground motion amplification (Yong et al., 2012; Seyhan et al., 2014; Stewart et al., 2014; Yong, 2016). These classes, however, were not sufficiently resolved because coarse-scaled SRTM DEMs were the basis for the categories (Yong, 2016). Thus, we develop a new framework consisting of more detailed polygonal global terrain classes to improve estimations of soil-type and material stiffness. We first prepare high resolution 250-m DEMs derived from the 2010 Global Multi-resolution Terrain Elevation Data (GMTED2010). As in IP07, we calculate three geometric signatures (slope, local convexity and surface texture) from the DEMs. We create additional polygons by using the same signatures and multi-resolution segmentation techniques on the GMTED2010. We consider two types of surface texture thresholds in different window sizes (3x3 and 13x13 pixels), in addition to slope and local convexity, to classify pixels within the DEM. Finally, we apply the k-means clustering and thresholding methods to the 250-m DEM and produce more detailed polygonal terrain classes. We compare the new terrain classification maps of Japan and California with geologic, aerial photography, and landslide distribution maps, and visually find good correspondence of key features. To predict ground motion amplification, we apply the Yong (2016) method for estimating VS30. The systematic classification of geomorphology has the potential to provide a better understanding of the susceptibility to geohazards, which is especially vital in populated areas.

Precipitation pulse use by an invasive woody legume: the role of soil texture and pulse size.

PubMed

Fravolini, Alessandra; Hultine, Kevin R; Brugnoli, Enrico; Gazal, Rico; English, Nathan B; Williams, David G

2005-08-01

Plant metabolic activity in arid and semi-arid environments is largely tied to episodic precipitation events or "pulses". The ability of plants to take up and utilize rain pulses during the growing season in these water-limited ecosystems is determined in part by pulse timing, intensity and amount, and by hydrological properties of the soil that translate precipitation into plant-available soil moisture. We assessed the sensitivity of an invasive woody plant, velvet mesquite (Prosopis velutina Woot.), to large (35 mm) and small (10 mm) isotopically labeled irrigation pulses on two contrasting soil textures (sandy-loam vs. loamy-clay) in semi-desert grassland in southeastern Arizona, USA. Predawn leaf water potential (psi(pd)), the isotopic abundance of deuterium in stem water (deltaD), the abundance of 13C in soluble leaf sugar (delta13C), and percent volumetric soil water content (theta(v)) were measured prior to irrigation and repeatedly for 2 weeks following irrigation. Plant water potential and the percent of pulse water present in the stem xylem indicated that although mesquite trees on both coarse- and fine-textured soils quickly responded to the large irrigation pulse, the magnitude and duration of this response substantially differed between soil textures. After reaching a maximum 4 days after the irrigation, the fraction of pulse water in stem xylem decreased more rapidly on the loamy-clay soil than the sandy-loam soil. Similarly, on both soil textures mesquite significantly responded to the 10-mm pulse. However, the magnitude of this response was substantially greater for mesquite on the sandy-loam soil compared to loamy-clay soil. The relationship between psi(pd) and delta13C of leaf-soluble carbohydrates over the pulse period did not differ between plants at the two sites, indicating that differences in photosynthetic response of mesquite trees to the moisture pulses was a function of soil water availability within the rooting zone rather than differences in plant biochemical or physiological constraints. Patterns of resource acquisition by mesquite during the dynamic wetting-drying cycle following rainfall pulses is controlled by a complex interaction between pulse size and soil hydraulic properties. A better understanding of how this interaction affects plant water availability and photosynthetic response is needed to predict how grassland structure and function will respond to climate change.

Grazing moderates increases in C3 grass abundance over seven decades across a soil texture gradient in shortgrass steppe

USDA-ARS?s Scientific Manuscript database

Questions: How does long-term grazing exclusion influence plant community composition in a semiarid grassland? Can spatial variation in the effects of grazing exclusion be explained by variation in soil texture? Location: The shortgrass steppe of northeastern Colorado, USA, located in the North Amer...

Height growth of red pine on fine-textured soils.

Treesearch

David H. Alban; Donald H. Prettyman

1984-01-01

Height growth was determined by stem analysis for red pine in 12 natural and 10 planted stands on well-drained, fine textured soils. Growth closely followed the Gervorkiantz site index curves. When calculating site index, an age adjustment is desirable if the trees take longer than 8 years to attain breast height.

Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping

NASA Astrophysics Data System (ADS)

Tarando, Sebastian Roberto; Fetita, Catalin; Brillet, Pierre-Yves

2017-03-01

The infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. Traditionally, such classification relies on a two-dimensional analysis of axial CT images. This paper proposes a cascade of the existing CNN based CAD system, specifically tuned-up. The advantage of using a deep learning approach is a better regularization of the classification output. In a preliminary evaluation, the combined approach was tested on a 13 patient database of various lung pathologies, showing an increase of 10% in True Positive Rate (TPR) with respect to the best suited state of the art CNN for this task.

Soil process-oriented modelling of within-field variability based on high-resolution 3D soil type distribution maps.

NASA Astrophysics Data System (ADS)

Bönecke, Eric; Lück, Erika; Gründling, Ralf; Rühlmann, Jörg; Franko, Uwe

2016-04-01

Today, the knowledge of within-field variability is essential for numerous purposes, including practical issues, such as precision and sustainable soil management. Therefore, process-oriented soil models have been applied for a considerable time to answer question of spatial soil nutrient and water dynamics, although, they can only be as consistent as their variation and resolution of soil input data. Traditional approaches, describe distribution of soil types, soil texture or other soil properties for greater soil units through generalised point information, e.g. from classical soil survey maps. Those simplifications are known to be afflicted with large uncertainties. Varying soil, crop or yield conditions are detected even within such homogenised soil units. However, recent advances of non-invasive soil survey and on-the-go monitoring techniques, made it possible to obtain vertical and horizontal dense information (3D) about various soil properties, particularly soil texture distribution which serves as an essential soil key variable affecting various other soil properties. Thus, in this study we based our simulations on detailed 3D soil type distribution (STD) maps (4x4 m) to adjacently built-up sufficient informative soil profiles including various soil physical and chemical properties. Our estimates of spatial STD are based on high-resolution lateral and vertical changes of electrical resistivity (ER), detected by a relatively new multi-sensor on-the-go ER monitoring device. We performed an algorithm including fuzzy-c-mean (FCM) logic and traditional soil classification to estimate STD from those inverted and layer-wise available ER data. STD is then used as key input parameter for our carbon, nitrogen and water transport model. We identified Pedological horizon depths and inferred hydrological soil variables (field capacity, permanent wilting point) from pedotransferfunctions (PTF) for each horizon. Furthermore, the spatial distribution of soil organic carbon (SOC), as essential input variable, was predicted by measured soil samples and associated to STD of the upper 30 cm. The comprehensive and high-resolution (4x4 m) soil profile information (up to 2 m soil depth) were then used to initialise a soil process model (Carbon and Nitrogen Dynamics - CANDY) for soil functional modelling (daily steps of matter fluxes, soil temperature and water balances). Our study was conducted on a practical field (~32,000 m²) of an agricultural farm in Central Germany with Chernozem soils under arid conditions (average rainfall < 550 mm). This soil region is known to have differences in soil structure mainly occurring within the subsoil, since topsoil conditions are described as homogenous. The modelled soil functions considered local climate information and practical farming activities. Results show, as expected, distinguished functional variability, both on spatial and temporal resolution for subsoil evident structures, e.g. visible differences for available water capacity within 0-100 cm but homogenous conditions for the topsoil.

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Peters-Lidard, Christa D.; Garcia, Matthew E.; Mocko, David M.; Tischler, Michael A.; Moran, M. Susan; Thoma, D. P.

2007-01-01

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

Identification and classification of similar looking food grains

NASA Astrophysics Data System (ADS)

Anami, B. S.; Biradar, Sunanda D.; Savakar, D. G.; Kulkarni, P. V.

2013-01-01

This paper describes the comparative study of Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers by taking a case study of identification and classification of four pairs of similar looking food grains namely, Finger Millet, Mustard, Soyabean, Pigeon Pea, Aniseed, Cumin-seeds, Split Greengram and Split Blackgram. Algorithms are developed to acquire and process color images of these grains samples. The developed algorithms are used to extract 18 colors-Hue Saturation Value (HSV), and 42 wavelet based texture features. Back Propagation Neural Network (BPNN)-based classifier is designed using three feature sets namely color - HSV, wavelet-texture and their combined model. SVM model for color- HSV model is designed for the same set of samples. The classification accuracies ranging from 93% to 96% for color-HSV, ranging from 78% to 94% for wavelet texture model and from 92% to 97% for combined model are obtained for ANN based models. The classification accuracy ranging from 80% to 90% is obtained for color-HSV based SVM model. Training time required for the SVM based model is substantially lesser than ANN for the same set of images.

Reduction of root-knot nematode, Meloidogyne javanica, and ozone mass transfer in soil treated with ozone.

PubMed

Qiu, Jinya Jack; Westerdahl, Becky B; Pryor, Alan

2009-09-01

Ozone gas (O₃) is a reactive oxidizing agent with biocidal properties. Because of the current phasing out of methyl bromide, investigations on the use of ozone gas as a soil-fumigant were conducted. Ozone gas was produced at a concentration of 1% in air by a conventional electrical discharge O₃ generator. Two O₃ dosages and three gas flow rates were tested on a sandy loam soil collected from a tomato field that had a resident population of root knot nematodes, Meloidogyne javanica. At dosages equivalent to 50 and 250 kg of O₃/ha, M. javanica were reduced by 24% and 68%, and free-living nematodes by 19% and 52%, respectively. The reduction for both M. javanica and free-living nematodes was dosage dependent and flow rate independent. The rates of O₃ mass transfer (OMT) through three soils of different texture were greater at low and high moisture levels than at intermediate ones. At any one soil moisture level, the OMT rate varied with soil texture and soil organic matter content. Results suggest that soil texture, moisture, and organic matter content should be considered in determining O₃ dosage needed for effective nematode control.

Soil Particle Size Analysis by Laser Diffractometry: Result Comparison with Pipette Method

NASA Astrophysics Data System (ADS)

Šinkovičová, Miroslava; Igaz, Dušan; Kondrlová, Elena; Jarošová, Miriam

2017-10-01

Soil texture as the basic soil physical property provides a basic information on the soil grain size distribution as well as grain size fraction representation. Currently, there are several methods of particle dimension measurement available that are based on different physical principles. Pipette method based on the different sedimentation velocity of particles with different diameter is considered to be one of the standard methods of individual grain size fraction distribution determination. Following the technical advancement, optical methods such as laser diffraction can be also used nowadays for grain size distribution determination in the soil. According to the literature review of domestic as well as international sources related to this topic, it is obvious that the results obtained by laser diffractometry do not correspond with the results obtained by pipette method. The main aim of this paper was to analyse 132 samples of medium fine soil, taken from the Nitra River catchment in Slovakia, from depths of 15-20 cm and 40-45 cm, respectively, using laser analysers: ANALYSETTE 22 MicroTec plus (Fritsch GmbH) and Mastersizer 2000 (Malvern Instruments Ltd). The results obtained by laser diffractometry were compared with pipette method and the regression relationships using linear, exponential, power and polynomial trend were derived. Regressions with the three highest regression coefficients (R2) were further investigated. The fit with the highest tightness was observed for the polynomial regression. In view of the results obtained, we recommend using the estimate of the representation of the clay fraction (<0.01 mm) polynomial regression, to achieve a highest confidence value R2 at the depths of 15-20 cm 0.72 (Analysette 22 MicroTec plus) and 0.95 (Mastersizer 2000), from a depth of 40-45 cm 0.90 (Analysette 22 MicroTec plus) and 0.96 (Mastersizer 2000). Since the percentage representation of clayey particles (2nd fraction according to the methodology of Complex Soil Survey done in Slovakia) in soil is the determinant for soil type specification, we recommend using the derived relationships in soil science when the soil texture analysis is done according to laser diffractometry. The advantages of laser diffraction method comprise the short analysis time, usage of small sample amount, application for the various grain size fraction and soil type classification systems, and a wide range of determined fractions. Therefore, it is necessary to focus on this issue further to address the needs of soil science research and attempt to replace the standard pipette method with more progressive laser diffraction method.

Effects of Spatial Variability of Soil Properties on the Triggering of Rainfall-Induced Shallow Landslides

NASA Astrophysics Data System (ADS)

Fan, Linfeng; Lehmann, Peter; Or, Dani

2015-04-01

Naturally-occurring spatial variations in soil properties (e.g., soil depth, moisture, and texture) affect key hydrological processes and potentially the mechanical response of soil to hydromechanical loading (relative to the commonly-assumed uniform soil mantle). We quantified the effects of soil spatial variability on the triggering of rainfall-induced shallow landslides at the hillslope- and catchment-scales, using a physically-based landslide triggering model that considers interacting soil columns with mechanical strength thresholds (represented by the Fiber Bundle Model). The spatial variations in soil properties are represented as Gaussian random distributions and the level of variation is characterized by the coefficient of variation and correlation lengths of soil properties (i.e., soil depth, soil texture and initial water content in this study). The impacts of these spatial variations on landslide triggering characteristics were measured by comparing the times to triggering and landslide volumes for heterogeneous soil properties and homogeneous cases. Results at hillslope scale indicate that for spatial variations of an individual property (without cross correlation), the increasing of coefficient of variation introduces weak spots where mechanical damage is accelerated and leads to earlier onset of landslide triggering and smaller volumes. Increasing spatial correlation length of soil texture and initial water content also induces early landslide triggering and small released volumes due to the transition of failure mode from brittle to ductile failure. In contrast, increasing spatial correlation length of soil depth "reduces" local steepness and postpones landslide triggering. Cross-correlated soil properties generally promote landslide initiation, but depending on the internal structure of spatial distribution of each soil property, landslide triggering may be reduced. The effects of cross-correlation between initial water content and soil texture were investigated in detail at the catchment scale by incorporating correlations of both variables with topography. Results indicate that the internal structure of the spatial distribution of each soil property together with their interplays determine the overall performance of the coupled spatial variability. This study emphasizes the importance of both the randomness and spatial structure of soil properties on landslide triggering and characteristics.

Comparisons of neural networks to standard techniques for image classification and correlation

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1994-01-01

Neural network techniques for multispectral image classification and spatial pattern detection are compared to the standard techniques of maximum-likelihood classification and spatial correlation. The neural network produced a more accurate classification than maximum-likelihood of a Landsat scene of Tucson, Arizona. Some of the errors in the maximum-likelihood classification are illustrated using decision region and class probability density plots. As expected, the main drawback to the neural network method is the long time required for the training stage. The network was trained using several different hidden layer sizes to optimize both the classification accuracy and training speed, and it was found that one node per class was optimal. The performance improved when 3x3 local windows of image data were entered into the net. This modification introduces texture into the classification without explicit calculation of a texture measure. Larger windows were successfully used for the detection of spatial features in Landsat and Magellan synthetic aperture radar imagery.

CFS-SMO based classification of breast density using multiple texture models.

PubMed

Sharma, Vipul; Singh, Sukhwinder

2014-06-01

It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46% is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS-SMO method outperforms all other classifiers giving a sensitivity of 100%. This makes it suitable to be taken as a second opinion in classifying breast tissue density.

Lava Morphology Classification of a Fast-Spreading Ridge Using Deep-Towed Sonar Data: East Pacific Rise

NASA Astrophysics Data System (ADS)

Meyer, J.; White, S.

2005-05-01

Classification of lava morphology on a regional scale contributes to the understanding of the distribution and extent of lava flows at a mid-ocean ridge. Seafloor classification is essential to understand the regional undersea environment at midocean ridges. In this study, the development of a classification scheme is found to identify and extract textural patterns of different lava morphologies along the East Pacific Rise using DSL-120 side-scan and ARGO camera imagery. Application of an accurate image classification technique to side-scan sonar allows us to expand upon the locally available visual ground reference data to make the first comprehensive regional maps of small-scale lava morphology present at a mid-ocean ridge. The submarine lava morphologies focused upon in this study; sheet flows, lobate flows, and pillow flows; have unique textures. Several algorithms were applied to the sonar backscatter intensity images to produce multiple textural image layers useful in distinguishing the different lava morphologies. The intensity and spatially enhanced images were then combined and applied to a hybrid classification technique. The hybrid classification involves two integrated classifiers, a rule-based expert system classifier and a machine learning classifier. The complementary capabilities of the two integrated classifiers provided a higher accuracy of regional seafloor classification compared to using either classifier alone. Once trained, the hybrid classifier can then be applied to classify neighboring images with relative ease. This classification technique has been used to map the lava morphology distribution and infer spatial variability of lava effusion rates along two segments of the East Pacific Rise, 17 deg S and 9 deg N. Future use of this technique may also be useful for attaining temporal information. Repeated documentation of morphology classification in this dynamic environment can be compared to detect regional seafloor change.

Soil Texture Often Exerts a Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns

NASA Astrophysics Data System (ADS)

Dong, Jingnuo; Ochsner, Tyson E.

2018-03-01

Soil moisture patterns are commonly thought to be dominated by land surface characteristics, such as soil texture, at small scales and by atmospheric processes, such as precipitation, at larger scales. However, a growing body of evidence challenges this conceptual model. We investigated the structural similarity and spatial correlations between mesoscale (˜1-100 km) soil moisture patterns and land surface and atmospheric factors along a 150 km transect using 4 km multisensor precipitation data and a cosmic-ray neutron rover, with a 400 m diameter footprint. The rover was used to measure soil moisture along the transect 18 times over 13 months. Spatial structures of soil moisture, soil texture (sand content), and antecedent precipitation index (API) were characterized using autocorrelation functions and fitted with exponential models. Relative importance of land surface characteristics and atmospheric processes were compared using correlation coefficients (r) between soil moisture and sand content or API. The correlation lengths of soil moisture, sand content, and API ranged from 12-32 km, 13-20 km, and 14-45 km, respectively. Soil moisture was more strongly correlated with sand content (r = -0.536 to -0.704) than with API for all but one date. Thus, land surface characteristics exhibit coherent spatial patterns at scales up to 20 km, and those patterns often exert a stronger influence than do precipitation patterns on mesoscale spatial patterns of soil moisture.

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

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.

Classification of Liss IV Imagery Using Decision Tree Methods

NASA Astrophysics Data System (ADS)

Verma, Amit Kumar; Garg, P. K.; Prasad, K. S. Hari; Dadhwal, V. K.

2016-06-01

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

Hyperspectral image segmentation using a cooperative nonparametric approach

NASA Astrophysics Data System (ADS)

Taher, Akar; Chehdi, Kacem; Cariou, Claude

2013-10-01

In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.

Constraints on tree seedling establishment in montane grasslands of the Valles Caldera, New Mexico

Treesearch

Jonathan D. Coop; Thomas J. Givnish

2008-01-01

Montane and subalpine grasslands are prominent, but poorly understood, features of the Rocky Mountains. These communities frequently occur below reversed tree lines on valley floors, where nightly cold air accumulation is spatially coupled with fine soil texture. We used field experiments to assess the roles of minimum temperature, soil texture, grass competition, and...

Textured Image Segmentation

DTIC Science & Technology

1980-01-01

descriminated by frequency domain features. It has been shown (201 that Fourier features provide useful information for aerial classification and for...Package for the Social. Sciences (SPSS). These descriminant algorithms are documented in Appendix C. Source textures are known, so that cluster

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

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

Fried, David V.; Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas; Tucker, Susan L.

2014-11-15

Purpose: To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC). Methods and Materials: We retrospectively reviewed 91 cases with stage III NSCLC treated with definitive chemoradiation therapy. All patients underwent pretreatment diagnostic contrast enhanced computed tomography (CE-CT) followed by 4-dimensional CT (4D-CT) for treatment simulation. We used the average-CT and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation wasmore » used for covariate selection and modeling. Models incorporating texture features from the 33 image types and CPFs were compared to those with models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on whether their predicted outcome was above or below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility. Results: Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (P=.046), LRC (P=.01), and FFDM (P=.005). The average CCCs were 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4% (±3.7% SD), 78.3% (±4.0% SD), and 78.8% (±3.9% SD) classification reproducibility in terms of OS, LRC, and FFDM, respectively. Conclusions: Pretreatment tumor texture may provide prognostic information beyond that obtained from CPFs. Models incorporating feature reproducibility achieved classification rates of ∼80%. External validation would be required to establish texture as a prognostic factor.« less

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

NASA Astrophysics Data System (ADS)

Shellito, Peter J.; Small, Eric E.; Livneh, Ben

2018-03-01

Drydown periods that follow precipitation events provide an opportunity to assess controls on soil evaporation on a continental scale. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates, and our work suggests that SMAP-observed drying is also predominantly affected by direct soil evaporation. Data cover the domain of the North American Land Data Assimilation System Phase 2 and span the first 1.8 years of SMAP's operation. Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

Texture analysis applied to second harmonic generation image data for ovarian cancer classification

NASA Astrophysics Data System (ADS)

Wen, Bruce L.; Brewer, Molly A.; Nadiarnykh, Oleg; Hocker, James; Singh, Vikas; Mackie, Thomas R.; Campagnola, Paul J.

2014-09-01

Remodeling of the extracellular matrix has been implicated in ovarian cancer. To quantitate the remodeling, we implement a form of texture analysis to delineate the collagen fibrillar morphology observed in second harmonic generation microscopy images of human normal and high grade malignant ovarian tissues. In the learning stage, a dictionary of "textons"-frequently occurring texture features that are identified by measuring the image response to a filter bank of various shapes, sizes, and orientations-is created. By calculating a representative model based on the texton distribution for each tissue type using a training set of respective second harmonic generation images, we then perform classification between images of normal and high grade malignant ovarian tissues. By optimizing the number of textons and nearest neighbors, we achieved classification accuracy up to 97% based on the area under receiver operating characteristic curves (true positives versus false positives). The local analysis algorithm is a more general method to probe rapidly changing fibrillar morphologies than global analyses such as FFT. It is also more versatile than other texture approaches as the filter bank can be highly tailored to specific applications (e.g., different disease states) by creating customized libraries based on common image features.

Cellular automata rule characterization and classification using texture descriptors

NASA Astrophysics Data System (ADS)

Machicao, Jeaneth; Ribas, Lucas C.; Scabini, Leonardo F. S.; Bruno, Odermir M.

2018-05-01

The cellular automata (CA) spatio-temporal patterns have attracted the attention from many researchers since it can provide emergent behavior resulting from the dynamics of each individual cell. In this manuscript, we propose an approach of texture image analysis to characterize and classify CA rules. The proposed method converts the CA spatio-temporal patterns into a gray-scale image. The gray-scale is obtained by creating a binary number based on the 8-connected neighborhood of each dot of the CA spatio-temporal pattern. We demonstrate that this technique enhances the CA rule characterization and allow to use different texture image analysis algorithms. Thus, various texture descriptors were evaluated in a supervised training approach aiming to characterize the CA's global evolution. Our results show the efficiency of the proposed method for the classification of the elementary CA (ECAs), reaching a maximum of 99.57% of accuracy rate according to the Li-Packard scheme (6 classes) and 94.36% for the classification of the 88 rules scheme. Moreover, within the image analysis context, we found a better performance of the method by means of a transformation of the binary states to a gray-scale.

Computer-aided diagnosis in phase contrast imaging X-ray computed tomography for quantitative characterization of ex vivo human patellar cartilage.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismuller, Axel

2013-10-01

Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: 0.94 ±0.08 ) and "Euler characteristic" (AUC: 0.94 ±0.07 ), and GLCM-derived feature "Correlation" (AUC: 0.93 ±0.07). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.

Compositon of sediments transported by the wind at different heights

NASA Astrophysics Data System (ADS)

Iturri, Antonela; Funk, Roger; Leue, Martin; Sommer, Michael; Buschiazzo, Daniel

2017-04-01

Wind erosion (WE) is one of the most important degradation process of soils in arid- and semiarid environments in the world, affecting soil properties and adjacent ecosystems, including human health. Estimations about the amount of eroded soil are available in Argentina and in the world, but the quality of the eroded sediments, particularly the sorting effects in agricultural soils, has been scarcely studied. The trend of the different mineral and organic soil compounds, which enrich in different size classes, can define height distribution profiles. Therefore, the uppermost 2.5 cm of four agricultural loess soils that differ in granulometric composition were used for WE simulations in a wind tunnel. Particles with a diameter smaller than 10 µm (PM10) were collected with a laboratory dust generator. The bulk soil and all the sediment samples were characterized by the granulometric composition, the soil organic carbon (SOC) content and the mineral and organic functional groups. Despite different texture, the soils were subjected to similar sorting processes in height, but differed depending on their granulometry. There was a separation between coarser and finer soil particles in coarser textured soils, while finer textured soils were more homogeneous in all heights. This correlated with the preferential transport of Si-O from quartz and C-H, C=O and C-C from soil organic matter (SOM), which were transported in larger and/or denser particles at lower heights. O-H from clay minerals and C-O-C and C-O from polysaccharides, carbohydrates and derivatives from SOM were transported in higher heights. Despite similar SOC content in the bulk soils, both the amount and composition in the PM10 fractions was different. The SOC transported at higher heights was mostly composed of polysaccharides, carbohydrates and derivatives associated with clay minerals. The SOC in PM10 fractions of coarser-textured soils was dominated by labile C-H groups. According to the determined height distribution profiles, it can be deduced that WE may affect both soil quality and the soil C balance due to the sorting effects during transport.

Combined effects of short-term rainfall patterns and soil texture on nitrogen cycling -- A Modeling Analysis

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

Gu, C.; Riley, W.J.

2009-11-01

Precipitation variability and magnitude are expected to change in many parts of the world over the 21st century. We examined the potential effects of intra-annual rainfall patterns on soil nitrogen (N) transport and transformation in the unsaturated soil zone using a deterministic dynamic modeling approach. The model (TOUGHREACT-N), which has been tested and applied in several experimental and observational systems, mechanistically accounts for microbial activity, soil-moisture dynamics that respond to precipitation variability, and gaseous and aqueous tracer transport in the soil. Here, we further tested and calibrated the model against data from a precipitation variability experiment in a tropical systemmore » in Costa Rica. The model was then used to simulate responses of soil moisture, microbial dynamics, nitrogen (N) aqueous and gaseous species, N leaching, and N trace-gas emissions to changes in rainfall patterns; the effect of soil texture was also examined. The temporal variability of nitrate leaching and NO, N{sub 2}, and N{sub 2}O effluxes were significantly influenced by rainfall dynamics. Soil texture combined with rainfall dynamics altered soil moisture dynamics, and consequently regulated soil N responses to precipitation changes. The clay loam soil more effectively buffered water stress during relatively long intervals between precipitation events, particularly after a large rainfall event. Subsequent soil N aqueous and gaseous losses showed either increases or decreases in response to increasing precipitation variability due to complex soil moisture dynamics. For a high rainfall scenario, high precipitation variability resulted in as high as 2.4-, 2.4-, 1.2-, and 13-fold increases in NH{sub 3}, NO, N{sub 2}O and NO{sub 3}{sup -} fluxes, respectively, in clay loam soil. In sandy loam soil, however, NO and N{sub 2}O fluxes decreased by 15% and 28%, respectively, in response to high precipitation variability. Our results demonstrate that soil N cycling responses to increasing precipitation variability depends on precipitation amount and soil texture, and that accurate prediction of future N cycling and gas effluxes requires models with relatively sophisticated representation of the relevant processes.« less

World Reference Base | FAO SOILS PORTAL | Food and Agriculture

Science.gov Websites

> Soil classification > World Reference Base FAO SOILS PORTAL Survey Assessment Biodiversity Management Degradation/Restoration Policies/Governance Publications Soil properties Soil classification World Reference Base FAO legend USDA soil taxonomy Universal soil classification National Systems Numerical

A Wavelet Polarization Decomposition Net Model for Polarimetric SAR Image Classification

NASA Astrophysics Data System (ADS)

He, Chu; Ou, Dan; Yang, Teng; Wu, Kun; Liao, Mingsheng; Chen, Erxue

2014-11-01

In this paper, a deep model based on wavelet texture has been proposed for Polarimetric Synthetic Aperture Radar (PolSAR) image classification inspired by recent successful deep learning method. Our model is supposed to learn powerful and informative representations to improve the generalization ability for the complex scene classification tasks. Given the influence of speckle noise in Polarimetric SAR image, wavelet polarization decomposition is applied first to obtain basic and discriminative texture features which are then embedded into a Deep Neural Network (DNN) in order to compose multi-layer higher representations. We demonstrate that the model can produce a powerful representation which can capture some untraceable information from Polarimetric SAR images and show a promising achievement in comparison with other traditional SAR image classification methods for the SAR image dataset.

A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.

PubMed

Anavi, Yaron; Kogan, Ilya; Gelbart, Elad; Geva, Ofer; Greenspan, Hayit

2015-08-01

In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different features, including binary features, texture features, and deep learning (CNN) features are examined. In addition, two approaches are investigated for the retrieval task. One approach is based on the distance of image descriptors using the above features (hereon termed the "descriptor"-based approach); the second approach ("classification"-based approach) is based on a probability descriptor, generated by a pair-wise classification of each two classes (pathologies) and their decision values using an SVM classifier. Best results are achieved using deep learning features in a classification scheme.

Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

NASA Astrophysics Data System (ADS)

Freitas, Nuno R.; Vieira, Pedro M.; Lima, Estevão; Lima, Carlos S.

2018-02-01

Correct classification of cystoscopy images depends on the interpreter’s experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform (DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value (HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.

General Approach for Rock Classification Based on Digital Image Analysis of Electrical Borehole Wall Images

NASA Astrophysics Data System (ADS)

Linek, M.; Jungmann, M.; Berlage, T.; Clauser, C.

2005-12-01

Within the Ocean Drilling Program (ODP), image logging tools have been routinely deployed such as the Formation MicroScanner (FMS) or the Resistivity-At-Bit (RAB) tools. Both logging methods are based on resistivity measurements at the borehole wall and therefore are sensitive to conductivity contrasts, which are mapped in color scale images. These images are commonly used to study the structure of the sedimentary rocks and the oceanic crust (petrologic fabric, fractures, veins, etc.). So far, mapping of lithology from electrical images is purely based on visual inspection and subjective interpretation. We apply digital image analysis on electrical borehole wall images in order to develop a method, which augments objective rock identification. We focus on supervised textural pattern recognition which studies the spatial gray level distribution with respect to certain rock types. FMS image intervals of rock classes known from core data are taken in order to train textural characteristics for each class. A so-called gray level co-occurrence matrix is computed by counting the occurrence of a pair of gray levels that are a certain distant apart. Once the matrix for an image interval is computed, we calculate the image contrast, homogeneity, energy, and entropy. We assign characteristic textural features to different rock types by reducing the image information into a small set of descriptive features. Once a discriminating set of texture features for each rock type is found, we are able to discriminate the entire FMS images regarding the trained rock type classification. A rock classification based on texture features enables quantitative lithology mapping and is characterized by a high repeatability, in contrast to a purely visual subjective image interpretation. We show examples for the rock classification between breccias, pillows, massive units, and horizontally bedded tuffs based on ODP image data.

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.

Classification of glioblastoma and metastasis for neuropathology intraoperative diagnosis: a multi-resolution textural approach to model the background

NASA Astrophysics Data System (ADS)

Ahmad Fauzi, Mohammad Faizal; Gokozan, Hamza Numan; Elder, Brad; Puduvalli, Vinay K.; Otero, Jose J.; Gurcan, Metin N.

2014-03-01

Brain cancer surgery requires intraoperative consultation by neuropathology to guide surgical decisions regarding the extent to which the tumor undergoes gross total resection. In this context, the differential diagnosis between glioblastoma and metastatic cancer is challenging as the decision must be made during surgery in a short time-frame (typically 30 minutes). We propose a method to classify glioblastoma versus metastatic cancer based on extracting textural features from the non-nuclei region of cytologic preparations. For glioblastoma, these regions of interest are filled with glial processes between the nuclei, which appear as anisotropic thin linear structures. For metastasis, these regions correspond to a more homogeneous appearance, thus suitable texture features can be extracted from these regions to distinguish between the two tissue types. In our work, we use the Discrete Wavelet Frames to characterize the underlying texture due to its multi-resolution capability in modeling underlying texture. The textural characterization is carried out in primarily the non-nuclei regions after nuclei regions are segmented by adapting our visually meaningful decomposition segmentation algorithm to this problem. k-nearest neighbor method was then used to classify the features into glioblastoma or metastasis cancer class. Experiment on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7% for glioblastoma, 87.5% for metastasis and 88.7% overall. Further studies are underway to incorporate nuclei region features into classification on an expanded dataset, as well as expanding the classification to more types of cancers.

