Sediment fluxes and the littoral drift along northeast Andhra Pradesh Coast, India: estimation by remote sensing.

PubMed

Kunte, Pravin D; Alagarsamy, R; Hursthouse, A S

2013-06-01

The littoral drift regime along the northeastern coast of India was investigated by analyzing coastal drift indicators and shoreline changes based on multitemporal satellite images. The study of offshore turbidity patterns and quantitative estimation of suspended sediments was undertaken to understand the magnitude and direction of movement of sediment fluxes. The study revealed that: (1) the character of coastal landforms and sedimentation processes indicate that the sediment transport is bidirectional and monsoon dependent; (2) multidate, multitemporal analysis of satellite images helps to show the nature of sediment transport along the coast. The dominant net sediment transport is in a NE direction along the eastern coast of India. Finally, this assessment demonstrates the potential of remote sensing technology in understanding the coastal morphometric changes, long-term sediment transport, shoreline changes, and offshore turbidity distribution pattern and the implications for the transport of sediment-associated pollutants.

A change detection method for remote sensing image based on LBP and SURF feature

NASA Astrophysics Data System (ADS)

Hu, Lei; Yang, Hao; Li, Jin; Zhang, Yun

2018-04-01

Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.

Study on paddy rice yield estimation based on multisource data and the Grey system theory

NASA Astrophysics Data System (ADS)

Deng, Wensheng; Wang, Wei; Liu, Hai; Li, Chen; Ge, Yimin; Zheng, Xianghua

2009-10-01

The paddy rice is our important crops. In study of the paddy rice yield estimation, compared with the scholars who usually only take the remote sensing data or meteorology as the influence factors, we combine the remote sensing and the meteorological data to make the monitoring result closer reality. Although the gray system theory has used in many aspects, it is applied very little in paddy rice yield estimation. This study introduces it to the paddy rice yield estimation, and makes the yield estimation model. This can resolve small data sets problem that can not be solved by deterministic model. It selects some regions in Jianghan plain for the study area. The data includes multi-temporal remote sensing image, meteorological and statistic data. The remote sensing data is the 16-day composite images (250-m spatial resolution) of MODIS. The meteorological data includes monthly average temperature, sunshine duration and rain fall amount. The statistical data is the long-term paddy rice yield of the study area. Firstly, it extracts the paddy rice planting area from the multi-temporal MODIS images with the help of GIS and RS. Then taking the paddy rice yield as the reference sequence, MODIS data and meteorological data as the comparative sequence, computing the gray correlative coefficient, it selects the yield estimation factor based on the grey system theory. Finally, using the factors, it establishes the yield estimation model and does the result test. The result indicated that the method is feasible and the conclusion is credible. It can provide the scientific method and reference value to carry on the region paddy rice remote sensing estimation.

Investigating change detection of archaeological sites by multiscale and multitemporal satellite imagery

NASA Astrophysics Data System (ADS)

Lasaponara, R.; Lanorte, A.; Coluzzi, R.; Masini, N.

2009-04-01

The systematic monitoring of cultural and natural heritage is a basic step for its conservation. Monitoring strategies should constitute an integral component of policies relating to land use, development, and planning. To this aim remote sensing technologies can be used profitably. This paper deals with the use of multitemporal, multisensors, and multiscale satellite data for assessing and monitoring changes affecting cultural landscapes and archaeological sites. The discussion is focused on some significant test cases selected in Peru (South America) and Southern Italy . Artifacts, unearthed sites, and marks of buried remains have been investigated by using multitemporal aerial and satellite data, such as Quickbird, ASTER, Landsat MSS and TM.

Evidential analysis of difference images for change detection of multitemporal remote sensing images

NASA Astrophysics Data System (ADS)

Chen, Yin; Peng, Lijuan; Cremers, Armin B.

2018-03-01

In this article, we develop two methods for unsupervised change detection in multitemporal remote sensing images based on Dempster-Shafer's theory of evidence (DST). In most unsupervised change detection methods, the probability of difference image is assumed to be characterized by mixture models, whose parameters are estimated by the expectation maximization (EM) method. However, the main drawback of the EM method is that it does not consider spatial contextual information, which may entail rather noisy detection results with numerous spurious alarms. To remedy this, we firstly develop an evidence theory based EM method (EEM) which incorporates spatial contextual information in EM by iteratively fusing the belief assignments of neighboring pixels to the central pixel. Secondly, an evidential labeling method in the sense of maximizing a posteriori probability (MAP) is proposed in order to further enhance the detection result. It first uses the parameters estimated by EEM to initialize the class labels of a difference image. Then it iteratively fuses class conditional information and spatial contextual information, and updates labels and class parameters. Finally it converges to a fixed state which gives the detection result. A simulated image set and two real remote sensing data sets are used to evaluate the two evidential change detection methods. Experimental results show that the new evidential methods are comparable to other prevalent methods in terms of total error rate.

Analyzing soil erosion using a multi-temporal UAV data set after one year of active agriculture in Navarra, Spain

NASA Astrophysics Data System (ADS)

Anders, Niels; Keesstra, Saskia; Masselink, Rens

2014-05-01

Unmanned Aerial System (UAS) are becoming popular tools in the geosciences due to improving technology and processing/analysis techniques. They can potentially fill the gap between spaceborne or manned aircraft remote sensing and terrestrial remote sensing, both in terms of spatial and temporal resolution. In this study we analyze a multi-temporal data set that was acquired with a fixed-wing UAS in an agricultural catchment (2 sq. km) in Navarra, Spain. The goal of this study is to register soil erosion activity after one year of agricultural activity. The aircraft was equipped with a Panasonic GX1 16MP pocket camera with a 20 mm lens to capture normal JPEG RGB images. The data set consisted of two sets of imagery acquired in the end of February in 2013 and 2014 after harvesting. The raw images were processed using Agisoft Photoscan Pro which includes the structure-from-motion (SfM) and multi-view stereopsis (MVS) algorithms producing digital surface models and orthophotos of both data sets. A discussion is presented that is focused on the suitability of multi-temporal UAS data and SfM/MVS processing for quantifying soil loss, mapping the distribution of eroded materials and analyzing re-occurrences of rill patterns after plowing.

MULTI-TEMPORAL REMOTE SENSING ANALYTICAL APPROACHES FOR CHARACTERIZING LANDSCAPE CHANGE

EPA Science Inventory

Changes in landscape composition and function result from both acute land-cover conversions and chronic landscape changes. Land-cover conversions are typically mediated by human land-use activities (e.g. conversion from forest to agriculture), while more subtle chronic l...

Remote Sensing Classification of Grass Seed Cropping Practices in Western Oregon

USDA-ARS?s Scientific Manuscript database

Multiband Landsat images and multi-temporal MODIS 16-day composite NDVI were classified into 16 categories representing the primary crop rotation options and stand establishment conditions present in western Oregon grass seed fields. Mismatch in resolution between MODIS and Landsat data was resolved...

Detection and Monitoring of Small-Scale Mining Operations in the Eastern Democratic Republic of the Congo (DRC) Using Multi-Temporal, Multi-Sensor Remote Sensing Data

NASA Astrophysics Data System (ADS)

Walther, Christian; Frei, Michaela

2017-04-01

Mining of so-called "conflict minerals" is often related with small-scale mining activities. The here discussed activities are located in forested areas in the eastern DRC, which are often remote, difficult to access and insecure for traditional geological field inspection. In order to accelerate their CTC (Certified Trading Chain)-certification process, remote sensing data are used for detection and monitoring of these small-scale mining operations. This requires a high image acquisition frequency due to mining site relocations and for compensation of year-round high cloud coverage, especially for optical data evaluation. Freely available medium resolution optical data of Sentinel-2 and Landsat-8 as well as SAR data of Sentinel-1 are used for detecting small mining targets with a minimum size of approximately 0.5 km2. The developed method enables a robust multi-temporal detection of mining sites, monitoring of mining site spatio-temporal relocations and environmental changes. Since qualitative and quantitative comparable results are generated, the followed change detection approach is objective and transparent and may push the certification process forward.

Effect of spatial image support in detecting long-term vegetation change from satellite time-series

USDA-ARS?s Scientific Manuscript database

Context Arid rangelands have been severely degraded over the past century. Multi-temporal remote sensing techniques are ideally suited to detect significant changes in ecosystem state; however, considerable uncertainty exists regarding the effects of changing image resolution on their ability to de...

Sequential Classifier Training for Rice Mapping with Multitemporal Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Guo, Y.; Jia, X.; Paull, D.

2017-10-01

Most traditional methods for rice mapping with remote sensing data are effective when they are applied to the initial growing stage of rice, as the practice of flooding during this period makes the spectral characteristics of rice fields more distinguishable. In this study, we propose a sequential classifier training approach for rice mapping that can be used over the whole growing period of rice for monitoring various growth stages. Rice fields are firstly identified during the initial flooding period. The identified rice fields are used as training data to train a classifier that separates rice and non-rice pixels. The classifier is then used as a priori knowledge to assist the training of classifiers for later rice growing stages. This approach can be applied progressively to sequential image data, with only a small amount of training samples being required from each image. In order to demonstrate the effectiveness of the proposed approach, experiments were conducted at one of the major rice-growing areas in Australia. The proposed approach was applied to a set of multitemporal remote sensing images acquired by the Sentinel-2A satellite. Experimental results show that, compared with traditional spectral-indexbased algorithms, the proposed method is able to achieve more stable and consistent rice mapping accuracies and it reaches higher than 80% during the whole rice growing period.

Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest

USGS Publications Warehouse

Chen, Xuexia; Vogelmann, James E.; Rollins, Matt; Ohlen, Donald; Key, Carl H.; Yang, Limin; Huang, Chengquan; Shi, Hua

2011-01-01

It is challenging to detect burn severity and vegetation recovery because of the relatively long time period required to capture the ecosystem characteristics. Multitemporal remote sensing data can providemultitemporal observations before, during and after a wildfire, and can improve the change detection accuracy. The goal of this study is to examine the correlations between multitemporal spectral indices and field-observed burn severity, and to provide a practical method to estimate burn severity and vegetation recovery. The study site is the Jasper Fire area in the Black Hills National Forest, South Dakota, that burned during August and September 2000. Six multitemporal Landsat images acquired from 2000 (pre-fire), 2001 (post-fire), 2002, 2003, 2005 and 2007 were used to assess burn severity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized burn ratio (NBR), integrated forest index (IFI) and the differences of these indices between the pre-fire and post-fire years were computed and analysed with 66 field-based composite burn index (CBI) plots collected in 2002. Results showed that differences of NDVI and differences of EVI between the pre-fire year and the first two years post-fire were highly correlated with the CBI scores. The correlations were low beyond the second year post-fire. Differences of NBR had good correlation with CBI scores in all study years. Differences of IFI had low correlation with CBI in the first year post-fire and had good correlation in later years. A CBI map of the burnt area was produced using regression tree models and the multitemporal images. The dynamics of four spectral indices from 2000 to 2007 indicated that both NBR and IFI are valuable for monitoring long-term vegetation recovery. The high burn severity areas had a much slower recovery than the moderate and low burn areas.

Land cover changes in central Sonora Mexico

Treesearch

Diego Valdez-Zamudio; Alejandro Castellanos-Villegas; Stuart Marsh

2000-01-01

Remote sensing techniques have been demonstrated to be very effective tools to help detect, analyze, and evaluate land cover changes in natural areas of the world. Changes in land cover can generally be attributed to either natural or anthropogenic forces. Multitemporal satellite imagery and airborne videography were used to detect, analyze, and evaluate land cover...

A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud

NASA Astrophysics Data System (ADS)

Zeng, Chao; Long, Di; Shen, Huanfeng; Wu, Penghai; Cui, Yaokui; Hong, Yang

2018-07-01

Land surface temperature (LST) is one of the most important parameters in land surface processes. Although satellite-derived LST can provide valuable information, the value is often limited by cloud contamination. In this paper, a two-step satellite-derived LST reconstruction framework is proposed. First, a multi-temporal reconstruction algorithm is introduced to recover invalid LST values using multiple LST images with reference to corresponding remotely sensed vegetation index. Then, all cloud-contaminated areas are temporally filled with hypothetical clear-sky LST values. Second, a surface energy balance equation-based procedure is used to correct for the filled values. With shortwave irradiation data, the clear-sky LST is corrected to the real LST under cloudy conditions. A series of experiments have been performed to demonstrate the effectiveness of the developed approach. Quantitative evaluation results indicate that the proposed method can recover LST in different surface types with mean average errors in 3-6 K. The experiments also indicate that the time interval between the multi-temporal LST images has a greater impact on the results than the size of the contaminated area.

Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics.

PubMed

Benedek, C; Descombes, X; Zerubia, J

2012-01-01

In this paper, we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: 1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low-level change information between the time layers and object-level building description to recognize and separate changed and unaltered buildings. 2) To answer the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature-based modules. 3) To simultaneously ensure the convergence, optimality, and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel nonuniform stochastic object birth process which generates relevant objects with higher probability based on low-level image features.

Multiscale and Multitemporal Urban Remote Sensing

NASA Astrophysics Data System (ADS)

Mesev, V.

2012-07-01

The remote sensing of urban areas has received much attention from scientists conducting studies on measuring sprawl, congestion, pollution, poverty, and environmental encroachment. Yet much of the research is case and data-specific where results are greatly influenced by prevailing local conditions. There seems to be a lack of epistemological links between remote sensing and conventional theoretical urban geography; in other words, an oversight for the appreciation of how urban theory fuels urban change and how urban change is measured by remotely sensed data. This paper explores basic urban theories such as centrality, mobility, materiality, nature, public space, consumption, segregation and exclusion, and how they can be measured by remote sensing sources. In particular, the link between structure (tangible objects) and function (intangible or immaterial behavior) is addressed as the theory that supports the wellknow contrast between land cover and land use classification from remotely sensed data. The paper then couches these urban theories and contributions from urban remote sensing within two analytical fields. The first is the search for an "appropriate" spatial scale of analysis, which is conveniently divided between micro and macro urban remote sensing for measuring urban structure, understanding urban processes, and perhaps contributions to urban theory at a variety of scales of analysis. The second is on the existence of a temporal lag between materiality of urban objects and the planning process that approved their construction, specifically how time-dependence in urban structural-functional models produce temporal lags that alter the causal links between societal and political functional demands and structural ramifications.

Operational monitoring of land-cover change using multitemporal remote sensing data

NASA Astrophysics Data System (ADS)

Rogan, John

2005-11-01

Land-cover change, manifested as either land-cover modification and/or conversion, can occur at all spatial scales, and changes at local scales can have profound, cumulative impacts at broader scales. The implication of operational land-cover monitoring is that researchers have access to a continuous stream of remote sensing data, with the long term goal of providing for consistent and repetitive mapping. Effective large area monitoring of land-cover (i.e., >1000 km2) can only be accomplished by using remotely sensed images as an indirect data source in land-cover change mapping and as a source for land-cover change model projections. Large area monitoring programs face several challenges: (1) choice of appropriate classification scheme/map legend over large, topographically and phenologically diverse areas; (2) issues concerning data consistency and map accuracy (i.e., calibration and validation); (3) very large data volumes; (4) time consuming data processing and interpretation. Therefore, this dissertation research broadly addresses these challenges in the context of examining state-of-the-art image pre-processing, spectral enhancement, classification, and accuracy assessment techniques to assist the California Land-cover Mapping and Monitoring Program (LCMMP). The results of this dissertation revealed that spatially varying haze can be effectively corrected from Landsat data for the purposes of change detection. The Multitemporal Spectral Mixture Analysis (MSMA) spectral enhancement technique produced more accurate land-cover maps than those derived from the Multitemporal Kauth Thomas (MKT) transformation in northern and southern California study areas. A comparison of machine learning classifiers showed that Fuzzy ARTMAP outperformed two classification tree algorithms, based on map accuracy and algorithm robustness. Variation in spatial data error (positional and thematic) was explored in relation to environmental variables using geostatistical interpolation techniques. Finally, the land-cover modification maps generated for three time intervals (1985--1990--1996--2000), with nine change-classes revealed important variations in land-cover gain and loss between northern and southern California study areas.

The development of machine technology processing for earth resource survey

NASA Technical Reports Server (NTRS)

Landgrebe, D. A.

1970-01-01

The following technologies are considered for automatic processing of earth resources data: (1) registration of multispectral and multitemporal images, (2) digital image display systems, (3) data system parameter effects on satellite remote sensing systems, and (4) data compression techniques based on spectral redundancy. The importance of proper spectral band and compression algorithm selections is pointed out.

Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure

Treesearch

T. Ryan McCarley; Crystal A. Kolden; Nicole M. Vaillant; Andrew T. Hudak; Alistair M. S. Smith; Brian M. Wing; Bryce S. Kellogg; Jason Kreitler

2017-01-01

Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots.While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often...

ATHENA: Remote Sensing Science Center for Cultural Heritage in Cyprus

NASA Astrophysics Data System (ADS)

Hadjimitsis, Diofantos G.; Agapiou, Athos; Lysandrou, Vasiliki; Themistocleous, Kyriakos; Cuca, Branka; Lasaponara, Rosa; Masini, Nicola; Krauss, Thomas; Cerra, Daniele; Gessner, Ursula; Schreier, Gunter

2016-04-01

The Cultural Heritage (CH) sector, especially those of monuments and sites has always been facing a number of challenges from environmental pressure, pollution, human intervention from tourism to destruction by terrorism.Within this context, CH professionals are seeking to improve currently used methodologies, in order to better understand, protect and valorise the common European past and common identity. "ATHENA" H2020-TWINN-2015 project will seek to improve and expand the capabilities of the Cyprus University of Technology, involving professionals dealing with remote sensing technologies for supporting CH sector from the National Research Center of Italy (CNR) and German Aerospace Centre (DLR). The ATHENA centre will be devoted to the development, introduction and systematic use of advanced remote sensing science and technologies in the field of archaeology, built cultural heritage, their multi-temporal analysis and interpretation and the distant monitoring of their natural and anthropogenic environment in the area of Eastern Mediterranean.

Multitask SVM learning for remote sensing data classification

NASA Astrophysics Data System (ADS)

Leiva-Murillo, Jose M.; Gómez-Chova, Luis; Camps-Valls, Gustavo

2010-10-01

Many remote sensing data processing problems are inherently constituted by several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task but relation to previous acquisitions should be properly considered. In such problems, different modalities of the data (temporal, spatial, angular) gives rise to changes between the training and test distributions, which constitutes a difficult learning problem known as covariate shift. Multitask learning methods aim at jointly solving a set of prediction problems in an efficient way by sharing information across tasks. This paper presents a novel kernel method for multitask learning in remote sensing data classification. The proposed method alleviates the dataset shift problem by imposing cross-information in the classifiers through matrix regularization. We consider the support vector machine (SVM) as core learner and two regularization schemes are introduced: 1) the Euclidean distance of the predictors in the Hilbert space; and 2) the inclusion of relational operators between tasks. Experiments are conducted in the challenging remote sensing problems of cloud screening from multispectral MERIS images and for landmine detection.

Supervised Semantic Classification for Nuclear Proliferation Monitoring

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

Vatsavai, Raju; Cheriyadat, Anil M; Gleason, Shaun Scott

2010-01-01

Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferationmore » monitoring using high resolution remote sensing images.« less

Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery

Treesearch

Arjan J. H. Meddens; Jeffrey A. Hicke; Lee A. Vierling; Andrew T. Hudak

2013-01-01

Bark beetles cause significant tree mortality in coniferous forests across North America. Mapping beetle-caused tree mortality is therefore important for gauging impacts to forest ecosystems and assessing trends. Remote sensing offers the potential for accurate, repeatable estimates of tree mortality in outbreak areas. With the advancement of multi-temporal disturbance...

Unveiling topographical changes using LiDAR mapping capability: case study of Belaga in Sarawak, East-Malaysia

NASA Astrophysics Data System (ADS)

Ganendra, T. R.; Khan, N. M.; Razak, W. J.; Kouame, Y.; Mobarakeh, E. T.

2016-06-01

The use of Light Detection and Ranging (LiDAR) remote sensing technology to scan and map landscapes has proven to be one of the most popular techniques to accurately map topography. Thus, LiDAR technology is the ultimate method of unveiling the surface feature under dense vegetation, and, this paper intends to emphasize the diverse techniques that can be utilized to elucidate topographical changes over the study area, using multi-temporal airborne full waveform LiDAR datasets collected in 2012 and 2014. Full waveform LiDAR data offers access to an almost unlimited number of returns per shot, which enables the user to explore in detail topographical changes, such as vegetation growth measurement. The study also found out topography changes at the study area due to earthwork activities contributing to soil consolidation, soil erosion and runoff, requiring cautious monitoring. The implications of this study not only concurs with numerous investigations undertaken by prominent researchers to improve decision making, but also corroborates once again that investigations employing multi-temporal LiDAR data to unveil topography changes in vegetated terrains, produce more detailed and accurate results than most other remote sensing data.

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

Zoran, Maria; Savastru, Roxana; Savastru, Dan

Satellite Earth observation data in the visible and near-infrared (VNIR) wavelengths represent a useful source of information for forest systems monitoring through derived biogeophysical parameters (vegetation index, leaf area index, canopy cover, fraction of absorbed photosynthetically active radiation, chlorophyll content, net primary production, canopy water stress, etc.). Use of satellite remote sensing data to assess forest spatio-temporal changes due to climatic or anthropogenic stressors is an excellent example of the value of multispectral and multitemporal observations. Fusion technique was applied to time-series multispectral and multitemporal satellite imagery (NOAA AVHRR, MODIS Terra/Aqua, Landsat ETM and IKONOS satellite data) for periurban forestmore » areas Cernica-Branesti, placed in the neighboring of Bucharest town, Romania, over 2002-2014 period.« less

Development of a Land Use Mapping and Monitoring Protocol for the High Plains Region: A Multitemporal Remote Sensing Application

NASA Technical Reports Server (NTRS)

Price, Kevin P.; Nellis, M. Duane

1996-01-01

The purpose of this project was to develop a practical protocol that employs multitemporal remotely sensed imagery, integrated with environmental parameters to model and monitor agricultural and natural resources in the High Plains Region of the United States. The value of this project would be extended throughout the region via workshops targeted at carefully selected audiences and designed to transfer remote sensing technology and the methods and applications developed. Implementation of such a protocol using remotely sensed satellite imagery is critical for addressing many issues of regional importance, including: (1) Prediction of rural land use/land cover (LULC) categories within a region; (2) Use of rural LULC maps for successive years to monitor change; (3) Crop types derived from LULC maps as important inputs to water consumption models; (4) Early prediction of crop yields; (5) Multi-date maps of crop types to monitor patterns related to crop change; (6) Knowledge of crop types to monitor condition and improve prediction of crop yield; (7) More precise models of crop types and conditions to improve agricultural economic forecasts; (8;) Prediction of biomass for estimating vegetation production, soil protection from erosion forces, nonpoint source pollution, wildlife habitat quality and other related factors; (9) Crop type and condition information to more accurately predict production of biogeochemicals such as CO2, CH4, and other greenhouse gases that are inputs to global climate models; (10) Provide information regarding limiting factors (i.e., economic constraints of pumping, fertilizing, etc.) used in conjunction with other factors, such as changes in climate for predicting changes in rural LULC; (11) Accurate prediction of rural LULC used to assess the effectiveness of government programs such as the U.S. Soil Conservation Service (SCS) Conservation Reserve Program; and (12) Prediction of water demand based on rural LULC that can be related to rates of draw-down of underground water supplies.

A Prototype Hydrologic Observatory for the Neuse River Basin Using Remote Sensing Data as a Part of the CUAHSI-HIS Effort

NASA Astrophysics Data System (ADS)

Kanwar, R.; Narayan, U.; Lakshmi, V.

2005-12-01

Remote sensing has the potential to immensely advance the science and application of hydrology as it provides multi-scale and multi-temporal measurements of several hydrologic parameters. There is a wide variety of remote sensing data sources available to a hydrologist with a myriad of data formats, access techniques, data quality issues and temporal and spatial extents. It is very important to make data availability and its usage as convenient as possible for potential users. The CUAHSI Hydrologic Information System (HIS) initiative addresses this issue of better data access and management for hydrologists with a focus on in-situ data, that is point measurements of water and energy fluxes which make up the 'more conventional' sources of hydrologic data. This paper explores various sources of remotely sensed hydrologic data available, their data formats and volumes, current modes of data acquisition by end users, metadata associated with data itself, and requirements from potential data models that would allow a seamless integration of remotely sensed hydrologic observations into the Hydrologic Information System. Further, a prototype hydrologic observatory (HO) for the Neuse River Basin is developed using surface temperature, vegetation indices and soil moisture estimates available from remote sensing. The prototype (HO) uses the CUAHSI digital library system (DLS) on the back (server) end. On the front (client) end, a rich visual environment has been developed in order to provide better decision making tools in order to make an optimal choice in the selection of remote sensing data for a particular application. An easy point and click interface to the remote sensing data is also implemented for common users who are just interested in location based query of hydrologic variable values.

Characterizing environmental change in interior Alaska (1982-2012) using multi-temporal, multi-scale remote sensing data and field measurements

Treesearch

Hans-Erik Andersen; Robert. Pattison

2012-01-01

We investigate how vegetation in the Tanana Valley of interior Alaska (120,000 km2) has responded to a changing climate over the preceding three decades (1982-2012). Expected impacts include: 1) drying of wetlands and subsequent encroachment of woody vegetation into areas previously dominated by herbaceous and bryoid vegetation types, 2) changes...

Identifying environmental features for land management decisions

NASA Technical Reports Server (NTRS)

1982-01-01

The major accomplishments of the Center for Remote Sensing and Cartography are outlined. The analysis and inventory of the Parker Mountain rangeland and the use of multitemporal data to study aspen succession stages are discussed. New and continuing projects are also described including a Salt Lake County land use study, Wasatch-Cache riparian study, and Humboldt River riparian habitat study. Finally, progress in digital processing techniques is reported.

Crop Identification Technology Assessment for Remote Sensing (CITARS)

NASA Technical Reports Server (NTRS)

Bauer, M. E.; Cary, T. K.; Davis, B. J.; Swain, P. H.

1975-01-01

The results of classifications and experiments performed for the Crop Identification Technology Assessment for Remote Sensing (CITARS) project are summarized. Fifteen data sets were classified using two analysis procedures. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. In addition, 20 data sets were classified using training statistics from another segment or date. The results of both the local and non-local classifications in terms of classification and proportion estimation are presented. Several additional experiments are described which were performed to provide additional understanding of the CITARS results. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, the spectral discriminability of corn, soybeans, and other, and analysis of aircraft multispectral data.

Capturing the fugitive: Applying remote sensing to terrestrial animal distribution and diversity

NASA Astrophysics Data System (ADS)

Leyequien, Euridice; Verrelst, Jochem; Slot, Martijn; Schaepman-Strub, Gabriela; Heitkönig, Ignas M. A.; Skidmore, Andrew

2007-02-01

Amongst many ongoing initiatives to preserve biodiversity, the Millennium Ecosystem Assessment again shows the importance to slow down the loss of biological diversity. However, there is still a gap in the overview of global patterns of species distributions. This paper reviews how remote sensing has been used to assess terrestrial faunal diversity, with emphasis on proxies and methodologies, while exploring prospective challenges for the conservation and sustainable use of biodiversity. We grouped and discussed papers dealing with the faunal taxa mammals, birds, reptiles, amphibians, and invertebrates into five classes of surrogates of animal diversity: (1) habitat suitability, (2) photosynthetic productivity, (3) multi-temporal patterns, (4) structural properties of habitat, and (5) forage quality. It is concluded that the most promising approach for the assessment, monitoring, prediction, and conservation of faunal diversity appears to be the synergy of remote sensing products and auxiliary data with ecological biodiversity models, and a subsequent validation of the results using traditional observation techniques.

Determination of Winter Wheat Phenology in Bavaria- A Contribution to Regional Crop Health Monitoring from Space

NASA Astrophysics Data System (ADS)

Bruggemann, Lena; Bach, Heike; Ruf, Tobias; Appel, Florian; Migdall, Silke; Hank, Tobias; Mauser, Wolfram; Eiblmeier, Peter

2016-08-01

The central topic of this study is the monitoring of winter wheat phenology and the detection of anthesis (flowering) using remotely sensed data as well as crop growth modeling. It is not possible to directly observe the flowering of wheat with optical satellite sensors. Thus, an approach that combines crop growth modeling with remote sensing data covering optical and microwave spectral ranges was developed. This was done in three steps: The hydro-agroecological land surface model PROMET was first run in a stand-alone version for selected sites distributed throughout Bavaria using only static input parameters (e.g. soil map) and current meteorological data as driving factors. Thus, multitemporal information from optical remote sensing data was assimilated into the model runs in a second step to improve the accuracy of the results. Finally, the use of radar data for anthesis detection in winter wheat was tested using Sentinel-1 data of 2015 in dual polarization mode (VV+VH).

Growth pattern research on the modern deposition of Ganjiang delta in Poyang lake basin by spatio-temporal remote sensing images

NASA Astrophysics Data System (ADS)

Zhou, Hongying; Yuan, Xuanjun; Zhang, Youyan; Dong, Wentong; Liu, Song

2016-11-01

It is of great importance for petroleum exploration to study the sedimentary features and the growth pattern of shoal water deltas in lake basins. Taking spatio-temporal remote sensing images as the principal data source, combined with field sedimentation survey, a quantitative research on the modern deposition of Ganjiang delta in the Poyang Lake Basin is described in this paper. Using 76 multi-temporal and multi-type remote sensing images acquired from 1973 to 2015, combined with field sedimentation survey, remote sensing interpretation analysis was conducted on the sedimentary facies of the Ganjiang delta. It is found that that the current Poyang Lake mainly consists of three types of sand body deposits including deltaic deposit, overflow channel deposit, and aeolian deposit, and the distribution of sand bodies was affected by the above three types of depositions jointly. The mid-branch channels of the Ganjiang delta increased on an exponential growth rhythm. The main growth pattern of the Ganjiang delta is dendritic and reticular, and the distributary channel mostly arborizes at lake inlet and was reworked to be reticulatus at late stage.

Palm Swamp Wetland Ecosystems of the Upper Amazon: Characterizing their Distribution and Inundation State Using Multiple Resolution Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schröder, R.; Pinto, N.; Zimmermann, R.; Horna, V.

2011-12-01

Palm swamp wetlands are prevalent in the Amazon basin, including extensive regions in northern Peru. These ecosystems are characterized by constant surface inundation and moderate seasonal water level variation. The combination of constantly saturated soils, giving rise to low oxygen conditions, and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, knowledge of their spatial extent and inundation state is crucial for assessing the associated land-atmosphere carbon exchange. Precise spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We are developing a remote sensing methodology using multiple resolution microwave remote sensing data to determine palm swamp distribution and inundation state over focus regions in the Amazon basin in northern Peru. For this purpose, two types of multi-temporal microwave data are used: 1) high-resolution (100 m) data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR) to derive maps of palm swamp extent and inundation from dual-polarization fine-beam and multi-temporal HH-polarized ScanSAR, and 2) coarse resolution (25 km) combined active and passive microwave data from QuikSCAT and AMSR-E to derive inundated area fraction on a weekly basis. We compare information content and accuracy of the coarse resolution products to the PALSAR-based datasets to ensure information harmonization. The synergistic combination of high and low resolution datasets will allow for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA/EORC. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Canadian SAR remote sensing for the Terrestrial Wetland Global Change Research Network (TWGCRN)

USGS Publications Warehouse

Kaya, Shannon; Brisco, Brian; Cull, Andrew; Gallant, Alisa L.; Sadinski, Walter J.; Thompson, Dean

2010-01-01

The Canada Centre for Remote Sensing (CCRS) has more than 30 years of experience investigating the use of SAR remote sensing for many applications related to terrestrial water resources. Recently, CCRS scientists began contributing to the Terrestrial Wetland Global Change Research Network (TWGCRN), a bi-national research network dedicated to assessing impacts of global change on interconnected wetland-upland landscapes across a vital portion of North America. CCRS scientists are applying SAR remote sensing to characterize wetland components of these landscapes in three ways. First, they are using a comprehensive set of RADARSAT-2 SAR data collected during April to September 2009 to extract multi-temporal surface water information for key TWGCRN study landscapes in North America. Second, they are analyzing polarimetric RADARSAT-2 data to determine areas where double-bounce represents the primary scattering mechanism and is indicative of flooded vegetation in these landscapes. Third, they are testing advanced interferometric SAR techniques to estimate water levels with RADARSAT-2 Fine Quad polarimetric image pairs. The combined information from these three SAR analysis activities will provide TWGCRN scientists with an integrated view and monitoring capability for these dynamic wetland-upland landscapes. These data are being used in conjunction with other remote sensing and field data to study interactions between landscape and animal (birds and amphibians) responses to climate/global change.

A novel multi-temporal approach to wet snow retrieval with Sentinel-1 images (Conference Presentation)

NASA Astrophysics Data System (ADS)

Marin, Carlo; Callegari, Mattia; Notarnicola, Claudia

2016-10-01

Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention. In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. Even though the analysis of snow pack can be conducted in the visible, near-infrared and short-wave infrared spectrum, the presence of clouds during the melting season, which may be pervasive in some parts of the World (e.g., polar regions), renders impossible the regular acquisition of information needed for the operational purposes. Therefore, the use of the microwave sensors, which signal can penetrate the clouds, can be an asset for the detection of snow proprieties. In particular, the SAR images have demonstrated to be effective and robust measurements to identify the wet snow. Among the several methods presented in the literature, the best results in wet snow mapping have been achieved by the bi-temporal change detection approach proposed by Nagler and Rott [1], or its slight improvements presented afterwards (e.g., [2]). Nonetheless, with the introduction of the Sentinel-1 by ESA, which provides free-of-charge SAR images every 6 days over the same geographical area with a resolution of 20m, the scientists have the opportunity to better investigate and improve the state-of-the-art methods for wet snow detection. In this work, we propose a novel method based on a supervised learning approach able to exploit both the experience of the state-of-the-art algorithms and the high multi-temporal information provided by the Sentinel-1 data. In detail, this is done by training the proposed method with examples extracted by [1] and refine this information by deriving additional training for the complex cases where the state-of-the-art algorithm fails. In addition, the multi-temporal information is fully exploited by modelling it as a series of statistical moments. Indeed, with a proper time sampling, statistical moments can describe the shape of the probability density function (pdf) of the backscattering time series ([3-4]). Given the description of the shape of the multi-temporal VV and VH backscattering pdfs, it is not necessary to explicitly identify which time instants in the time series are to be assigned to the reference image as done in the bi-temporal approach. This information is implicit in the shape of the pdf and it is used in the training procedure for solving the wet snow detection problem based on the available training samples. The proposed approach is designed to work in an alpine environment and it is validated considering ground truth measurements provided by automatic weather stations that record snow depth and snow temperature over 10 sites deployed in the South Tyrol region in northern Italy. References: [1] Nagler, T.; Rott, H., "Retrieval of wet snow by means of multitemporal SAR data," in Geoscience and Remote Sensing, IEEE Transactions on , vol.38, no.2, pp.754-765, Mar 2000. [2] Storvold, R., Malnes, E., and Lauknes, I., "Using ENVISAT ASAR wideswath data to retrieve snow covered area in mountainous regions", EARSeL eProceedings 4, 2/2006 [3] Inglada, J and Mercier, G., "A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis," in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1432-1445, May 2007. [4] Bujor, F., Trouve, E., Valet, L., Nicolas J. M., and Rudant, J. P., "Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2073-2084, Oct. 2004.

Applicability Assessment of Uavsar Data in Wetland Monitoring: a Case Study of Louisiana Wetland

NASA Astrophysics Data System (ADS)

Zhao, J.; Niu, Y.; Lu, Z.; Yang, J.; Li, P.; Liu, W.

2018-04-01

Wetlands are highly productive and support a wide variety of ecosystem goods and services. Monitoring wetland is essential and potential. Because of the repeat-pass nature of satellite orbit and airborne, time-series of remote sensing data can be obtained to monitor wetland. UAVSAR is a NASA L-band synthetic aperture radar (SAR) sensor compact pod-mounted polarimetric instrument for interferometric repeat-track observations. Moreover, UAVSAR images can accurately map crustal deformations associated with natural hazards, such as volcanoes and earthquakes. And its polarization agility facilitates terrain and land-use classification and change detection. In this paper, the multi-temporal UAVSAR data are applied for monitoring the wetland change. Using the multi-temporal polarimetric SAR (PolSAR) data, the change detection maps are obtained by unsupervised and supervised method. And the coherence is extracted from the interfometric SAR (InSAR) data to verify the accuracy of change detection map. The experimental results show that the multi-temporal UAVSAR data is fit for wetland monitor.

Multi-temporal RADARSAT-1 and ERS backscattering signatures of coastal wetlands in southeastern Louisiana

USGS Publications Warehouse

Kwoun, Oh-Ig; Lu, Z.

2009-01-01

Using multi-temporal European Remote-sensing Satellites (ERS-1/-2) and Canadian Radar Satellite (RADARSAT-1) synthetic aperture radar (SAR) data over the Louisiana coastal zone, we characterize seasonal variations of radar backscat-tering according to vegetation type. Our main findings are as follows. First, ERS-1/-2 and RADARSAT-1 require careful radiometric calibration to perform multi-temporal backscattering analysis for wetland mapping. We use SAR backscattering signals from cities for the relative calibration. Second, using seasonally averaged backscattering coefficients from ERS-1/-2 and RADARSAT-1, we can differentiate most forests (bottomland and swamp forests) and marshes (freshwater, intermediate, brackish, and saline marshes) in coastal wetlands. The student t-test results support the usefulness of season-averaged backscatter data for classification. Third, combining SAR backscattering coefficients and an optical-sensor-based normalized difference vegetation index can provide further insight into vegetation type and enhance the separation between forests and marshes. Our study demonstrates that SAR can provide necessary information to characterize coastal wetlands and monitor their changes.

Landscape dynamics analysis of the Yongding River watershed (Mentougou section) by multi-temporal Landsat imagery

NASA Astrophysics Data System (ADS)

Zhang, Yuhu; Yu, Changqing; Qi, Jiaguo; Zhang, Zili; Shi, Qinshan

2007-11-01

The problem of efficient use of multi-temporal remotely sensed data for land-cover and landscape pattern dynamics has already considerable attention in landscape ecology and some other disciplines. This research develops and tests a methodological approach to monitor and analysis landscape dynamics change of Yongding river watershed (Mentougou section) as study area from 1988 to 2005, The result shows that the OIF is the best method of optimal bands selection in Landsat TM remote sensing data, TM3, 4, 5 bands is optimal band combination ;the Mentougou Reach of Yongding river watershed landscape changed significantly in terms of its composition over the period 1988-2005, The total landscape patches of study area in 2005 are more those in 1988,2001, Mean patch size(MPS)decreased sharply, Number of patches(NP) increased sharply, The landscape pattern takes on the fragmentation trends under the effect on the human activity. The forest (woodland and shrubland)are the main landscape matrix. with a significant decrease in croplands and a increase in built-up (residential, urban land) and industrial minerals mining land(coal, open-pit)over the 17 years, And the underlying socio-economic and other drivers of landscape change in study area are discussed.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

NASA Astrophysics Data System (ADS)

Waldhoff, Guido; Lussem, Ulrike; Bareth, Georg

2017-09-01

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008-2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

Geometric registration of remotely sensed data with SAMIR

NASA Astrophysics Data System (ADS)

Gianinetto, Marco; Barazzetti, Luigi; Dini, Luigi; Fusiello, Andrea; Toldo, Roberto

2015-06-01

The commercial market offers several software packages for the registration of remotely sensed data through standard one-to-one image matching. Although very rapid and simple, this strategy does not take into consideration all the interconnections among the images of a multi-temporal data set. This paper presents a new scientific software, called Satellite Automatic Multi-Image Registration (SAMIR), able to extend the traditional registration approach towards multi-image global processing. Tests carried out with high-resolution optical (IKONOS) and high-resolution radar (COSMO-SkyMed) data showed that SAMIR can improve the registration phase with a more rigorous and robust workflow without initial approximations, user's interaction or limitation in spatial/spectral data size. The validation highlighted a sub-pixel accuracy in image co-registration for the considered imaging technologies, including optical and radar imagery.

Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data

NASA Astrophysics Data System (ADS)

Chen, Jingbo; Wang, Chengyi; Yue, Anzhi; Chen, Jiansheng; He, Dongxu; Zhang, Xiuyan

2017-10-01

The tremendous success of deep learning models such as convolutional neural networks (CNNs) in computer vision provides a method for similar problems in the field of remote sensing. Although research on repurposing pretrained CNN to remote sensing tasks is emerging, the scarcity of labeled samples and the complexity of remote sensing imagery still pose challenges. We developed a knowledge-guided golf course detection approach using a CNN fine-tuned on temporally augmented data. The proposed approach is a combination of knowledge-driven region proposal, data-driven detection based on CNN, and knowledge-driven postprocessing. To confront data complexity, knowledge-derived cooccurrence, composition, and area-based rules are applied sequentially to propose candidate golf regions. To confront sample scarcity, we employed data augmentation in the temporal domain, which extracts samples from multitemporal images. The augmented samples were then used to fine-tune a pretrained CNN for golf detection. Finally, commission error was further suppressed by postprocessing. Experiments conducted on GF-1 imagery prove the effectiveness of the proposed approach.

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronand; Russell, Jeff; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the foundation of any interoperability or change detection technique. Satellite intercomparisons and accurate vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), require the generation of accurate reflectance maps (NDVI is used to describe or infer a wide variety of biophysical parameters and is defined in terms of near-infrared (NIR) and red band reflectances). Accurate reflectance-map generation from satellite imagery relies on the removal of solar and satellite geometry and of atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance has been widely applied to a few systems only. The ability to obtain atmospherically corrected imagery and products from various satellites is essential to enable widescale use of remotely sensed, multitemporal imagery for a variety of applications. An atmospheric correction approach derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that can be applied to high-spatial-resolution satellite imagery under many conditions was evaluated to demonstrate a reliable, effective reflectance map generation method. Additional information is included in the original extended abstract.

Pan-Tropical Forest Mapping by Exploiting Textures of Multi-Temporal High Resolution SAR Data

NASA Astrophysics Data System (ADS)

Knuth, R.; Eckardt, R.; Richter, N.; Schmullius, C.

2012-12-01

Even though the first commitment period of the Kyoto Protocol is in the offing, there is still a strong demand for profound, reliable, and up to date information in order to bridge the gap of knowledge of the land cover conversion. Despite the fact that land use change is one of the largest carbon contribution factors, it is still poorly quantified. This is particularly true for many tropical forest areas worldwide. Here, preservation of such pristine forest areas is critically endangered. Enormous population growth, the increasing global demand for various resources, and the ongoing unsustainable management practices put the remaining tropical forests under a huge pressure. Yet, only the United Nations Food and Agriculture Organization's (FAO) global Forest Resources Assessment (FRA) report provides the crucial quantitative information every 5 years on a regional scale. Nonetheless, the assembled information of the FRA reports bear the burden of ambiguity and vagueness, because they were compiled based on autonomously gathered statistics, which are usually driven by the individual country needs. There is a broad consensus among the different scientific disciplines, that only the remote sensing technology allows for a large scale robust monitoring of these widespread, and remote forest areas. Consequently, the FAO decided to supplementary analyze remote sensing data for the present (2010) and upcoming FRAs. However, it is also widely accepted that currently only microwave remote sensing techniques allow for an all-day, weather independent monitoring of the frequently cloud-covered tropics. In this context, high resolution Synthetic Aperture Radar (SAR) images of the German satellites TerraSAR-X and TanDEM-X have been investigated within the pan-tropics to support the latest FRA 2010 report. Data of more than 304 predominantly cloud-covered sites in Latin America (188), Central Africa (45) and Southeast Asia (71) have been acquired. Upon delivery, the corresponding radar images were processed using an operational processing chain that includes radiometric transformation, noise reduction, and georeferencing of the SAR data. In places with pronounced topography both satellites were used as single pass interferometer to derive a digital surface model in order to perform an orthorectification followed by a topographic normalization of the SAR backscatter values. As prescribed by the FAO, the final segment-based classification algorithm was fed by multi-temporal backscatter information, a set of textural features, and information on the degree of coherence between the multi-temporal acquisitions. Validation with available high resolution optical imagery suggests that the produced forest maps possess an overall accuracy of 75 percent or higher.

Registration and rectification needs of geology

NASA Technical Reports Server (NTRS)

Chavez, P. S., Jr.

1982-01-01

Geologic applications of remotely sensed imaging encompass five areas of interest. The five areas include: (1) enhancement and analysis of individual images; (2) work with small area mosaics of imagery which have been map projection rectified to individual quadrangles; (3) development of large area mosaics of multiple images for several counties or states; (4) registration of multitemporal images; and (5) data integration from several sensors and map sources. Examples for each of these types of applications are summarized.

Evolving land cover classification algorithms for multispectral and multitemporal imagery

NASA Astrophysics Data System (ADS)

Brumby, Steven P.; Theiler, James P.; Bloch, Jeffrey J.; Harvey, Neal R.; Perkins, Simon J.; Szymanski, John J.; Young, Aaron C.

2002-01-01

The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification.

Yield estimation of corn based on multitemporal LANDSAT-TM data as input for an agrometeorological model

NASA Astrophysics Data System (ADS)

Bach, Heike

1998-07-01

In order to test remote sensing data with advanced yield formation models for accuracy and timeliness of yield estimation of corn, a project was conducted for the State Ministry for Rural Environment, Food, and Forestry of Baden-Württemberg (Germany). This project was carried out during the course of the `Special Yield Estimation', a regular procedure conducted for the European Union, to more accurately estimate agricultural yield. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on four LANDSAT-derived estimates (between May and August) and daily meteorological data, the grain yield of corn fields was determined for 1995. The modelled yields were compared with results gathered independently within the Special Yield Estimation for 23 test fields in the upper Rhine valley. The agreement between LANDSAT-based estimates (six weeks before harvest) and Special Yield Estimation (at harvest) shows a relative error of 2.3%. The comparison of the results for single fields shows that six weeks before harvest, the grain yield of corn was estimated with a mean relative accuracy of 13% using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results for yield prediction with remote sensing.

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

USGS Publications Warehouse

Stow, Douglas A.; Hope, Allen; McGuire, David; Verbyla, David; Gamon, John A.; Huemmrich, Fred; Houston, Stan; Racine, Charles H.; Sturm, Matthew; Tape, Ken D.; Hinzman, Larry D.; Yoshikawa, Kenji; Tweedie, Craig E.; Noyle, Brian; Silapaswan, Cherie; Douglas, David C.; Griffith, Brad; Jia, Gensuo; Howard E. Epstein,; Walker, Donald A.; Daeschner, Scott; Petersen, Aaron; Zhou, Liming; Myneni, Ranga B.

2004-01-01

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations.The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.

Compositing multitemporal remote sensing data sets

USGS Publications Warehouse

Qi, J.; Huete, A.R.; Hood, J.; Kerr, Y.

1993-01-01

To eliminate cloud and atmosphere-affected pixels, the compositing of multi temporal remote sensing data sets is done by selecting the maximum vale of the normalized different vegetation index (NDVI) within a compositing period. The NDVI classifier, however, is strongly affected by surface type and anisotropic properties, sensor viewing geometries, and atmospheric conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external effects. To improve the accuracy of compositing products, two key approaches can be taken: one is to refine the compositing classifier (NDVI) and the other is to improve existing compositing algorithms. In this project, an alternative classifier was developed and an alternative pixel selection criterion was proposed for compositing. The new classifier and the alternative compositing algorithm were applied to an advanced very high resolution radiometer data set of different biome types in the United States. The results were compared with the maximum value compositing and the best index slope extraction algorithms. The new approaches greatly reduced the high frequency noises related to the external factors and repainted more reliable data. The results suggest that the geometric-optical canopy properties of specific biomes may be needed in compositing. Limitations of the new approaches include the dependency of pixel selection on the length of the composite period and data discontinuity.

Taiga forest stands and SAR: Monitoring for subarctic global change

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

Way, J.; Kwok, R.; Viereck, L.

1992-03-01

In preparation for the first European Earth Remote Sensing (ERS-1) mission, a series of multitemporal, multifrequency, multipolarization aircraft synthetic aperture radar (SAR) data sets were acquired over the Bonanza Creek Experimental Forest near Fairbanks, Alaska in March 1988. Significant change in radar backscatter was observed over the two-week experimental period due to changing environmental conditions. These preliminary results are presented to illustrate the opportunity afforded by the ERS-1 SAR to monitor temporal change in forest ecosystems.

Satellite remote sensing of primary production

NASA Technical Reports Server (NTRS)

Tucker, C. J.; Sellers, P. J.

1986-01-01

Leaf structure and function are shown to result in distinctive variations in the absorption and reflection of solar radiation from plant canopies. The leaf properties that determine the radiation-interception characteristics of plant canopies are directly linked to photosynthesis, stomatal resistance and evapotranspiration and can be inferred from measurements of reflected solar energy. The effects of off-nadir viewing and atmospheric constituents, coupled with the need to measure changing surface conditions, emphasize the need for multitemporal measurements of reflected radiation if primary production is to be estimated.

Criteria for the optimal selection of remote sensing optical images to map event landslides

NASA Astrophysics Data System (ADS)

Fiorucci, Federica; Giordan, Daniele; Santangelo, Michele; Dutto, Furio; Rossi, Mauro; Guzzetti, Fausto

2018-01-01

Landslides leave discernible signs on the land surface, most of which can be captured in remote sensing images. Trained geomorphologists analyse remote sensing images and map landslides through heuristic interpretation of photographic and morphological characteristics. Despite a wide use of remote sensing images for landslide mapping, no attempt to evaluate how the image characteristics influence landslide identification and mapping exists. This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type (monoscopic or stereoscopic), on landslide mapping. We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system (dGPS) survey, which served as a benchmark. The eight maps were compared pairwise and to a benchmark. The mismatch between each map pair was quantified by the error index, E. Results show that the map closest to the benchmark delineation of the landslide was obtained using the higher resolution image, where the landslide signature was primarily photographical (in the landslide source and transport area). Conversely, where the landslide signature was mainly morphological (in the landslide deposit) the best mapping result was obtained using the stereoscopic images. Albeit conducted on a single landslide, the experiment results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps.