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

PubMed

Karacavus, Seyhan; Yılmaz, Bülent; Tasdemir, Arzu; Kayaaltı, Ömer; Kaya, Eser; İçer, Semra; Ayyıldız, Oguzhan

2018-04-01

We investigated the association between the textural features obtained from 18 F-FDG images, metabolic parameters (SUVmax , SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

PubMed

Agner, Shannon C; Soman, Salil; Libfeld, Edward; McDonald, Margie; Thomas, Kathleen; Englander, Sarah; Rosen, Mark A; Chin, Deanna; Nosher, John; Madabhushi, Anant

2011-06-01

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

Description of textures by a structural analysis.

PubMed

Tomita, F; Shirai, Y; Tsuji, S

1982-02-01

A structural analysis system for describing natural textures is introduced. The analyzer automatically extracts the texture elements in an input image, measures their properties, classifies them into some distinctive classes (one ``ground'' class and some ``figure'' classes), and computes the distributions of the gray level, the shape, and the placement of the texture elements in each class. These descriptions are used for classification of texture images. An analysis-by-synthesis method for evaluating texture analyzers is also presented. We propose a synthesizer which generates a texture image based on the descriptions. By comparing the reconstructed image with the original one, we can see what information is preserved and what is lost in the descriptions.

Relation between L-band soil emittance and soil water content

NASA Technical Reports Server (NTRS)

Stroosnijder, L.; Lascano, R. J.; Van Bavel, C. H. M.; Newton, R. W.

1986-01-01

An experimental relation between soil emittance (E) at L-band and soil surface moisture content (M) is compared with a theoretical one. The latter depends on the soil dielectric constant, which is a function of both soil moisture content and of soil texture. It appears that a difference of 10 percent in the surface clay content causes a change in the estimate of M on the order of 0.02 cu m/cu m. This is based on calculations with a model that simulates the flow of water and energy, in combination with a radiative transfer model. It is concluded that an experimental determination of the E-M relation for each soil type is not required, and that a rough estimate of the soil texture will lead to a sufficiently accurate estimate of soil moisture from a general, theoretical relationship obtained by numerical simulation.

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

Effect of a water-based drilling waste on receiving soil properties and plants growth.

PubMed

Saint-Fort, Roger; Ashtani, Sahar

2014-01-01

This investigation was undertaken to determine the relative effects of recommended land spraying while drilling (LWD) loading rate application for a source of water-based drilling waste material on selected soil properties and phytotoxicity. Drilling waste material was obtained from a well where a nitrate gypsum water based product was used to formulate the drilling fluid. The fluid and associated drill cuttings were used as the drilling waste source to conduct the experiment. The study was carried out in triplicate and involved five plant species, four drilling waste loading rates and a representative agricultural soil type in Alberta. Plant growth was monitored for a period of ten days. Drilling waste applied at 10 times above the recommended loading rate improved the growth and germination rate of all plants excluding radish. Loading rates in excess of 40 and 50 times had a deleterious effect on radish, corn and oat but not on alfalfa and barley. Germination rate decreased as waste loading rate increased. Effects on soil physical and chemical properties were more pronounced at the 40 and 50 times exceeding recommended loading rate. Significant changes in soil parameters occurred at the higher rates in terms of increase in soil porosity, pH, EC, hydraulic conductivity, SAR and textural classification. This study indicates that the applications of this type of water based drill cutting if executed at an optimal loading rate, may improve soil quality and results in better plant growth.

Dielectric properties of soils as a function of moisture content

NASA Technical Reports Server (NTRS)

Cihlar, J.; Ulaby, F. T.

1974-01-01

Soil dielectric constant measurements are reviewed and the dependence of the dielectric constant on various soil parameters is determined. Moisture content is given special attention because of its practical significance in remote sensing and because it represents the single most influential parameter as far as soil dielectric properties are concerned. Relative complex dielectric constant curves are derived as a function of volumetric soil water content at three frequencies (1.3 GHz, 4.0 GHz, and 10.0 GHz) for each of three soil textures (sand, loam, and clay). These curves, presented in both tabular and graphical form, were chosen as representative of the reported experimental data. Calculations based on these curves showed that the power reflection coefficient and emissivity, unlike skin depth, vary only slightly as a function of frequency and soil texture.

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.

Role of soil texture on mesquite water relations and response to summer precipitation

Treesearch

Alessandra Fravolini; Kevin R. Hultine; Dan F. Koepke; David G. Williams

2003-01-01

In the arid Southwest United States, monsoon precipitation plays a key role in ecosystem water balance and productivity. The sensitivity of deeply rooted plants to pulses of summer precipitation is, in part, controlled by the interaction between soil texture, precipitation intensity, and plant rooting depth and activity. In this study we evaluated the water relations...

How do earthworms, soil texture and plant composition affect infiltration along an experimental plant diversity gradient in grassland?

PubMed

Fischer, Christine; Roscher, Christiane; Jensen, Britta; Eisenhauer, Nico; Baade, Jussi; Attinger, Sabine; Scheu, Stefan; Weisser, Wolfgang W; Schumacher, Jens; Hildebrandt, Anke

2014-01-01

Infiltration is a key process in determining the water balance, but so far effects of earthworms, soil texture, plant species diversity and their interaction on infiltration capacity have not been studied. We measured infiltration capacity in subplots with ambient and reduced earthworm density nested in plots of different plant species (1, 4, and 16 species) and plant functional group richness and composition (1 to 4 groups; legumes, grasses, small herbs, tall herbs). In summer, earthworm presence significantly increased infiltration, whereas in fall effects of grasses and legumes on infiltration were due to plant-mediated changes in earthworm biomass. Effects of grasses and legumes on infiltration even reversed effects of texture. We propose two pathways: (i) direct, probably by modifying the pore spectrum and (ii) indirect, by enhancing or suppressing earthworm biomass, which in turn influenced infiltration capacity due to change in burrowing activity of earthworms. Overall, the results suggest that spatial and temporal variations in soil hydraulic properties can be explained by biotic processes, especially the presence of certain plant functional groups affecting earthworm biomass, while soil texture had no significant effect. Therefore biotic parameters should be taken into account in hydrological applications.

Net nitrogen mineralization and net nitrification rates in soils following deforestation for pasture across the southwestern Brazilian Amazon Basin landscape.

PubMed

Neill, Christopher; Piccolo, Marisa C; Cerri, Carlos C; Steudler, Paul A; Melillo, Jerry M; Brito, Marciano

1997-04-01

Previous studies of the effect of tropical forest conversion to cattle pasture on soil N dynamics showed that rates of net N mineralization and net nitrification were lower in pastures compared with the original forest. In this study, we sought to determine the generality of these patterns by examining soil inorganic N concentrations, net mineralization and nitrification rates in 6 forests and 11 pastures 3 years old or older on ultisols and oxisols that encompassed a wide variety of soil textures and spanned a 700-km geographical range in the southwestern Brazilian Amazon Basin state of Rondônia. We sampled each site during October-November and April-May. Forest soils had higher extractable NO 3 - -N and total inorganic N concentrations than pasture soils, but substantial NO 3 - -N occurred in both forest and pasture soils. Rates of net N mineralization and net nitrification were higher in forest soils. Greater concentrations of soil organic matter in finer textured soils were associated with greater rates of net N mineralization and net nitrification, but this relationship was true only under native forest vegetation; rates were uniformly low in pastures, regardless of soil type or texture. Net N mineralization and net nitrification rates per unit of total soil organic matter showed no pattern across the different forest sites, suggesting that controls of net N mineralization may be broadly similar across a wide range of soil types. Similar reductions in rates of net N transformations in pastures 3 years old or older across a range of textures on these soils suggest that changes to soil N cycling caused by deforestation for pasture may be Basin-wide in extent. Lower net N mineralization and net nitrification rates in established pastures suggest that annual N losses from largely deforested landscapes may be lower than losses from the original forest. Total ecosystem N losses since deforestation are likely to depend on the balance between lower N loss rates from established pastures and the magnitude and duration of N losses that occur in the years immediately following forest clearing.

Reduction of Root-Knot Nematode, Meloidogyne javanica, and Ozone Mass Transfer in Soil Treated with Ozone

PubMed Central

Qiu, Jinya Jack; Pryor, Alan

2009-01-01

Ozone gas (O3) is a reactive oxidizing agent with biocidal properties. Because of the current phasing out of methyl bromide, investigations on the use of ozone gas as a soil-fumigant were conducted. Ozone gas was produced at a concentration of 1% in air by a conventional electrical discharge O3 generator. Two O3 dosages and three gas flow rates were tested on a sandy loam soil collected from a tomato field that had a resident population of root knot nematodes, Meloidogyne javanica. At dosages equivalent to 50 and 250 kg of O3/ha, M. javanica were reduced by 24% and 68%, and free-living nematodes by 19% and 52%, respectively. The reduction for both M. javanica and free-living nematodes was dosage dependent and flow rate independent. The rates of O3 mass transfer (OMT) through three soils of different texture were greater at low and high moisture levels than at intermediate ones. At any one soil moisture level, the OMT rate varied with soil texture and soil organic matter content. Results suggest that soil texture, moisture, and organic matter content should be considered in determining O3 dosage needed for effective nematode control. PMID:22736821

Biochar Properties Influencing Greenhouse Gas Emissions in Tropical Soils Differing in Texture and Mineralogy.

PubMed

Butnan, Somchai; Deenik, Jonathan L; Toomsan, Banyong; Antal, Michael J; Vityakon, Patma

2016-09-01

The ability of biochar applications to alter greenhouse gases (GHGs) (CO, CH, and NO) has been attracting research interest. However, inconsistent published results necessitate further exploration of potential influencing factors, including biochar properties, biochar rates, soil textures and mineralogy, and their interactions. Two short-term laboratory incubations were conducted to evaluate the effects of different biochars: a biochar with low ash (2.4%) and high-volatile matter (VM) (35.8%) contents produced under low-temperature (350°C) traditional kiln and a biochar with high ash (3.9%) and low-VM (14.7%) contents produced with a high-temperature (800°C) Flash Carbonization reactor and different biochar rates (0, 2, and 4% w/w) on the GHG emissions in a loamy-sand Ultisol and a silty-clay-loam Oxisol. In the coarse-textured, low-buffer Ultisol, cumulative CO and CH emissions increased with increasing VM content of biochars; however, CO emission sharply decreased at 83 μg VM g soil. In the fine-textured, high-buffer Oxisol, there were significant positive effects of VM content on cumulative CO emission without suppression effects. Regarding cumulative NO emission, there were significant positive effects in the Mn-rich Oxisol. Ash-induced increases in soil pH had negative effects on all studied GHG emissions. Possible mechanisms include the roles biochar VM played as microbial substrates, a source of toxic compounds and complexing agents reducing the toxicity of soil aluminum and manganese, and the role of biochar ash in increasing soil pH affecting GHG emissions in these two contrasting soils. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Project implementation : classification of organic soils and classification of marls - training of INDOT personnel.

DOT National Transportation Integrated Search

2012-09-01

This is an implementation project for the research completed as part of the following projects: SPR3005 Classification of Organic Soils : and SPR3227 Classification of Marl Soils. The methods developed for the classification of both soi...

Employing wavelet-based texture features in ammunition classification

NASA Astrophysics Data System (ADS)

Borzino, Ángelo M. C. R.; Maher, Robert C.; Apolinário, José A.; de Campos, Marcello L. R.

2017-05-01

Pattern recognition, a branch of machine learning, involves classification of information in images, sounds, and other digital representations. This paper uses pattern recognition to identify which kind of ammunition was used when a bullet was fired based on a carefully constructed set of gunshot sound recordings. To do this task, we show that texture features obtained from the wavelet transform of a component of the gunshot signal, treated as an image, and quantized in gray levels, are good ammunition discriminators. We test the technique with eight different calibers and achieve a classification rate better than 95%. We also compare the performance of the proposed method with results obtained by standard temporal and spectrographic techniques

Detection of damaged areas caused by the oil extraction in a steppe region using winter landsat imagery

NASA Astrophysics Data System (ADS)

Mjachina, Ksenya; Hu, Zhiyong; Chibilyev, Alexander

2018-01-01

Oil production in a steppe region disturbs the landscape and damages the steppe ecosystem. The objective of this research was to detect areas damaged by oil production in an oil field within the Russian Volga-Ural steppe region using winter Landsat imagery. We developed a practicable and effective approach using winter snow season multispectral Landsat satellite imagery. To this end, we applied seven algorithms of spectral or texture-based transformation: K-means, maximum likelihood estimation, topsoil grain size index, soil brightness, normalized differential snow index, tasselled cap, and co-occurrence measures. The co-occurrence texture measure variance shows the optimal result of identifying damaged areas. The unique feature of our method is that it can differentiate damaged areas from the bare soil of cropland within a cold steppe region where the area damaged by oil production is mixed with bare (fallow) croplands that have a polygonal shape similar to well pads. Such similarities can lead to confusion in object-based classification. Using the co-occurrence measures, we found that from 1988 to 2015, damaged area is nearly three times as big in the peak period of the oil field development (2001 and 2009) as in 1988. Landscape fragmentation also peaked in 2001 and 2009. Our approach for this project is useful and cost effective regular monitoring of damages from oil production for both the Volga-Ural steppe region and other cold steppe regions.

Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?

PubMed

Yang, Fan; Xu, Ying-Ying; Shen, Hong-Bin

2014-01-01

Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.

Composition and diversity of weed communities in Al-Jouf province, northern Saudi Arabia

PubMed Central

Gomaa, Nasr H.

2012-01-01

The aim of this study was to identify the main weed communities in Al-Jouf province in northern Saudi Arabia. Moreover, the composition and diversity of these communities were studied in relation to soil variables and crop type. Some 54 stands representing olive orchards, date palm orchards, wheat crop and watermelon crop were studied, using ten quadrats (1 × 1 m) per stand. A total of 71 species belonging to 22 families and 61 genera were observed. The classification of vegetation using the Two Way Indicator Species Analysis (TWINSPAN) resulted in the recognition of four vegetation groups representing wheat crop, orchards in winter season, orchards in summer season and watermelon crop. These results suggested the importance of both crop and season for the formation of weed community. Detrended Correspondence Analysis (DCA) showed that these groups are clearly distinguished by the first two DCA axes. The species richness was higher in both olive and date palm orchards than in wheat and watermelon crops. This pattern of species richness could be related to farm management practices and habitat micro-heterogeneity. Soil electrical conductivity, organic carbon and soil texture showed significant correlations with species richness and the cover values of some dominant species, suggesting the significant role of soil characteristics in weed community structure and diversity. PMID:23961198

Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices.

PubMed

Padma, A; Sukanesh, R

2013-01-01

A computer software system is designed for the segmentation and classification of benign from malignant tumour slices in brain computed tomography (CT) images. This paper presents a method to find and select both the dominant run length and co-occurrence texture features of region of interest (ROI) of the tumour region of each slice to be segmented by Fuzzy c means clustering (FCM) and evaluate the performance of support vector machine (SVM)-based classifiers in classifying benign and malignant tumour slices. Two hundred and six tumour confirmed CT slices are considered in this study. A total of 17 texture features are extracted by a feature extraction procedure, and six features are selected using Principal Component Analysis (PCA). This study constructed the SVM-based classifier with the selected features and by comparing the segmentation results with the experienced radiologist labelled ground truth (target). Quantitative analysis between ground truth and segmented tumour is presented in terms of segmentation accuracy, segmentation error and overlap similarity measures such as the Jaccard index. The classification performance of the SVM-based classifier with the same selected features is also evaluated using a 10-fold cross-validation method. The proposed system provides some newly found texture features have an important contribution in classifying benign and malignant tumour slices efficiently and accurately with less computational time. The experimental results showed that the proposed system is able to achieve the highest segmentation and classification accuracy effectiveness as measured by jaccard index and sensitivity and specificity.

Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

NASA Astrophysics Data System (ADS)

Kato, Noriji; Fukui, Motofumi; Isozaki, Takashi

2009-02-01

Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize texture analysis approaches with second and higher order statistics, and show successful classification result among various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives, which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes (ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.

Soil-Structural Stability as Affected by Clay Mineralogy, Soil Texture and Polyacrylamide Application

USDA-ARS?s Scientific Manuscript database

Soil-structural stability (expressed in terms of aggregate stability and pore size distribution) depends on (i) soil inherent properties, (ii) extrinsic condition prevailing in the soil that may vary temporally and spatially, and (iii) addition of soil amendments. Different soil management practices...

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia.

PubMed

Diamond, James; Anderson, Neil H; Bartels, Peter H; Montironi, Rodolfo; Hamilton, Peter W

2004-09-01

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at x 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 x 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.

Automated Texture Classification of the Mawrth Vallis Landing Site Region

NASA Astrophysics Data System (ADS)

Parente, M.; Bayley, L.; Hunkins, L.; McKeown, N. K.; Bishop, J. L.

2009-12-01

Supervised classification techniques have been developed to discriminate geomorphologic units in HiRISE images of Mawrth Vallis on Mars, one of the MSL candidate landing sites. A variety of clay minerals that indicate water was once present have been identified in the ancient bedrock at Mawrth Vallis [1-7]. These clay-rich rocks exhibit distinct surface textures in HiRISE images, where the nontronite-bearing unit consists of two primary textures: 2-5 m irregular inverted polygons and irregular parallel fracture sets ([8,13], Fig. b-c). In contrast, the montmorillonite-bearing unit consists of 0.5-1.5 m regular polygons ([8,13], Fig. e). We also characterized dunes (Fig. d), and the spectrally unremarkable caprock unit (Fig. a). Classification of these textures was performed by extracting discriminatory features from gray-level run length matrices (GLRLMs) [9], gray-level co-occurrence matrices (GLCMs) [10], and semivariograms [11] calculated for small blocks of data in HiRISE images. Preliminary results using an algorithm containing eight of these classification features produced a texture classification technique that is 85 percent accurate. The discriminant analysis (e.g. [12]) classifier we used modeled a linear discriminant function for each class based on the training feature vectors for that class. The test vector with the largest value for its discriminant function was then assigned to each class. We assumed linear functions were acceptable for small training sets and we performed automated selection in order to identify the most discriminative features for the textures in Mawrth Vallis. Continued efforts are underway to test and refine this procedure in order to optimize texture recognition on a broader collection of textures, representing additional surface components from Mawrth Vallis and other landing sites on Mars. [1] Bibring, J.-P., et al. (2005) Science, 307, 1576-1581. [2] Poulet, F., et al. (2005) Nature, 438, 632-627. [3] Bishop, J. L., et al. (2008) Science, 321, 830-833. [4] Wray, J. J., et al. (2008) GRL, 35, L12202. [5] Loizeau, D., et al. (2009) Icarus, (in press). [6] McKeown, N. K., et al. (2009) JGR- Planets, (in press). [7] Noe Dobrea, E. Z., et al. (2009) JGR- Planets, (in revision). [8] McKeown, N. K. et al. (2009) LPSC abs. #2433. [9] Galloway, M. M., (1975),Computer Graphics and Image Processing 4, 172-179. [10] Haralick, R. M., (1973) IEEE Trans. on Systems, Man and Cybernetics 3, 610-621. [11] Curran, P. J., Remote Sensing of Environment 24, 493-507, 1988. [12] Hastie T., et al. (2005), The elements of statistical learning. Springer. [13] McKeown, N. K., et al. (2009) AGU

Visual Representations of Texture

DTIC Science & Technology

1988-12-15

mm 󈧏 I I I In o,. ITY CkASISIFICATION OF THIS G , - - -REPORT DOCUMENTATION PAGE la. REPORT SECURITY CLASSIFICATION b . RESTRICTIVE MARKINGS ,ij...experiments investigating the interaction of size and contrast in texture segregation,( b ) compared our experimental results with the calculated outputs of a M...investigating the interaction of size and contrast in texture segregation, ( b ) compared our experimental results with the calculated outputs of a 2D

Independent Component Analysis of Textures

NASA Technical Reports Server (NTRS)

Manduchi, Roberto; Portilla, Javier

2000-01-01

A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.

Quantifying Urban Texture in Nairobi, Kenya and its Implications for Understanding Natural Hazard Impact

NASA Astrophysics Data System (ADS)

Taylor, Faith E.; Malamud, Bruce D.; Millington, James D. A.

2016-04-01

The configuration of infrastructure networks such as roads, drainage and power lines can both affect and be affected by natural hazards such as earthquakes, intense rain, wildfires and extreme temperatures. In this paper, we present and compare two methods to quantify urban topology on approximate scales of 0.0005 km2 to 10 km2 and create classifications of different 'urban textures' that relate to risk of natural hazard impact in an area. The methods we use focus on applicability in urban developing country settings, where access to high resolution and high quality data may be difficult. We use the city of Nairobi, Kenya to trial these methods. Nairobi has a population >3 million, and is a mix of informal settlements, residential and commercial development. The city and its immediate surroundings are subject to a variety of natural hazards such as floods, landslides, fires, drought, hail, heavy wind and extreme temperatures; all of these hazards can occur singly, but also have the potential for one to trigger another, thus providing a 'cascade' of hazards, or for two of the hazards to occur spatially and temporally near each other and interact. We use two measures of urban texture: (i) Street block textures, (ii) Google Earth land cover textures. Street block textures builds on the methodology of Louf and Barthelemy (2014) and uses Open Street Map data to analyse the shape, size, complexity and pattern of individual blocks of land created by fully enclosed loops of the major and minor road network of Nairobi. We find >4000 of these blocks ranging in size from approximately 0.0005 km2 to 10 km2, with approximately 5 classifications of urban texture. Google Earth land cover texture is a visual classification of homogeneous parcels of land performed in Google Earth using high-resolution airborne imagery and a qualitative criteria for each land cover type. Using the Google Earth land cover texture method, we identify >40 'urban textures' based on visual characteristics such as colour, texture, shadow and setting and have created a clear criteria for classifying an area based on its visual characteristics. These two methods for classifying urban texture in Nairobi are compared in a GIS and in the field to investigate whether there is a link between the visual appearance of an area and its network topology. From these urban textures, we may start to identify areas where (a) urban texture types may indicate a relative propensity to certain hazards and their interactions and (b) urban texture types that may increase or decrease the impact of a hazard that occurs in that area.

Caracterisation des occupations du sol en milieu urbain par imagerie radar

NASA Astrophysics Data System (ADS)

Codjia, Claude

This study aims to test the relevance of medium and high-resolution SAR images on the characterization of the types of land use in urban areas. To this end, we have relied on textural approaches based on second-order statistics. Specifically, we look for texture parameters most relevant for discriminating urban objects. We have used in this regard Radarsat-1 in fine polarization mode and Radarsat-2 HH fine mode in dual and quad polarization and ultrafine mode HH polarization. The land uses sought were dense building, medium density building, low density building, industrial and institutional buildings, low density vegetation, dense vegetation and water. We have identified nine texture parameters for analysis, grouped into families according to their mathematical definitions in a first step. The parameters of similarity / dissimilarity include Homogeneity, Contrast, the Differential Inverse Moment and Dissimilarity. The parameters of disorder are Entropy and the Second Angular Momentum. The Standard Deviation and Correlation are the dispersion parameters and the Average is a separate family. It is clear from experience that certain combinations of texture parameters from different family used in classifications yield good results while others produce kappa of very little interest. Furthermore, we realize that if the use of several texture parameters improves classifications, its performance ceils from three parameters. The calculation of correlations between the textures and their principal axes confirm the results. Despite the good performance of this approach based on the complementarity of texture parameters, systematic errors due to the cardinal effects remain on classifications. To overcome this problem, a radiometric compensation model was developed based on the radar cross section (SER). A radar simulation from the digital surface model of the environment allowed us to extract the building backscatter zones and to analyze the related backscatter. Thus, we were able to devise a strategy of compensation of cardinal effects solely based on the responses of the objects according to their orientation from the plane of illumination through the radar's beam. It appeared that a compensation algorithm based on the radar cross section was appropriate. Some examples of the application of this algorithm on HH polarized RADARSAT-2 images are presented as well. Application of this algorithm will allow considerable gains with regard to certain forms of automation (classification and segmentation) at the level of radar imagery thus generating a higher level of quality in regard to visual interpretation. Application of this algorithm on RADARSAT-1 and RADARSAT-2 images with HH, HV, VH, and VV polarisations helped make considerable gains and eliminate most of the classification errors due to the cardinal effects.

Effect of buffer strips and soil texture on runoff losses of flufenacet and isoxaflutole from maize fields.

PubMed

Milan, Marco; Ferrero, Aldo; Letey, Marilisa; De Palo, Fernando; Vidotto, Francesco

2013-01-01

The influence of buffer strips and soil texture on runoff of flufenacet and isoxaflutole was studied for two years in Northern Italy. The efficacy of buffer strips was evaluated on six plots characterized by different soil textures; two plots had Riva soil (18.6% sand, 63.1% silt, 18.3% clay) while the remaining four plots had Tetto Frati (TF) soil (37.1% sand, 57% silt, 5.9% clay). Additionally, the width of the buffer strips, constituted of spontaneous vegetation grown after crop sowing, was also compared for their ability to abate runoff waters. Chemical residues in water following runoff events were investigated, as well as their dissipation in the soil. After the first runoff events, concentrations of herbicides in water samples collected from Riva plots were as much as four times lower in waters from TF plots. On average of two growing seasons, the field half-life of flufenacet in the upper soil layer (5 cm) ranged between 8.1 and 12.8 days in Riva soil, 8.5 and 9.3 days in TF soil. Isoxaflutole field half-life was less than 1 day. The buffer strip was very affective by the uniformity of the vegetative cover, particularly, at the beginning of the season. In TF plots, concentration differences were generally due to the presence or absence of the buffer strip, regardless of its width.

Hydraulic lift through transpiration suppression in shrubs from two arid ecosystems: patterns and control mechanisms.

PubMed

Prieto, Iván; Martínez-Tillería, Karina; Martínez-Manchego, Luis; Montecinos, Sonia; Pugnaire, Francisco I; Squeo, Francisco A

2010-08-01

Hydraulic lift (HL) is the passive movement of water through the roots from deep wet to dry shallow soil layers when stomata are closed. HL has been shown in different ecosystems and species, and it depends on plant physiology and soil properties. In this study we explored HL patterns in several arid land shrubs, and developed a simple model to simulate the temporal evolution and magnitude of HL during a soil drying cycle under relatively stable climatic conditions. This model was then used to evaluate the influence of soil texture on the quantity of water lifted by shrubs in different soil types. We conducted transpiration suppression experiments during spring 2005 in Chile and spring 2008 in Spain on five shrub species that performed HL, Flourensia thurifera, Senna cumingii and Pleocarphus revolutus (Chile), Retama sphaerocarpa and Artemisia barrelieri (Spain). Shrubs were covered with a black, opaque plastic fabric for a period of 48-72 h, and soil water potential was recorded at different depths under the shrubs. While the shrubs remained covered, water potential continuously increased in shallow soil layers until the cover was removed. The model output indicated that the amount of water lifted by shrubs is heavily dependent on soil texture, as shrubs growing in loamy soils redistributed up to 3.6 times more water than shrubs growing on sandy soils. This could be an important consideration for species growing in soils with different textures, as their ability to perform HL would be context dependent.

Factors affecting HCH and DDT in soils around watersheds of Beijing reservoirs, China.

PubMed

Hu, Wenyou; Lu, Yonglong; Wang, Tieyu; Luo, Wei; Zhang, Xiang; Geng, Jing; Wang, Guang; Shi, Yajuan; Jiao, Wentao; Chen, Chunli

2010-04-01

The factors that influence the dynamics of hexachlorocyclohexane (HCH) and dichlorodiphenyltrichloroethane (DDT) in soils around the watersheds of Beijing reservoirs were examined. Compared with other studies on HCH and DDT in soils and established reference values, the concentrations of HCH and DDT in soils around our study area were relatively low. The relationships between HCH and DDT concentrations and land use, soil texture, and soil properties were discussed. HCH and DDT concentrations were higher in arable soils than those in uncultivated fallow soils. Although land use was the most important factor affecting HCH and DDT residues, additional factors such as soil texture and soil total organic carbon were also involved in pesticide retention in soils. The results indicated that the historical agricultural applications of HCH and DDT were the major source of their residues. Atmospheric deposition, as well as long-distance transportation and inputs from surrounding weathered agricultural soils may also serve as important sources of HCH and DDT residues in the watersheds.

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

PubMed

Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

2017-08-01

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Dynamics And Remediation Of Fine Textured Soils And Ground Water Contaminated With Salts And Chlorinated Organic Compounds

NASA Astrophysics Data System (ADS)

Murata, Alison; Naeth, M. Anne

2017-04-01

Soil and ground water are frequently contaminated by industrial activities, posing a potential risk to human and environmental health and limiting land use. Proper site management and remediation treatments can return contaminated areas to safe and useful states. Most remediation research focuses on single contaminants in coarse and medium textured soils. Contaminant mixtures are common and make remediation efforts complex due to differing chemical properties. Remediation in fine textured soils is difficult since their low hydraulic conductivities hinder addition of amendments into and removal of contaminated media out of the impacted zone. The objective of this research is to assess contaminant dynamics and potential remediation techniques for fine textured soil and ground water impacted by multiple contaminants in Edmonton, Alberta, Canada. The University of Alberta's Ellerslie Waste Management Facility was used to process liquid laboratory waste from 1972 to 2007. A waste water pond leak prior to 1984 resulted in salt and chlorinated organic compound contamination. An extensive annual ground water monitoring data set for the site is available since 1988. Analytical parameters include pH, electrical conductivity, major ions, volatile organic compounds, and metals. Data have been compared to Alberta Tier 1 Soil and Groundwater Remediation Guidelines to identify exceedances. The parameters of greatest concern, based on magnitude and frequency of detection, are electrical conductivity, sodium, chloride, chloroform, and dichloromethane. Spatial analyses of the data show that the contamination is focused in and down gradient of the former waste water pond. Temporal analyses show different trends depending on monitoring well location. Laboratory column experiments were used to assess leaching as a potential treatment for salt contamination in fine textured soils. Saturated hydraulic conductivity was measured for seven soils from two depth intervals with or without calcium nitrate amendment. Results show all factors and interactions were significant. Leachate electrical conductivity was measured for five soils from two depth intervals with or without calcium nitrate amendment for eight sequential pore volumes. Results show highest electrical conductivity for the initial pore volume and decreasing electrical conductivities for subsequent pore volumes. Laboratory microcosm experiments are being used to assess anaerobic biodegradation as a potential treatment for chloroform contamination in fine textured soils and ground water. The first experiment investigates the bioremediation potential for indigenous microorganisms using acetate, lactate, canola oil, nitrate, and sulfate as carbon source or terminal electron acceptor amendments. The second experiment investigates the bioremediation potential for microorganisms from a secondary contaminated site which could be used as a microbial inoculation source. The same amendments except lactate were used. Headspace chloroform analysis results do not indicate the occurrence of biodegradation in any treatment meaning that bioremediation may not be a viable option. Results from this research will be used to conduct a risk assessment for the site incorporating site and contaminant characteristics. A management and remediation plan will be developed so the land can be safely used and the university's lease can be terminated. The research will contribute to our knowledge on remediation with contaminant mixtures and fine textured soils.

Development of soil taxation and soil classification as furthered by the Austrian Soil Science Society

NASA Astrophysics Data System (ADS)

Baumgarten, Andreas

2013-04-01

Soil taxation and soil classification are important drivers of soil science in Austria. However, the tasks are quite different: whereas soil taxation aims at the evaluation of the productivity potential of the soil, soil classification focusses on the natural development and - especially nowadays - on functionality of the soil. Since the foundation of the Austrian Soil Science Society (ASSS), representatives both directions of the description of the soil have been involved in the common actions of the society. In the first years it was a main target to improve and standardize field descriptions of the soil. Although both systems differ in the general layout, the experts should comply with identical approaches. According to this work, a lot of effort has been put into the standardization of the soil classification system, thus ensuring a common basis. The development, state of the art and further development of both classification and taxation systems initiated and carried out by the ASSS will be shown.

Feasibility of opportunistic osteoporosis screening in routine contrast-enhanced multi detector computed tomography (MDCT) using texture analysis.