Combining optical remote sensing, agricultural statistics and field observations for culture recognition over a peri-urban region

NASA Astrophysics Data System (ADS)

Delbart, Nicolas; Emmanuelle, Vaudour; Fabienne, Maignan; Catherine, Ottlé; Jean-Marc, Gilliot

2017-04-01

This study explores the potential of multi-temporal optical remote sensing, with high revisit frequency, to derive missing information on agricultural calendar and crop types over the agricultural lands in the Versailles plain in the western Paris suburbs. This study comes besides past and ongoing studies on the use of radar and high spatial resolution optical remote sensing to monitor agricultural practices in this study area (e.g. Vaudour et al. 2014). Agricultural statistics, such as the Land Parcel Identification System (LPIS) for France, permit to know the nature of annual crops for each digitized declared field of this land parcel registry. However, within each declared field several cropped plots and a diversity of practices may exist, being marked by agricultural rotations which vary both spatially and temporally within it and differ from one year to the other. Even though the new LPIS to be released in 2016 is expected to describe individual plots within declared fields, its attributes may not enable to discriminate between winter and spring crops. Here we evaluate the potential of high observation frequency remote sensing to differentiate seasonal crops based essentially on the seasonality of the spectral properties. In particular, we use the Landsat data to spatially disaggregate the LPIS statistical data, on the basis of the analysis of the remote sensing spectral seasonality measured on a number of selected ground-observed fields. This work is carried out in the framework of the CNES TOSCA-PLEIADES-CO of the French Space Agency.

Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

NASA Astrophysics Data System (ADS)

Sofina, N.; Ehlers, M.

2012-08-01

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

Satellite data in aquatic area research - Some ideas for future studies

NASA Technical Reports Server (NTRS)

Raitala, Jouko T.

1986-01-01

Attempts to apply aquatic remote sensing to the preparation of parametric map-like presentations, quantitative evaluations and time-related investigations in various water areas in Finland are presented. The potential use of Landsat MSS data in aquatic area studies, including limology, aquatic botany, geomorphology and engineering is evaluated using computer-aided digital remote sensing techniques. MSS data may provide information about depth, Secchi disc values, humus content in water, and productivity. Aquatic vegetation classification using MSS is possible only where vegetation units are large enough in respect to the 0.5 hectares ground resolution. Multitemporal satellite imagery has been used to evaluate alterations in the littoral areas of some Finnish water reservoirs between successive periods of high water. It is concluded that although MSS data can be of use in aquatic studies, it should be used in connection with field data and/or TM and SPOT data.

Sample project: establishing a global forest monitoring capability using multi-resolution and multi-temporal remotely sensed data sets

USGS Publications Warehouse

Hansen, Matt; Stehman, Steve; Loveland, Tom; Vogelmann, Jim; Cochrane, Mark

2009-01-01

Quantifying rates of forest-cover change is important for improved carbon accounting and climate change modeling, management of forestry and agricultural resources, and biodiversity monitoring. A practical solution to examining trends in forest cover change at global scale is to employ remotely sensed data. Satellite-based monitoring of forest cover can be implemented consistently across large regions at annual and inter-annual intervals. This research extends previous research on global forest-cover dynamics and land-cover change estimation to establish a robust, operational forest monitoring and assessment system. The approach integrates both MODIS and Landsat data to provide timely biome-scale forest change estimation. This is achieved by using annual MODIS change indicator maps to stratify biomes into low, medium and high change categories. Landsat image pairs can then be sampled within these strata and analyzed for estimating area of forest cleared.

On the use of COSMO-SkyMed time series for the identification of Archaeological traces dating from the Eastern-Han to Northern-Wei Dynasties in Luoyang city.

NASA Astrophysics Data System (ADS)

Chen, Fulong; Masini, Nicola; Yang, Ruixia; Feng, Dexian; Lasaponara, Rosa

2015-04-01

The availability of Very High Resolution (VHR) Synthetic Aperture SAR (SAR) data (Lasaponara and Masini 2013, Tapete et al. 2013), such as TerraSAR-X and Cosmo Sky Med launched in 2007, opened a new era in the spaceborne SAR remote sensing, including archaeology remote sensing previous mainly based on optical data (see for example Lasaponara and Masini 2012, Ciminale et al. 2009, Masini and Lasaponara 2006). They provide powerful tools, based on active sensors from space operating in the microwave frequency range, which are useful to extract information about the contemporary landscape and make possible, in some conditions, to infer changes in the former environment and to detect archaeological remains. Nevertheless, the capability of satellite radar technology in archaeology has so far not been fully assessed. This paper (Chen et al 2015) is a pioneering effort to assess the potential of satellite SAR X-band data in the detection of archaeological marks. We focus on the results obtained from a collaborative contribution jointly carried out by archaeologists and remote sensing experts in order to test the use of COSMO-SkyMed data in different contexts and environmental conditions. The methodological approach we adopted is based on multi-temporal analysis performed to reduce noise and highlight archaeological marks. Results from multi-temporal data analysis, conducted using 40 scenes from COSMO-SkyMed X-band Stripmap data (27 February to 17 October 2013), enable us to detect unknown archaeological crop, soil, and shadow marks representing Luoyang city, dating from the Eastern-Han to Northern-Wei Dynasties. Reference Chen F., Masini N., Yang R., Milillo P., Feng D., Lasaponara R., 2015 A Space View of Radar Archaeological Marks: First Applications of COSMO-SkyMed X-Band Data. Remote Sens. 2015, 7, 24-50; doi:10.3390/rs70100024. Lasaponara R., Masini N. 2013, Satellite Synthetic Aperture Radar in Archaeology and Cultural Landscape: An Overview. Archaeological Prospection, 20, 71-78, doi: 10.1002/arp.1452 Tapete D., Cigna F., Masini N., Lasaponara R. 2013. Prospection and monitoring of the archaeological heritage of Nasca, Peru, with ENVISAT ASAR, Archaeological Prospection, 20, 133-147, doi: 10.1002/arp.1449. Ciminale M, D Gallo, R Lasaponara, N Masini, 2009 A multiscale approach for reconstructing archaeological landscapes: applications in Northern Apulia (Italy) Archaeological Prospection 16 (3), 143-153 Lasaponara R, N Masini, 2012 Satellite Remote Sensing, A New Tool for Archaeology (Series Remote Sensing and Digital Image) Springer book Masini N, R Lasaponara, 2006, Satellite-based recognition of landscape archaeological features related to ancient human transformation Journal of Geophysics and Engineering 3 (3), 230.

Measuring deforestation using remote sensing and its implication for conservation in Gunung Palung National Park, West Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Fawzi, N. I.; Husna, V. N.; Helms, J. A.

2018-05-01

Gunung Palung National Park (1,080 km2, 1°3’ – 1°22’ S, 109°54’ – 110°28’ E) was first protected in 1937 and is now one of the largest remaining primary lowland mixed dipterocarp forests on Borneo. To help inform conservation efforts, we measured forest cover change in the protected area using 11 multi-temporal Landsat series images with path/row 121/61. Annual deforestation rates have declined since measurement began in 1989, to around 68 hectares per year in 2011 and 112 hectares per year in 2017. Halting deforestation in this protected area requires to tackle its underlying economic and social causes, and find ways for communities to meet their needs without resorting to forest clearing. Community empowerment, forest rehabilitation, and health care incentives as payment for ecosystem services can help reduce deforestation in Gunung Palung National Park. This becomes a positive trend which we must continue to always work in forest conservation. Future forest monitoring will be dependency with remote sensing analysis and open source remote sensing data such as Landsat and Sentinel data remain an important data source for historical forest change monitoring.

On the use of Multisensor and multitemporal data for monitoring risk degradation and looting in archaeological site

NASA Astrophysics Data System (ADS)

Masini, Nicola; Lasaponara, Rosa

2015-04-01

Illegal excavations represent one of the main risks which affect the archaeological heritage all over the world. They cause a massive loss of artefacts but also, and above all, a loss of the cultural context, which makes the subsequent interpretation of archaeological remains very difficult. Remote sensing offers a suitable chance to quantify and analyse this phenomenon, especially in those countries, from Southern America to Middle East, where the surveillance on site is not much effective and time consuming or non practicable due to military or political restrictions. In this paper we focus on the use of GeoEye and Google Earth imagery to quantitatively assess looting in Ventarron (Lambayeque, Peru) that is one of most important archaeological sites in Southern America. Multitemporal satellite images acquired for the study area have been processed by using both autocorrelation statistics and unsupervised classification to highlight and extract looting patterns. The mapping of areas affected by looting offered the opportunity to investigate such areas not previously systematically documented. Reference Lasaponara R.; Giovanni Leucci; Nicola Masini; Raffaele Persico 2014 ": Investigating archaeological looting using very high resolution satellite images and georadar: the experience in Lambayeque in North Peru JASC13-61R1 Cigna Francesca, Deodato Tapete, Rosa Lasaponara and Nicola Masini, 2013 Amplitude Change Detection with ENVISAT ASAR to Image the Cultural Landscape of the Nasca Region, Peru (pages 117-131). Archeological Prospection Article first published online: 21 MAY 2013 | DOI: 10.1002/arp.1451 Tapete Deodato, Francesca Cigna, Nicola Masini and Rosa Lasaponara 2013. Prospection and Monitoring of the Archaeological Heritage of Nasca, Peru, with ENVISAT ASAR Archeological Prospection (pages 133-147) Article first published online: 21 MAY 2013 | DOI: 10.1002/arp.1449 Lasaponara Rosa 2013: Geospatial analysis from space: Advanced approaches for data processing, information extraction and interpretation. Int. J. Applied Earth Observation and Geoinformation 20 Lasaponara . R &N. Masini "Satellite Remote Sensing: A NewTool for Archaeology" Springer February 2012 (http://www.amazon.com/Satellite-Remote-Sensing-Archaeology-Processing/dp/9048188008) Lasaponara, R., Lanorte, A., 2012. Satellite time-series analysis. Int. J. Remote Sens.33 (15), 4649-4652, http://dx.doi.org/10.1080/01431161.2011.638342.

Monitoring of rock glacier dynamics by multi-temporal UAV images

NASA Astrophysics Data System (ADS)

Morra di Cella, Umberto; Pogliotti, Paolo; Diotri, Fabrizio; Cremonese, Edoardo; Filippa, Gianluca; Galvagno, Marta

2015-04-01

During the last years several steps forward have been made in the comprehension of rock glaciers dynamics mainly for their potential evolution into rapid mass movements phenomena. Monitoring the surface movement of creeping mountain permafrost is important for understanding the potential effect of ongoing climate change on such a landforms. This study presents the reconstruction of two years of surface movements and DEM changes obtained by multi-temporal analysis of UAV images (provided by SenseFly Swinglet CAM drone). The movement rate obtained by photogrammetry are compared to those obtained by differential GNSS repeated campaigns on almost fifty points distributed on the rock glacier. Results reveals a very good agreements between both rates velocities obtained by the two methods and vertical displacements on fixed points. Strengths, weaknesses and shrewdness of this methods will be discussed. Such a method is very promising mainly for remote regions with difficult access.

Modeling the impacts of phenological and inter-annual changes in landscape metrics on local biodiversity of agricultural lands of Eastern Ontario using multi-spatial and multi-temporal remote sensing data

NASA Astrophysics Data System (ADS)

Alavi-Shoushtari, N.; King, D.

2017-12-01

Agricultural landscapes are highly variable ecosystems and are home to many local farmland species. Seasonal, phenological and inter-annual agricultural landscape dynamics have potential to affect the richness and abundance of farmland species. Remote sensing provides data and techniques which enable monitoring landscape changes in multiple temporal and spatial scales. MODIS high temporal resolution remote sensing images enable detection of seasonal and phenological trends, while Landsat higher spatial resolution images, with its long term archive enables inter-annual trend analysis over several decades. The objective of this study to use multi-spatial and multi-temporal remote sensing data to model the response of farmland species to landscape metrics. The study area is the predominantly agricultural region of eastern Ontario. 92 sample landscapes were selected within this region using a protocol designed to maximize variance in composition and configuration heterogeneity while controlling for amount of forest and spatial autocorrelation. Two sample landscape extents (1×1km and 3×3km) were selected to analyze the impacts of spatial scale on biodiversity response. Gamma diversity index data for four taxa groups (birds, butterflies, plants, and beetles) were collected during the summers of 2011 and 2012 within the cropped area of each landscape. To extract the seasonal and phenological metrics a 2000-2012 MODIS NDVI time-series was used, while a 1985-2012 Landsat time-series was used to model the inter-annual trends of change in the sample landscapes. The results of statistical modeling showed significant relationships between farmland biodiversity for several taxa and the phenological and inter-annual variables. The following general results were obtained: 1) Among the taxa groups, plant and beetles diversity was most significantly correlated with the phenological variables; 2) Those phenological variables which are associated with the variability in the start of season date across the sample landscapes and the variability in the corresponding NDVI values at that date showed the strongest correlation with the biodiversity indices; 3) The significance of the models improved when using 3×3km site extent both for MODIS and Landsat based models due most likely to the larger sample size over 3x3km.

Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images

NASA Astrophysics Data System (ADS)

He, Yingqing; Ai, Bin; Yao, Yao; Zhong, Fajun

2015-06-01

Cellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model's parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou city, located in the core of China's Pearl River Delta. The initial urban land was directly classified from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model's parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation.

Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data.

PubMed

Nielsen, Allan Aasbjerg

2002-01-01

This paper describes two- and multiset canonical correlations analysis (CCA) for data fusion, multisource, multiset, or multitemporal exploratory data analysis. These techniques transform multivariate multiset data into new orthogonal variables called canonical variates (CVs) which, when applied in remote sensing, exhibit ever-decreasing similarity (as expressed by correlation measures) over sets consisting of 1) spectral variables at fixed points in time (R-mode analysis), or 2) temporal variables with fixed wavelengths (T-mode analysis). The CVs are invariant to linear and affine transformations of the original variables within sets which means, for example, that the R-mode CVs are insensitive to changes over time in offset and gain in a measuring device. In a case study, CVs are calculated from Landsat Thematic Mapper (TM) data with six spectral bands over six consecutive years. Both Rand T-mode CVs clearly exhibit the desired characteristic: they show maximum similarity for the low-order canonical variates and minimum similarity for the high-order canonical variates. These characteristics are seen both visually and in objective measures. The results from the multiset CCA R- and T-mode analyses are very different. This difference is ascribed to the noise structure in the data. The CCA methods are related to partial least squares (PLS) methods. This paper very briefly describes multiset CCA-based multiset PLS. Also, the CCA methods can be applied as multivariate extensions to empirical orthogonal functions (EOF) techniques. Multiset CCA is well-suited for inclusion in geographical information systems (GIS).

Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

NASA Astrophysics Data System (ADS)

Hagensieker, Ron; Roscher, Ribana; Rosentreter, Johannes; Jakimow, Benjamin; Waske, Björn

2017-12-01

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain. The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.

Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?

PubMed Central

Armaş, Iuliana; Mendes, Diana A.; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana

2017-01-01

The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992–2010 from ERS-1/-2 and ENVISAT, and 2011–2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements. PMID:28252103

Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?

PubMed

Armaş, Iuliana; Mendes, Diana A; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana

2017-03-02

The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992-2010 from ERS-1/-2 and ENVISAT, and 2011-2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements.

Bamboo mapping of Ethiopia, Kenya and Uganda for the year 2016 using multi-temporal Landsat imagery

NASA Astrophysics Data System (ADS)

Zhao, Yuanyuan; Feng, Duole; Jayaraman, Durai; Belay, Daniel; Sebrala, Heiru; Ngugi, John; Maina, Eunice; Akombo, Rose; Otuoma, John; Mutyaba, Joseph; Kissa, Sam; Qi, Shuhua; Assefa, Fiker; Oduor, Nellie Mugure; Ndawula, Andrew Kalema; Li, Yanxia; Gong, Peng

2018-04-01

Mapping the spatial distribution of bamboo in East Africa is necessary for biodiversity conservation, resource management and policy making for rural poverty reduction. In this study, we produced a contemporary bamboo cover map of Ethiopia, Kenya and Uganda for the year 2016 using multi-temporal Landsat imagery series at 30 m spatial resolution. This is the first bamboo map generated using remotely sensed data for these three East African countries that possess most of the African bamboo resource. The producer's and user's accuracies of bamboos are 79.2% and 84.0%, respectively. The hotspots with large amounts of bamboo were identified and the area of bamboo coverage for each region was estimated according to the map. The seasonal growth status of two typical bamboo zones (one highland bamboo and one lowland bamboo) were analyzed and the multi-temporal imagery proved to be useful in differentiating bamboo from other vegetation classes. The images acquired in September to February are less contaminated by clouds and shadows, and the image series cover the dying back process of lowland bamboo, which were helpful for bamboo identification in East Africa.

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

NASA Technical Reports Server (NTRS)

Beck, L. R.; Rodriguez, M. H.; Dister, S. W.; Rodriguez, A. D.; Washino, R. K.; Roberts, D. R.; Spanner, M. A.

1997-01-01

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Studies of recognition with multitemporal remote sensor data

NASA Technical Reports Server (NTRS)

Malila, W. A.; Hieber, R. H.; Cicone, R. C.

1975-01-01

Characteristics of multitemporal data and their use in recognition processing were investigated. Principal emphasis was on satellite data collected by the LANDSAT multispectral scanner and on temporal changes throughout a growing season. The effects of spatial misregistration on recognition performance with multitemporal data were examined. A capability to compute probabilities of detection and false alarm was developed and used with simulated distributions for misregistered pixels. Wheat detection was found to be degraded and false alarms increased by misregistration effects. Multitemporal signature characteristics and multitemporal recognition processing were studied to gain insights into problems associated with this approach and possible improvements. Recognition performance with one multitemporal data set displayed marked improvements over results from single-time data.

Monitoring Change in Temperate Coniferous Forest Ecosystems

NASA Technical Reports Server (NTRS)

Williams, Darrel (Technical Monitor); Woodcock, Curtis E.

2004-01-01

The primary goal of this research was to improve monitoring of temperate forest change using remote sensing. In this context, change includes both clearing of forest due to effects such as fire, logging, or land conversion and forest growth and succession. The Landsat 7 ETM+ proved an extremely valuable research tool in this domain. The Landsat 7 program has generated an extremely valuable transformation in the land remote sensing community by making high quality images available for relatively low cost. In addition, the tremendous improvements in the acquisition strategy greatly improved the overall availability of remote sensing images. I believe that from an historical prespective, the Landsat 7 mission will be considered extremely important as the improved image availability will stimulate the use of multitemporal imagery at resolutions useful for local to regional mapping. Also, Landsat 7 has opened the way to global applications of remote sensing at spatial scales where important surface processes and change can be directly monitored. It has been a wonderful experience to have participated on the Landsat 7 Science Team. The research conducted under this project led to contributions in four general domains: I. Improved understanding of the information content of images as a function of spatial resolution; II. Monitoring Forest Change and Succession; III. Development and Integration of Advanced Analysis Methods; and IV. General support of the remote sensing of forests and environmental change. This report is organized according to these topics. This report does not attempt to provide the complete details of the research conducted with support from this grant. That level of detail is provided in the 16 peer reviewed journal articles, 7 book chapters and 5 conference proceedings papers published as part of this grant. This report attempts to explain how the various publications fit together to improve our understanding of how forests are changing and how to monitor forest change with remote sensing. There were no new inventions that resulted from this grant.

Environmental mapping and monitoring of Iceland by remote sensing (EMMIRS)

NASA Astrophysics Data System (ADS)

Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.

2016-04-01

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remote sensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remote sensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remote sensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remote sensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project investigates the complex landscape dynamics between geological and ecological processes. This is done through cross-correlation of mapping results and implementation of modelling techniques that simulate geological and ecological processes in order to extrapolate the landscape evolution

Multi-temporal UAV based data for mapping crop type and structure in smallholder dominated Tanzanian agricultural landscape

NASA Astrophysics Data System (ADS)

Nagol, J. R.; Chung, C.; Dempewolf, J.; Maurice, S.; Mbungu, W.; Tumbo, S.

2015-12-01

Timely mapping and monitoring of crops like Maize, an important food security crop in Tanzania, can facilitate timely response by government and non-government organizations to food shortage or surplus conditions. Small UAVs can play an important role in linking the spaceborne remote sensing data and ground based measurement to improve the calibration and validation of satellite based estimates of in-season crop metrics. In Tanzania most of the growing season is often obscured by clouds. UAV data, if collected within a stratified statistical sampling framework, can also be used to directly in lieu of spaceborne data to infer mid-season yield estimates at regional scales.Here we present an object based approach to estimate crop metrics like crop type, area, and height using multi-temporal UAV based imagery. The methods were tested at three 1km2 plots in Kilosa, Njombe, and Same districts in Tanzania. At these sites both ground based and UAV based data were collected on a monthly time-step during the year 2015 growing season. SenseFly eBee drone with RGB and NIR-R-G camera was used to collect data. Crop type classification accuracies of above 85% were easily achieved.

Rotation and scale invariant shape context registration for remote sensing images with background variations

NASA Astrophysics Data System (ADS)

Jiang, Jie; Zhang, Shumei; Cao, Shixiang

2015-01-01

Multitemporal remote sensing images generally suffer from background variations, which significantly disrupt traditional region feature and descriptor abstracts, especially between pre and postdisasters, making registration by local features unreliable. Because shapes hold relatively stable information, a rotation and scale invariant shape context based on multiscale edge features is proposed. A multiscale morphological operator is adapted to detect edges of shapes, and an equivalent difference of Gaussian scale space is built to detect local scale invariant feature points along the detected edges. Then, a rotation invariant shape context with improved distance discrimination serves as a feature descriptor. For a distance shape context, a self-adaptive threshold (SAT) distance division coordinate system is proposed, which improves the discriminative property of the feature descriptor in mid-long pixel distances from the central point while maintaining it in shorter ones. To achieve rotation invariance, the magnitude of Fourier transform in one-dimension is applied to calculate angle shape context. Finally, the residual error is evaluated after obtaining thin-plate spline transformation between reference and sensed images. Experimental results demonstrate the robustness, efficiency, and accuracy of this automatic algorithm.

Rice Crop Monitoring Using Microwave and Optical Remotely Sensed Image Data

NASA Astrophysics Data System (ADS)

Suga, Y.; Konishi, T.; Takeuchi, S.; Kitano, Y.; Ito, S.

Hiroshima Institute of Technology HIT is operating the direct down-links of microwave and optical satellite data in Japan This study focuses on the validation for rice crop monitoring using microwave and optical remotely sensed image data acquired by satellites referring to ground truth data such as height of crop ratio of crop vegetation cover and leaf area index in the test sites of Japan ENVISAT-1 ASAR data has a capability to capture regularly and to monitor during the rice growing cycle by alternating cross polarization mode images However ASAR data is influenced by several parameters such as landcover structure direction and alignment of rice crop fields in the test sites In this study the validation was carried out combined with microwave and optical satellite image data and ground truth data regarding rice crop fields to investigate the above parameters Multi-temporal multi-direction descending and ascending and multi-angle ASAR alternating cross polarization mode images were used to investigate rice crop growing cycle LANDSAT data were used to detect landcover structure direction and alignment of rice crop fields corresponding to the backscatter of ASAR As the result of this study it was indicated that rice crop growth can be precisely monitored using multiple remotely sensed data and ground truth data considering with spatial spectral temporal and radiometric resolutions

Backscatter Analysis Using Multi-Temporal SENTINEL-1 SAR Data for Crop Growth of Maize in Konya Basin, Turkey

NASA Astrophysics Data System (ADS)

Abdikan, S.; Sekertekin, A.; Ustunern, M.; Balik Sanli, F.; Nasirzadehdizaji, R.

2018-04-01

Temporal monitoring of crop types is essential for the sustainable management of agricultural activities on both national and global levels. As a practical and efficient tool, remote sensing is widely used in such applications. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was utilized to investigate the performance of the sensor backscatter image on crop monitoring. Multi-temporal C-band VV and VH polarized SAR images were acquired simultaneously by in-situ measurements which was conducted at Konya basin, central Anatolia Turkey. During the measurements, plant height of maize plant was collected and relationship between backscatter values and plant height was analysed. The maize growth development was described under Biologische Bundesanstalt, bundessortenamt und CHemische industrie (BBCH). Under BBCH stages, the test site was classified as leaf development, stem elongation, heading and flowering in general. The correlation coefficient values indicated high correlation for both polarimetry during the early stages of the plant, while late stages indicated lower values in both polarimetry. As a last step, multi-temporal coverage of crop fields was analysed to map seasonal land use. To this aim, object based image classification was applied following image segmentation. About 80 % accuracies of land use maps were created in this experiment. As preliminary results, it is concluded that Sentinel-1 data provides beneficial information about plant growth. Dual-polarized Sentinel-1 data has high potential for multi-temporal analyses for agriculture monitoring and reliable mapping.

Quick multitemporal approach to get cloudless improved multispectral imagery for large geographical areas

NASA Astrophysics Data System (ADS)

Colaninno, Nicola; Marambio Castillo, Alejandro; Roca Cladera, Josep

2017-10-01

The demand for remotely sensed data is growing increasingly, due to the possibility of managing information about huge geographic areas, in digital format, at different time periods, and suitable for analysis in GIS platforms. However, primary satellite information is not such immediate as desirable. Beside geometric and atmospheric limitations, clouds, cloud shadows, and haze generally contaminate optical images. In terms of land cover, such a contamination is intended as missing information and should be replaced. Generally, image reconstruction is classified according to three main approaches, i.e. in-painting-based, multispectral-based, and multitemporal-based methods. This work relies on a multitemporal-based approach to retrieve uncontaminated pixels for an image scene. We explore an automatic method for quickly getting daytime cloudless and shadow-free image at moderate spatial resolution for large geographical areas. The process expects two main steps: a multitemporal effect adjustment to avoid significant seasonal variations, and a data reconstruction phase, based on automatic selection of uncontaminated pixels from an image stack. The result is a composite image based on middle values of the stack, over a year. The assumption is that, for specific purposes, land cover changes at a coarse scale are not significant over relatively short time periods. Because it is largely recognized that satellite imagery along tropical areas are generally strongly affected by clouds, the methodology is tested for the case study of the Dominican Republic at the year 2015; while Landsat 8 imagery are employed to test the approach.

A method for the selection of relevant pattern indices for monitoring of spatial forest cover pattern at a regional scale

NASA Astrophysics Data System (ADS)

De Clercq, Eva M.; Vandemoortele, Femke; De Wulf, Robert R.

2006-06-01

When signing Agenda 21, several countries agreed to monitor the status of forests to ensure their sustainable use. For reporting on the change in spatial forest cover pattern on a regional scale, pattern metrics are widely used. These indices are not often thoroughly evaluated as to their sensitivity to remote sensing data characteristics. Hence, one would not know whether the change in the metric values was due to actual landscape pattern changes or to characteristic variation of multitemporal remote sensing data. The objective of this study is to empirically test an array of pattern metrics for the monitoring of spatial forest cover. Different user requirements are used as point of departure. This proved to be a straightforward method for selecting relevant pattern indices. We strongly encourage the systematic screening of these indices prior to use in order to get a deeper understanding of the results obtained by them.

A new method of Quickbird own image fusion

NASA Astrophysics Data System (ADS)

Han, Ying; Jiang, Hong; Zhang, Xiuying

2009-10-01

With the rapid development of remote sensing technology, the means of accessing to remote sensing data become increasingly abundant, thus the same area can form a large number of multi-temporal, different resolution image sequence. At present, the fusion methods are mainly: HPF, IHS transform method, PCA method, Brovey, Mallat algorithm and wavelet transform and so on. There exists a serious distortion of the spectrums in the IHS transform, Mallat algorithm omits low-frequency information of the high spatial resolution images, the integration results of which has obvious blocking effects. Wavelet multi-scale decomposition for different sizes, the directions, details and the edges can have achieved very good results, but different fusion rules and algorithms can achieve different effects. This article takes the Quickbird own image fusion as an example, basing on wavelet transform and HVS, wavelet transform and IHS integration. The result shows that the former better. This paper introduces the correlation coefficient, the relative average spectral error index and usual index to evaluate the quality of image.

Abstracting of suspected illegal land use in urban areas using case-based classification of remote sensing images

NASA Astrophysics Data System (ADS)

Chen, Fulong; Wang, Chao; Yang, Chengyun; Zhang, Hong; Wu, Fan; Lin, Wenjuan; Zhang, Bo

2008-11-01

This paper proposed a method that uses a case-based classification of remote sensing images and applied this method to abstract the information of suspected illegal land use in urban areas. Because of the discrete cases for imagery classification, the proposed method dealt with the oscillation of spectrum or backscatter within the same land use category, and it not only overcame the deficiency of maximum likelihood classification (the prior probability of land use could not be obtained) but also inherited the advantages of the knowledge-based classification system, such as artificial intelligence and automatic characteristics. Consequently, the proposed method could do the classifying better. Then the researchers used the object-oriented technique for shadow removal in highly dense city zones. With multi-temporal SPOT 5 images whose resolution was 2.5×2.5 meters, the researchers found that the method can abstract suspected illegal land use information in urban areas using post-classification comparison technique.

Data and techniques for studying the urban heat island effect in Johannesburg

NASA Astrophysics Data System (ADS)

Hardy, C. H.; Nel, A. L.

2015-04-01

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg's residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

Mapping Post-Fire Vegetation Recovery at Different Lithologies of Taygetos mt (greece) with Multi-Temporal Remote Sensing Data

NASA Astrophysics Data System (ADS)

Vassilakis, Emmanuel; Mallinis, George; Christopoulou, Anastasia; Farangitakis, Georgios-Pavlos; Papanikolaou, Ioannis; Arianoutsou, Margarita

2017-04-01

Mt Taygetos (2407m), located at southern Peloponnese (Greece) suffered a large fire during the summer of 2007. The fire burned approximately 45% of the area covered by the endemic Greek fir (Abies cephalonica) and Black Pine (Pinus nigra) forest ecosystems. The aim of the current study is to examine the potential differences on post-fire vegetation recovery imposed by the lithology as well as the geomorphology of the given area over sites of the same climatic and landscape conditions (elevation, aspect, slope etc.). The main lithologies consist of carbonate, permeable, not easily erodible formations (limestones and marbles) and clastic, impermeable (schists, slate and flysch) erodible ones. A time-series of high spatial resolution satellite images were interpreted, analyzed and compared in order to detect changes in vegetation coverage which could prioritize areas of interest for fieldwork campaigns. The remote sensing datasets were acquired before (Ikonos-2), a few months after (Quickbird-2) and some years after (Worldview-3) the 2007 fire. High resolution Digital Elevation Model was used for the ortho-rectification and co-registration of the remote sensing data, but also for the extraction of the mountainous landscape characteristics. The multi-temporal image dataset was analyzed through GEographic-Object Based Image Analysis (GEOBIA). Objects corresponding to different vegetation types through time were identified through spectral and textural features. The classification results were combined with basic layers such as lithological outcrops, pre-fire vegetation, landscape morphology etc., supplementing a spatial geodatabase used for classifying burnt areas with varying post-fire plant community recovery. We validated the results of the classification during fieldwork and found that at a local scale, where the landscape features are quite similar, the bedrock type proves to be an important factor for vegetation recovery, as it clearly defines the soil generation along with its properties. Plant species recovery seems to be controlled by the local lithology as it was found weaker in plots overlying limestones and marbles, comparing to that observed over schists, even for the same species. In conclusion, post-fire vegetation recovery seems to be a complex process controlled not only from species biology, but also from the geological features.

Susceptibility Evaluation and Mapping of CHINA'S Landslide Disaster Based on Multi-Temporal Ground and Remote Sensing Satellite Data

NASA Astrophysics Data System (ADS)

Liu, C.; Li, W.; Lu, P.; Sang, K.; Hong, Y.; Li, R.

2012-07-01

Under the circumstances of global climate change, nowadays landslide occurs in China more frequently than ever before. The landslide hazard and risk assessment remains an international focus on disaster prevention and mitigation. It is also an important approach for compiling and quantitatively characterizing landslide damages. By integrating empirical models for landslide disasters, and through multi-temporal ground data and remote sensing data, this paper will perform a landslide susceptibility assessment throughout China. A landslide susceptibility (LS) map will then be produced, which can be used for disaster evaluation, and provide basis for analyzing China's major landslide-affected regions. Firstly, based on previous research of landslide susceptibility assessment, this paper collects and analyzes the historical landslide event data (location, quantity and distribution) of past sixty years in China as a reference for late-stage studies. Secondly, this paper will make use of regional GIS data of the whole country provided by the National Geomatics Centre and China Meteorological Administration, including regional precipitation data, and satellite remote sensing data such as from TRMM and MODIS. By referring to historical landslide data of past sixty years, it is possible to develop models for assessing LS, including producing empirical models for prediction, and discovering both static and dynamic key factors, such as topography and landforms (elevation, curvature and slope), geologic conditions (lithology of the strata), soil type, vegetation cover, hydrological conditions (flow distribution). In addition, by analyzing historical data and combining empirical models, it is possible to synthesize a regional statistical model and perform a LS assessment. Finally, based on the 1km×1km grid, the LS map is then produced by ANN learning and multiplying the weighted factor layers. The validation is performed with reference to the frequency and distribution of historical data. This research reveals the spatiotemporal distribution of landslide disasters in China. The study develops a complete algorithm of data collecting, processing, modelling and synthesizing, which fulfils the assessment of landslide susceptibility, and provides theoretical basis for prediction and forecast of landslide disasters throughout China.

Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

NASA Astrophysics Data System (ADS)

Shew, A. M.; Ghosh, A.

2017-10-01

Remote sensing in the optical domain is widely used in agricultural monitoring; however, such initiatives pose a challenge for developing countries due to a lack of high quality in situ information. Our proposed methodology could help developing countries bridge this gap by demonstrating the potential to quantify patterns of dry season rice production in Bangladesh. To analyze approximately 90,000 km2 of cultivated land in Bangladesh at 30 m spatial resolution, we used two decades of remote sensing data from the Landsat archive and Google Earth Engine (GEE), a cloud-based geospatial data analysis platform built on Google infrastructure and capable of processing petabyte-scale remote sensing data. We reconstructed the seasonal patterns of vegetation indices (VIs) for each pixel using a harmonic time series (HTS) model, which minimizes the effects of missing observations and noise. Next, we combined the seasonality information of VIs with our knowledge of rice cultivation systems in Bangladesh to delineate rice areas in the dry season, which are predominantly hybrid and High Yielding Varieties (HYV). Based on historical Landsat imagery, the harmonic time series of vegetation indices (HTS-VIs) model estimated 4.605 million ha, 3.519 million ha, and 4.021 million ha of rice production for Bangladesh in 2005, 2010, and 2015 respectively. Fine spatial scale information on HYV rice over the last 20 years will greatly improve our understanding of double-cropped rice systems, current status of production, and potential for HYV rice adoption in Bangladesh during the dry season.

Assessment of Wildfire Risk in Southern California with Live Fuel Moisture Measurement and Remotely Sensed Vegetation Water Content Proxies

NASA Astrophysics Data System (ADS)

Jia, S.; Kim, S. H.; Nghiem, S. V.; Kafatos, M.

2017-12-01

Live fuel moisture (LFM) is the water content of live herbaceous plants expressed as a percentage of the oven-dry weight of plant. It is a critical parameter in fire ignition in Mediterranean climate and routinely measured in sites selected by fire agencies across the U.S. Vegetation growing cycle, meteorological metrics, soil type, and topography all contribute to the seasonal and inter-annual variation of LFM, and therefore, the risk of wildfire. The optical remote sensing-based vegetation indices (VIs) have been used to estimate the LFM. Comparing to the VIs, microwave remote sensing products have advantages like less saturation effect in greenness and representing the water content of the vegetation cover. In this study, we established three models to evaluate the predictability of LFM in Southern California using MODIS NDVI, vegetation temperature condition index (VTCI) from downscaled Soil Moisture Active Passive (SMAP) products, and vegetation optical depth (VOD) derived by Land Parameter Retrieval Model. Other ancillary variables, such as topographic factors (aspects and slope) and meteorological metrics (air temperature, precipitation, and relative humidity), are also considered in the models. The model results revealed an improvement of LFM estimation from SMAP products and VOD, despite the uncertainties introduced in the downscaling and parameter retrieval. The estimation of LFM using remote sensing data can provide an assessment of wildfire danger better than current methods using NDVI-based growing seasonal index. Future study will test the VOD estimation from SMAP data using the multi-temporal dual channel algorithm (MT-DCA) and extend the LFM modeling to a regional scale.

[Spatial-temporal evolution characterization of land subsidence by multi-temporal InSAR method and GIS technology].

PubMed

Chen, Bei-Bei; Gong, Hui-Li; Li, Xiao-Juan; Lei, Kun-Chao; Duan, Guang-Yao; Xie, Jin-Rong

2014-04-01

Long-term over-exploitation of underground resources, and static and dynamic load increase year by year influence the occurrence and development of regional land subsidence to a certain extent. Choosing 29 scenes Envisat ASAR images covering plain area of Beijing, China, the present paper used the multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches, and obtained monitoring information of regional land subsidence. Under different situation of space development and utilization, the authors chose five typical settlement areas; With classified information of land-use, multi-spectral remote sensing image, and geological data, and adopting GIS spatial analysis methods, the authors analyzed the time series evolution characteristics of uneven settlement. The comprehensive analysis results suggests that the complex situations of space development and utilization affect the trend of uneven settlement; the easier the situation of space development and utilization, the smaller the settlement gradient, and the less the uneven settlement trend.

Shoreline change after 12 years of tsunami in Banda Aceh, Indonesia: a multi-resolution, multi-temporal satellite data and GIS approach

NASA Astrophysics Data System (ADS)

Sugianto, S.; Heriansyah; Darusman; Rusdi, M.; Karim, A.

2018-04-01

The Indian Ocean Tsunami event on the 26 December 2004 has caused severe damage of some shorelines in Banda Aceh City, Indonesia. Tracing back the impact can be seen using remote sensing data combined with GIS. The approach is incorporated with image processing to analyze the extent of shoreline changes with multi-temporal data after 12 years of tsunami. This study demonstrates multi-resolution and multi-temporal satellite images of QuickBird and IKONOS to demarcate the shoreline of Banda Aceh shoreline from before and after tsunami. The research has demonstrated a significant change to the shoreline in the form of abrasion between 2004 and 2005 from few meters to hundred meters’ change. The change between 2004 and 2011 has not returned to the previous stage of shoreline before the tsunami, considered post tsunami impact. The abrasion occurs between 18.3 to 194.93 meters. Further, the change in 2009-2011 shows slowly change of shoreline of Banda Aceh, considered without impact of tsunami e.g. abrasion caused by ocean waves that erode the coast and on specific areas accretion occurs caused by sediment carried by the river flow into the sea near the shoreline of the study area.

Modelling coastal processes by means of innovative integration of remote sensing and modelling analysis

NASA Astrophysics Data System (ADS)

Taramelli, A.; Zanuttigh, B.; Zucca, F.; Dejana, M.; Valentini, E.

2011-12-01

Coastal marine and inland landforms are dynamic systems undergoing adjustments in form at different time and space scales in response to varying conditions external to the system. Coastal emerged and shallow submerged nearshore areas, affected by short-term perturbations, return to their pre-disturbance morphology and generally reach a dynamic equilibrium. Worldwide in the last century we have experienced in increased coastal inundation, erosion and ecosystem losses. However, erosion can result from a number of other factors, such as altered wind and current patterns, high-energy waves, and reduced fluvial sediment inputs. Direct impacts of human activities, including reclamation of coastal wetlands, deforestation, damming, channelization, diversions of coastal waterways, construction of seawalls and other structures, alter circulation patterns. Also indirect human impacts such as land-uses changes through time (eg. from agricultural to industrial use) have affected coastal ecosystems. The objective of this research is to propose innovative remote sensing applications to monitor specific coastal processes in order to use them within a physical modelling to quantify and model their time evolution. The research was applied in two dynamic and densely populated deltas and coastal areas (the Po and the Plymouth delta) by combining multi-sensor spaceborne remote sensing (SAR and OPTICAL) to physical modelling. The main results are: a) deformation and spatiotemporal variations maps in coastal morphology with a special focus to point out the temporal subsidence evolution, b) inter and intra-annual change detection maps that are both used a to feed a coastal physical modelling (MIKE 21). The basic strategy was to highlight the different components of the coastal system environment through: 1) deformation and spatio-temporal variations maps of coastal morphology, by the use of time-stack from 1992 up today of ESA SAR data (ERS-1/2 and ENVISAT-ASAR sensors) were used to produce deformation maps and to point out the temporal evolution and 2) multitemporal hyperspectral endmembers fractions map of coastal morphology, 3) numerical model well-established through remote sensed based procedures and results in order to produce spatio-temporal scenario in coastal areas. The objective was to locate and characterize important coastal indicators for different regions using multitemporal data from the multi-hyperspectral sensors, as well as topographic elevation, SAR and derived products (eg. coherence) data. The identification of different indicators was based on land spectral properties, topography/landforms (low topography), disturbed areas (agricultural, construction), and vegetation distribution. Moreover, the indicators were assessed at seasonal and interannual time scales over two temporal decades horizons starting from 1990 and 2000.

Extraction and Analysis of Traditional Chinese Medicine Crops Based on Multitemporal High Resolution Data-Taking Qiaocheng District of Bozhou as AN Example

NASA Astrophysics Data System (ADS)

Yu, H.; He, J.; Zhou, H.; Guan, F.; Li, L.; Ren, B.; Wang, Z.

2018-04-01

Remote sensing technology has become an important method to rapidly acquireing of planting layout and composition of regional crops.It is very important to accurately master the planting area of Chinese medicine crops in the Characteristic planting area because it relations to accurately master the cultivation of Chinese medicine crops, formulate related policies and adjustment of crop planting structure.The author puts forward a method of using remote sencing technology for momitoring Chinese medicine which has good applicability and generalization. This paper took Qiaocheng District of Bozhou as an example to Verify the feasibility of the method, providing a reference for solving the problem of interpretation and extraction of Chinese medicinal materials in the region.

Multitemporal satellite change detection investigations for documentation and valorization of cultural landscape

NASA Astrophysics Data System (ADS)

Lasaponara, R.; Masini, n.

2012-04-01

The paper focus on the setting up of a methodology for analyzing cultural landscapes to extract information about ancient civilization settlements, land-use variations, stratified anthropogenic environment, human impacts on landscape, as well as climate driven changes over short, medium, and long periods of time. The analysis of cultural landscape along with its protection and preservation strategies requires the contribution of integrated disciplines and data source, and, above all, the fusion of multi-temporal and multi dimensional data available from different sources. In this contest satellite time series may help us in improve knowledge content of cultural landscape and heritage . The methodology approach we devised is focused on multitemporal/multisource/multiscale data analysis as a support for extracting (i) archaeological settlements and (ii) potential ancient land-use patterning. To these aims, DTM from SRTM and ASTER along multispectral data from TM, ASTER and Quikbird have been used. In order to make the satellite data more meaningful and more exploitable for investigations, reliable data processing have been carried out. Over the years a great variety of digital image enhancement techniques have been devised for specific application fields according to data availability. Nevertheless, only recently these methods have captured great attention also in the field of archaeology for an easier extraction of quantitative information using effective and reliable semiautomatic data processing. The setting up of fully-automatic methodologies is a big challenge to be strategically addressed by research communities in the next years. Multitemporal, multiscale and multisensor satellite data sets can provide useful tool for extracting information and traces related both to modern and ancient civilizations still fossilized in the modern landscape. Reference Lasaponara R, Masini N (2006a) On the potential of panchromatic and multispectral Quickbird data for archaeological prospection. Int J Remote Sens 27: 3607-3614. Lasaponara R, Masini N (2006b) Identification of archaeological buried remains based on Normalized Difference Vegetation Index (NDVI) from Quickbird satellite data. IEEE Geosci Remote S 3(3): 325-328. Lasaponara R, Masini N (2007a) Detection of archaeological crop marks by using satellite QuickBird multispectral imagery. J Archaeol Sci 34: 214-21. Lasaponara R, Masini N (2007b) Improving satellite Quickbird - based identification of landscape archaeological features trough tasselled cup transformation and PCA. 21st CIPA Symposium, Atene, 1-6 giugno 2007. Lasaponara R, Masini N (2010) Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite imagery. In: Taniar D et al (Eds), Proceedings of ICSSA, The 2010 International Conference on Computational Science and its Application (Fukuoka-Japan, March 23 - 26, 2010), Springer, Berlin, 261-269. Lasaponara R, Masini N (2011) Satellite Remote Sensing in Archaeology : past, present and future. J Archaeol Sc 38: 1995-2002. Lasaponara R, Masini N, Rizzo E, Orefici G (2011) New discoveries in the Piramide Naranjada in Cahuachi (Peru) using satellite, Ground Probing Radar and magnetic investigations. J Archaeol Sci 38: 2031-2039. Lasaponara R, Masini N, Scardozzi G (2008) Satellite based archaeological research in ancient territory of Hierapolis. 1st International EARSeL Workshop. Advances in Remote Sensing for Archaeology and Cultural Heritage Management", CNR, Rome, September 30-October 4, Aracne, Rome, pp.11-16. Lillesand T M, Kiefer R W (2000) Remote Sensing and Image interpretation. John Wiley and Sons, New York. Masini N, Lasaponara R (2006) Satellite-based recognition of landscape archaeological features related to ancient human transformation. J Geophys Eng 3: 230-235, doi:10.1088/1742-2132/3/3/004. Masini N, Lasaponara R (2007) Investigating the spectral capability of QuickBird data to detect archaeological remains buried under vegetated and not vegetated areas. J Cult Heri 8 (1): 53-60.

Earthquake Building Damage Mapping Based on Feature Analyzing Method from Synthetic Aperture Radar Data

NASA Astrophysics Data System (ADS)

An, L.; Zhang, J.; Gong, L.

2018-04-01

Playing an important role in gathering information of social infrastructure damage, Synthetic Aperture Radar (SAR) remote sensing is a useful tool for monitoring earthquake disasters. With the wide application of this technique, a standard method, comparing post-seismic to pre-seismic data, become common. However, multi-temporal SAR processes, are not always achievable. To develop a post-seismic data only method for building damage detection, is of great importance. In this paper, the authors are now initiating experimental investigation to establish an object-based feature analysing classification method for building damage recognition.

a Spiral-Based Downscaling Method for Generating 30 M Time Series Image Data

NASA Astrophysics Data System (ADS)

Liu, B.; Chen, J.; Xing, H.; Wu, H.; Zhang, J.

2017-09-01

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

2008-01-01

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify a variety of plant phenomena and improve monitoring capabilities.

Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

PubMed

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

PubMed Central

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly. PMID:23967112

Vegetation cover change detection and assessment in arid environment using multi-temporal remote sensing images and ecosystem management approach

NASA Astrophysics Data System (ADS)

Abdelrahman Aly, Anwar; Mosa Al-Omran, Abdulrasoul; Shahwan Sallam, Abdulazeam; Al-Wabel, Mohammad Ibrahim; Shayaa Al-Shayaa, Mohammad

2016-04-01

Vegetation cover (VC) change detection is essential for a better understanding of the interactions and interrelationships between humans and their ecosystem. Remote sensing (RS) technology is one of the most beneficial tools to study spatial and temporal changes of VC. A case study has been conducted in the agro-ecosystem (AE) of Al-Kharj, in the center of Saudi Arabia. Characteristics and dynamics of total VC changes during a period of 26 years (1987-2013) were investigated. A multi-temporal set of images was processed using Landsat images from Landsat4 TM 1987, Landsat7 ETM+2000, and Landsat8 to investigate the drivers responsible for the total VC pattern and changes, which are linked to both natural and social processes. The analyses of the three satellite images concluded that the surface area of the total VC increased by 107.4 % between 1987 and 2000 and decreased by 27.5 % between years 2000 and 2013. The field study, review of secondary data, and community problem diagnosis using the participatory rural appraisal (PRA) method suggested that the drivers for this change are the deterioration and salinization of both soil and water resources. Ground truth data indicated that the deteriorated soils in the eastern part of the Al-Kharj AE are frequently subjected to sand dune encroachment, while the southwestern part is frequently subjected to soil and groundwater salinization. The groundwater in the western part of the ecosystem is highly saline, with a salinity ≥ 6 dS m-1. The ecosystem management approach applied in this study can be used to alike AE worldwide.

Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization

NASA Astrophysics Data System (ADS)

Tuia, Devis; Marcos, Diego; Camps-Valls, Gustau

2016-10-01

Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes. In order to adapt and transfer models across image acquisitions, one must be able to cope with datasets that are not co-registered, acquired under different illumination and atmospheric conditions, by different sensors, and with scarce ground references. Traditionally, methods based on histogram matching have been used. However, they fail when densities have very different shapes or when there is no corresponding band to be matched between the images. An alternative builds upon manifold alignment. Manifold alignment performs a multidimensional relative normalization of the data prior to product generation that can cope with data of different dimensionality (e.g. different number of bands) and possibly unpaired examples. Aligning data distributions is an appealing strategy, since it allows to provide data spaces that are more similar to each other, regardless of the subsequent use of the transformed data. In this paper, we study a methodology that aligns data from different domains in a nonlinear way through kernelization. We introduce the Kernel Manifold Alignment (KEMA) method, which provides a flexible and discriminative projection map, exploits only a few labeled samples (or semantic ties) in each domain, and reduces to solving a generalized eigenvalue problem. We successfully test KEMA in multi-temporal and multi-source very high resolution classification tasks, as well as on the task of making a model invariant to shadowing for hyperspectral imaging.

Structural health monitoring of engineered structures using a space-borne synthetic aperture radar multi-temporal approach: from cultural heritage sites to war zones

NASA Astrophysics Data System (ADS)

Milillo, Pietro; Tapete, Deodato; Cigna, Francesca; Perissin, Daniele; Salzer, Jacqueline; Lundgren, Paul; Fielding, Eric; Burgmann, Roland; Biondi, Filippo; Milillo, Giovanni; Serio, Carmine

2016-10-01

Structural health monitoring (SHM) of engineered structures consists of an automated or semi-automated survey system that seeks to assess the structural condition of an anthropogenic structure. The aim of an SHM system is to provide insights into possible induced damage or any inherent signals of deformation affecting the structure in terms of detection, localization, assessment, and prediction. During the last decade there has been a growing interest in using several remote sensing techniques, such as synthetic aperture radar (SAR), for SHM. Constellations of SAR satellites with short repeat time acquisitions permit detailed surveys temporal resolution and millimetric sensitivity to deformation that are at the scales relevant to monitoring large structures. The all-weather multi-temporal characteristics of SAR make its products suitable for SHM systems, especially in areas where in situ measurements are not feasible or not cost effective. To illustrate this capability, we present results from COSMO-SkyMed (CSK) and TerraSAR-X SAR observations applied to the remote sensing of engineered structures. We show how by using multiple-geometry SAR-based products which exploit both phase and amplitude of the SAR signal we can address the main objectives of an SHM system including detection and localization. We highlight that, when external data such as rain or temperature records are available or simple elastic models can be assumed, the SAR-based SHM capability can also provide an interpretation in terms of assessment and prediction. We highlight examples of the potential for such imaging capabilities to enable advances in SHM from space, focusing on dams and cultural heritage areas.

Detection of tamarisk defoliation by the northern tamarisk beetle based on multitemporal Landsat 5 thematic mapper imagery

USGS Publications Warehouse

Meng, Ran; Dennison, Philip E.; Jamison, Levi R.; van Riper, Charles; Nager, Pamela; Hultine, Kevin R.; Bean, Dan W.; Dudley, Tom

2012-01-01

The spread of tamarisk (Tamarix spp., also known as saltcedar) is a significant ecological disturbance in western North America and has long been targeted for control, leading to the importation of the northern tamarisk beetle (Diorhabda carinulata) as a biological control agent. Following its initial release along the Colorado River near Moab, Utah in 2004, the beetle has successfully established and defoliated tamarisk across much of the upper Colorado River Basin. However, the spatial distribution and seasonal timing of defoliation are complex and difficult to quantify over large areas. To address this challenge, we tested and compared two remote sensing approaches to mapping tamarisk defoliation: Disturbance Index (DI) and a decision tree method called Random Forest (RF). Based on multitemporal Landsat 5 TM imagery for 2006-2010, changes in DI and defoliation probability from RF were calculated to detect tamarisk defoliation along the banks of Green, Colorado, Dolores and San Juan rivers within the Colorado Plateau area. Defoliation mapping accuracy was assessed based on field surveys partitioned into 10 km sections of river and on regions of interest created for continuous riparian vegetation. The DI method detected 3711 ha of defoliated area in 2007, 7350 ha in 2008, 10,457 ha in 2009 and 5898 ha in 2010. The RF method detected much smaller areas of defoliation but proved to have higher accuracy, as demonstrated by accuracy assessment and sensitivity analysis, with 784 ha in 2007, 960 ha in 2008, 934 ha in 2009, and 1008 ha in 2010. Results indicate that remote sensing approaches are likely to be useful for studying spatiotemporal patterns of tamarisk defoliation as the tamarisk leaf beetle spreads throughout the western United States.

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

USGS Publications Warehouse

Corcoran, Jennifer M.; Knight, Joseph F.; Gallant, Alisa L.

2013-01-01

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Imagery for Disaster Response and Recovery

NASA Astrophysics Data System (ADS)

Bethel, G. R.

2011-12-01

Exposing the remotely sensed imagery for disaster response and recovery can provide the basis for an unbiased understanding of current conditions. Having created consolidated remotely sensed and geospatial data sources documents for US and Foreign disasters over the past six years, availability and usability are continuing to evolve. By documenting all existing sources of imagery and value added products, the disaster response and recovery community can develop actionable information. The past two years have provided unique situations to use imagery including a major humanitarian disaster and response effort in Haiti, a major environmental disaster in the Gulf of Mexico, a killer tornado in Joplin Missouri and long-term flooding in the Midwest. Each disaster presents different challenges and requires different spatial resolutions, spectral properties and/or multi-temporal collections. The community of data providers continues to expand with organized actives such as the International Charter for Space and Major Disasters and acquisitions by the private sector for the public good rather than for profit. However, data licensing, the lack of cross-calibration and inconsistent georeferencing hinder optimal use. Recent pre-event imagery is a critial component to any disaster response.

The method for detecting biological parameter of rice growth and early planting of paddy crop by using multi temporal remote sensing data

NASA Astrophysics Data System (ADS)

Domiri, D. D.

2017-01-01

Rice crop is the most important food crop for the Asian population, especially in Indonesia. During the growth of rice plants have four main phases, namely the early planting or inundation phase, the vegetative phase, the generative phase, and bare land phase. Monitoring the condition of the rice plant needs to be conducted in order to know whether the rice plants have problems or not in its growth. Application of remote sensing technology, which uses satellite data such as Landsat 8 and others which has a spatial and temporal resolution is high enough for monitoring the condition of crops such as paddy crop in a large area. In this study has been made an algorithm for monitoring rapidly of rice growth condition using Maximum of Vegetation Index (EVI Max). The results showed that the time of early planting can be estimated if known when EVI Max occurred. The value of EVI Max and when it occured can be known by trough spatial analysis of multitemporal EVI Landsat 8 or other medium spatial resolution satellites.

Exploitation of multi-temporal Earth Observation imagery for monitoring land cover change in mining sites

NASA Astrophysics Data System (ADS)

Petropoulos, G.; Partsinevelos, P.; Mitraka, Z.

2012-04-01

Surface mining has been shown to cause intensive environmental degradation in terms of landscape, vegetation and biological communities. Nowadays, the commercial availability of remote sensing imagery at high spatiotemporal scales, has improved dramatically our ability to monitor surface mining activity and evaluate its impact on the environment and society. In this study we investigate the potential use of Landsat TM imagery combined with diverse classification techniques, namely artificial neural networks and support vector machines for delineating mining exploration and assessing its effect on vegetation in various surface mining sites in the Greek island of Milos. Assessment of the mining impact in the study area is validated through the analysis of available QuickBird imagery acquired nearly concurrently to the TM overpasses. Results indicate the capability of the TM sensor combined with the image analysis applied herein as a potential economically viable solution to provide rapidly and at regular time intervals information on mining activity and its impact to the local environment. KEYWORDS: mining environmental impact, remote sensing, image classification, change detection, land reclamation, support vector machines, neural networks

Extraction of land cover change information from ENVISAT-ASAR data in Chengdu Plain

NASA Astrophysics Data System (ADS)

Xu, Wenbo; Fan, Jinlong; Huang, Jianxi; Tian, Yichen; Zhang, Yong

2006-10-01

Land cover data are essential to most global change research objectives, including the assessment of current environmental conditions and the simulation of future environmental scenarios that ultimately lead to public policy development. Chinese Academy of Sciences generated a nationwide land cover database in order to carry out the quantification and spatial characterization of land use/cover changes (LUCC) in 1990s. In order to improve the reliability of the database, we will update the database anytime. But it is difficult to obtain remote sensing data to extract land cover change information in large-scale. It is hard to acquire optical remote sensing data in Chengdu plain, so the objective of this research was to evaluate multitemporal ENVISAT advanced synthetic aperture radar (ASAR) data for extracting land cover change information. Based on the fieldwork and the nationwide 1:100000 land cover database, the paper assesses several land cover changes in Chengdu plain, for example: crop to buildings, forest to buildings, and forest to bare land. The results show that ENVISAT ASAR data have great potential for the applications of extracting land cover change information.

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, B.C.; Tieszen, L.T.

2003-01-01

Time integrated normalized difference vegetation index (??NDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989-1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ??NDVI and the ??NDVI coefficient of variation (CV ??NDVI) used as a proxy for interranual climate variability is analysed. Results suggest that the differences in the long-term climate control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primary C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ??NDVI values.

Farmland Drought Evaluation Based on the Assimilation of Multi-Temporal Multi-Source Remote Sensing Data into AquaCrop Model

NASA Astrophysics Data System (ADS)

Yang, Guijun; Yang, Hao; Jin, Xiuliang; Pignatti, Stefano; Casa, Faffaele; Silverstro, Paolo Cosmo

2016-08-01

Drought is the most costly natural disasters in China and all over the world. It is very important to evaluate the drought-induced crop yield losses and further improve water use efficiency at regional scale. Firstly, crop biomass was estimated by the combined use of Synthetic Aperture Radar (SAR) and optical remote sensing data. Then the estimated biophysical variable was assimilated into crop growth model (FAO AquaCrop) by the Particle Swarm Optimization (PSO) method from farmland scale to regional scale.At farmland scale, the most important crop parameters of AquaCrop model were determined to reduce the used parameters in assimilation procedure. The Extended Fourier Amplitude Sensitivity Test (EFAST) method was used for assessing the contribution of different crop parameters to model output. Moreover, the AquaCrop model was calibrated using the experiment data in Xiaotangshan, Beijing.At regional scale, spatial application of our methods were carried out and validated in the rural area of Yangling, Shaanxi Province, in 2014. This study will provide guideline to make irrigation decision of balancing of water consumption and yield loss.

Localized geohazards in West Texas, captured by multi-temporal Sentinel-1A/B interferometry

NASA Astrophysics Data System (ADS)

Kim, J. W.; Lu, Z.

2017-12-01

West Texas contains the Permian Basin and is particularly composed of three major geologic sedimentary basins: Delaware Basin, Central Basin Platform, and Midland Basin. Because the vast region was once covered by a shallow sea and had experienced long-lasting evaporation million years ago, the West Texas is underlain by a thick layer of water soluble rocks including the carbonate and evaporite rocks. In addition, the geologic composition provided abundant hydrocarbons in the depth of several kilometers, but the human activities exploiting the massive oil and gas from the subsurface made negative impacts on the stability of underground and ground surface. Most deformation and localized geohazards have been unnoticed by means of field measurements or remote sensing methods, because the West Texas is located in the low populated, remote region. The Sentinel-1A/B has continuously acquired the SAR imagery with a large swath of 250 km over the region, and its multi-temporal measurements can provide clues on what are really taking place on the ground surface, what are the causes to trigger the localized subsidence/uplift, and what should be done to prevent more severe disasters in the future. We have established an automated Sentinel-1A/B InSAR processing system on SMU supercomputer (Maneframe), its continuous monitoring will help us unveil the current status of deformation occurring in West Texas.

Mapping grass communities based on multi-temporal Landsat TM imagery and environmental variables

NASA Astrophysics Data System (ADS)

Zeng, Yuandi; Liu, Yanfang; Liu, Yaolin; de Leeuw, Jan

2007-06-01

Information on the spatial distribution of grass communities in wetland is increasingly recognized as important for effective wetland management and biological conservation. Remote sensing techniques has been proved to be an effective alternative to intensive and costly ground surveys for mapping grass community. However, the mapping accuracy of grass communities in wetland is still not preferable. The aim of this paper is to develop an effective method to map grass communities in Poyang Lake Natural Reserve. Through statistic analysis, elevation is selected as an environmental variable for its high relationship with the distribution of grass communities; NDVI stacked from images of different months was used to generate Carex community map; the image in October was used to discriminate Miscanthus and Cynodon communities. Classifications were firstly performed with maximum likelihood classifier using single date satellite image with and without elevation; then layered classifications were performed using multi-temporal satellite imagery and elevation with maximum likelihood classifier, decision tree and artificial neural network separately. The results show that environmental variables can improve the mapping accuracy; and the classification with multitemporal imagery and elevation is significantly better than that with single date image and elevation (p=0.001). Besides, maximum likelihood (a=92.71%, k=0.90) and artificial neural network (a=94.79%, k=0.93) perform significantly better than decision tree (a=86.46%, k=0.83).

Application of SAR remote sensing and crop modeling for operational rice crop monitoring in South and South East Asian Countries

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Holecz, F.; Khan, N. I.; Barbieri, M.; Maunahan, A. A.; Gatti, L.; Quicho, E. D.; Pazhanivelan, S.; Campos-Taberner, M.; Collivignarelli, F.; Haro, J. G.; Intrman, A.; Phuong, D.; Boschetti, M.; Prasadini, P.; Busetto, L.; Minh, V. Q.; Tuan, V. Q.

2017-12-01

This study uses multi-temporal SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations in South and South Asian countries and assimilate the information into ORYZA Crop Growth Simulation Model (CGSM) to monitor rice yield. The study demonstrates examples of operational application of this rice monitoring system in: (1) detecting drought impact on rice planting in Central Thailand and Tamil Nadu, India, (2) mapping heat stress impact on rice yield in Andhra Pradesh, India, and (3) generating historical rice yield data for districts in Red River Delta, Vietnam.

Monitoring gypsy moth defoliation by applying change detection techniques to Landsat imagery

NASA Technical Reports Server (NTRS)

Williams, D. L.; Stauffer, M. L.

1978-01-01

The overall objective of a research effort at NASA's Goddard Space Flight Center is to develop and evaluate digital image processing techniques that will facilitate the assessment of the intensity and spatial distribution of forest insect damage in Northeastern U.S. forests using remotely sensed data from Landsats 1, 2 and C. Automated change detection techniques are presently being investigated as a method of isolating the areas of change in the forest canopy resulting from pest outbreaks. In order to follow the change detection approach, Landsat scene correction and overlay capabilities are utilized to provide multispectral/multitemporal image files of 'defoliation' and 'nondefoliation' forest stand conditions.

Effects of land cover and regional climate variations on long-term spatiotemporal changes in sagebrush ecosystems

USGS Publications Warehouse

Xian, George Z.; Homer, Collin G.; Aldridge, Cameron L.

2012-01-01

This research investigated the effects of climate and land cover change on variation in sagebrush ecosystems. We combined information of multi-year sagebrush distribution derived from multitemporal remote sensing imagery and climate data to study the variation patterns of sagebrush ecosystems under different potential disturbances. We found that less than 40% of sagebrush ecosystem changes involved abrupt changes directly caused by landscape transformations and over 60% of the variations involved gradual changes directly related to climatic perturbations. The primary increases in bare ground and declines in sagebrush vegetation abundance were significantly correlated with the 1996-2006 decreasing trend in annual precipitation.

Multi-temporal change image inference towards false alarms reduction for an operational photogrammetric rockfall detection system

NASA Astrophysics Data System (ADS)

Partsinevelos, Panagiotis; Kallimani, Christina; Tripolitsiotis, Achilleas

2015-06-01

Rockfall incidents affect civil security and hamper the sustainable growth of hard to access mountainous areas due to casualties, injuries and infrastructure loss. Rockfall occurrences cannot be easily prevented, whereas previous studies for rockfall multiple sensor early detection systems have focused on large scale incidents. However, even a single rock may cause the loss of a human life along transportation routes thus, it is highly important to establish methods for the early detection of small-scale rockfall incidents. Terrestrial photogrammetric techniques are prone to a series of errors leading to false alarm incidents, including vegetation, wind, and non relevant change in the scene under consideration. In this study, photogrammetric monitoring of rockfall prone slopes is established and the resulting multi-temporal change imagery is processed in order to minimize false alarm incidents. Integration of remote sensing imagery analysis techniques is hereby applied to enhance early detection of a rockfall. Experimental data demonstrated that an operational system able to identify a 10-cm rock movement within a 10% false alarm rate is technically feasible.

Detection of urban expansion in an urban-rural landscape with multitemporal QuickBird images

PubMed Central

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

2011-01-01

Accurately detecting urban expansion with remote sensing techniques is a challenge due to the complexity of urban landscapes. This paper explored methods for detecting urban expansion with multitemporal QuickBird images in Lucas do Rio Verde, Mato Grosso, Brazil. Different techniques, including image differencing, principal component analysis (PCA), and comparison of classified impervious surface images with the matched filtering method, were used to examine urbanization detection. An impervious surface image classified with the hybrid method was used to modify the urbanization detection results. As a comparison, the original multispectral image and segmentation-based mean-spectral images were used during the detection of urbanization. This research indicates that the comparison of classified impervious surface images with matched filtering method provides the best change detection performance, followed by the image differencing method based on segmentation-based mean spectral images. The PCA is not a good method for urban change detection in this study. Shadows and high spectral variation within the impervious surfaces represent major challenges to the detection of urban expansion when high spatial resolution images are used. PMID:21799706

Detecting deforestation with a spectral change detection approach using multitemporal Landsat data: a case study of Kinabalu Park, Sabah, Malaysia.

PubMed

Phua, Mui-How; Tsuyuki, Satoshi; Furuya, Naoyuki; Lee, Jung Soo

2008-09-01

Tropical deforestation is occurring at an alarming rate, threatening the ecological integrity of protected areas. This makes it vital to regularly assess protected areas to confirm the efficacy of measures that protect that area from clearing. Satellite remote sensing offers a systematic and objective means for detecting and monitoring deforestation. This paper examines a spectral change approach to detect deforestation using pattern decomposition (PD) coefficients from multitemporal Landsat data. Our results show that the PD coefficients for soil and vegetation can be used to detect deforestation using change vector analysis (CVA). CVA analysis demonstrates that deforestation in the Kinabalu area, Sabah, Malaysia has significantly slowed from 1.2% in period 1 (1973 and 1991) to 0.1% in period 2 (1991 and 1996). A comparison of deforestation both inside and outside Kinabalu Park has highlighted the effectiveness of the park in protecting the tropical forest against clearing. However, the park is still facing pressure from the area immediately surrounding the park (the 1 km buffer zone) where the deforestation rate has remained unchanged.

Disease detection in sugar beet fields: a multi-temporal and multi-sensoral approach on different scales

NASA Astrophysics Data System (ADS)

Mahlein, Anne-Katrin; Hillnhütter, Christian; Mewes, Thorsten; Scholz, Christine; Steiner, Ulrike; Dehne, Heinz-Willhelm; Oerke, Erich-Christian

2009-09-01

Depending on environmental factors fungal diseases of crops are often distributed heterogeneously in fields. Precision agriculture in plant protection implies a targeted fungicide application adjusted these field heterogeneities. Therefore an understanding of the spatial and temporal occurrence of pathogens is elementary. As shown in previous studies, remote sensing techniques can be used to detect and observe spectral anomalies in the field. In 2008, a sugar beet field site was observed at different growth stages of the crop using different remote sensing techniques. The experimental field site consisted of two treatments. One plot was sprayed with a fungicide to avoid fungal infections. In order to obtain sugar beet plants infected with foliar diseases the other plot was not sprayed. Remote sensing data were acquired from the high-resolution airborne hyperspectral imaging ROSIS in July 2008 at sugar beet growth stage 39 and from the HyMap sensor systems in August 2008 at sugar beet growth stage 45, respectively. Additionally hyperspectral signatures of diseased and non-diseased sugar beet plants were measured with a non-imaging hand held spectroradiometer at growth stage 49 in September. Ground truth data, in particular disease severity were collected at 50 sampling points in the field. Changes of reflection rates were related to disease severity increasing with time. Erysiphe betae causing powdery mildew was the most frequent leaf pathogen. A classification of healthy and diseased sugar beets in the field was possible by using hyperspectral vegetation indices calculated from canopy reflectance.

Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image

NASA Astrophysics Data System (ADS)

Wicaksono, Pramaditya; Danoedoro, Projo; Hartono, Hartono; Nehren, Udo; Ribbe, Lars

2011-11-01

Mangrove forest is an important ecosystem located in coastal area that provides various important ecological and economical services. One of the services provided by mangrove forest is the ability to act as carbon sink by sequestering CO2 from atmosphere through photosynthesis and carbon burial on the sediment. The carbon buried on mangrove sediment may persist for millennia before return to the atmosphere, and thus act as an effective long-term carbon sink. Therefore, it is important to understand the distribution of carbon stored within mangrove forest in a spatial and temporal context. In this paper, an effort to map carbon stocks in mangrove forest is presented using remote sensing technology to overcome the handicap encountered by field survey. In mangrove carbon stock mapping, the use of medium spatial resolution Landsat 7 ETM+ is emphasized. Landsat 7 ETM+ images are relatively cheap, widely available and have large area coverage, and thus provide a cost and time effective way of mapping mangrove carbon stocks. Using field data, two image processing techniques namely Vegetation Index and Linear Spectral Unmixing (LSU) were evaluated to find the best method to explain the variation in mangrove carbon stocks using remote sensing data. In addition, we also tried to estimate mangrove carbon sequestration rate via multitemporal analysis. Finally, the technique which produces significantly better result was used to produce a map of mangrove forest carbon stocks, which is spatially extensive and temporally repetitive.

Reconstructing time series water volumes of drying lakes in Central Asia with ZY-3 stereo remote sensing data

NASA Astrophysics Data System (ADS)

Li, J.; Warner, T.; Bao, A.

2017-12-01

Central Asia is one of the world most vulnerable areas responding to global change. Lakes in arid regions of Central Asia remain sensitive to climatic change and fluctuate with temperature and precipitation variations. Study showed that some central asian inland lakes in showed a trend of area shrinkage or extinct in the last decades. Quantitative analysis of lake volume changes in spatio-temporal processes will improve our understanding water resource utilization in arid regions and their responses to regional climate change. However, due to the lack of lake bathmetry or observation data, the volumes of these lakes remain unknown. In this paper, three lakes, such as Chaiwopu lake, Alik Lake and Selectyteniz Lake in Central Asia are used to reconstruct lake volume changes. Firstly, stereo mapping technologies derived from ZY-3 high resolution data are used to map the high-precision 3-D lake bathmetry, so as to create "Area-Level-Volume" based on contours of lake bathmetry. Secondly, time series lake areas in the last 50 years are mapped with multi-source and multi-temporal remote sensing images. Based on lake storage curves and time series lake areas, lake volumes in the last 5 decades can be reconstructed, and the spatio-temporal characteristics of lake volume changes and their mechanisms are also analyzed. The results showed that the high-precision lake hydrological elements are reconstructed on arid drying lakes through the application of stereo mapping technology in remote sensing.

Geothermal Prospecting with Remote Sensing and Geographical Information System Technologies in Xilingol Volcanic Field in the Eastern Inner Mongolia, NE China

NASA Astrophysics Data System (ADS)

Peng, F.; Huang, S.; Xiong, Y.; Zhao, Y.; Cheng, Y.

2013-05-01

Geothermal energy is a renewable and low-carbon energy source independent of climate change. It is most abundant in Cenozoic volcanic areas where high temperature can be obtained within a relatively shallow depth. Like other geological resources, geothermal resource prospecting and exploration require a good understanding of the host media. Remote sensing (RS) has the advantages of high spatial and temporal resolution and broad spatial coverage over the conventional geological and geophysical prospecting, while geographical information system (GIS) has intuitive, flexible, and convenient characteristics. In this study, we apply RS and GIS technics in prospecting the geothermal energy potential in Xilingol, a Cenozoic volcanic field in the eastern Inner Mongolia, NE China. Landsat TM/ETM+ multi-temporal images taken under clear-sky conditions, digital elevation model (DEM) data, and other auxiliary data including geological maps of 1:2,500,000 and 1:200,000 scales are used in this study. The land surface temperature (LST) of the study area is retrieved from the Landsat images with the single-channel algorithm on the platform of ENVI developed by ITT Visual Information Solutions. Information of linear and circular geological structure is then extracted from the LST maps and compared to the existing geological data. Several useful technologies such as principal component analysis (PCA), vegetation suppression technique, multi-temporal comparative analysis, and 3D Surface View based on DEM data are used to further enable a better visual geologic interpretation with the Landsat imagery of Xilingol. The Preliminary results show that major faults in the study area are mainly NE and NNE oriented. Several major volcanism controlling faults and Cenozoic volcanic eruption centers have been recognized from the linear and circular structures in the remote images. Seven areas have been identified as potential targets for further prospecting geothermal energy based on the visual interpretation of the geological structures. The study shows that GIS and RS have great application potential in the geothermal exploration in volcanic areas and will promote the exploration of renewable energy resources of great potential.

Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

2015-12-01

Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in combination with advanced analytic and extraction techniques provides a vital remote sensing tool for decision makers and scientists with a high-degree of flexibility to adapt to different uses.

Spline function approximation techniques for image geometric distortion representation. [for registration of multitemporal remote sensor imagery

NASA Technical Reports Server (NTRS)

Anuta, P. E.

1975-01-01

Least squares approximation techniques were developed for use in computer aided correction of spatial image distortions for registration of multitemporal remote sensor imagery. Polynomials were first used to define image distortion over the entire two dimensional image space. Spline functions were then investigated to determine if the combination of lower order polynomials could approximate a higher order distortion with less computational difficulty. Algorithms for generating approximating functions were developed and applied to the description of image distortion in aircraft multispectral scanner imagery. Other applications of the techniques were suggested for earth resources data processing areas other than geometric distortion representation.

Monitoring of environmental conditions in the Alaskan forests using ERS-1 SAR data

NASA Technical Reports Server (NTRS)

Rignot, Eric; Way, Jobea; Mcdonald, Kyle; Viereck, Leslie; Adams, Phyllis

1992-01-01

Preliminary results from an analysis of the multitemporal radar backscatter signatures of tree species acquired by European Remote Sensing Satellite (ERS-1) synthetic aperture radar (SAR) data are presented. Significant changes in radar backscatter are detected. Correlation of these differences with ground truth observations indicate that these are due to changes in soil and liquid water content as a result of freeze/thaw events. C-band observations acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (JPL AIRSAR) instrument demonstrate the potential of a C-band radar instrument to monitor drought/flood events. The potential of ERS-1 for monitoring phenologic changes in the forest and for classifying tree species is less promising.

Implementation of Sentinel-2 Data in the M4Land System for the Generation of Continuous Information Products in Agriculture

NASA Astrophysics Data System (ADS)

Klug, P.; Schlenz, F.; Hank, T.; Migdall, S.; Weiß, I.; Danner, M.; Bach, H.; Mauser, W.

2016-08-01

The analysis system developed in the frame of the M4Land project (Model based, Multi-temporal, Multi scale and Multi sensorial retrieval of continuous land management information) has proven its capabilities of classifying crop type and creating products on the intensity of agricultural production using optical remote sensing data from Landsat and RapidEye. In this study, Sentinel-2 data is used for the first time together with Landsat 7 ETM+ and 8 OLI data within the M4Land analysis system to derive continuously crop type and the agricultural intensity of fields in an area north of Munich, Germany and the year 2015.

A 3D convolutional neural network approach to land cover classification using LiDAR and multi-temporal Landsat imagery

NASA Astrophysics Data System (ADS)

Xu, Z.; Guan, K.; Peng, B.; Casler, N. P.; Wang, S. W.

2017-12-01

Landscape has complex three-dimensional features. These 3D features are difficult to extract using conventional methods. Small-footprint LiDAR provides an ideal way for capturing these features. Existing approaches, however, have been relegated to raster or metric-based (two-dimensional) feature extraction from the upper or bottom layer, and thus are not suitable for resolving morphological and intensity features that could be important to fine-scale land cover mapping. Therefore, this research combines airborne LiDAR and multi-temporal Landsat imagery to classify land cover types of Williamson County, Illinois that has diverse and mixed landscape features. Specifically, we applied a 3D convolutional neural network (CNN) method to extract features from LiDAR point clouds by (1) creating occupancy grid, intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into a 3D CNN feature extractor for many epochs of learning. The learned features (e.g., morphological features, intensity features, etc) were combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. We used photo interpretation for training and testing data generation. The classification results show that our approach outperforms traditional methods using LiDAR derived feature maps, and promises to serve as an effective methodology for creating high-quality land cover maps through fusion of complementary types of remote sensing data.

The use of satellite data for monitoring temporal and spatial patterns of fire: a comprehensive review

NASA Astrophysics Data System (ADS)

Lasaponara, R.

2009-04-01

Remotely sensed (RS) data can fruitfully support both research activities and operative monitoring of fire at different temporal and spatial scales with a synoptic view and cost effective technologies. "The contribution of remote sensing (RS) to forest fires may be grouped in three categories, according to the three phases of fire management: (i) risk estimation (before fire), (ii) detection (during fire) and (iii) assessment (after fire)" Chuvieco (2006). Relating each phase, wide research activities have been conducted over the years. (i) Risk estimation (before fire) has been mainly based on the use of RS data for (i) monitoring vegetation stress and assessing variations in vegetation moisture content, (ii) fuel type mapping, at different temporal and spatial scales from global, regional down to a local scale (using AVHRR, MODIS, TM, ASTER, Quickbird images and airborne hyperspectral and LIDAR data). Danger estimation has been mainly based on the use of AVHRR (onborad NOAA), MODIS (onboard TERRA and AQUA), VEGETATION (onboard SPOT) due to the technical characteristics (i.e. spectral, spatial and temporal resolution). Nevertheless microwave data have been also used for vegetation monitoring. (ii) Detection: identification of active fires, estimation of fire radiative energy and fire emission. AVHRR was one of the first satellite sensors used for setting up fire detection algorithms. The availbility of MODIS allowed us to obtain global fire products free downloaded from NASA web site. Sensors onboard geostationary satellite platforms, such as GOES, SEVIRI, have been used for fire detection, to obtain a high temporal resolution (at around 15 minutes) monitoring of active fires. (iii) Post fire damage assessment includes: burnt area mapping, fire emission, fire severity, vegetation recovery, fire resilience estimation, and, more recently, fire regime characterization. Chuvieco E. L. Giglio, C. Justice, 2008 Global charactrerization of fire activity: toward defining fire regimes from Earth observation data Global Change Biology vo. 14. doi: 10.1111/j.1365-2486.2008.01585.x 1-15, Chuvieco E., P. Englefield, Alexander P. Trishchenko, Yi Luo Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2381-2396 Chuvieco Emilio 2006, Remote Sensing of Forest Fires: Current limitations and future prospects in Observing Land from Space: Science, Customers and Technology, Advances in Global Change Research Vol. 4 pp 47-51 De Santis A., E. Chuvieco Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models, Remote Sensing of Environment, Volume 108, Issue 4, 29 June 2007, Pages 422-435. De Santis A., E. Chuvieco, Patrick J. Vaughan, Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models, Remote Sensing of Environment, Volume 113, Issue 1, 15 January 2009, Pages 126-136 García M., E. Chuvieco, H. Nieto, I. Aguado Combining AVHRR and meteorological data for estimating live fuel moisture content Remote Sensing of Environment, Volume 112, Issue 9, 15 September 2008, Pages 3618-3627 Ichoku C., L. Giglio, M. J. Wooster, L. A. Remer Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy. Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2950-2962. Lasaponara R. and Lanorte, On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape Ecological Modelling Volume 204, Issues 1-2, 24 May 2007, Pages 79-84 Lasaponara R., A. Lanorte, S. Pignatti,2006 Multiscale fuel type mapping in fragmented ecosystems: preliminary results from Hyperspectral MIVIS and Multispectral Landsat TM data, Int. J. Remote Sens., vol. 27 (3) pp. 587-593. Lasaponara R., V. Cuomo, M. F. Macchiato, and T. Simoniello, 2003 .A self-adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection.n International Journal of Remote Sensing, vol. 24, No 8, 1723-1749. Minchella A., F. Del Frate, F. Capogna, S. Anselmi, F. Manes Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas Remote Sensing of Environment, In Press Næsset E., T. Gobakken Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 3079-3090 Peterson S. H, Dar A. Roberts, Philip E. Dennison Mapping live fuel moisture with MODIS data: A multiple regression approach, Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4272-4284. Schroeder Wilfrid, Elaine Prins, Louis Giglio, Ivan Csiszar, Christopher Schmidt, Jeffrey Morisette, Douglas Morton Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2711-2726 Shi J., T. Jackson, J. Tao, J. Du, R. Bindlish, L. Lu, K.S. Chen Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4285-4300 Tansey, K., Grégoire, J-M., Defourny, P., Leigh, R., Pekel, J-F., van Bogaert, E. and Bartholomé, E., 2008 A New, Global, Multi-Annual (2000-2007) Burnt Area Product at 1 km Resolution and Daily Intervals Geophysical Research Letters, VOL. 35, L01401, doi:10.1029/2007GL031567, 2008. Telesca L. and Lasaponara R., 2006; "Pre-and Post- fire Behaviural trends revealed in satellite NDVI time series" Geophysical Research Letters,., 33, L14401, doi:10.1029/2006GL026630 Telesca L. and Lasaponara R 2005 Discriminating Dynamical Patterns in Burned and Unburned Vegetational Covers by Using SPOT-VGT NDVI Data. Geophysical Research Letters,, 32, L21401, doi:10.1029/2005GL024391. Telesca L. and Lasaponara R. Investigating fire-induced behavioural trends in vegetation covers , Communications in Nonlinear Science and Numerical Simulation, 13, 2018-2023, 2008 Telesca L., A. Lanorte and R. Lasaponara, 2007. Investigating dynamical trends in burned and unburned vegetation covers by using SPOT-VGT NDVI data. Journal of Geophysics and Engineering, Vol. 4, pp. 128-138, 2007 Telesca L., R. Lasaponara, and A. Lanorte, Intra-annual dynamical persistent mechanisms in Mediterranean ecosystems revealed SPOT-VEGETATION Time Series, Ecological Complexity, 5, 151-156, 2008 Verbesselt, J., Somers, B., Lhermitte, S., Jonckheere, I., van Aardt, J., and Coppin, P. (2007) Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sensing of Environment 108: 357-368. Zhang X., S. Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897 Zhang X., Shobha Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897

Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area

USGS Publications Warehouse

Norman, Laura M.; Middleton, Barry R.; Wilson, Natalie R.

2018-01-01

Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000  km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, Bradley C.; Tieszen, Larry L.

2003-01-01

Time integrated normalized difference vegetation index (ΣNDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989–1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxy for interannual climate variability is analysed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ΣNDVI values.

Investigating flood susceptible areas in inaccessible regions using remote sensing and geographic information systems.

PubMed

Lim, Joongbin; Lee, Kyoo-Seock

2017-03-01

Every summer, North Korea (NK) suffers from floods, resulting in decreased agricultural production and huge economic loss. Besides meteorological reasons, several factors can accelerate flood damage. Environmental studies about NK are difficult because NK is inaccessible due to the division of Korea. Remote sensing (RS) can be used to delineate flood inundated areas in inaccessible regions such as NK. The objective of this study was to investigate the spatial characteristics of flood susceptible areas (FSAs) using multi-temporal RS data and digital elevation model data. Such study will provide basic information to restore FSAs after reunification. Defining FSAs at the study site revealed that rice paddies with low elevation and low slope were the most susceptible areas to flood in NK. Numerous sediments from upper streams, especially streams through crop field areas on steeply sloped hills, might have been transported and deposited into stream channels, thus disturbing water flow. In conclusion, NK floods may have occurred not only due to meteorological factors but also due to inappropriate land use for flood management. In order to mitigate NK flood damage, reforestation is needed for terraced crop fields. In addition, drainage capacity for middle stream channel near rice paddies should be improved.

Monitoring, analyzing and simulating of spatial-temporal changes of landscape pattern over mining area

NASA Astrophysics Data System (ADS)

Liu, Pei; Han, Ruimei; Wang, Shuangting

2014-11-01

According to the merits of remotely sensed data in depicting regional land cover and Land changes, multi- objective information processing is employed to remote sensing images to analyze and simulate land cover in mining areas. In this paper, multi-temporal remotely sensed data were selected to monitor the pattern, distri- bution and trend of LUCC and predict its impacts on ecological environment and human settlement in mining area. The monitor, analysis and simulation of LUCC in this coal mining areas are divided into five steps. The are information integration of optical and SAR data, LULC types extraction with SVM classifier, LULC trends simulation with CA Markov model, landscape temporal changes monitoring and analysis with confusion matrixes and landscape indices. The results demonstrate that the improved data fusion algorithm could make full use of information extracted from optical and SAR data; SVM classifier has an efficient and stable ability to obtain land cover maps, which could provide a good basis for both land cover change analysis and trend simulation; CA Markov model is able to predict LULC trends with good performance, and it is an effective way to integrate remotely sensed data with spatial-temporal model for analysis of land use / cover change and corresponding environmental impacts in mining area. Confusion matrixes are combined with landscape indices to evaluation and analysis show that, there was a sustained downward trend in agricultural land and bare land, but a continues growth trend tendency in water body, forest and other lands, and building area showing a wave like change, first increased and then decreased; mining landscape has undergone a from small to large and large to small process of fragmentation, agricultural land is the strongest influenced landscape type in this area, and human activities are the primary cause, so the problem should be pay more attentions by government and other organizations.

Tracking Avian Reservoirs of Arboviruses using Remote Sensing and Radiotelemetry

NASA Technical Reports Server (NTRS)

Beck, L.; Wright, S.; Schmidt, C.; Lobitz, B.; Bell, D.; Brown, D.; Brass, James A. (Technical Monitor)

2002-01-01

Encephalitis is caused by a virus that is transmitted by mosquitoes between mammalian hosts. The virus is closely related to the West Nile virus (WNV), which started in New York in 1999, and has since spread to 25 states. Like encephalitis, WNV is vectored by mosquitoes, and the primary hosts are birds; humans are accidental, or'dead-end' hosts. Very little is understood about the behavior of these bird populations, and how they intersect - both in time and in space - with mosquito populations. Exploring these relationships is the first step in developing models for encephalitis and WNV transmission risk. This project combines remotely sensed data with radiotelemetry to create a spatiotemporal map of encephalitis viral activity in bird and mosquito populations in the Sacramento Valley of California. Specifically, remote sensing (RS) and geographic information system (GIS) technologies were used to characterize habitats utilized by both avian viral reservoirs and the mosquitoes that vector encephalitis. Radiotelemetry and serosurveys (blood) were then used to spatially and temporally track the patterns of infection. The project uses Landsat ETM+ multitemporal satellite data to characterize habitats utilized by both birds and the mosquito vectors. Mist nets were used to sample members of individual flocks of blackbirds and cowbirds over a period of several months; these birds were then bled to assess their viral status, banded, and fitted with transmitters. Radiotelemetry was used to spatially and temporally track the distribution of banded birds and their associated flocks. The movement of these indicator flocks were compared with the location of remotely sensed (adult and larval) mosquito habitats to determine the intersection of bird's and vectors; this is key in understanding where and when transmission occurs from bird to bird, as well as from bird to mammal, via mosquito. The relationships found during the project are being used to generate a model of encephalitis transmission risk in California.

Landslide inventory maps: New tools for an old problem

NASA Astrophysics Data System (ADS)

Guzzetti, Fausto; Mondini, Alessandro Cesare; Cardinali, Mauro; Fiorucci, Federica; Santangelo, Michele; Chang, Kang-Tsung

2012-04-01

Landslides are present in all continents, and play an important role in the evolution of landscapes. They also represent a serious hazard in many areas of the world. Despite their importance, we estimate that landslide maps cover less than 1% of the slopes in the landmasses, and systematic information on the type, abundance, and distribution of landslides is lacking. Preparing landslide maps is important to document the extent of landslide phenomena in a region, to investigate the distribution, types, pattern, recurrence and statistics of slope failures, to determine landslide susceptibility, hazard, vulnerability and risk, and to study the evolution of landscapes dominated by mass-wasting processes. Conventional methods for the production of landslide maps rely chiefly on the visual interpretation of stereoscopic aerial photography, aided by field surveys. These methods are time consuming and resource intensive. New and emerging techniques based on satellite, airborne, and terrestrial remote sensing technologies, promise to facilitate the production of landslide maps, reducing the time and resources required for their compilation and systematic update. In this work, we first outline the principles for landslide mapping, and we review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories. Next, we examine recent and new technologies for landslide mapping, considering (i) the exploitation of very-high resolution digital elevation models to analyze surface morphology, (ii) the visual interpretation and semi-automatic analysis of different types of satellite images, including panchromatic, multispectral, and synthetic aperture radar images, and (iii) tools that facilitate landslide field mapping. Next, we discuss the advantages and the limitations of the new remote sensing data and technology for the production of geomorphological, event, seasonal, and multi-temporal inventory maps. We conclude by arguing that the new tools will help to improve the quality of landslide maps, with positive effects on all derivative products and analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluations.

Reducing uncertainty on satellite image classification through spatiotemporal reasoning

NASA Astrophysics Data System (ADS)

Partsinevelos, Panagiotis; Nikolakaki, Natassa; Psillakis, Periklis; Miliaresis, George; Xanthakis, Michail

2014-05-01

The natural habitat constantly endures both inherent natural and human-induced influences. Remote sensing has been providing monitoring oriented solutions regarding the natural Earth surface, by offering a series of tools and methodologies which contribute to prudent environmental management. Processing and analysis of multi-temporal satellite images for the observation of the land changes include often classification and change-detection techniques. These error prone procedures are influenced mainly by the distinctive characteristics of the study areas, the remote sensing systems limitations and the image analysis processes. The present study takes advantage of the temporal continuity of multi-temporal classified images, in order to reduce classification uncertainty, based on reasoning rules. More specifically, pixel groups that temporally oscillate between classes are liable to misclassification or indicate problematic areas. On the other hand, constant pixel group growth indicates a pressure prone area. Computational tools are developed in order to disclose the alterations in land use dynamics and offer a spatial reference to the pressures that land use classes endure and impose between them. Moreover, by revealing areas that are susceptible to misclassification, we propose specific target site selection for training during the process of supervised classification. The underlying objective is to contribute to the understanding and analysis of anthropogenic and environmental factors that influence land use changes. The developed algorithms have been tested upon Landsat satellite image time series, depicting the National Park of Ainos in Kefallinia, Greece, where the unique in the world Abies cephalonica grows. Along with the minor changes and pressures indicated in the test area due to harvesting and other human interventions, the developed algorithms successfully captured fire incidents that have been historically confirmed. Overall, the results have shown that the use of the suggested procedures can contribute to the reduction of the classification uncertainty and support the existing knowledge regarding the pressure among land-use changes.

Mapping the Snow Line Altitude for Large Glacier Samples from Multitemporal Landsat Imagery

NASA Astrophysics Data System (ADS)

Rastner, P.; Nicholson, L. I.; Notarnicola, C.; Prinz, R.; Sailer, R.

2015-12-01

The cryosphere of mountain regions is fastly changing in response to climate change. This is particularly evident in global-scale glacier retreat. Trends in snow cover, however, are more difficult to determine, as annual fluctuations can be very large. Snow is an important parameter in the energy and mass balance of glaciers and the snow line altitude (SLA) at the end of the melting period can be considered as a proxy for the equilibrium line altitude (ELA). By frequently observing the SLA from satellite, region-wide monitoring of glaciers and improved calibration and validation of transient glacier (mass balance) models is possible. In the near future, frequent mapping of the SLA will be strongly facilitated by satellite missions like Sentinel 2A/B, where the same region will be covered every 5 days with 10 m spatial resolution. For this study we have developed an automated tool to derive the SLA for large glacier samples from remote sensing data. The method is first applied in the Ötztal Alps (Austria) where reliable in-situ data of mass balance and ELA are available for several glaciers over a 30-years period. The algorithm currently works with multi-temporal Landsat imagery (1972-2015), digital glacier outlines and a high-quality national DEM. All input datasets are atmospherically and topographically pre-processed before the SLA is automatically retrieved for each glacier. The remote-sensing derived SLA is generally about 200 m lower than the ELA, however, a clear trend in the altitude of the end of summer snow line is detectable (~ 200 m), which is in agreement with the ELA trend observed in the field. After bias correction and conversion to mass balance, the variability in observed mass balance can be well reproduced from the satellite-derived SLA time series. This is promising for application of the approach in other regions.

A methodology to estimate representativeness of LAI station observation for validation: a case study with Chinese Ecosystem Research Network (CERN) in situ data

NASA Astrophysics Data System (ADS)

Xu, Baodong; Li, Jing; Liu, Qinhuo; Zeng, Yelu; Yin, Gaofei

2014-11-01

Leaf Area Index (LAI) is known as a key vegetation biophysical variable. To effectively use remote sensing LAI products in various disciplines, it is critical to understand the accuracy of them. The common method for the validation of LAI products is firstly establish the empirical relationship between the field data and high-resolution imagery, to derive LAI maps, then aggregate high-resolution LAI maps to match moderate-resolution LAI products. This method is just suited for the small region, and its frequencies of measurement are limited. Therefore, the continuous observing LAI datasets from ground station network are important for the validation of multi-temporal LAI products. However, due to the scale mismatch between the point observation in the ground station and the pixel observation, the direct comparison will bring the scale error. Thus it is needed to evaluate the representativeness of ground station measurement within pixel scale of products for the reasonable validation. In this paper, a case study with Chinese Ecosystem Research Network (CERN) in situ data was taken to introduce a methodology to estimate representativeness of LAI station observation for validating LAI products. We first analyzed the indicators to evaluate the observation representativeness, and then graded the station measurement data. Finally, the LAI measurement data which can represent the pixel scale was used to validate the MODIS, GLASS and GEOV1 LAI products. The result shows that the best agreement is reached between the GLASS and GEOV1, while the lowest uncertainty is achieved by GEOV1 followed by GLASS and MODIS. We conclude that the ground station measurement data can validate multi-temporal LAI products objectively based on the evaluation indicators of station observation representativeness, which can also improve the reliability for the validation of remote sensing products.

Study on Mosaic and Uniform Color Method of Satellite Image Fusion in Large Srea

NASA Astrophysics Data System (ADS)

Liu, S.; Li, H.; Wang, X.; Guo, L.; Wang, R.

2018-04-01

Due to the improvement of satellite radiometric resolution and the color difference for multi-temporal satellite remote sensing images and the large amount of satellite image data, how to complete the mosaic and uniform color process of satellite images is always an important problem in image processing. First of all using the bundle uniform color method and least squares mosaic method of GXL and the dodging function, the uniform transition of color and brightness can be realized in large area and multi-temporal satellite images. Secondly, using Color Mapping software to color mosaic images of 16bit to mosaic images of 8bit based on uniform color method with low resolution reference images. At last, qualitative and quantitative analytical methods are used respectively to analyse and evaluate satellite image after mosaic and uniformity coloring. The test reflects the correlation of mosaic images before and after coloring is higher than 95 % and image information entropy increases, texture features are enhanced which have been proved by calculation of quantitative indexes such as correlation coefficient and information entropy. Satellite image mosaic and color processing in large area has been well implemented.

Monitoring cover crops using radar remote sensing in southern Ontario, Canada

NASA Astrophysics Data System (ADS)

Shang, J.; Huang, X.; Liu, J.; Wang, J.

2016-12-01

Information on agricultural land surface conditions is important for developing best land management practices to maintain the overall health of the fields. The climate condition supports one harvest per year for the majority of the field crops in Canada, with a relative short growing season between May and September. During the non-growing-season months (October to the following April), many fields are traditionally left bare. In more recent year, there has been an increased interest in planting cover crops. Benefits of cover crops include boosting soil organic matters, preventing soil from erosion, retaining soil moisture, and reducing surface runoff hence protecting water quality. Optical remote sensing technology has been exploited for monitoring cover crops. However limitations inherent to optical sensors such as cloud interference and signal saturation (when leaf area index is above 2.5) impeded its operational application. Radar remote sensing on the other hand is not hindered by unfavorable weather conditions, and the signal continues to be sensitive to crop growth beyond the saturation point of optical sensors. It offers a viable means for capturing timely information on field surface conditions (with or without crop cover) or crop development status. This research investigated the potential of using multi-temporal RADARSAT-2 C-band synthetic aperture radar (SAR) data collected in 2015 over multiple fields of winter wheat, corn and soybean crops in southern Ontario, Canada, to retrieve information on the presence of cover crops and their growth status. Encouraging results have been obtained. This presentation will report the methodology developed and the results obtained.

Developing a thermal characteristic index for lithology identification using thermal infrared remote sensing data

NASA Astrophysics Data System (ADS)

Wei, Jiali; Liu, Xiangnan; Ding, Chao; Liu, Meiling; Jin, Ming; Li, Dongdong

2017-01-01

In remote sensing petrology fields, studies have mainly concentrated on spectroscopy remote sensing research, and methods to identify minerals and rocks are mainly based on the analysis and enhancement of spectral features. Few studies have reported the application of thermodynamics for lithology identification. This paper aims to establish a thermal characteristic index (TCI) to explore rock thermal behavior responding to defined environmental systems. The study area is located in the northern Qinghai Province, China, on the northern edge of the Qinghai-Tibet Plateau, where mafic-ultramafic rock, quartz-rich rock, alkali granite rock and carbonate rock are well exposed; the pixel samples of these rocks and vegetation were obtained based on relevant indices and geological maps. The scatter plots of TCI indicate that mafic-ultramafic rock and quartz-rich rock can be well extracted from other surface objects when interference from vegetation is lower. On account of the complexity of environmental systems, three periods of TCI were used to construct a three-dimensional scatter plot, named the multi-temporal thermal feature space (MTTFS) model. Then, the Bayes discriminant analysis algorithm was applied to the MTTFS model to extract rocks quantitatively. The classification accuracy of mafic-ultramafic rock is more than 75% in both training data and test data, which suggests TCI can act as a sensitive indicator to distinguish rocks and the MTTFS model can accurately extract mafic-ultramafic rock from other surface objects. We deduce that the use of thermodynamics is promising in lithology identification when an effective index is constructed and an appropriated model is selected.

Regional landslide susceptibility assessment using multi-stage remote sensing data along the coastal range highway in northeastern Taiwan

NASA Astrophysics Data System (ADS)

Lee, Ching-Fang; Huang, Wei-Kai; Chang, Yu-Lin; Chi, Shu-Yeong; Liao, Wu-Chang

2018-01-01

Typhoons Megi (2010) and Saola (2012) brought torrential rainfall which triggered regional landslides and flooding hazards along Provincial Highway No. 9 in northeastern Taiwan. To reduce property loss and saving lives, this study combines multi-hazard susceptibility assessment with environmental geology map a rock mass rating system (RMR), remote sensing analysis, and micro-topography interpretation to develop an integrated landslide hazard assessment approach and reflect the intrinsic state of slopeland from the past toward the future. First, the degree of hazard as indicated by historical landslides was used to determine many landslide regions in the past. Secondly, geo-mechanical classification of rock outcroppings was performed by in-situ investigation along the vulnerable road sections. Finally, a high-resolution digital elevation model was extracted from airborne LiDAR and multi-temporal remote sensing images which was analyzed to discover possible catastrophic landslide hotspot shortly. The results of the analysis showed that 37% of the road sections in the study area were highly susceptible to landslide hazards. The spatial distribution of the road sections revealed that those characterized by high susceptibility were located near the boundaries of fault zones and in areas of lithologic dissimilarity. Headward erosion of gullies and concave-shaped topographic features had an adverse effect and was the dominant factor triggering landslides. Regional landslide reactivation on this coastal highway are almost related to the past landslide region based on hazard statistics. The final results of field validation demonstrated that an accuracy of 91% could be achieved for forecasting geohazard followed by intense rainfall events and typhoons.

Monitoring land use/cover changes on the Romanian Black Sea Coast

NASA Astrophysics Data System (ADS)

Zoran, L. F. V.; Dida, A. I.; Zoran, M. A.

2014-10-01

Remotely sensed satellite data are critical to understanding the coastal zones' physical and social systems interaction, complementing ground based methods and providing accurate wide range, objective and comparable, at widely-varying scales, synoptically data. For some environmental agreements remote sensing may provide the only viable means of compliance verification because the phenomena are monitored occurs over large and inaccessible geographic areas. The main aim of this paper was the assessment of coastal zone land cover/use changes based on fusion technique of satellite remote sensing imagery. The evaluation of coastal zone landscapes was based upon different sub-functions which refer to landscape features such as water, soil, land-use, buildings, groundwater, biotope types. A newly proposed sub-pixel mapping algorithm was applied to a set of multispectral and multitemporal satellite data for Danube Delta, Constantza and Black Sea coastal zone areas in Romania. A land cover classification and subsequent environmental quality analysis for change detection was done based on Landsat TM , Landsat ETM, QuickBird satellite images over 1990 to 2013 period of time. Spectral signatures of different terrain features have been used to separate and classify surface units of coastal zone and sub-coastal zone area.The change in the position of the coastline in Constantza area was examined in relation with the urban expansion. A distinction was made between landfill/sedimentation processes on the one hand and dredging/erosion processes on the other. We considered the Romanian Black Sea coastal zone dynamics in connection with the spatio-temporal variation of physical and biogeochemical processes and their influences on the environmental state in the near-shore area.