PubMed

Mookiah, M R K; Rohrmeier, A; Dieckmeyer, M; Mei, K; Kopp, F K; Noel, P B; Kirschke, J S; Baum, T; Subburaj, K

2018-04-01

This study investigated the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. The results showed an acceptable reproducibility of texture features, and these features could discriminate healthy/osteoporotic fracture cohort with an accuracy of 83%. This aim of this study is to investigate the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. We performed texture analysis at the spine in routine MDCT exams and investigated the effect of intravenous contrast medium (IVCM) (n = 7), slice thickness (n = 7), the long-term reproducibility (n = 9), and the ability to differentiate healthy/osteoporotic fracture cohort (n = 9 age and gender matched pairs). Eight texture features were extracted using gray level co-occurrence matrix (GLCM). The independent sample t test was used to rank the features of healthy/fracture cohort and classification was performed using support vector machine (SVM). The results revealed significant correlations between texture parameters derived from MDCT scans with and without IVCM (r up to 0.91) slice thickness of 1 mm versus 2 and 3 mm (r up to 0.96) and scan-rescan (r up to 0.59). The performance of the SVM classifier was evaluated using 10-fold cross-validation and revealed an average classification accuracy of 83%. Opportunistic osteoporosis screening at the spine using specific texture parameters (energy, entropy, and homogeneity) and SVM can be performed in routine contrast-enhanced MDCT exams.

Deep-learning derived features for lung nodule classification with limited datasets

NASA Astrophysics Data System (ADS)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

Computer-aided diagnosis with textural features for breast lesions in sonograms.

PubMed

Chen, Dar-Ren; Huang, Yu-Len; Lin, Sheng-Hsiung

2011-04-01

Computer-aided diagnosis (CAD) systems provided second beneficial support reference and enhance the diagnostic accuracy. This paper was aimed to develop and evaluate a CAD with texture analysis in the classification of breast tumors for ultrasound images. The ultrasound (US) dataset evaluated in this study composed of 1020 sonograms of region of interest (ROI) subimages from 255 patients. Two-view sonogram (longitudinal and transverse views) and four different rectangular regions were utilized to analyze each tumor. Six practical textural features from the US images were performed to classify breast tumors as benign or malignant. However, the textural features always perform as a high dimensional vector; high dimensional vector is unfavorable to differentiate breast tumors in practice. The principal component analysis (PCA) was used to reduce the dimension of textural feature vector and then the image retrieval technique was performed to differentiate between benign and malignant tumors. In the experiments, all the cases were sampled with k-fold cross-validation (k=10) to evaluate the performance with receiver operating characteristic (ROC) curve. The area (A(Z)) under the ROC curve for the proposed CAD system with the specific textural features was 0.925±0.019. The classification ability for breast tumor with textural information is satisfactory. This system differentiates benign from malignant breast tumors with a good result and is therefore clinically useful to provide a second opinion. Copyright © 2010 Elsevier Ltd. All rights reserved.

Extracting built-up areas from TerraSAR-X data using object-oriented classification method

NASA Astrophysics Data System (ADS)

Wang, SuYun; Sun, Z. C.

2017-02-01

Based on single-polarized TerraSAR-X, the approach generates homogeneous segments on an arbitrary number of scale levels by applying a region-growing algorithm which takes the intensity of backscatter and shape-related properties into account. The object-oriented procedure consists of three main steps: firstly, the analysis of the local speckle behavior in the SAR intensity data, leading to the generation of a texture image; secondly, a segmentation based on the intensity image; thirdly, the classification of each segment using the derived texture file and intensity information in order to identify and extract build-up areas. In our research, the distribution of BAs in Dongying City is derived from single-polarized TSX SM image (acquired on 17th June 2013) with average ground resolution of 3m using our proposed approach. By cross-validating the random selected validation points with geo-referenced field sites, Quick Bird high-resolution imagery, confusion matrices with statistical indicators are calculated and used for assessing the classification results. The results demonstrate that an overall accuracy 92.89 and a kappa coefficient of 0.85 could be achieved. We have shown that connect texture information with the analysis of the local speckle divergence, combining texture and intensity of construction extraction is feasible, efficient and rapid.

Land cover classification accuracy from electro-optical, X, C, and L-band Synthetic Aperture Radar data fusion

NASA Astrophysics Data System (ADS)

Hammann, Mark Gregory

The fusion of electro-optical (EO) multi-spectral satellite imagery with Synthetic Aperture Radar (SAR) data was explored with the working hypothesis that the addition of multi-band SAR will increase the land-cover (LC) classification accuracy compared to EO alone. Three satellite sources for SAR imagery were used: X-band from TerraSAR-X, C-band from RADARSAT-2, and L-band from PALSAR. Images from the RapidEye satellites were the source of the EO imagery. Imagery from the GeoEye-1 and WorldView-2 satellites aided the selection of ground truth. Three study areas were chosen: Wad Medani, Sudan; Campinas, Brazil; and Fresno- Kings Counties, USA. EO imagery were radiometrically calibrated, atmospherically compensated, orthorectifed, co-registered, and clipped to a common area of interest (AOI). SAR imagery were radiometrically calibrated, and geometrically corrected for terrain and incidence angle by converting to ground range and Sigma Naught (?0). The original SAR HH data were included in the fused image stack after despeckling with a 3x3 Enhanced Lee filter. The variance and Gray-Level-Co-occurrence Matrix (GLCM) texture measures of contrast, entropy, and correlation were derived from the non-despeckled SAR HH bands. Data fusion was done with layer stacking and all data were resampled to a common spatial resolution. The Support Vector Machine (SVM) decision rule was used for the supervised classifications. Similar LC classes were identified and tested for each study area. For Wad Medani, nine classes were tested: low and medium intensity urban, sparse forest, water, barren ground, and four agriculture classes (fallow, bare agricultural ground, green crops, and orchards). For Campinas, Brazil, five generic classes were tested: urban, agriculture, forest, water, and barren ground. For the Fresno-Kings Counties location 11 classes were studied: three generic classes (urban, water, barren land), and eight specific crops. In all cases the addition of SAR to EO resulted in higher overall classification accuracies. In many cases using more than a single SAR band also improved the classification accuracy. There was no single best SAR band for all cases; for specific study areas or LC classes, different SAR bands were better. For Wad Medani, the overall accuracy increased nearly 25% over EO by using all three SAR bands and GLCM texture. For Campinas, the improvement over EO was 4.3%; the large areas of vegetation were classified by EO with good accuracy. At Fresno-Kings Counties, EO+SAR fusion improved the overall classification accuracy by 7%. For times or regions where EO is not available due to extended cloud cover, classification with SAR is often the only option; note that SAR alone typically results in lower classification accuracies than when using EO or EO-SAR fusion. Fusion of EO and SAR was especially important to improve the separability of orchards from other crops, and separating urban areas with buildings from bare soil; those classes are difficult to accurately separate with EO. The outcome of this dissertation contributes to the understanding of the benefits of combining data from EO imagery with different SAR bands and SAR derived texture data to identify different LC classes. In times of increased public and private budget constraints and industry consolidation, this dissertation provides insight as to which band packages could be most useful for increased accuracy in LC classification.

Classification of human carcinoma cells using multispectral imagery

NASA Astrophysics Data System (ADS)

Ćinar, Umut; Y. Ćetin, Yasemin; Ćetin-Atalay, Rengul; Ćetin, Enis

2016-03-01

In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options.

Classification of JERS-1 Image Mosaic of Central Africa Using A Supervised Multiscale Classifier of Texture Features

NASA Technical Reports Server (NTRS)

Saatchi, Sassan; DeGrandi, Franco; Simard, Marc; Podest, Erika

1999-01-01

In this paper, a multiscale approach is introduced to classify the Japanese Research Satellite-1 (JERS-1) mosaic image over the Central African rainforest. A series of texture maps are generated from the 100 m mosaic image at various scales. Using a quadtree model and relating classes at each scale by a Markovian relationship, the multiscale images are classified from course to finer scale. The results are verified at various scales and the evolution of classification is monitored by calculating the error at each stage.

Topographic Controls on Spatial Patterns of Soil Texture and Moisture in a Semi-arid Montane Catchment with Aspect-Dependent Vegetation

NASA Astrophysics Data System (ADS)

Lehman, B. M.; Niemann, J. D.

2008-12-01

Soil moisture exerts significant control over the partitioning of latent and sensible energy fluxes, the magnitude of both vertical and lateral water fluxes, the physiological and water-use characteristics of vegetation, and nutrient cycling. Considerable progress has been made in determining how soil characteristics, topography, and vegetation influence spatial patterns of soil moisture in humid environments at the catchment, hillslope, and plant scales. However, understanding of the controls on soil moisture patterns beyond the plant scale in semi-arid environments remains more limited. This study examines the relationships between the spatial patterns of near surface soil moisture (upper 5 cm), terrain indices, and soil properties in a small, semi-arid, montane catchment. The 8 ha catchment, located in the Cache La Poudre River Canyon in north-central Colorado, has a total relief of 115 m and an average elevation of 2193 m. It is characterized by steep slopes and shallow, gravelly/sandy soils with scattered granite outcroppings. Depth to bedrock ranges from 0 m to greater than 1 m. Vegetation in the catchment is highly correlated with topographic aspect. In particular, north-facing hillslopes are predominately vegetated by ponderosa pines, while south-facing slopes are mostly vegetated by several shrub species. Soil samples were collected at a 30 m resolution to characterize soil texture and bulk density, and several datasets consisting of more than 300 point measurements of soil moisture were collected using time domain reflectometry (TDR) between Fall 2007 and Summer 2008 at a 15 m resolution. Results from soil textural analysis performed with sieving and the ASTM standard hydrometer method show that soil texture is finer on the north-facing hillslope than on the south-facing hillslope. Cos(aspect) is the best univariate predictor of silts, while slope is the best predictor of coarser fractions up to fine gravel. Bulk density increases with depth but shows no significant relationship with topographic indices. When the catchment average soil moisture is low, the variance of soil moisture increases with the average. When the average is high, the variance remains relatively constant. Little of the variation in soil moisture is explained by topographic indices when the catchment is either very wet or dry; however, when the average soil moisture takes on intermediate values, cos(aspect) is consistently the best predictor among the terrain indices considered.

Mammographic phenotypes of breast cancer risk driven by breast anatomy

NASA Astrophysics Data System (ADS)

Gastounioti, Aimilia; Oustimov, Andrew; Hsieh, Meng-Kang; Pantalone, Lauren; Conant, Emily F.; Kontos, Despina

2017-03-01

Image-derived features of breast parenchymal texture patterns have emerged as promising risk factors for breast cancer, paving the way towards personalized recommendations regarding women's cancer risk evaluation and screening. The main steps to extract texture features of the breast parenchyma are the selection of regions of interest (ROIs) where texture analysis is performed, the texture feature calculation and the texture feature summarization in case of multiple ROIs. In this study, we incorporate breast anatomy in these three key steps by (a) introducing breast anatomical sampling for the definition of ROIs, (b) texture feature calculation aligned with the structure of the breast and (c) weighted texture feature summarization considering the spatial position and the underlying tissue composition of each ROI. We systematically optimize this novel framework for parenchymal tissue characterization in a case-control study with digital mammograms from 424 women. We also compare the proposed approach with a conventional methodology, not considering breast anatomy, recently shown to enhance the case-control discriminatory capacity of parenchymal texture analysis. The case-control classification performance is assessed using elastic-net regression with 5-fold cross validation, where the evaluation measure is the area under the curve (AUC) of the receiver operating characteristic. Upon optimization, the proposed breast-anatomy-driven approach demonstrated a promising case-control classification performance (AUC=0.87). In the same dataset, the performance of conventional texture characterization was found to be significantly lower (AUC=0.80, DeLong's test p-value<0.05). Our results suggest that breast anatomy may further leverage the associations of parenchymal texture features with breast cancer, and may therefore be a valuable addition in pipelines aiming to elucidate quantitative mammographic phenotypes of breast cancer risk.

Different approaches for the texture classification of a remote sensing image bank

NASA Astrophysics Data System (ADS)

Durand, Philippe; Brunet, Gerard; Ghorbanzadeh, Dariush; Jaupi, Luan

2018-04-01

In this paper, we summarize and compare two different approaches used by the authors, to classify different natural textures. The first approach, which is simple and inexpensive in computing time, uses a data bank image and an expert system able to classify different textures from a number of rules established by discipline specialists. The second method uses the same database and a neural networks approach.

Effect of slice thickness on brain magnetic resonance image texture analysis

PubMed Central

2010-01-01

Background The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients. Methods We averaged the intensities of three consecutive 1-mm slices to simulate 3-mm slices. Two hundred sixty-four texture parameters were calculated for both the original and the averaged slices. Wilcoxon's signed ranks test was used to find differences between the regions of interest representing white matter and multiple sclerosis plaques. Linear and nonlinear discriminant analyses were applied with several separate training and test sets to determine the actual classification accuracy. Results Only moderate differences in distributions of the texture parameter value for 1-mm and simulated 3-mm-thick slices were found. Our study also showed that white matter areas are well separable from multiple sclerosis plaques even if the slice thickness differs between training and test sets. Conclusions Three-millimeter-thick magnetic resonance image slices acquired with a 1.5 T clinical magnetic resonance scanner seem to be sufficient for texture analysis of multiple sclerosis plaques and white matter tissue. PMID:20955567

Can Infrared Spectroscopy Be Used to Measure Change in Potassium Nitrate Concentration as a Proxy for Soil Particle Movement?

PubMed Central

Luleva, Mila Ivanova; van der Werff, Harald; Jetten, Victor; van der Meer, Freek

2011-01-01

Displacement of soil particles caused by erosion influences soil condition and fertility. To date, the cesium 137 isotope (137Cs) technique is most commonly used for soil particle tracing. However when large areas are considered, the expensive soil sampling and analysis present an obstacle. Infrared spectral measurements would provide a solution, however the small concentrations of the isotope do not influence the spectral signal sufficiently. Potassium (K) has similar electrical, chemical and physical properties as Cs. Our hypothesis is that it can be used as possible replacement in soil particle tracing. Soils differing in texture were sampled for the study. Laboratory soil chemical analyses and spectral sensitivity analyses were carried out to identify the wavelength range related to K concentration. Different concentrations of K fertilizer were added to soils with varying texture properties in order to establish spectral characteristics of the absorption feature associated with the element. Changes in position of absorption feature center were observed at wavelengths between 2,450 and 2,470 nm, depending on the amount of fertilizer applied. Other absorption feature parameters (absorption band depth, width and area) were also found to change with K concentration with coefficient of determination between 0.85 and 0.99. Tracing soil particles using K fertilizer and infrared spectral response is considered suitable for soils with sandy and sandy silt texture. It is a new approach that can potentially grow to a technique for rapid monitoring of soil particle movement over large areas. PMID:22163843

Temporal evolution of soil moisture statistical fractal and controls by soil texture and regional groundwater flow

NASA Astrophysics Data System (ADS)

Ji, Xinye; Shen, Chaopeng; Riley, William J.

2015-12-01

Soil moisture statistical fractal is an important tool for downscaling remotely-sensed observations and has the potential to play a key role in multi-scale hydrologic modeling. The fractal was first introduced two decades ago, but relatively little is known regarding how its scaling exponents evolve in time in response to climatic forcings. Previous studies have neglected the process of moisture re-distribution due to regional groundwater flow. In this study we used a physically-based surface-subsurface processes model and numerical experiments to elucidate the patterns and controls of fractal temporal evolution in two U.S. Midwest basins. Groundwater flow was found to introduce large-scale spatial structure, thereby reducing the scaling exponents (τ), which has implications for the transferability of calibrated parameters to predict τ. However, the groundwater effects depend on complex interactions with other physical controls such as soil texture and land use. The fractal scaling exponents, while in general showing a seasonal mode that correlates with mean moisture content, display hysteresis after storm events that can be divided into three phases, consistent with literature findings: (a) wetting, (b) re-organizing, and (c) dry-down. Modeling experiments clearly show that the hysteresis is attributed to soil texture, whose "patchiness" is the primary contributing factor. We generalized phenomenological rules for the impacts of rainfall, soil texture, groundwater flow, and land use on τ evolution. Grid resolution has a mild influence on the results and there is a strong correlation between predictions of τ from different resolutions. Overall, our results suggest that groundwater flow should be given more consideration in studies of the soil moisture statistical fractal, especially in regions with a shallow water table.

Impact of Surface Roughness and Soil Texture on Mineral Dust Emission Fluxes Modeling

NASA Technical Reports Server (NTRS)

Menut, Laurent; Perez, Carlos; Haustein, Karsten; Bessagnet, Bertrand; Prigent, Catherine; Alfaro, Stephane

2013-01-01

Dust production models (DPM) used to estimate vertical fluxes of mineral dust aerosols over arid regions need accurate data on soil and surface properties. The Laboratoire Inter-Universitaire des Systemes Atmospheriques (LISA) data set was developed for Northern Africa, the Middle East, and East Asia. This regional data set was built through dedicated field campaigns and include, among others, the aerodynamic roughness length, the smooth roughness length of the erodible fraction of the surface, and the dry (undisturbed) soil size distribution. Recently, satellite-derived roughness length and high-resolution soil texture data sets at the global scale have emerged and provide the opportunity for the use of advanced schemes in global models. This paper analyzes the behavior of the ERS satellite-derived global roughness length and the State Soil Geographic data base-Food and Agriculture Organization of the United Nations (STATSGO-FAO) soil texture data set (based on wet techniques) using an advanced DPM in comparison to the LISA data set over Northern Africa and the Middle East. We explore the sensitivity of the drag partition scheme (a critical component of the DPM) and of the dust vertical fluxes (intensity and spatial patterns) to the roughness length and soil texture data sets. We also compare the use of the drag partition scheme to a widely used preferential source approach in global models. Idealized experiments with prescribed wind speeds show that the ERS and STATSGO-FAO data sets provide realistic spatial patterns of dust emission and friction velocity thresholds in the region. Finally, we evaluate a dust transport model for the period of March to July 2011 with observed aerosol optical depths from Aerosol Robotic Network sites. Results show that ERS and STATSGO-FAO provide realistic simulations in the region.

Loamy, two-storied soils on the outwash plains of southwestern lower Michigan: Pedoturbation of loess with the underlying sand

USGS Publications Warehouse

Luehmann, Michael D.; Peter, Brad G.; Connallon, Christopher B.; Schaetzl, Randall J.; Smidt, Samuel J.; Liu, Wei; Kincare, Kevin A.; Walkowiak, Toni A.; Thorlund, Elin; Holler, Marie S.

2016-01-01

Soils on many of the outwash plains in southwestern Michigan have loamy upper profiles, despite being underlain by sand-textured outwash. The origin of this upper, loamy material has long been unknown. The purpose of this study is to analyze the spatio-textural characteristics of these loamy-textured sediments to ascertain their origin(s). The textural curves of this material have distinct bimodality, with clear silt and sand peaks. Because the sand peaks align with those in the outwash below, we conclude that the upper material is a mixture of an initially silty material with the sand from below, forming loamy textures. By applying a textural filtering operation to the data, we determined its original characteristics; nearly all of the soils originally had silt loam upper profiles, typical for loess. Field data showed that the loamy material is thickest east of a broad, north–south trending valley (the Niles-Thornapple Spillway) that once carried glacial meltwater. The material becomes thinner, generally better sorted, and finer in texture eastward, away from this channel. We conclude that the loamy mantle on many of the adjacent outwash plains is silt-rich loess, derived from the Niles-Thornapple Spillway and its tributary channels and transported on mainly westerly winds. The spillway was active between ca. 17.3 and 16.8 k cal. years ago. At this time, a large network of tunnel channels existed beneath the stagnant Saginaw lobe ice. Meltwater from the lobe funneled silt-rich sediment into the spillway, rendering it a prodigious silt source.

Response of three soil water sensors to variable solution electrical conductivity in different soils

USDA-ARS?s Scientific Manuscript database

Commercial dielectric soil water sensors may improve management of irrigated agriculture by providing continuous field soil water information. Use of these sensors is partly limited by sensor sensitivity to variations in soil salinity and texture, which force expensive, time consuming, soil specific...

Mapping Soil hydrologic features in a semi-arid irrigated area in Spain

NASA Astrophysics Data System (ADS)

Jiménez-Aguirre, M.° Teresa; Isidoro, Daniel; Usón, Asunción

2016-04-01

The lack of soil information is a managerial problem in irrigated areas in Spain. The Violada Irrigation District (VID; 5234 ha) is a gypsic, semi-arid region in the Middle Ebro River Basin, northeast Spain. VID is under irrigation since the 1940's. The implementation of the flood irrigation system gave rise to waterlogging problems, solved along the years with the installation of an artificial drainage network. Aggregated water balances have been performed in VID since the early 1980's considering average soil properties and aggregated irrigation data for the calculations (crop evapotranspiration, canal seepage, and soil drainage). In 2008-2009, 91% of the VID was modernized to sprinkler irrigation. This new system provides detailed irrigation management information that together with detailed soil information would allow for disaggregated water balances for a better understanding of the system. Our goal was to draw a semi-detailed soil map of VID presenting the main soil characteristics related to irrigation management. A second step of the work was to set up pedotransfer functions (PTF) to estimate the water content and saturated hydraulic conductivity (Ks) from easily measurable parameters. Thirty four pits were opened, described and sampled for chemical and physical properties. Thirty three additional auger holes were sampled for water holding capacity (WHC; down to 60 cm), helping to draw the soil units boundaries. And 15 Ks tests (inverse auger hole method) were made. The WHC was determined as the difference between the field capacity (FC) and wilting point (WP) measured in samples dried at 40°C during 5 days. The comparison with old values dried at 105°C for 2 days highlighted the importance of the method when gypsum is present in order to avoid water removal from gypsum molecules. The soil map was drawn down to family level. Thirteen soil units were defined by the combination of five subgroups [Typic Calcixerept (A), Petrocalcic Calcixerept (B), Gypsic Haploxerept (C), Typic Xerorthent (D), and Typic Xerofluvent (E)] and six particle size families [Fine (1), Fine-silty (2), Fine-loamy (3), Coarse-loamy (4), Loamy Superficial (5) and Loamy-skeletal (6)]. Two great soil zones were defined: the more calcic glacis (A and B subgroups) dominated by coarse textures (4-6); and the more gypsic, fine textured valley floors (C, D and E) (1-2-3) with the exception of the superficial gypsic high lands (D5). In all the soils in VID Calcium Carbonate Equivalent (CCE) was high (though lower in the valleys) and silt was the main textural fraction. The coarser textured glacis had low Gypsum Content (GC), lower WHC and higher Ks while the valley bottoms had high GC, fine textures and lower Ks. The soil water retention properties (FC and WP) could be calculated from textural properties (clay, and fine silt fractions) and the Ks could be related to sand and GC by means of meaningful PTF's. The use of disaggregated soil information (combined with distributed irrigation data) may lead to improved water balance calculations and suggest management options for a better water use in VID.

Comparison of Morphologies of Apollo 17 Dust Particles with Lunar Simulant, JSC-1

NASA Technical Reports Server (NTRS)

Liu, Yang; Taylor, Lawrence A.; Hill, Eddy; Kihm, Kenneth D.; Day, James D. M.

2005-01-01

Lunar dust (< 20 microns) makes up approx.20 wt.% of the lunar soil. Because of the abrasive and adhering nature of lunar soil, a detailed knowledge of the morphology (size, shape and abundance) of lunar dust is important for dust mitigation on the Moon. This represents a critical step towards the establishment of long-term human presence on the Moon (Taylor et al. 2005). Machinery design for in-situ resource utilization (ISRU) on the Moon also requires detailed information on dust morphology and general physical/chemical characteristics. Here, we report a morphological study of Apollo 17 dust sample 70051 and compare it to lunar soil stimulant, JSC-1. W e have obtained SEM images of dust grains from sample 70051 soil (Fig. 1). The dust grains imaged are composed of fragments of minerals, rocks, agglutinates and glass. Most particles consist largely of agglutinitic impact glass with their typical vesicular textures (fine bubbles). All grains show sub-angular to angular shapes, commonly with sharp edges, common for crushed glass fragments. There are mainly four textures: (1) ropey-textured pieces (typical for agglutinates), (2) angular shards, (3) blocky bits, and (4) Swiss-cheese grains. This last type with its high concentration of submicron bubbles, occurs on all scales. Submicron cracks are also present in most grains. Dust-sized grains of lunar soil simulant, JSC-1, were also studied. JSC-1 is a basaltic tuff with relatively high glass content (approx.50%; McKay et al. 1994). It was initially chosen in the early 90s to approximate the geotechnical properties of the average lunar soil (Klosky et al. 1996). JSC-1 dust grains also show angular blocky and shard textures (Fig. 2), similar to those of lunar dust. However, the JSC-1 grains lack the Swiss-cheese textured particles, as well as submicron cracks and bubbles in most grains.

Statistical process control applied to mechanized peanut sowing as a function of soil texture.

PubMed

Zerbato, Cristiano; Furlani, Carlos Eduardo Angeli; Ormond, Antonio Tassio Santana; Gírio, Lucas Augusto da Silva; Carneiro, Franciele Morlin; da Silva, Rouverson Pereira

2017-01-01

The successful establishment of agricultural crops depends on sowing quality, machinery performance, soil type and conditions, among other factors. This study evaluates the operational quality of mechanized peanut sowing in three soil types (sand, silt, and clay) with variable moisture contents. The experiment was conducted in three locations in the state of São Paulo, Brazil. The track-sampling scheme was used for 80 sampling locations of each soil type. Descriptive statistics and statistical process control (SPC) were used to evaluate the quality indicators of mechanized peanut sowing. The variables had normal distributions and were stable from the viewpoint of SPC. The best performance for peanut sowing density, normal spacing, and the initial seedling growing stand was found for clayey soil followed by sandy soil and then silty soil. Sandy or clayey soils displayed similar results regarding sowing depth, which was deeper than in the silty soil. Overall, the texture and the moisture of clayey soil provided the best operational performance for mechanized peanut sowing.

Factors influencing the contents of metals and as in soils around the watershed of Guanting Reservoir, China.

PubMed

Xu, Li; Wang, Tieyu; Luo, Wei; Ni, Kun; Liu, Shijie; Wang, Lin; Li, Qiushuang; Lu, Yonglong

2013-03-01

Topsoil samples from 61 sites around the Guanting Reservoir, China, were measured for Cu, Zn, Cr, Ni, Cd, Pb and As concentrations. The mean concentrations of Cu, Zn, Cr, Ni, Cd, Pb and As were 16.8, 59.4, 37.8, 18.3, 0.32, 20.1 and 8.67 mg/kg dry weight, respectively. Factors that influence the dynamics of these metals in soils around the watersheds of Beijing reservoirs were examined. The influence of atmospheric deposition, land use, soil texture, soil type and soil chemical parameters on metal contents in soils was investigated. Atmospheric deposition, land use and soil texture were the important factors affecting heavy metal residues. Soil type and soil chemical parameters were also involved in heavy metal retention in soils. The data provided in this study are considered crucial for reservoir remediation, especially since the Guanting Reservoir will serve as one of the main drinking water sources for Beijing in the foreseeable future.

Statistical process control applied to mechanized peanut sowing as a function of soil texture

PubMed Central

Furlani, Carlos Eduardo Angeli; da Silva, Rouverson Pereira

2017-01-01

The successful establishment of agricultural crops depends on sowing quality, machinery performance, soil type and conditions, among other factors. This study evaluates the operational quality of mechanized peanut sowing in three soil types (sand, silt, and clay) with variable moisture contents. The experiment was conducted in three locations in the state of São Paulo, Brazil. The track-sampling scheme was used for 80 sampling locations of each soil type. Descriptive statistics and statistical process control (SPC) were used to evaluate the quality indicators of mechanized peanut sowing. The variables had normal distributions and were stable from the viewpoint of SPC. The best performance for peanut sowing density, normal spacing, and the initial seedling growing stand was found for clayey soil followed by sandy soil and then silty soil. Sandy or clayey soils displayed similar results regarding sowing depth, which was deeper than in the silty soil. Overall, the texture and the moisture of clayey soil provided the best operational performance for mechanized peanut sowing. PMID:28742095

How Do Earthworms, Soil Texture and Plant Composition Affect Infiltration along an Experimental Plant Diversity Gradient in Grassland?

PubMed Central

Fischer, Christine; Roscher, Christiane; Jensen, Britta; Eisenhauer, Nico; Baade, Jussi; Attinger, Sabine; Scheu, Stefan; Weisser, Wolfgang W.; Schumacher, Jens; Hildebrandt, Anke

2014-01-01

Background Infiltration is a key process in determining the water balance, but so far effects of earthworms, soil texture, plant species diversity and their interaction on infiltration capacity have not been studied. Methodology/Principal Findings We measured infiltration capacity in subplots with ambient and reduced earthworm density nested in plots of different plant species (1, 4, and 16 species) and plant functional group richness and composition (1 to 4 groups; legumes, grasses, small herbs, tall herbs). In summer, earthworm presence significantly increased infiltration, whereas in fall effects of grasses and legumes on infiltration were due to plant-mediated changes in earthworm biomass. Effects of grasses and legumes on infiltration even reversed effects of texture. We propose two pathways: (i) direct, probably by modifying the pore spectrum and (ii) indirect, by enhancing or suppressing earthworm biomass, which in turn influenced infiltration capacity due to change in burrowing activity of earthworms. Conclusions/Significance Overall, the results suggest that spatial and temporal variations in soil hydraulic properties can be explained by biotic processes, especially the presence of certain plant functional groups affecting earthworm biomass, while soil texture had no significant effect. Therefore biotic parameters should be taken into account in hydrological applications. PMID:24918943

Decision Tree Repository and Rule Set Based Mingjiang River Estuarine Wetlands Classifaction

NASA Astrophysics Data System (ADS)

Zhang, W.; Li, X.; Xiao, W.

2018-05-01

The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the random sampling method is used to conduct the field investigation, achieve an overall accuracy of 90.31 %, and the Kappa coefficient is 0.88. The classification method based on decision tree threshold values and rule set developed by the repository, outperforms the results obtained from the traditional methodology. Our decision tree repository and rule set based object-oriented classification technique was an effective method for producing comparable and consistency wetlands data set.

Texture segmentation by genetic programming.

PubMed

Song, Andy; Ciesielski, Vic

2008-01-01

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

NASA Astrophysics Data System (ADS)

Taşkin Kaya, Gülşen

2013-10-01

Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The HDMR method is developed to improve the efficiency of the deducing high dimensional behaviors. The method is formed by a particular organization of low dimensional component functions, in which each function is the contribution of one or more input variables to the output variables.

Subaquatic soils in the Volga, Don and Kuban Rivers deltas.

NASA Astrophysics Data System (ADS)

Tkachenko, Anna; Gerasimova, Maria; Lychagin, Mikhail

2015-04-01

River deltas occupy a special interface position in the environment and are characterized by contrasting hydrological and landscape-geochemical regimes. Small depth of water and weak currents contribute to suspended matter deposition. Significant spread of aquatic plants provides the enrichment of subaquatic soils in organic matter. All these factors contribute to the formation of different subaquatic soils. Possibility of including them in the classification systems is discussed by many authors (Demas and Rabenhorst, 2001; Stolt et al., 2011); there is also a special subaquatic qualifier for submerged soils in WRB; however, they are still absent in many national classification systems, as well as in the recent Russian one (2008). The purpose of this research is to reveal the properties of the subaquatic soils in the Volga, Don and Kuban Rivers deltaic areas and to propose pedogenetic approaches to categorize AQUAZEMS. Investigations of deltaic areas were performed in 2010-2012 in deltaic lagoons, fresh-water bays, small channels, oxbow lakes, and also in the part of deltaic near-shore zone. Morphological descriptions of distinguishable layers (colour, texture, thickness, boundaries, consistence, plant residues and shell debris) were made in columns obtained by augering as it is done by other researchers (Stolt et al., 2011), and supplemented with analytical data (pH, Eh, TDS, particle-size composition, and Corg). It is suggested to name the horizons in aquazems in the same way as in terrestrial soils in the recent Russian soil classification system, and apply symbols starting with the combination of caps - AQ. Most typical for aquazems is their aquagley AQG horizon that has features similar to terrestrial gleys - homogeneity in color and consistence, permeation by clay, predominance of dove grey colour. The AQG horizon gradually merges into parent material - stratified bottom sediments. The "topsoil" is usually enriched in organic matter and may be different in accordance with plant communities. The highest Corg content (4-6%) was recorded under lotus (Nelumbo sp.) and reed (Phragmites australis); reed is hard to decompose and its residues preserve recognizable plant tissues. Hence, two variants of upper horizons may be identified: aquahumus horizon - AQA and aquapeat horizon - AQT. Floating plants do not create any stable horizon under active hydrodynamic processes, whereas if they are weak, a discontinuous greyish-bluish horizon, 2-3 cm thick, is formed with Сorg content not exceeding 1-2%. In active channels, mixing of the upper part of aquazem profiles by currents results in the formation of a thin yellowish-grey oxidized layer (AQOX) with a very low content of Corg: less than 1%. Following the rules of the new system of soil classification of Russia (2004, 2008) aquazems may be tentatively classified in the following way. All aquazems may be referred to the trunk of synlithogenic soils as a special aquazem order; aquazem types may be specified by the combinations of horizons, hence, typical (AQA-AQG-AQC-C), organogenic (AQT-AQG-AQC-C), and oxidized (AQA-AQOX-AQG-AQC-C). The extension of studies is sure to find new types.

Spatial variability in the soil water content of a Mediterranean agroforestry system with high soil heterogeneity

NASA Astrophysics Data System (ADS)

Molina, Antonio Jaime; Llorens, Pilar; Aranda, Xavier; Savé, Robert; Biel, Carmen

2013-04-01

Variability of soil water content is known to increase with the size of spatial domain in which measurements are taken. At field scale, heterogeneity in soil, vegetation, topography, water input volume and management affects, among other factors, hydrologic plot behaviour under different mean soil water contents. The present work studies how the spatial variability of soil water content (SWC) is affected by soil type (texture, percentage of stones and the combination of them) in a timber-orientated plantation of cherry tree (Prunus avium) under Mediterranean climatic conditions. The experimental design is a randomized block one with 3 blocks * 4 treatments, based on two factors: irrigation (6 plots irrigated versus 6 plots not irrigated) and soil management (6 plots tillaged versus 6 plots not tillaged). SWC is continuously measured at 25, 50 and 100 cm depth with FDR sensors, located at two positions in each treatment: under tree influence and 2.5 m apart. This study presents the results of the monitoring during 2012 of the 24 sensors located at the 25 cm depth. In each of the measurement point, texture and percentage of stones were measured. Sandy-loam, sandy-clay-loam and loam textures were found together with a percentage of stones ranging from 20 to 70 %. The results indicated that the relationship between the daily mean SWC and its standard deviation, a common procedure used to study spatial variability, changed with texture, percentage of stones and the estimation of field capacity from the combination of both. Temporal stability analysis of SWC showed a clear pattern related to field capacity, with the measurement points of the sandy-loam texture and the high percentage of stones showing the maximun negative diference with the global mean. The high range in the mean relative difference observed (± 75 %), could indicate that the studied plot may be considered as a good field-laboratory to extrapolate results at higher spatial scales. Furthermore, the pattern in the temporal stability of tree growth was clearly related to that one in SWC. Nevertheless, the treatments that represent the mean conditions in growth were not exactly the same than those in SWC, which could be attributable to other characteristics than soil.

Using geometrical, textural, and contextual information of land parcels for classification of detailed urban land use

USGS Publications Warehouse

Wu, S.-S.; Qiu, X.; Usery, E.L.; Wang, L.

2009-01-01

Detailed urban land use data are important to government officials, researchers, and businesspeople for a variety of purposes. This article presents an approach to classifying detailed urban land use based on geometrical, textural, and contextual information of land parcels. An area of 6 by 14 km in Austin, Texas, with land parcel boundaries delineated by the Travis Central Appraisal District of Travis County, Texas, is tested for the approach. We derive fifty parcel attributes from relevant geographic information system (GIS) and remote sensing data and use them to discriminate among nine urban land uses: single family, multifamily, commercial, office, industrial, civic, open space, transportation, and undeveloped. Half of the 33,025 parcels in the study area are used as training data for land use classification and the other half are used as testing data for accuracy assessment. The best result with a decision tree classification algorithm has an overall accuracy of 96 percent and a kappa coefficient of 0.78, and two naive, baseline models based on the majority rule and the spatial autocorrelation rule have overall accuracy of 89 percent and 79 percent, respectively. The algorithm is relatively good at classifying single-family, multifamily, commercial, open space, and undeveloped land uses and relatively poor at classifying office, industrial, civic, and transportation land uses. The most important attributes for land use classification are the geometrical attributes, particularly those related to building areas. Next are the contextual attributes, particularly those relevant to the spatial relationship between buildings, then the textural attributes, particularly the semivariance texture statistic from 0.61-m resolution images.

The performance improvement of automatic classification among obstructive lung diseases on the basis of the features of shape analysis, in addition to texture analysis at HRCT

NASA Astrophysics Data System (ADS)

Lee, Youngjoo; Kim, Namkug; Seo, Joon Beom; Lee, JuneGoo; Kang, Suk Ho

2007-03-01

In this paper, we proposed novel shape features to improve classification performance of differentiating obstructive lung diseases, based on HRCT (High Resolution Computerized Tomography) images. The images were selected from HRCT images, obtained from 82 subjects. For each image, two experienced radiologists selected rectangular ROIs with various sizes (16x16, 32x32, and 64x64 pixels), representing each disease or normal lung parenchyma. Besides thirteen textural features, we employed additional seven shape features; cluster shape features, and Top-hat transform features. To evaluate the contribution of shape features for differentiation of obstructive lung diseases, several experiments were conducted with two different types of classifiers and various ROI sizes. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess the performance and cross-validation of the system, 5-folding method was used. In comparison to employing only textural features, adding shape features yields significant enhancement of overall sensitivity(5.9, 5.4, 4.4% in the Bayesian and 9.0, 7.3, 5.3% in the SVM), in the order of ROI size 16x16, 32x32, 64x64 pixels, respectively (t-test, p<0.01). Moreover, this enhancement was largely due to the improvement on class-specific sensitivity of mild centrilobular emphysema and bronchiolitis obliterans which are most hard to differentiate for radiologists. According to these experimental results, adding shape features to conventional texture features is much useful to improve classification performance of obstructive lung diseases in both Bayesian and SVM classifiers.

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

PubMed Central

Caetano dos Santos, Florentino Luciano; Skottman, Heli; Juuti-Uusitalo, Kati; Hyttinen, Jari

2016-01-01

Aims A fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification. Methods For image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level. Dataset and Results The RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%. Conclusions Here we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors’ accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f. PMID:26895509

Early classification of Alzheimer's disease using hippocampal texture from structural MRI

NASA Astrophysics Data System (ADS)

Zhao, Kun; Ding, Yanhui; Wang, Pan; Dou, Xuejiao; Zhou, Bo; Yao, Hongxiang; An, Ningyu; Zhang, Yongxin; Zhang, Xi; Liu, Yong

2017-03-01

Convergent evidence has been collected to support that Alzheimer's disease (AD) is associated with reduction in hippocampal volume based on anatomical magnetic resonance imaging (MRI) and impaired functional connectivity based on functional MRI. Radiomics texture analysis has been previously successfully used to identify MRI biomarkers of several diseases, including AD, mild cognitive impairment and multiple sclerosis. In this study, our goal was to determine if MRI hippocampal textures, including the intensity, shape, texture and wavelet features, could be served as an MRI biomarker of AD. For this purpose, the texture marker was trained and evaluated from MRI data of 48 AD and 39 normal samples. The result highlights the presence of hippocampal texture abnormalities in AD, and the possibility that texture may serve as a neuroimaging biomarker for AD.

The Impact of Solar Arrays on Arid Soil Hydrology: Some Numerical Simulations

NASA Astrophysics Data System (ADS)

Luo, Y.; Berli, M.; Koonce, J.; Shillito, R.; Dijkema, J.; Ghezzehei, T. A.; Yu, Z.

2016-12-01

Hot deserts are prime locations for solar energy generation but also recognized as particularly fragile environments. Minimizing the impact of facility-scale solar installations on desert environments is therefore of increasing concern. This study focuses on the impact of photovoltaic solar arrays on the water balance of arid soil underneath the array. The goal was to explore whether concentrated rainwater infiltration along the solar panel drip lines would lead to deeper infiltration and an increase in soil water storage in the long term. A two-dimensional HYDRUS model was developed to simulate rainwater infiltration into the soil within a photovoltaic solar array. Results indicate that rainwater infiltrates deeper below the drip lines compared to the areas between solar panels but only for coarse textured soil. Finer-textured soils redistribute soil moisture horizontally and the concentrating effect of solar panels on rainwater infiltration appears to be small.

Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

NASA Astrophysics Data System (ADS)

Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

2017-12-01

Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

Demonstrations in Solute Transport Using Dyes: Part I. Procedures and Results.

ERIC Educational Resources Information Center

Butters, Greg; Bandaranayake, Wije

1993-01-01

Presents the general theory to explain chemical movement in soil. Describes classroom demonstrations with visually stimulating results that show the effects of soil structure, soil texture, soil pH, and soluble organic matter on that movement. (MDH)

Profile soil property estimation using a VIS-NIR-EC-force probe

USDA-ARS?s Scientific Manuscript database

Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. ...

Hydraulic Property and Soil Textural Classification Measurements for Rainier Mesa, Nevada Test Site, Nevada

USGS Publications Warehouse

Ebel, Brian A.; Nimmo, John R.

2010-01-01

This report presents particle size analysis, field-saturated hydraulic conductivity measurements, and qualitative descriptions of surficial materials at selected locations at Rainier Mesa, Nevada. Measurements and sample collection were conducted in the Rainier Mesa area, including unconsolidated sediments on top of the mesa, an ephemeral wash channel near the mesa edge, and dry U12n tunnel pond sediments below the mesa. Particle size analysis used a combination of sieving and optical diffraction techniques. Field-saturated hydraulic conductivity measurements employed a single-ring infiltrometer with analytical formulas that correct for falling head and spreading outside the ring domain. These measurements may prove useful to current and future efforts at Rainier Mesa aimed at understanding infiltration and its effect on water fluxes and radionuclide transport in the unsaturated zone.

Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature.

PubMed

Shigeta, Hironori; Mashita, Tomohiro; Kikuta, Junichi; Seno, Shigeto; Takemura, Haruo; Ishii, Masaru; Matsuda, Hideo

2017-10-01

Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.

Decomposition of organic carbon in fine soil particles is likely more sensitive to warming than in coarse particles: an incubation study with temperate grassland and forest soils in northern China.

PubMed

Ding, Fan; Huang, Yao; Sun, Wenjuan; Jiang, Guangfu; Chen, Yue

2014-01-01

It is widely recognized that global warming promotes soil organic carbon (SOC) decomposition, and soils thus emit more CO2 into the atmosphere because of the warming; however, the response of SOC decomposition to this warming in different soil textures is unclear. This lack of knowledge limits our projection of SOC turnover and CO2 emission from soils after future warming. To investigate the CO2 emission from soils with different textures, we conducted a 107-day incubation experiment. The soils were sampled from temperate forest and grassland in northern China. The incubation was conducted over three short-term cycles of changing temperature from 5°C to 30°C, with an interval of 5°C. Our results indicated that CO2 emissions from sand (>50 µm), silt (2-50 µm), and clay (<2 µm) particles increased exponentially with increasing temperature. The sand fractions emitted more CO2 (CO2-C per unit fraction-C) than the silt and clay fractions in both forest and grassland soils. The temperature sensitivity of the CO2 emission from soil particles, which is expressed as Q10, decreased in the order clay>silt>sand. Our study also found that nitrogen availability in the soil facilitated the temperature dependence of SOC decomposition. A further analysis of the incubation data indicated a power-law decrease of Q10 with increasing temperature. Our results suggested that the decomposition of organic carbon in fine-textured soils that are rich in clay or silt could be more sensitive to warming than those in coarse sandy soils and that SOC might be more vulnerable in boreal and temperate regions than in subtropical and tropical regions under future warming.

Benign-malignant mass classification in mammogram using edge weighted local texture features

NASA Astrophysics Data System (ADS)

Rabidas, Rinku; Midya, Abhishek; Sadhu, Anup; Chakraborty, Jayasree

2016-03-01

This paper introduces novel Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for the classification of mammographic masses as benign or malignant. Mass is one of the common, however, challenging evidence of breast cancer in mammography and diagnosis of masses is a difficult task. Since DRLBP and DRLTP overcome the drawbacks of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) by discriminating a brighter object against the dark background and vice-versa, in addition to the preservation of the edge information along with the texture information, several edge-preserving texture features are extracted, in this study, from DRLBP and DRLTP. Finally, a Fisher Linear Discriminant Analysis method is incorporated with discriminating features, selected by stepwise logistic regression method, for the classification of benign and malignant masses. The performance characteristics of DRLBP and DRLTP features are evaluated using a ten-fold cross-validation technique with 58 masses from the mini-MIAS database, and the best result is observed with DRLBP having an area under the receiver operating characteristic curve of 0.982.

Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Karahaliou, A.; Vassiou, K.; Skiadopoulos, S.; Kanavou, T.; Yiakoumelos, A.; Costaridou, L.

2009-07-01

The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960±0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.

Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

NASA Astrophysics Data System (ADS)

Korfiatis, P.; Kalogeropoulou, C.; Daoussis, D.; Petsas, T.; Adonopoulos, A.; Costaridou, L.

2009-07-01

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Ammonia volatilization loss from surface applied livestock manure.

PubMed

Paramasivam, S; Jayaraman, K; Wilson, Takela C; Alva, Ashok K; Kelson, Luma; Jones, Leandra B

2009-03-01

Ammonia (NH(3)) emission from livestock manures used in agriculture reduces N uptake by crops and negatively impacts air quality. This laboratory study was conducted to evaluate NH(3)emission from different livestock manures applied to two soils: Candler fins sand (CFS; light-textured soil, pH 6.8 and field capacity soil water content of 70 g kg(-1)) from Lake Alfred, Florida and Ogeechee loamy sand (OLS; medium-textured soil, pH 5.2 and field capacity soil water content of 140 g kg(-1)) from Savannah, Georgia. Poultry litter (PL) collected from a poultry farm near Douglas, Georgia, and fresh solid separate of swine manure (SM) collected from a farm near Clinton, North Carolina were used. Each of the soil was weighed in 100 g sub samples and amended with either PL or SM at rates equivalent to either 0, 2.24, 5.60, 11.20, or 22.40 Mg ha(-1) in 1L Mason jars and incubated in the laboratory at field capacity soil water content for 19 days to monitor NH(3) volatilization. Results indicated a greater NH(3) loss from soils amended with SM compared to that with PL. The cumulative NH(3)volatilization loss over 19 days ranged from 4 to 27% and 14 to 32% of total N applied as PL and SM, respectively. Volatilization of NH(3) was greater from light-textured CFS than that from medium-textured OLS. Volatilization loss increased with increasing rates of manure application. Ammonia volatilization was lower at night time than that during the day time. Differences in major factors such as soil water content, temperature, soil type and live stock manure type influenced the diurnal variation in volatilization loss of NH(3) from soils. A significant portion (> 50%) of cumulative NH(3) emission over 19 d occurred during the first 5-7 d following the application of livestock manures. Results of this study demonstrate that application of low rates of livestock manure (< or = 5.60 Mg ha(-1)) is recommended to minimize NH(3) emissions.

Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

PubMed

Fesharaki, Nooshin Jafari; Pourghassem, Hossein

2013-07-01

Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

Analysis on the Utility of Satellite Imagery for Detection of Agricultural Facility

NASA Astrophysics Data System (ADS)

Kang, J.-M.; Baek, S.-H.; Jung, K.-Y.

2012-07-01

Now that the agricultural facilities are being increase owing to development of technology and diversification of agriculture and the ratio of garden crops that are imported a lot and the crops cultivated in facilities are raised in Korea, the number of vinyl greenhouses is tending upward. So, it is important to grasp the distribution of vinyl greenhouses as much as that of rice fields, dry fields and orchards, but it is difficult to collect the information of wide areas economically and correctly. Remote sensing using satellite imagery is able to obtain data of wide area at the same time, quickly and cost-effectively collect, monitor and analyze information from every object on earth. In this study, in order to analyze the utilization of satellite imagery at detection of agricultural facility, image classification was performed about the agricultural facility, vinyl greenhouse using Formosat-2 satellite imagery. The training set of sea, vegetation, building, bare ground and vinyl greenhouse was set to monitor the agricultural facilities of the object area and the training set for the vinyl greenhouses that are main monitoring object was classified and set again into 3 types according the spectral characteristics. The image classification using 4 kinds of supervise classification methods applied by the same training set were carried out to grasp the image classification method which is effective for monitoring agricultural facilities. And, in order to minimize the misclassification appeared in the classification using the spectral information, the accuracy of classification was intended to be raised by adding texture information. The results of classification were analyzed regarding the accuracy comparing with that of naked-eyed detection. As the results of classification, the method of Mahalanobis distance was shown as more efficient than other methods and the accuracy of classification was higher when adding texture information. Hence the more effective monitoring of agricultural facilities is expected to be available if the characteristics such as texture information including satellite images or spatial pattern are studied in detail.

Semi-Automated Classification of Seafloor Data Collected on the Delmarva Inner Shelf

NASA Astrophysics Data System (ADS)

Sweeney, E. M.; Pendleton, E. A.; Brothers, L. L.; Mahmud, A.; Thieler, E. R.

2017-12-01

We tested automated classification methods on acoustic bathymetry and backscatter data collected by the U.S. Geological Survey (USGS) and National Oceanic and Atmospheric Administration (NOAA) on the Delmarva inner continental shelf to efficiently and objectively identify sediment texture and geomorphology. Automated classification techniques are generally less subjective and take significantly less time than manual classification methods. We used a semi-automated process combining unsupervised and supervised classification techniques to characterize seafloor based on bathymetric slope and relative backscatter intensity. Statistical comparison of our automated classification results with those of a manual classification conducted on a subset of the acoustic imagery indicates that our automated method was highly accurate (95% total accuracy and 93% Kappa). Our methods resolve sediment ridges, zones of flat seafloor and areas of high and low backscatter. We compared our classification scheme with mean grain size statistics of samples collected in the study area and found that strong correlations between backscatter intensity and sediment texture exist. High backscatter zones are associated with the presence of gravel and shells mixed with sand, and low backscatter areas are primarily clean sand or sand mixed with mud. Slope classes further elucidate textural and geomorphologic differences in the seafloor, such that steep slopes (>0.35°) with high backscatter are most often associated with the updrift side of sand ridges and bedforms, whereas low slope with high backscatter correspond to coarse lag or shell deposits. Low backscatter and high slopes are most often found on the downdrift side of ridges and bedforms, and low backscatter and low slopes identify swale areas and sand sheets. We found that poor acoustic data quality was the most significant cause of inaccurate classification results, which required additional user input to mitigate. Our method worked well along the primarily sandy Delmarva inner continental shelf, and outlines a method that can be used to efficiently and consistently produce surficial geologic interpretations of the seafloor from ground-truthed geophysical or hydrographic data.

News from Online: Digging up Earth Day Resources

ERIC Educational Resources Information Center

Coldwell, Bernadette A.

2006-01-01

The soil science and soil chemistry is incorporated into teaching materials for earth day and beyond. It revealed some of the chemical properties of the soil through color and texture and the chemical processes relevant to soils abound, including the carbon and nitrogen cycles in the soil, acidification of soils through acid deposition, leaching…

In-situ field capacity and soil water retention measurements in two contrasting soil textures

USDA-ARS?s Scientific Manuscript database

Knowledge of the in-situ field capacity and soil-water retention curve for soils is important for effective irrigation management and scheduling. The primary objective of this study was to estimate in-situ field capacity and soil water retention curves in the field using continually monitoring soil ...

In-situ Field Capacity and Soil Water Retention Measurements in Two Contrasting Soil Textures

USDA-ARS?s Scientific Manuscript database

Knowledge of the in-situ field capacity and soil-water retention curve for soils is important for effective irrigation management and scheduling. The primary objective of this study was to estimate in-situ field capacity and soil water retention curves in the field using continually monitoring soil ...

Detection of flood effects in montane streams based on fusion of 2D and 3D information from UAV imagery

NASA Astrophysics Data System (ADS)

Langhammer, Jakub; Vacková, Tereza

2017-04-01

In the contribution, we are presenting a novel method, enabling objective detection and classification of the alluvial features resulting from flooding, based on the imagery, acquired by the unmanned aerial vehicles (UAVs, drones). We have proposed and tested a workflow, using two key data products of the UAV photogrammetry - the 2D orthoimage and 3D digital elevation model, together with derived information on surface texture for the consequent classification of erosional and depositional features resulting from the flood. The workflow combines the photogrammetric analysis of the UAV imagery, texture analysis of the DEM, and the supervised image classification. Application of the texture analysis and use of DEM data is aimed to enhance 2D information, resulting from the high-resolution orthoimage by adding the newly derived bands, which enhance potential for detection and classification of key types of fluvial features in the stream and the floodplain. The method was tested on the example of a snowmelt-driven flood in a montane stream in Sumava Mts., Czech Republic, Central Europe, that occurred in December 2015. Using the UAV platform DJI Inspire 1 equipped with the RGB camera there was acquired imagery covering a 1 km long stretch of a meandering creek with elevated fluvial dynamics. Agisoft Photoscan Pro was used to derive a point cloud and further the high-resolution seamless orthoimage and DEM, Orfeo toolkit and SAGA GIS tools were used for DEM analysis. From the UAV-based data inputs, a multi-band dataset was derived as a source for the consequent classification of fluvial landforms. The RGB channels of the derived orthoimage were completed by the selected texture feature layers and the information on 3D properties of the riverscape - the normalized DEM and terrain ruggedness. Haralick features, derived from the RGB channels, are used for extracting information on the surface texture, the terrain ruggedness index is used as a measure of local topographical variability. Based on this dataset, the supervised classification was performed to identify the fluvial features, including the fresh and old accumulations of different size, fresh bank erosion, in-stream features and the riparian zone vegetation, verified later by the field survey. The classification based on the fusion of high-resolution 2D and 3D data, derived from UAV imagery, enabled to identify and quantify the extent of recent and old accumulations, to distinguish the coarse and fine sediments or to separate the shallow and deep zones in the submerged zone of the channel. With the high operability of the data acquisition process, the proposed method appears to be a promising tool for rapid mapping and classification of flood effects in streams and floodplains.

Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible.

PubMed

Mannil, Manoj; von Spiczak, Jochen; Manka, Robert; Alkadhi, Hatem

2018-06-01

The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images. In this institutional review board-approved retrospective study, we included non-contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls. Texture analysis of the left ventricle was performed using free-hand regions of interest, and texture features were classified twice (Model I: controls versus acute MI versus chronic MI; Model II: controls versus acute and chronic MI). For both classifications, 6 commonly used machine learning classifiers were used: decision tree C4.5 (J48), k-nearest neighbors, locally weighted learning, RandomForest, sequential minimal optimization, and an artificial neural network employing deep learning. In addition, 2 blinded, independent readers visually assessed noncontrast CCT images for the presence or absence of MI. In Model I, best classification results were obtained using the k-nearest neighbors classifier (sensitivity, 69%; specificity, 85%; false-positive rate, 0.15). In Model II, the best classification results were found with the locally weighted learning classification (sensitivity, 86%; specificity, 81%; false-positive rate, 0.19) with an area under the curve from receiver operating characteristics analysis of 0.78. In comparison, both readers were not able to identify MI in any of the noncontrast, low radiation dose CCT images. This study indicates the ability of texture analysis and machine learning in detecting MI on noncontrast low radiation dose CCT images being not visible for the radiologists' eye.

Ecological risk assessment: influence of texture on background concentration of microelements in soils of Russia.

NASA Astrophysics Data System (ADS)

Beketskaya, Olga

2010-05-01

In Russia quality standards of contaminated substances values in environment consist of ecological and sanitary rate-setting. The sanitary risk assessment base on potential risk that contaminants pose to protect human beings. The main purpose of the ecological risk assessment is to protect ecosystem. To determine negative influence on living organisms in the sanitary risk assessment in Russia we use MPC. This value of contaminants show how substances affected on different part of environment, biological activity and soil processes. The ecological risk assessment based on comparison compounds concentration with background concentration for definite territories. Taking into account high interval of microelements value in soils, we suggest using statistic method for determination of concentration levels of chemical elements concentration in soils of Russia. This method is based on determination middle levels of elements content in natural condition. The top limit of middle chemical elements concentration in soils is value, which exceed middle regional background level in three times standard deviation. The top limit of natural concentration excess we can explain as anthropogenic impact. At first we study changing in the middle content value of microelements in soils of geographic regions in European part of Russia on the basis of cartographical analysis. Cartographical analysis showed that the soil of mountainous and mountain surrounding regions is enriched with microelements. On the plain territory of European part of Russia for most of microelements was noticed general direction of increasing their concentration in soils from north to south, also in the same direction soil clay content rise for majority of soils. For all other territories a clear connection has been noticed between the distribution of sand sediment. By our own investigation and data from scientific literature data base was created. This data base consist of following soil properties: texture, organic matter content, concentration of microelements and pH value. On the basis of this data base massive of data for Forest-steppe and Steppe regions was create, which was divided by texture. For all data statistics method was done and was calculated maximum level natural microelements content for soils with different texture (?+3*δ). As a result of our statistic calculation we got middle and the top limit of background concentration of microelements in sandy and clay soils (conditional border - sandy loam) of two regions. We showed, that for all territory of European part of Russia and for Forest-steppe and Steppe regions separately middle content and maximum level natural microelements concentrations (?+3*σ) are higher in clay soils, rather then in sandy soils. Data characterizing soils, in different regions, of similar texture differs less than the data collected for sandy and clay soils of the same region. After all this calculation we can notice, that data of middle and top limit of background microelements concentration in soils, based on statistic method, can be used in the aim of ecological risk assessment. Using offered method allow to calculate top limit of background concentration for sandy and clay soils for large-scale geographic regions, exceeding which will be evidence of anthropogenic contamination of soil.

The Effect of Soil Hydraulic Properties vs. Soil Texture in Land Surface Models

NASA Technical Reports Server (NTRS)

Gutmann, E. D.; Small, E. E.

2005-01-01

This study focuses on the effect of Soil Hydraulic Property (SHP) selection on modeled surface fluxes following a rain storm in a semi-arid environment. SHPs are often defined based on a Soil Texture Class (STC). To examine the effectiveness of this approach, the Noah land surface model was run with each of 1306 soils in a large SHP database. Within most STCs, the outputs have a range of 350 W/m2 for latent and sensible heat fluxes, and 8K for surface temperature. The average difference between STC median values is only 100 W/m2 for latent and sensible heat. It is concluded that STC explains 5-15% of the variance in model outputs and should not be used to determine SHPs.

Comparison of field and laboratory VNIR spectroscopy for profile soil property estimation

USDA-ARS?s Scientific Manuscript database

In-field, in-situ data collection with soil sensors has potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate important soil properties, such as soil carbon, nitrogen, water content, and texture. Most pre...

Estimation of soil profile physical and chemical properties using a VIS-NIR-EC-force probe

USDA-ARS?s Scientific Manuscript database

Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. ...

Evaluation of soil processing conditions on mineralizable C and N across a textural gradient

USDA-ARS?s Scientific Manuscript database

Soil biological activity is an important component of a well-functioning soil. Methodologies for estimating this process in soil vary due to a variety of theoretical, functional, and expediency considerations. We tested the effects of soil processing (sieve size), water delivery method (from top a...

Effects of spoil texture on growth of K-31 tall fescue

Treesearch

David H. Van Lear

1971-01-01

Growth of K-31 tall fescue (Festuca arundinacea) was significantly affected by the particle-size distribution, or texture, of four spoils from eastern Kentucky. Growth on spoils having no toxic chemical properties generally was greatest where texture consisted of about equal quantities of soil-size material and a coarser fraction (2 mm. to 6.4 mm.),...

An Active Patch Model for Real World Texture and Appearance Classification

PubMed Central

Mao, Junhua; Zhu, Jun; Yuille, Alan L.

2014-01-01

This paper addresses the task of natural texture and appearance classification. Our goal is to develop a simple and intuitive method that performs at state of the art on datasets ranging from homogeneous texture (e.g., material texture), to less homogeneous texture (e.g., the fur of animals), and to inhomogeneous texture (the appearance patterns of vehicles). Our method uses a bag-of-words model where the features are based on a dictionary of active patches. Active patches are raw intensity patches which can undergo spatial transformations (e.g., rotation and scaling) and adjust themselves to best match the image regions. The dictionary of active patches is required to be compact and representative, in the sense that we can use it to approximately reconstruct the images that we want to classify. We propose a probabilistic model to quantify the quality of image reconstruction and design a greedy learning algorithm to obtain the dictionary. We classify images using the occurrence frequency of the active patches. Feature extraction is fast (about 100 ms per image) using the GPU. The experimental results show that our method improves the state of the art on a challenging material texture benchmark dataset (KTH-TIPS2). To test our method on less homogeneous or inhomogeneous images, we construct two new datasets consisting of appearance image patches of animals and vehicles cropped from the PASCAL VOC dataset. Our method outperforms competing methods on these datasets. PMID:25531013

Statistical analyses of soil properties on a quaternary terrace sequence in the upper sava river valley, Slovenia, Yugoslavia

USGS Publications Warehouse

Vidic, N.; Pavich, M.; Lobnik, F.

1991-01-01

Alpine glaciations, climatic changes and tectonic movements have created a Quaternary sequence of gravely carbonate sediments in the upper Sava River Valley, Slovenia, Yugoslavia. The names for terraces, assigned in this model, Gu??nz, Mindel, Riss and Wu??rm in order of decreasing age, are used as morphostratigraphic terms. Soil chronosequence on the terraces was examined to evaluate which soil properties are time dependent and can be used to help constrain the ages of glaciofluvial sedimentation. Soil thickness, thickness of Bt horizons, amount and continuity of clay coatings and amount of Fe and Me concretions increase with soil age. The main source of variability consists of solutions of carbonate, leaching of basic cations and acidification of soils, which are time dependent and increase with the age of soils. The second source of variability is the content of organic matter, which is less time dependent, but varies more within soil profiles. Textural changes are significant, presented by solution of carbonate pebbles and sand, and formation is silt loam matrix, which with age becomes finer, with clay loam or clayey texture. The oldest, Gu??nz, terrace shows slight deviation from general progressive trends of changes of soil properties with time. The hypothesis of single versus multiple depositional periods of deposition was tested with one-way analysis of variance (ANOVA) on a staggered, nested hierarchical sampling design on a terrace of largest extent and greatest gravel volume, the Wu??rm terrace. The variability of soil properties is generally higher within subareas than between areas of the terrace, except for the soil thickness. Observed differences in soil thickness between the areas of the terrace could be due to multiple periods of gravel deposition, or to the initial differences of texture of the deposits. ?? 1991.

Drivers of small scale variability in soil-atmosphere fluxes of CH4, N2O and CO2 in a forest soil

NASA Astrophysics Data System (ADS)

Maier, Martin; Nicolai, Clara; Wheeler, Denis; Lang, Friedeike; Paulus, Sinikka

2016-04-01

Soil-atmosphere fluxes of CH4, N2O and CO2 can vary on different spatial scales, on large scales between ecosystems but also within apparently homogenous sites. While CO2 and CH4 consumption is rather evenly distibuted in well aerated soils, the production of N2O and CH4 seems to occur at hot spots that can be associated with anoxic or suboxic conditions. Small-scale variability in soil properties is well-known from field soil assesment, affecting also soil aeration and thus theoretically, greenhouse gas fluxes. In many cases different plant species are associated with different soil conditions and vegetation mapping should therefor combined with soil mapping. Our research objective was explaining the small scale variability of greenhouse gas fluxes in an apparently homogeneous 50 years old Scots Pine stand in a former riparian flood plain.We combined greenhouse gas measurements and soil physical lab measurments with field soil assessment and vegetation mapping. Measurements were conducted with at 60 points at a plot of 30 X 30 m at the Hartheim monitoring site (SW Germany). For greenhouse gas measurements a non-steady state chamber system and laser analyser, and a photoacoustic analyser were used. Our study shows that the well aerated site was a substantial sink for atmospheric CH4 (-2.4 nmol/m² s) and also a for N2O (-0.4 nmol/m² s), but less pronounced, whereas CO2 production was a magnitude larger (2.6 μmol/m² s). The spatial variability of the CH4 consumption of the soils could be explained by the variability of the soil gas diffusivity (measured in situ + using soil cores). Deviations of this clear trend were only observed at points where decomposing woody debris was directly under the litter layer. Soil texture ranged from gravel, coarse sand, fine sand to pure silt, with coarser texture having higher soil gas diffusivity. Changes in texture were rather abrupt at some positions or gradual at other positions, and were well reflected in the vegetation structure. On patches of gravel and coarse sand there was hardly any ground vegatation, and a shrublayer was found only at silty patches Our results indicate that a stratification and regionalisation approach based on vegetation structure and soil texture represents a promising tool for the adjustment of sampling designs for soil gas flux measurement. Acknowledgement This research was financially supported by the project DFG (MA 5826/2-1).

Evaluation of soil manipulation to prepare engineered earthen waste covers for revegetation

DOE PAGES

Waugh, W. Joseph; Benson, Craig H.; Albright, William H.; ...