A fully automatic tool to perform accurate flood mapping by merging remote sensing imagery and ancillary data

NASA Astrophysics Data System (ADS)

D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco; Pasquariello, Guido

2016-04-01

Flooding is one of the most frequent and expansive natural hazard. High-resolution flood mapping is an essential step in the monitoring and prevention of inundation hazard, both to gain insight into the processes involved in the generation of flooding events, and from the practical point of view of the precise assessment of inundated areas. Remote sensing data are recognized to be useful in this respect, thanks to the high resolution and regular revisit schedules of state-of-the-art satellites, moreover offering a synoptic overview of the extent of flooding. In particular, Synthetic Aperture Radar (SAR) data present several favorable characteristics for flood mapping, such as their relative insensitivity to the meteorological conditions during acquisitions, as well as the possibility of acquiring independently of solar illumination, thanks to the active nature of the radar sensors [1]. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground: the presence of many land cover types, each one with a particular signature in presence of flood, requires modelling the behavior of different objects in the scene in order to associate them to flood or no flood conditions [2]. Generally, the fusion of multi-temporal, multi-sensor, multi-resolution and/or multi-platform Earth observation image data, together with other ancillary information, seems to have a key role in the pursuit of a consistent interpretation of complex scenes. In the case of flooding, distance from the river, terrain elevation, hydrologic information or some combination thereof can add useful information to remote sensing data. Suitable methods, able to manage and merge different kind of data, are so particularly needed. In this work, a fully automatic tool, based on Bayesian Networks (BNs) [3] and able to perform data fusion, is presented. It supplies flood maps describing the dynamics of each analysed event, combining time series of images, acquired by different sensors, with ancillary information. Some experiments have been performed by combining multi-temporal SAR intensity images, InSAR coherence and optical data, with geomorphic and other ground information. The tool has been tested on different flood events occurred in the Basilicata region (Italy) during the last years, showing good capabilities of identification of a large area interested by the flood phenomenon, partially overcoming the obstacle constituted by the presence of scattering/coherence classes corresponding to different land cover types, which respond differently to the presence of water and to inundation evolution [1] A. Refice et al, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2711-2722, 2014. [2] L. Pulvirenti et al., IEEE Trans. Geosci. Rem. Sens., Vol. PP, pp. 1- 13, 2015. [3] A. D'Addabbo et al., "A Bayesian Network for Flood Detection combining SAR Imagery and Ancillary Data," IEEE Trans. Geosci. Rem. Sens., in press.

GMES Initial Operations - Network for Earth Observation Research Training (GIONET)

NASA Astrophysics Data System (ADS)

Nicolas-Perea, V.; Balzter, H.

2012-12-01

GMES Initial Operations - Network for Earth Observation Research Training (GIONET) is a Marie Curie funded project that aims to establish the first of a kind European Centre of Excellence for Earth Observation Research Training. GIONET is a partnership of leading Universities, research institutes and private companies from across Europe aiming to cultivate a community of early stage researchers in the areas of optical and radar remote sensing skilled for the emerging GMES land monitoring services during the GMES Initial Operations period (2011-2013) and beyond. GIONET is expected to satisfy the demand for highly skilled researchers and provide personnel for operational phase of the GMES and monitoring and emergency services. It will achieve this by: -Providing postgraduate training in Earth Observation Science that exposes students to different research disciplines and complementary skills, providing work experiences in the private and academic sectors, and leading to a recognized qualification (Doctorate). -Enabling access to first class training in both fundamental and applied research skills to early-stage researchers at world-class academic centers and market leaders in the private sector. -Building on the experience from previous GMES research and development projects in the land monitoring and emergency information services. The training program through supervised research focuses on 14 research topics (each carried out by an Early Stage Researchers based in one of the partner organization) divided in 5 main areas: Forest monitoring: Global biomass information systems Forest Monitoring of the Congo Basin using Synthetic Aperture radar (SAR) Multi-concept Earth Observation Capabilities for Biomass Mapping and Change Detection: Synergy of Multi-temporal and Multi-frequency Interferometric Radar and Optical Satellite Data Land cover and change: Multi-scale Remote Sensing Synergy for Land Process Studies: from field Spectrometry to Airborne Hyperspectral and Lidar Campaigns to Radar-Optical Satellite Data Multi-temporal, multi-frequency SAR for landscape dynamics Coastal zone and freshwater monitoring: Optical and SAR-based EO in support of Integrated Coastal Zone Management Dynamics and conservation ecology of emergent and submerged macrophytes in Lake Balaton using airborne remote sensing Satellite remote sensing of water quality (chlorophyll and suspended sediment) using MODIS and ship-mounted LIDAR Geohazards and emergency response: Methods for detection and monitoring of small scale land surface feature changes in complex crisis situations Monitoring landslide displacements with Radar Interferometry DINSAR/PSI hybrid methodologies for ground-motion monitoring Climate adaptation and emergency response: Earth Observation based analysis of regional impact of climate change induced water stress patterns fuelling human crisis and conflict situations in semi dry climate regimes Satellite Derived Information for Drought Detection and Estimation of the Water Balance GIONET will also cover methodologies including (i) modelling fundamental radiative processes determining the satellite signal, (ii) atmospheric correction and calibration, (iii) processing higher-order data products, (iii) developing information products from satellite data to meet user requirements, and (iv) statistical methods for assessing the quality and accuracy of data products.

A multi-temporal fusion-based approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan

An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York, USA). The first step in the process was the construction of land cover maps from freely available, high temporal resolution, low spatial resolution MODIS imagery using a time-series approach. We used the variability in the temporal signatures among different land cover classes for classification. The time series-specific features were defined by various physical properties of a pixel, such as variation in vegetation cover and water content over time. The pixels were classified into four land cover classes - forest, urban, water, and vegetation - using Euclidean and Mahalanobis distance metrics. On the other hand, a high spatial resolution commercial satellite, such as RapidEye, can be tasked to capture images over the affected area in the case of a nuclear event. This imagery served as a second source of data to augment results from the time series approach. The classifications from the two approaches were integrated using an a posteriori probability-based fusion approach. This was done by establishing a relationship between the classes, obtained after classification of the two data sources. Despite the coarse spatial resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion-based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. This fusion thus contributed to classification accuracy refinement, with a few additional advantages, such as correction for cloud cover and providing for an approach that is robust against point-in-time seasonal anomalies, due to the inclusion of multi-temporal data. We concluded that this approach is capable of generating land cover maps of acceptable accuracy and rapid turnaround, which in turn can yield reliable estimates of crop acreage of a region. The final algorithm is part of an automated software tool, which can be used by emergency response personnel to generate a nuclear ingestion pathway information product within a few hours of data collection.

Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area

NASA Astrophysics Data System (ADS)

Kustas, William P.; Alfieri, Joseph G.; Anderson, Martha C.; Colaizzi, Paul D.; Prueger, John H.; Evett, Steven R.; Neale, Christopher M. U.; French, Andrew N.; Hipps, Lawrence E.; Chávez, José L.; Copeland, Karen S.; Howell, Terry A.

2012-12-01

Application and validation of many thermal remote sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal thermal remote sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) [1], and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) [2]. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the remote sensing observations with the local flux measurement footprint.

On the use of wavelet for extracting feature patterns from Multitemporal google earth satellite data sets

NASA Astrophysics Data System (ADS)

Lasaponara, R.

2012-04-01

The great amount of multispectral VHR satellite images, even available free of charge in Google earth has opened new strategic challenges in the field of remote sensing for archaeological studies. These challenges substantially deal with: (i) the strategic exploitation of satellite data as much as possible, (ii) the setting up of effective and reliable automatic and/or semiautomatic data processing strategies and (iii) the integration with other data sources from documentary resources to the traditional ground survey, historical documentation, geophysical prospection, etc. VHR satellites provide high resolution data which can improve knowledge on past human activities providing precious qualitative and quantitative information developed to such an extent that currently they share many of the physical characteristics of aerial imagery. This makes them ideal for investigations ranging from a local to a regional scale (see. for example, Lasaponara and Masini 2006a,b, 2007a, 2011; Masini and Lasaponara 2006, 2007, Sparavigna, 2010). Moreover, satellite data are still the only data source for research performed in areas where aerial photography is restricted because of military or political reasons. Among the main advantages of using satellite remote sensing compared to traditional field archaeology herein we briefly focalize on the use of wavelet data processing for enhancing google earth satellite data with particular reference to multitemporal datasets. Study areas selected from Southern Italy, Middle East and South America are presented and discussed. Results obtained point out the use of automatic image enhancement can successfully applied as first step of supervised classification and intelligent data analysis for semiautomatic identification of features of archaeological interest. Reference Lasaponara R, Masini N (2006a) On the potential of panchromatic and multispectral Quickbird data for archaeological prospection. Int J Remote Sens 27: 3607-3614. Lasaponara R, Masini N (2006b) Identification of archaeological buried remains based on Normalized Difference Vegetation Index (NDVI) from Quickbird satellite data. IEEE Geosci Remote S 3(3): 325-328. Lasaponara R, Masini N (2007a) Detection of archaeological crop marks by using satellite QuickBird multispectral imagery. J Archaeol Sci 34: 214-21. Lasaponara R, Masini N (2007b) Improving satellite Quickbird - based identification of landscape archaeological features trough tasselled cup transformation and PCA. 21st CIPA Symposium, Atene, 1-6 giugno 2007. Lasaponara R, Masini N (2010) Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite imagery. In: Taniar D et al (Eds), Proceedings of ICSSA, The 2010 International Conference on Computational Science and its Application (Fukuoka-Japan, March 23 - 26, 2010), Springer, Berlin, 261-269. Lasaponara R, Masini N (2011) Satellite Remote Sensing in Archaeology : past, present and future. J Archaeol Sc 38: 1995-2002. Lasaponara R, Masini N, Rizzo E, Orefici G (2011) New discoveries in the Piramide Naranjada in Cahuachi (Peru) using satellite, Ground Probing Radar and magnetic investigations. J Archaeol Sci 38: 2031-2039. Lasaponara R, Masini N, Scardozzi G (2008) Satellite based archaeological research in ancient territory of Hierapolis. 1st International EARSeL Workshop. Advances in Remote Sensing for Archaeology and Cultural Heritage Management", CNR, Rome, September 30-October 4, Aracne, Rome, pp.11-16. Lillesand T M, Kiefer R W (2000) Remote Sensing and Image interpretation. John Wiley and Sons, New York. Masini N, Lasaponara R (2006) Satellite-based recognition of landscape archaeological features related to ancient human transformation. J Geophys Eng 3: 230-235, doi:10.1088/1742-2132/3/3/004. Masini N, Lasaponara R (2007) Investigating the spectral capability of QuickBird data to detect archaeological remains buried under vegetated and not vegetated areas. J Cult Heri 8 (1): 53-60. Sparavigna Enhancing the Google imagery using a wavelet filter, A.C. Sparavigna,http://arxiv.org/abs/1009.1590

The use of historical imagery in the remediation of an urban hazardous waste site

USGS Publications Warehouse

Slonecker, E.T.

2011-01-01

The information derived from the interpretation of historical aerial photographs is perhaps the most basic multitemporal application of remote-sensing data. Aerial photographs dating back to the early 20th century can be extremely valuable sources of historical landscape activity. In this application, imagery from 1918 to 1927 provided a wealth of information about chemical weapons testing, storage, handling, and disposal of these hazardous materials. When analyzed by a trained photo-analyst, the 1918 aerial photographs resulted in 42 features of potential interest. When compared with current remedial activities and known areas of contamination, 33 of 42 or 78.5% of the features were spatially correlated with areas of known contamination or other remedial hazardous waste cleanup activity. ?? 2010 IEEE.

The use of historical imagery in the remediation of an urban hazardous waste site

USGS Publications Warehouse

Slonecker, E.T.

2011-01-01

The information derived from the interpretation of historical aerial photographs is perhaps the most basic multitemporal application of remote-sensing data. Aerial photographs dating back to the early 20th century can be extremely valuable sources of historical landscape activity. In this application, imagery from 1918 to 1927 provided a wealth of information about chemical weapons testing, storage, handling, and disposal of these hazardous materials. When analyzed by a trained photo-analyst, the 1918 aerial photographs resulted in 42 features of potential interest. When compared with current remedial activities and known areas of contamination, 33 of 42 or 78.5% of the features were spatially correlated with areas of known contamination or other remedial hazardous waste cleanup activity.

Crop identification technology assessment for remote sensing (CITARS). Volume 10: Interpretation of results

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Feiveson, A. H.; Hall, F. G.; Bauer, M. E.; Davis, B. J.; Malila, W. A.; Rice, D. P.

1975-01-01

The CITARS was an experiment designed to quantitatively evaluate crop identification performance for corn and soybeans in various environments using a well-defined set of automatic data processing (ADP) techniques. Each technique was applied to data acquired to recognize and estimate proportions of corn and soybeans. The CITARS documentation summarizes, interprets, and discusses the crop identification performances obtained using (1) different ADP procedures; (2) a linear versus a quadratic classifier; (3) prior probability information derived from historic data; (4) local versus nonlocal recognition training statistics and the associated use of preprocessing; (5) multitemporal data; (6) classification bias and mixed pixels in proportion estimation; and (7) data with differnt site characteristics, including crop, soil, atmospheric effects, and stages of crop maturity.

Remote and terrestrial ground monitoring techniques integration for hazard assessment in mountain areas

NASA Astrophysics Data System (ADS)

Chinellato, Giulia; Kenner, Robert; Iasio, Christian; Mair, Volkmar; Mosna, David; Mulas, Marco; Phillips, Marcia; Strada, Claudia; Zischg, Andreas

2014-05-01

In high mountain regions the choice of appropriate sites for infrastructure such as roads, railways, cable cars or hydropower dams is often very limited. In parallel, the increasing demand for supply infrastructure in the Alps induces a continuous transformation of the territory. The new role played by the precautionary monitoring in the risk governance becomes fundamental and may overcome the modeling of future events, which represented so far the predominant approach to these sort of issues. Furthermore the consequence of considering methodologies alternative to those more exclusive allow to reduce costs and increasing the frequency of measurements, updating continuously the cognitive framework of existing hazard condition in most susceptible territories. The scale factor of the observed area and the multiple purpose of such regional ordinary surveys make it convenient to adopt Radar Satellite-based systems, but they need to be integrated with terrestrial systems for validation and eventual early warning purposes. Significant progress over the past decade in Remote Sensing (RS), Proximal Sensing and integration-based sensor networks systems now provide technologies, that allow to implement monitoring systems for ordinary surveys of extensive areas or regions, which are affected by active natural processes and slope instability. The Interreg project SloMove aims to provide solutions for such challenges and focuses on using remote sensing monitoring techniques for the monitoring of mass movements in two test sites, in South Tyrol (Italy) and in Grisons Canton (Switzerland). The topics faced in this project concern mass movements and slope deformation monitoring techniques, focusing mainly on the integration of multi-temporal interferometry, new generation of terrestrial technologies for differential digital terrain model elaboration provided by laser scanner (TLS), and GNSS-based topographic surveys, which are used not only for validation purpose, but also for adding value and information to the whole monitoring survey. The test sites are currently observed by an original integrated methodology specifically developed within the aim of the project. The integrated monitoring design includes reference targets for the different monitoring systems placed together on the same point or rigid foundation, to facilitate the comparison of the data and, in the operational use, to be able to switch consistently from one to the other system. The principal goal of the project is to define a shared procedure to select scalable technologies, best practices and institutional action plans more adequate to deal with different sort of hazard related to ground displacement, in densely populated mountain areas containing recreational and critical infrastructures. Keywords: integrated monitoring, multi-temporal interferometry, artificial reflectors; mass movement, SloMove.eu

Integrating Remote Sensing Data with Directional Two- Dimensional Wavelet Analysis and Open Geospatial Techniques for Efficient Disaster Monitoring and Management.

PubMed

Lin, Yun-Bin; Lin, Yu-Pin; Deng, Dong-Po; Chen, Kuan-Wei

2008-02-19

In Taiwan, earthquakes have long been recognized as a major cause oflandslides that are wide spread by floods brought by typhoons followed. Distinguishingbetween landslide spatial patterns in different disturbance regimes is fundamental fordisaster monitoring, management, and land-cover restoration. To circumscribe landslides,this study adopts the normalized difference vegetation index (NDVI), which can bedetermined by simply applying mathematical operations of near-infrared and visible-redspectral data immediately after remotely sensed data is acquired. In real-time disastermonitoring, the NDVI is more effective than using land-cover classifications generatedfrom remotely sensed data as land-cover classification tasks are extremely time consuming.Directional two-dimensional (2D) wavelet analysis has an advantage over traditionalspectrum analysis in that it determines localized variations along a specific direction whenidentifying dominant modes of change, and where those modes are located in multi-temporal remotely sensed images. Open geospatial techniques comprise a series ofsolutions developed based on Open Geospatial Consortium specifications that can beapplied to encode data for interoperability and develop an open geospatial service for sharing data. This study presents a novel approach and framework that uses directional 2Dwavelet analysis of real-time NDVI images to effectively identify landslide patterns andshare resulting patterns via open geospatial techniques. As a case study, this study analyzedNDVI images derived from SPOT HRV images before and after the ChiChi earthquake(7.3 on the Richter scale) that hit the Chenyulan basin in Taiwan, as well as images aftertwo large typhoons (Xangsane and Toraji) to delineate the spatial patterns of landslidescaused by major disturbances. Disturbed spatial patterns of landslides that followed theseevents were successfully delineated using 2D wavelet analysis, and results of patternrecognitions of landslides were distributed simultaneously to other agents using geographymarkup language. Real-time information allows successive platforms (agents) to work withlocal geospatial data for disaster management. Furthermore, the proposed is suitable fordetecting landslides in various regions on continental, regional, and local scales usingremotely sensed data in various resolutions derived from SPOT HRV, IKONOS, andQuickBird multispectral images.

Glacier changes in the Nanga Parbat Himalayas: a re-photographic survey between the 1930s and now

NASA Astrophysics Data System (ADS)

Schmidt, S.; Nüsser, M.

2009-04-01

In contrast to the relatively well investigated glacier and landscape changes in the mountains of Europe and North America, very little investigations and documentations using repeat photography have been undertaken in the Himalayas and other high mountain regions of Asia. The present study seeks to investigate glacier and landscape changes in the Nanga Parbat region (NW-Himalaya) using a multi-temporal and multi-spatial approach which is based on terrestrial repeat photography and remote sensing data. A comprehensive collection of historical landscape photographs, taken by members of the German Himalaya expeditions 1934 and 1937, forms a valuable baseline data set for the area. Recent fieldwork made it possible to repeat a large number of these photographs viewpoints identical to the earlier ones, and the direct comparisons illustrate glacier dynamics and landscape changes over a span of seventy years. Furthermore, in order to fill the temporal gap and to analyze temporal and spatial dynamics of glaciers over the last 40 years we use different satellite sensors (Corona, Aster, Landsat, Spot, QuickBird). First investigations were carried out at the Raikot Glacier, which is located at the northern declivity of the Nanga Parbat, the ninth highest peak on earth. The multi-temporal comparison detects only small down-wasting rates of the Raikot Glacier over the last 70 years and a retreat of the terminus of about 250 m which is characterized by great fluctuations. Based on this multi-temporal and multi-data approach, we will detect and analyze glacier and landscape changes in the whole Nanga Parbat region.

Multiscale, multispectral and multitemporal satellite data to identify archaeological remains in the archaeological area of Tiwanaku (Bolivia)

NASA Astrophysics Data System (ADS)

Masini, Nicola; Lasaponara, Rosa

2015-04-01

The aim of this paper is to investigate the cultural landscape of the archaeological area of Tiwanaku (Bolivia) using multiscale, multispectral and multitemporal satellite data. Geospatial analysis techniques were applied to the satellite data sets in order to enhance and map traces of past human activities and perform a spatial characterization of environmental and cultural patterns. In particular, in the Tiwanaku area, the approach based on local indicators of spatial autocorrelation (LISA) applied to ASTER data allowed us to identify traces of a possible ancient hydrographic network with a clear spatial relation with the well-known moat surrounding the core of the monumental area. The same approach applied to QuickBird data, allowed us to identify numerous traces of archaeological interest, in Mollo Kontu mound, less investigated than the monumental area. Some of these traces were in perfect accordance with the results of independent studies, other were completely unknown. As a whole, the detected features, composing a geometric pattern with roughly North-South orientation, closely match those of the other residential contexts at Tiwanaku. These new insights, captured from multitemporal ASTER and QuickBird data processing, suggested new questions on the ancient landscape and provided important information for planning future field surveys and archaeogeophyical investigations. Reference [1] Lasaponara R., Masini N. 2014. Beyond modern landscape features: New insights in thearchaeological area of Tiwanaku in Bolivia from satellite data. International Journal of Applied Earth Observation and Geoinformation, 26, 464-471, http://dx.doi.org/10.1016/j.jag.2013.09.00. [2] Tapete D., Cigna F., Masini N., Lasaponara R. 2013. Prospection and monitoring of the archaeological heritage of Nasca, Peru, with ENVISAT ASAR, Archaeological Prospection, 20, 133-147, doi: 10.1002/arp.1449. [3] Lasaponara R, N Masini, 2012 Satellite Remote Sensing, A New Tool for Archaeology (Series Remote Sensing and Digital Image) Springer book [4] Masini N., Lasaponara N., Orefici G. 2009, Addressing the challenge of detecting archaeological adobe structures in Southern Peru using QuickBird imagery, Journal of Cultural Heritage, 10S, pp. e3-e9 [doi:10.1016/j.culher.2009.10.005]. [5] Masini N, R Lasaponara, 2006, Satellite-based recognition of landscape archaeological features related to ancient human transformation Journal of Geophysics and Engineering 3 (3), 230. Lasaponara R., Masini N. 2013, Satellite Synthetic Aperture Radar in Archaeology and Cultural Landscape: An Overview. Archaeological Prospection, 20, 71-78, doi: 10.1002/arp.1452

Using endmembers in AVIRIS images to estimate changes in vegetative biomass

NASA Technical Reports Server (NTRS)

Smith, Milton O.; Adams, John B.; Ustin, Susan L.; Roberts, Dar A.

1992-01-01

Field techniques for estimating vegetative biomass are labor intensive, and rarely are used to monitor changes in biomass over time. Remote-sensing offers an attractive alternative to field measurements; however, because there is no simple correspondence between encoded radiance in multispectral images and biomass, it is not possible to measure vegetative biomass directly from AVIRIS images. Ways to estimate vegetative biomass by identifying community types and then applying biomass scalars derived from field measurements are investigated. Field measurements of community-scale vegetative biomass can be made, at least for local areas, but it is not always possible to identify vegetation communities unambiguously using remote measurements and conventional image-processing techniques. Furthermore, even when communities are well characterized in a single image, it typically is difficult to assess the extent and nature of changes in a time series of images, owing to uncertainties introduced by variations in illumination geometry, atmospheric attenuation, and instrumental responses. Our objective is to develop an improved method based on spectral mixture analysis to characterize and identify vegetative communities, that can be applied to multi-temporal AVIRIS and other types of images. In previous studies, multi-temporal data sets (AVIRIS and TM) of Owens Valley, CA were analyzed and vegetation communities were defined in terms of fractions of reference (laboratory and field) endmember spectra. An advantage of converting an image to fractions of reference endmembers is that, although fractions in a given pixel may vary from image to image in a time series, the endmembers themselves typically are constant, thus providing a consistent frame of reference.

Potentials and limitations of remote fire monitoring in protected areas.

PubMed

Dos Santos, João Flávio Costa; Romeiro, Joyce Machado Nunes; de Assis, José Batuíra; Torres, Fillipe Tamiozzo Pereira; Gleriani, José Marinaldo

2018-03-01

Protected areas (PAs) play an important role in maintaining the biodiversity and ecological processes of the site. One of the greatest challenges for the PA management in several biomes in the world is wildfires. The objective of this work was to evaluate the potentialities and limitations of the use of data obtained by orbital remote sensing in the monitoring fire occurrence in PAs. Fire Occurrence Records (FORs) were analyzed in Serra do Brigadeiro State Park, Minas Gerais, Brazil, from 2007 to 2015, using photo interpreted data from TM, ETM + and OLI sensors of the Landsat series and the Hot Spot Database (HSD) from the Brazilian Institute of Space Research - INPE. It was also observed the time of permanence of the scar left by fire on the landscape, through the multitemporal analysis of the behavior of NDVI (Normalized Difference Vegetation Index) and NBR (Normalized Burn Ratio) indexes, before and after the occurrence. The greatest limitation found for the orbital remote monitoring was the presence of clouds in the passage of the sensor in dates close to the occurrence of the fires. The burned area identified by photo interpretation was 54.9% less than the area contained in the FOR. Although the HSD reported fire occurrences in the buffer zone (up to 10km from the Park), no FORs were found at a distance greater than 1100m from the boundaries of the PA. As the main potential of remote sensing, the possibility of identifying burned areas throughout the park and surroundings is highlighted, with low costs and greater accuracy. Copyright © 2017 Elsevier B.V. All rights reserved.

Change Detection in High-Resolution Remote Sensing Images Using Levene-Test and Fuzzy Evaluation

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Liu, H. J.

2018-04-01

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

Assessing Change in Agricultural Productivity Caused by Drought and Conflict in Northern Syria using Landsat Imagery.

NASA Astrophysics Data System (ADS)

Girgin, T.; Ozdogan, M.

2015-12-01

Until recently, agricultural production in Syria has been an important source of revenue and food security for the country. At its peak, agriculture in Syria accounted for 25 percent of the country's GDP. In 2014, Syrian agriculture accounted for less than 5 percent of the GDP. This decline in agricultural productivity is the cause of a 3-year long drought that started in 2007, followed by a still-ongoing conflict that started in mid-2011. Using remote sensing tools, this paper focuses on the impact that the 2007-2010 drought had on agricultural production, as well as the impact that the ongoing conflict had on the agricultural production in northern Syria. Remote sensing is a powerful and great solution to study regions of the world that are hard-to-reach due to conflict and/or other limitations. It is particularly useful when studying a region that inaccessible due to an ongoing conflict, such as in northern Syria. Using multi-temporal Landsat 5 and Landsat 8 images from August 2006, 2010 and 2014 and utilizing the neural networks algorithm, we assessed for agricultural output change in northern Syria. We conclude that the ongoing Syrian conflict has had a bigger impact on the agricultural output in northern Syria than the 3-year long drought.

Multilayer Markov Random Field models for change detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Benedek, Csaba; Shadaydeh, Maha; Kato, Zoltan; Szirányi, Tamás; Zerubia, Josiane

2015-09-01

In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our purposes are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of Ground Truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quantitative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches.

Integration of remote sensing technique and hydrologic model for monitoring tidal flat dynamics of Juiduansha in Shanghai

NASA Astrophysics Data System (ADS)

Zheng, Zongsheng; Zhou, Yunxuan; Jiang, Xuezhong

2007-06-01

Ground survey is restricted by the difficulty of access to wide-range and dynamic salt marsh. Waterline method and hydrodynamic model were investigated to construct Digital Elevation Model (DEM) at Jiudunasha Shoals. A series of waterlines were extracted from multi-temporal remotely sensing images collected over the period of 2000-2004. The assignment of an elevation to each waterline at the satellite overpass was performed according to hydrodynamic model. The corrected waterlines labeled elevations were used to construct Triangulated Irregular Networks (TINs). Then an interpolation for each grid elevation was performed in accordance with the associated triangle. This initial DEM, produced using the corrected waterline set, was then used to refine the topography in the intertidal zone, and the model was re-run to produce improved water levels and a new DEM. This procedure was iterated by comparing modeled and actual waterlines until no further improvement occurred. Three DEMs of different intervals were built by this approach and were compared to evaluate the effect of Deep Water Channel Project (DWCP) at the north of Jiuduansha Island. Waterline method combined with numerical model, is an effective tool for constructing digital elevation model of mudflats. The result can provide invaluable information for coastal land use and engineer construction.

Advances in satellite remote sensing of environmental variables for epidemiological applications.

PubMed

Goetz, S J; Prince, S D; Small, J

2000-01-01

Earth-observing satellites have provided an unprecedented view of the land surface but have been exploited relatively little for the measurement of environmental variables of particular relevance to epidemiology. Recent advances in techniques to recover continuous fields of air temperature, humidity, and vapour pressure deficit from remotely sensed observations have significant potential for disease vector monitoring and related epidemiological applications. We report on the development of techniques to map environmental variables with relevance to the prediction of the relative abundance of disease vectors and intermediate hosts. Improvements to current methods of obtaining information on vegetation properties, canopy and surface temperature and soil moisture over large areas are also discussed. Algorithms used to measure these variables incorporate visible, near-infrared and thermal infrared radiation observations derived from time series of satellite-based sensors, focused here primarily but not exclusively on the Advanced Very High Resolution Radiometer (AVHRR) instruments. The variables compare favourably with surface measurements over a broad array of conditions at several study sites, and maps of retrieved variables captured patterns of spatial variability comparable to, and locally more accurate than, spatially interpolated meteorological observations. Application of multi-temporal maps of these variables are discussed in relation to current epidemiological research on the distribution and abundance of some common disease vectors.

Assessing the Tundra-taiga Boundary with Multi-Sensor Satellite Data

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Sun, G.; Kharuk, V. I.; Kovacs, K.

2004-01-01

Monitoring the dynamics of the circumpolar boreal forest (taiga) and Arctic tundra boundary is important for understanding the causes and consequences of changes observed in these areas. This ecotone, the world's largest, stretches for over 13,400 km and marks the transition between the northern limits of forests and the southern margin of the tundra. Because of the inaccessibility and large extent of this zone, remote sensing data can play an important role for mapping the characteristics and monitoring the dynamics. Basic understanding of the capabilities of existing space borne instruments for these purposes is required. In this study we examined the use of several remote sensing techniques for identifying the existing tundra- taiga ecotone. These include Landsat-7, MISR, MODIS and RADARSAT data. Historical cover maps, recent forest stand measurements and high-resolution IKONOS images were used for local ground truth. It was found that a tundra-taiga transitional area can be characterized using multi- spectral Landsat ETM+ summer images, multi-angle MISR red band reflectance images, RADARSAT images with larger incidence angle, or multi-temporal and multi-spectral MODIS data. Because of different resolutions and spectral regions covered, the transition zone maps derived from different data types were not identical, but the general patterns were consistent.

Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands

USGS Publications Warehouse

Poitras, Travis; Villarreal, Miguel; Waller, Eric K.; Nauman, Travis; Miller, Mark E.; Duniway, Michael C.

2018-01-01

Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.

Quantifying different types of urban growth and the change dynamic in Guangzhou using multi-temporal remote sensing data

NASA Astrophysics Data System (ADS)

Sun, Cheng; Wu, Zhi-feng; Lv, Zhi-qiang; Yao, Na; Wei, Jian-bing

2013-04-01

There is a widespread concern about urban sprawl. It has negative impacts on natural resources, economic health, and community character. Without a universal definition of urban sprawl, its quantification and modeling is difficult. Traditionally, urban sprawl was described using qualitative terms, and landscape patterns. Quantitative methods are required to help local, regional and state land use planners to better identify, understand and address it. In this study, an integrated approach of remote sensing and GIS was used to identify three urban growth types of infilling growth, outlying growth and edge-expansion growth at the city of Guangzhou, China. Spatial metrics were used to characterize long-term trends and patterns of urban growth. Result shows that the proposed method can identify and visualize different urban growth types. Infilling growth is the dominant expansion type. Edge-expansion is concentrated at suburban areas. Outlying growth mainly occurs relatively far from the urban core. The analysis shows that initially the urban area expands mainly as outlying growth, causing increased fragmentation and dispersion of urban areas. Next, growth filled in vacant non-urban area inwards, resulting into a more compact and aggregated urban pattern. The study shows an improved understanding of urban growth, and helps to provide an effective way for urban planning.

Where the Rubber Meets the Road; Varied Techniques for Measuring the Land-Atmosphere Exchange of Water and Energy in a California Watershed and the Driving Influences on this Exchange

NASA Astrophysics Data System (ADS)

Kochendorfer, J.; Viers, J.; Niswonger, R.; Paw U, K.; Haas, E.; Reck, R. A.

2005-12-01

In conjunction with the Cosumnes Research Group, we performed a field study along the Cosumnes River in California's Central Valley. The study included tower-based evapotranspiration estimates, continuous hydrologic measurements, and analysis of remote sensing data. We estimated the effects of phreatophytic evapotranspiration on groundwater from scales as small as an individual stand of trees to as large as the watershed and explored the climactic and hydrologic controls over riparian evapotranspiration. Tower-based evapotranspiration measurements included one eddy covariance tower within a cottonwood forest (Populus fremontii), and one surface temperature/micrometeorological evapotranspiration tower within a willow stand (Salix lasiolepis). The technique used on the surface temperature/micrometeorological evapotranspiration tower was developed and chosen in preference to eddy covariance for a site where a considerable quantity of the riparian ecosystem to atmosphere exchange is advective. Hydrologic techniques included measurements of groundwater depth and volumetric soil moisture. We also examined multitemporal, multiresolution remotely sensed imagery to correlate evapotranspiration rates for a restored cottonwood forest with derived vegetation indices. These indices were evaluated for applicability to other restored riparian habitats within the Cosumnes River Preserve and to help guide future restoration actions as a function of hydrologic connectivity and water demand.

Yield estimation of corn with multispectral data and the potential of using imaging spectrometers

NASA Astrophysics Data System (ADS)

Bach, Heike

1997-05-01

In the frame of the special yield estimation, a regular procedure conducted for the European Union to more accurately estimate agricultural yield, a project was conducted for the state minister for Rural Environment, Food and Forestry of Baden-Wuerttemberg, Germany) to test remote sensing data with advanced yield formation models for accuracy and timelines of yield estimation of corn. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on 4 LANDSAT-derived estimates and daily meteorological data the grain yield of corn stands was determined for 1995. The modeled yield was compared with results independently gathered within the special yield estimation for 23 test fields in the Upper Rhine Valley. The agrement between LANDSAT-based estimates and Special Yield Estimation shows a relative error of 2.3 percent. The comparison of the results for single fields shows, that six weeks before harvest the grain yield of single corn fields was estimated with a mean relative accuracy of 13 percent using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results or yield prediction with remote sensing.

S.I.I.A for monitoring crop evolution and anomaly detection in Andalusia by remote sensing

NASA Astrophysics Data System (ADS)

Rodriguez Perez, Antonio Jose; Louakfaoui, El Mostafa; Munoz Rastrero, Antonio; Rubio Perez, Luis Alberto; de Pablos Epalza, Carmen

2004-02-01

A new remote sensing application was developed and incorporated to the Agrarian Integrated Information System (S.I.I.A), project which is involved on integrating the regional farming databases from a geographical point of view, adding new values and uses to the original information. The project is supported by the Studies and Statistical Service, Regional Government Ministry of Agriculture and Fisheries (CAP). The process integrates NDVI values from daily NOAA-AVHRR and monthly IRS-WIFS images, and crop classes location maps. Agrarian local information and meteorological information is being included in the working process to produce a synergistic effect. An updated crop-growing evaluation state is obtained by 10-days periods, crop class, sensor type (including data fusion) and administrative geographical borders. Last ten years crop database (1992-2002) has been organized according to these variables. Crop class database can be accessed by an application which helps users on the crop statistical analysis. Multi-temporal and multi-geographical comparative analysis can be done by the user, not only for a year but also for a historical point of view. Moreover, real time crop anomalies can be detected and analyzed. Most of the output products will be available on Internet in the near future by a on-line application.

Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

USGS Publications Warehouse

Dieye, A.M.; Roy, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

2012-01-01

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

Using NASA UAVSAR Datasets to Link Soil Moisture to Crop Conditions

NASA Astrophysics Data System (ADS)

Davitt, A. W. D.; McDonald, K. C.; Azarderakhsh, M.; Winter, J.

2015-12-01

California and The Central Valley are experiencing one of that region's worst, persistent droughts, which represents the continuation of a prolonged drought that started in the early 2000's. Due to the continued drought, many agricultural regions in The Central Valley have been experiencing water shortages, negatively impacting agricultural production and the socio-economics of the region. Due to these impacts, there has been an increased incentive to find new ways to conserve water for use in irrigation. Recent advances in remote sensing techniques provide the ability for end users to better understand field conditions so they may make more informed decisions on irrigation timing and amounts. However, a good understanding of soil moisture and its role in crop health and yield is lacking to support informed water management decisions. Though known to be important, a robust understanding of the role of the spatio-temporal patterns in soil moisture linked to crop health is lacking. Remote sensing platforms such as NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) provide the capacity to obtain within-field measurements to estimate within-field and field-to-field variability in soil moisture. UAVSAR radar images acquired from 2010 to 2014 for Yolo County, California are being examined to determine the suitability of high resolution (field scale) multi-temporal L-band radar backscatter imagery for soil moisture assessment and crop conditions through the growing season. By using such data and linking to in-situ meteorology measurements, modeling (MIMICS), and other remote sensing derived datasets (Sentinel, Landsat, MODIS, and TOPS-SIMS), an integrated monitoring system can potentially support the assessment of agricultural field conditions. This allows growers to optimize the use of limited water supplies through informed water management practices, potentially improving crop conditions and yield in a water stressed region.

Assessment of Various Remote Sensing Technologies in Biomass and Nitrogen Content Estimation Using AN Agricultural Test Field

NASA Astrophysics Data System (ADS)

Näsi, R.; Viljanen, N.; Kaivosoja, J.; Hakala, T.; Pandžić, M.; Markelin, L.; Honkavaara, E.

2017-10-01

Multispectral and hyperspectral imaging is usually acquired by satellite and aircraft platforms. Recently, miniaturized hyperspectral 2D frame cameras have showed great potential to precise agriculture estimations and they are feasible to combine with lightweight platforms, such as drones. Drone platform is a flexible tool for remote sensing applications with environment and agriculture. The assessment and comparison of different platforms such as satellite, aircraft and drones with different sensors, such as hyperspectral and RGB cameras is an important task in order to understand the potential of the data provided by these equipment and to select the most appropriate according to the user applications and requirements. In this context, open and permanent test fields are very significant and helpful experimental environment, since they provide a comparative data for different platforms, sensors and users, allowing multi-temporal analyses as well. Objective of this work was to investigate the feasibility of an open permanent test field in context of precision agriculture. Satellite (Sentinel-2), aircraft and drones with hyperspectral and RGB cameras were assessed in this study to estimate biomass, using linear regression models and in-situ samples. Spectral data and 3D information were used and compared in different combinations to investigate the quality of the models. The biomass estimation accuracies using linear regression models were better than 90 % for the drone based datasets. The results showed that the use of spectral and 3D features together improved the estimation model. However, estimation of nitrogen content was less accurate with the evaluated remote sensing sensors. The open and permanent test field showed to be suitable to provide an accurate and reliable reference data for the commercial users and farmers.

Identification and characterization of agro-ecological infrastructures by remote sensing

NASA Astrophysics Data System (ADS)

Ducrot, D.; Duthoit, S.; d'Abzac, A.; Marais-Sicre, C.; Chéret, V.; Sausse, C.

2015-10-01

Agro-Ecological Infrastructures (AEIs) include many semi-natural habitats (hedgerows, grass strips, grasslands, thickets…) and play a key role in biodiversity preservation, water quality and erosion control. Indirect biodiversity indicators based on AEISs are used in many national and European public policies to analyze ecological processes. The identification of these landscape features is difficult and expensive and limits their use. Remote sensing has a great potential to solve this problem. In this study, we propose an operational tool for the identification and characterization of AEISs. The method is based on segmentation, contextual classification and fusion of temporal classifications. Experiments were carried out on various temporal and spatial resolution satellite data (20-m, 10-m, 5-m, 2.5-m, 50-cm), on three French regions southwest landscape (hilly, plain, wooded, cultivated), north (open-field) and Brittany (farmland closed by hedges). The results give a good idea of the potential of remote sensing image processing methods to map fine agro-ecological objects. At 20-m spatial resolution, only larger hedgerows and riparian forests are apparent. Classification results show that 10-m resolution is well suited for agricultural and AEIs applications, most hedges, forest edges, thickets can be detected. Results highlight the multi-temporal data importance. The future Sentinel satellites with a very high temporal resolution and a 10-m spatial resolution should be an answer to AEIs detection. 2.50-m resolution is more precise with more details. But treatments are more complicated. At 50-cm resolution, accuracy level of details is even higher; this amplifies the difficulties previously reported. The results obtained allow calculation of statistics and metrics describing landscape structures.

Digital spatial soil and land information for agriculture development

NASA Astrophysics Data System (ADS)

Sharma, R. K.; Laghathe, Pankaj; Meena, Ranglal; Barman, Alok Kumar; Das, Satyendra Nath

2006-12-01

Natural resource management calls for study of natural system prevailing in the country. In India floods and droughts visit regularly, causing extensive damages of natural wealth including agriculture that are crucial for sustenance of economic growth. The Indian Sub-continent drained by many major rivers and their tributaries where watershed, the hydrological unit forms a natural system that allows management and development of land resources following natural harmony. Acquisition of various kinds and levels of soil and land characteristics using both conventional and remote sensing techniques and subsequent development of digital spatial data base are essential to evolve strategy for planning watershed development programmes, their monitoring and impact evaluation. The multi-temporal capability of remote sensing sensors helps to update the existing data base which are of dynamic in nature. The paper outlines the concept of spatial data base development, generation using remote sensing techniques, designing of data structure, standardization and integration with watershed layers and various non spatial attribute data for various applications covering watershed development planning, alternate land use planning, soil and water conservation, diversified agriculture practices, generation of soil health card, soil and land reclamation, etc. The soil and land characteristics are vital to derive various interpretative groupings or master table that helps to generate the desired level of information of various clients using the GIS platform. The digital spatial data base on soils and watersheds generated by All India Soil and Land Use Survey will act as a sub-server of the main GIS based Web Server being hoisted by the planning commission for application of spatial data for planning purposes under G2G domain. It will facilitate e-governance for natural resource management using modern technology.

Landsat data availability from the EROS Data Center and status of future plans

USGS Publications Warehouse

Pohl, Russell A.; Metz, G.G.

1977-01-01

The Department of Interior's EROS Data Center, managed by the U.S. Geological Survey, was established in 1972, in Sioux Falls, South Dakota, to serve as a principal dissemination facility for Landsat and other remotely Sensed data. Through the middle of 1977, the Center has supplied approximately 1.7 million copies of images from the more than 5 million images of the Earth's surface archived at the Center. Landsat accounted for half of these images plus approximately 5,800 computer-compatible tapes of Landsat data were also supplied to users. New methods for processing data products to make them more useful are being developed, and new accession aids for determining data availability are being placed in operation. The Center also provides assistance and training to resource specialists and land managers in the use of Landsat and other remotely sensed data. A Data Analysis Laboratory is operated at the Center to provide both digital and analog multispectral/multitemporal image analysis capabilities in support of the training and assistance programs. In addition to conventionally processed data products, radiometrically enhanced Landsat imagery are now available from the Center in limited quantities. In mid-1978, the Center will convert to an all-digital processing system for Landsat data that will provide improved products for user analysis in production quantities. The Department of Interior and NASA are currently studying concepts that use communication satellites to relay Landsat data between U.S. ground stations, Goddard Space Flight Center and the EROS Data Center which would improve the timeliness of data availability. The Data Center also works closely with the remote sensing programs and Landsat data receiving and processing facilities being developed in foreign countries.

Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

NASA Astrophysics Data System (ADS)

Wang, Hui-Lin; An, Ru; You, Jia-jun; Wang, Ying; Chen, Yuehong; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballard, Jonathan Arthur

2017-10-01

Soil moisture plays an important role in the water cycle within the surface ecosystem, and it is the basic condition for the growth of plants. Currently, the spatial resolutions of most soil moisture data from remote sensing range from ten to several tens of km, while those observed in-situ and simulated for watershed hydrology, ecology, agriculture, weather, and drought research are generally <1 km. Therefore, the existing coarse-resolution remotely sensed soil moisture data need to be downscaled. This paper proposes a universal and multitemporal soil moisture downscaling method suitable for large areas. The datasets comprise land surface, brightness temperature, precipitation, and soil and topographic parameters from high-resolution data and active/passive microwave remotely sensed essential climate variable soil moisture (ECV_SM) data with a spatial resolution of 25 km. Using this method, a total of 288 soil moisture maps of 1-km resolution from the first 10-day period of January 2003 to the last 10-day period of December 2010 were derived. The in-situ observations were used to validate the downscaled ECV_SM. In general, the downscaled soil moisture values for different land cover and land use types are consistent with the in-situ observations. Mean square root error is reduced from 0.070 to 0.061 using 1970 in-situ time series observation data from 28 sites distributed over different land uses and land cover types. The performance was also assessed using the GDOWN metric, a measure of the overall performance of the downscaling methods based on the same dataset. It was positive in 71.429% of cases, indicating that the suggested method in the paper generally improves the representation of soil moisture at 1-km resolution.

Identification of rice field using Multi-Temporal NDVI and PCA method on Landsat 8 (Case Study: Demak, Central Java)

NASA Astrophysics Data System (ADS)

Sukmono, Abdi; Ardiansyah

2017-01-01

Paddy is one of the most important agricultural crop in Indonesia. Indonesia’s consumption of rice per capita in 2013 amounted to 78,82 kg/capita/year. In 2017, the Indonesian government has the mission of realizing Indonesia became self-sufficient in food. Therefore, the Indonesian government should be able to seek the stability of the fulfillment of basic needs for food, such as rice field mapping. The accurate mapping for rice field can use a quick and easy method such as Remote Sensing. In this study, multi-temporal Landsat 8 are used for identification of rice field based on Rice Planting Time. It was combined with other method for extract information from the imagery. The methods which was used Normalized Difference Vegetation Index (NDVI), Principal Component Analysis (PCA) and band combination. Image classification is processed by using nine classes, those are water, settlements, mangrove, gardens, fields, rice fields 1st, rice fields 2nd, rice fields 3rd and rice fields 4th. The results showed the rice fields area obtained from the PCA method was 50,009 ha, combination bands was 51,016 ha and NDVI method was 45,893 ha. The accuracy level was obtained PCA method (84.848%), band combination (81.818%), and NDVI method (75.758%).

Development of user applications for earth resources survey data in urban and regional planning in the Puget Sound area

NASA Technical Reports Server (NTRS)

Westerlund, F. V.

1975-01-01

User applications of remote sensing in Washington State are described. The first project created a multi-temporal land use/land cover data base for the environs of the Seattle-Tacoma International Airport, to serve planning and management operations of the Port of Seattle. The second is an on-going effort to develop a capability within the Puget Sound Governmental Conference, a council of governments (COG), to inventory and monitor land use within its four county jurisdiction. Developmental work has focused on refinement of land use/cover classification systems applicable at this regional scale and various levels of detail in relation to program requirements of the agency. Related research, refinement of manual methods, user training and approaches to technology transfer are discussed.

Forest Structure Characterization Using JPL's UAVSAR Multi-Baseline Polarimetric SAR Interferometry and Tomography

NASA Technical Reports Server (NTRS)

Neumann, Maxim; Hensley, Scott; Lavalle, Marco; Ahmed, Razi

2013-01-01

This paper concerns forest remote sensing using JPL's multi-baseline polarimetric interferometric UAVSAR data. It presents exemplary results and analyzes the possibilities and limitations of using SAR Tomography and Polarimetric SAR Interferometry (PolInSAR) techniques for the estimation of forest structure. Performance and error indicators for the applicability and reliability of the used multi-baseline (MB) multi-temporal (MT) PolInSAR random volume over ground (RVoG) model are discussed. Experimental results are presented based on JPL's L-band repeat-pass polarimetric interferometric UAVSAR data over temperate and tropical forest biomes in the Harvard Forest, Massachusetts, and in the La Amistad Park, Panama and Costa Rica. The results are partially compared with ground field measurements and with air-borne LVIS lidar data.

Forest Structure Characterization Using Jpl's UAVSAR Multi-Baseline Polarimetric SAR Interferometry and Tomography

NASA Technical Reports Server (NTRS)

Neumann, Maxim; Hensley, Scott; Lavalle, Marco; Ahmed, Razi

2013-01-01

This paper concerns forest remote sensing using JPL's multi-baseline polarimetric interferometric UAVSAR data. It presents exemplary results and analyzes the possibilities and limitations of using SAR Tomography and Polarimetric SAR Interferometry (PolInSAR) techniques for the estimation of forest structure. Performance and error indicators for the applicability and reliability of the used multi-baseline (MB) multi-temporal (MT) PolInSAR random volume over ground (RVoG) model are discussed. Experimental results are presented based on JPL's L-band repeat-pass polarimetric interferometric UAVSAR data over temperate and tropical forest biomes in the Harvard Forest, Massachusetts, and in the La Amistad Park, Panama and Costa Rica. The results are partially compared with ground field measurements and with air-borne LVIS lidar data.

Applying remote sensing and GIS for chimpanzee habitat change detection, behaviour and conservation

NASA Astrophysics Data System (ADS)

Pintea, Lilian

Chimpanzees (Pan troglodytes), our closest living relatives, are declining alarmingly in abundance and distribution all across Africa. Clearing of forests and woodlands has one of the most rapid and devastating impacts, leaving chimpanzees in isolated, small populations that face edge effects and elevated risk of extinction. Satellite imagery could be a powerful tool to map chimpanzee habitats and threats at the landscape scale even in the most remote, difficult to access areas. However, few applications exist to demonstrate how remote sensing methods can be used in Africa for chimpanzee research and conservation in practice. In chapter one, I investigate the use of Landsat MSS and ETM+ satellite imagery to monitor dry tropical forests and miombo woodlands change between 1972-1999 inside and outside Gombe National Park, Tanzania. I show that canopy cover increased in the northern and middle parts of the park but with severe canopy loss outside protected area. Deforestation has had unequal effects on the three chimpanzee communities inside the park. The Kasekela chimpanzees have been least affected by canopy loss outside the park. In contrast, the Mitumba and Kalande communities have likely lost key range areas. In chapter two, I use 25 years of data on Gombe chimpanzees to investigate to what extent vegetation variables detected from multi-temporal satellite images can be applied to understand changes in chimpanzee feeding and party size. NDVI positively correlated with the time chimpanzees spent feeding but had no affect on the average number of adult males in the party. Instead the number of males in the party increased with proximity to hostile neighboring communities. In chapter three, I use Landsat and SPOT satellite imagery as the basis for Threat Reduction Assessment to evaluate conservation outcomes of a ten year community based conservation project in Tanzania. The findings suggest that the remote sensing methods applied in this study could provide new exciting prospects for monitoring chimpanzee habitats, socioecological research and a baseline to measure our conservation success.

Weak Learner Method for Estimating River Discharges using Remotely Sensed Data: Central Congo River as a Testbed

NASA Astrophysics Data System (ADS)

Kim, D.; Lee, H.; Yu, H.; Beighley, E.; Durand, M. T.; Alsdorf, D. E.; Hwang, E.