2015-10-21

Seven ripping treatments designed to improve soil physical conditions for revegetation were compared on a test pad simulating an earthen cover for a waste disposal cell. The field test was part of study of methods to convert compacted-soil waste covers into evapotranspiration covers. The test pad consisted of a compacted layer of fine-textured soil simulating a barrier protection layer overlain by a gravelly sand bedding layer and a cobble armor layer. Treatments included combinations of soil-ripping implements (conventional shank [CS], wing-tipped shank [WTS], and parabolic oscillating shank with wings [POS]), ripping depths, and number of passes. Dimensions, dry density, moisturemore » content, and particle size distribution of disturbance zones were determined in two trenches excavated across rip rows. The goal was to create a root-zone dry density between 1.2 and 1.6 Mg m-3 and a seedbed soil texture ranging from clay loam to sandy loam with low rock content. All treatments created V-shaped disturbance zones as measured on trench faces. Disturbance zone size was most influenced by ripping depth. Winged implements created larger disturbance zones. All treatments lifted fines into the bedding layer, moved gravel and cobble down into the fine-textured protection layer, and thereby disrupted the capillary barrier at the interface. Changes in dry density within disturbance zones were comparable for the CS and WTS treatments but were highly variable among POS treatments. Water content increased in the bedding layer and decreased in the protection layer after ripping. The POS at 1.2-m depth and two passes created the largest zone with a low dry density (1.24 Mg m-3) and the most favorable seedbed soil texture (gravely silt loam). Furthermore, ripping also created large soil aggregates and voids in the protection layer that may produce preferential flow paths and reduce water storage capacity.« less

Volumetric characterization of human patellar cartilage matrix on phase contrast x-ray computed tomography

NASA Astrophysics Data System (ADS)

Abidin, Anas Z.; Nagarajan, Mahesh B.; Checefsky, Walter A.; Coan, Paola; Diemoz, Paul C.; Hobbs, Susan K.; Huber, Markus B.; Wismüller, Axel

2015-03-01

Phase contrast X-ray computed tomography (PCI-CT) has recently emerged as a novel imaging technique that allows visualization of cartilage soft tissue, subsequent examination of chondrocyte patterns, and their correlation to osteoarthritis. Previous studies have shown that 2D texture features are effective at distinguishing between healthy and osteoarthritic regions of interest annotated in the radial zone of cartilage matrix on PCI-CT images. In this study, we further extend the texture analysis to 3D and investigate the ability of volumetric texture features at characterizing chondrocyte patterns in the cartilage matrix for purposes of classification. Here, we extracted volumetric texture features derived from Minkowski Functionals and gray-level co-occurrence matrices (GLCM) from 496 volumes of interest (VOI) annotated on PCI-CT images of human patellar cartilage specimens. The extracted features were then used in a machine-learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with GLCM features correlation (AUC = 0.83 +/- 0.06) and homogeneity (AUC = 0.82 +/- 0.07), which significantly outperformed all Minkowski Functionals (p < 0.05). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving GLCM-derived statistical features can distinguish between healthy and osteoarthritic tissue with high accuracy.

Benefits of Red-Edge Spectral Band and Texture Features for the Object-based Classification using RapidEye sSatellite Image data

NASA Astrophysics Data System (ADS)

Kim, H. O.; Yeom, J. M.

2014-12-01

Space-based remote sensing in agriculture is particularly relevant to issues such as global climate change, food security, and precision agriculture. Recent satellite missions have opened up new perspectives by offering high spatial resolution, various spectral properties, and fast revisit rates to the same regions. Here, we examine the utility of broadband red-edge spectral information in multispectral satellite image data for classifying paddy rice crops in South Korea. Additionally, we examine how object-based spectral features affect the classification of paddy rice growth stages. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the crop condition in heterogeneous paddy rice crop environments, particularly when single-season image data were used. This positive effect appeared to be offset by the multi-temporal image data. Additional texture information brought only a minor improvement or a slight decline, although it is well known to be advantageous for object-based classification in general. We conclude that broadband red-edge information derived from conventional multispectral satellite data has the potential to improve space-based crop monitoring. Because the positive or negative effects of texture features for object-based crop classification could barely be interpreted, the relationships between the textual properties and paddy rice crop parameters at the field scale should be further examined in depth.

A software tool for automatic classification and segmentation of 2D/3D medical images

NASA Astrophysics Data System (ADS)

Strzelecki, Michal; Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur

2013-02-01

Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided.

Subsurface Chloride Transport in Shallow Groundwater

USDA-ARS?s Scientific Manuscript database

High soil spatial heterogeneity was observed at the USDA-ARS Beltsville OPE3 field site using geophysical surveys (ground-penetrating radar) and soil textural analysis. This was confirmed with data on crop yields and pesticide concentrations in wells. To assess effects of soil heterogeneity on soil ...

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.

Texture-based segmentation of temperate-zone woodland in panchromatic IKONOS imagery

NASA Astrophysics Data System (ADS)

Gagnon, Langis; Bugnet, Pierre; Cavayas, Francois

2003-08-01

We have performed a study to identify optimal texture parameters for woodland segmentation in a highly non-homogeneous urban area from a temperate-zone panchromatic IKONOS image. Texture images are produced with the sum- and difference-histograms depend on two parameters: window size f and displacement step p. The four texture features yielding the best discrimination between classes are the mean, contrast, correlation and standard deviation. The f-p combinations 17-1, 17-2, 35-1 and 35-2 are those which give the best performance, with an average classification rate of 90%.

Interactions of Soil Order and Land Use Management on Soil Properties in the Kukart Watershed, Kyrgyzstan

USDA-ARS?s Scientific Manuscript database

Surveys of soil properties related to soil functioning for many regions of Kyrgyzstan are limited. This study established ranges of selected chemical [soil organic matter (SOM), pH and total N (TN)], physical (soil texture), and biochemical (six enzyme activities of C, N, P and S cycling) character...

Analysis of the inhibitory effects of chloropicrin fumigation on nitrification in various soil types.

PubMed

Yan, Dongdong; Wang, Qiuxia; Li, Yuan; Ouyang, Canbin; Guo, Meixia; Cao, Aocheng

2017-05-01

Chloropicrin retards the conversion of ammonia to nitrite during the nitrification process in soil. In our study, the dynamic effect of chloropicrin fumigation on soil nitrification was evaluated in five different soil types to identify relationships between soil properties and the effect of fumigation on nitrification. Chloropicrin significantly inhibited nitrification in all soils; however, the recovery of nitrification varied greatly between the soils. Following chloropicrin fumigation, nitrification recovered to the control level in all soils, except in the acidic Guangxi soil. Nitrification recovered faster in fumigated sandy loam Beijing soil than in the other four fumigated soils. Soil texture and pH were two important factors that influenced chloropicrin's inhibitory effect on nitrification. An S-shaped function was fitted to soil NO 3 - -N content to assess the nitrification recovery tendency in different soils. The time taken to reach maximum nitrification (t max ) ranged from 2.4 to 3.0 weeks in all unfumigated soils. Results demonstrated that t max was greater in all fumigated soils than in untreated soils. Correlation calculations showed that t max was strongly correlated to soil texture. The correlation analysis results indicated that the recovery rate of nitrification after chloropicrin fumigation is much faster in sandy loam soil than silty loam soil. Copyright © 2017. Published by Elsevier Ltd.

Plant communities on infertile soils are less sensitive to climate change.

PubMed

Harrison, Susan; Damschen, Ellen; Fernandez-Going, Barbara; Eskelinen, Anu; Copeland, Stella

2015-11-01

Much evidence suggests that plant communities on infertile soils are relatively insensitive to increased water deficit caused by increasing temperature and/or decreasing precipitation. However, a multi-decadal study of community change in the western USA does not support this conclusion. This paper tests explanations related to macroclimatic differences, overstorey effects on microclimate, variation in soil texture and plant functional traits. A re-analysis was undertaken of the changes in the multi-decadal study, which concerned forest understorey communities on infertile (serpentine) and fertile soils in an aridifying climate (southern Oregan) from 1949-1951 to 2007-2008. Macroclimatic variables, overstorey cover and soil texture were used as new covariates. As an alternative measure of climate-related change, the community mean value of specific leaf area was used, a functional trait measuring drought tolerance. We investigated whether these revised analyses supported the prediction of lesser sensitivity to climate change in understorey communities on infertile serpentine soils. Overstorey cover, but not macroclimate or soil texture, was a significant covariate of community change over time. It strongly buffered understorey temperatures, was correlated with less change and averaged >50 % lower on serpentine soils, thereby counteracting the lower climate sensitivity of understorey herbs on these soils. Community mean specific leaf area showed the predicted pattern of less change over time in serpentine than non-serpentine communities. Based on the current balance of evidence, plant communities on infertile serpentine soils are less sensitive to changes in the climatic water balance than communities on more fertile soils. However, this advantage may in some cases be lessened by their sparser overstorey cover. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology

PubMed Central

Heil, Kurt

2017-01-01

Fast and accurate assessment of within-field variation is essential for detecting field-wide heterogeneity and contributing to improvements in the management of agricultural lands. The goal of this paper is to provide an overview of field scale characterization by electromagnetic induction, firstly with a focus on the applications of EM38 to salinity, soil texture, water content and soil water turnover, soil types and boundaries, nutrients and N-turnover and soil sampling designs. Furthermore, results concerning special applications in agriculture, horticulture and archaeology are included. In addition to these investigations, this survey also presents a wide range of practical methods for use. Secondly, the effectiveness of conductivity readings for a specific target in a specific locality is determined by the intensity at which soil factors influence these values in relationship to the desired information. The interpretation and utility of apparent electrical conductivity (ECa) readings are highly location- and soil-specific, so soil properties influencing the measurement of ECa must be clearly understood. From the various calibration results, it appears that regression constants for the relationships between ECa, electrical conductivity of aqueous soil extracts (ECe), texture, yield, etc., are not necessarily transferable from one region to another. The modelling of ECa, soil properties, climate and yield are important for identifying the location to which specific utilizations of ECa technology (e.g., ECa−texture relationships) can be appropriately applied. In general, the determination of absolute levels of ECa is frequently not possible, but it appears to be quite a robust method to detect relative differences, both spatially and temporally. Often, the use of ECa is restricted to its application as a covariate or the use of the readings in a relative sense rather than as absolute terms. PMID:29113048

Application of Multitemporal Remotely Sensed Soil Moisture for the Estimation of Soil Physical Properties

NASA Technical Reports Server (NTRS)

Mattikalli, N. M.; Engman, E. T.; Jackson, T. J.; Ahuja, L. R.

1997-01-01

This paper demonstrates the use of multitemporal soil moisture derived from microwave remote sensing to estimate soil physical properties. The passive microwave ESTAR instrument was employed during June 10-18, 1992, to obtain brightness temperature (TB) and surface soil moisture data in the Little Washita watershed, Oklahoma. Analyses of spatial and temporal variations of TB and soil moisture during the dry-down period revealed a direct relationship between changes in T and soil moisture and soil physical (viz. texture) and hydraulic (viz. saturated hydraulic conductivity, K(sat)) properties. Statistically significant regression relationships were developed for the ratio of percent sand to percent clay (RSC) and K(sat), in terms of change components of TB and surface soil moisture. Validation of results using field measured values and soil texture map indicated that both RSC and K(sat) can be estimated with reasonable accuracy. These findings have potential applications of microwave remote sensing to obtain quick estimates of the spatial distributions of K(sat), over large areas for input parameterization of hydrologic models.

SOIL QUALITY RECOVERY IN PREVIOUSLY FARMED FIELDS SEEDED TO PERENNIAL WARM SEASON NATIVE GRASS

EPA Science Inventory

A study of twelve Conservation Reserve Program sites in northeastern Kansas was conducted to determine native grass species and selected soil textures influence on soil quality recovery.
Plant productivity, plant carbon and nitrogen concentrations, total soil nitrogen and car...

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...

A simplified regional-scale electromagnetic induction - Salinity calibration model using ANOCOVA modeling techniques

USDA-ARS?s Scientific Manuscript database

Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity (ECa) is a potential means of characterizing the spatial variability of any soil property that influences ECa including soil salinity, water content, texture, bulk density, organic matter, and cation exc...

Water and chloride transport in a fine-textured soil in a feedlot pen.

PubMed

Veizaga, E A; Rodríguez, L; Ocampo, C J

2015-11-01

Cattle feeding in feedlot pens produces large amounts of manure and animal urine. Manure solutions resulting from surface runoff are composed of numerous chemical constituents whose leaching causes salinization of the soil profile. There is a relatively large number of studies on preferential flow characterization and modeling in clayed soils. However, research on water flow and solute transport derived from cattle feeding operations in fine-textured soils under naturally occurring precipitation events is less frequent. A field monitoring and modeling investigation was conducted at two plots on a fine-textured soil near a feedlot pen in Argentina to assess the potential of solute leaching into the soil profile. Soil pressure head and chloride concentration of the soil solution were used in combination with HYDRUS-1D numerical model to simulate water flow and chloride transport resorting to the concept of mobile/immobile-MIM water for solute transport. Pressure head sensors located at different depths registered a rapid response to precipitation suggesting the occurrence of preferential flow-paths for infiltrating water. Cracks and small fissures were documented at the field site where the % silt and % clay combined is around 94%. Chloride content increased with depth for various soil pressure head conditions, although a dilution process was observed as precipitation increased. The MIM approach improved numerical results at one of the tested sites where the development of cracks and macropores is likely, obtaining a more dynamic response in comparison with the advection-dispersion equation. Copyright © 2015 Elsevier B.V. All rights reserved.

Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences

NASA Astrophysics Data System (ADS)

Poletti, Enea; Veronese, Elisa; Calabrese, Massimiliano; Bertoldo, Alessandra; Grisan, Enrico

2012-02-01

The automatic segmentation of brain tissues in magnetic resonance (MR) is usually performed on T1-weighted images, due to their high spatial resolution. T1w sequence, however, has some major downsides when brain lesions are present: the altered appearance of diseased tissues causes errors in tissues classification. In order to overcome these drawbacks, we employed two different MR sequences: fluid attenuated inversion recovery (FLAIR) and double inversion recovery (DIR). The former highlights both gray matter (GM) and white matter (WM), the latter highlights GM alone. We propose here a supervised classification scheme that does not require any anatomical a priori information to identify the 3 classes, "GM", "WM", and "background". Features are extracted by means of a local multi-scale texture analysis, computed for each pixel of the DIR and FLAIR sequences. The 9 textures considered are average, standard deviation, kurtosis, entropy, contrast, correlation, energy, homogeneity, and skewness, evaluated on a neighborhood of 3x3, 5x5, and 7x7 pixels. Hence, the total number of features associated to a pixel is 56 (9 textures x3 scales x2 sequences +2 original pixel values). The classifier employed is a Support Vector Machine with Radial Basis Function as kernel. From each of the 4 brain volumes evaluated, a DIR and a FLAIR slice have been selected and manually segmented by 2 expert neurologists, providing 1st and 2nd human reference observations which agree with an average accuracy of 99.03%. SVM performances have been assessed with a 4-fold cross-validation, yielding an average classification accuracy of 98.79%.

An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

PubMed

Rasouli, Mahdi; Chen, Yi; Basu, Arindam; Kukreja, Sunil L; Thakor, Nitish V

2018-04-01

Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.

Statistical analysis of texture in trunk images for biometric identification of tree species.

PubMed

Bressane, Adriano; Roveda, José A F; Martins, Antônio C G

2015-04-01

The identification of tree species is a key step for sustainable management plans of forest resources, as well as for several other applications that are based on such surveys. However, the present available techniques are dependent on the presence of tree structures, such as flowers, fruits, and leaves, limiting the identification process to certain periods of the year. Therefore, this article introduces a study on the application of statistical parameters for texture classification of tree trunk images. For that, 540 samples from five Brazilian native deciduous species were acquired and measures of entropy, uniformity, smoothness, asymmetry (third moment), mean, and standard deviation were obtained from the presented textures. Using a decision tree, a biometric species identification system was constructed and resulted to a 0.84 average precision rate for species classification with 0.83accuracy and 0.79 agreement. Thus, it can be considered that the use of texture presented in trunk images can represent an important advance in tree identification, since the limitations of the current techniques can be overcome.

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.

How to interpret rheometry, an innovative technique in soil physicochemistry, correctly - explained by critical users

NASA Astrophysics Data System (ADS)

Holthusen, Dörthe; Paulus, Eloi; Pértile, Patricia; Reichert, José Miguel; José Reinert, Dalvan; Horn, Rainer

2015-04-01

In soil physics some new methods were developed in the past years, e.g. with regard to observe soil structure non-invasively by means of micro-computed tomography, while the range of methods that investigate a soil's behavior in motion, e.g. under the impact of mechanic stresses, was enlarged by rheometry. The adjustment to the specific conditions of soil as a relatively dry and heterogeneous media opened a new field compared to the materials under investigations till then, like concrete or clay dispersions. The potential of this methodology is not fully appreciated yet which we would like to change with the help of our findings and the deduced guidelines. Rheology itself has been making part of soil science already for many years; however, the technical investigation was mostly done at a large scale, by means of triaxial shear or shear frame or compression tests etc. The corresponding measuring device, the rheometer, hence was tested and developed by and for the industries of food, cosmetics, paintings and coatings and only quite recently found to be applicable to soil also. The most interesting aspect was the possibility to combine soil physics with soil chemistry, namely to relate the behavior of soil under mechanical stresses to chemical interactions like e.g. particle charge and friction between single particles by means of an amplitude sweep test. Meanwhile, many different soils of different texture, organic matter and oxides and hydroxides content, and fertilization levels have been investigated at different matric potentials and allowed for a comparatively broad classification system. Therewith, the microstructural stability of a given soil by means of stiffness degradation during an amplitude sweep test can be categorized as a function of texture and matric potential. The micro scale soil stability furthermore influences the processes and thus soil structural stability at larger scales, which supports the investigations of relationships between different scales. Transferability to field situations is given due to the character of the mechanic stress which equals those appearing e.g. under a tractor tire exerting vibrating, compressing and shearing forces. Unfortunately, the multiple results of this single test can impede the use of rheometry as their interpretation requires intensive data analysis. By pointing out the single results and their meaning for soil structure, we aim at facilitating the use of rheometry. We also suggest moderate alterations to take into account more strongly the vertical compression of soil for a more comprehensive implementation of a micro shear test. Additionally, we point out further uses of a rheometer in soil physics, which is the viscosity, i. e. the ability of a fluid to penetrate into a porous system and to be relocated within. Herewith, quite complex fluid characteristics, e.g. of animal manure, anaerobic digestates, but also pesticides in aqueous solution, root mucilage etc. can be determined and applied as improved, because more detailed, model parameters in soil water transport. As a conclusion, our contribution aims at further promoting a promising method for investigating the fundamentals of soil physics at the interface to soil chemistry.

Classification scheme for sedimentary and igneous rocks in Gale crater, Mars

NASA Astrophysics Data System (ADS)

Mangold, N.; Schmidt, M. E.; Fisk, M. R.; Forni, O.; McLennan, S. M.; Ming, D. W.; Sautter, V.; Sumner, D.; Williams, A. J.; Clegg, S. M.; Cousin, A.; Gasnault, O.; Gellert, R.; Grotzinger, J. P.; Wiens, R. C.

2017-03-01

Rocks analyzed by the Curiosity rover in Gale crater include a variety of clastic sedimentary rocks and igneous float rocks transported by fluvial and impact processes. To facilitate the discussion of the range of lithologies, we present in this article a petrological classification framework adapting terrestrial classification schemes to Mars compositions (such as Fe abundances typically higher than for comparable lithologies on Earth), to specific Curiosity observations (such as common alkali-rich rocks), and to the capabilities of the rover instruments. Mineralogy was acquired only locally for a few drilled rocks, and so it does not suffice as a systematic classification tool, in contrast to classical terrestrial rock classification. The core of this classification involves (1) the characterization of rock texture as sedimentary, igneous or undefined according to grain/crystal sizes and shapes using imaging from the ChemCam Remote Micro-Imager (RMI), Mars Hand Lens Imager (MAHLI) and Mastcam instruments, and (2) the assignment of geochemical modifiers based on the abundances of Fe, Si, alkali, and S determined by the Alpha Particle X-ray Spectrometer (APXS) and ChemCam instruments. The aims are to help understand Gale crater geology by highlighting the various categories of rocks analyzed by the rover. Several implications are proposed from the cross-comparisons of rocks of various texture and composition, for instance between in place outcrops and float rocks. All outcrops analyzed by the rover are sedimentary; no igneous outcrops have been observed. However, some igneous rocks are clasts in conglomerates, suggesting that part of them are derived from the crater rim. The compositions of in-place sedimentary rocks contrast significantly with the compositions of igneous float rocks. While some of the differences between sedimentary rocks and igneous floats may be related to physical sorting and diagenesis of the sediments, some of the sedimentary rocks (e.g., potassic rocks) cannot be paired with any igneous rocks analyzed so far. In contrast, many float rocks, which cannot be classified from their poorly defined texture, plot on chemistry diagrams close to float rocks defined as igneous from their textures, potentially constraining their nature.

Classification scheme for sedimentary and igneous rocks in Gale crater, Mars

DOE PAGES

Mangold, Nicolas; Schmidt, Mariek E.; Fisk, Martin R.; ...

2016-11-05

Rocks analyzed by the Curiosity rover in Gale crater include a variety of clastic sedimentary rocks and igneous float rocks transported by fluvial and impact processes. Here, to facilitate the discussion of the range of lithologies, we present in this article a petrological classification framework adapting terrestrial classification schemes to Mars compositions (such as Fe abundances typically higher than for comparable lithologies on Earth), to specific Curiosity observations (such as common alkali-rich rocks), and to the capabilities of the rover instruments. Mineralogy was acquired only locally for a few drilled rocks, and so it does not suffice as a systematicmore » classification tool, in contrast to classical terrestrial rock classification. The core of this classification involves (1) the characterization of rock texture as sedimentary, igneous or undefined according to grain/crystal sizes and shapes using imaging from the ChemCam Remote Micro-Imager (RMI), Mars Hand Lens Imager (MAHLI) and Mastcam instruments, and (2) the assignment of geochemical modifiers based on the abundances of Fe, Si, alkali, and S determined by the Alpha Particle X-ray Spectrometer (APXS) and ChemCam instruments. The aims are to help understand Gale crater geology by highlighting the various categories of rocks analyzed by the rover. Several implications are proposed from the cross-comparisons of rocks of various texture and composition, for instance between in place outcrops and float rocks. All outcrops analyzed by the rover are sedimentary; no igneous outcrops have been observed. However, some igneous rocks are clasts in conglomerates, suggesting that part of them are derived from the crater rim. The compositions of in-place sedimentary rocks contrast significantly with the compositions of igneous float rocks. While some of the differences between sedimentary rocks and igneous floats may be related to physical sorting and diagenesis of the sediments, some of the sedimentary rocks (e.g., potassic rocks) cannot be paired with any igneous rocks analyzed so far. Finally, in contrast, many float rocks, which cannot be classified from their poorly defined texture, plot on chemistry diagrams close to float rocks defined as igneous from their textures, potentially constraining their nature.« less

Classification scheme for sedimentary and igneous rocks in Gale crater, Mars

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

Mangold, Nicolas; Schmidt, Mariek E.; Fisk, Martin R.

Rocks analyzed by the Curiosity rover in Gale crater include a variety of clastic sedimentary rocks and igneous float rocks transported by fluvial and impact processes. Here, to facilitate the discussion of the range of lithologies, we present in this article a petrological classification framework adapting terrestrial classification schemes to Mars compositions (such as Fe abundances typically higher than for comparable lithologies on Earth), to specific Curiosity observations (such as common alkali-rich rocks), and to the capabilities of the rover instruments. Mineralogy was acquired only locally for a few drilled rocks, and so it does not suffice as a systematicmore » classification tool, in contrast to classical terrestrial rock classification. The core of this classification involves (1) the characterization of rock texture as sedimentary, igneous or undefined according to grain/crystal sizes and shapes using imaging from the ChemCam Remote Micro-Imager (RMI), Mars Hand Lens Imager (MAHLI) and Mastcam instruments, and (2) the assignment of geochemical modifiers based on the abundances of Fe, Si, alkali, and S determined by the Alpha Particle X-ray Spectrometer (APXS) and ChemCam instruments. The aims are to help understand Gale crater geology by highlighting the various categories of rocks analyzed by the rover. Several implications are proposed from the cross-comparisons of rocks of various texture and composition, for instance between in place outcrops and float rocks. All outcrops analyzed by the rover are sedimentary; no igneous outcrops have been observed. However, some igneous rocks are clasts in conglomerates, suggesting that part of them are derived from the crater rim. The compositions of in-place sedimentary rocks contrast significantly with the compositions of igneous float rocks. While some of the differences between sedimentary rocks and igneous floats may be related to physical sorting and diagenesis of the sediments, some of the sedimentary rocks (e.g., potassic rocks) cannot be paired with any igneous rocks analyzed so far. Finally, in contrast, many float rocks, which cannot be classified from their poorly defined texture, plot on chemistry diagrams close to float rocks defined as igneous from their textures, potentially constraining their nature.« less

Russia: Saratov

Atmospheric Science Data Center

2013-04-17

... to differences in brightness and texture between bare soil and vegetated land. The chestnut-colored soils in this region are brighter ... of vegetation relative to the nadir camera, which sees more soil. In spring, therefore, the scene is brightest in the vertical view and ...

Shape from texture: an evaluation of visual cues

NASA Astrophysics Data System (ADS)

Mueller, Wolfgang; Hildebrand, Axel

1994-05-01

In this paper an integrated approach is presented to understand and control the influence of texture on shape perception. Following Gibson's hypotheses, which states that texture is a mathematically and psychological sufficient stimulus for surface perception, we evaluate different perceptual cues. Starting out from a perception-based texture classification introduced by Tamura et al., we build up a uniform sampled parameter space. For the synthesis of some of our textures we use the texture description language HiLDTe. To acquire the desired texture specification we take advantage of a genetic algorithm. Employing these textures we practice a number of psychological tests to evaluate the significance of the different texture features. A comprehension of the results derived from the psychological tests is done to constitute new shape analyzing techniques. Since the vanishing point seems to be an important visual cue we introduce the Hough transform. A prospective of future work within the field of visual computing is provided within the final section.

Rangeland degradation in savannas of South Africa: spatial patterns of soil and vegetation

NASA Astrophysics Data System (ADS)

Sandhage-Hofmann, Alexandra; Löffler, Jörg; du Preez, Chris; Kotzé, Elmarie; Weijers, Stef; Wundram, Dirk; Zacharias, Maximilan; Amelung, Wulf

2017-04-01

Extensive bush encroachment by Acacia mellifera and associated woody species at semi-arid and arid sites are the most notable forms of rangeland degradation in savannas of South Africa. Concerns are growing over the threat of suppression and loss of nutritious perennial grass species. Grazing and different rangeland management systems (communal and freehold) are considered to be of major importance for degradation, but the process of encroachment is not restricted to communal land. A vegetation change is mostly accompanied by changes in soil properties, where soils in savanna systems can profit from woody species due to litter fall, root distribution, shadow and animal resting time. Savannas are very heterogeneous systems with high spatial variation of patches with wood, herbaceous species and bare ground. We hypothesized that the spatial patterns of soil properties in South Africás rangelands are controlled by present or past vegetation, modulated by the tenure systems with higher rangeland degradation in communal areas. To test this, we sampled soils at communal and commercial land in the Kuruman area of South Africa with the following design: three farms per tenure system, 6 randomly chosen plots (100x100m) per farm, and 25 soil samples (0-10 cm) per plot, each in a 5x5m sampling area. At every sampling point, information of overlying vegetation was recorded (species or bare soil, canopy size, height). For each sampling area, if present, trees/ shrubs were sampled and their ages estimated through the counting of annual growth rings. For each plot, high resolution UAV aerial photos were taken to evaluate the extent of bush encroachment. Analyses involved main physical and chemical soil parameters and isotopic analyses. The results of a rough aerial image classification (grass, woody species, bare ground) revealed significant differences between the tenure systems with higher coverage of bare ground and shrubs at communal farms, and higher grass cover at commercial farms. The tenure systems had no differences in main texture classes of the soils, but significant differences in the composition of the sand fraction, with higher levels of fine sand and lower levels of coarse sand in communal farms. The chemical soil properties showed a high variability both within and between the farms, with much higher variability within communal than commercial farms. Additionally, concentrations of nitrogen, carbon, calcium and pH were significant higher in communal farms. Isotopic analyses in soils showed significant differences for 15N with higher levels in commercial farms. Different photosynthetic pathways are responsible for differences found in 13C values, with higher levels (-16-18‰) in C4-grassland and lower values (-22-26‰) in soils under Acacia (C3). We found relationships between soil properties and species or bare ground, where differences in texture likely interact with both, vegetation cover and soil properties.

Soil compaction effects on water status of ponderosa pine assessed through 13C/12C composition.

PubMed

Gomez, G Armando; Singer, Michael J; Powers, Robert F; Horwath, William R

2002-05-01

Soil compaction is a side effect of forest reestablishment practices resulting from use of heavy equipment and site preparation. Soil compaction often alters soil properties resulting in changes in plant-available water. The use of pressure chamber methods to assess plant water stress has two drawbacks: (1) the measurements are not integrative; and (2) the method is difficult to apply extensively to establish seasonal soil water status. We evaluated leaf carbon isotopic composition (delta13C) as a means of assessing effects of soil compaction on water status and growth of young ponderosa pine (Pinus ponderosa var. ponderosa Dougl. ex Laws) stands across a range of soil textures. Leaf delta13C in cellulose and whole foliar tissue were highly correlated. Leaf delta13C in both whole tissue and cellulose (holocellulose) was up to 1.0 per thousand lower in trees growing in non-compacted (NC) loam or clay soils than in compacted (SC) loam or clay soils. Soil compaction had the opposite effect on leaf delta13C in trees growing on sandy loam soil, indicating that compaction increased water availability in this soil type. Tree growth response to compaction also varied with soil texture, with no effect, a negative effect and a positive effect as a result of compaction of loam, clay and sandy loam soils, respectively. There was a significant correlation between 13C signature and tree growth along the range of soil textures. Leaf delta13C trends were correlated with midday stem water potentials. We conclude that leaf delta13C can be used to measure retrospective water status and to assess the impact of site preparation on tree growth. The advantage of the leaf delta13C approach is that it provides an integrative assessment of past water status in different aged leaves.

Trends in Soil Moisture Reflect More Than Slope Position: Soils on San Cristóbal Island, Galápagos as a Case Study

NASA Astrophysics Data System (ADS)

Percy, M.; Singha, K.; Benninger, L. K.; Riveros-Iregui, D. A.; Mirus, B. B.

2015-12-01

The spatial and temporal distribution of soil moisture in tropical critical zones depends upon a number of variables including topographic position, soil texture, overlying vegetation, and local microclimates. We investigate the influences on soil moisture on a tropical basaltic island (San Cristóbal, Galápagos) across a variety of microclimates during the transition from the wetter to the drier season. We used multiple approaches to characterize spatial and temporal patterns in soil moisture at four sites across microclimates ranging from arid to very humid. The microclimates on San Cristóbal vary with elevation, so our monitoring includes two sites in the transitional zone at lower elevations, one in the humid zone at moderate elevations, and one in the very humid zone in higher elevations. We made over 250 near-surface point measurements per site using a Hydrosense II probe, and estimated the lateral variability in soil moisture across each site with an EM-31 electrical conductivity meter. We also monitored continuous time-series of in-situ soil moisture dynamics using three nested TDR probes collocated with meteorological stations at each of the sites. Preliminary analysis indicates that soils in the very humid zone have lower electrical conductivities across all the hillslopes as compared to the humid and transitional zones, which suggests that additional factors beyond climate and slope position are important. While soil texture across the very humid site is fairly uniform, variations in vegetation have a strong control on soil moisture patterns. At the remaining sites the vegetation patterns also have a very strong local influence on soil moisture, but correlation between the depth to clay layers and soil moisture patterns suggests that mineralogy is also important. Our findings suggest that the microclimatic setting is a crucial consideration for understanding relations between vegetation, soil texture, and soil-moisture dynamics in tropical critical zones.

Textural features for image classification

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Dinstein, I.; Shanmugam, K.

1973-01-01

Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

The use of computer-assisted image analysis in the evaluation of the effect of management systems on changes in the color, chemical composition and texture of m. longissimus dorsi in pigs.