2017-12-01

River discharge is a prerequisite for an understanding of flood hazard and water resource management, yet we have poor knowledge of it, especially over remote basins. Previous studies have successfully used a classic hydraulic geometry, at-many-stations hydraulic geometry (AMHG), and Manning's equation to estimate the river discharge. Theoretical bases of these empirical methods were introduced by Leopold and Maddock (1953) and Manning (1889), and those have been long used in the field of hydrology, water resources, and geomorphology. However, the methods to estimate the river discharge from remotely sensed data essentially require bathymetric information of the river or are not applicable to braided rivers. Furthermore, the methods used in the previous studies adopted assumptions of river conditions to be steady and uniform. Consequently, those methods have limitations in estimating the river discharge in complex and unsteady flow in nature. In this study, we developed a novel approach to estimating river discharges by applying the weak learner method (here termed WLQ), which is one of the ensemble methods using multiple classifiers, to the remotely sensed measurements of water levels from Envisat altimetry, effective river widths from PALSAR images, and multi-temporal surface water slopes over a part of the mainstem Congo. Compared with the methods used in the previous studies, the root mean square error (RMSE) decreased from 5,089 m3s-1 to 3,701 m3s-1, and the relative RMSE (RRMSE) improved from 12% to 8%. It is expected that our method can provide improved estimates of river discharges in complex and unsteady flow conditions based on the data-driven prediction model by machine learning (i.e. WLQ), even when the bathymetric data is not available or in case of the braided rivers. Moreover, it is also expected that the WLQ can be applied to the measurements of river levels, slopes and widths from the future Surface Water Ocean Topography (SWOT) mission to be launched in 2021.

Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring In Northeast China

NASA Astrophysics Data System (ADS)

Bendig, J.; Willkomm, M.; Tilly, N.; Gnyp, M. L.; Bennertz, S.; Qiang, C.; Miao, Y.; Lenz-Wiedemann, V. I. S.; Bareth, G.

2013-08-01

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed geodata in the last years (Hardin & Jensen 2011). Various applications in numerous fields of research like archaeology (Hendrickx et al., 2011), forestry or geomorphology evolved (Martinsanz, 2012). This contribution deals with the generation of multi-temporal crop surface models (CSMs) with very high resolution by means of low-cost equipment. The concept of the generation of multi-temporal CSMs using Terrestrial Laserscanning (TLS) has already been introduced by Hoffmeister et al. (2010). For this study, data acquisition was performed with a low-cost and low-weight Mini-UAV (< 5 kg). UAVs in general and especially smaller ones, like the system presented here, close a gap in small scale remote sensing (Berni et al., 2009; Watts et al., 2012). In precision agriculture frequent remote sensing on such scales during the vegetation period provides important spatial information on the crop status. Crop growth variability can be detected by comparison of the CSMs in different phenological stages. Here, the focus is on the detection of this variability and its dependency on cultivar and plant treatment. The method has been tested for data acquired on a barley experiment field in Germany. In this contribution, it is applied to a different crop in a different environment. The study area is an experiment field for rice in Northeast China (Sanjiang Plain). Three replications of the cultivars Kongyu131 and Longjing21 were planted in plots that were treated with different amounts of N-fertilizer. In July 2012 three UAV-campaigns were carried out. Establishment of ground control points (GCPs) allowed for ground truth. Additionally, further destructive and non-destructive field data were collected. The UAV-system is an MK-Okto by Hisystems (http://www.mikrokopter.de) which was equipped with the high resolution Panasonic Lumix GF3 12 megapixel consumer camera. The self-built and self-maintained system has a payload of up to 1 kg and an average flight time of 15 minutes. The maximum speed is around 30 km/h and the system can be operated up to a wind speed of less than 19 km/h (Beaufort scale number 3 for wind speed). Using a suitable flight plan stereo images can be captured. For this study, a flying height of 50 m and a 44% side and 90% forward overlap was chosen. The images are processed into CSMs under the use of the Structure from Motion (SfM)-based software Agisoft Photoscan 0.9.0. The resulting models have a resolution of 0.02 m and an average number of about 12 million points. Further data processing in Esri ArcGIS allows for quantitative comparison of the plant heights. The multi-temporal datasets are analysed on a plot size basis. The results can be compared to and combined with the additional field data. Detecting plant height with non-invasive measurement techniques enables analysis of its correlation to biomass and other crop parameters (Hansen & Schjoerring, 2003; Thenkabail et al., 2000) measured in the field. The method presented here can therefore be a valuable addition for the recognition of such correlations.

Unmanned aerial systems for forest reclamation monitoring: throwing balloons in the air

NASA Astrophysics Data System (ADS)

Andrade, Rita; Vaz, Eric; Panagopoulos, Thomas; Guerrero, Carlos

2014-05-01

Wildfires are a recurrent phenomenon in Mediterranean landscapes, deteriorating environment and ecosystems, calling out for adequate land management. Monitoring burned areas enhances our abilities to reclaim them. Remote sensing has become an increasingly important tool for environmental assessment and land management. It is fast, non-intrusive, and provides continuous spatial coverage. This paper reviews remote sensing methods, based on space-borne, airborne or ground-based multispectral imagery, for monitoring the biophysical properties of forest areas for site specific management. The usage of satellite imagery for land use management has been frequent in the last decades, it is of great use to determine plants health and crop conditions, allowing a synergy between the complexity of environment, anthropogenic landscapes and multi-temporal understanding of spatial dynamics. Aerial photography increments on spatial resolution, nevertheless it is heavily dependent on airborne availability as well as cost. Both these methods are required for wide areas management and policy planning. Comprising an active and high resolution imagery source, that can be brought at a specific instance, reducing cost while maintaining locational flexibility is of utmost importance for local management. In this sense, unmanned aerial vehicles provide maximum flexibility with image collection, they can incorporate thermal and multispectral sensors, however payload and engine operation time limit flight time. Balloon remote sensing is becoming increasingly sought after for site specific management, catering rapid digital analysis, permitting greater control of the spatial resolution as well as of datasets collection in a given time. Different wavelength sensors may be used to map spectral variations in plant growth, monitor water and nutrient stress, assess yield and plant vitality during different stages of development. Proximity could be an asset when monitoring forest plants vitality. Early predictions of re-vegetation success facilitate precise and timely diagnosis of stress, thus remedial actions can be taken at localized detail.

An economic value of remote-sensing information—Application to agricultural production and maintaining groundwater quality

USGS Publications Warehouse

Forney, William M.; Raunikar, Ronald P.; Bernknopf, Richard L.; Mishra, Shruti K.

2012-01-01

Does remote-sensing information provide economic benefits to society, and can a value be assigned to those benefits? Can resource management and policy decisions be better informed by coupling past and present Earth observations with groundwater nitrate measurements? Using an integrated assessment approach, the U.S. Geological Survey (USGS) applied an established conceptual framework to answer these questions, as well as to estimate the value of information (VOI) for remote-sensing imagery. The approach uses moderate-resolution land-imagery (MRLI) data from the Landsat and Advanced Wide Field Sensor satellites that has been classified by the National Agricultural Statistics Service into the Cropland Data Layer (CDL). Within the constraint of the U.S. Environmental Protection Agency's public health threshold for potable groundwater resources, the USGS modeled the relation between a population of the CDL's land uses and dynamic nitrate (NO3-) contamination of aquifers in a case study region in northeastern Iowa. Employing various multiscaled, multitemporal geospatial datasets with MRLI to maximize the value of agricultural production, the approach develops and uses multiple environmental science models to address dynamic nitrogen loading and transport at specified distances from specific sites (wells) and at landscape scales (for example, across 35 counties and two aquifers). In addition to the ecosystem service of potable groundwater, this effort focuses on the use of MRLI for the management of the major land uses in the study region-the production of corn and soybeans, which can impact groundwater quality. Derived methods and results include (1) economic and dynamic nitrate-pollution models, (2) probabilities of the survival of groundwater, and (3) a VOI for remote sensing. For the northeastern Iowa study region, the marginal benefit of the MRLI VOI (in 2010 dollars) is $858 million ±$197 million annualized, which corresponds to a net present value of $38.1 billion ±$8.8 billion for that flow of benefits in perpetuity. Given that these economic estimates are derived from one case study in a part of only one State, the estimates provide a lower estimate related to the potential value of the Landsat Data Continuity Mission.

Great Basin vegetation response to groundwater fluctuation, climate variability, and previous land cultivation: The application of multitemporal measurements from remote sensing data to regional vegetation dynamics

NASA Astrophysics Data System (ADS)

Elmore, Andrew James

The conversion of large natural basins to managed watersheds for the purpose of providing water to urban centers has had a negative impact on semiarid ecosystems, worldwide. We view semiarid plant communities as being adapted to short, regular periods of drought. However, human induced changes in the water balance often remove these systems from the range of natural variability that has been historically established. This thesis explores vegetation changes over a 13-yr period for Owens Valley, in eastern California. Using remotely sensed measurements of vegetation cover, an extensive vegetation survey, field data and observations, precipitation records, and data on water table depth, I identify the key modes of response of xeric, phreatophytic, and exotic Great Basin plant communities. Three specific advancements were reached as a result of this work. (1) A change classification technique was developed that was used to separate regions of land-cover that were dependent on precipitation from regions dependent on groundwater. This technique utilized Spectral Mixture Analysis of annually acquired Landsat Thematic Mapper remote sensing data, to retrieve regional estimates of percent vegetation cover. (2) A threshold response related to depth-to-water dependence was identified for phreatophytic Alkali Meadow communities. Plant communities that were subject to groundwater depths below this threshold exhibited greater invasion by precipitation sensitive plants. (3) The floristic differences between previously cultivated and uncultivated land were found to account for an increased sensitivity of plant communities to precipitation variability. Through (2) and (3), two human influences (groundwater decline and previous land cultivation) were shown to alter land cover such that the land became more sensitive to precipitation change. Climate change predictions include a component of increased climate variability for the western United States; therefore, these results place serious doubt on the sustainability of human activities in this region. The results from this work broadly cover topics from remote sensing techniques to the ecology of Great Basin plant communities and are applicable wherever large regions of land are being managed in an era of changing environmental conditions.

Unsupervised Change Detection for Geological and Ecological Monitoring via Remote Sensing: Application on a Volcanic Area

NASA Astrophysics Data System (ADS)

Falco, N.; Pedersen, G. B. M.; Vilmunandardóttir, O. K.; Belart, J. M. M. C.; Sigurmundsson, F. S.; Benediktsson, J. A.

2016-12-01

The project "Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS)" aims at providing fast and reliable mapping and monitoring techniques on a big spatial scale with a high temporal resolution of the Icelandic landscape. Such mapping and monitoring will be crucial to both mitigate and understand the scale of processes and their often complex interlinked feedback mechanisms.In the EMMIRS project, the Hekla volcano area is one of the main sites under study, where the volcanic eruptions, extreme weather and human activities had an extensive impact on the landscape degradation. The development of innovative remote sensing approaches to compute earth observation variables as automatically as possible is one of the main tasks of the EMMIRS project. Furthermore, a temporal remote sensing archive is created and composed by images acquired by different sensors (Landsat, RapidEye, ASTER and SPOT5). Moreover, historical aerial stereo photos allowed decadal reconstruction of the landscape by reconstruction of digital elevation models. Here, we propose a novel architecture for automatic unsupervised change detection analysis able to ingest multi-source data in order to detect landscape changes in the Hekla area. The change detection analysis is based on multi-scale analysis, which allows the identification of changes at different level of abstraction, from pixel-level to region-level. For this purpose, operators defined in mathematical morphology framework are implemented to model the contextual information, represented by the neighbour system of a pixel, allowing the identification of changes related to both geometrical and spectral domains. Automatic radiometric normalization strategy is also implemented as pre-processing step, aiming at minimizing the effect of different acquisition conditions. The proposed architecture is tested on multi-temporal data sets acquired over different time periods coinciding with the last three eruptions (1980-1981, 1991, 2000) occurred on Hekla volcano. The results reveal emplacement of new lava flows and the initial vegetation succession, providing insightful information on the evolving of vegetation in such environment. Shadow and snow patch changes are resolved in post-processing by exploiting the available spectral information.

Serving Satellite Remote Sensing Data to User Community through the OGC Interoperability Protocols

NASA Astrophysics Data System (ADS)

di, L.; Yang, W.; Bai, Y.

2005-12-01

Remote sensing is one of the major methods for collecting geospatial data. Hugh amount of remote sensing data has been collected by space agencies and private companies around the world. For example, NASA's Earth Observing System (EOS) is generating more than 3 Tb of remote sensing data per day. The data collected by EOS are processed, distributed, archived, and managed by the EOS Data and Information System (EOSDIS). Currently, EOSDIS is managing several petabytes of data. All of those data are not only valuable for global change research, but also useful for local and regional application and decision makings. How to make the data easily accessible to and usable by the user community is one of key issues for realizing the full potential of these valuable datasets. In the past several years, the Open Geospatial Consortium (OGC) has developed several interoperability protocols aiming at making geospatial data easily accessible to and usable by the user community through Internet. The protocols particularly relevant to the discovery, access, and integration of multi-source satellite remote sensing data are the Catalog Service for Web (CS/W) and Web Coverage Services (WCS) Specifications. The OGC CS/W specifies the interfaces, HTTP protocol bindings, and a framework for defining application profiles required to publish and access digital catalogues of metadata for geographic data, services, and related resource information. The OGC WCS specification defines the interfaces between web-based clients and servers for accessing on-line multi-dimensional, multi-temporal geospatial coverage in an interoperable way. Based on definitions by OGC and ISO 19123, coverage data include all remote sensing images as well as gridded model outputs. The Laboratory for Advanced Information Technology and Standards (LAITS), George Mason University, has been working on developing and implementing OGC specifications for better serving NASA Earth science data to the user community for many years. We have developed the NWGISS software package that implements multiple OGC specifications, including OGC WMS, WCS, CS/W, and WFS. As a part of NASA REASON GeoBrain project, the NWGISS WCS and CS/W servers have been extended to provide operational access to NASA EOS data at data pools through OGC protocols and to make both services chainable in the web-service chaining. The extensions in the WCS server include the implementation of WCS 1.0.0 and WCS 1.0.2, and the development of WSDL description of the WCS services. In order to find the on-line EOS data resources, the CS/W server is extended at the backend to search metadata in NASA ECHO. This presentation reports those extensions and discuss lessons-learned on the implementation. It also discusses the advantage, disadvantages, and future improvement of OGC specifications, particularly the WCS.

Himalayan glaciers: understanding contrasting patterns of glacier behavior using multi-temporal satellite imagery

NASA Astrophysics Data System (ADS)

Racoviteanu, A.

2014-12-01

High rates of glacier retreat for the last decades are often reported, and believed to be induced by 20th century climate changes. However, regional glacier fluctuations are complex, and depend on a combination of climate and local topography. Furthermore, in ares such as the Hindu-Kush Himalaya, there are concerns about warming, decreasing monsoon precipitation and their impact on local glacier regimes. Currently, the challenge is in understanding the magnitude of feedbacks between large-scale climate forcing and small-scale glacier behavior. Spatio-temporal patterns of glacier distribution are still llimited in some areas of the high Hindu-Kush Himalaya, but multi-temporal satellite imagery has helped fill spatial and temporal gaps in regional glacier parameters in the last decade. Here I present a synopsis of the behavior of glaciers across the Himalaya, following a west to east gradient. In particular, I focus on spatial patterns of glacier parameters in the eastern Himalaya, which I investigate at multi-spatial scales using remote sensing data from declassified Corona, ASTER, Landsat ETM+, Quickbird and Worldview2 sensors. I also present the use of high-resolution imagery, including texture and thermal analysis for mapping glacier features at small scale, which are particularly useful in understanding surface trends of debris-covered glaciers, which are prevalent in the Himalaya. I compare and contrast spatial patterns of glacier area and élévation changes in the monsoon-influenced eastern Himalaya (the Everest region in the Nepal Himalaya and Sikkim in the Indian Himalaya) with other observations from the dry western Indian Himalaya (Ladakh and Lahul-Spiti), both field measurements and remote sensing-based. In the eastern Himalaya, results point to glacier area change of -0.24 % ± 0.08% per year from the 1960's to the 2006's, with a higher rate of retreat in the last decade (-0.43% /yr). Debris-covered glacier tongues show thinning trends of -30.8 m± 39 m on average over the last four decades, similar to other studies in the same climatic area. However, at small scales, the behavior of glaciers is highly heterogenous, with contrasting patterns of thickening glacier termini versus retreating nad thinning glacier tongues.

Deforestation and Rice: Using Methods in Modeling and Remote Sensing to Project Patterns of Forest Change in Eastern Madagascar

NASA Astrophysics Data System (ADS)

Armstrong, A. H.; Fatoyinbo, T. E.; Fischer, R.; Huth, A.; Shugart, H. H.

2013-12-01

In the species rich tropics, forest conservation is often eclipsed by anthropogenic disturbance, resulting in a heightened need for an accurate assessment of biomass and the gaining of predictive capability before these ecosystems disappear. The combination of multi-temporal remote sensing data, field data and forest growth modeling to quantify carbon stocks and flux is therefore of great importance. In this study, we utilize these methods to (1) improve forest biomass and carbon flux estimates for the study region in Eastern Madagascar, and (2) initialize an individual-based growth model that incorporates the anthropogenic factors causing deforestation to project ecosystem response to future environmental change. Recent studies have shown that there is a direct correlation between the international rice market and rates of deforestation in tropical countries such as Madagascar (see Minten et al., 2006). Further, although law protects the remaining forest areas, dictatorships and recent political unrest have lead to poor or non-existent enforcement of precious wood and forest protection over the past 35 years. Our approach combined multi-temporal remote sensing analysis and ecological modeling using a theoretical and mathematical approach to assess biomass change and to understand how tree growth and life history (growth response patterns) relate to past and present economic variability in Madagascar forests of the eastern Toamasina region. We measured rates of change of deforestation with respect to politics and the price of rice by classifying and comparing biomass using 30m Landsat during 5 political regime time periods (1985-1992, 1993-1996, 1997-2001, 2002-2008, 2009 to present). Forest biomass estimations were calibrated using forest inventory data collected over 3 growing seasons over the study region (130 small circular plots in primary forest). This information was then built into the previously parameterized (Armstrong et al., in prep and Fischer et al in review) Madagascar FORMIX3 Model (see Huth and Ditzer, 2000) by incorporating rice economy, selective logging and political stability modules into the model to control certain species groups (i.e. selective harvest) and fire frequency (encroachment). The improved FORMIX3 model was then used to investigate and project forest growth response to a variety of impact scenarios ranging from an increase in overall deforestation to a decrease in deforestation and increase in protection enforcement. Our findings showed a significant positive correlation between increasing deforestation rates and higher local rice prices due to political regime and international market factors. This research resulted in the first quantitative analysis of the relationship between the international rice market and local land-use in terms of slash and burn agriculture, illegal logging of precious hardwood in Madagascar.

Co-Registration Between Multisource Remote-Sensing Images

NASA Astrophysics Data System (ADS)

Wu, J.; Chang, C.; Tsai, H.-Y.; Liu, M.-C.

2012-07-01

Image registration is essential for geospatial information systems analysis, which usually involves integrating multitemporal and multispectral datasets from remote optical and radar sensors. An algorithm that deals with feature extraction, keypoint matching, outlier detection and image warping is experimented in this study. The methods currently available in the literature rely on techniques, such as the scale-invariant feature transform, between-edge cost minimization, normalized cross correlation, leasts-quares image matching, random sample consensus, iterated data snooping and thin-plate splines. Their basics are highlighted and encoded into a computer program. The test images are excerpts from digital files created by the multispectral SPOT-5 and Formosat-2 sensors, and by the panchromatic IKONOS and QuickBird sensors. Suburban areas, housing rooftops, the countryside and hilly plantations are studied. The co-registered images are displayed with block subimages in a criss-cross pattern. Besides the imagery, the registration accuracy is expressed by the root mean square error. Toward the end, this paper also includes a few opinions on issues that are believed to hinder a correct correspondence between diverse images.

Scalable Earth-observation Analytics for Geoscientists: Spacetime Extensions to the Array Database SciDB

NASA Astrophysics Data System (ADS)

Appel, Marius; Lahn, Florian; Pebesma, Edzer; Buytaert, Wouter; Moulds, Simon

2016-04-01

Today's amount of freely available data requires scientists to spend large parts of their work on data management. This is especially true in environmental sciences when working with large remote sensing datasets, such as obtained from earth-observation satellites like the Sentinel fleet. Many frameworks like SpatialHadoop or Apache Spark address the scalability but target programmers rather than data analysts, and are not dedicated to imagery or array data. In this work, we use the open-source data management and analytics system SciDB to bring large earth-observation datasets closer to analysts. Its underlying data representation as multidimensional arrays fits naturally to earth-observation datasets, distributes storage and computational load over multiple instances by multidimensional chunking, and also enables efficient time-series based analyses, which is usually difficult using file- or tile-based approaches. Existing interfaces to R and Python furthermore allow for scalable analytics with relatively little learning effort. However, interfacing SciDB and file-based earth-observation datasets that come as tiled temporal snapshots requires a lot of manual bookkeeping during ingestion, and SciDB natively only supports loading data from CSV-like and custom binary formatted files, which currently limits its practical use in earth-observation analytics. To make it easier to work with large multi-temporal datasets in SciDB, we developed software tools that enrich SciDB with earth observation metadata and allow working with commonly used file formats: (i) the SciDB extension library scidb4geo simplifies working with spatiotemporal arrays by adding relevant metadata to the database and (ii) the Geospatial Data Abstraction Library (GDAL) driver implementation scidb4gdal allows to ingest and export remote sensing imagery from and to a large number of file formats. Using added metadata on temporal resolution and coverage, the GDAL driver supports time-based ingestion of imagery to existing multi-temporal SciDB arrays. While our SciDB plugin works directly in the database, the GDAL driver has been specifically developed using a minimum amount of external dependencies (i.e. CURL). Source code for both tools is available from github [1]. We present these tools in a case-study that demonstrates the ingestion of multi-temporal tiled earth-observation data to SciDB, followed by a time-series analysis using R and SciDBR. Through the exclusive use of open-source software, our approach supports reproducibility in scalable large-scale earth-observation analytics. In the future, these tools can be used in an automated way to let scientists only work on ready-to-use SciDB arrays to significantly reduce the data management workload for domain scientists. [1] https://github.com/mappl/scidb4geo} and \\url{https://github.com/mappl/scidb4gdal

Predicting Intra-Urban Population Densities in Africa using SAR and Optical Remote Sensing Data

NASA Astrophysics Data System (ADS)

Linard, C.; Steele, J.; Forget, Y.; Lopez, J.; Shimoni, M.

2017-12-01

The population of Africa is predicted to double over the next 40 years, driving profound social, environmental and epidemiological changes within rapidly growing cities. Estimations of within-city variations in population density must be improved in order to take urban heterogeneities into account and better help urban research and decision making, especially for vulnerability and health assessments. Satellite remote sensing offers an effective solution for mapping settlements and monitoring urbanization at different spatial and temporal scales. In Africa, the urban landscape is covered by slums and small houses, where the heterogeneity is high and where the man-made materials are natural. Innovative methods that combine optical and SAR data are therefore necessary for improving settlement mapping and population density predictions. An automatic method was developed to estimate built-up densities using recent and archived optical and SAR data and a multi-temporal database of built-up densities was produced for 48 African cities. Geo-statistical methods were then used to study the relationships between census-derived population densities and satellite-derived built-up attributes. Best predictors were combined in a Random Forest framework in order to predict intra-urban variations in population density in any large African city. Models show significant improvement of our spatial understanding of urbanization and urban population distribution in Africa in comparison to the state of the art.

Study of the urban evolution of Brasilia with the use of LANDSAT data

NASA Technical Reports Server (NTRS)

Deoliveira, M. D. N. (Principal Investigator); Foresti, C.; Niero, M.; Parreiras, E. M. D. F.

1984-01-01

The urban growth of Brasilia within the last ten years is analyzed with special emphasis on the utilization of remote sensing orbital data and automatic image processing. The urban spatial structure and the monitoring of its temporal changes were focused in a whole and dynamic way by the utilization of MSS-LANDSAT images for June 1973, 1978 and 1983. In order to aid data interpretation, a registration algorithm implemented at the Interactive Multispectral Image Analysis System (IMAGE-100) was utilized aiming at the overlap of multitemporal images. The utilization of suitable digital filters, combined with the images overlap, allowed a rapid identification of areas of possible urban growth and oriented the field work. The results obtained permitted an evaluation of the urban growth of Brasilia, taking as reference the proposed stated for the construction of the city.

Theoretical Foundations of Remote Sensing for Glacier Assessment and Mapping

NASA Technical Reports Server (NTRS)

Bishop, Michael P.; Bush, Andrew B. G.; Furfaro, Roberto; Gillespie, Alan R.; Hall, Dorothy K.; Haritashya, Umesh K.; Shroder, John F., Jr.

2014-01-01

The international scientific community is actively engaged in assessing ice sheet and alpine glacier fluctuations at a variety of scales. The availability of stereoscopic, multitemporal, and multispectral satellite imagery from the optical wavelength regions of the electromagnetic spectrum has greatly increased our ability to assess glaciological conditions and map the cryosphere. There are, however, important issues and limitations associated with accurate satellite information extraction and mapping, as well as new opportunities for assessment and mapping that are all rooted in understanding the fundamentals of the radiation transfer cascade. We address the primary radiation transfer components, relate them to glacier dynamics and mapping, and summarize the analytical approaches that permit transformation of spectral variation into thematic and quantitative parameters. We also discuss the integration of satellite-derived information into numerical modeling approaches to facilitate understandings of glacier dynamics and causal mechanisms.

Forest fuel treatment detection using multi-temporal airborne Lidar data and high resolution aerial imagery ---- A case study at Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Su, Y.; Guo, Q.; Collins, B.; Fry, D.; Kelly, M.

2014-12-01

Forest fuel treatments (FFT) are often employed in Sierra Nevada forest (located in California, US) to enhance forest health, regulate stand density, and reduce wildfire risk. However, there have been concerns that FFTs may have negative impacts on certain protected wildlife species. Due to the constraints and protection of resources (e.g., perennial streams, cultural resources, wildlife habitat, etc.), the actual FFT extents are usually different from planned extents. Identifying the actual extent of treated areas is of primary importance to understand the environmental influence of FFTs. Light detection and ranging (Lidar) is a powerful remote sensing technique that can provide accurate forest structure measurements, which provides great potential to monitor forest changes. This study used canopy height model (CHM) and canopy cover (CC) products derived from multi-temporal airborne Lidar data to detect FFTs by an approach combining a pixel-wise thresholding method and a object-of-interest segmentation method. We also investigated forest change following the implementation of landscape-scale FFT projects through the use of normalized difference vegetation index (NDVI) and standardized principle component analysis (PCA) from multi-temporal high resolution aerial imagery. The same FFT detection routine was applied on the Lidar data and aerial imagery for the purpose of comparing the capability of Lidar data and aerial imagery on FFT detection. Our results demonstrated that the FFT detection using Lidar derived CC products produced both the highest total accuracy and kappa coefficient, and was more robust at identifying areas with light FFTs. The accuracy using Lidar derived CHM products was significantly lower than that of the result using Lidar derived CC, but was still slightly higher than using aerial imagery. FFT detection results using NDVI and standardized PCA using multi-temporal aerial imagery produced almost identical total accuracy and kappa coefficient. Both methods showed relatively limited capacity to detect light FFT areas, and had higher false detection rate (recognized untreated areas as treated areas) compared to the methods using Lidar derived parameters.

Development of Waterfall Cliff Face: An Implication from Multitemporal High-definition Topographic Data

NASA Astrophysics Data System (ADS)

Hayakawa, Y. S.; Obanawa, H.

2015-12-01

Bedrock knickpoints (waterfalls) often act as erosional front in bedrock rivers, whose geomorphological processes are various. In waterfalls with vertical cliffs, both fluvial erosion and mass movement are feasible to form the landscape. Although morphological changes of such steep cliffs are sometimes visually observed, quantitative and precise measurements of their spatiotemporal distribution have been limited due to poor accessibility to such cliffs. For the clarification of geomorphological processes in such cliffs, multi-temporal mapping of the cliff face at a high resolution can be advantaged by short-range remote sensing approaches. Here we carry out multi-temporal terrestrial laser scanning (TLS), as well as structure-from-motion multi-view stereo (SfM-MVS) photogrammetry based on unmanned aerial system (UAS) for accurate topographic mapping of cliffs around a waterfall. The study site is Kegon Falls in central Japan, having a vertical drop of surface water from top of its overhanging cliff and groundwater outflows from its lower portions. The bedrock consists of alternate layers of jointed andesite lava and conglomerates. The latest major rockfall in 1986 caused approximately 8-m recession of the waterfall lip. Three-dimensional changes of the rock surface were detected by multi-temporal measurements by TLS over years, showing the portions of small rockfalls and surface lowering in the bedrock. Erosion was frequently observed in relatively weak the conglomerates layer, whereas small rockfalls were often found in the andesite layers. Wider areas of the waterfall and cliff were also measured by UAS-based SfM-MVS photogrammetry, improving the mapping quality of the cliff morphology. Point clouds are also projected on a vertical plane to generate a digital elevation model (DEM), and cross-sectional profiles extracted from the DEM indicate the presence of a distinct, 5-10-m deep depression in the cliff face. This appears to have been formed by freeze-thaw and/or wet-dry weathering following the recession in 1986. The long-term development of the waterfall cliff face is then discussed comprising various processes of rockfalls, water pressure and weathering.

Spatial changes of estuary in Ernakulam district, Southern India for last seven decades, using multi-temporal satellite data.

PubMed

Dipson, P T; Chithra, S V; Amarnath, A; Smitha, S V; Harindranathan Nair, M V; Shahin, Adhem

2015-01-15

The study area, located in the western side of Kerala State, South India, is a part of Vembanad-Kol wetlands - the largest estuary in India's western coastal wetland system and one of the Ramsar Sites of Kerala. Major portion of this estuary comes under the Ernakulam district which includes the Cochin City - the business and Industrial hub of Kerala, which has seen fast urbanization since independence (1947). Recently, this region is subjected to a characteristic fast urban sprawl, whereas, the estuarine zone is subjected to tremendous land use/land cover changes (LULC). Periodic monitoring of the estuary is essential for the formulation of viable management options for the sustainable utilization of this vital environmental resource. Remote sensing coupled with GIS applications has proved to be a useful tool in monitoring wetland changes. In the present study, the changes this estuarine region have undergone from 1944 to 2009 have been monitored with the help of multi-temporal satellite data. Estuarine areas were mapped with the help of Landsat MSS (1973), Landsat ETM (1990) and IRS LISS-III (1998 and 2009) using visual interpretation and digitization techniques in ArcGIS 9.3 Environment. The study shows a progressive decrease in the estuarine area, the reasons of which are identified chronologically. Copyright © 2014 Elsevier Ltd. All rights reserved.

Use of multi-temporal SPOT-5 satellite images for land degradation assessment in Cameron Highlands, Malaysia using Geospatial techniques

NASA Astrophysics Data System (ADS)

Nampak, Haleh; Pradhan, Biswajeet

2016-07-01

Soil erosion is the common land degradation problem worldwide because of its economic and environmental impacts. Therefore, land-use change detection has become one of the major concern to geomorphologists, environmentalists, and land use planners due to its impact on natural ecosystems. The objective of this paper is to evaluate the relationship between land use/cover changes and land degradation in the Cameron highlands (Malaysia) through multi-temporal remotely sensed satellite images and ancillary data. Land clearing in the study area has resulted increased soil erosion due to rainfall events. Also unsustainable development and agriculture, mismanagement and lacking policies contribute to increasing soil erosion rates. The LULC distribution of the study area was mapped for 2005, 2010, and 2015 through SPOT-5 satellite imagery data which were classified based on object-based classification. A soil erosion model was also used within a GIS in order to study the susceptibility of the areas affected by changes to overland flow and rain splash erosion. The model consists of four parameters, namely soil erodibility, slope, vegetation cover and overland flow. The results of this research will be used in the selection of the areas that require mitigation processes which will reduce their degrading potential. Key words: Land degradation, Geospatial, LULC change, Soil erosion modelling, Cameron highlands.

Multi-temporal analysis of land surface temperature in highly urbanized districts

NASA Astrophysics Data System (ADS)

Kaya, S.; Celik, B.; Sertel, E.; Bayram, B.; Seker, D. Z.

2017-12-01

Istanbul is one of the largest cities around the world with population over 15 million and it has 39 districts. Due to high immigration rate after the 1980s, parallel to the urbanization rapid population increase has occurred in some of these districts. Thus, a significant increase in land surface temperature were monitored and this subject became one of the most popular subject of different researches. Natural landscapes transformed into residential areas with impervious surfaces that causes rise in land surface temperatures which is one of the component of urban heat islands. This study focuses on determining the land use/land cover changes and land surface temperature in highly urbanized districts for last 32 years and examining the relationship between these two parameters using multi-temporal optical and thermal remotely sensed data. In this study, Landsat5 Thematic Mapper and Landsat8 OLI/TIR imagery with acquisition dates June 1984 and June 2016 were used. In order to assess the land use/cover change between 1984 and 2016, Vegetation Impervious Surface-soil (V-I-S) model is used. Each end-member spectra are extracted from ASTER spectral library. Additionally, V-I-S model, NDVI, NDBI and NDBaI indices have been derived for further investigation of land cover changes. The results of the study, presented that in the last 32 years, the amount of impervious surfaces substantially increased along with land surface temperatures.

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

Monitoring of urban growth and its related environmental impacts: Niamey case study

NASA Astrophysics Data System (ADS)

Perotti, Luigi; Tankari Dan-Badjo, Abdourahamane; De Luca, Domenico Antonio; Antonella Dino, Giovanna; Lasagna, Manuela; Spadafora, Francesco; Yadji, Guero; Konaté, Moussa

2016-04-01

The present contribution is about a preliminary study of the evolution of Niamey city (Niger) during last decades. Such research is part of an UNICOO project (funded by the University of Turin) and connected to the Edulink Cooperation Project (R.U.S.S.A.D.E.), a multidisciplinary project between Italy, Niger, Burkina Faso and Tchad funded on ACP- EU cooperation program in Higher Education. Recent advances in remote sensing, both in satellite hardware technology (i.e. image availability) and image processing algorithm development, provide opportunities for collection and analysis of multitemporal information on urban form and size that can be useful for policy and planning. In spite of these developments, there are also limitations to remote sensing and its application in practice. Some opportunities for, and limitations on, monitoring urban growth using remote sensing data are shown in the present contribution; moreover examples of environmental impacts of urban growth, as monitored with remote sensing, are provided. Niamey is the capital of Niger and is the first city in the country in size and economic importance. Its population increased gradually, from about 3,000 units in 1930 to about 30,000 in 1960, rising to 250,000 in 1980 and, according to estimates, to 800,000 units in 2000. Its patterns of population distribution, livelihoods, and its dominant role within the national economy of Niger make it a good representative case study for West Africa. This case study will consider the recent historical context of continued urban growth and will assess potential future impacts of settlement patterns. The rapid growth of Niamey in the last decades brought relative prosperity but it certainly affected patterns of land use within the city and the emerging urban system. After a preliminary sketch of the georesources in the city (qualitative and quantitative characterization of the surface water and groundwater, and of aggregates), an analyses of the urban growth and the evolution of the city using remote sensing data are reported. Moreover the presence of quarries, using satellite images, was highlighted. Indeed, the important enlargement of the city is certainly connected to a growing use of aggregates for construction. To plan a correct building and infrastructure activities, a survey about aggregate production and needs, and about the potential production on recycled aggregates from demolition and excavation activities, is necessary. At last, quarries enlargement ,during the decades, and, eventually, the evolution of quarries in landfills (controlled or not) are evaluated using remote sensing data. The results of this study are of interest for the identification of the areas most likely subjected to contamination, due to waste erroneous management, of soils and water (surface water and groundwater). Moreover, all the information arising from the present work are useful for local decision makers to enhance Niamey georesources management.

Evaluation of sensor, environment and operational factors impacting the use of multiple sensor constellations for long term resource monitoring

NASA Astrophysics Data System (ADS)

Rengarajan, Rajagopalan

Moderate resolution remote sensing data offers the potential to monitor the long and short term trends in the condition of the Earth's resources at finer spatial scales and over longer time periods. While improved calibration (radiometric and geometric), free access (Landsat, Sentinel, CBERS), and higher level products in reflectance units have made it easier for the science community to derive the biophysical parameters from these remotely sensed data, a number of issues still affect the analysis of multi-temporal datasets. These are primarily due to sources that are inherent in the process of imaging from single or multiple sensors. Some of these undesired or uncompensated sources of variation include variation in the view angles, illumination angles, atmospheric effects, and sensor effects such as Relative Spectral Response (RSR) variation between different sensors. The complex interaction of these sources of variation would make their study extremely difficult if not impossible with real data, and therefore, a simulated analysis approach is used in this study. A synthetic forest canopy is produced using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and its measured BRDFs are modeled using the RossLi canopy BRDF model. The simulated BRDF matches the real data to within 2% of the reflectance in the red and the NIR spectral bands studied. The BRDF modeling process is extended to model and characterize the defoliation of a forest, which is used in factor sensitivity studies to estimate the effect of each factor for varying environment and sensor conditions. Finally, a factorial experiment is designed to understand the significance of the sources of variation, and regression based analysis are performed to understand the relative importance of the factors. The design of experiment and the sensitivity analysis conclude that the atmospheric attenuation and variations due to the illumination angles are the dominant sources impacting the at-sensor radiance.

Sen2-Agri country level demonstration for Ukraine

NASA Astrophysics Data System (ADS)

Kussul, N.; Kolotii, A.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

Due to launch of Sentinel-2 mission European Space Agency (ESA) started Sentinel-2 for Agriculture (Sen2-Agri) project coordinated by Universite catholique de Louvain (UCL). Ukraine is selected as one of 3 country level demonstration sites for benchmarking Sentinel-2 data due to wide range of main crops (both winter and summer), big fields and high enough climate variability over the territory [1-2]. Within this county level demonstration main objectives are following: i) Sentinel's products quality assessment and their suitability estimation for the territory of Ukraine [2]; ii) demonstration in order to convince decision makers and state authorities; iii) assessment of the personnel and facilities required to run the Sen2-Agri system and creation of Sen-2 Agri products (crop type maps and such essential climatic variable as Leaf Area Index - LAI [3]). During this project ground data were collected for crop land mapping and crop type classification along the roads within main agro-climatic zones of Ukraine. For LAI estimation we used indirect non-destructive method which is based on DHP-images and VALERI protocol. Products created with use of Sen2-Agri system deployed during project execution and results of neural-network approach utilization will be compared. References Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508. Kussul, N., Skakun, S., Shelestov, A., Lavreniuk, M., Yailymov, B., & Kussul, O. (2015). Regional scale crop mapping using multi-temporal satellite imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 45-52. Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736

Automated Detection of Thermo-Erosion in High Latitude Ecosystems

NASA Astrophysics Data System (ADS)

Lara, M. J.; Chipman, M. L.; Hu, F.

2017-12-01

Detecting permafrost disturbance is of critical importance as the severity of climate change and associated increase in wildfire frequency and magnitude impacts regional to global carbon dynamics. However, it has not been possible to evaluate spatiotemporal patterns of permafrost degradation over large regions of the Arctic, due to limited spatial and temporal coverage of high resolution optical, radar, lidar, or hyperspectral remote sensing products. Here we present the first automated multi-temporal analysis for detecting disturbance in response to permafrost thaw, using meso-scale high-frequency remote sensing products (i.e. entire Landsat image archive). This approach was developed, tested, and applied in the Noatak National Preserve (26,500km2) in northwestern Alaska. We identified thermo-erosion (TE), by capturing the indirect spectral signal associated with episodic sediment plumes in adjacent waterbodies following TE disturbance. We isolated this turbidity signal within lakes during summer (mid-summer & late-summer) and annual time-period image composites (1986-2016), using the cloud-based geospatial parallel processing platform, Google Earth Engine™API. We validated the TE detection algorithm using seven consecutive years of sub-meter high resolution imagery (2009-2015) covering 798 ( 33%) of the 2456 total lakes in the Noatak lowlands. Our approach had "good agreement" with sediment pulses and landscape deformation in response to permafrost thaw (overall accuracy and kappa coefficient of 85% and 0.61). We identify active TE to impact 10.4% of all lakes, but was inter-annually variable, with the highest and lowest TE years represented by 1986 ( 41.1%) and 2002 ( 0.7%), respectively. We estimate thaw slumps, lake erosion, lake drainage, and gully formation to account for 23.3, 61.8, 12.5, and 1.3%, of all active TE across the Noatak National Preserve. Preliminary analysis, suggests TE may be subject to a hysteresis effect following extreme climatic conditions or wildfire. This work demonstrates the utility of meso-scale high frequency remote sensing products for advancing high latitude permafrost research.

Toward accelerating landslide mapping with interactive machine learning techniques

NASA Astrophysics Data System (ADS)

Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

2013-04-01

Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also included an experimental evaluation of the uncertainties of manual mappings from multiple experts and demonstrated strong relationships between the uncertainty of the experts and the machine learning model.

Development and Validation of Remote Sensing-Based Surface Inundation Products for Vector-Borne Disease Risk in East Africa

NASA Astrophysics Data System (ADS)

Jensen, K.; McDonald, K. C.; Ceccato, P.; Schroeder, R.; Podest, E.

2014-12-01

The potential impact of climate variability and change on the spread of infectious disease is of increasingly critical concern to public health. Newly-available remote sensing datasets may be combined with predictive modeling to develop new capabilities to mitigate risks of vector-borne diseases such as malaria, leishmaniasis, and rift valley fever. We have developed improved remote sensing-based products for monitoring water bodies and inundation dynamics that have potential utility for improving risk forecasts of vector-borne disease epidemics. These products include daily and seasonal surface inundation based on the global mappings of inundated area fraction derived at the 25-km scale from active and passive microwave instruments ERS, QuikSCAT, ASCAT, and SSM/I data - the Satellite Water Microwave Product Series (SWAMPS). Focusing on the East African region, we present validation of this product using multi-temporal classification of inundated areas in this region derived from high resolution PALSAR (100m) and Landsat (30m) observations. We assess historical occurrence of malaria in the east African country of Eritrea with respect to the time series SWAMPS datasets, and we aim to construct a framework for use of these new datasets to improve prediction of future malaria risk in this region. This work is supported through funding from the NASA Applied Sciences Program, the NASA Terrestrial Ecology Program, and the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. This study is also supported and monitored by National Oceanic and Atmospheric Administration (NOAA) under Grant - CREST Grant # NA11SEC4810004. The statements contained within the manuscript/research article are not the opinions of the funding agency or the U.S. government, but reflect the authors' opinions. This work was conducted in part under the framework of the ALOS Kyoto and Carbon Initiative. ALOS PALSAR data were provided by JAXA EORC.

Fractal Characterization of Multitemporal Scaled Remote Sensing Data

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Lam, Nina Siu-Ngan; Qiu, Hong-lie

1998-01-01

Scale is an "innate" concept in geographic information systems. It is recognized as something that is intrinsic to the ingestion, storage, manipulation, analysis, modeling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GISs become more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GISS. Scale is fundamental to the characterization of geo-spatial data as represented in GISS, but we have relatively little insight on the effects of, or how to measure the effects of, scale in representing multiscaled data; i.e., data that are acquired in different formats (e.g., map, digital) and exist in varying spatial, temporal, and in the case of remote sensing data, radiometric, configurations. This is particularly true in the emerging era of Integrated GISs (IGIS), wherein spatial data in a variety of formats (e.g., raster, vector) are combined with multiscaled remote sensing data, capable of performing highly sophisticated space-time data analyses and modeling. Moreover, the complexities associated with the integration of multiscaled data sets in a multitude of formats are exacerbated by the confusion of what the term "scale" is from a multidisciplinary perspective; i.e., "scale" takes on significantly different meanings depending upon one's disciplinary background and spatial perspective which can lead to substantive confusion in the input, manipulation, analyses, and output of IGISs (Quattrochi, 1993). Hence, we must begin to look at the universality of scale and begin to develop the theory, methods, and techniques necessary to advance knowledge on the "Science of Scale" across a wide number of spatial disciplines that use GISs.

Does remote sensing help translating local SGD investigation to large spatial scales?

NASA Astrophysics Data System (ADS)

Moosdorf, N.; Mallast, U.; Hennig, H.; Schubert, M.; Knoeller, K.; Neehaul, Y.

2016-02-01

Within the last 20 years, studies on submarine groundwater discharge (SGD) have revealed numerous processes, temporal behavior and quantitative estimations as well as best-practice and localization methods. This plethora on information is valuable regarding the understanding of magnitude and effects of SGD for the respective location. Yet, since given local conditions vary, the translation of local understanding, magnitudes and effects to a regional or global scale is not trivial. In contrast, modeling approaches (e.g. 228Ra budget) tackling SGD on a global scale do provide quantitative global estimates but have not been related to local investigations. This gap between the two approaches, local and global, and the combination and/or translation of either one to the other represents one of the mayor challenges the SGD community currently faces. But what if remote sensing can provide certain information that may be used as translation between the two, similar to transfer functions in many other disciplines allowing an extrapolation from in-situ investigated and quantified SGD (discrete information) to regional scales or beyond? Admittedly, the sketched future is ambitious and we will certainly not be able to present a solution to the raised question. Nonetheless, we will show a remote sensing based approach that is already able to identify potential SGD sites independent on location or hydrogeological conditions. Based on multi-temporal thermal information of the water surface as core of the approach, SGD influenced sites display a smaller thermal variation (thermal anomalies) than surrounding uninfluenced areas. Despite the apparent simplicity, the automatized approach has helped to localize several sites that could be validated with proven in-situ methods. At the same time it embodies the risk to identify false positives that can only be avoided if we can `calibrate' the so obtained thermal anomalies to in-situ data. We will present all pros and cons of our approach with the intention to contribute to the solution of translating SGD investigation to larger scales.

Nuclear Power Plant environment`s surveillance by satellite remote sensing and in-situ monitoring data

NASA Astrophysics Data System (ADS)

Zoran, Maria

The main environmental issues affecting the broad acceptability of nuclear power plant are the emission of radioactive materials, the generation of radioactive waste, and the potential for nuclear accidents. All nuclear fission reactors, regardless of design, location, operator or regulator, have the potential to undergo catastrophic accidents involving loss of control of the reactor core, failure of safety systems and subsequent widespread fallout of hazardous fission products. Risk is the mathematical product of probability and consequences, so lowprobability and high-consequence accidents, by definition, have a high risk. NPP environment surveillance is a very important task in frame of risk assessment. Satellite remote sensing data had been applied for dosimeter levels first time for Chernobyl NPP accident in 1986. Just for a normal functioning of a nuclear power plant, multitemporal and multispectral satellite data in complementarily with field data are very useful tools for NPP environment surveillance and risk assessment. Satellite remote sensing is used as an important technology to help environmental research to support research analysis of spatio-temporal dynamics of environmental features nearby nuclear facilities. Digital processing techniques applied to several LANDSAT, MODIS and QuickBird data in synergy with in-situ data are used to assess the extent and magnitude of radiation and non-radiation effects on the water, near field soil, vegetation and air. As a test case the methodology was applied for for Nuclear Power Plant (NPP) Cernavoda, Romania. Thermal discharge from nuclear reactors cooling is dissipated as waste heat in Danube-Black -Sea Canal and Danube River. Water temperatures captured in thermal IR imagery are correlated with meteorological parameters. If during the winter thermal plume is localized to an area of a few km of NPP, the temperature difference between the plume and non-plume areas being about 1.5 oC, during summer and fall , is a larger thermal plume up to 5-6 km far along Danube Black Sea Canal ,the temperature change is about 1.0 oC.

Corn and soybean Landsat MSS classification performance as a function of scene characteristics

NASA Technical Reports Server (NTRS)

Batista, G. T.; Hixson, M. M.; Bauer, M. E.

1982-01-01

In order to fully utilize remote sensing to inventory crop production, it is important to identify the factors that affect the accuracy of Landsat classifications. The objective of this study was to investigate the effect of scene characteristics involving crop, soil, and weather variables on the accuracy of Landsat classifications of corn and soybeans. Segments sampling the U.S. Corn Belt were classified using a Gaussian maximum likelihood classifier on multitemporally registered data from two key acquisition periods. Field size had a strong effect on classification accuracy with small fields tending to have low accuracies even when the effect of mixed pixels was eliminated. Other scene characteristics accounting for variability in classification accuracy included proportions of corn and soybeans, crop diversity index, proportion of all field crops, soil drainage, slope, soil order, long-term average soybean yield, maximum yield, relative position of the segment in the Corn Belt, weather, and crop development stage.

Applications of remote sensing technology to U.S. Water resource management

NASA Technical Reports Server (NTRS)

Weber, J. D.

1982-01-01

Applications of Landsat data to assessing the available water supply in the U.S. for agricultural puposes as a program of the NASA Office of Space Science and Applications are described. Snow melt runoff predictions are performed with multitemporal Landsat imagery to measure the extent of mountain snow packs in the Rockies in order to produce stream flow forecast models. The data is used for decisions of which crops to plant, based on irrigation water which will be accessible in eleven western U.S. states. Imagery has tracked the growth of irrigated lands watered from the Ogallala aquifer in the central U.S., and is providing a data base for calculating the aquifer depletion rates. Studies are preceeding in temporally mapping the kinds of vegetation growing in the Suwannee Sound in Florida to monitor the intrusion of salt water, which would reduce the River's usefulness for irrigation. Finally, capabilities of the Thematic Mapper are reviewed.

Application of LANDSAT data to the study of urban development in Brasilia

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Deoliveira, M. D. L. N.; Foresti, C.; Niero, M.; Parreira, E. M. D. M. F.

1984-01-01

The urban growth of Brasilia within the last ten years is analyzed with special emphasis on the utilization of remote sensing orbital data and automatic image processing. The urban spatial structure and the monitoring of its temporal changes were examined in a whole and dynamic way by the utilization of MSS-LANDSAT images for June (1973, 1978 and 1983). In order to aid data interpretation, a registration algorithm implemented in the Interactive Multispectral Image Analysis System (IMAGE-100) was utilized aiming at the overlap of multitemporal images. The utilization of suitable digital filters, combined with the images overlap, allowed a rapid identification of areas of possible urban growth and oriented the field work. The results obtained in this work permitted an evaluation of the urban growth of Brasilia, taking as reference the proposal stated for the construction of the city in the Pilot Plan elaborated by Lucio Costa.

Using Multi-Dimensional Microwave Remote Sensing Information for the Retrieval of Soil Surface Roughness

NASA Astrophysics Data System (ADS)

Marzahn, P.; Ludwig, R.