PubMed

Zapotoczny, Piotr; Kozera, Wojciech; Karpiesiuk, Krzysztof; Pawłowski, Rodian

2014-08-01

The effect of management systems on selected physical properties and chemical composition of m. longissimus dorsi was studied in pigs. Muscle texture parameters were determined by computer-assisted image analysis, and the color of muscle samples was evaluated using a spectrophotometer. Highly significant correlations were observed between chemical composition and selected texture variables in the analyzed images. Chemical composition was not correlated with color or spectral distribution. Subject to the applied classification methods and groups of variables included in the classification model, the experimental groups were identified correctly in 35-95%. No significant differences in the chemical composition of m. longissimus dorsi were observed between experimental groups. Significant differences were noted in color lightness (L*) and redness (a*). Copyright © 2014 Elsevier Ltd. All rights reserved.

Local feature saliency classifier for real-time intrusion monitoring

NASA Astrophysics Data System (ADS)

Buch, Norbert; Velastin, Sergio A.

2014-07-01

We propose a texture saliency classifier to detect people in a video frame by identifying salient texture regions. The image is classified into foreground and background in real time. No temporal image information is used during the classification. The system is used for the task of detecting people entering a sterile zone, which is a common scenario for visual surveillance. Testing is performed on the Imagery Library for Intelligent Detection Systems sterile zone benchmark dataset of the United Kingdom's Home Office. The basic classifier is extended by fusing its output with simple motion information, which significantly outperforms standard motion tracking. A lower detection time can be achieved by combining texture classification with Kalman filtering. The fusion approach running at 10 fps gives the highest result of F1=0.92 for the 24-h test dataset. The paper concludes with a detailed analysis of the computation time required for the different parts of the algorithm.

29 CFR Appendix A to Subpart P of... - Soil Classification

Code of Federal Regulations, 2013 CFR

2013-07-01

... 29 Labor 8 2013-07-01 2013-07-01 false Soil Classification A Appendix A to Subpart P of Part 1926..., App. A Appendix A to Subpart P of Part 1926—Soil Classification (a) Scope and application—(1) Scope. This appendix describes a method of classifying soil and rock deposits based on site and environmental...

29 CFR Appendix A to Subpart P of... - Soil Classification

Code of Federal Regulations, 2012 CFR

2012-07-01

... 29 Labor 8 2012-07-01 2012-07-01 false Soil Classification A Appendix A to Subpart P of Part 1926..., App. A Appendix A to Subpart P of Part 1926—Soil Classification (a) Scope and application—(1) Scope. This appendix describes a method of classifying soil and rock deposits based on site and environmental...

29 CFR Appendix A to Subpart P of... - Soil Classification

Code of Federal Regulations, 2014 CFR

2014-07-01

... 29 Labor 8 2014-07-01 2014-07-01 false Soil Classification A Appendix A to Subpart P of Part 1926..., App. A Appendix A to Subpart P of Part 1926—Soil Classification (a) Scope and application—(1) Scope. This appendix describes a method of classifying soil and rock deposits based on site and environmental...

29 CFR Appendix A to Subpart P of... - Soil Classification

Code of Federal Regulations, 2011 CFR

2011-07-01

... 29 Labor 8 2011-07-01 2011-07-01 false Soil Classification A Appendix A to Subpart P of Part 1926..., App. A Appendix A to Subpart P of Part 1926—Soil Classification (a) Scope and application—(1) Scope. This appendix describes a method of classifying soil and rock deposits based on site and environmental...

Detecting blind building façades from highly overlapping wide angle aerial imagery

NASA Astrophysics Data System (ADS)

Burochin, Jean-Pascal; Vallet, Bruno; Brédif, Mathieu; Mallet, Clément; Brosset, Thomas; Paparoditis, Nicolas

2014-10-01

This paper deals with the identification of blind building façades, i.e. façades which have no openings, in wide angle aerial images with a decimeter pixel size, acquired by nadir looking cameras. This blindness characterization is in general crucial for real estate estimation and has, at least in France, a particular importance on the evaluation of legal permission of constructing on a parcel due to local urban planning schemes. We assume that we have at our disposal an aerial survey with a relatively high stereo overlap along-track and across-track and a 3D city model of LoD 1, that can have been generated with the input images. The 3D model is textured with the aerial imagery by taking into account the 3D occlusions and by selecting for each façade the best available resolution texture seeing the whole façade. We then parse all 3D façades textures by looking for evidence of openings (windows or doors). This evidence is characterized by a comprehensive set of basic radiometric and geometrical features. The blindness prognostic is then elaborated through an (SVM) supervised classification. Despite the relatively low resolution of the images, we reach a classification accuracy of around 85% on decimeter resolution imagery with 60 × 40 % stereo overlap. On the one hand, we show that the results are very sensitive to the texturing resampling process and to vegetation presence on façade textures. On the other hand, the most relevant features for our classification framework are related to texture uniformity and horizontal aspect and to the maximal contrast of the opening detections. We conclude that standard aerial imagery used to build 3D city models can also be exploited to some extent and at no additional cost for facade blindness characterisation.

Corn response to nitrogen is influenced by soil texture and weather

USDA-ARS?s Scientific Manuscript database

Soil properties and weather conditions are known to affect soil nitrogen (N) availability and plant N uptake. However, studies examining N response as affected by soil and weather sometimes give conflicting results. Meta-analysis is a statistical method for estimating treatment effects in a se...

Random-Forest Classification of High-Resolution Remote Sensing Images and Ndsm Over Urban Areas

NASA Astrophysics Data System (ADS)

Sun, X. F.; Lin, X. G.

2017-09-01

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample's category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

Detailed soil mapping and relationships between soil characteristics and tree growth in an alluvial plain (Lombardy, Italy)

NASA Astrophysics Data System (ADS)

Ferré, Chiara; Comolli, Roberto

2015-04-01

The study area is located in an abandoned meander of the Oglio river (southern Lombardy, Italy), with young soils of alluvial origin (Calcaric Fluvisols). During 2002, in an area covering 20 hectares, a tree plant for wood production was realized (oak, hornbeam, ash, alder, and walnut; poplar only in the first part of the growth cycle). Objective of the study was to verify the existence of correlations between tree growth and soil characteristics. In 2004, the soil was sampled at 126 points, according to a regular grid, taking the surface soil horizon (Ap). The collected soil samples were analyzed in laboratory, measuring pH in H2O and KCl, texture, total carbonates, soil organic C (SOC), available P (Olsen), and exchangeable K. The pH in H2O varies between 7.7 and 8.1; the pH in KCl varies between 7.2 and 7.7; the more frequent particle-size classes are loam and sandy loam; SOC varies between 0.4 and 1.1%; total carbonates from 23 to 45%; exchangeable K between 0.01 and 0.25 cmol(+) kg-1; available P between 1.2 and 16.8 mg kg-1. At a distance of 12 years, in 2014, diameters at breast height of all the trees (2513 in total) were measured and their height was estimated on the basis of empirical equations obtained for each species, in order to calculate the tree volume. Spatial variability of soil properties was evaluated and mapped using multivariate geostatistical techniques. The analyses revealed the presence of different scales of spatial variation: micro-scale, short range scale (about 80 m for texture) and long range scale (about 220 m for texture). The spatial pattern of most soil properties (mainly texture and total carbonates) was probably associated with fluvial depositional processes. To evaluate soil-plant relationships, soil characteristics were collocated into the plant data set by estimating specific soil properties at each individual tree location. Soil spatial variability was reflected by the differences in plant growth. Statistical analysis of the collected data highlighted a number of statistically significant correlations between tree growth and soil features: clay content and total carbonates were almost always negatively correlated with tree growth; sand content, pH in KCl, available P and exchangeable K were almost always positively correlated; SOC content was negatively correlated, but only for oak.

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

The combined use of liming and Sarcocornia fruticosa development for phytomanagement of salt marsh soils polluted by mine wastes.

PubMed

González-Alcaraz, María Nazaret; Conesa, Héctor Miguel; Tercero, María del Carmen; Schulin, Rainer; Alvarez-Rogel, José; Egea, Consuelo

2011-02-15

The aim of this study was to evaluate the combined effects of liming and behaviour of Sarcocornia fruticosa as a strategy of phytomanagement of metal polluted salt marsh soils. Soils were taken from two polluted salt marshes (one with fine texture and pH∼6.4 and the other one with sandy texture and pH∼3.1). A lime amendment derived from the marble industry was added to each soil at a rate of 20 g kg(-1), giving four treatments: neutral soil with/without liming and acidic soil with/without liming. Cuttings of S. fruticosa were planted in pots filled with these substrates and grown for 10 months. The pots were irrigated with eutrophicated water. As expected, lime amendment decreased the soluble metal concentrations. In both soils, liming favoured the growth of S. fruticosa and enhanced the capacity of the plants to phytostabilise metals in roots. Copyright © 2010 Elsevier B.V. All rights reserved.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

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

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J., E-mail: bje@mayo.edu

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O{sup 6}-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiersmore » were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.« less

Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis.

PubMed

Hu, Shan; Xu, Chao; Guan, Weiqiao; Tang, Yong; Liu, Yana

2014-01-01

Osteosarcoma is the most common malignant bone tumor among children and adolescents. In this study, image texture analysis was made to extract texture features from bone CR images to evaluate the recognition rate of osteosarcoma. To obtain the optimal set of features, Sym4 and Db4 wavelet transforms and gray-level co-occurrence matrices were applied to the image, with statistical methods being used to maximize the feature selection. To evaluate the performance of these methods, a support vector machine algorithm was used. The experimental results demonstrated that the Sym4 wavelet had a higher classification accuracy (93.44%) than the Db4 wavelet with respect to osteosarcoma occurrence in the epiphysis, whereas the Db4 wavelet had a higher classification accuracy (96.25%) for osteosarcoma occurrence in the diaphysis. Results including accuracy, sensitivity, specificity and ROC curves obtained using the wavelets were all higher than those obtained using the features derived from the GLCM method. It is concluded that, a set of texture features can be extracted from the wavelets and used in computer-aided osteosarcoma diagnosis systems. In addition, this study also confirms that multi-resolution analysis is a useful tool for texture feature extraction during bone CR image processing.

The effect of vegetation and soil texture on the nature of organics in runoff from a catchment supplying water for domestic consumption.

PubMed

Awad, John; van Leeuwen, John; Abate, Dawit; Pichler, Markus; Bestland, Erick; Chittleborough, David J; Fleming, Nigel; Cohen, Jonathan; Liffner, Joel; Drikas, Mary

2015-10-01

The influence of vegetation and soil texture on the concentration and character of dissolved organic matter (DOM) present in runoff from the surface and sub-surface of zero order catchments of the Myponga Reservoir-catchment (South Australia) was investigated to determine the impacts of catchment characteristics and land management practices on the quality of waters used for domestic supply. Catchments selected have distinct vegetative cover (grass, native vegetation or pine) and contrasting texture of the surface soil horizon (sand or clay loam/clay). Water samples were collected from three slope positions (upper, middle, and lower) at soil depths of ~30 cm and ~60 cm in addition to overland flows. Filtered (0.45 μm) water samples were analyzed for dissolved organic carbon (DOC) and UV-visible absorbance and by F-EEM and HPSEC with UV and fluorescence detection to characterize the DOM. Surface and sub-surface runoff from catchments with clay soils and native vegetation or grass had lower DOC concentrations and lower relative abundances of aromatic, humic-like and high molecular weight organics than runoff from sandy soils with these vegetative types. Sub-surface flows from two catchments with Pinus radiata had similar DOC concentrations and DOM character, regardless of marked variation in surface soil texture. Runoff from catchments under native vegetation and grass on clay soils resulted in lower DOC concentrations and hence would be expected to have lower coagulant demand in conventional treatment for potable water supply than runoff from corresponding sandy soil catchments. However, organics in runoff from clay catchments would be more difficult to remove by coagulation. Surface waters from the native vegetation and grass catchments were generally found to have higher relative abundance of organic compounds amenable to removal by coagulation compared with sub-surface waters. Biophysical and land management practices combine to have a marked influence on the quality of source water used for domestic supply. Copyright © 2015 Elsevier B.V. All rights reserved.

Forest tree species clssification based on airborne hyper-spectral imagery

NASA Astrophysics Data System (ADS)

Dian, Yuanyong; Li, Zengyuan; Pang, Yong

2013-10-01

Forest precision classification products were the basic data for surveying of forest resource, updating forest subplot information, logging and design of forest. However, due to the diversity of stand structure, complexity of the forest growth environment, it's difficult to discriminate forest tree species using multi-spectral image. The airborne hyperspectral images can achieve the high spatial and spectral resolution imagery of forest canopy, so it will good for tree species level classification. The aim of this paper was to test the effective of combining spatial and spectral features in airborne hyper-spectral image classification. The CASI hyper spectral image data were acquired from Liangshui natural reserves area. Firstly, we use the MNF (minimum noise fraction) transform method for to reduce the hyperspectral image dimensionality and highlighting variation. And secondly, we use the grey level co-occurrence matrix (GLCM) to extract the texture features of forest tree canopy from the hyper-spectral image, and thirdly we fused the texture and the spectral features of forest canopy to classify the trees species using support vector machine (SVM) with different kernel functions. The results showed that when using the SVM classifier, MNF and texture-based features combined with linear kernel function can achieve the best overall accuracy which was 85.92%. It was also confirm that combine the spatial and spectral information can improve the accuracy of tree species classification.

Statistical analysis of textural features for improved classification of oral histopathological images.

PubMed

Muthu Rama Krishnan, M; Shah, Pratik; Chakraborty, Chandan; Ray, Ajoy K

2012-04-01

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback-Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier's performance from 80.69% to 90.75%. Results are here studied and discussed.

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.

Investigating the Impacts of Particle Size and Wind Speed on Brownout

DTIC Science & Technology

2015-03-26

mixture of sand, silt, clay , and organic material, classified based on its size and texture. Sand is the largest of the particle materials, with...smallest soil component is clay , with particle sizes less than 0.002 mm. Ultra-fine in texture, clay feels sticky when wet, is extremely cohesive, and does...not allow air to move through it easily. Clay makes a soil dense and is hard as concrete when dry. Loam is a nearly even mixture of sand and silt

Reflecting on the structure of soil classification systems: insights from a proposal for integrating subsoil data into soil information systems

NASA Astrophysics Data System (ADS)

Dondeyne, Stefaan; Juilleret, Jérôme; Vancampenhout, Karen; Deckers, Jozef; Hissler, Christophe

2017-04-01

Classification of soils in both World Reference Base for soil resources (WRB) and Soil Taxonomy hinges on the identification of diagnostic horizons and characteristics. However as these features often occur within the first 100 cm, these classification systems convey little information on subsoil characteristics. An integrated knowledge of the soil, soil-to-substratum and deeper substratum continuum is required when dealing with environmental issues such as vegetation ecology, water quality or the Critical Zone in general. Therefore, we recently proposed a classification system of the subsolum complementing current soil classification systems. By reflecting on the structure of the subsoil classification system which is inspired by WRB, we aim at fostering a discussion on some potential future developments of WRB. For classifying the subsolum we define Regolite, Saprolite, Saprock and Bedrock as four Subsolum Reference Groups each corresponding to different weathering stages of the subsoil. Principal qualifiers can be used to categorize intergrades of these Subsoil Reference Groups while morphologic and lithologic characteristics can be presented with supplementary qualifiers. We argue that adopting a low hierarchical structure - akin to WRB and in contrast to a strong hierarchical structure as in Soil Taxonomy - offers the advantage of having an open classification system avoiding the need for a priori knowledge of all possible combinations which may be encountered in the field. Just as in WRB we also propose to use principal and supplementary qualifiers as a second level of classification. However, in contrast to WRB we propose to reserve the principal qualifiers for intergrades and to regroup the supplementary qualifiers into thematic categories (morphologic or lithologic). Structuring the qualifiers in this manner should facilitate the integration and handling of both soil and subsoil classification units into soil information systems and calls for paying attention to these structural issues in future developments of WRB.

Realistic Expectations for Rock Identification.

ERIC Educational Resources Information Center

Westerback, Mary Elizabeth; Azer, Nazmy

1991-01-01

Presents a rock classification scheme for use by beginning students. The scheme is based on rock textures (glassy, crystalline, clastic, and organic framework) and observable structures (vesicles and graded bedding). Discusses problems in other rock classification schemes which may produce confusion, misidentification, and anxiety. (10 references)…

Wavelet-based energy features for glaucomatous image classification.

PubMed

Dua, Sumeet; Acharya, U Rajendra; Chowriappa, Pradeep; Sree, S Vinitha

2012-01-01

Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.

Soil-Site Factors Affecting Southern Upland Oak Managment and Growth

Treesearch

John K. Francis

1980-01-01

Soil supplies trees with physical support, moisture, oxygen, and nutrients. Amount of moisture most limits tree growth; and soil and topographic factors such as texture and aspect, which influence available soil moisture. are most useful in predicting growth. Equations that include soil and topographic variables can be used to predict site index. Foresters can also...

Texture Classification by Texton: Statistical versus Binary

PubMed Central

Guo, Zhenhua; Zhang, Zhongcheng; Li, Xiu; Li, Qin; You, Jane

2014-01-01

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor. PMID:24520346

Texture analysis of common renal masses in multiple MR sequences for prediction of pathology

NASA Astrophysics Data System (ADS)

Hoang, Uyen N.; Malayeri, Ashkan A.; Lay, Nathan S.; Summers, Ronald M.; Yao, Jianhua

2017-03-01

This pilot study performs texture analysis on multiple magnetic resonance (MR) images of common renal masses for differentiation of renal cell carcinoma (RCC). Bounding boxes are drawn around each mass on one axial slice in T1 delayed sequence to use for feature extraction and classification. All sequences (T1 delayed, venous, arterial, pre-contrast phases, T2, and T2 fat saturated sequences) are co-registered and texture features are extracted from each sequence simultaneously. Random forest is used to construct models to classify lesions on 96 normal regions, 87 clear cell RCCs, 8 papillary RCCs, and 21 renal oncocytomas; ground truths are verified through pathology reports. The highest performance is seen in random forest model when data from all sequences are used in conjunction, achieving an overall classification accuracy of 83.7%. When using data from one single sequence, the overall accuracies achieved for T1 delayed, venous, arterial, and pre-contrast phase, T2, and T2 fat saturated were 79.1%, 70.5%, 56.2%, 61.0%, 60.0%, and 44.8%, respectively. This demonstrates promising results of utilizing intensity information from multiple MR sequences for accurate classification of renal masses.

Hydraulic Property and Soil Textural Classification Measurements for Rainier Mesa, Nevada Test Site, Nevada

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

Ebel, Brian A.; Nimmo, John R.

2009-12-29

This report presents particle size analysis, field-saturated hydraulic conductivity measurements, and qualitative descriptions of surficial materials at selected locations at Rainier Mesa, Nevada. Measurements and sample collection were conducted in the Rainier Mesa area, including unconsolidated sediments on top of the mesa, an ephemeral wash channel near the mesa edge, and dry U12n tunnel pond sediments below the mesa. Particle size analysis used a combination of sieving and optical diffraction techniques. Field-saturated hydraulic conductivity measurements employed a single-ring infiltrometer with analytical formulas that correct for falling head and spreading outside the ring domain. These measurements may prove useful to currentmore » and future efforts at Rainier Mesa aimed at understanding infiltration and its effect on water fluxes and radionuclide transport in the unsaturated zone.« less

Correlation of the Rock Mass Rating (RMR) System with the Unified Soil Classification System (USCS): Introduction of the Weak Rock Mass Rating System (W-RMR)

NASA Astrophysics Data System (ADS)

Warren, Sean N.; Kallu, Raj R.; Barnard, Chase K.

2016-11-01

Underground gold mines in Nevada are exploiting increasingly deeper ore bodies comprised of weak to very weak rock masses. The Rock Mass Rating (RMR) classification system is widely used at underground gold mines in Nevada and is applicable in fair to good-quality rock masses, but is difficult to apply and loses reliability in very weak rock mass to soil-like material. Because very weak rock masses are transition materials that border engineering rock mass and soil classification systems, soil classification may sometimes be easier and more appropriate to provide insight into material behavior and properties. The Unified Soil Classification System (USCS) is the most likely choice for the classification of very weak rock mass to soil-like material because of its accepted use in tunnel engineering projects and its ability to predict soil-like material behavior underground. A correlation between the RMR and USCS systems was developed by comparing underground geotechnical RMR mapping to laboratory testing of bulk samples from the same locations, thereby assigning a numeric RMR value to the USCS classification that can be used in spreadsheet calculations and geostatistical analyses. The geotechnical classification system presented in this paper including a USCS-RMR correlation, RMR rating equations, and the Geo-Pick Strike Index is collectively introduced as the Weak Rock Mass Rating System (W-RMR). It is the authors' hope that this system will aid in the classification of weak rock masses and more usable design tools based on the RMR system. More broadly, the RMR-USCS correlation and the W-RMR system help define the transition between engineering soil and rock mass classification systems and may provide insight for geotechnical design in very weak rock masses.

Texture of Frozen Food

NASA Astrophysics Data System (ADS)

Wani, Kohmei

Quantitative determination of textural quality of frozen food due to freezing and storage conditions is complicated,since the texture is consisted of multi-dimensiona1 factors. The author reviewed the importance of texture in food quality and the factors which is proposed by a priori estimation. New classification of expression words of textural properties by subjective evaluation and an application of four elements mechanical model for analysis of physical characteristics was studied on frozen meat patties. Combination of freezing-thawing condition on the subjective properties and physiochemical characteristics of beef lean meat and hamachi fish (Yellow-tail) meat was studied. Change of the plasticity and the deformability of these samples differed by freezing-thawing rate and cooking procedure. Also optimum freezing-thawing condition was differed from specimens.

Comparison of Forest Soil Carbon Dynamics at Five Sites Along a Latitudinal Gradient

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

Garten Jr, Charles T

2011-01-01

Carbon stocks, and C:N ratios, were measured in the forest floor, mineral soil, and two mineral soil fractions (particulate and mineral-associated organic matter, POM and MOM, respectively) at five forest sites, ranging from 60 to 100 years old, along a latitudinal gradient in the eastern United States. Sampling at four sites was replicated over two consecutive years. For many measurements (like forest floor carbon stocks, cumulative soil organic carbon stocks to 20 cm, and the fraction of whole soil carbon in POM), there was no significant difference between years at each site despite the use of somewhat different sampling methods.more » With one exception, forest floor and mineral soil carbon stocks increased from warm, southern, sites (with fine-textured soils) to northern, cool, sites (with more coarse-textured soils). The exception was a northern site, with less than 10% silt-clay content, that had a soil organic carbon stock similar to those measured at southern sites. Soil carbon at each site was partitioned into two pools (labile and stable) on the basis of carbon measured in the forest floor and POM and MOM fractions from the mineral soil. A two-compartment steady-state model, with randomly varying parameter values, was used in probabilistic calculations to estimate the turnover time of labile soil organic carbon (MRTU) and the annual transfer of labile carbon to stable carbon (k2) at each site in two different years. Based on empirical data, the turnover time of stable soil carbon (MRTS) was determined by mean annual temperature and increased from 30 to 100 years from south to north. Moving from south to north, MRTU increased from approximately 5 to 14 years. Consistent with prior studies, 13C enrichment factors ( ) from the Rayleigh equation, that describe the rate of change in 13C through the soil profile, were an indicator of soil carbon turnover times along the latitudinal gradient. Consistent with its role in stabilization of soil organic carbon, silt-clay content along the gradient was positively correlated (r = 0.91; P 0.001) with parameter k2. Mean annual temperature was indicated as the environmental factor most strongly associated with south to north differences in the storage and turnover of labile soil carbon. However, soil texture appeared to override the influence of temperature when there was too little silt-clay content to stabilize labile soil carbon and thereby protect it from decomposition. Irrespective of latitudinal differences in measured soil carbon stocks, each study site had a relatively high proportion of labile soil carbon (approximately 50% of whole soil carbon to a depth of 20 cm). Depending on unknown temperature sensitivities, large labile pools of forest soil carbon are potentially at risk of depletion by decomposition in a warming climate, and losses could be disproportionately higher from coarse textured forest soils.« less

Field infiltration measurements in grassed roadside drainage ditches: Spatial and temporal variability

NASA Astrophysics Data System (ADS)

Ahmed, Farzana; Gulliver, John S.; Nieber, J. L.

2015-11-01

Roadside drainage ditches (grassed swales) are an attractive stormwater control measure (SCM) since they can reduce runoff volume by infiltrating water into the soil, filter sediments and associated pollutants out of the water, and settle solids onto the soil surface. In this study a total of 722 infiltration measurements were collected in five swales located in Twin-Cities, MN and one swale located in Madison, WI to characterize the field-saturated hydraulic conductivity (Kfs) derived from the infiltration measurements of these swales. Measurements were taken with a falling head device, the Modified Philip Dunne (MPD) infiltrometer, which allows the collection of simultaneous infiltration measurements at multiple locations with several infiltrometers. Field-saturated hydraulic conductivity was higher than expected for different soil texture classes. We hypothesize that this is due to plant roots creating macropores that break up the soil for infiltration. Statistical analysis was performed on the Kfs values to analyze the effect of initial soil moisture content, season, soil texture class and distance in downstream direction on the geometric mean Kfs value of a swale. Because of the high spatial variation of Kfs in the same swale no effect of initial soil moisture content, season and soil texture class was observed on the geometric mean Kfs value. But the distance in downstream direction may have positive or negative effect on the Kfs value. An uncertainty analysis on the Kfs value indicated that approximately twenty infiltration measurements is the minimum number to obtain a representative geometric mean Kfs value of a swale that is less than 350 m long within an acceptable level of uncertainty.

Variable rate application of nematicides on cotton fields: a promising site-specific management strategy.

PubMed

Ortiz, Brenda V; Perry, Calvin; Sullivan, Dana; Lu, Ping; Kemerait, Robert; Davis, Richard F; Smith, Amanda; Vellidis, George; Nichols, Robert

2012-03-01

Field tests were conducted to determine if differences in response to nematicide application (i.e., root-knot nematode (RKN) populations, cotton yield, and profitability) occurred among RKN management zones (MZ). The MZ were delineated using fuzzy clustering of five terrain (TR) and edaphic (ED) field features related to soil texture: apparent soil electrical conductivity shallow (ECa-shallow) and deep (ECa-deep), elevation (EL), slope (SL), and changes in bare soil reflectance. Zones with lowest mean values of ECa- shallow, ECa- deep, NDVI, and SL were designated as at greater risk for high RKN levels. Nematicide-treated plots (4 rows wide and 30 m long) were established in a randomized complete block design within each zone, but the number of replications in each zone varied from four to six depending on the size of the zone.The nematicides aldicarb (Temik 15 G) and 1,3-dichloropropene (1,3-D,Telone II) were applied at two rates (0.51 and 1.0 kg a.i./ha for aldicarb, and 33.1 and 66.2 kg a.i./ha for 1,3-D) to RKN MZ in commercial fields between 2007 and 2009. A consolidated analysis over the entire season showed that regardless of the zone, there were not differences between aldicarb rates and 1,3-D rates. The result across zones showed that 1,3-D provided better RKN control than did aldicarb in zones with low ECa values (high RKN risk zones exhibiting more coarse-textured sandy soils). In contrast, in low risk zones with relatively higher ECa values (heavier textured soil), the effects of 1,3-D and aldicarb were equal and application of any of the treatments provided sufficient control. In low RKN risk zones, a farmer would often have lost money if a high rate of 1,3-D was applied. This study showed that the effect of nematicide type and rate on RKN control and cotton yield varied across management zones (MZ) with the most expensive treatment likely to provide economic benefit only in zones with coarser soil texture. This study demonstrates the value of site specific application of nematicides based on management zones, although this approach might not be economically beneficial in fields with little variability in soil texture.

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.

Image enhancements of Landsat 8 (OLI) and SAR data for preliminary landslide identification and mapping applied to the central region of Kenya

NASA Astrophysics Data System (ADS)

Mwaniki, M. W.; Kuria, D. N.; Boitt, M. K.; Ngigi, T. G.

2017-04-01

Image enhancements lead to improved performance and increased accuracy of feature extraction, recognition, identification, classification and hence change detection. This increases the utility of remote sensing to suit environmental applications and aid disaster monitoring of geohazards involving large areas. The main aim of this study was to compare the effect of image enhancement applied to synthetic aperture radar (SAR) data and Landsat 8 imagery in landslide identification and mapping. The methodology involved pre-processing Landsat 8 imagery, image co-registration, despeckling of the SAR data, after which Landsat 8 imagery was enhanced by Principal and Independent Component Analysis (PCA and ICA), a spectral index involving bands 7 and 4, and using a False Colour Composite (FCC) with the components bearing the most geologic information. The SAR data were processed using textural and edge filters, and computation of SAR incoherence. The enhanced spatial, textural and edge information from the SAR data was incorporated to the spectral information from Landsat 8 imagery during the knowledge based classification. The methodology was tested in the central highlands of Kenya, characterized by rugged terrain and frequent rainfall induced landslides. The results showed that the SAR data complemented Landsat 8 data which had enriched spectral information afforded by the FCC with enhanced geologic information. The SAR classification depicted landslides along the ridges and lineaments, important information lacking in the Landsat 8 image classification. The success of landslide identification and classification was attributed to the enhanced geologic features by spectral, textural and roughness properties.

Soil and Crop management: Lessons from the laboratory biosphere 2002-2004

NASA Astrophysics Data System (ADS)

Silverstone, S.; Nelson, M.; Alling, A.; Allen, J.

During the years 2002 and 2003, three closed system experiments were carried out in the "Laboratory Biosphere" facility located in Santa Fe, New Mexico. The program involved experimentation with "Hoyt" Soy Beans, USU Apogee Wheat and TU-82-155 sweet potato using a 5.37 m2 soil planting bed which was 30 cm deep. The soil texture, 40% clay, 31% sand and 28% silt (a clay loam), was collected from an organic farm in New Mexico to avoid chemical residues. Soil management practices involved minimal tillage, mulching and returning crop residues to the soil after each experiment. Between experiment #2 and #3, the top 15 cm of the soil was amended using a mix of peat moss, green sand, humates and pumice to improve soil texture, lower soil pH and increase nutrient availability. Soil analyses for all three experiments are presented to show how the soils have changed with time and how the changes relate to crop selection and rotation, soil selection and management, water management and pest control. The experience and information gained from these experiments are being applied to the future design of the Mars On Earth facility.

Local binary pattern texture-based classification of solid masses in ultrasound breast images

NASA Astrophysics Data System (ADS)

Matsumoto, Monica M. S.; Sehgal, Chandra M.; Udupa, Jayaram K.

2012-03-01

Breast cancer is one of the leading causes of cancer mortality among women. Ultrasound examination can be used to assess breast masses, complementarily to mammography. Ultrasound images reveal tissue information in its echoic patterns. Therefore, pattern recognition techniques can facilitate classification of lesions and thereby reduce the number of unnecessary biopsies. Our hypothesis was that image texture features on the boundary of a lesion and its vicinity can be used to classify masses. We have used intensity-independent and rotation-invariant texture features, known as Local Binary Patterns (LBP). The classifier selected was K-nearest neighbors. Our breast ultrasound image database consisted of 100 patient images (50 benign and 50 malignant cases). The determination of whether the mass was benign or malignant was done through biopsy and pathology assessment. The training set consisted of sixty images, randomly chosen from the database of 100 patients. The testing set consisted of forty images to be classified. The results with a multi-fold cross validation of 100 iterations produced a robust evaluation. The highest performance was observed for feature LBP with 24 symmetrically distributed neighbors over a circle of radius 3 (LBP24,3) with an accuracy rate of 81.0%. We also investigated an approach with a score of malignancy assigned to the images in the test set. This approach provided an ROC curve with Az of 0.803. The analysis of texture features over the boundary of solid masses showed promise for malignancy classification in ultrasound breast images.

Classification Scheme for Diverse Sedimentary and Igneous Rocks Encountered by MSL in Gale Crater

NASA Technical Reports Server (NTRS)

Schmidt, M. E.; Mangold, N.; Fisk, M.; Forni, O.; McLennan, S.; Ming, D. W.; Sumner, D.; Sautter, V.; Williams, A. J.; Gellert, R.

2015-01-01

The Curiosity Rover landed in a lithologically and geochemically diverse region of Mars. We present a recommended rock classification framework based on terrestrial schemes, and adapted for the imaging and analytical capabilities of MSL as well as for rock types distinctive to Mars (e.g., high Fe sediments). After interpreting rock origin from textures, i.e., sedimentary (clastic, bedded), igneous (porphyritic, glassy), or unknown, the overall classification procedure (Fig 1) involves: (1) the characterization of rock type according to grain size and texture; (2) the assignment of geochemical modifiers according to Figs 3 and 4; and if applicable, in depth study of (3) mineralogy and (4) geologic/stratigraphic context. Sedimentary rock types are assigned by measuring grains in the best available resolution image (Table 1) and classifying according to the coarsest resolvable grains as conglomerate/breccia, (coarse, medium, or fine) sandstone, silt-stone, or mudstone. If grains are not resolvable in MAHLI images, grains in the rock are assumed to be silt sized or smaller than surface dust particles. Rocks with low color contrast contrast between grains (e.g., Dismal Lakes, sol 304) are classified according to minimum size of apparent grains from surface roughness or shadows outlining apparent grains. Igneous rocks are described as intrusive or extrusive depending on crystal size and fabric. Igneous textures may be described as granular, porphyritic, phaneritic, aphyric, or glassy depending on crystal size. Further descriptors may include terms such as vesicular or cumulate textures.

A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Moraes, Diego R.; Reche, Gustavo A.; Borges, Lucas R.; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

2017-03-01

This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).

Modeling uncertainty and correlation in soil properties using Restricted Pairing and implications for ensemble-based hillslope-scale soil moisture and temperature estimation

NASA Astrophysics Data System (ADS)

Flores, A. N.; Entekhabi, D.; Bras, R. L.

2007-12-01

Soil hydraulic and thermal properties (SHTPs) affect both the rate of moisture redistribution in the soil column and the volumetric soil water capacity. Adequately constraining these properties through field and lab analysis to parameterize spatially-distributed hydrology models is often prohibitively expensive. Because SHTPs vary significantly at small spatial scales individual soil samples are also only reliably indicative of local conditions, and these properties remain a significant source of uncertainty in soil moisture and temperature estimation. In ensemble-based soil moisture data assimilation, uncertainty in the model-produced prior estimate due to associated uncertainty in SHTPs must be taken into account to avoid under-dispersive ensembles. To treat SHTP uncertainty for purposes of supplying inputs to a distributed watershed model we use the restricted pairing (RP) algorithm, an extension of Latin Hypercube (LH) sampling. The RP algorithm generates an arbitrary number of SHTP combinations by sampling the appropriate marginal distributions of the individual soil properties using the LH approach, while imposing a target rank correlation among the properties. A previously-published meta- database of 1309 soils representing 12 textural classes is used to fit appropriate marginal distributions to the properties and compute the target rank correlation structure, conditioned on soil texture. Given categorical soil textures, our implementation of the RP algorithm generates an arbitrarily-sized ensemble of realizations of the SHTPs required as input to the TIN-based Realtime Integrated Basin Simulator with vegetation dynamics (tRIBS+VEGGIE) distributed parameter ecohydrology model. Soil moisture ensembles simulated with RP- generated SHTPs exhibit less variance than ensembles simulated with SHTPs generated by a scheme that neglects correlation among properties. Neglecting correlation among SHTPs can lead to physically unrealistic combinations of parameters that exhibit implausible hydrologic behavior when input to the tRIBS+VEGGIE model.

Decomposition of Organic Carbon in Fine Soil Particles Is Likely More Sensitive to Warming than in Coarse Particles: An Incubation Study with Temperate Grassland and Forest Soils in Northern China

PubMed Central

Ding, Fan; Huang, Yao; Sun, Wenjuan; Jiang, Guangfu; Chen, Yue

2014-01-01

It is widely recognized that global warming promotes soil organic carbon (SOC) decomposition, and soils thus emit more CO2 into the atmosphere because of the warming; however, the response of SOC decomposition to this warming in different soil textures is unclear. This lack of knowledge limits our projection of SOC turnover and CO2 emission from soils after future warming. To investigate the CO2 emission from soils with different textures, we conducted a 107-day incubation experiment. The soils were sampled from temperate forest and grassland in northern China. The incubation was conducted over three short-term cycles of changing temperature from 5°C to 30°C, with an interval of 5°C. Our results indicated that CO2 emissions from sand (>50 µm), silt (2–50 µm), and clay (<2 µm) particles increased exponentially with increasing temperature. The sand fractions emitted more CO2 (CO2-C per unit fraction-C) than the silt and clay fractions in both forest and grassland soils. The temperature sensitivity of the CO2 emission from soil particles, which is expressed as Q10, decreased in the order clay>silt>sand. Our study also found that nitrogen availability in the soil facilitated the temperature dependence of SOC decomposition. A further analysis of the incubation data indicated a power-law decrease of Q10 with increasing temperature. Our results suggested that the decomposition of organic carbon in fine-textured soils that are rich in clay or silt could be more sensitive to warming than those in coarse sandy soils and that SOC might be more vulnerable in boreal and temperate regions than in subtropical and tropical regions under future warming. PMID:24736659

Classification of anthropogenic soils by new diagnostic criteria involved in the Slovak Soil Classification System (2014)

NASA Astrophysics Data System (ADS)

Sobocká, Jaroslava; Balkovič, Juraj; Bedrna, Zoltán

2017-04-01

Anthropogenic soils can be found mostly in SUITMA areas. The issue of adequate and correct description and classification of these soils occurs very often and can result in inconsistent even in contradictory opinions. In the new version of the anthropogenic soil classification system in Slovakia some new diagnostics criteria were involved and applied for better understanding the inherent nature of these soils. The group of the former anthropogenic soils was divided following scheme of soil reference groups in the WRB 2014 (Anthrozem and Technozem). According to the new version of the Slovak anthropogenic soils classification (2014) there have been distinguished 2 groups of anthropogenic soils: 1) cultivated soils group including 2 soil types (in Slovak terminology): Kultizem and Hortizem and 2) technogenic soils group having 2 soil types: Antrozem and Technozem. Cultivated soil group represents soils developing or forming "in-situ" with diagnostic horizons characterized by human deeply influenced cultivated processes. Technogenic soil group are soils developing like "ex-situ" soils. The key features recognizing technogenic soil group are human-transported and altered material (HTAM = ex-situ aspect), and artefacts content. Diagnostic horizons (top and subsoil) were described as various material affected by physical-mechanical excavation, transportation and spread, mixing, and containing artefacts (the new diagnostic feature). Kultizems are differentiated by cultivated horizon(s) and Technozems by anthropogenic horizon(s). Cultivated horizons are mostly well-known described horizon in many scientific references. Anthropogenic horizons for Technozem are developed from the human-induced transported and altered material which origin is from the other ecological locality that adjacent area. Materials (or substrates) can consist of various material (natural, technogenic or their mixing) with thickness ≥ 60 cm. Artefacts are the second diagnostic feature which presence authenticates the "artificial origin" of the soil. Natural material contains ≤ 10 % artefacts; natural-technogenic 10-40 % artefacts; and technogenic ≥ 40 %. In the soil survey anthropogenic transported or altered layer is very simply recognizable in soil profile if it is compared with adjacent natural horizons. The classification problem is to define and distinguish not only artefacts in soil profile but recognize the origin of the material. The completed manual for these issues is missing. In the contribution, there graphically individual basic soil types of Antrozems and Technozems with some subtypes will be illustrated. Also the basic schema of classification units in Slovakia will be depicted.

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...

When is a soil remediated? Comparison of biopiled and windrowed soils contaminated with bunker-fuel in a full-scale trial.

PubMed

Coulon, Frédéric; Al Awadi, Mohammed; Cowie, William; Mardlin, David; Pollard, Simon; Cunningham, Colin; Risdon, Graeme; Arthur, Paul; Semple, Kirk T; Paton, Graeme I

2010-10-01

A six month field scale study was carried out to compare windrow turning and biopile techniques for the remediation of soil contaminated with bunker C fuel oil. End-point clean-up targets were defined by human risk assessment and ecotoxicological hazard assessment approaches. Replicate windrows and biopiles were amended with either nutrients and inocula, nutrients alone or no amendment. In addition to fractionated hydrocarbon analysis, culturable microbial characterisation and soil ecotoxicological assays were performed. This particular soil, heavy in texture and historically contaminated with bunker fuel was more effectively remediated by windrowing, but coarser textures may be more amendable to biopiling. This trial reveals the benefit of developing risk and hazard based approaches in defining end-point bioremediation of heavy hydrocarbons when engineered biopile or windrow are proposed as treatment option. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Structural texture similarity metrics for image analysis and retrieval.

PubMed

Zujovic, Jana; Pappas, Thrasyvoulos N; Neuhoff, David L

2013-07-01

We develop new metrics for texture similarity that accounts for human visual perception and the stochastic nature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviations between textures that according to human judgment are essentially identical. The proposed metrics extend the ideas of structural similarity and are guided by research in texture analysis-synthesis. They are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in sliding windows. We conduct systematic tests to investigate metric performance in the context of "known-item search," the retrieval of textures that are "identical" to the query texture. This eliminates the need for cumbersome subjective tests, thus enabling comparisons with human performance on a large database. Our experimental results indicate that the proposed metrics outperform peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM) and its variations, as well as state-of-the-art texture classification metrics, using standard statistical measures.

Classification Features of US Images Liver Extracted with Co-occurrence Matrix Using the Nearest Neighbor Algorithm

NASA Astrophysics Data System (ADS)

Moldovanu, Simona; Bibicu, Dorin; Moraru, Luminita; Nicolae, Mariana Carmen

2011-12-01

Co-occurrence matrix has been applied successfully for echographic images characterization because it contains information about spatial distribution of grey-scale levels in an image. The paper deals with the analysis of pixels in selected regions of interest of an US image of the liver. The useful information obtained refers to texture features such as entropy, contrast, dissimilarity and correlation extract with co-occurrence matrix. The analyzed US images were grouped in two distinct sets: healthy liver and steatosis (or fatty) liver. These two sets of echographic images of the liver build a database that includes only histological confirmed cases: 10 images of healthy liver and 10 images of steatosis liver. The healthy subjects help to compute four textural indices and as well as control dataset. We chose to study these diseases because the steatosis is the abnormal retention of lipids in cells. The texture features are statistical measures and they can be used to characterize irregularity of tissues. The goal is to extract the information using the Nearest Neighbor classification algorithm. The K-NN algorithm is a powerful tool to classify features textures by means of grouping in a training set using healthy liver, on the one hand, and in a holdout set using the features textures of steatosis liver, on the other hand. The results could be used to quantify the texture information and will allow a clear detection between health and steatosis liver.

Exploring the Potential of High Resolution Remote Sensing Data for Mapping Vegetation and the Age Groups of Oil Palm Plantation

NASA Astrophysics Data System (ADS)

Kamiran, N.; Sarker, M. L. R.

2014-02-01

The land use/land cover transformation in Malaysia is enormous due to palm oil plantation which has provided huge economical benefits but also created a huge concern for carbon emission and biodiversity. Accurate information about oil palm plantation and the age of plantation is important for a sustainable production, estimation of carbon storage capacity, biodiversity and the climate model. However, the problem is that this information cannot be extracted easily due to the spectral signature for forest and age group of palm oil plantations is similar. Therefore, a noble approach "multi-scale and multi-texture algorithms" was used for mapping vegetation and different age groups of palm oil plantation using a high resolution panchromatic image (WorldView-1) considering the fact that pan imagery has a potential for more detailed and accurate mapping with an effective image processing technique. Seven texture algorithms of second-order Grey Level Co-occurrence Matrix (GLCM) with different scales (from 3×3 to 39×39) were used for texture generation. All texture parameters were classified step by step using a robust classifier "Artificial Neural Network (ANN)". Results indicate that single spectral band was unable to provide good result (overall accuracy = 34.92%), while higher overall classification accuracies (73.48%, 84.76% and 93.18%) were obtained when textural information from multi-scale and multi-texture approach were used in the classification algorithm.

Plant absorption of trace elements in sludge amended soils and correlation with soil chemical speciation.

PubMed

Torri, Silvana; Lavado, Raúl

2009-07-30

The aim of the present study was to investigate the relationship between Lolium perenne L. uptake of Cd, Cu, Pb, and Zn in sludge amended soils and soil availability of these elements assessed by soil sequential extraction. A greenhouse experiment was set with three representative soils of the Pampas Region, Argentina, amended with sewage sludge and sewage sludge enriched with its own incinerated ash. After the stabilization period of 60 days, half of the pots were sampled for soil analysis; the rest of the pots were sown with L. perenne and harvested 8, 12, 16 and 20 weeks after sowing, by cutting just above the soil surface. Cadmium and Pb concentrations in aerial tissues of L. perenne were below detection limits, in good agreement with the soil fractionation study. Copper and Zn concentration in the first harvest were significantly higher in the coarse textured soil compared to the fine textured soil, in contrast with soil chemical speciation. In the third harvest, there was a positive correlation between Cu and Zn concentration in aerial biomass and soil fractions usually considered of low availability. We conclude that the most available fractions obtained by soil sequential extraction did not provide the best indicator of Cu and Zn availability to L. perenne.

Soil physical properties regulate lethal heating during burning of woody residues

Treesearch

Matt Busse; Carol Shestak; Ken Hubbert; Eric Knapp

2010-01-01

Temperatures well in excess of the lethal threshold for roots (60°C) have been measured in forest soils when woody fuels are burned. Whether this heat pulse is strongly moderated by soil moisture or soil texture is not fully understood, however. We measured soil heat profi les during 60 experimental burns, identifying changes in maximum soil temperature and heat...

Three-Dimensional Soil Landscape Modeling: A Potential Earth Science Teaching Tool

ERIC Educational Resources Information Center

Schmid, Brian M.; Manu, Andrew; Norton, Amy E.

2009-01-01

Three-dimensional visualization is helpful in understanding soils, and three dimensional (3-D) tools are gaining popularity in teaching earth sciences. Those tools are still somewhat underused in soil science, yet soil properties such as texture, color, and organic carbon content vary both vertically and horizontally across the landscape. These…

Missouri Ozark forest soils: perspectives and realities

Treesearch

R. David. Hammer

1997-01-01

Ozark forest soils are dynamic in space and time, and most formed in multiple parent materials. Erosion and mass movement have been variable and extensive. Soil attributes including texture, cation exchange capacity, and mineralogy are related to geologic strata and to geomorphic conditions. Soil organic carbon content is influenced by surface shape, position in...

A Meta-Analysis quantifying the relationships between response to nitrogen fertilization vs soil texture and weather

USDA-ARS?s Scientific Manuscript database

Weather and soil properties are known to affect soil nitrogen (N) availability and plant N uptake. Studies examining N response as affected by soil and weather sometimes give conflicting results. Meta-analysis is a statistical method for estimating treatment effects in a series of experiments...

Determination of field-effective soil properties in the tidewater region of North Carolina

Treesearch

J. McFero Grace; R.W. Skaggs

2013-01-01

Soils vary spatially in texture, structure, depth of horizons, and macropores, which can lead to a large variation in soil physical properties. In particular, saturated hydraulic conductivity (Ksat) and drainable porosity are critical properties required to model field hydrology in poorly drained lands. These soil-property values can be measured...

Quantitative Relationship of Soil Texture with the Observed Population Density Reduction of Heterodera glycines after Annual Corn Rotation in Nebraska

PubMed Central

Pérez-Hernández, Oscar; Giesler, Loren J.

2014-01-01

Soil texture has been commonly associated with the population density of Heterodera glycines (soybean cyst nematode: SCN), but such an association has been mainly described in terms of textural classes. In this study, multivariate analysis and a generalized linear modeling approach were used to elucidate the quantitative relationship of soil texture with the observed SCN population density reduction after annual corn rotation in Nebraska. Forty-five commercial production fields were sampled in 2009, 2010, and 2011 and SCN population density (eggs/100 cm3 of soil) for each field was determined before (Pi) and after (Pf) annual corn rotation from ten 3 × 3-m sampling grids. Principal components analysis revealed that, compared with silt and clay, sand had a stronger association with SCN Pi and Pf. Cluster analysis using the average linkage method and confirmed through 1,000 bootstrap simulations identified two groups: one corresponding to predominant silt-and-clay fields and other to sand-predominant fields. This grouping suggested that SCN relative percent population decline was higher in the sandy than in the silt-and-clay predominant group. However, when groups were compared for their SCN population density reduction using Pf as the response, Pi as a covariate, and incorporating the year and field variability, a negative binomial generalized linear model indicated that the SCN population density reduction was not statistically different between the sand-predominant field group and the silt-and-clay predominant group. PMID:24987160

Temporal observations of bright soil exposures at Gusev crater, Mars

USGS Publications Warehouse

Rice, M.S.; Bell, J.F.; Cloutis, E.A.; Wray, J.J.; Herkenhoff, K. E.; Sullivan, R.; Johnson, J. R.; Anderson, R.B.

2011-01-01

The Mars Exploration Rover Spirit has discovered bright soil deposits in its wheel tracks that previously have been confirmed to contain ferric sulfates and/or opaline silica. Repeated Pancam multispectral observations have been acquired at four of these deposits to monitor spectral and textural changes over time during exposure to Martian surface conditions. Previous studies suggested that temporal spectral changes occur because of mineralogic changes (e.g., phase transitions accompanying dehydration). In this study, we present a multispectral and temporal analysis of eight Pancam image sequences at the Tyrone exposure, three at the Gertrude Weise exposure, two at the Kit Carson exposure, and ten at the Ulysses exposure that have been acquired as of sol 2132 (1 January 2010). We compare observed variations in Pancam data to spectral changes predicted by laboratory experiments for the dehydration of ferric sulfates. We also present a spectral analysis of repeated Mars Reconnaissance Orbiter HiRISE observations spanning 32 sols and a textural analysis of Spirit Microscopic Imager observations of Ulysses spanning 102 sols. At all bright soil exposures, we observe no statistically significant spectral changes with time that are uniquely diagnostic of dehydration and/or mineralogic phase changes. However, at Kit Carson and Ulysses, we observe significant textural changes, including slumping within the wheel trench, movement of individual grains, disappearance of fines, and dispersal of soil clods. All observed textural changes are consistent with aeolian sorting and/or minor amounts of air fall dust deposition.

Temporal observations of bright soil exposures at Gusev crater, Mars

USGS Publications Warehouse

Rice, M.S.; Bell, J.F.; Cloutis, E.A.; Wray, J.J.; Herkenhoff, K. E.; Sullivan, R.; Johnson, J. R.; Anderson, R.B.

2011-01-01

The Mars Exploration Rover Spirit has discovered bright soil deposits in its wheel tracks that previously have been confirmed to contain ferric sulfates and/or opaline silica. Repeated Pancam multispectral observations have been acquired at four of these deposits to monitor spectral and textural changes over time during exposure to Martian surface conditions. Previous studies suggested that temporal spectral changes occur because of mineralogic changes (e.g., phase transitions accompanying dehydration). In this study, we present a multispectral and temporal analysis of eight Pancam image sequences at the Tyrone exposure, three at the Gertrude Weise exposure, two at the Kit Carson exposure, and ten at the Ulysses exposure that have been acquired as of sol 2132 (1 January 2010). We compare observed variations in Pancam data to spectral changes predicted by laboratory experiments for the dehydration of ferric sulfates. We also present a spectral analysis of repeated Mars Reconnaissance Orbiter HiRISE observations spanning 32 sols and a textural analysis of Spirit Microscopic Imager observations of Ulysses spanning 102 sols. At all bright soil exposures, we observe no statistically significant spectral changes with time that are uniquely diagnostic of dehydration and/or mineralogic phase changes. However, at Kit Carson and Ulysses, we observe significant textural changes, including slumping within the wheel trench, movement of individual grains, disappearance of fines, and dispersal of soil clods. All observed textural changes are consistent with aeolian sorting and/or minor amounts of air fall dust deposition. Copyright 2011 by the American Geophysical Union.

Multi-spectral texture analysis for IED detection

NASA Astrophysics Data System (ADS)

Petersson, Henrik; Gustafsson, David

2016-10-01

The use of Improvised Explosive Devices (IEDs) has increased significantly over the world and is a globally widespread phenomenon. Although measures can be taken to anticipate and prevent the opponent's ability to deploy IEDs, detection of IEDs will always be a central activity. There is a wide range of different sensors that are useful but also simple means, such as a pair of binoculars, can be crucial to detect IEDs in time. Disturbed earth (disturbed soil), such as freshly dug areas, dumps of clay on top of smooth sand or depressions in the ground, could be an indication of a buried IED. This paper brie y describes how a field trial was set-up to provide a realistic data set on a road section containing areas with disturbed soil due to buried IEDs. The road section was imaged using a forward looking land-based sensor platform consisting of visual imaging sensors together with long-, mid-, and shortwave infrared imaging sensors. The paper investigates the presence of discriminatory information in surface texture comparing areas with disturbed against undisturbed soil. The investigation is conducted for the different wavelength bands available. To extract features that describe texture, image processing tools such as 'Histogram of Oriented Gradients', 'Local Binary Patterns', 'Lacunarity', 'Gabor Filtering' and 'Co-Occurence' is used. It is found that texture as characterized here may provide discriminatory information to detect disturbed soil, but the signatures we found are weak and can not be used alone in e.g. a detector system.

Soil mineralogy and microbes determine forest life history strategy and carbon cycling in humid tropical forests

NASA Astrophysics Data System (ADS)

Soong, J.; Verbruggen, E.; Peñuelas, J.; Janssens, I. A.; Grau, O.

2017-12-01

Tropical forests account for over one third of global terrestrial gross primary productivity and cycle more C than any other ecosystem on Earth. However, we still lack a mechanistic understanding of how such high productivity is maintained on the old, highly weathered and phosphorus depleted soils in the tropics. We hypothesized that heterogeneity in soil texture, mineralogy and microbial community composition may be the major drivers of differences in soil C storage and P limitation across tropical forests. We sampled 12 forest sites across a 200 km transect in the humid neo-tropics of French Guiana that varied in soil texture, precipitation and mineralogy. We found that soil texture was a major driver of soil carbon stocks and forest life history strategy, where sandy forests have lower soil C stocks, slower turnover and decomposition and a more closed nutrient cycle while clayey forests have higher soil C stocks, faster turnover and a more leaky nutrient cycle (using natural abundance stable isotope evidence). We found that although the presence of Al and Fe oxides in the clayey soils occludes soil organic matter and P, a greater abundance of arbuscular mycorrhizal fungi help forests to access occluded P in clayey soils fueling higher turnover and faster decomposition rates. Evidence from a laboratory incubation of tropical soils with nutrient additions further demonstrates the de-coupling of microbial P demands from C:N limitations providing further evidence for the need to examine microbial stoichiometry to explain C cycling in the P-limited tropics. We argue that microbial community composition and physiological demands, constrained within the limitations of soil mineralogical reactivity, largely controls nutrient and C cycling in tropical forest soils. Together our observational field study and laboratory incubation provide a unique dataset to shed light on the mineralogical and microbial controls on C and nutrient cycling in tropical soils. By integrating microbial, soil, litter and forest metrics we describe how microbes, minerals and soil organic matter act as an ecosystem property driving forest dynamics via microbial and plant stoichiometric constraints.

Terahertz Spectroscopy for Proximal Soil Sensing: An Approach to Particle Size Analysis

PubMed Central

Dworak, Volker; Mahns, Benjamin; Selbeck, Jörn; Weltzien, Cornelia

2017-01-01

Spatially resolved soil parameters are some of the most important pieces of information for precision agriculture. These parameters, especially the particle size distribution (texture), are costly to measure by conventional laboratory methods, and thus, in situ assessment has become the focus of a new discipline called proximal soil sensing. Terahertz (THz) radiation is a promising method for nondestructive in situ measurements. The THz frequency range from 258 gigahertz (GHz) to 350 GHz provides a good compromise between soil penetration and the interaction of the electromagnetic waves with soil compounds. In particular, soil physical parameters influence THz measurements. This paper presents investigations of the spectral transmission signals from samples of different particle size fractions relevant for soil characterization. The sample thickness ranged from 5 to 17 mm. The transmission of THz waves was affected by the main mineral particle fractions, sand, silt and clay. The resulting signal changes systematically according to particle sizes larger than half the wavelength. It can be concluded that THz spectroscopic measurements provide information about soil texture and penetrate samples with thicknesses in the cm range. PMID:29048392

Evaluation of airborne image data for mapping riparian vegetation within the Grand Canyon

USGS Publications Warehouse

Davis, Philip A.; Staid, Matthew I.; Plescia, Jeffrey B.; Johnson, Jeffrey R.

2002-01-01

This study examined various types of remote-sensing data that have been acquired during a 12-month period over a portion of the Colorado River corridor to determine the type of data and conditions for data acquisition that provide the optimum classification results for mapping riparian vegetation. Issues related to vegetation mapping included time of year, number and positions of wavelength bands, and spatial resolution for data acquisition to produce accurate vegetation maps versus cost of data. Image data considered in the study consisted of scanned color-infrared (CIR) film, digital CIR, and digital multispectral data, whose resolutions from 11 cm (photographic film) to 100 cm (multispectral), that were acquired during the Spring, Summer, and Fall seasons in 2000 for five long-term monitoring sites containing riparian vegetation. Results show that digitally acquired data produce higher and more consistent classification accuracies for mapping vegetation units than do film products. The highest accuracies were obtained from nine-band multispectral data; however, a four-band subset of these data, that did not include short-wave infrared bands, produced comparable mapping results. The four-band subset consisted of the wavelength bands 0.52-0.59 µm, 0.59-0.62 µm, 0.67-0.72 µm, and 0.73-0.85 µm. Use of only three of these bands that simulate digital CIR sensors produced accuracies for several vegetation units that were 10% lower than those obtained using the full multispectral data set. Classification tests using band ratios produced lower accuracies than those using band reflectance for scanned film data; a result attributed to the relatively poor radiometric fidelity maintained by the film scanning process, whereas calibrated multispectral data produced similar classification accuracies using band reflectance and band ratios. This suggests that the intrinsic band reflectance of the vegetation is more important than inter-band reflectance differences in attaining high mapping accuracies. These results also indicate that radiometrically calibrated sensors that record a wide range of radiance produce superior results and that such sensors should be used for monitoring purposes. When texture (spatial variance) at near-infrared wavelength is combined with spectral data in classification, accuracy increased most markedly (20-30%) for the highest resolution (11-cm) CIR film data, but decreased in its effect on accuracy in lower-resolution multi-spectral image data; a result observed in previous studies (Franklin and McDermid 1993, Franklin et al. 2000, 2001). While many classification unit accuracies obtained from the 11-cm film CIR band with texture data were in fact higher than those produced using the 100-cm, nine-band multispectral data with texture, the 11-cm film CIR data produced much lower accuracies than the 100-cm multispectral data for the more sparsely populated vegetation units due to saturation of picture elements during the film scanning process in vegetation units with a high proportion of alluvium. Overall classification accuracies obtained from spectral band and texture data range from 36% to 78% for all databases considered, from 57% to 71% for the 11-cm film CIR data, and from 54% to 78% for the 100-cm multispectral data. Classification results obtained from 20-cm film CIR band and texture data, which were produced by applying a Gaussian filter to the 11-cm film CIR data, showed increases in accuracy due to texture that were similar to those observed using the original 11-cm film CIR data. This suggests that data can be collected at the lower resolution and still retain the added power of vegetation texture. Classification accuracies for the riparian vegetation units examined in this study do not appear to be influenced by season of data acquisition, although data acquired under direct sunlight produced higher overall accuracies than data acquired under overcast conditions. The latter observation, in addition to the importance of band reflectance for classification, implies that data should be acquired near summer solstice when sun elevation and reflectance is highest and when shadows cast by steep canyon walls are minimized.

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

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.

Effects of buried obstacles on penetration resistance in cohesionless soils

NASA Technical Reports Server (NTRS)

Deluca, E. W.; Carrasco, L. H.

1972-01-01

Recent experiments concerning penetration of cohesionless soils in special molds that include solid obstacles embedded within the soil matrix are reported. The relative effects of these obstacles with respect to the soil properties of relative density, texture, and gradation are also discussed. Because lunar soil is fairly cohesionless, special attention was given to the Apollo lunar simulant, AP-12.

Verification of High Resolution Soil Moisture and Latent Heat in Germany

NASA Astrophysics Data System (ADS)

Samaniego, L. E.; Warrach-Sagi, K.; Zink, M.; Wulfmeyer, V.

2012-12-01

Improving our understanding of soil-land-surface-atmosphere feedbacks is fundamental to make reliable predictions of water and energy fluxes on land systems influenced by anthropogenic activities. Estimating, for instance, which would be the likely consequences of changing climatic regimes on water availability and crop yield, requires of high resolution soil moisture. Modeling it at large-scales, however, is difficult and uncertain because of the interplay between state variables and fluxes and the significant parameter uncertainty of the predicting models. At larger scales, the sub-grid variability of the variables involved and the nonlinearity of the processes complicate the modeling exercise even further because parametrization schemes might be scale dependent. Two contrasting modeling paradigms (WRF/Noah-MP and mHM) were employed to quantify the effects of model and data complexity on soil moisture and latent heat over Germany. WRF/Noah-MP was forced ERA-interim on the boundaries of the rotated CORDEX-Grid (www.meteo.unican.es/wiki/cordexwrf) with a spatial resolution of 0.11o covering Europe during the period from 1989 to 2009. Land cover and soil texture were represented in WRF/Noah-MP with 1×1~km MODIS images and a single horizon, coarse resolution European-wide soil map with 16 soil texture classes, respectively. To ease comparison, the process-based hydrological model mHM was forced with daily precipitation and temperature fields generated by WRF during the same period. The spatial resolution of mHM was fixed at 4×4~km. The multiscale parameter regionalization technique (MPR, Samaniego et al. 2010) was embedded in mHM to be able to estimate effective model parameters using hyper-resolution input data (100×100~km) obtained from Corine land cover and detailed soil texture fields for various horizons comprising 72 soil texture classes for Germany, among other physiographical variables. mHM global parameters, in contrast with those of Noah-MP, were obtained by closing the water balance over major river basins in Germany. Simulated soil moisture and latent heat flux were also evaluated at several eddy covariance sites in Germany. Comparison of monthly soil moisture and latent heat fields obtained with both models over Germany exhibited significant differences, which are mainly attributed to the subgrid variability of key model parameters such as porosity and aerodynamic resistance. Comparison of soil moisture fields obtained with WRF/Noah-MP and mHM forced with grided metereological observations (German Meteorological Service) showed that the differences between both models are mainly due to a combination of precipitation bias and different soil texture resolution. However, EOF analyses indicate that CORDEX results start recovering structures due to soil and vegetation properties. This experiment clearly highlighted the importance of hyper resolution input data to address these challenge. High resolution mHM simulations also indicate that the parametric uncertainty of land surface models is significant, and should not be neglected if a model is to be employed for application at regional scales, e.g. for drought monitoring.

Partitioning the relative contributions of inorganic plant composition and soil characteristics to the quality of Helichrysum italicum subsp. italicum (Roth) G. Don fil. essential oil.

PubMed

Bianchini, Ange; Santoni, François; Paolini, Julien; Bernardini, Antoine-François; Mouillot, David; Costa, Jean

2009-07-01

Composition of Helichrysum italicum subsp. italicum essential oil showed chemical variability according to vegetation cycle, environment, and geographic origins. In the present work, 48 individuals of this plant at different development stages and the corresponding root soils were sampled: i) 28 volatile components were identified and measured in essential oil by using GC and GC/MS; ii) ten elements from plants and soils have been estimated using colorimetry in continuous flux, flame atomic absorption spectrometry, or emission spectrometry (FAAS/FAES); iii) texture and acidity (real and potential) of soil samples were also reported. Relationships between the essential-oil composition, the inorganic plant composition, and the soil characteristics (inorganic composition, texture, and acidity) have been established using multivariate analysis such as Principal Component Analysis (PCA) and partial Redundancy Analysis (RDA). This study demonstrates a high level of intraspecific differences in oil composition due to environmental factors and, more particularly, soil characteristics.

Effect of soil texture on phytoremediation of arsenic-contaminated soils

NASA Astrophysics Data System (ADS)

Pallud, C. E.; Matzen, S. L.; Olson, A.