2016-06-01

In this Paper the potential of multi parametric polarimetric SAR (PolSAR) data for soil surface roughness estimation is investigated and its potential for hydrological modeling is evaluated. The study utilizes microwave backscatter collected from the Demmin testsite in the North-East Germany during AgriSAR 2006 campaign using fully polarimetric L-Band airborne SAR data. For ground truthing extensive soil surface roughness in addition to various other soil physical properties measurements were carried out using photogrammetric image matching techniques. The correlation between ground truth roughness indices and three well established polarimetric roughness estimators showed only good results for Re[ρRRLL] and the RMS Height s. Results in form of multitemporal roughness maps showed only satisfying results due to the fact that the presence and development of particular plants affected the derivation. However roughness derivation for bare soil surfaces showed promising results.

Wetland Feature Extraction in Poyang Lake from Muti-Sensor and Multi-Temporal Images

NASA Astrophysics Data System (ADS)

Zhang, Li; Desnos, Yves-Louis; Wang, Yeqiao; Chen, Xiaoling; Zmuda, Andy; Yesou, Herve

2016-08-01

Under the high dynamic hydrological variations and impacts from human activities, the nature wetlands of Poyang Lake face major challenges in biodiversity decline and wetland degradation. Variations of Poyang Lake wetlands are difficult to map by a single source or one time remote sensing imagery because the landscape is dominated by herbaceous vegetation and aquatic macrophytes which are altered and controlled by the water level. This study selected and combined time series NDVI, Green Ratio Vegetation Index (GRVI) and Modified Normalized Different Water Index (MNDWI), Backscattering coefficients(σ0) (VV&VH mode), Shannon Entropy (SE) and H/α wishart classification value derived from Sentinel 1A and Sentinel 2A to investigate the spatial-temporal variation of wetlands in autumn and spring growing season with discussions about the possibility of monitoring the wetland vegetation by C-band dual-pol datasets.

A combined field/remote sensing approach for characterizing landslide risk in coastal areas

NASA Astrophysics Data System (ADS)

Francioni, Mirko; Coggan, John; Eyre, Matthew; Stead, Doug

2018-05-01

Understanding the key factors controlling slope failure mechanisms in coastal areas is the first and most important step for analyzing, reconstructing and predicting the scale, location and extent of future instability in rocky coastlines. Different failure mechanisms may be possible depending on the influence of the engineering properties of the rock mass (including the fracture network), the persistence and type of discontinuity and the relative aspect or orientation of the coastline. Using a section of the North Coast of Cornwall, UK, as an example we present a multi-disciplinary approach for characterizing landslide risk associated with coastal instabilities in a blocky rock mass. Remotely captured terrestrial and aerial LiDAR and photogrammetric data were interrogated using Geographic Information System (GIS) techniques to provide a framework for subsequent analysis, interpretation and validation. The remote sensing mapping data was used to define the rock mass discontinuity network of the area and to differentiate between major and minor geological structures controlling the evolution of the North Coast of Cornwall. Kinematic instability maps generated from aerial LiDAR data using GIS techniques and results from structural and engineering geological surveys are presented. With this method, it was possible to highlight the types of kinematic failure mechanism that may generate coastal landslides and highlight areas that are more susceptible to instability or increased risk of future instability. Multi-temporal aerial LiDAR data and orthophotos were also studied using GIS techniques to locate recent landslide failures, validate the results obtained from the kinematic instability maps through site observations and provide improved understanding of the factors controlling the coastal geomorphology. The approach adopted is not only useful for academic research, but also for local authorities and consultancy's when assessing the likely risks of coastal instability.

Classification of very high resolution satellite remote sensing data in a pilot phase of the forest cover classification of the Democratic Republic of Congo, Forêts d'Afrique Central Evaluées par Télédetection (FACET) product

NASA Astrophysics Data System (ADS)

Singa Monga Lowengo, C.

2012-12-01

The Observatoire Satellital des Forêts d'Afrique Centrale (OSFAC) based in Kinshasa, serves as the focal point of the GOFC-GOLD network for Central Africa. OSFAC's long term objective is building regional capacity to use remotely sensed data to map forest cover and forest cover change across Central Africa. OSFAC archives and disseminates satellite data, offers training in geospatial data applications in coordination with the University of Kinshasa, and provides technical support to CARPE partners. Forêts d'Afrique Centrale Évaluées par Télédétection (FACET) is an OSFAC initiative that implements the UMD/SDSU methodology at the national level and quantitatively evaluates the spatiotemporal dynamics of forest cover in Central Africa. The multi-temporal series of FACET data is a useful contribution to many projects, such as biodiversity monitoring, climate modeling, conservation, natural resource management, land use planning, agriculture and REDD+. I am working as Remote Sensing and GIS Officer in various projects of OSFAC. My activities include forest cover and lands dynamics monitoring in Congo Basin. I am familiar with the use of digital mapping software, GIS and RS (Arc GIS, ENVI and PCI Geomatica etc.), classification and spatial Analysis of satellite images, 3D modeling, etc. I started as an intern at OSFAC, Assistant Trainer (Professional Training) and Consultant than permanent employee since October 2009. To assist in the OSFAC activities regarding the monitoring of forest cover and the CARPE program in the context of natural resources management, I participated in the development of the FACET Atlas (Republic of Congo). I received data from Matt Hansen (map.img), WRI and Brazzaville (shapefiles). With all these data I draw maps of the ROC Atlas and statistics of forest cover and forest loss. We organize field work on land to collect data to validate the FACET product. Therefore, to assess forest cover in the region of Kwamouth and Kahuzi-Maiko Biega landscape with very high resolution data and field work for validating FACET product (Remotelly Sensing Product).;

Remote Sensing as a First Step in Geothermal Exploration in the Xilingol Volcanic Field in NE China

NASA Astrophysics Data System (ADS)

Peng, F.; Huang, S.; Xiong, Y.

2013-12-01

Geothermal energy is a renewable and low-carbon energy source independent of climate change. It is most abundant in Cenozoic volcanic areas where high temperature can be obtained within a relatively shallow depth. Geological structures play an important role in the transfer and storage of geothermal energy. Like other geological resources, geothermal resource prospecting and exploration require a good understanding of the host media. Remote sensing (RS) has the advantages of high spatial and temporal resolution and broad spatial coverage over the conventional geological and geophysical prospecting techniques, while geographical information system (GIS) has intuitive, flexible, and convenient characteristics. In this study, RS and GIS techniques are utilized to prospect the geothermal energy potential in Xilingol, a Cenozoic volcanic area in the eastern Inner Mongolia, NE China. Landsat TM/ETM+ multi-temporal images taken under clear-sky conditions, digital elevation model (DEM) data, and other auxiliary data including geological maps of 1:2,500,000 and 1:200,000 scales are used in this study. The land surface temperature (LST) of the study area is retrieved from the Landsat images with a single-channel algorithm. Prior to the LST retrieval, the imagery data are preprocessed to eliminate abnormal values by reference to the normalized difference vegetation index (NDVI) and the improved normalized water index (MNDWI) on the ENVI platform developed by ITT Visual Information Solutions. Linear and circular geological structures are then inferred through visual interpretation of the LST maps with references to the existing geological maps in conjunction with the computer automatic interpretation features such as lineament frequency, lineament density, and lineament intersection. Several useful techniques such as principal component analysis (PCA), image classification, vegetation suppression, multi-temporal comparative analysis, and 3D Surface View based on DEM data are used to further enable a better visual geologic interpretation with the Landsat imagery of Xilingol. Several major volcanism controlling faults and Cenozoic volcanic eruption centers have been recognized from the linear and circular structures in the remote sensing images. The result shows that the major faults in the study area are mainly NEE oriented. Hidden faults and deep structures are inferred from the analysis of distribution regularities of linear and circular structures. Especially, the swarms of craters northwest to the Dalinuoer Lake appear to be controlled by some NEE trending hidden basement fractures. The intersecting areas of the NEE linear structures with NW trending structures overlapped by the circular structures are the favorable regions for geothermal resources. Seven areas have been preliminarily identified as the targets for further prospecting geothermal energy based on the visual interpretation of the geological structures. The study shows that RS and GIS have great application potential in the geothermal exploration in volcanic areas and will promote the exploration of renewable energy resources of great potential.

Improving classification accuracy using multi-date IRS/LISS data and development of thermal stress index for Asiatic lion habitat

NASA Astrophysics Data System (ADS)

Gupta, Rajendra Kumar

The increase in lion and leopard population in the GIR wild life sanctuary and National Park (Gir Protected Area) demands periodic and precision monitoring of habitat at close intervals using space based remote sensing data. Besides characterizing the different forest classes, remote sensing needs to support for the assessment of thermal stress zones and identification of possible corridors for lion dispersion to new home ranges. The study focuses on assessing the thematic forest classification accuracies in percentage terms(CA) attainable using single date post-monsoon (CA=60, kappa = 0.514) as well as leaf shedding (CA=48.4, kappa = 0.372) season data in visible and Near-IR spectral bands of IRS/LISS-III at 23.5 m spatial resolution; and improvement of CA by using joint two date (multi-temporal) data sets (CA=87.2, kappa = 0.843) in the classification. The 188 m spatial resolution IRS/WiFS and 23.5 m spatial resolution LISS-III data were used to study the possible corridors for dispersion of Lions from GIR protected areas (PA). A relative thermal stress index (RTSI) for Gir PA has been developed using NOAA/ AVHRR data sets of post-monsoon, leaf shedded and summer seasons. The paper discusses the role of RTSI as a tool to work out forest management plans using leaf shedded season data to combat the thermal stress in the habitat, by identifying locations for artificial water holes during the ensuing summer season.

Integrated remote sensing for multi-temporal analysis of urban land cover-climate interactions

NASA Astrophysics Data System (ADS)

Savastru, Dan M.; Zoran, Maria A.; Savastru, Roxana S.

2016-08-01

Climate change is considered to be the biggest environmental threat in the future in the South- Eastern part of Europe. In frame of predicted global warming, urban climate is an important issue in scientific research. Surface energy processes have an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. This paper investigated the influences of urban growth on thermal environment in relationship with other biophysical variables in Bucharest metropolitan area of Romania. Remote sensing data from Landsat TM/ETM+ and time series MODIS Terra/Aqua sensors have been used to assess urban land cover- climate interactions over period between 2000 and 2015 years. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) at city level. Based on these parameters, the urban growth, and urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

Using Multitemporal Remote Sensing Imagery and Inundation Measures to Improve Land Change Estimates in Coastal Wetlands

USGS Publications Warehouse

Allen, Y.C.; Couvillion, B.R.; Barras, J.A.

2012-01-01

Remote sensing imagery can be an invaluable resource to quantify land change in coastal wetlands. Obtaining an accurate measure of land change can, however, be complicated by differences in fluvial and tidal inundation experienced when the imagery is captured. This study classified Landsat imagery from two wetland areas in coastal Louisiana from 1983 to 2010 into categories of land and water. Tide height, river level, and date were used as independent variables in a multiple regression model to predict land area in the Wax Lake Delta (WLD) and compare those estimates with an adjacent marsh area lacking direct fluvial inputs. Coefficients of determination from regressions using both measures of water level along with date as predictor variables of land extent in the WLD, were higher than those obtained using the current methodology which only uses date to predict land change. Land change trend estimates were also improved when the data were divided by time period. Water level corrected land gain in the WLD from 1983 to 2010 was 1 km 2 year -1, while rates in the adjacent marsh remained roughly constant. This approach of isolating environmental variability due to changing water levels improves estimates of actual land change in a dynamic system, so that other processes that may control delta development such as hurricanes, floods, and sediment delivery, may be further investigated. ?? 2011 Coastal and Estuarine Research Federation (outside the USA).

Vegetation Mapping in a Dryland Ecosystem Using Multi-temporal Sentinel-2 Imagery and Ensemble Learning

NASA Astrophysics Data System (ADS)

Enterkine, J.; Spaete, L.; Glenn, N. F.; Gallagher, M.

2017-12-01

Remote sensing and mapping of dryland ecosystem vegetation is notably problematic due to the low canopy cover and fugacious growing seasons. Recent improvements in available satellite imagery and machine learning techniques have enabled enhanced approaches to mapping and monitoring vegetation across dryland ecosystems. The Sentinel-2 satellites (launched June 2015 and March 2017) of ESA's Copernicus Programme offer promising developments from existing multispectral satellite systems such as Landsat. Freely-available, Sentinel-2 imagery offers a five-day revisit frequency, thirteen spectral bands (in the visible, near infrared, and shortwave infrared), and high spatial resolution (from 10m to 60m). Three narrow spectral bands located between the visible and the near infrared are designed to observe changes in photosynthesis. The high temporal, spatial, and spectral resolution of this imagery makes it ideal for monitoring vegetation in dryland ecosystems. In this study, we calculated a large number of vegetation and spectral indices from Sentinel-2 imagery spanning a growing season. This data was leveraged with robust field data of canopy cover at precise geolocations. We then used a Random Forests ensemble learning model to identify the most predictive variables for each landcover class, which were then used to impute landcover over the study area. The resulting vegetation map product will be used by land managers, and the mapping approaches will serve as a basis for future remote sensing projects using Sentinel-2 imagery and machine learning.

Monitoring Powdery Mildew of Winter Wheat by Using Moderate Resolution Multi-Temporal Satellite Imagery

PubMed Central

Zhang, Jingcheng; Pu, Ruiliang; Yuan, Lin; Wang, Jihua; Huang, Wenjiang; Yang, Guijun

2014-01-01

Powdery mildew is one of the most serious diseases that have a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study attempted to use multi-temporal moderate resolution satellite-based data of surface reflectances in blue (B), green (G), red (R) and near infrared (NIR) bands from HJ-CCD (CCD sensor on Huanjing satellite) to monitor disease at a regional scale. In a suburban area in Beijing, China, an extensive field campaign for disease intensity survey was conducted at key growth stages of winter wheat in 2010. Meanwhile, corresponding time series of HJ-CCD images were acquired over the study area. In this study, a number of single-stage and multi-stage spectral features, which were sensitive to powdery mildew, were selected by using an independent t-test. With the selected spectral features, four advanced methods: mahalanobis distance, maximum likelihood classifier, partial least square regression and mixture tuned matched filtering were tested and evaluated for their performances in disease mapping. The experimental results showed that all four algorithms could generate disease maps with a generally correct distribution pattern of powdery mildew at the grain filling stage (Zadoks 72). However, by comparing these disease maps with ground survey data (validation samples), all of the four algorithms also produced a variable degree of error in estimating the disease occurrence and severity. Further, we found that the integration of MTMF and PLSR algorithms could result in a significant accuracy improvement of identifying and determining the disease intensity (overall accuracy of 72% increased to 78% and kappa coefficient of 0.49 increased to 0.59). The experimental results also demonstrated that the multi-temporal satellite images have a great potential in crop diseases mapping at a regional scale. PMID:24691435

Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure

USGS Publications Warehouse

McCarley, T. Ryan; Kolden, Crystal A.; Vaillant, Nicole M.; Hudak, Andrew T.; Smith, Alistair M.S.; Wing, Brian M.; Kellogg, Bryce; Kreitler, Jason R.

2017-01-01

Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots. While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often lacks validation with specific variables of change. Additional uncertainty remains regarding how best to account for environmental variables influencing fire effects (e.g., weather) for which observational data cannot easily be acquired, and whether pre-fire agents of change such as bark beetle and timber harvest impact model accuracy. This study quantifies wildfire effects by correlating changes in forest structure derived from multi-temporal Light Detection and Ranging (LiDAR) acquisitions to multi-temporal spectral changes captured by the Landsat Thematic Mapper and Operational Land Imager for the 2012 Pole Creek Fire in central Oregon. Spatial regression modeling was assessed as a methodology to account for spatial autocorrelation, and model consistency was quantified across areas impacted by pre-fire mountain pine beetle and timber harvest. The strongest relationship (pseudo-r2 = 0.86, p < 0.0001) was observed between the ratio of shortwave infrared and near infrared reflectance (d74) and LiDAR-derived estimate of canopy cover change. Relationships between percentage of LiDAR returns in forest strata and spectral indices generally increased in strength with strata height. Structural measurements made closer to the ground were not well correlated. The spatial regression approach improved all relationships, demonstrating its utility, but model performance declined across pre-fire agents of change, suggesting that such studies should stratify by pre-fire forest condition. This study establishes that spectral indices such as d74 and dNBR are most sensitive to wildfire-caused structural changes such as reduction in canopy cover and perform best when that structure has not been reduced pre-fire.

Comparison of Hyperspectral and Multispectral Satellites for Discriminating Land Cover in Northern California

NASA Astrophysics Data System (ADS)

Clark, M. L.; Kilham, N. E.

2015-12-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as spectrally-mixed woodlands and forests.

Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery.

PubMed

Zhang, Jingcheng; Pu, Ruiliang; Yuan, Lin; Wang, Jihua; Huang, Wenjiang; Yang, Guijun

2014-01-01

Powdery mildew is one of the most serious diseases that have a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study attempted to use multi-temporal moderate resolution satellite-based data of surface reflectances in blue (B), green (G), red (R) and near infrared (NIR) bands from HJ-CCD (CCD sensor on Huanjing satellite) to monitor disease at a regional scale. In a suburban area in Beijing, China, an extensive field campaign for disease intensity survey was conducted at key growth stages of winter wheat in 2010. Meanwhile, corresponding time series of HJ-CCD images were acquired over the study area. In this study, a number of single-stage and multi-stage spectral features, which were sensitive to powdery mildew, were selected by using an independent t-test. With the selected spectral features, four advanced methods: mahalanobis distance, maximum likelihood classifier, partial least square regression and mixture tuned matched filtering were tested and evaluated for their performances in disease mapping. The experimental results showed that all four algorithms could generate disease maps with a generally correct distribution pattern of powdery mildew at the grain filling stage (Zadoks 72). However, by comparing these disease maps with ground survey data (validation samples), all of the four algorithms also produced a variable degree of error in estimating the disease occurrence and severity. Further, we found that the integration of MTMF and PLSR algorithms could result in a significant accuracy improvement of identifying and determining the disease intensity (overall accuracy of 72% increased to 78% and kappa coefficient of 0.49 increased to 0.59). The experimental results also demonstrated that the multi-temporal satellite images have a great potential in crop diseases mapping at a regional scale.

Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans

NASA Astrophysics Data System (ADS)

Archer, Reginald S.

This research focuses on measuring and monitoring long term recovery progress from the impacts of Hurricane Katrina on New Orleans, LA. Remote sensing has frequently been used for emergency response and damage assessment after natural disasters. However, techniques for analysis of long term disaster recovery using remote sensing have not been widely explored. With increased availability and lower costs, remote sensing offers an objective perspective, systematic and repeatable analysis, and provides a substitute to multiple site visits. In addition, remote sensing allows access to large geographical areas and areas where ground access may be disrupted, restricted or denied. This dissertation addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators. Maximum likelihood classification and post-classification change detection were applied to multi-temporal high resolution aerial images to quantitatively measure the progress of recovery. Images were classified to automatically identify disaster recovery indicators and exploit the indicators that are visible within each image. The spectral analysis demonstrated that employing maximum likelihood classification to high resolution true color aerial images performed adequately and provided a good indication of spectral pattern recognition, despite the limited spectral information. Applying the change detection to the classified images was effective for determining the temporal trajectory of indicators categorized as blue tarps, FEMA trailers, houses, vegetation, bare earth and pavement. The results of the post classification change detection revealed a dominant change trajectory from bluetarp to house, as damaged houses became permanently repaired. Specifically, the level of activity of blue tarps, housing, vegetation, FEMA trailers (temporary housing) pavement and bare earth were derived from aerial image processing to measure and monitor the progress of recovery. Trajectories of recovery for each individual indicator were examined to provide a better understanding of activity during reconstruction. A collection of spatial metrics was explored in order to identify spatial patterns and characterize classes in terms of patches of pixels. One of the key findings of the spatial analysis is that patch shapes were more complex in the presence of debris and damaged or destroyed buildings. The combination of spectral, temporal, and spatial analysis provided a satisfactory, though limited, solution to the question of whether remote sensing alone, can be used to quantitatively assess and monitor the progress of long term recovery following a major disaster. The research described in this dissertation provided a detailed illustration of the level of activity experienced by different recovery indicators during the long term recovery process. It also addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators identified from classified high resolution true color aerial imagery. The results produced in this research demonstrate that the observed trajectories for actual indicators of recovery indicate different levels of recovery activity even within the same community. The level of activity of the long term reconstruction phase observed in the Kates model is not consistent with the level of activity of key recovery indicators in the Lower 9th Ward during the same period. Used in the proper context, these methods and results provide decision making information for determining resources. KEYWORDS: Change detection, classification, Katrina, New Orleans, remote sensing, disaster recovery, spatial metrics

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

USGS Publications Warehouse

Selkowitz, D.J.

2010-01-01

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 ??N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10??, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.

Direct Satellite Data Acquisition and its Application for Large -scale Monitoring Projects in Russia

NASA Astrophysics Data System (ADS)

Gershenzon, O.

2011-12-01

ScanEx RDC created an infrastructure (ground stations network) to acquire and process remote sensing data from different satellites: Terra, Aqua, Landsat, IRS-P5/P6, SPOT 4/5, FORMOSAT-2, EROS A/B, RADARSAT-1/2, ENVISAT-1. It owns image archives from these satellites as well as from SPOT-2 and CARTOSAT-2. ScanEx RDC builds and delivers remote sensing ground stations (working with up to 15 satellites); and owns the ground stations network to acquire data for Russia and surrounding territory. ScanEx stations are the basic component in departmental networks of remote sensing data acquisition for different state authorities (Roshydromet, Ministry of Natural Recourses, Emercom) and University- based remote sensing data acquisition and processing centers in Russia and abroad. ScanEx performs large-scale projects in collaboration with government agencies to monitor forests, floods, fires, sea surface pollution, and ice situation in Northern Russia. During 2010-2011 ScanEx conducted daily monitoring of wild fires in Russia detecting and registering thermal anomalies using data from Terra, Aqua, Landsat and SPOT satellites. Detailed SPOT 4/5 data is used to analyze burnt areas and to assess damage caused by fire. Satellite data along with other information about fire situation in Russia was daily updated and published via free-access Internet geoportal. A few projects ScanEx conducted together with environmental NGO. Project "Satellite monitoring of Especially Protected Natural Areas of Russia and its results visualization on geoportal was conducted in cooperation with NGO "Transparent World". The project's goal was to observe natural phenomena and economical activity, including illegal, by means of Earth remote sensing data. Monitoring is based on multi-temporal optical space imagery of different spatial resolution. Project results include detection of anthropogenic objects that appeared in the vicinity or even within the border of natural territories, that have never been touched by civilization before. "Satellite based technology for monitoring ship ice navigation and its influence on seal population in the White Sea" project was conducted in cooperation with IFAW. Results of the near real-time satellite monitoring were published on specially designed open web source. This allows project team to put image interpretation results in near real-time mode for on-line access to all interesting external stakeholders. During project realization Envisat, Radarsat, SPOT, EROS space images were used. In addition the methodology to locate seal population using EROS space images was developed. This methodology is based on detection of vital functions and displacement traces. Environmental satellite monitoring of Northern Russian territory and Arctic seas projects where the results are published via free-access Internet geoportal has a significant social importance.

The geomorphological evidences of subsidence in the Nile Delta: Analysis of high resolution topographic DEM and multi-temporal satellite images

NASA Astrophysics Data System (ADS)

El Bastawesy, M.; Cherif, O. H.; Sultan, M.

2017-12-01

This paper investigates the relevance of landforms to the subsidence of the Nile Delta using a high resolution topographic digital elevation model (DEM) and sets of multi-temporal Landsat satellite images. 195 topographic map sheets produced in 1946 at 1:25,000 scale were digitized, and the DEM was interpolated. The undertaken processing techniques have distinguished all the natural low-lying closed depressions from the artificial errors induced by the interpolation of the DEM. The local subsidence of these depressions from their surroundings reaches a maximum depth of 2.5 m. The regional subsidence of the Nile Delta has developed inverted topography, where the tracts occupied by the contemporary distributary channels are standing at higher elevations than the areas in between. This inversion could be related to the differences in the hydrological and sedimentological properties of underlying sediments, as the channels are underlain by water-saturated sands while the successions of clay and silt on flood plains are prone to compaction. Furthermore, the analysis of remote sensing and topographic data clearly show significant changes in the land cover and land use, particularly in the northern lagoons and adjacent sabkhas, which are dominated by numerous low subsiding depressions. The areas covered by water logging and ponds are increasing on the expense of agricultural areas, and aquaculture have been practiced instead. The precise estimation of subsidence rates and distribution should be worked out to evaluate probable changes in land cover and land use.

Dealing with missing data in remote sensing images within land and crop classification

NASA Astrophysics Data System (ADS)

Skakun, Sergii; Kussul, Nataliia; Basarab, Ruslan

Optical remote sensing images from space provide valuable data for environmental monitoring, disaster management [1], agriculture mapping [2], so forth. In many cases, a time-series of satellite images is used to discriminate or estimate particular land parameters. One of the factors that influence the efficiency of satellite imagery is the presence of clouds. This leads to the occurrence of missing data that need to be addressed. Numerous approaches have been proposed to fill in missing data (or gaps) and can be categorized into inpainting-based, multispectral-based, and multitemporal-based. In [3], ancillary MODIS data are utilized for filling gaps and predicting Landsat data. In this paper we propose to use self-organizing Kohonen maps (SOMs) for missing data restoration in time-series of satellite imagery. Such approach was previously used for MODIS data [4], but applying this approach for finer spatial resolution data such as Sentinel-2 and Landsat-8 represents a challenge. Moreover, data for training the SOMs are selected manually in [4] that complicates the use of the method in an automatic mode. SOM is a type of artificial neural network that is trained using unsupervised learning to produce a discretised representation of the input space of the training samples, called a map. The map seeks to preserve the topological properties of the input space. The reconstruction of satellite images is performed for each spectral band separately, i.e. a separate SOM is trained for each spectral band. Pixels that have no missing values in the time-series are selected for training. Selecting the number of training pixels represent a trade-off, in particular increasing the number of training samples will lead to the increased time of SOM training while increasing the quality of restoration. Also, training data sets should be selected automatically. As such, we propose to select training samples on a regular grid of pixels. Therefore, the SOM seeks to project a large number of non-missing data to the subspace vectors in the map. Restoration of the missing values is performed in the following way. The multi-temporal pixel values (with gaps) are put to the neural network. A neuron-winner (or a best matching unit, BMU) in the SOM is selected based on the distance metric (for example, Euclidian). It should be noted that missing values are omitted from metric estimation when selecting BMU. When the BMU is selected, missing values are substituted by corresponding components of the BMU values. The efficiency of the proposed approach was tested on a time-series of Landsat-8 images over the JECAM test site in Ukraine and Sich-2 images over Crimea (Sich-2 is Ukrainian remote sensing satellite acquiring images at 8m spatial resolution). Landsat-8 images were first converted to the TOA reflectance, and then were atmospherically corrected so each pixel value represents a surface reflectance in the range from 0 to 1. The error of reconstruction (error of quantization) on training data was: band-2: 0.015; band-3: 0.020; band-4: 0.026; band-5: 0.070; band-6: 0.060; band-7: 0.055. The reconstructed images were also used for crop classification using a multi-layer perceptron (MLP). Overall accuracy was 85.98% and Cohen's kappa was 0.83. References. 1. Skakun, S., Kussul, N., Shelestov, A. and Kussul, O. “Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia,” Risk Analysis, 2013, doi: 10.1111/risa.12156. 2. Gallego, F.J., Kussul, N., Skakun, S., Kravchenko, O., Shelestov, A., Kussul, O. “Efficiency assessment of using satellite data for crop area estimation in Ukraine,” International Journal of Applied Earth Observation and Geoinformation, vol. 29, pp. 22-30, 2014. 3. Roy D.P., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., and Lindquist, E., “Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data,” Remote Sensing of Environment, 112(6), pp. 3112-3130, 2008. 4. Latif, B.A., and Mercier, G., “Self-Organizing maps for processing of data with missing values and outliers: application to remote sensing images,” Self-Organizing Maps. InTech, pp. 189-210, 2010.

Environmental monitoring of El Hierro Island submarine volcano, by combining low and high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Eugenio, F.; Martin, J.; Marcello, J.; Fraile-Nuez, E.

2014-06-01

El Hierro Island, located at the Canary Islands Archipelago in the Atlantic coast of North Africa, has been rocked by thousands of tremors and earthquakes since July 2011. Finally, an underwater volcanic eruption started 300 m below sea level on October 10, 2011. Since then, regular multidisciplinary monitoring has been carried out in order to quantify the environmental impacts caused by the submarine eruption. Thanks to this natural tracer release, multisensorial satellite imagery obtained from MODIS and MERIS sensors have been processed to monitor the volcano activity and to provide information on the concentration of biological, chemical and physical marine parameters. Specifically, low resolution satellite estimations of optimal diffuse attenuation coefficient (Kd) and chlorophyll-a (Chl-a) concentration under these abnormal conditions have been assessed. These remote sensing data have played a fundamental role during field campaigns guiding the oceanographic vessel to the appropriate sampling areas. In addition, to analyze El Hierro submarine volcano area, WorldView-2 high resolution satellite spectral bands were atmospherically and deglinted processed prior to obtain a high-resolution optimal diffuse attenuation coefficient model. This novel algorithm was developed using a matchup data set with MERIS and MODIS data, in situ transmittances measurements and a seawater radiative transfer model. Multisensor and multitemporal imagery processed from satellite remote sensing sensors have demonstrated to be a powerful tool for monitoring the submarine volcanic activities, such as discolored seawater, floating material and volcanic plume, having shown the capabilities to improve the understanding of submarine volcanic processes.

Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960-2005.

PubMed

Dewan, Ashraf M; Yamaguchi, Yasushi

2009-03-01

This paper illustrates the result of land use/cover change in Dhaka Metropolitan of Bangladesh using topographic maps and multi-temporal remotely sensed data from 1960 to 2005. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images such as SPOT, IRS, IKONOS and field data. The overall accuracy of land cover change maps, generated from Landsat and IRS-1D data, ranged from 85% to 90%. The analysis indicated that the urban expansion of Dhaka Metropolitan resulted in the considerable reduction of wetlands, cultivated land, vegetation and water bodies. The maps showed that between 1960 and 2005 built-up areas increased approximately 15,924 ha, while agricultural land decreased 7,614 ha, vegetation decreased 2,336 ha, wetland/lowland decreased 6,385 ha, and water bodies decreased about 864 ha. The amount of urban land increased from 11% (in 1960) to 344% in 2005. Similarly, the growth of landfill/bare soils category was about 256% in the same period. Much of the city's rapid growth in population has been accommodated in informal settlements with little attempt being made to limit the risk of environmental impairments. The study quantified the patterns of land use/cover change for the last 45 years for Dhaka Metropolitan that forms valuable resources for urban planners and decision makers to devise sustainable land use and environmental planning.

Spectrally-Based Assessment of Crop Seasonal Performance and Yield

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Borisova, Denitsa; Georgiev, Georgy

The rapid advances of space technologies concern almost all scientific areas from aeronautics to medicine, and a wide range of application fields from communications to crop yield predictions. Agricultural monitoring is among the priorities of remote sensing observations for getting timely information on crop development. Monitoring agricultural fields during the growing season plays an important role in crop health assessment and stress detection provided that reliable data is obtained. Successfully spreading is the implementation of hyperspectral data to precision farming associated with plant growth and phenology monitoring, physiological state assessment, and yield prediction. In this paper, we investigated various spectral-biophysical relationships derived from in-situ reflectance measurements. The performance of spectral data for the assessment of agricultural crops condition and yield prediction was examined. The approach comprisesd development of regression models between plant spectral and state-indicative variables such as biomass, vegetation cover fraction, leaf area index, etc., and development of yield forecasting models from single-date (growth stage) and multitemporal (seasonal) reflectance data. Verification of spectral predictions was performed through comparison with estimations from biophysical relationships between crop growth variables. The study was carried out for spring barley and winter wheat. Visible and near-infrared reflectance data was acquired through the whole growing season accompanied by detailed datasets on plant phenology and canopy structural and biochemical attributes. Empirical relationships were derived relating crop agronomic variables and yield to various spectral predictors. The study findings were tested using airborne remote sensing inputs. A good correspondence was found between predicted and actual (ground-truth) estimates

Using Imaging Spectrometry to Approach Crop Classification from a Water Management Perspective

NASA Astrophysics Data System (ADS)

Shivers, S.; Roberts, D. A.

2017-12-01

We use hyperspectral remote sensing imagery to classify crops in the Central Valley of California at a level that would be of use to water managers. In California irrigated agriculture uses 80 percent of the state's water supply with differences in water application rate varying by as large as a factor of three, dependent on crop type. Therefore, accurate water resource accounting is dependent upon accurate crop mapping. While on-the-ground crop accounting at the county level requires significant labor and time inputs, remote sensing has the potential to map crops over a greater spatial area with more frequent time intervals. Specifically, imaging spectrometry with its wide spectral range has the ability to detect small spectral differences at the field-level scale that may be indiscernible to multispectral sensors such as Landsat. In this study, crops in the Central Valley were classified into nine categories defined and used by the California Department of Water Resources as having similar water usages. We used the random forest classifier on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery from June 2013, 2014 and 2015 to analyze accuracy of multi-temporal images and to investigate the extent to which cropping patterns have changed over the course of the 2013-2015 drought. Initial results show accuracies of over 90% for all three years, indicating that hyperspectral imagery has the potential to identify crops by water use group at a single time step with a single sensor, allowing cropping patterns to be monitored in anticipation of water needs.

A Webgis Framework for Disseminating Processed Remotely Sensed on Land Cover Transformations

NASA Astrophysics Data System (ADS)

Caradonna, Grazia; Novelli, Antonio; Tarantino, Eufemia; Cefalo, Raffaela; Fratino, Umberto

2016-06-01

Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data.

SENTINEL-1 and SENTINEL-2 Data Fusion for Wetlands Mapping: Balikdami, Turkey

NASA Astrophysics Data System (ADS)

Kaplan, G.; Avdan, U.

2018-04-01

Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy. In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared. The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90 % in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques.

An Improved dem Construction Method for Mudflats Based on BJ-1 Small Satellite Images: a Case Study on Bohai Bay

NASA Astrophysics Data System (ADS)

Wu, D.; Du, Y.; Su, F.; Huang, W.; Zhang, L.

2018-04-01

The topographic measurement of muddy tidal flat is restricted by the difficulty of access to the complex, wide-range and dynamic tidal conditions. Then the waterline detection method (WDM) has the potential to investigate the morph-dynamics quantitatively by utilizing large archives of satellite images. The study explores the potential for using WDM with BJ-1 small satellite images to construct a digital elevation model (DEM) of a wide and grading mudflat. Three major conclusions of the study are as follows: (1) A new intelligent correlating model of waterline detection considering different tidal stages and local geographic conditions was explored. With this correlative algorithm waterline detection model, a series of waterlines were extracted from multi-temporal remotely sensing images collected over the period of a year. The model proved to detect waterlines more efficiently and exactly. (2) The spatial structure of elevation superimposing on the points of waterlines was firstly constructed and a more accurate hydrodynamic ocean tide grid model was used. By the newly constructed abnormal hydrology evaluation model, a more reasonable and reliable set of waterline points was acquired to construct a smoother TIN and GRID DEM. (3) DEM maps of Bohai Bay, with a spatial resolution of about 30 m and height accuracy of about 0.35 m considering LiDAR and 0.19 m considering RTK surveying were constructed over an area of about 266 km2. Results show that remote sensing research in extremely turbid estuaries and tidal areas is possible and is an effective tool for monitoring the tidal flats.

Large scale maps of cropping intensity in Asia from MODIS

NASA Astrophysics Data System (ADS)

Gray, J. M.; Friedl, M. A.; Frolking, S. E.; Ramankutty, N.; Nelson, A.

2013-12-01

Agricultural systems are geographically extensive, have profound significance to society, and also affect regional energy, carbon, and water cycles. Since most suitable lands worldwide have been cultivated, there is growing pressure to increase yields on existing agricultural lands. In tropical and sub-tropical regions, multi-cropping is widely used to increase food production, but regional-to-global information related to multi-cropping practices is poor. Such information is of critical importance to ensure sustainable food production while mitigating against negative environmental impacts associated with agriculture such as contamination and depletion of freshwater resources. Unfortunately, currently available large-area inventory statistics are inadequate because they do not capture important spatial patterns in multi-cropping, and are generally not available in a timeframe that can be used to help manage cropping systems. High temporal resolution sensors such as MODIS provide an excellent source of information for addressing this need. However, relative to studies that document agricultural extensification, systematic assessment of agricultural intensification via multi-cropping has received relatively little attention. The goal of this work is to help close this methodological and information gap by developing methods that use multi-temporal remote sensing to map multi-cropping systems in Asia. Image time series analysis is especially challenging in Asia because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low quality remote sensing observations, especially during the Asian Monsoon. The methodology that we use for this work builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but employs refined methods to segment, smooth, and gap-fill 8-day EVI time series calculated from MODIS BRDF corrected surface reflectances. Crop cycle segments are identified based on changes in slope for linear regressions estimated for local windows, and constrained by the EVI amplitude and length of crop cycles that are identified. The procedure can be used to map seasonal or long-term average cropping strategies, and to characterize changes in cropping intensity over longer time periods. The datasets produced using this method therefore provide information related to global cropping systems, and more broadly, provide important information that is required to ensure sustainable management of Earth's resources and ensure food security. To test our algorithm, we applied it to time series of MODIS EVI images over Asia from 2000-2012. Our results demonstrate the utility of multi-temporal remote sensing for characterizing multi-cropping practices in some of the most important and intensely agricultural regions in the world. To evaluate our approach, we compared results from MODIS to field-scale survey data at the pixel scale, and agricultural inventory statistics at sub-national scales. We then mapped changes in multi-cropped area in Asia from the early MODIS period (2001-2004) to present (2009-2012), and characterizes the magnitude and location of changes in cropping intensity over the last 12 years. We conclude with a discussion of the challenges, future improvements, and broader impacts of this work.

Evaluating and monitoring forest fuel treatments using remote sensing applications in Arizona, U.S.A.

USGS Publications Warehouse

Petrakis, Roy; Villarreal, Miguel; Wu, Zhuoting; Hetzler, Robert; Middleton, Barry R.; Norman, Laura M.

2018-01-01

The practice of fire suppression across the western United States over the past century has led to dense forests, and when coupled with drought has contributed to an increase in large and destructive wildfires. Forest management efforts aimed at reducing flammable fuels through various fuel treatments can help to restore frequent fire regimes and increase forest resilience. Our research examines how different fuel treatments influenced burn severity and post-fire vegetative stand dynamics on the San Carlos Apache Reservation, in east-central Arizona, U.S.A. Our methods included the use of multitemporal remote sensing data and cloud computing to evaluate burn severity and post-fire vegetation conditions as well as statistical analyses. We investigated how forest thinning, commercial harvesting, prescribed burning, and resource benefit burning (managed wildfire) related to satellite measured burn severity (the difference Normalized Burn Ratio – dNBR) following the 2013 Creek Fire and used spectral measures of post-fire stand dynamics to track changes in land surface characteristics (i.e., brightness, greenness and wetness). We found strong negative relationships between dNBR and post-fire greenness and wetness, and a positive non-linear relationship between dNBR and brightness, with greater variability at higher severities. Fire severity and post-fire surface changes also differed by treatment type. Our results showed harvested and thinned sites that were not treated with prescribed fire had the highest severity fire. When harvesting was followed by a prescribed burn, the sites experienced lower burn severity and reduced post-fire changes in vegetation greenness and wetness. Areas that had previously experienced resource benefit burns had the lowest burn severities and the highest post-fire greenness measurements compared to all other treatments, except for where the prescribed burn had occurred. These results suggest that fire treatments may be most effective at reducing the probability of hazardous fire and increasing post-fire recovery. This research demonstrates the utility of remote sensing and spatial data to inform forest management, and how various fuel treatments can influence burn severity and post-fire vegetation response within ponderosa pine forests across the southwestern U.S.

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States

PubMed Central

Swetnam, Tyson L.; Gillan, Jeffrey K.; Sankey, Temuulen T.; McClaran, Mitchel P.; Nichols, Mary H.; Heilman, Philip; McVay, Jason

2018-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft. PMID:29379511

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States.

PubMed

Swetnam, Tyson L; Gillan, Jeffrey K; Sankey, Temuulen T; McClaran, Mitchel P; Nichols, Mary H; Heilman, Philip; McVay, Jason

2017-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft.

Topographic mapping on large-scale tidal flats with an iterative approach on the waterline method

NASA Astrophysics Data System (ADS)

Kang, Yanyan; Ding, Xianrong; Xu, Fan; Zhang, Changkuan; Ge, Xiaoping

2017-05-01

Tidal flats, which are both a natural ecosystem and a type of landscape, are of significant importance to ecosystem function and land resource potential. Morphologic monitoring of tidal flats has become increasingly important with respect to achieving sustainable development targets. Remote sensing is an established technique for the measurement of topography over tidal flats; of the available methods, the waterline method is particularly effective for constructing a digital elevation model (DEM) of intertidal areas. However, application of the waterline method is more limited in large-scale, shifting tidal flats areas, where the tides are not synchronized and the waterline is not a quasi-contour line. For this study, a topographical map of the intertidal regions within the Radial Sand Ridges (RSR) along the Jiangsu Coast, China, was generated using an iterative approach on the waterline method. A series of 21 multi-temporal satellite images (18 HJ-1A/B CCD and three Landsat TM/OLI) of the RSR area collected at different water levels within a five month period (31 December 2013-28 May 2014) was used to extract waterlines based on feature extraction techniques and artificial further modification. These 'remotely-sensed waterlines' were combined with the corresponding water levels from the 'model waterlines' simulated by a hydrodynamic model with an initial generalized DEM of exposed tidal flats. Based on the 21 heighted 'remotely-sensed waterlines', a DEM was constructed using the ANUDEM interpolation method. Using this new DEM as the input data, it was re-entered into the hydrodynamic model, and a new round of water level assignment of waterlines was performed. A third and final output DEM was generated covering an area of approximately 1900 km2 of tidal flats in the RSR. The water level simulation accuracy of the hydrodynamic model was within 0.15 m based on five real-time tide stations, and the height accuracy (root mean square error) of the final DEM was 0.182 m based on six transects of measured data. This study aimed at construction of an accurate DEM for a large-scale, high-variable zone within a short timespan based on an iterative way of the waterline method.

Damage extraction of buildings in the 2015 Gorkha, Nepal earthquake from high-resolution SAR data

NASA Astrophysics Data System (ADS)

Yamazaki, Fumio; Bahri, Rendy; Liu, Wen; Sasagawa, Tadashi

2016-05-01

Satellite remote sensing is recognized as one of the effective tools for detecting and monitoring affected areas due to natural disasters. Since SAR sensors can capture images not only at daytime but also at nighttime and under cloud-cover conditions, they are especially useful at an emergency response period. In this study, multi-temporal high-resolution TerraSAR-X images were used for damage inspection of the Kathmandu area, which was severely affected by the April 25, 2015 Gorkha Earthquake. The SAR images obtained before and after the earthquake were utilized for calculating the difference and correlation coefficient of backscatter. The affected areas were identified by high values of the absolute difference and low values of the correlation coefficient. The post-event high-resolution optical satellite images were employed as ground truth data to verify our results. Although it was difficult to estimate the damage levels for individual buildings, the high resolution SAR images could illustrate their capability in detecting collapsed buildings at emergency response times.

Using Multitemporal and Multispectral Airborne Lidar to Assess Depth of Peat Loss and Correspondence With a New Active Normalized Burn Ratio for Wildfires

NASA Astrophysics Data System (ADS)

Chasmer, L. E.; Hopkinson, C. D.; Petrone, R. M.; Sitar, M.

2017-12-01

Accuracy of depth of burn (an indicator of consumption) in peatland soils using prefire and postfire airborne light detection and ranging (lidar) data is determined within a wetland-upland forest environment near Fort McMurray, Alberta, Canada. The relationship between peat soil burn depth and an "active" normalized burn ratio (ANBR) is also examined beneath partially and fully burned forest and understory canopies using state-of-the-art active reflectance from a multispectral lidar compared with normalized burn ratio (NBR) derived from Landsat 7 ETM+. We find significant correspondence between depth of burn, lidar-derived ANBR, and difference NBR (dNBR) from Landsat. However, low-resolution optical imagery excludes peatland burn losses in transition zones, which are highly sensitive to peat loss via combustion. The findings presented here illustrate the utility of this new remote sensing technology for expanding an area of research where it has previously been challenging to spatially detect and quantify such wildfire burn losses.

Mapping the spatial and temporal dynamics of the velvet mesquite with MODIS and AVIRIS: Case study at the Santa Rita Experimental Range

NASA Astrophysics Data System (ADS)

Kaurivi, Jorry Zebby Ujama

The general objective of this research is to develop a methodology that will allow mapping and quantifying shrub encroachment with remote sensing. The multitemporal properties of the Moderate Resolution Imaging Spectroradiometer (MODIS) -250m, 16-day vegetation index products were combined with the hyperspectral and high spatial resolution (3.6m) computation of the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) to detect the dynamics of mesquite and grass/soil matrix at two sites of high (19.5%) and low (5.7%) mesquite cover in the Santa Rita Experimental Range (SRER). MODIS results showed separability between grassland and mesquite based on phenology. Mesquite landscapes had longer green peak starting in April through February, while the grassland only peaked during the monsoon season (July through October). AVIRIS revealed spectral separability, but high variation in the data implicated high heterogeneity in the landscape. Nonetheless, the methodology for larger data was developed in this study and combines ground, air and satellite data.

Potential of VIIRS Time Series Data for Aiding the USDA Forest Service Early Warning System for Forest Health Threats: A Gypsy Moth Defoliation Case Study

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Ryan, Robert E.; McKellip, Rodney

2008-01-01

The Healthy Forest Restoration Act of 2003 mandated that a national forest threat Early Warning System (EWS) be developed. The USFS (USDA Forest Service) is currently building this EWS. NASA is helping the USFS to integrate remotely sensed data into the EWS, including MODIS data for monitoring forest disturbance at broad regional scales. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for contribution to the EWS. In doing so, the RPC project employed multitemporal simulated VIIRS and MODIS data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria despar). Gypsy moth is an invasive species threatening eastern U.S. hardwood forests. It is one of eight major forest insect threats listed in the Healthy Forest Restoration Act of 2003. This RPC experiment is relevant to several nationally important mapping applications, including carbon management, ecological forecasting, coastal management, and disaster management

Monitoring landslide kinematics by multi-temporal radar interferometry - the Corvara landslide case study

NASA Astrophysics Data System (ADS)

Thiebes, Benni; Cuozzo, Giovanni; Callegari, Mattia; Schlögel, Romy; Mulas, Marco; Corsini, Alessandro; Mair, Volkmar

2016-04-01

Corvara landslide in the Italian Dolomites is slow-moving landslide on which extensive research activities have been carried out since the 1990ies, including sub-surface techniques (e.g. drillings, piezometers and inclinometers), surface methods (e.g. geomorphological mapping and GPS measurements), and remote sensing techniques (e.g. multi-temporal radar interferometry (MTI), and recently amplitude-based offset-tracking and UAV-based photogrammetry). The currently active volume of Corvara landslide has been estimated to be approximately 25 million m³ with shear surfaces at depths of 40 m. Displacement velocities greatly vary spatially and temporally, with only a few cm per year in the accumulation zone, and more than 20 m per year in the highly active source zone. Autumn rainfall and spring snow melt, as well as accumulation of snow during winter have been identified as the major displacement triggering and accelerating events. The ongoing landslide movements pose a threat to the municipality of Corvara, the national road 244, extensive ski resort infrastructure and a golf course. Over the last years, the focus for monitoring the Corvara landslide was put on MTI using 16 artificial corner reflectors and on permanent and periodic differential GPS measurements. This aimed for (1) assessing the ongoing displacements of an active and complex landslide, and (2) analysing the benefits and limitations of MTI for landslide monitoring from the perspective of geomorphologists but also for administrative end-user such as civil protection and Geological surveys. Here, we present the latest results of these analyses, and report on the potential of MTI and related investigations, as well as future fields of research.

Rice crop growth monitoring using ENVISAT-1/ASAR AP mode

NASA Astrophysics Data System (ADS)

Konishi, Tomohisa; Suga, Yuzo; Omatu, Shigeru; Takeuchi, Shoji; Asonuma, Kazuyoshi

2007-10-01

Hiroshima Institute of Technology (HIT) is operating the direct down-links of microwave and optical earth observation satellite data in Japan. This study focuses on the validation for rice crop monitoring using microwave remotely sensed image data acquired by ENIVISAT-1 referring to ground truth data such as height of rice crop, vegetation cover rate and leaf area index in the test sites of Hiroshima district, the western part of Japan. ENVISAT-1/ASAR data has the capabilities for the monitoring of the rice crop growing cycle by using alternating cross polarization mode images. However, ASAR data is influenced by several parameters such as land cover structure, direction and alignment of rice crop fields in the test sites. In this study, the validation was carried out to be combined with microwave image data and ground truth data regarding rice crop fields to investigate the above parameters. Multi-temporal, multi-direction (descending and ascending) and multi-angle ASAR alternating cross polarization mode images were used to investigate during the rice crop growing cycle. On the other hand, LANDSAT-7/ETM+ data were used to detect land cover structure, direction and alignment of rice crop fields corresponding to the backscatter of ASAR. Finally, the extraction of rice planted area was attempted by using multi-temporal ASAR AP mode data such as VV/VH and HH/HV. As the result of this study, it is clear that the estimated rice planted area coincides with the existing statistical data for area of the rice crop field. In addition, HH/HV is more effective than VV/VH in the rice planted area extraction.

Comparative data mining analysis for information retrieval of MODIS images: monitoring lake turbidity changes at Lake Okeechobee, Florida

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Daranpob, Ammarin; Yang, Y. Jeffrey; Jin, Kang-Ren

2009-09-01

In the remote sensing field, a frequently recurring question is: Which computational intelligence or data mining algorithms are most suitable for the retrieval of essential information given that most natural systems exhibit very high non-linearity. Among potential candidates might be empirical regression, neural network model, support vector machine, genetic algorithm/genetic programming, analytical equation, etc. This paper compares three types of data mining techniques, including multiple non-linear regression, artificial neural networks, and genetic programming, for estimating multi-temporal turbidity changes following hurricane events at Lake Okeechobee, Florida. This retrospective analysis aims to identify how the major hurricanes impacted the water quality management in 2003-2004. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite imageries were used to retrieve the spatial patterns of turbidity distributions for comparison against the visual patterns discernible in the in-situ observations. By evaluating four statistical parameters, the genetic programming model was finally selected as the most suitable data mining tool for classification in which the MODIS band 1 image and wind speed were recognized as the major determinants by the model. The multi-temporal turbidity maps generated before and after the major hurricane events in 2003-2004 showed that turbidity levels were substantially higher after hurricane episodes. The spatial patterns of turbidity confirm that sediment-laden water travels to the shore where it reduces the intensity of the light necessary to submerged plants for photosynthesis. This reduction results in substantial loss of biomass during the post-hurricane period.

Monitoring of Building Construction by 4D Change Detection Using Multi-temporal SAR Images

NASA Astrophysics Data System (ADS)

Yang, C. H.; Pang, Y.; Soergel, U.

2017-05-01

Monitoring urban changes is important for city management, urban planning, updating of cadastral map, etc. In contrast to conventional field surveys, which are usually expensive and slow, remote sensing techniques are fast and cost-effective alternatives. Spaceborne synthetic aperture radar (SAR) sensors provide radar images captured rapidly over vast areas at fine spatiotemporal resolution. In addition, the active microwave sensors are capable of day-and-night vision and independent of weather conditions. These advantages make multi-temporal SAR images suitable for scene monitoring. Persistent scatterer interferometry (PSI) detects and analyses PS points, which are characterized by strong, stable, and coherent radar signals throughout a SAR image sequence and can be regarded as substructures of buildings in built-up cities. Attributes of PS points, for example, deformation velocities, are derived and used for further analysis. Based on PSI, a 4D change detection technique has been developed to detect disappearance and emergence of PS points (3D) at specific times (1D). In this paper, we apply this 4D technique to the centre of Berlin, Germany, to investigate its feasibility and application for construction monitoring. The aims of the three case studies are to monitor construction progress, business districts, and single buildings, respectively. The disappearing and emerging substructures of the buildings are successfully recognized along with their occurrence times. The changed substructures are then clustered into single construction segments based on DBSCAN clustering and α-shape outlining for object-based analysis. Compared with the ground truth, these spatiotemporal results have proven able to provide more detailed information for construction monitoring.

Automated Image Registration Using Morphological Region of Interest Feature Extraction

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2005-01-01

With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

Satellite passive microwave remote sensing for estimating diurnal variation of leaf water content, as a proxy of evapotranspiration, in the Dry Chaco Forest, Argentina

NASA Astrophysics Data System (ADS)

Barraza Bernadas, V.; Grings, F.; Ferrazzoli, P.; Carbajo, A.; Fernandez, R.; Karszenbaum, H.

2012-12-01

Evapotranspiration (ET) is a key component of water cycle, which is strongly linked with environmental condition and vegetation functioning. Since it is very difficult to robustly estimate it from remote sensing data at regional scale it is usually inferred from other proxies using water balance. This work describes a procedure to estimate ET in a dry forest by monitoring diurnal variation of leaf water content (LWC), using multitemporal passive microwave remote sensing observations. Hourly observations provide the opportunity to monitor repetitive diurnal variations of passive microwave observations, which can only be accounted by changes in LWC (which is itself related to water vapor that enters to the atmosphere from land surface). To this end, we calculated the vegetation frequency index (FI) as FI= 2*(TBKa-TBX)/ ((TBKa +TBX)), where TBKa and TBX indicate brightness temperatures at 37 and 10.6 GHz respectively. There is both theoretical and experimental evidence that link this index to microwave to LWC. The index was computed for vertical polarization, because it presents higher correlation with vegetation state. At diurnal temporal scale, changes in LWC are commonly very small. Nevertheless, it was previously shown that passive remote sensing data (FI computed using Ku and Ka bands) acquired at different hours can be used to estimate the seasonal changes in ET. In this work, we present a procedure based on the hourly changes of FI, which are interpreted as changes in LWC. In order to present a quantitative estimation, the discrete forest model described in (Ferrazzoli and Guerriero, 1996) has been used to simulate the variations of FI with LWC. To illustrate the procedure, AMSR-E and WINDSAT data from 2007-2009 at X and Ka bands were used, and up to four observations per day at four different local times (2.30 am, 7.00 am, 2.30 pm and 7.00 pm) were analyzed. The region addressed is the area of the Dry Chaco forest located in Bermejo River Basin in Argentina (22-27°S, and 58-66°W).This area is characterized for being an open dry forest (20% of tree crown cover), with mean annual temperatures between 20 and 22 °C, mean summer temperatures between 24 and 27 °C and minimal annual rainfall (500 mm). The annual behavior of diurnal LWC shows a range increase in summer and a decrease in winter, being correlated with vegetation annual growing season (foliation/defoliation). For summertime, our results show a decrease of FI values from 7.00 am to 2.30 pm and an increase between 2.30 pm to 7.00 pm. According to the interaction model, these observed changes of FI corresponded to an increase in LWC from ~0.4 g/g to 0.5 g/g in six hours (7.00 am - 2.30 pm) and then a similar decrease for the 2.30 pm to 7.00 pm period. We hypothesize that this daily variations (an increase of LWC during the sunny hours) could be related to more water being available at the leaves, to cope with evaporation needs in order to achieve positive diurnal water balance during the hot diurnal hours. This could be a specific adaptation to high temperature and drought environmental conditions, like the ones present in Chaco. Ferrazzoli P., Guerriero L.,(1996)"Passive microwave remote sensing of forests: a model investigation", IEEE Transactions on Geoscience and Remote Sensing, vol.34, n. 2, pp.433-443.

Change detection in a time series of polarimetric SAR data by an omnibus test statistic and its factorization (Conference Presentation)

NASA Astrophysics Data System (ADS)

Nielsen, Allan A.; Conradsen, Knut; Skriver, Henning

2016-10-01

Test statistics for comparison of real (as opposed to complex) variance-covariance matrices exist in the statistics literature [1]. In earlier publications we have described a test statistic for the equality of two variance-covariance matrices following the complex Wishart distribution with an associated p-value [2]. We showed their application to bitemporal change detection and to edge detection [3] in multilook, polarimetric synthetic aperture radar (SAR) data in the covariance matrix representation [4]. The test statistic and the associated p-value is described in [5] also. In [6] we focussed on the block-diagonal case, we elaborated on some computer implementation issues, and we gave examples on the application to change detection in both full and dual polarization bitemporal, bifrequency, multilook SAR data. In [7] we described an omnibus test statistic Q for the equality of k variance-covariance matrices following the complex Wishart distribution. We also described a factorization of Q = R2 R3 … Rk where Q and Rj determine if and when a difference occurs. Additionally, we gave p-values for Q and Rj. Finally, we demonstrated the use of Q and Rj and the p-values to change detection in truly multitemporal, full polarization SAR data. Here we illustrate the methods by means of airborne L-band SAR data (EMISAR) [8,9]. The methods may be applied to other polarimetric SAR data also such as data from Sentinel-1, COSMO-SkyMed, TerraSAR-X, ALOS, and RadarSat-2 and also to single-pol data. The account given here closely follows that given our recent IEEE TGRS paper [7]. Selected References [1] Anderson, T. W., An Introduction to Multivariate Statistical Analysis, John Wiley, New York, third ed. (2003). [2] Conradsen, K., Nielsen, A. A., Schou, J., and Skriver, H., "A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing 41(1): 4-19, 2003. [3] Schou, J., Skriver, H., Nielsen, A. A., and Conradsen, K., "CFAR edge detector for polarimetric SAR images," IEEE Transactions on Geoscience and Remote Sensing 41(1): 20-32, 2003. [4] van Zyl, J. J. and Ulaby, F. T., "Scattering matrix representation for simple targets," in Radar Polarimetry for Geoscience Applications, Ulaby, F. T. and Elachi, C., eds., Artech, Norwood, MA (1990). [5] Canty, M. J., Image Analysis, Classification and Change Detection in Remote Sensing,with Algorithms for ENVI/IDL and Python, Taylor & Francis, CRC Press, third revised ed. (2014). [6] Nielsen, A. A., Conradsen, K., and Skriver, H., "Change detection in full and dual polarization, single- and multi-frequency SAR data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(8): 4041-4048, 2015. [7] Conradsen, K., Nielsen, A. A., and Skriver, H., "Determining the points of change in time series of polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing 54(5), 3007-3024, 2016. [9] Christensen, E. L., Skou, N., Dall, J., Woelders, K., rgensen, J. H. J., Granholm, J., and Madsen, S. N., "EMISAR: An absolutely calibrated polarimetric L- and C-band SAR," IEEE Transactions on Geoscience and Remote Sensing 36: 1852-1865 (1998).

QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A report on field monitoring, remote sensing MMV, GIS integration, and modeling results for forestry field validation test to quantify aboveground tree biomass and carbon

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

Lee Spangler; Lee A. Vierling; Eva K. Stand

2012-04-01

Sound policy recommendations relating to the role of forest management in mitigating atmospheric carbon dioxide (CO{sub 2}) depend upon establishing accurate methodologies for quantifying forest carbon pools for large tracts of land that can be dynamically updated over time. Light Detection and Ranging (LiDAR) remote sensing is a promising technology for achieving accurate estimates of aboveground biomass and thereby carbon pools; however, not much is known about the accuracy of estimating biomass change and carbon flux from repeat LiDAR acquisitions containing different data sampling characteristics. In this study, discrete return airborne LiDAR data was collected in 2003 and 2009 acrossmore » {approx}20,000 hectares (ha) of an actively managed, mixed conifer forest landscape in northern Idaho, USA. Forest inventory plots, established via a random stratified sampling design, were established and sampled in 2003 and 2009. The Random Forest machine learning algorithm was used to establish statistical relationships between inventory data and forest structural metrics derived from the LiDAR acquisitions. Aboveground biomass maps were created for the study area based on statistical relationships developed at the plot level. Over this 6-year period, we found that the mean increase in biomass due to forest growth across the non-harvested portions of the study area was 4.8 metric ton/hectare (Mg/ha). In these non-harvested areas, we found a significant difference in biomass increase among forest successional stages, with a higher biomass increase in mature and old forest compared to stand initiation and young forest. Approximately 20% of the landscape had been disturbed by harvest activities during the six-year time period, representing a biomass loss of >70 Mg/ha in these areas. During the study period, these harvest activities outweighed growth at the landscape scale, resulting in an overall loss in aboveground carbon at this site. The 30-fold increase in sampling density between the 2003 and 2009 did not affect the biomass estimates. Overall, LiDAR data coupled with field reference data offer a powerful method for calculating pools and changes in aboveground carbon in forested systems. The results of our study suggest that multitemporal LiDAR-based approaches are likely to be useful for high quality estimates of aboveground carbon change in conifer forest systems.« less

A multi-sensor approach to assess erosion risk in low mountain range landscapes - a comparative case study in western Germany

NASA Astrophysics Data System (ADS)

Seeling, S.; Buddenbaum, H.; Seeger, M.; Löhnertz, M.

2009-04-01

In this presentation we summarize our experience in the derivation of variables for identification of erosion and areas endagered of erosion from different remote sensing sensors. The field study is situated at the "Zemmer-Plateau" (north-east from Trier) and was undertaken to compare the ability of different, passive and active, remote sensing sensors to derive several process parameters of soil erosion in agricultural landscapes. Additionally the added value of sensor combinations was investigated. Backscatter of C-Band microwave instruments is known to be sensitive to soil roughness and surface soil moisture. If landuse and roughness is approximately constant, backscatter is mostly affected by temporal changes in soil moisture. For the test site multitemporal imagery from the ASAR and ERS2 sensors was available. For the identification of areas prone to waterlogging an approach based on principal component analysis was used. Multitemporal imagery from optical sensors like Landsat and SPOT HRV allow the assessment of slow changes within the landscape and annual changes of vegetation cover. We used Landsat imagery from 1975, 1984 and 2000 to map the changes in landuse and associated soil development, multi temporal imagery from SPOT 4 and 5 satellites was used to identify different crop types. Additionally we investigated which areas that are prone to erosion by their topography position, have, due to maladjusted land management, not been protected by vegetation cover during the main annual rainfall season in 2003. Airborne Laser Scanning (ALS) data is well suited for discovering areas susceptible of erosion. Even under forest canopies ALS can provide high-resolution terrain models that can be used for identifying trenches, linear features, steep hills and other terrain features, which trigger erosion or are even results of erosion. ALS-derived DTMs usually have a spatial resolution of about 1 m, while DTMs from other data sources are much coarser. A key problem when working with ALS is finding the echoes that have really been reflected by the ground and not by buildings or vegetation. This is achieved by filtering the last and only return laser points. The investigations were aided be the analyses of two Quickbird datasets. The information layers derived from different sensors were merged into a preliminary erosion information system. This data base allows the identification of areas prone to erosion risk. Furthermore the results allow setting the focus on the most effective methods for further investigations.

MITRA Virtual laboratory for operative application of satellite time series for land degradation risk estimation

NASA Astrophysics Data System (ADS)

Nole, Gabriele; Scorza, Francesco; Lanorte, Antonio; Manzi, Teresa; Lasaponara, Rosa

2015-04-01

This paper aims to present the development of a tool to integrate time series from active and passive satellite sensors (such as of MODIS, Vegetation, Landsat, ASTER, COSMO, Sentinel) into a virtual laboratory to support studies on landscape and archaeological landscape, investigation on environmental changes, estimation and monitoring of natural and anthropogenic risks. The virtual laboratory is composed by both data and open source tools specifically developed for the above mentioned applications. Results obtained for investigations carried out using the implemented tools for monitoring land degradation issues and subtle changes ongoing on forestry and natural areas are herein presented. In detail MODIS, SPOT Vegetation and Landsat time series were analyzed comparing results of different statistical analyses and the results integrated with ancillary data and evaluated with field survey. The comparison of the outputs we obtained for the Basilicata Region from satellite data analyses and independent data sets clearly pointed out the reliability for the diverse change analyses we performed, at the pixel level, using MODIS, SPOT Vegetation and Landsat TM data. Next steps are going to be implemented to further advance the current Virtual Laboratory tools, by extending current facilities adding new computational algorithms and applying to other geographic regions. Acknowledgement This research was performed within the framework of the project PO FESR Basilicata 2007/2013 - Progetto di cooperazione internazionale MITRA "Remote Sensing tecnologies for Natural and Cultural heritage Degradation Monitoring for Preservation and valorization" funded by Basilicata Region Reference 1. A. Lanorte, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbance International Journal of Applied Earth Observation and Geoinformation 2441-446 2. G Calamita, A Lanorte, R Lasaponara, B Murgante, G Nole 2013 Analyzing urban sprawl applying spatial autocorrelation techniques to multi-temporal satellite data. Urban and Regional Data Management: UDMS Annual 2013, 161 3. R Lasaponara 2013 Geospatial analysis from space: Advanced approaches for data processing, information extraction and interpretation International Journal of Applied Earth Observations and Geoinformation 20 . 1-3 4. R Lasaponara, A Lanorte 2011 Satellite time-series analysis International Journal of Remote Sensing 33 (15), 4649-4652 5. G Nolè, M Danese, B Murgante, R Lasaponara, A Lanorte Using spatial autocorrelation techniques and multi-temporal satellite data for analyzing urban sprawl Computational Science and Its Applications-ICCSA 2012, 512-527

Propagation Limitations in Remote Sensing.

DTIC Science & Technology

Contents: Multi-sensors and systems in remote sensing ; Radar sensing systems over land; Remote sensing techniques in oceanography; Influence of...propagation media and background; Infrared techniques in remote sensing ; Photography in remote sensing ; Analytical studies in remote sensing .

Comparison of Hyperspectral and Multispectral Satellites for Forest Alliance Classification in the San Francisco Bay Area

NASA Astrophysics Data System (ADS)

Clark, M. L.

2016-12-01

The goal of this study was to assess multi-temporal, Hyperspectral Infrared Imager (HyspIRI) satellite imagery for improved forest class mapping relative to multispectral satellites. The study area was the western San Francisco Bay Area, California and forest alliances (e.g., forest communities defined by dominant or co-dominant trees) were defined using the U.S. National Vegetation Classification System. Simulated 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery were processed from image data acquired by NASA's AVIRIS airborne sensor in year 2015, with summer and multi-temporal (spring, summer, fall) data analyzed separately. HyspIRI reflectance was used to generate a suite of hyperspectral metrics that targeted key spectral features related to chemical and structural properties. The Random Forests classifier was applied to the simulated images and overall accuracies (OA) were compared to those from real Landsat 8 images. For each image group, broad land cover (e.g., Needle-leaf Trees, Broad-leaf Trees, Annual agriculture, Herbaceous, Built-up) was classified first, followed by a finer-detail forest alliance classification for pixels mapped as closed-canopy forest. There were 5 needle-leaf tree alliances and 16 broad-leaf tree alliances, including 7 Quercus (oak) alliance types. No forest alliance classification exceeded 50% OA, indicating that there was broad spectral similarity among alliances, most of which were not spectrally pure but rather a mix of tree species. In general, needle-leaf (Pine, Redwood, Douglas Fir) alliances had better class accuracies than broad-leaf alliances (Oaks, Madrone, Bay Laurel, Buckeye, etc). Multi-temporal data classifications all had 5-6% greater OA than with comparable summer data. For simulated data, HyspIRI metrics had 4-5% greater OA than Landsat 8 and Sentinel-2 multispectral imagery and 3-4% greater OA than HyspIRI reflectance. Finally, HyspIRI metrics had 8% greater OA than real Landsat 8 imagery. In conclusion, forest alliance classification was found to be a difficult remote sensing application with moderate resolution (30 m) satellite imagery; however, of the data tested, HyspIRI spectral metrics had the best performance relative to multispectral satellites.

A Multitemporal, Multisensor Approach to Mapping the Canadian Boreal Forest

NASA Astrophysics Data System (ADS)

Reith, Ernest

The main anthropogenic source of CO2 emissions is the combustion of fossil fuels, while the clearing and burning of forests contribute significant amounts as well. Vegetation represents a major reservoir for terrestrial carbon stocks, and improving our ability to inventory vegetation will enhance our understanding of the impacts of land cover and climate change on carbon stocks and fluxes. These relationships may be an indication of a series of troubling biosphere-atmospheric feedback mechanisms that need to be better understood and modeled. Valuable land cover information can be provided to the global climate change modeling community using advanced remote sensing capabilities such as Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR). Individually and synergistically, data were successfully used to characterize the complex nature of the Canadian boreal forest land cover types. The multiple endmember spectral mixture analysis process was applied against seasonal AVIRIS data to produce species-level vegetated land cover maps of two study sites in the Canadian boreal forest: Old Black Spruce (OBS) and Old Jack Pine (OJP). The highest overall accuracy was assessed to be at least 66% accurate to the available reference map, providing evidence that high-quality, species-level land cover mapping of the Canadian boreal forest is achievable at accuracy levels greater than other previous research efforts in the region. Backscatter information from multichannel, polarimetric SAR utilizing a binary decision tree-based classification technique methodology was moderately successfully applied to AIRSAR to produce maps of the boreal land cover types at both sites, with overall accuracies at least 59%. A process, centered around noise whitening and principal component analysis features of the minimum noise fraction transform, was implemented to leverage synergies contained within spatially coregistered multitemporal and multisensor AVIRIS and AIRSAR data sets to successfully produce high-accuracy boreal forest land cover maps. Overall land cover map accuracies of 78% and 72% were assessed for OJP and OBS sites, respectively, for either seasonal or multitemporal data sets. High individual land cover accuracies appeared to be independent of site, season, or multisensor combination in the minimum-noise fraction-based approach.

Determination of Destructed and Infracted Forest Areas with Multi-temporal High Resolution Satellite Images

NASA Astrophysics Data System (ADS)

Seker, D. Z.; Unal, A.; Kaya, S.; Alganci, U.

2015-12-01

Migration from rural areas to city centers and their surroundings is an important problem of not only our country but also the countries that under development stage. This uncontrolled and huge amount of migration brings out urbanization and socio - economic problems. The demand on settling the industrial areas and commercial activities nearby the city centers results with a negative change in natural land cover on cities. Negative impacts of human induced activities on natural resources and land cover has been continuously increasing for decades. The main human activities that resulted with destruction and infraction of forest areas can be defined as mining activities, agricultural activities, industrial / commercial activities and urbanization. Temporal monitoring of the changes in spatial distribution of forest areas is significantly important for effective management and planning progress. Changes can occur as spatially large destructions or small infractions. Therefore there is a need for reliable, fast and accurate data sources. At this point, satellite images proved to be a good data source for determination of the land use /cover changes with their capability of monitoring large areas with reasonable temporal resolutions. Spectral information derived from images provides discrimination of land use/cover types from each other. Developments in remote sensing technology in the last decade improved the spatial resolution of satellites and high resolution images were started to be used to detect even small changes in the land surface. As being the megacity of Turkey, Istanbul has been facing a huge migration for the last 20 years and effects of urbanization and other human based activities over forest areas are significant. Main focus of this study is to determine the destructions and infractions in forest areas of Istanbul, Turkey with 2.5m resolution SPOT 5 multi-temporal satellite imagery. Analysis was mainly constructed on threshold based classification of multi-temporal vegetation index data derived from satellite images. Determined changes were exported to GIS environment and spatial overlay and intersection analyses were performed with use of forest type maps and authorized area maps in order to demonstrate the actual situation of destructions and infractions.

Active fire detection using a peat fire radiance model

NASA Astrophysics Data System (ADS)

Kushida, K.; Honma, T.; Kaku, K.; Fukuda, M.

2011-12-01

The fire fractional area and radiances at 4 and 11 μm of active fires in Indonesia were estimated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. Based on these fire information, a stochastic fire model was used for evaluating two fire detection algorithms of Moderate Resolution Imaging Spectroradiometer (MODIS). One is single-image stochastic fire detection, and the other is multitemporal stochastic fire detection (Kushida, 2010 - IEEE Geosci. Remote Sens. Lett.). The average fire fractional area per one 1 km2 ×1 km2 pixel was 1.7%; this value corresponds to 32% of that of Siberian and Mongolian boreal forest fires. The average radiances at 4 and 11 μm of active fires were 7.2 W/(m2.sr.μm) and 11.1 W/(m2.sr.μm); these values correspond to 47% and 91% of those of Siberian and Mongolian boreal forest fires, respectively. In order to get false alarms less than 20 points per 106 km2 area, for the Siberian and Mongolian boreal forest fires, omission errors (OE) of 50-60% and about 40% were expected for the detections by using the single and multitemporal images, respectively. For Indonesian peat fires, OE of 80-90% was expected for the detections by using the single images. For the peat-fire detections by using the multitemporal images, OE of about 40% was expected, provided that the background radiances were estimated from past multitemporal images with less than the standard deviation of 1K. The analyses indicated that it was difficult to obtain sufficient active-fire information of Indonesian peat fires from single MODIS images for the fire fighting, and that the use of the multitemporal images was important.

Earth view: A business guide to orbital remote sensing

NASA Technical Reports Server (NTRS)

Bishop, Peter C.

1990-01-01

The following subject areas are covered: Earth view - a guide to orbital remote sensing; current orbital remote sensing systems (LANDSAT, SPOT image, MOS-1, Soviet remote sensing systems); remote sensing satellite; and remote sensing organizations.

Planning Tripoli Metro Network by the Use of Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Alhusain, O.; Engedy, Gy.; Milady, A.; Paulini, L.; Soos, G.

2012-08-01

Tripoli, the capital city of Libya is going through significant and integrated development process, this development is expected to continue in the next few decades. The Libyan authorities have put it as their goal to develop Tripoli to an important metropolis in North Africa. To achieve this goal, they identified goals for the city's future development in all human, economic, cultural, touristic, and nonetheless infrastructure levels. On the infrastructure development level, among other things, they have identified the development of public transportation as one of the important development priorities. At present, public transportation in Tripoli is carried out by a limited capacity bus network alongside of individual transportation. However, movement in the city is characterized mainly by individual transportation with all its disadvantages such as traffic jams, significant air pollution with both carbon monoxide and dust, and lack of parking space. The Libyan authorities wisely opted for an efficient, modern, and environment friendly solution for public transportation, this was to plan a complex Metro Network as the backbone of public transportation in the city, and to develop and integrate the bus network and other means of transportation to be in harmony with the planned Metro network. The Metro network is planned to provide convenient connections to Tripoli International Airport and to the planned Railway station. They plan to build a system of Park and Ride (P+R) facilities at suitable locations along the Metro lines. This paper will present in details the planned Metro Network, some of the applied technological solutions, the importance of applying remote sensing and GIS technologies in different planning phases, and problems and benefits associated with the use of multi-temporal-, multi-format spatial data in the whole network planning phase.

Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates.

PubMed

Carrea, Laura; Embury, Owen; Merchant, Christopher J

2015-11-01

Datasets containing information to locate and identify water bodies have been generated from data locating static-water-bodies with resolution of about 300 m (1/360 ∘ ) recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The LC CCI water-bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR on Envisat between 2005 and 2010. The new derived datasets provide coherently: distance to land, distance to water, water-body identifiers and lake-centre locations. The water-body identifier dataset locates the water bodies assigning the identifiers of the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. Information on distance-to-land for each water cell and the distance-to-water for each land cell has many potential applications in remote sensing, where the applicability of geophysical retrieval algorithms may be affected by the presence of water or land within a satellite field of view (image pixel). During the generation and validation of the datasets some limitations of the GLWD database and of the LC CCI water-bodies mask have been found. Some examples of the inaccuracies/limitations are presented and discussed. Temporal change in water-body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated.

Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery.

PubMed

Chen, Zhong; Zhang, Yifei; Ouyang, Chao; Zhang, Feng; Ma, Jie

2018-03-09

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities.

Using aerial images for establishing a workflow for the quantification of water management measures

NASA Astrophysics Data System (ADS)

Leuschner, Annette; Merz, Christoph; van Gasselt, Stephan; Steidl, Jörg

2017-04-01

Quantified landscape characteristics, such as morphology, land use or hydrological conditions, play an important role for hydrological investigations as landscape parameters directly control the overall water balance. A powerful assimilation and geospatial analysis of remote sensing datasets in combination with hydrological modeling allows to quantify landscape parameters and water balances efficiently. This study focuses on the development of a workflow to extract hydrologically relevant data from aerial image datasets and derived products in order to allow an effective parametrization of a hydrological model. Consistent and self-contained data source are indispensable for achieving reasonable modeling results. In order to minimize uncertainties and inconsistencies, input parameters for modeling should be extracted from one remote-sensing dataset mainly if possbile. Here, aerial images have been chosen because of their high spatial and spectral resolution that permits the extraction of various model relevant parameters, like morphology, land-use or artificial drainage-systems. The methodological repertoire to extract environmental parameters range from analyses of digital terrain models, multispectral classification and segmentation of land use distribution maps and mapping of artificial drainage-systems based on spectral and visual inspection. The workflow has been tested for a mesoscale catchment area which forms a characteristic hydrological system of a young moraine landscape located in the state of Brandenburg, Germany. These dataset were used as input-dataset for multi-temporal hydrological modelling of water balances to detect and quantify anthropogenic and meteorological impacts. ArcSWAT, as a GIS-implemented extension and graphical user input interface for the Soil Water Assessment Tool (SWAT) was chosen. The results of this modeling approach provide the basis for anticipating future development of the hydrological system, and regarding system changes for the adaption of water resource management decisions.

Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater.

PubMed

Kobryn, H T; Lantzke, R; Bell, R; Admiraal, R

2015-03-01

The installation of deep drains is an engineering approach to remediate land salinised by the influence of shallow groundwater. It is a costly treatment and its economic viability is, in part, dependent on the lateral extent to which the drain increases biological productivity by lowering water tables and soil salinity (referred to as the drains' zone of benefit). Such zones may be determined by assessing the biological productivity response of adjacent vegetation over time. We tested a multi-temporal satellite remote sensing method to analyse temporal and spatial changes in vegetation condition surrounding deep drainage sites at five locations in the Western Australian wheatbelt affected by dryland salinity-Morawa, Pithara, Beacon, Narembeen and Dumbleyung. Vegetation condition as a surrogate for biological productivity was assessed by Normalised Difference Vegetation Index (NDVI) during the peak growing season. Analysis was at the site scale within a 1000 m buffer zone from the drains. There was clear evidence of NDVI increasing with elevation, slope and distance from the drain. After accounting for elevation, slope and distance from the drain, there was a significant increase in NDVI across the five locations after installation of deep drains. Changes in NDVI after drainage were broadly consistent with measured changes at each site in groundwater levels after installation of the deep drains. However, this study assessed the lateral extent of benefit for biological productivity and gave a measure of the area of benefit along the entire length of the drain. The method demonstrated the utility of spring NDVI images for rapid and relatively simple assessment of the change in site condition after implementation of drainage, but approaches for further improvement of the procedure were identified. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery

PubMed Central

Chen, Zhong; Zhang, Yifei; Ouyang, Chao; Zhang, Feng; Ma, Jie

2018-01-01

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities. PMID:29522424

Improving Landslide Susceptibility Modeling Using an Empirical Threshold Scheme for Excluding Landslide Deposition

NASA Astrophysics Data System (ADS)

Tsai, F.; Lai, J. S.; Chiang, S. H.

2015-12-01

Landslides are frequently triggered by typhoons and earthquakes in Taiwan, causing serious economic losses and human casualties. Remotely sensed images and geo-spatial data consisting of land-cover and environmental information have been widely used for producing landslide inventories and causative factors for slope stability analysis. Landslide susceptibility, on the other hand, can represent the spatial likelihood of landslide occurrence and is an important basis for landslide risk assessment. As multi-temporal satellite images become popular and affordable, they are commonly used to generate landslide inventories for subsequent analysis. However, it is usually difficult to distinguish different landslide sub-regions (scarp, debris flow, deposition etc.) directly from remote sensing imagery. Consequently, the extracted landslide extents using image-based visual interpretation and automatic detections may contain many depositions that may reduce the fidelity of the landslide susceptibility model. This study developed an empirical thresholding scheme based on terrain characteristics for eliminating depositions from detected landslide areas to improve landslide susceptibility modeling. In this study, Bayesian network classifier is utilized to build a landslide susceptibility model and to predict sequent rainfall-induced shallow landslides in the Shimen reservoir watershed located in northern Taiwan. Eleven causative factors are considered, including terrain slope, aspect, curvature, elevation, geology, land-use, NDVI, soil, distance to fault, river and road. Landslide areas detected using satellite images acquired before and after eight typhoons between 2004 to 2008 are collected as the main inventory for training and verification. In the analysis, previous landslide events are used as training data to predict the samples of the next event. The results are then compared with recorded landslide areas in the inventory to evaluate the accuracy. Experimental results demonstrate that the accuracies of landslide susceptibility analysis in all sequential predictions have been improved significantly after eliminating landslide depositions.

Remote sensing and spatial analysis based study for detecting deforestation and the associated drivers

NASA Astrophysics Data System (ADS)

El-Abbas, Mustafa M.; Csaplovics, Elmar; Deafalla, Taisser H.

2013-10-01

Nowadays, remote-sensing technologies are becoming increasingly interlinked to the issue of deforestation. They offer a systematized and objective strategy to document, understand and simulate the deforestation process and its associated causes. In this context, the main goal of this study, conducted in the Blue Nile region of Sudan, in which most of the natural habitats were dramatically destroyed, was to develop spatial methodologies to assess the deforestation dynamics and its associated factors. To achieve that, optical multispectral satellite scenes (i.e., ASTER and LANDSAT) integrated with field survey in addition to multiple data sources were used for the analyses. Spatiotemporal Object Based Image Analysis (STOBIA) was applied to assess the change dynamics within the period of study. Broadly, the above mentioned analyses include; Object Based (OB) classifications, post-classification change detection, data fusion, information extraction and spatial analysis. Hierarchical multi-scale segmentation thresholds were applied and each class was delimited with semantic meanings by a set of rules associated with membership functions. Consequently, the fused multi-temporal data were introduced to create detailed objects of change classes from the input LU/LC classes. The dynamic changes were quantified and spatially located as well as the spatial and contextual relations from adjacent areas were analyzed. The main finding of the present study is that, the forest areas were drastically decreased, while the agrarian structure in conversion of forest into agricultural fields and grassland was the main force of deforestation. In contrast, the capability of the area to recover was clearly observed. The study concludes with a brief assessment of an 'oriented' framework, focused on the alarming areas where serious dynamics are located and where urgent plans and interventions are most critical, guided with potential solutions based on the identified driving forces.

Tracing grassland degradation on the Eastern Tibetan Plateau with multi-temporal remote sensing data

NASA Astrophysics Data System (ADS)

Fassnacht, Fabian E.; Li, Li; Fritz, Andreas

2017-04-01

The Tibetan Plateau in Western China is the world's largest alpine landscape, sheltering a rich diversity of native flora and fauna. In the past few decades, the Tibetan Plateau was found to suffer from grassland degradation processes. Grassland degradation is assumed to not only endanger biodiversity but also to increase the risk for natural hazards in other parts of the country which are ecologically and hydrologically connected to the area. Grassland degradation is furthermore, changing the albedo of the surfaces of the Plateau and may therefore even notably affect atmospheric and climatic processes. However, the mechanisms behind the degradation processes remain poorly understood due to scarce baseline data and insufficient scientific research as well as manifold potential influences on the degradation processes including pastoral management, climate, herbivor mammals and administrative decisions. This study tries to contribute to this research gap by tracing grassland degradation processes by time-series analysis of multi-spectral Landsat data. After identifying the degraded areas, it is examined whether the degradation patterns relate to topographic properties, climatic gradients or administrative borders. Results from a first study showed that most degradation occurred in high-altitude areas, while slope and aspect where not having a notable influence. Furthermore, a climatic gradient within the study area was found to correlate with the degradation patterns observed for large extents. Currently, the study is being expanded over a larger area and more detailed spatially-adaptive analysis concerning the degradation drivers are being developed. Corresponding results will be presented. We conclude that remotely sensed patterns of grassland degradation can contribute to an improved understanding of the degradation processed on the Tibetan Plateau by providing spatially and temporally explicit information on the degradation processes at an adequate scale.

Wet Snow Mapping in Southern Ontario with Sentinel-1A Observations

NASA Astrophysics Data System (ADS)

Chen, H.; Kelly, R. E. J.

2017-12-01

Wet snow is defined as snow with liquid water present in an ice-water mix. It is can be an indicator for the onset of the snowmelt period. Knowledge about the extent of wet snow area can be of great importance for the monitoring of seasonal snowmelt runoff with climate-induced changes in snowmelt duration having implications for operational hydrological and ecological applications. Spaceborne microwave remote sensing has been used to observe seasonal snow under all-weather conditions. Active microwave observations of snow at C-band are sensitive to wet snow due to the high dielectric contrast with non-wet snow surfaces and synthetic aperture radar (SAR) is now openly available to identify and map the wet snow areas globally at relatively fine spatial resolutions ( 100m). In this study, a semi-automated workflow is developed from the change detection method of Nagler et al. (2016) using multi-temporal Sentinel-1A (S1A) dual-polarization observations of Southern Ontario. Weather station data and visible-infrared satellite observations are used to refine the wet snow area estimates. Wet snow information from National Operational Hydrologic Remote Sensing Center (NOHRSC) is used to compare with the S1A estimates. A time series of wet snow maps shows the variations in backscatter from wet snow on a pixel basis. Different land cover types in Southern Ontario are assessed with respect to their impacts on wet snow estimates. While forests and complex land surfaces can impact the ability to map wet snow, the approach taken is robust and illustrates the strong sensitivity of the approach to wet snow backscattering characteristics. The results indicate the feasibility of the change detection method on non-mountainous large areas and address the usefulness of Sentinel-1A data for wet snow mapping.

Soil aggregate stability and wind erodible fraction in a semi-arid environment of White Nile State, Sudan

NASA Astrophysics Data System (ADS)

Elhaja, Mohamed Eltom; Ibrahim, Ibrahim Saeed; Adam, Hassan Elnour; Csaplovics, Elmar

2014-11-01

One of the most important recent issues facing White Nile State, Sudan, as well as Sub Saharan Africa, is the threat of continued land degradation and desertification as a result of climatic factors and human activities. Remote sensing and satellites imageries with multi-temporal and spectral and GIS capability, plays a major role in developing a global and local operational capability for monitoring land degradation and desertification in dry lands, as well as in White Nile State. The process of desertification in form of sand encroachment in White Nile State has increased rapidly, and much effort has been devoted to define and study its causes and impacts. This study depicts the capability afforded by remote sensing and GIS to analyze and map the aggregate stability as indicator for the ability of soil to wind erosion process in White Nile State by using Geo-statistical techniques. Cloud-free subset Landsat; Enhance Thematic Mapper plus (ETM +) scenes covering the study area dated 2008 was selected in order to identify the different features covering the study area as well as to make the soil sampling map. Wet-sieving method was applied to determine the aggregate stability. The geo-statistical methods in EARDAS 9.1 software was used for mapping the aggregate stability. The results showed that the percentage of aggregate stability ranged from (0 to 61%) in the study area, which emphasized the phenomena of sand encroachment from the western part (North Kordofan) to the eastern part (White Nile State), following the wind direction. The study comes out with some valuable recommendations and comments, which could contribute positively in reducing sand encroachments

Analysis of Urban Growth in Edwardsville Illinois Using Remote Sensing and Population Change

NASA Astrophysics Data System (ADS)

Onuoha, Hilda U.

Rapid urbanization is one of the many critical, global issues. This very significant social and economic phenomenon has brought about much debate in the past twenty years and has become a very important policy issue. Understanding its dynamics and patterns is important to develop appropriate policies and make more informed planning decisions. Many dimensions to the urban land growth have been identified in related literature including drivers, relationship with other factors like population, impacts, and methods of measurement. In this study, urban growth in the Edwardsville area (composed of Edwardsville and Glen Carbon, Illinois) is analyzed spatio-temporally using remote sensing and population change from 1990 to 2015. The objectives of this study are (a) identifying the major land use changes in the Edwardsville area from 1990 to 2015, (b) analyzing the rate of urban growth and its relationship to population change in the area from 1990 to 2015, (c) identifying the general pattern and direction of urban growth in the study area. Using multi-temporal satellite images to classify and derive changes in land cover classes during the study period, results showed that the land cover classes with major changes are the urban/built-up land and agricultural/grassland, with a steady increase in the former and steady decrease in the later. Results also show the highest rate of increase in urban land was between 2000 and 2010. In comparison to population, the both show increase over the study years but urban land shows a higher rate of increase indicating dispersion. To analyze urban growth pattern in the area, the study area was divided into three zones: NE, SE, and W. The SE zone showed the highest amount of the growth and from the results, the infill type of growth was inferred.

Technology study of quantum remote sensing imaging

NASA Astrophysics Data System (ADS)

Bi, Siwen; Lin, Xuling; Yang, Song; Wu, Zhiqiang

2016-02-01

According to remote sensing science and technology development and application requirements, quantum remote sensing is proposed. First on the background of quantum remote sensing, quantum remote sensing theory, information mechanism, imaging experiments and prototype principle prototype research situation, related research at home and abroad are briefly introduced. Then we expounds compress operator of the quantum remote sensing radiation field and the basic principles of single-mode compression operator, quantum quantum light field of remote sensing image compression experiment preparation and optical imaging, the quantum remote sensing imaging principle prototype, Quantum remote sensing spaceborne active imaging technology is brought forward, mainly including quantum remote sensing spaceborne active imaging system composition and working principle, preparation and injection compression light active imaging device and quantum noise amplification device. Finally, the summary of quantum remote sensing research in the past 15 years work and future development are introduced.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

Semi-Automatic Normalization of Multitemporal Remote Images Based on Vegetative Pseudo-Invariant Features

PubMed Central

Garcia-Torres, Luis; Caballero-Novella, Juan J.; Gómez-Candón, David; De-Castro, Ana Isabel

2014-01-01

A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method’s efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified. PMID:24604031

Inside and around the roman town of Grumentum: the contribution of LiDAR and historical air photography

NASA Astrophysics Data System (ADS)

Cianciarulo, Dario; Guariglia, Annibale; Lasaponara, Rosa; Masini, Nicola

2013-04-01

The papers deals with the integration of aerial laser scanning, multitemporal satellite and aerial dataset to provide information on the 'forma urbis' of the Grumentum roman town, to detect new archaeological features in its close surrounding and to analyze changes of the landscape over the time. Grumentum is an ancient town, 50 km south of Potenza (Southern Italy), located near the 'Via Herculea' connecting Venusia, in the north est of Basilicata, with Heraclea in the Ionian coast. The first settlement date back to the 6th century BC. Then, it was resettled by the Romans in the 3rd century BC. The town, which evidences a long history from the Republican age to late Antiquity (III BC-V AD), is characterized by the typical urban pattern of 'cardi' and 'decumani'. Its excavated ruins include a large amphitheatre, a theatre, the thermae, the Forum and some temples. LiDAR data, adequately filtered, classified and post processed by using geostatistics methods(Lasaponara et al. 2012), enabled to detect features linked to tombs under a dense vegetation located close to the urban perimeter. The analysis of historical air photos, draped over the ground surface obtained from the LiDAR survey, put in evidence some unknown crop-marks linked to roman urban fabric. Finally, the same photos along with the satellite multitemporal dataset allowed us to reconstruct the recent history of the landscape from the Agrarian Reform, in the 50s, up today. Reference Lasaponara R., Masini N., Holmgren R., Backe Forsberg Y., Integration of aerial and satellite remote sensing for archaeological investigations: a case study of the Etruscan site San Giovenale, Journal of Geophysics and Engineering, vol. 9, S26-S39, doi:10.1088/1742-2132/9/4/S26

Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

NASA Astrophysics Data System (ADS)

Shelestov, Andrii; Lavreniuk, Mykola; Kussul, Nataliia; Novikov, Alexei; Skakun, Sergii

2017-02-01

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory ( 28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

Monitoring the effect of restoration measures in Indonesian peatlands by radar satellite imagery.

PubMed

Jaenicke, J; Englhart, S; Siegert, F

2011-03-01

In the context of the ongoing climate change discussions the importance of peatlands as carbon stores is increasingly recognised in the public. Drainage, deforestation and peat fires are the main reasons for the release of huge amounts of carbon from peatlands. Successful restoration of degraded tropical peatlands is of high interest due to their huge carbon store and sequestration potential. The blocking of drainage canals by dam building has become one of the most important measures to restore the hydrology and the ecological function of the peat domes. This study investigates the capability of using multitemporal radar remote sensing imagery for monitoring the hydrological effects of these measures. The study area is the former Mega Rice Project area in Central Kalimantan, Indonesia, where peat drainage and forest degradation is especially intense. Restoration measures started in July 2004 by building 30 large dams until June 2008. We applied change detection analysis with more than 80 ENVISAT ASAR and ALOS PALSAR images, acquired between 2004 and 2009. Radar signal increases of up to 1.36 dB show that high frequency multitemporal radar satellite imagery can be used to detect an increase in peat soil moisture after dam construction, especially in deforested areas with a high density of dams. Furthermore, a strong correlation between cross-polarised radar backscatter coefficients and groundwater levels above -50 cm was found. Monitoring peatland rewetting and quantifying groundwater level variations is important information for vegetation re-establishment, fire hazard warning and making carbon emission mitigation tradable under the voluntary carbon market or REDD (Reducing Emissions from Deforestation and Degradation) mechanism. Copyright © 2010 Elsevier Ltd. All rights reserved.

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm.

PubMed

Naghibi, Fereydoun; Delavar, Mahmoud Reza; Pijanowski, Bryan

2016-12-14

Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm

PubMed Central

Naghibi, Fereydoun; Delavar, Mahmoud Reza; Pijanowski, Bryan

2016-01-01

Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits. PMID:27983633

Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm

NASA Astrophysics Data System (ADS)

Liu, Meiling; Liu, Xiangnan; Li, Jin; Ding, Chao; Jiang, Jiale

2014-12-01

Satellites routinely provide frequent, large-scale, near-surface views of many oceanographic variables pertinent to plankton ecology. However, the nutrient fertility of water can be challenging to detect accurately using remote sensing technology. This research has explored an approach to estimate the nutrient fertility in coastal waters through the fusion of synthetic aperture radar (SAR) images and optical images using the random forest (RF) algorithm. The estimation of total inorganic nitrogen (TIN) in the Hong Kong Sea, China, was used as a case study. In March of 2009 and May and August of 2010, a sequence of multi-temporal in situ data and CCD images from China's HJ-1 satellite and RADARSAT-2 images were acquired. Four sensitive parameters were selected as input variables to evaluate TIN: single-band reflectance, a normalized difference spectral index (NDSI) and HV and VH polarizations. The RF algorithm was used to merge the different input variables from the SAR and optical imagery to generate a new dataset (i.e., the TIN outputs). The results showed the temporal-spatial distribution of TIN. The TIN values decreased from coastal waters to the open water areas, and TIN values in the northeast area were higher than those found in the southwest region of the study area. The maximum TIN values occurred in May. Additionally, the estimation accuracy for estimating TIN was significantly improved when the SAR and optical data were used in combination rather than a single data type alone. This study suggests that this method of estimating nutrient fertility in coastal waters by effectively fusing data from multiple sensors is very promising.

Applications of Remote Sensing to Emergency Management.

DTIC Science & Technology

1980-02-15

Contents: Foundations of Remote Sensing : Data Acquisition and Interpretation; Availability of Remote Sensing Technology for Disaster Response...Imaging Systems, Current and Near Future Satellite and Aircraft Remote Sensing Systems; Utilization of Remote Sensing in Disaster Response: Categories of...Disasters, Phases of Monitoring Activities; Recommendations for Utilization of Remote Sensing Technology in Disaster Response; Selected Reading List.

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

REMOTE SENSING TECHNOLOGIES APPLICATIONS RESEARCH

EPA Science Inventory

Remote sensing technologies applications research supports the ORD Landscape Sciences Program (LSP) in two separate areas: operational remote sensing, and remote sensing research and development. Operational remote sensing is provided to the LSP through the use of current and t...

The Dynamics of Population, Built-up Areas and their Evolving Associations in Gridded Population across Time and Space

NASA Astrophysics Data System (ADS)

Stevens, F. R.; Gaughan, A. E.; Tatem, A. J.; Linard, C.; Sorichetta, A.; Nieves, J. J.; Reed, P.

2017-12-01

Gridded population data is commonly used to understand the `now' of hazard risk and mitigation management, health and disease modelling, and global change-, economic-, environmental-, and sustainability-related research. But to understand how human population change at local to global scales influences and is influenced by environmental changes requires novel ways of treating data and statistically describing associations of measured population counts with associated covariates. One of the most critical components in such gridded estimates is the relationship between built-up areas and population located in these areas. This relationship is rarely static and accurately estimating changes in built-areas through time and the changing human population around them is critical when applying gridded population datasets in studies of other environmental change. The research presented here discusses these issues in the context of multitemporal, gridded population data, using new technologies and sources of remotely-sensed and modeled built-up areas. We discuss applications of these data in environmental analyses and intercomparisons with other such data across scales.

Long term changes in forest cover and land use of Similipal Biosphere Reserve of India using satellite remote sensing data

NASA Astrophysics Data System (ADS)

Saranya, K. R. L.; Reddy, C. Sudhakar

2016-04-01

The spatial changes in forest cover of Similipal biosphere reserve, Odisha, India over eight decades (1930-2012) has been quantified by using multi-temporal data from different sources. Over the period, the forest cover reduced by 970.8 km2 (23.6% of the total forest), and most significantly during the period, 1930-1975. Human-induced activities like conversion of forest land for agriculture, construction of dams and mining activities have been identified as major drivers of deforestation. Spatial analysis indicates that 399 grids (1 grid = 1 × 1 km) have undergone large-scale changes in forest cover (>75 ha) during 1930-1975, while only 3 grids have shown >75 ha loss during 1975-1990. Annual net rate of deforestation was 0.58 during 1930-1975, which has been reduced substantially during 1975-1990 (0.04). Annual gross rate of deforestation in 2006-2012 is indeed low (0.01) as compared to the national and global average. This study highlights the impact and effectiveness of conservation practices in minimizing the rate of deforestation and protecting the Similipal Biosphere Reserve.

Quantifying multi-temporal urban development characteristics in Las Vegas from Landsat and ASTER data

USGS Publications Warehouse

Xian, G.; Crane, M.; McMahon, C.

2008-01-01

Urban development has expanded rapidly in Las Vegas, Nevada of the United States, over the last fifty years. A major environmental change associated with this urbanization trend is the transformation of the landscape from natural cover types to increasingly anthropogenic impervious surface. This research utilizes remote sensing data from both the Landsat and Terra-Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instruments in conjunction with digital orthophotography to estimate urban extent and its temporal changes by determining sub-pixel impervious surfaces. Percent impervious surface area has shown encouraging agreement with urban land extent and development density. Results indicate that total urban land-use increases approximately 110 percent from 1984 to 2002. Most of the increases are associated with medium-to high-density urban development. Places having significant increases in impervious surfaces are in the northwestern and southeastern parts of Las Vegas. Most high-density urban development, however, appears in central Las Vegas. Impervious surface conditions for 2002 measured from Landsat and ASTER satellite data are compared in terms of their accuracy.

[Relationships of wetland landscape fragmentation with climate change in middle reaches of Heihe River, China].

PubMed

Jiang, Peng-Hui; Zhao, Rui-Feng; Zhao, Hai-Li; Lu, Li-Peng; Xie, Zuo-Lun

2013-06-01

Based on the 1975-2010 multi-temporal remotely sensed TM and ETM images and meteorological data, in combining with wavelet analysis, trend surface simulation, and interpolation method, this paper analyzed the meteorological elements' spatial distribution and change characteristics in the middle reaches of Heihe River, and elucidated the process of wetland landscape fragmentation in the study area by using the landscape indices patch density (PD), mean patch size (MPS), and patch shape fragment index (FS). The relationships between the wetland landscape fragmentation and climate change were also approached through correlation analysis and multiple stepwise regression analysis. In 1975-2010, the overall distribution patterns of precipitation and temperature in the study area were low precipitation in high temperature regions and high precipitation in low temperature regions, and the main characteristics of climate change were the conversion from dry to wet and from cold to warm. In the whole study period, the wetland landscape fragmentation was mainly manifested in the decrease of MPS, with a decrement of 48.95 hm2, and the increase of PD, with an increment of 0.006 ind x hm(-2).

Continuous Change Detection of Urban Lakes in Wuhan, China Using Multi-Temporal Remote Sensing Images

NASA Astrophysics Data System (ADS)

Zhang, W.; Kong, X.; Tan, G.; Zheng, S.

2018-04-01

Urban lakes are important natural, scenic and pattern attractions of the city, and they are potential development resources as well. However, lots of urban lakes in China have been shrunk significantly or disappeared due to rapid urbanization. In this study, four Landsat images were used to perform a case study for lake change detection in downtown Wuhan, China, which were acquired on 1991, 2002, 2011 and 2017, respectively. Modified NDWI (MNDWI) was adopted to extract water bodies of urban areas from all these images, and OTSU was used to optimize the threshold selection. Furthermore, the variation of lake shrinkage was analysed in detail according to SVM classification and post-classification comparison, and the coverage of urban lakes in central area of Wuhan has decreased by 47.37 km2 between 1991 and 2017. The experimental results revealed that there were significant changes in the surface area of urban lakes over the 27 years, and it also indicated that rapid urbanization has a strong impact on the losses of urban water resources.

Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach

PubMed Central

Alexakis, Dimitrios D.; Mexis, Filippos-Dimitrios K.; Vozinaki, Anthi-Eirini K.; Daliakopoulos, Ioannis N.; Tsanis, Ioannis K.