2015-12-01

Soil arsenic (As) contamination is a global problem, resulting in part from anthropogenic activities, including the use of arsenical pesticides and treated wood, mining, and irrigated agriculture. Phytoextraction using the hyperaccumulating fern Pteris vittata is a promising new technology to remediate soils with shallow arsenic contamination with minimal site disturbance. However, many challenges still lie ahead for a global application of phytoremediation. For example, remediation times using P. vittata are on the order of decades. In addition, most research on As phytoextraction with P. vittata has examined As removal from sandy soils, where As is more available, with little research focusing on As removal from clayey soils, where As is less available. The objective of this study is to determine the effects of soil texture and soil fertilization on As extraction by P. vittata, to optimize remediation efficiency and decrease remediation time under complex field conditions. A field study was established 2.5 years ago in an abandoned railroad grade contaminated with As (average 85.5 mg kg-1) with texture varying from sandy loam to silty clay loam. Organic N, inorganic N, organic P, inorganic P, and compost were applied to separate sub-plots; control ferns were grown in untreated soil. In a parallel greenhouse experiment, ferns were grown in sandy loam soil extracted from the field (180 mg As kg-1), with similar treatments as those used at the field site, plus a high phosphate treatment and treatments with arbuscular mycorrhizal fungi. In the field study, fern mortality was 24% higher in clayey soil than in sandy soil due to waterlogging, while As was primarily associated with sandy soil. Results from the sandy loam soil indicate that soil treatments did not significantly increase As phytoextraction, which was lower in phosphate-treated ferns than in control ferns, both in the field and greenhouse study. Under greenhouse conditions, ferns treated with organic N were largest and accumulated the most total As, while under field conditions, control and compost-treated ferns accumulated the most total As. Under greenhouse conditions, leaching appeared to account for most As removed from sandy loam soil. Results from a similar greenhouse study now underway in clayey soil will be discussed.

Study of microarthopod communities to assess soil quality in different managed vineyards

NASA Astrophysics Data System (ADS)

Gagnarli, E.; Goggioli, D.; Tarchi, F.; Guidi, S.; Nannelli, R.; Vignozzi, N.; Valboa, G.; Lottero, M. R.; Corino, L.; Simoni, S.

2015-01-01

Land use influences the abundance and diversity of soil arthropods. The evaluation of the impact of different management strategies on soil quality is increasingly requested. The determination of communities' structures of edaphic fauna can represent an efficient tool. In this study, in some vineyards in Piedmont (Italy), the effects of two different management systems, organic and integrated pest management (IPM), on soil biota were evaluated. As microarthropods living in soil surface are an important component of soil ecosystem interacting with all the other system components, a multi disciplinary approach was adopted by characterizing also some soil physical and chemical characteristics (soil texture, soil pH, total organic carbon, total nitrogen, calcium carbonate). Soil samplings were carried out on Winter 2011 and Spring 2012. All specimens were counted and determined up to the order level. The biological quality of the soil was defined through the determination of ecological indices, such as QBS-ar, species richness and indices of Shannon-Weaver, Pielou, Margalef and Simpson. The mesofauna abundance was affected by both the type of management and the soil texture. The analysis of microarthropod communities by QBS-ar showed higher values in organic than in IPM managed vineyards; in particular, the values registered in organic vineyards were similar to those characteristic of preserved soils.

About soil cover heterogeneity of agricultural research stations' experimental fields

NASA Astrophysics Data System (ADS)

Rannik, Kaire; Kõlli, Raimo; Kukk, Liia

2013-04-01

Depending on local pedo-ecological conditions (topography, (geo) diversity of soil parent material, meteorological conditions) the patterns of soil cover and plant cover determined by soils are very diverse. Formed in the course of soil-plant mutual relationship, the natural ecosystems are always influenced to certain extent by the other local soil forming conditions or they are site specific. The agricultural land use or the formation of agro-ecosystems depends foremost on the suitability of soils for the cultivation of feed and food crops. As a rule, the most fertile or the best soils of the area, which do not present any or present as little as possible constraints for agricultural land use, are selected for this purpose. Compared with conventional field soils, the requirements for the experimental fields' soil cover quality are much higher. Experimental area soils and soil cover composition should correspond to local pedo-ecological conditions and, in addition to that, represent the soil types dominating in the region, whereas the fields should be as homogeneous as possible. The soil cover heterogeneity of seven arable land blocks of three research stations (Jõgeva, Kuusiku and Olustvere) was studied 1) by examining the large scale (1:10 000) digital soil map (available via the internet), and 2) by field researches using the transect method. The stages of soils litho-genetic and moisture heterogeneities were estimated by using the Estonian normal soils matrix, however, the heterogeneity of top- and subsoil texture by using the soil texture matrix. The quality and variability of experimental fields' soils humus status, was studied more thoroughly from the aspect of humus concentration (g kg-1), humus cover thickness (cm) and humus stocks (Mg ha-1). The soil cover of Jõgeva experimental area, which presents an accumulative drumlin landscape (formed during the last glacial period), consist from loamy Luvisols and associated to this Cambisols. In Kuusiku area, which landscape is characterized by till and limestone plains with thin Quaternary cover, the soil cover is more heterogeneous than in previous area. Kuusiku soil cover is more variegated by the soil texture and as well as by the genesis of soils. In addition to Cambisols, Leptosols, Gleysols and Luvisols may be found here as well. The dominating soils in Olustvere research area, which is situated on wavy upland plateau, are Albeluvisols.

[Characteristics of soil water infiltration in sub-alpine dark coniferous ecosystem of upper reaches of Yangtze River].

PubMed

Yu, Xinxiao; Zhao, Yutao; Zhang, Zhiqiang; Cheng, Genwei

2003-01-01

Dark coniferous forest is the predominant type of vegetation in the upper reaches of Yangtze River. Difference among different types of soil exists. The sand content of soil is higher and the soil texture is coarser in the early stage of forest succession. The sand content of soil decreases with the advancement of the forest succession while that of soil in Abies fabri over-mature forest is the lowest. In slope wash soil, the sand content of soil decreases with the increasing soil depth. The soil porosity and soil water-holding capacity increases and soil bulk density decreases with the advancement of forest succession and decrease of soil depth. The deeper soil depth or the smaller soil water content are, the smaller the unsaturated hydraulic conductivity of soil measured by CGA method. Moreover, the correlation of soil water content with unsaturated hydraulic conductivity of soil can be simulated by an exponential function. The saturated hydraulic conductivity of soil decreases exponentially with the increasing soil depth. The time to attain the stable infiltration rate is different among different soil depth, while the deeper the soil depth is, the longer the time needs. The variation in soil texture, soil physical properties and the high infiltration rate of soil there implicated that there are scarce surface runoff, but abundant in subsurface flow, return flow and seepage, which is the result of regulation by dark coniferous forest on hydrological processes.

Keys to soil taxonomy by soil survey staff (sixth edition)

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

NONE

1994-12-31

This publication, Keys to Soil Taxonomy, serves two purposes. It provides the taxonomic keys necessary for the classification of soils according to Soil Taxonomy in a form that can be used easily in the field, and it also acquaints users of Soil Taxonomy with recent changes in the classification system. This volume includes all revisions of the keys that have so far been approved, replacing the original keys in Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys (1975), the work on which this abridged version, first published in 1983, is based. This publication incorporatesmore » all amendments approved to date and published in National Soil Taxonomy Handbook (NSTH) Issues 1-17.« less

Modeling nitrate at domestic and public-supply well depths in the Central Valley, California

USGS Publications Warehouse

Nolan, Bernard T.; Gronberg, JoAnn M.; Faunt, Claudia C.; Eberts, Sandra M.; Belitz, Ken

2014-01-01

Aquifer vulnerability models were developed to map groundwater nitrate concentration at domestic and public-supply well depths in the Central Valley, California. We compared three modeling methods for ability to predict nitrate concentration >4 mg/L: logistic regression (LR), random forest classification (RFC), and random forest regression (RFR). All three models indicated processes of nitrogen fertilizer input at the land surface, transmission through coarse-textured, well-drained soils, and transport in the aquifer to the well screen. The total percent correct predictions were similar among the three models (69–82%), but RFR had greater sensitivity (84% for shallow wells and 51% for deep wells). The results suggest that RFR can better identify areas with high nitrate concentration but that LR and RFC may better describe bulk conditions in the aquifer. A unique aspect of the modeling approach was inclusion of outputs from previous, physically based hydrologic and textural models as predictor variables, which were important to the models. Vertical water fluxes in the aquifer and percent coarse material above the well screen were ranked moderately high-to-high in the RFR models, and the average vertical water flux during the irrigation season was highly significant (p < 0.0001) in logistic regression.

Soil Classification and Treatment.

ERIC Educational Resources Information Center

Clemson Univ., SC. Vocational Education Media Center.

This instructional unit was designed to enable students, primarily at the secondary level, to (1) classify soils according to current capability classifications of the Soil Conservation Service, (2) select treatments needed for a given soil class according to current recommendations provided by the Soil Conservation Service, and (3) interpret a…

Bayesian Fusion of Color and Texture Segmentations

NASA Technical Reports Server (NTRS)

Manduchi, Roberto

2000-01-01

In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine "soft" segmentations computed for two or more features independently. Our algorithm merges models according to a mean entropy criterion, and allows to choose the appropriate number of classes for the final grouping. This technique also allows to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture.)

Detection of Focal Cortical Dysplasia Lesions in MRI Using Textural Features

NASA Astrophysics Data System (ADS)

Loyek, Christian; Woermann, Friedrich G.; Nattkemper, Tim W.

Focal cortical dysplasia (FCD) is a frequent cause of medically refractory partial epilepsy. The visual identification of FCD lesions on magnetic resonance images (MRI) is a challenging task in standard radiological analysis. Quantitative image analysis which tries to assist in the diagnosis of FCD lesions is an active field of research. In this work we investigate the potential of different texture features, in order to explore to what extent they are suitable for detecting lesional tissue. As a result we can show first promising results based on segmentation and texture classification.

Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames.

PubMed

Depeursinge, Adrien; Van de Ville, Dimitri; Platon, Alexandra; Geissbuhler, Antoine; Poletti, Pierre-Alexandre; Müller, Henning

2012-07-01

We propose near-affine-invariant texture descriptors derived from isotropic wavelet frames for the characterization of lung tissue patterns in high-resolution computed tomography (HRCT) imaging. Affine invariance is desirable to enable learning of nondeterministic textures without a priori localizations, orientations, or sizes. When combined with complementary gray-level histograms, the proposed method allows a global classification accuracy of 76.9% with balanced precision among five classes of lung tissue using a leave-one-patient-out cross validation, in accordance with clinical practice.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multi-class texture analysis in colorectal cancer histology

NASA Astrophysics Data System (ADS)

Kather, Jakob Nikolas; Weis, Cleo-Aron; Bianconi, Francesco; Melchers, Susanne M.; Schad, Lothar R.; Gaiser, Timo; Marx, Alexander; Zöllner, Frank Gerrit

2016-06-01

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

Inline inspection of textured plastics surfaces

NASA Astrophysics Data System (ADS)

Michaeli, Walter; Berdel, Klaus

2011-02-01

This article focuses on the inspection of plastics web materials exhibiting irregular textures such as imitation wood or leather. They are produced in a continuous process at high speed. In this process, various defects occur sporadically. However, current inspection systems for plastics surfaces are able to inspect unstructured products or products with regular, i.e., highly periodic, textures, only. The proposed inspection algorithm uses the local binary pattern operator for texture feature extraction. For classification, semisupervised as well as supervised approaches are used. A simple concept for semisupervised classification is presented and applied for defect detection. The resulting defect-maps are presented to the operator. He assigns class labels that are used to train the supervised classifier in order to distinguish between different defect types. A concept for parallelization is presented allowing the efficient use of standard multicore processor PC hardware. Experiments with images of a typical product acquired in an industrial setting show a detection rate of 97% while achieving a false alarm rate below 1%. Real-time tests show that defects can be reliably detected even at haul-off speeds of 30 m/min. Further applications of the presented concept can be found in the inspection of other materials.

Dust emission parameterization scheme over the MENA region: Sensitivity analysis to soil moisture and soil texture

NASA Astrophysics Data System (ADS)

Gherboudj, Imen; Beegum, S. Naseema; Marticorena, Beatrice; Ghedira, Hosni

2015-10-01

The mineral dust emissions from arid/semiarid soils were simulated over the MENA (Middle East and North Africa) region using the dust parameterization scheme proposed by Alfaro and Gomes (2001), to quantify the effect of the soil moisture and clay fraction in the emissions. For this purpose, an extensive data set of Soil Moisture and Ocean Salinity soil moisture, European Centre for Medium-Range Weather Forecasting wind speed at 10 m height, Food Agricultural Organization soil texture maps, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index, and erodibility of the soil surface were collected for the a period of 3 years, from 2010 to 2013. Though the considered data sets have different temporal and spatial resolution, efforts have been made to make them consistent in time and space. At first, the simulated sandblasting flux over the region were validated qualitatively using MODIS Deep Blue aerosol optical depth and EUMETSAT MSG (Meteosat Seciond Generation) dust product from SEVIRI (Meteosat Spinning Enhanced Visible and Infrared Imager) and quantitatively based on the available ground-based measurements of near-surface particulate mass concentrations (PM10) collected over four stations in the MENA region. Sensitivity analyses were performed to investigate the effect of soil moisture and clay fraction on the emissions flux. The results showed that soil moisture and soil texture have significant roles in the dust emissions over the MENA region, particularly over the Arabian Peninsula. An inversely proportional dependency is observed between the soil moisture and the sandblasting flux, where a steep reduction in flux is observed at low friction velocity and a gradual reduction is observed at high friction velocity. Conversely, a directly proportional dependency is observed between the soil clay fraction and the sandblasting flux where a steep increase in flux is observed at low friction velocity and a gradual increase is observed at high friction velocity. The magnitude of the percentage reduction/increase in the sandblasting flux decreases with the increase of the friction velocity for both soil moisture and soil clay fraction. Furthermore, these variables are interdependent leading to a gradual decrease in the percentage increase in the sandblasting flux for higher soil moisture values.

Data documentation for the bare soil experiment at the University of Arkansas

NASA Technical Reports Server (NTRS)

Waite, W. P.; Scott, H. D. (Principal Investigator); Hancock, G. D.

1980-01-01

The reflectivities of several controlled moisture test plots were investigated. These test plots were of a similar soil texture which was clay loam and were prepared to give a desired initial soil moisture and density profile. Measurements were conducted on the plots as the soil water redistributed for both long term and diurnal cycles. These measurements included reflectivity, gravimetric and volumetric soil moisture, soil moisture potential, and soil temperature.

Soil geomorphic classification, soil taxonomy, and effects on soil richness assessments

Treesearch

Jonathan D. Phillips; Daniel A. Marion

2007-01-01

The study of pedodiversity and soil richness depends on the notion of soils as discrete entities. Soil classifications are often criticized in this regard because they depend in part on arbitrary or subjective criteria. In this study soils were categorized on the basis of the presence or absence of six lithological and morphological characteristics. Richness vs. area...

Effects of biotic and abiotic indices on long term soil moisture data in a grassland biodiversity experiment

NASA Astrophysics Data System (ADS)

Fischer, Christine; Hohenbrink, Tobias; Leimer, Sophia; Roscher, Christiane; Ravenek, Janneke; de Kroon, Hans; Kreutziger, Yvonne; Wirth, Christian; Eisenhauer, Nico; Gleixner, Gerd; Weigelt, Alexandra; Mommer, Liesje; Beßler, Holger; Schröder, Boris; Hildebrandt, Anke

2015-04-01

Soil moisture is the dynamic link between climate, soil and vegetation and the dynamics and variation are affected by several often interrelated factors such as soil texture, soil structural parameters (soil organic carbon) and vegetation parameters (belowground- and aboveground biomass). For the characterization and estimation of soil moisture and its variability and the resulting water fluxes and solute transports, the knowledge of the relative importance of these factors is of major challenge for hydrology and bioclimatology. Because of the heterogeneity of these factors, soil moisture varies strongly over time and space. Our objective was to assess the spatio-temporal variability of soil moisture and factors which could explain that variability, like soil properties and vegetation cover, in in a long term biodiversity experiment (Jena Experiment). The Jena Experiment consist 86 plots on which plant species richness (0, 1, 2, 4, 8, 16, and 60) and functional groups (legumes, grasses, tall herbs, and small herbs) were manipulated in a factorial design Soil moisture measurements were performed weekly April to September 2003-2005 and 2008-2013 using Delta T theta probe. Measurements were integrated to three depth intervals: 0.0 - 0.20, 0.20 - 0.40 and 0.40 - 0.70 m. We analyze the spatio-temporal patterns of soil water content on (i) the normalized time series and (ii) the first components obtained from a principal component analysis (PCA). Both were correlated with the design variables of the Jena Experiment (plant species richness and plant functional groups) and other influencing factors such as soil texture, soil structural variables and vegetation parameters. For the time stability of soil water content, the analysis showed that plots containing grasses was consistently drier than average at the soil surface in all observed years while plots containing legumes comparatively moister, but only up to the year 2008. In 0.40 - 0.70 m soil deep plots presence of small herbs led to higher than average soil moisture in some years (2008, 2012, 2013). Interestingly, plant species richness led to moister than average subsoil at the beginning of the experiment (2003 and 2004), which changed to lower than average up to the year 2010 in all depths. There was no effect of species diversity in the years since 2010, although species diversity generally increases leaf area index and aboveground biomass. The first component from the PCA analysis described the mean behavior in time of all soil moisture time series. The second component reflected the impact of soil depth. The first two components explained 76% of the data set total variance. The third component is linked to plant species richness and explained about 4 % of the total variance of soil moisture data. The fourth component, which explained 2.4 %, showed a high correlation to soil texture. Within this study we investigate the dominant factors controlling spatio-temporal patterns of soil moisture at several soil depths. Although climate and soil depths were the most important drivers, other factors like plant species richness and soil texture affected the temporal variation while certain plant functional groups were important for the spatial variability.

Multiresolution Local Binary Pattern texture analysis for false positive reduction in computerized detection of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Choi, Jae Young; Kim, Dae Hoe; Choi, Seon Hyeong; Ro, Yong Man

2012-03-01

We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive (FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core region and its margin has clearly proven to be significant and is not sensitive to the precise location of the boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine (SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts features designed for false positive reduction.

A Clinical Decision Support System Using Ultrasound Textures and Radiologic Features to Distinguish Metastasis From Tumor-Free Cervical Lymph Nodes in Patients With Papillary Thyroid Carcinoma.

PubMed

Abbasian Ardakani, Ali; Reiazi, Reza; Mohammadi, Afshin

2018-03-30

This study investigated the potential of a clinical decision support approach for the classification of metastatic and tumor-free cervical lymph nodes (LNs) in papillary thyroid carcinoma on the basis of radiologic and textural analysis through ultrasound (US) imaging. In this research, 170 metastatic and 170 tumor-free LNs were examined by the proposed clinical decision support method. To discover the difference between the groups, US imaging was used for the extraction of radiologic and textural features. The radiologic features in the B-mode scans included the echogenicity, margin, shape, and presence of microcalcification. To extract the textural features, a wavelet transform was applied. A support vector machine classifier was used to classify the LNs. In the training set data, a combination of radiologic and textural features represented the best performance with sensitivity, specificity, accuracy, and area under the curve (AUC) values of 97.14%, 98.57%, 97.86%, and 0.994, respectively, whereas the classification based on radiologic and textural features alone yielded lower performance, with AUCs of 0.964 and 0.922. On testing the data set, the proposed model could classify the tumor-free and metastatic LNs with an AUC of 0.952, which corresponded to sensitivity, specificity, and accuracy of 93.33%, 96.66%, and 95.00%. The clinical decision support method based on textural and radiologic features has the potential to characterize LNs via 2-dimensional US. Therefore, it can be used as a supplementary technique in daily clinical practice to improve radiologists' understanding of conventional US imaging for characterizing LNs. © 2018 by the American Institute of Ultrasound in Medicine.

N2O emissions from humid tropical agricultural soils: effects of soil moisture, texture and nitrogen availability

Treesearch

A.M. Weitza; E. Linderb; S. Frolkingc; P.M. Crillc; M. Keller

2001-01-01

We studied soil moisture dynamics and nitrous oxide (N2O) ¯uxes from agricultural soils in the humid tropics of Costa Rica. Using a splitplot design on two soils (clay, loam) we compared two crop types (annual, perennial) each unfertilized and fertilized. Both soils are of andic origin. Their properties include relatively low bulk density and high organic matter...

[Effects of the grain size and thickness of dust deposits on soil water and salt movement in the hinterland of the Taklimakan Desert].

PubMed

Sun, Yan-Wei; Li, Sheng-Yu; Xu, Xin-Wen; Zhang, Jian-Guo; Li, Ying

2009-08-01

By using mcirolysimeter, a laboratory simulation experiment was conducted to study the effects of the grain size and thickness of dust deposits on the soil water evaporation and salt movement in the hinterland of the Taklimakan Desert. Under the same initial soil water content and deposition thickness condition, finer-textured (<0.063 mm) deposits promoted soil water evaporation, deeper soil desiccation, and surface soil salt accumulation, while coarse-textured (0.063-2 mm) deposits inhibited soil water evaporation and decreased deeper soil water loss and surface soil salt accumulation. The inhibition effect of the grain size of dust deposits on soil water evaporation had an inflection point at the grain size 0.20 mm, i. e., increased with increasing grain size when the grain size was 0.063-0.20 mm but decreased with increasing grain size when the grain size was > 0.20 mm. With the increasing thickness of dust deposits, its inhibition effect on soil water evaporation increased, and there existed a logarithmic relationship between the dust deposits thickness and water evaporation. Surface soil salt accumulation had a negative correlation with dust deposits thickness. In sum, the dust deposits in study area could affect the stability of arid desert ecosystem.

The Resource beneath Our Feet

ERIC Educational Resources Information Center

Clary, Renee

2015-01-01

This article describes activities in which students sample, investigate, classify, and compare characteristics (i.e., texture, color, density, porosity) of local soils, evaluating whether the soils are healthy or at risk. Students investigate correlations between geology and geography, predict which soil types may go extinct in their state, and…

Soil variability effects on canopy temperature in a limited irrigation experiment

USDA-ARS?s Scientific Manuscript database

Canopy temperature was monitored on a continuous basis in a limited irrigation maize experiment, with 12 separate irrigation treatments and 4 replicates of each treatment. Soil electroconductivity (EC) was measured and mapped to quantify variation in soil texture throughout the plots, and was correl...

Sustainable landscaping practices for enhancing vegetation establishment.

DOT National Transportation Integrated Search

2016-02-01

Soil compaction can severely limit the success of vegetation establishment. Current grading and landscaping : practices commonly produce compacted soils of varied textures and profiles within SHA medians and roadsides, : resulting in limited capacity...

Soil Genesis and Development, Lesson 5 - Soil Geography and Classification

USDA-ARS?s Scientific Manuscript database

The system of soil classification developed by the United States Department of Agriculture (USDA) is called Soil Taxonomy. Soil Taxonomy consists of a hierarchy of six levels which, from highest to lowest, are: Order, Suborder, Great Group, Subgroup, family, and series. This lesson will focus on bro...

Effects of Temperature on Solute Transport Parameters in Differently-Textured Soils at Saturated Condition

NASA Astrophysics Data System (ADS)

Hamamoto, S.; Arihara, M.; Kawamoto, K.; Nishimura, T.; Komatsu, T.; Moldrup, P.

2014-12-01

Subsurface warming driven by global warming, urban heat islands, and increasing use of shallow geothermal heating and cooling systems such as the ground source heat pump, potentially causes changes in subsurface mass transport. Therefore, understanding temperature dependency of the solute transport characteristics is essential to accurately assess environmental risks due to increased subsurface temperature. In this study, one-dimensional solute transport experiments were conducted in soil columns under temperature control to investigate effects of temperature on solute transport parameters, such as solute dispersion and diffusion coefficients, hydraulic conductivity, and retardation factor. Toyoura sand, Kaolin clay, and intact loamy soils were used in the experiments. Intact loamy soils were taken during a deep well boring at the Arakawa Lowland in Saitama Prefecture, Japan. In the transport experiments, the core sample with 5-cm diameter and 4-cm height was first isotropically consolidated, whereafter 0.01M KCl solution was injected to the sample from the bottom. The concentrations of K+ and Cl- in the effluents were analyzed by an ion chromatograph to obtain solute breakthrough curves. The solute transport parameters were calculated from the breakthrough curves. The experiments were conducted under different temperature conditions (15, 25, and 40 oC). The retardation factor for the intact loamy soils decreased with increasing temperature, while water permeability increased due to reduced viscosity of water at higher temperature. Opposite, the effect of temperature on solute dispersivity for the intact loamy soils was insignificant. The effects of soil texture on the temperature dependency of the solute transport characteristics will be further investigated from comparison of results from differently-textured samples.

Wind sorting affects differently the organo-mineral composition of saltating and particulate materials in contrasting texture agricultural soils

NASA Astrophysics Data System (ADS)

Iturri, Laura Antonela; Funk, Roger; Leue, Martin; Sommer, Michael; Buschiazzo, Daniel Eduardo

2017-10-01

There is little information about the mineral and organic composition of sediments eroded by wind at different heights. Because of that, wind tunnel simulations were performed on four agricultural loess soils of different granulometry and their saltating materials collected at different heights. The particulate matter with an aerodynamic diameter mainly smaller than 10 μm (PM10) of these soils was obtained separately by a laboratory method. Results indicated that the granulometric composition of sediments collected at different heights was more homogeneous in fine- than in sandy-textured soils, which were more affected by sorting effects during wind erosion. This agrees with the preferential transport of quartz at low heights and of clay minerals at greater heights. SOC contents increased with height, but the composition of the organic materials was different: stable carboxylic acids, aldehydes, amides and aromatics were preferentially transported close to the ground because their were found in larger aggregates, while plant debris and polysaccharides, carbohydrates and derivatives of microbial origin from organic matter dominated at greater heights for all soil types. The amount of SOC in the PM10 fraction was higher when it was emitted from sandy than from fine textured soils. Because of the sorting process produced by wind erosion, the stable organic matter compounds will be transported at low heights and local scales, modifying soil fertility due to nutrient exportation, while less stable organic compounds will be part of the suspension losses, which are known to affect some processes at regional- or global scale.

Effect of soil texture and chemical properties on laboratory-generated dust emissions from SW North America

NASA Astrophysics Data System (ADS)

Mockford, T.; Zobeck, T. M.; Lee, J. A.; Gill, T. E.; Dominguez, M. A.; Peinado, P.

2012-12-01

Understanding the controls of mineral dust emissions and their particle size distributions during wind-erosion events is critical as dust particles play a significant impact in shaping the earth's climate. It has been suggested that emission rates and particle size distributions are independent of soil chemistry and soil texture. In this study, 45 samples of wind-erodible surface soils from the Southern High Plains and Chihuahuan Desert regions of Texas, New Mexico, Colorado and Chihuahua were analyzed by the Lubbock Dust Generation, Analysis and Sampling System (LDGASS) and a Beckman-Coulter particle multisizer. The LDGASS created dust emissions in a controlled laboratory setting using a rotating arm which allows particle collisions. The emitted dust was transferred to a chamber where particulate matter concentration was recorded using a DataRam and MiniVol filter and dust particle size distribution was recorded using a GRIMM particle analyzer. Particle size analysis was also determined from samples deposited on the Mini-Vol filters using a Beckman-Coulter particle multisizer. Soil textures of source samples ranged from sands and sandy loams to clays and silts. Initial results suggest that total dust emissions increased with increasing soil clay and silt content and decreased with increasing sand content. Particle size distribution analysis showed a similar relationship; soils with high silt content produced the widest range of dust particle sizes and the smallest dust particles. Sand grains seem to produce the largest dust particles. Chemical control of dust emissions by calcium carbonate content will also be discussed.

Analyzing the Sand-fixing Effect of Feldspathic Sandstone from the Texture Characteristics

NASA Astrophysics Data System (ADS)

Zhang, lu; Ban, Jichang

2018-01-01

The purpose of this research was aimed to study the sand fixing effect of feldspathic sandstone in Mu Us Sandy Land, to provide a scienticic basis for desertification control, soil and water conservation and development of farming there. Methods of mixing feldspathic sandstone and aeolian sandy soil according to 1: 0, 1: 1, 1: 2, 1: 5, and 0: 1 mass ratioes, the graded composition and characteristics were studied with laser particle size analyzer. The result showed that these features of sand-based, loosely structured, easy to wind erosion of aeolian sandy soil were changed before feldspathic sandstone and aeolian sandy soil compounding. The <0.05 mm particle mass increased with feldspathic sandstone mass increasing. The texture presented this kind of change from sand to sandy loam to loam to silt loam. The small particle size distribution, good homogeneity and other features of aeolian sandy soil were improved to a certain degree, and the particle size distribution became broad before feldspathic sandstone and aeolian sandy soil compounding. The particle grading was continuous, and the grading characteristic was good when m(F): m(S) was 1: 5(Cu was 54.71 and Cc was 2.54) or when m(F): m(S) was 1: 2(Cu was 76.21, Cc was 1.12). The conclusion is that feldspathic sandstone has sand-fixing effect in texture characteristics, which heightens with feldspathic sandstone mass increasing, and when the mass ratio of feldspathic sandstone: aeolian sandy soil is 1: 2 or 1: 5 which compound better.

Relationship of grapevine yield and growth to nematode densities.

PubMed

Ferris, H; McKenry, M V

1975-07-01

Yield, growth, and vigor of individual grape vines were correlated with nematode population densities in a series of California vineyards. In a Hanford sandy loam soil, Xiphinema americanum densities showed negative correlations with yield, growth, and vigor of vines. When vines were categorized according to vigor, X. americanurn densities had little relationship to yield of high-vigor vines, but were negatively correlated with yield of low-vigor vines. Densities of Paratylenchus harnatus were positively correlated with yield, growth, and vigor of vines. Correlations between Meloidogyne spp. densities and vine performance were variable, even when the vines were separated according to soil type and plant vigor. Densities of Meloidogyne spp. populations were generally higher on coarser-textured, sandy soils and the vines were less vigorous there. Densities of P. hamatus were greater in fine-textured soils.

Physical parameters of Fluvisols on flooded and non-flooded terraces

NASA Astrophysics Data System (ADS)

Kercheva, Milena; Sokołowska, Zofia; Hajnos, Mieczysław; Skic, Kamil; Shishkov, Toma

2017-01-01

The heterogeneity of soil physical properties of Fluvisols, lack of large pristine areas, and different moisture regimes on non-flooded and flooded terraces impede the possibility to find a soil profile which can serve as a baseline for estimating the impact of natural or anthropogenic factors on soil evolution. The aim of this study is to compare the pore size distribution of pristine Fluvisols on flooded and non-flooded terraces using the method of the soil water retention curve, mercury intrusion porosimetry, nitrogen adsorption isotherms, and water vapour sorption. The pore size distribution of humic horizons of pristine Fluvisols on the non-flooded terrace differs from pore size distribution of Fluvisols on the flooded terrace. The peaks of textural and structural pores are higher in the humic horizons under more humid conditions. The structural characteristics of subsoil horizons depend on soil texture and evolution stage. The peaks of textural pores at about 1 mm diminish with lowering of the soil organic content. Structureless horizons are characterized by uni-modal pore size distribution. Although the content of structural pores of the subsoil horizons of Fluvisols on the non-flooded terrace is low, these pores are represented by biopores, as the coefficient of filtration is moderately high. The difference between non-flooded and flooded profiles is well expressed by the available water storage, volume and mean radius of pores, obtained by mercury intrusion porosimetry and water desorption, which are higher in the surface horizons of frequently flooded Fluvisols.

Feature detection in satellite images using neural network technology

NASA Technical Reports Server (NTRS)

Augusteijn, Marijke F.; Dimalanta, Arturo S.

1992-01-01

A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

Adjustment of corn nitrogen in-season fertilization based on soil texture and weather conditions: a Meta-analysis of North American trials

USDA-ARS?s Scientific Manuscript database

Soil properties and weather conditions are known to affect soil nitrogen (N) availability and plant N uptake. However, studies examining N response as affected by soil and weather sometimes give conflicting results. Meta-analysis is a statistical method for estimating treatment effects in a series o...

Land Application of Wastes: An Educational Program. Soil as a Treatment Medium - Module 3, Objectives, Script and Booklet.

ERIC Educational Resources Information Center

Clarkson, W. W.; And Others

This module examines the basic properties of soil which have an influence on the success of land treatment of wastes. These relevant properties include soil texture, soil structure, permeability, infiltration, available water capacity, and cation exchange capacity. Biological, chemical and physical mechanisms work to remove and renovate wastes…

Assessing heterogeneity in soil nitrogen cycling: a plot-scale approach

Treesearch

Peter Baas; Jacqueline E. Mohan; David Markewitz; Jennifer D. Knoepp

2014-01-01

The high level of spatial and temporal heterogeneity in soil N cycling processes hinders our ability to develop an ecosystem-wide understanding of this cycle. This study examined how incorporating an intensive assessment of spatial variability for soil moisture, C, nutrients, and soil texture can better explain ecosystem N cycling at the plot scale. Five sites...

[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.