2017-01-01

A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R2 values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies. PMID:28635625

Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach.

PubMed

Alexakis, Dimitrios D; Mexis, Filippos-Dimitrios K; Vozinaki, Anthi-Eirini K; Daliakopoulos, Ioannis N; Tsanis, Ioannis K

2017-06-21

A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R² values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies.

[Monitoring "green tide" in the Yellow Sea and the East China Sea using multi-temporal and multi-source remote sensing images].

PubMed

Xing, Qian-Guo; Zheng, Xiang-Yang; Shi, Ping; Hao, Jia-Jia; Yu, Ding-Feng; Liang, Shou-Zhen; Liu, Dong-Yan; Zhang, Yuan-Zhi

2011-06-01

Landsat-TM (Theme Mapper) and EOS (Earth Observing System)-MODIS (MODerate resolution Imaging Spectrora-diometer) Terra/Aqua images were used to monitor the macro-algae (Ulva prolifera) bloom since 2007 at the Yellow Sea and the East China Sea. At the turbid waters of Northern Jiangsu Shoal, there is strong spectral mixing behavior, and satellite images with finer spatical resolution are more effective in detection of macro-algae patches. Macro-algae patches were detected by the Landsat images for the first time at the Sheyang estuary where is dominated by very turbid waters. The MODIS images showed that the macro-algae from the turbid waters near the Northern Jiangsu Shoal drifted southwardly in the early of May and affected the East China Sea waters; with the strengthening east-asian Summer Monsoon, macro-algae patches mainly drifted in a northward path which was mostly observed at the Yellow Sea. Macro-algae patches were also found to drift eastwardly towards the Korea Peninsular, which are supposed to be driven by the sea surface wind.

Forest canopy growth dynamic modeling based on remote sensing prodcuts and meteorological data in Daxing'anling of Northeast China

NASA Astrophysics Data System (ADS)

Wu, Qiaoli; Song, Jinling; Wang, Jindi; Xiao, Zhiqiang

2014-11-01

Leaf Area Index (LAI) is an important biophysical variable for vegetation. Compared with vegetation indexes like NDVI and EVI, LAI is more capable of monitoring forest canopy growth quantitatively. GLASS LAI is a spatially complete and temporally continuous product derived from AVHRR and MODIS reflectance data. In this paper, we present the approach to build dynamic LAI growth models for young and mature Larix gmelinii forest in north Daxing'anling in Inner Mongolia of China using the Dynamic Harmonic Regression (DHR) model and Double Logistic (D-L) model respectively, based on the time series extracted from multi-temporal GLASS LAI data. Meanwhile we used the dynamic threshold method to attract the key phenological phases of Larix gmelinii forest from the simulated time series. Then, through the relationship analysis between phenological phases and the meteorological factors, we found that the annual peak LAI and the annual maximum temperature have a good correlation coefficient. The results indicate this forest canopy growth dynamic model to be very effective in predicting forest canopy LAI growth and extracting forest canopy LAI growth dynamic.

Efficient Method for Scalable Registration of Remote Sensing Images

NASA Astrophysics Data System (ADS)

Prouty, R.; LeMoigne, J.; Halem, M.

2017-12-01

The goal of this project is to build a prototype of a resource-efficient pipeline that will provide registration within subpixel accuracy of multitemporal Earth science data. Accurate registration of Earth-science data is imperative to proper data integration and seamless mosaicing of data from multiple times, sensors, and/or observation geometries. Modern registration methods make use of many arithmetic operations and sometimes require complete knowledge of the image domain. As such, while sensors become more advanced and are able to provide higher-resolution data, the memory resources required to properly register these data become prohibitive. The proposed pipeline employs a region of interest extraction algorithm in order to extract image subsets with high local feature density. These image subsets are then used to generate local solutions to the global registration problem. The local solutions are then 'globalized' to determine the deformation model that best solves the registration problem. The region of interest extraction and globalization routines are tested for robustness among the variety of scene-types and spectral locations provided by Earth-observing instruments such as Landsat, MODIS, or ASTER.

Multi-temporal remote sensing analysis of salars in El Loa Province, Chile: Implications for water resource management

NASA Astrophysics Data System (ADS)

Markovich, K.; Pierce, S. A.

2011-12-01

Salar de Ascotán and Salar de Carcote are internally drained, evaporative basins located in the Atacama Desert, 200 km northeast of Antofogasta in Region II, Chile. The two salars are part of a regional groundwater system that recharges in the adjacent uplands to the east and terminates in the regional topographic low at Salar de Uyuni, Bolivia. This regional groundwater system is discharged locally as spring-fed perennial surface water that flows across the salar surface and either evaporates, or reinfiltrates, in lagoon-like environments. This perennial surface water supports diverse flora and fauna in the salar basins, including flamingo, vicuña, and the endemic fish species Orestias ascotanensis. Mining projects in the region began pumping the groundwater system in the Ascotán basin in the mid-1990's, leading to concern about the preservation of spring-fed surface flows. While hydrologic and ecologic monitoring efforts have been coordinated, data collection is limited to in-situ measurements and antecedent records precede extraction by approximately six months. Remote sensing can provide a means for large scale monitoring of the salars, as well as providing additional historical data to support environmental management of the systems. This comparative study utilizes satellite imagery to detect changes in surface water extent in the two salars and evaluate the results for possible correlation with climatic and/or anthropogenic factors. Landsat TM and ETM+ images from the time period of 1986-2011 are analyzed for surface water extent, and geographic information technologies are used to integrate the remotely sensed data with in-situ measurements. Early results indicate that surface water extent on the salar surface has diminished from 1986 and present day conditions. The decrease is most pronounced in the Ascotán basin, suggesting a possible correlation to anthropogenic influences. Also, the rate of decrease in surface water presence is most elevated in the first years following the onset of pumping, but decreases in the latter part of the time period. Key controls on the water balance in the basins include climatic and hydrologic conditions, human-induced changes to surface structures, water resource extraction, and artificial recharge efforts recently implemented to mitigate the effects of pumping.

Detecting vegetation cover change on the summit of Cadillac Mountain using multi-temporal remote sensing datasets: 1979, 2001, and 2007.

PubMed

Kim, Min-Kook; Daigle, John J

2011-09-01

This study examines the efficacy of management strategies implemented in 2000 to reduce visitor-induced vegetation impact and enhance vegetation recovery at the summit loop trail on Cadillac Mountain at Acadia National Park, Maine. Using single-spectral high-resolution remote sensing datasets captured in 1979, 2001, and 2007, pre-classification change detection analysis techniques were applied to measure fractional vegetation cover changes between the time periods. This popular sub-alpine summit with low-lying vegetation and attractive granite outcroppings experiences dispersed visitor use away from the designated trail, so three pre-defined spatial scales (small, 0-30 m; medium, 0-60 m; and large, 0-90 m) were examined in the vicinity of the summit loop trail with visitor use (experimental site) and a site chosen nearby in a relatively pristine undisturbed area (control site) with similar spatial scales. Results reveal significant changes in terms of rates of vegetation impact between 1979 and 2001 extending out to 90 m from the summit loop trail with no management at the site. No significant differences were detected among three spatial zones (inner, 0-30 m; middle, 30-60 m; and outer, 60-90 m) at the experimental site, but all were significantly higher rates of impact compared to similar spatial scales at the control site (all p < 0.001). In contrast, significant changes in rates of recovery between 2001 and 2007 were observed in the medium and large spatial scales at the experimental site under management as compared to the control site (all p < 0.05). Also during this later period a higher rate of recovery was observed in the outer zone as compared to the inner zone at the experimental site (p < 0.05). The overall study results suggest a trend in the desired direction for the site and visitor management strategies designed to reduce vegetation impact and enhance vegetation recovery at the summit loop trail of Cadillac Mountain since 2000. However, the vegetation recovery has been rather minimal and did not reach the level of cover observed during the 1979 time period. In addition, the advantages and some limitations of using remote sensing technologies are discussed in detecting vegetation change in this setting and potential application to other recreation settings.

Land cover trajectory in the Caatinga biome: analysing trends, drivers and consequences with high resolution satellite time series in Paraiba, Brazil

NASA Astrophysics Data System (ADS)

Rufino, Iana; Cunha, John; Carlos, Galvão; Nailson, Silva

2017-04-01

The Caatinga Biome is a unique Earth ecosystem with only 1% of conserved and protected areas (Oliveira et al, 2012). Human activities pressures high threaten Caatinga Biodiversity. Along the last decades, native green areas are changed by crops, livestock or those areas are reached by urban areas (Oliveira et al 2012; Fiaschi e Pianni, 2009; Sivakumar, 2007; Castelleti et al, 2004; Pereira et al, 2013; le Polain de Waroux & Lambin, 2012; Apgaua et al, 2013). Precipitation rates have high variability in space and time. High temperatures with small inter annual variability drives evapotranspiration up and turns the water scarcity the main challenge for sustainable life in rural areas. Sánchez-Azofeifa et al., (2005) try establishing research priorities for tropical dry forests and they recommend Scientific Community to focus on ecology and social aspects and possibilities of remote sensing techniques in those studies. Specific algorithms to produce estimates of energy balance and evapotranspiration of water to the atmosphere can process satellite images derived from several sensors. These estimates, combined with the analysis of historical time-series, allow the detection of changes in the terrestrial plant systems and can be used to discriminate the influences from human occupation and those from climate variability and/or change on energy fluxes and land cover. The algorithms have to be calibrated and validated using ground-based data. Thus, a large multiple source set of satellite and ground data has to be processed and comparatively analyzed. However, the high computational cost for image processing introduce further processing challenges. In order to face those challenges, this research explores the possibilities of using these medium resolution remote sensing products (30 meters), presenting a multitemporal long term analysis (24 months) to identify the land trajectory of one Semi-arid area (pilot) in the Caatinga biome. All processing steps use the statistical R package and GIS based tools in a automatic approach for the SEBAL (Bastiaanssen, 2000) and Fmask algorithms (Zhu e Woodcock, 2012). The main goal is to develop and provide an efficient remote sensing approach for a better understanding of "land cover trajectory" on an extremely vulnerable ecosystem driven by shifts on precipitation seasonality and extreme weather conditions.

Tunnel-Site Selection by Remote Sensing Techniques

DTIC Science & Technology

A study of the role of remote sensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remote sensing was defined...conventional remote sensing . Future research directions are suggested, and the extension of remote sensing to include airborne passive microwave

System and method for evaluating wind flow fields using remote sensing devices

DOEpatents

Schroeder, John; Hirth, Brian; Guynes, Jerry

2016-12-13

The present invention provides a system and method for obtaining data to determine one or more characteristics of a wind field using a first remote sensing device and a second remote sensing device. Coordinated data is collected from the first and second remote sensing devices and analyzed to determine the one or more characteristics of the wind field. The first remote sensing device is positioned to have a portion of the wind field within a first scanning sector of the first remote sensing device. The second remote sensing device is positioned to have the portion of the wind field disposed within a second scanning sector of the second remote sensing device.

Exploring Models and Data for Remote Sensing Image Caption Generation

NASA Astrophysics Data System (ADS)

Lu, Xiaoqiang; Wang, Binqiang; Zheng, Xiangtao; Li, Xuelong

2018-04-01

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal

Research on active imaging information transmission technology of satellite borne quantum remote sensing

NASA Astrophysics Data System (ADS)

Bi, Siwen; Zhen, Ming; Yang, Song; Lin, Xuling; Wu, Zhiqiang

2017-08-01

According to the development and application needs of Remote Sensing Science and technology, Prof. Siwen Bi proposed quantum remote sensing. Firstly, the paper gives a brief introduction of the background of quantum remote sensing, the research status and related researches at home and abroad on the theory, information mechanism and imaging experiments of quantum remote sensing and the production of principle prototype.Then, the quantization of pure remote sensing radiation field, the state function and squeezing effect of quantum remote sensing radiation field are emphasized. It also describes the squeezing optical operator of quantum light field in active imaging information transmission experiment and imaging experiments, achieving 2-3 times higher resolution than that of coherent light detection imaging and completing the production of quantum remote sensing imaging prototype. The application of quantum remote sensing technology can significantly improve both the signal-to-noise ratio of information transmission imaging and the spatial resolution of quantum remote sensing .On the above basis, Prof.Bi proposed the technical solution of active imaging information transmission technology of satellite borne quantum remote sensing, launched researches on its system composition and operation principle and on quantum noiseless amplifying devices, providing solutions and technical basis for implementing active imaging information technology of satellite borne Quantum Remote Sensing.

Introduction to the physics and techniques of remote sensing

NASA Technical Reports Server (NTRS)

Elachi, Charles

1987-01-01

This book presents a comprehensive overview of the basics behind remote-sensing physics, techniques, and technology. The physics of wave/matter interactions, techniques of remote sensing across the electromagnetic spectrum, and the concepts behind remote sensing techniques now established and future ones under development are discussed. Applications of remote sensing are described for a wide variety of earth and planetary atmosphere and surface sciences. Solid surface sensing across the electromagnetic spectrum, ocean surface sensing, basic principles of atmospheric sensing and radiative transfer, and atmospheric remote sensing in the microwave, millimeter, submillimeter, and infrared regions are examined.

[Thematic Issue: Remote Sensing.

ERIC Educational Resources Information Center

Howkins, John, Ed.

1978-01-01

Four of the articles in this publication discuss the remote sensing of the Earth and its resources by satellites. Among the topics dealt with are the development and management of remote sensing systems, types of satellites used for remote sensing, the uses of remote sensing, and issues involved in using information obtained through remote…

75 FR 65304 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-22

... Commercial Remote Sensing (ACCRES); Request for Nominations AGENCY: National Oceanic and Atmospheric... Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was... Atmosphere, on matters relating to the U.S. commercial remote sensing industry and NOAA's activities to carry...

GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed, Ethiopia.

PubMed

Nigatu Wondrade; Dick, Øystein B; Tveite, Havard

2014-03-01

Classifying multi-temporal image data to produce thematic maps and quantify land cover changes is one of the most common applications of remote sensing. Mapping land cover changes at the regional level is essential for a wide range of applications including land use planning, decision making, land cover database generation, and as a source of information for sustainable management of natural resources. Land cover changes in Lake Hawassa Watershed, Southern Ethiopia, were investigated using Landsat MSS image data of 1973, and Landsat TM images of 1985, 1995, and 2011, covering a period of nearly four decades. Each image was partitioned in a GIS environment, and classified using an unsupervised algorithm followed by a supervised classification method. A hybrid approach was employed in order to reduce spectral confusion due to high variability of land cover. Classification of satellite image data was performed integrating field data, aerial photographs, topographical maps, medium resolution satellite image (SPOT 20 m), and visual image interpretation. The image data were classified into nine land cover types: water, built-up, cropland, woody vegetation, forest, grassland, swamp, bare land, and scrub. The overall accuracy of the LULC maps ranged from 82.5 to 85.0 %. The achieved accuracies were reasonable, and the observed classification errors were attributable to coarse spatial resolution and pixels containing a mixture of cover types. Land cover change statistics were extracted and tabulated using the ERDAS Imagine software. The results indicated an increase in built-up area, cropland, and bare land areas, and a reduction in the six other land cover classes. Predominant land cover is cropland changing from 43.6 % in 1973 to 56.4 % in 2011. A significant portion of land cover was converted into cropland. Woody vegetation and forest cover which occupied 21.0 and 10.3 % in 1973, respectively, diminished to 13.6 and 5.6 % in 2011. The change in water body was very peculiar in that the area of Lake Hawassa increased from 91.9 km(2) in 1973 to 95.2 km(2) in 2011, while that of Lake Cheleleka whose area was 11.3 km(2) in 1973 totally vanished in 2011 and transformed into mud-flat and grass dominated swamp. The "change and no change" analysis revealed that more than one third (548.0 km(2)) of the total area was exposed to change between 1973 and 2011. This study was useful in identifying the major land cover changes, and the analysis pursued provided a valuable insight into the ongoing changes in the area under investigation.

Design, Development and Testing of Web Services for Multi-Sensor Snow Cover Mapping

NASA Astrophysics Data System (ADS)

Kadlec, Jiri

This dissertation presents the design, development and validation of new data integration methods for mapping the extent of snow cover based on open access ground station measurements, remote sensing images, volunteer observer snow reports, and cross country ski track recordings from location-enabled mobile devices. The first step of the data integration procedure includes data discovery, data retrieval, and data quality control of snow observations at ground stations. The WaterML R package developed in this work enables hydrologists to retrieve and analyze data from multiple organizations that are listed in the Consortium of Universities for the Advancement of Hydrologic Sciences Inc (CUAHSI) Water Data Center catalog directly within the R statistical software environment. Using the WaterML R package is demonstrated by running an energy balance snowpack model in R with data inputs from CUAHSI, and by automating uploads of real time sensor observations to CUAHSI HydroServer. The second step of the procedure requires efficient access to multi-temporal remote sensing snow images. The Snow Inspector web application developed in this research enables the users to retrieve a time series of fractional snow cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) for any point on Earth. The time series retrieval method is based on automated data extraction from tile images provided by a Web Map Tile Service (WMTS). The average required time for retrieving 100 days of data using this technique is 5.4 seconds, which is significantly faster than other methods that require the download of large satellite image files. The presented data extraction technique and space-time visualization user interface can be used as a model for working with other multi-temporal hydrologic or climate data WMTS services. The third, final step of the data integration procedure is generating continuous daily snow cover maps. A custom inverse distance weighting method has been developed to combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the MODIS snow cover product. The method is demonstrated by producing a continuous daily time step snow presence probability map dataset for the Czech Republic region. The ability of the presented methodology to reconstruct MODIS snow cover under cloud is validated by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. Using crowdsourcing data (volunteer snow reports and ski tracks) improves the map accuracy by 0.7--1.2%. The output snow probability map data sets are published online using web applications and web services. Keywords: crowdsourcing, image analysis, interpolation, MODIS, R statistical software, snow cover, snowpack probability, Tethys platform, time series, WaterML, web services, winter sports.

Multi-temporal thermal analyses for submarine groundwater discharge (SGD) detection over large spatial scales in the Mediterranean

NASA Astrophysics Data System (ADS)

Hennig, Hanna; Mallast, Ulf; Merz, Ralf

2015-04-01

Submarine groundwater discharge (SGD) sites act as important pathways for nutrients and contaminants that deteriorate marine ecosystems. In the Mediterranean it is estimated that 75% of freshwater input is contributed from karst aquifers. Thermal remote sensing can be used for a pre-screening of potential SGD sites in order to optimize field surveys. Although different platforms (ground-, air- and spaceborne) may serve for thermal remote sensing, the most cost-effective are spaceborne platforms (satellites) that likewise cover the largest spatial scale (>100 km per image). Therefore an automatized and objective approach that uses thermal satellite images from Landsat 7 and Landsat 8 was used to localize potential SGD sites on a large spatial scale. The method using descriptive statistic parameter specially range and standard deviation by (Mallast et al., 2014) was adapted to the Mediterranean Sea. Since the method was developed for the Dead Sea were satellite images with cloud cover are rare and no sea level change occurs through tidal cycles it was essential to adapt the method to a region where tidal cycles occur and cloud cover is more frequent . These adaptations include: (1) an automatic and adaptive coastline detection (2) include and process cloud covered scenes to enlarge the data basis, (3) implement tidal data in order to analyze low tide images as SGD is enhanced during these phases and (4) test the applicability for Landsat 8 images that will provide data in the future once Landsat 7 stops working. As previously shown, the range method shows more accurate results compared to the standard deviation. However, the result exclusively depends on two scenes (minimum and maximum) and is largely influenced by outliers. Counteracting on this drawback we developed a new approach. Since it is assumed that sea surface temperature (SST) is stabilized by groundwater at SGD sites, the slope of a bootstrapped linear model fitted to sorted SST per pixel would be less steep than the slope of the surrounding area, resulting in less influence through outliers and an equal weighting of all integrated scenes. Both methods could be used to detect SGD sites in the Mediterranean regardless to the discharge characteristics (diffuse and focused) exceptions are sites with deep emergences. Better results could be shown in bays compared to more exposed sites. Since the range of the SST is mostly influenced by maximum and minimum of the scenes, the slope approach can be seen as a more representative method using all scenes. References: Mallast, U., Gloaguen, R., Friesen, J., Rödiger, T., Geyer, S., Merz, R., Siebert, C., 2014. How to identify groundwater-caused thermal anomalies in lakes based on multi-temporal satellite data in semi-arid regions. Hydrol. Earth Syst. Sci. 18 (7), 2773-2787.

Cropland management dynamics as a driver of forest cover change in European Russia (Invited)

NASA Astrophysics Data System (ADS)

Tyukavina, A.; Krylov, A.; Potapov, P.; Turubanova, S.; Hansen, M.; McCarty, J. L.

2013-12-01

The European part of Russia spans over 40% of the European subcontinent and comprises most of Europe's temperate and boreal forests. The region has undergone a socio-economic transition during the last two decades that has resulted in radical changes in land management. Large-scale agriculture land abandonment caused massive afforestation in the Central and Northern parts of the region (Alcantara et al. 2012). Afforestation of former croplands is currently not included in the official forestry statistical reports (Potapov et al. 2012), but is likely to have major impacts on regional carbon budgets (Kuemmerle et al. 2009). We employed a complete archive of Landsat TM and ETM+ imagery and automatic data processing algorithm to create regional time-sequential image composites and multi-temporal metrics for 1985-2012. Spectral metrics were used as independent variables to map forest cover and change with help of supervised machine learning algorithms and trend analysis. Forest cover loss was attributed to fires, harvesting, and wind/disease dynamics, while forest cover gain was disaggregated into reforestation and afforestation using pre-1990 TM imagery as baseline data. Special attention was paid to agricultural abandonment. Fire events of the last decade have been further characterized by ignition place, time, and burning intensity using MODIS fire detection data. Change detection products have been validated using field data collected during summer 2012 and 2013 and high resolution imagery. Massive arable land abandonment caused forest area increase within Central agricultural regions. While total logging area decreased after the USSR breakdown, logging and other forms of clearing increased within the Central and Western parts of the region. Gross forest gain and loss were nearly balanced within region; however, the most populated regions of European Russia featured the highest rate of net forest cover loss during the last decade. The annual burned forest area as well as area of windstorms damage significantly increased, especially in the Central regions. Fires predominantly affected pine forests and drained peatlands prone to summer droughts. Fire date and ignition analysis showed that forest fires are not related to extensive spring-time agricultural burning. References: Alcantara, C., T. Kuemmerle, A. V. Prishchepov & V. C. Radeloff. 2012. Mapping abandoned agriculture with multi-temporal MODIS satellite data. 334-347. Remote Sensing of Environment. Kuemmerle, T., O. Chaskovskyy, J. Knorn, V. C. Radeloff, I. Kruhlov, W. S. Keeton & P. Hostert. 2009. Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007. Remote Sensing of Environment, 113, 1194-1207. Potapov, P., S. Turubanova, I. Zhuravleva, M. Hansen, A. Yaroshenko & A. Manisha. 2012. Forest Cover Change within the Russian European North after the Breakdown of Soviet Union (1990-2005) 1-11. International Journal of Forestry Research.

Literature relevant to remote sensing of water quality

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Marcell, R. F.

1983-01-01

References relevant to remote sensing of water quality were compiled, organized, and cross-referenced. The following general categories were included: (1) optical properties and measurement of water characteristics; (2) interpretation of water characteristics by remote sensing, including color, transparency, suspended or dissolved inorganic matter, biological materials, and temperature; (3) application of remote sensing for water quality monitoring; (4) application of remote sensing according to water body type; and (5) manipulation, processing and interpretation of remote sensing digital water data.

Learning Methods of Remote Sensing In the 2013 Curriculum of Secondary School

NASA Astrophysics Data System (ADS)

Lili Somantri, Nandi

2016-11-01

The new remote sensing material included in the subjects of geography in the curriculum of 1994. For geography teachers generation of 90s and over who in college do not get the material remote sensing, for teaching is a tough matter. Most teachers only give a theoretical matter, and do not carry out practical reasons in the lack of facilities and infrastructure of computer laboratories. Therefore, in this paper studies the importance about the method or manner of teaching remote sensing material in schools. The purpose of this paper is 1) to explain the position of remote sensing material in the study of geography, 2) analyze the Geography Curriculum 2013 Subjects related to remote sensing material, 3) describes a method of teaching remote sensing material in schools. The method used in this paper is a descriptive analytical study supported by the literature. The conclusion of this paper that the position of remote sensing in the study of geography is a method or a way to obtain spatial data earth's surface. In the 2013 curriculum remote sensing material has been applied to the study of land use and transportation. Remote sensing methods of teaching must go through a practicum, which starts from the introduction of the theory of remote sensing, data extraction phase of remote sensing imagery to produce maps, both visually and digitally, field surveys, interpretation of test accuracy, and improved maps.

JPRS Report, Science & Technology, China, Remote Sensing Systems, Applications.

DTIC Science & Technology

1991-01-17

Partial Contents: Short Introduction to Nation’s Remote Sensing Units, Domestic Airborne Remote - Sensing System, Applications in Monitoring Natural...Disasters, Applications of Imagery From Experimental Satellites Launched in 1985, 1986, Current Status, Future Prospects for Domestic Remote - Sensing -Satellite...Ground Station, and Radar Remote - Sensing Technology Used to Monitor Yellow River Delta,

[A review on polarization information in the remote sensing detection].

PubMed

Gong, Jie-Qiong; Zhan, Hai-Gang; Liu, Da-Zhao

2010-04-01

Polarization is one of the inherent characteristics. Because the surface of the target structure, internal structure, and the angle of incident light are different, the earth's surface and any target in atmosphere under optical interaction process will have their own characteristic nature of polarization. Polarimetric characteristics of radiation energy from the targets are used in polarization remote sensing detection as detective information. Polarization remote sensing detection can get the seven-dimensional information of targets in complicated backgrounds, detect well-resolved outline of targets and low-reflectance region of objectives, and resolve the problems of atmospheric detection and identification camouflage detection which the traditional remote sensing detection can not solve, having good foreground in applications. This paper introduces the development of polarization information in the remote sensing detection from the following four aspects. The rationale of polarization remote sensing detection is the base of polarization remote sensing detection, so it is firstly introduced. Secondly, the present researches on equipments that are used in polarization remote sensing detection are particularly and completely expatiated. Thirdly, the present exploration of theoretical simulation of polarization remote sensing detection is well detailed. Finally, the authors present the applications research home and abroad of the polarization remote sensing detection technique in the fields of remote sensing, atmospheric sounding, sea surface and underwater detection, biology and medical diagnosis, astronomical observation and military, summing up the current problems in polarization remote sensing detection. The development trend of polarization remote sensing detection technology in the future is pointed out in order to provide a reference for similar studies.

Seperating Long-term Hydrological Loading and Tectonic Deformation Observed with Multi-temporal SAR Interferometry and GPS in Qinghai-Tibetan Plateau

NASA Astrophysics Data System (ADS)

LI, G.; Lin, H.

2014-12-01

From 2000 till present, most endorheic lakes in Tibetan plateau experienced quick increasing. Several largest lakes, gathered several meters depth water during one decade. Such massive mass increasing will lead to elastic and visco-elastic deformation of the ground. Qinghai-Tibetan Plateau is one the most active tectonic places in the world; monitoring its ground deformation is essential, when loading effect is a nuisance item. Due to the sparse distribution of GPS sites and most are roving sites, it is hard to distinguish tectonic component from mass loading effect. In this research we took Selin Co Lake located at Nujiang-Bangoin suture zone and evaluated long time ground deformation at hundred kilometers scale by multi-temporal SAR interferometry and simulate the ground deformation by loading history evaluated by multi mission satellite altimetry and optical images observation. At Nujiang-Bangoin suture zone, where GPS presented the maximum ground subsidence in Qinghai-Tibetan Plateau of 3.6mm/a which was found at the shore of Selin Co Lake from 1999 to 2011, when it experienced water level increasing of 0.7m/a. A model of elastic plate lying over Newtonian viscous half-space matches well with the results of multi-temporal SAR interferometry and GPS observations. We concluded that near Selin Co Lake area, ground deformation is composed by both tectonic and hydrological loading part. As SAR image coverage is much smaller than tectonic scale, we contribute the deformation detected by InSAR to loading effect. After evaluating and removing the hydrological loading effect, we founds that Nujiang-Bangoin suture zone did not experience quick subsidence, but only limited to 0.5mm/a. Selin Co Lake's quick volume increasing caused 3mm/a subsidence rate to the nearest GPS site. The Second nearest site showed the 1.4mm/a subsidence totally, which were composed by 1.05mm/a hydrological loading effect and the rest was tectonic. We also found that Young's Modulus is the most essential parameter for loading effect simulation, and our simulation gave the similar Young's Modulus as the previous seismic tomographic INDEPTH III program did. Therefore with accurate seismic tomographic results and loading history detected by remote sensing could accurately simulate ground deformation caused by hydrological loading.

Research support of the WETNET Program

NASA Technical Reports Server (NTRS)

Estes, John E.; Mcgwire, Kenneth C.; Scepan, Joseph; Henderson, SY; Lawless, Michael

1995-01-01

This study examines various aspects of the Microwave Vegetation Index (MVI). MVI is a derived signal created by differencing the spectral response of the 37 GHz horizontally and vertically polarized passive microwave signals. The microwave signal employed to derive this index is thought to be primarily influenced by vegetation structure, vegetation growth, standing water, and precipitation. The state of California is the study site for this research. Imagery from the Special Sensor Microwave/Imager (SSM/I) is used for the creation of MVI datasets analyzed in this research. The object of this research is to determine whether MVI corresponds with some quantifiable vegetation parameter (such as vegetation density) or whether the index is more affected by known biogeophysical parameters such antecedent precipitation. A secondary question associated with the above is whether the vegetation attributes that MVI is employed to determine can be more easily and accurately evaluated by other remote sensing means. An important associated question to be addressed in the study is the effect of different multi-temporal composting techniques on the derived MVI dataset. This work advances our understanding of the fundamental nature of MVI by studying vegetation as a mixture of structural types, such as forest and grassland. The study further advances our understanding by creating multitemporal precipitation datasets to compare the affects of precipitation upon MVI. This work will help to lay the groundwork for the use of passive microwave spectral information either as an adjunct to visible and near infrared imagery in areas where that is feasible or for the use of passive microwave alone in areas of moderate cloud coverage. In this research, an MVI dataset, spanning the period February 15, 1989 through April 25, 1990, has been created using National Aeronautic and Space Administration (NASA) supplied brightness temperature data. Information from the DMSP satellite 37 GHz wavelength SSM/I sensor in both horizontal and vertical polarization has been processed using the MVI algorithm. In conjunction with the MVI algorithm a multitemporal compositing technique was used to create datasets that correspond to 14 day periods. In this technical report, Section Two contains background information on the State of California and the three MVI study sites. Section Three describes the methods used to create the MVI and independent variables datasets. Section Four presents the results of the experiment. Section Five summarizes and concludes the work.

Convolutional Neural Network for Multi-Source Deep Learning Crop Classification in Ukraine

NASA Astrophysics Data System (ADS)

Lavreniuk, M. S.

2016-12-01

Land cover and crop type maps are one of the most essential inputs when dealing with environmental and agriculture monitoring tasks [1]. During long time neural network (NN) approach was one of the most efficient and popular approach for most applications, including crop classification using remote sensing data, with high an overall accuracy (OA) [2]. In the last years the most popular and efficient method for multi-sensor and multi-temporal land cover classification is convolution neural networks (CNNs). Taking into account presence clouds in optical data, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of optical imagery from Landsat-8 satellite. After missing data restoration, optical data from Landsat-8 was merged with Sentinel-1A radar data for better crop types discrimination [3]. An ensemble of CNNs is proposed for multi-temporal satellite images supervised classification. Each CNN in the corresponding ensemble is a 1-d CNN with 4 layers implemented using the Google's library TensorFlow. The efficiency of the proposed approach was tested on a time-series of Landsat-8 and Sentinel-1A images over the JECAM test site (Kyiv region) in Ukraine in 2015. Overall classification accuracy for ensemble of CNNs was 93.5% that outperformed an ensemble of multi-layer perceptrons (MLPs) by +0.8% and allowed us to better discriminate summer crops, in particular maize and soybeans. For 2016 we would like to validate this method using Sentinel-1 and Sentinel-2 data for Ukraine territory within ESA project on country level demonstration Sen2Agri. 1. A. Kolotii et al., "Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine," The Int. Arch. of Photogram., Rem. Sens. and Spatial Inform. Scie., vol. 40, no. 7, pp. 39-44, 2015. 2. F. Waldner et al., "Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity," Int. Journal of Rem. Sens. vol. 37, no. 14, pp 3196-3231, 2016. 3. S. Skakun et al., "Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine," IEEE Journal of Selected Topics in Applied Earth Observ. and Rem. Sens., 2015, DOI: 10.1109/JSTARS.2015.2454297.

Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

NASA Astrophysics Data System (ADS)

Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

2017-09-01

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

Near-earth orbital guidance and remote sensing

NASA Technical Reports Server (NTRS)

Powers, W. F.

1972-01-01

The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

Operational programs in forest management and priority in the utilization of remote sensing

NASA Technical Reports Server (NTRS)

Douglass, R. W.

1978-01-01

A speech is given on operational remote sensing programs in forest management and the importance of remote sensing in forestry is emphasized. Forest service priorities in using remote sensing are outlined.

Remote sensing, land use, and demography - A look at people through their effects on the land

NASA Technical Reports Server (NTRS)

Paul, C. K.; Landini, A. J.

1976-01-01

Relevant causes of failure by the remote sensing community in the urban scene are analyzed. The reasons for the insignificant role of remote sensing in urban land use data collection are called the law of realism, the incompatibility of remote sensing and urban management system data formats is termed the law of nominal/ordinal systems compatibility, and the land use/population correlation dilemma is referred to as the law of missing persons. The study summarizes the three laws of urban land use information for which violations, avoidance, or ignorance have caused the decline of present remote sensing research. Particular attention is given to the rationale for urban land use information and for remote sensing. It is shown that remote sensing of urban land uses compatible with the three laws can be effectively developed by realizing the 10 percent contribution of remote sensing to urban land use planning data collection.

Thematic Conference on Geologic Remote Sensing, 8th, Denver, CO, Apr. 29-May 2, 1991, Proceedings. Vols. 1 & 2

NASA Technical Reports Server (NTRS)

1991-01-01

The proceedings contain papers discussing the state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing, along with the research and development activities aimed at increasing the future capabilities of this technology. The following topics are addressed: spectral geology, U.S. and international hydrocarbon exporation, radar and thermal infrared remote sensing, engineering geology and hydrogeology, mineral exploration, remote sensing for marine and environmental applications, image processing and analysis, geobotanical remote sensing, and data integration and geographic information systems. Particular attention is given to spectral alteration mapping with imaging spectrometers, mapping the coastal plain of the Congo with airborne digital radar, applications of remote sensing techniques to the assessment of dam safety, remote sensing of ferric iron minerals as guides for gold exploration, principal component analysis for alteration mappping, and the application of remote sensing techniques for gold prospecting in the north Fujian province.

Methods of training the graduate level and professional geologist in remote sensing technology

NASA Technical Reports Server (NTRS)

Kolm, K. E.

1981-01-01

Requirements for a basic course in remote sensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remote sensing theory, the theory and data types relating to different remote sensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remote sensing in a given study, the integration of other data bases (geophysical and geochemical) into a remote sensing study, and geological remote sensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotely sensed data.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1993-01-01

Progress report on remote sensing of Earth terrain covering the period from Jan. to June 1993 is presented. Areas of research include: radiative transfer model for active and passive remote sensing of vegetation canopy; polarimetric thermal emission from rough ocean surfaces; polarimetric passive remote sensing of ocean wind vectors; polarimetric thermal emission from periodic water surfaces; layer model with tandom spheriodal scatterers for remote sensing of vegetation canopy; application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Remote sensing by satellite - Technical and operational implications for international cooperation

NASA Technical Reports Server (NTRS)

Doyle, S. E.

1976-01-01

International cooperation in the U.S. Space Program is discussed and related to the NASA program for remote sensing of the earth. Satellite remote sensing techniques are considered along with the selection of the best sensors and wavelength bands. The technology of remote sensing satellites is considered with emphasis on the Landsat system configuration. Future aspects of remote sensing satellites are considered.

Application possibilities of aerial and terrain data evaluation in particulate pollution effects

NASA Astrophysics Data System (ADS)

Kozma-Bognar, V.; Berke, J.; Martin, G.

2012-04-01

Recently, remote sensing has become a widely used technology in order to acquire information about our environment. Data collected using remote sensing technology indispensible criteria to recognise and monitor environmental problems caused by contamination from various human activities. According to great technological change and development in the previous decade high spectral and geometric resolution sensors are more often used. The higher resolution technology allows getting more accurate and reliable results in the research processes of the environmental pollution impacts. At University of Pannonia, Georgikon Faculty (Hungary) plant-soil-atmosphere system analyses are carried out for detecting the potential harmful effects of heavy metal pollution originated from vehicle industry. Related to this research at the Department of Meteorology and Water Management, black carbon and cadmium pollution effects are being analysed on maize crops. Testing area is situated at Agro-meteorological Research Station in Keszthely, where the first time in 2011 aerial imaging technology was used in parallel with field analyses. The experiment aims to analyses correlation of the field data with aerial data. During aerial photography were taken in different spectral bands (Visible, Near Infrared, Far Infrared). High intensity, spectral and spatial resolution data was an important part of the multitemporal imagine sensing and evaluating technology, therefore original technical solutions were applied. These resolutions served accurate plot-level evaluation. Fractal structure and intensity measurement evaluation methods were applied to examine black carbon and cadmium polluted and control maize canopy after data pre-processing. Research also focused on the examination of potential negative or positive effects of irrigation so that differences between irrigated and non-irrigated maize was investigated. For the period of growing season of 2011 time-series analyses were carried out in various phonological phases of maize. Finally, valued aerial and terrain parameters - including e.g. micro-climatic conditions, relative humidity, albedo, etc. - were compared. This article was made under the project TÁMOP-4.2.1/B-09/1/KONV-2010-0003 and TÁMOP-4.2.2/B-10/1-2010-0025. These projects are supported by the European Union and co-financed by the European Social Fund.

Remote sensing-based Information for crop monitoring: contribution of SAR and Moderate resolution optical data on Asian rice production

NASA Astrophysics Data System (ADS)

Boschetti, Mirco; Holectz, Francesco; Manfron, Giacinto; Collivignarelli, Francesco; Nelson, Andrew

2013-04-01

Updated information on crop typology and status are strongly required to support suitable action to better manage agriculture production and reduce food insecurity. In this field, remote sensing has been demonstrated to be a suitable tool to monitor crop condition however rarely the tested system became really operative. The ones today available, such as the European Commission MARS, are mainly based on the analysis of NDVI time series and required ancillary external information like crop mask to interpret the seasonal signal. This condition is not always guarantied worldwide reducing the potentiality of the remote sensing monitoring. Moreover in tropical countries cloud contamination strongly reduce the possibility of using optical remote sensing data for crop monitoring. In this framework we focused our analysis on the rice production monitoring in Asian tropical area. Rice is in fact the staple food for half of the world population (FAO 2004), in Asia almost 90% of the world's rice is produced and consumed and Rice and poverty often coincide. In this contest the production of reliable rice production information is of extreme interest. We tried to address two important issue in terms of required geospatial information for crop monitoring: rice crop detection (rice map) and seasonal dynamics analysis (phenology). We use both SAR and Optical data in order to exploit the potential complementarity of this system. Multi-temporal ASAR Wide Swath data are in fact the best option to deal with cloud contamination. SAR can easily penetrate the clouds providing information on the surface target. Temporal analysis of archive ASAR data allowed to derived accurate map, at 100m spatial resolution, of permanent rice cultivated areas. On the other and high frequency revisiting optical data, in this case MODIS, have been used to extract seasonal information for the year under analysis. MOD09A1 Surface Reflectance 8-Day L3 Global 500m have been exploited to derive time series of Vegetation Index. A temporal smoothing procedure based on Savitzky-Golay polynomial filter function was applied to the original 8-day composite VI data (EVI and NDVI) in order to eliminate spurious data which affect the time series and to produce an interpolated VI temporal profile. Finally within the area previously identify as rice by SAR analysis phenological estimation have been conducted. Crop growth minima and maxima, respectively indicator of rice transplanting and heading, have been identify from the derivative analysis time series. This procedure was tested in Bangladesh for the year 2011. Results showed that the combined use of both data typology represents the more suitable multisource framework to provide reliable information on rice crop growth. Preliminary maps analysis reveals how SAR rice detection was in agreement with local information and phenology extracted by MODIS data provided spatially distributed data comparable with local knowledge of crop calendar.

Remote sensing in operational range management programs in Western Canada

NASA Technical Reports Server (NTRS)

Thompson, M. D.

1977-01-01

A pilot program carried out in Western Canada to test remote sensing under semi-operational conditions and display its applicability to operational range management programs was described. Four agencies were involved in the program, two in Alberta and two in Manitoba. Each had different objectives and needs for remote sensing within its range management programs, and each was generally unfamiliar with remote sensing techniques and their applications. Personnel with experience and expertise in the remote sensing and range management fields worked with the agency personnel through every phase of the pilot program. Results indicate that these agencies have found remote sensing to be a cost effective tool and will begin to utilize remote sensing in their operational work during ensuing seasons.

PROCEEDINGS OF THE FOURTH SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT; 12, 13, 14 APRIL 1966.

DTIC Science & Technology

The symposium was conducted as part of a continuing program investigating the field of remote sensing , its potential in scientific research and...information on all aspects of remote sensing , with special emphasis on such topics as needs for remotely sensed data, data management, and the special... remote sensing programs, data acquisition, data analysis and application, and equipment design, were presented. (Author)

Remote sensing and image interpretation

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Kiefer, R. W. (Principal Investigator)

1979-01-01

A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.

Geotechnical applications of remote sensing and remote data transmission; Proceedings of the Symposium, Cocoa Beach, FL, Jan. 31-Feb. 1, 1986

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

Johnson, A.I.; Pettersson, C.B.

1988-01-01

Papers and discussions concerning the geotechnical applications of remote sensing and remote data transmission, sources of remotely sensed data, and glossaries of remote sensing and remote data transmission terms, acronyms, and abbreviations are presented. Aspects of remote sensing use covered include the significance of lineaments and their effects on ground-water systems, waste-site use and geotechnical characterization, the estimation of reservoir submerging losses using CIR aerial photographs, and satellite-based investigation of the significance of surficial deposits for surface mining operations. Other topics presented include the location of potential ground subsidence and collapse features in soluble carbonate rock, optical Fourier analysis ofmore » surface features of interest in geotechnical engineering, geotechnical applications of U.S. Government remote sensing programs, updating the data base for a Geographic Information System, the joint NASA/Geosat Test Case Project, the selection of remote data telemetry methods for geotechnical applications, the standardization of remote sensing data collection and transmission, and a comparison of airborne Goodyear electronic mapping system/SAR with satelliteborne Seasat/SAR radar imagery.« less

Education in Environmental Remote Sensing: Potentials and Problems.

ERIC Educational Resources Information Center

Kiefer, Ralph W.; Lillesand, Thomas M.

1983-01-01

Discusses remote sensing principles and applications and the status and needs of remote sensing education in the United States. A summary of the fundamental policy issues that will determine remote sensing's future role in environmental and resource managements is included. (Author/BC)

THE EPA REMOTE SENSING ARCHIVE

EPA Science Inventory

What would you do if you were faced with organizing 30 years of remote sensing projects that had been haphazardly stored at two separate locations for years then combined? The EPA Remote Sensing Archive, currently located in Las Vegas, Nevada. contains the remote sensing data and...

Research on remote sensing image pixel attribute data acquisition method in AutoCAD

NASA Astrophysics Data System (ADS)

Liu, Xiaoyang; Sun, Guangtong; Liu, Jun; Liu, Hui

2013-07-01

The remote sensing image has been widely used in AutoCAD, but AutoCAD lack of the function of remote sensing image processing. In the paper, ObjectARX was used for the secondary development tool, combined with the Image Engine SDK to realize remote sensing image pixel attribute data acquisition in AutoCAD, which provides critical technical support for AutoCAD environment remote sensing image processing algorithms.

Bibliography of Remote Sensing Techniques Used in Wetland Research.

DTIC Science & Technology

1993-01-01

remote sensing technology for detecting changes in wetland environments. This report documents a bibliographic search conducted as part of that work unit on applications of remote sensing techniques in wetland research. Results were used to guide research efforts on the use of remote sensing technology for wetland change detection and assessment. The citations are presented in three appendixes, organized by wetland type, sensor type, and author.... Change detection, Wetland assessment, Remote sensing ,

Kite Aerial Photography as a Tool for Remote Sensing

ERIC Educational Resources Information Center

Sallee, Jeff; Meier, Lesley R.

2010-01-01

As humans, we perform remote sensing nearly all the time. This is because we acquire most of our information about our surroundings through the senses of sight and hearing. Whether viewed by the unenhanced eye or a military satellite, remote sensing is observing objects from a distance. With our current technology, remote sensing has become a part…

Remote sensing for detecting and mapping whitefly (Bemisia tabaci) infestations

USDA-ARS?s Scientific Manuscript database

Remote sensing technology has long been used for detecting insect infestations on agricultural crops. With recent advances in remote sensing sensors and other spatial information technologies such as Global Position Systems (GPS) and Geographic Information Systems (GIS), remote sensing is finding mo...

Reflections on Earth--Remote-Sensing Research from Your Classroom.

ERIC Educational Resources Information Center

Campbell, Bruce A.

2001-01-01

Points out the uses of remote sensing in different areas, and introduces the program "Reflections on Earth" which provides access to basic and instructional information on remote sensing to students and teachers. Introduces students to concepts related to remote sensing and measuring distances. (YDS)

Remote-Sensing Practice and Potential

DTIC Science & Technology

1974-05-01

Six essential processes that must be accomplished if use of a remote - sensing system is to result in useful information are defined as problem...to be useful in remote - sensing projects are described. An overview of the current state-of-the-art of remote sensing is presented.

History and future of remote sensing technology and education

NASA Technical Reports Server (NTRS)

Colwell, R. N.

1980-01-01

A historical overview of the discovery and development of photography, related sciences, and remote sensing technology is presented. The role of education to date in the development of remote sensing is discussed. The probable future and potential of remote sensing and training is described.

On the characterization of subpixel effects for passive microwave remote sensing of snow in montane environments

NASA Astrophysics Data System (ADS)

Vander Jagt, Benjamin John

Snow and its water equivalent plays a vital role in global water and energy balances, with particular relevance in mountainous areas with arid and semi-arid climate regimes. Spaceborne passive microwave (PM) remote sensing measurements are attractive for snowpack characterization due to their continuous global coverage and historical record; over 30 years of research has been invested in the development of methods to characterize large-scale snow water resources from PM-based measurements. Historically, use of PM data for snowpack characterization in montane enviroments has been obstructed by the complex subpixel variability of snow properties within the PM measurement footprint. The main subpixel effects can be grouped as: the effect of snow microstructure (e.g. snow grain size) and stratigraphy on snow microwave emission, vegetation attenuation of PM measurements, and the sensitivity PM brightness temperature (Tb) observation to the variability of different subpixel properties at spaceborne measurement scales. This dissertation is focused on a systematic examination of these issues, which thus far have prevented the widespread integration of snow water equivalent (SWE) retrieval methods. It is meant to further our comprehension of the underlying processes at work in these rugged, remote, a hydrologically important areas. The role that snow microstructure plays in the PM retrievals of SWE is examined first. Traditional estimates of grain size are subjective and prone to error. Objective techniques to characterize grain size are described and implemented, including near infrared (NIR), stereology, and autocorrelation based approaches. Results from an intensive Colorado field study in which independent estimates of grain size and their modeled brightness temperature (Tb) emission are evaluated against PM Tb observations are included. The coarse resolution of the passive microwave measurements provides additional challenges when trying to resolve snow states via remote sensing observations. The natural heterogeneity of snowpack (e.g. depth, stratigraphy, etc) and vegetative states within the PM footprint occurs at spatial scales smaller than PM observation scales. The sensitivity to changes in snow depth given sub-pixel variability in snow and vegetation is explored and quantified using the comprehensive dataset acquired during the Cold Land Processes experiment (CLPX). Lastly, vegetation has long been an obstacle in efforts to derive snow depth and mass estimates from passive microwave (PM) measurements of brightness temperature (Tb). We introduce a vegetation transmissivity model that is derived entirely from multi-scale and multi-temporal PM Tb observations and a globally available vegetation dataset, specifically the Leaf Area Index (LAI). This newly constructed model characterizes the attenuation of PM Tb observations at frequencies typically employed for snow retrieval algorithms, as a function of LAI. Additionally, the model is used to predict how much SWE is observable within the major river basins of Colorado and the central Rockies.

Ten ways remote sensing can contribute to conservation

USGS Publications Warehouse

Rose, Robert A.; Byler, Dirck; Eastman, J. Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A.; Laporte, Nadine; Leidner, Allison K.; Leimgruber, Peter; Morisette, Jeffrey T.; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C.; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2014-01-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Ten ways remote sensing can contribute to conservation.

PubMed

Rose, Robert A; Byler, Dirck; Eastman, J Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A; Laporte, Nadine; Leidner, Allison; Leimgruber, Peter; Morisette, Jeffrey; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2015-04-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions? © 2014 Society for Conservation Biology.

Role of remote sensing in documenting living resources

NASA Technical Reports Server (NTRS)

Wagner, P. E.; Anderson, R. R.; Brun, B.; Eisenberg, M.; Genys, J. B.; Lear, D. W., Jr.; Miller, M. H.

1978-01-01

Specific cases of known or potentially useful applications of remote sensing in assessing biological resources are discussed. It is concluded that the more usable remote sensing techniques relate to the measurement of population fluctuations in aquatic systems. Sensing of the flora and the fauna of the Bay is considered with emphasis on direct sensing of aquatic plant populations and of water quality. Recommendations for remote sensing projects are given.

Commercial future: making remote sensing a media event

NASA Astrophysics Data System (ADS)

Lurie, Ian

1999-12-01

The rapid growth of commercial remote sensing has made high quality digital sensing data widely available -- now, remote sensing must become and remain a strong, commercially viable industry. However, this new industry cannot survive without an educated consumer base. To access markets, remote sensing providers must make their product more accessible, both literally and figuratively: Potential customers must be able to find the data they require, when they require it, and they must understand the utility of the information available to them. The Internet and the World Wide Web offer the perfect medium to educate potential customers and to sell remote sensing data to those customers. A well-designed web presence can provide both an information center and a market place for companies offering their data for sale. A very high potential web-based market for remote sensing lies in media. News agencies, web sites, and a host of other visual media services can use remote sensing data to provide current, relevant information regarding news around the world. This paper will provide a model for promotion and sale of remote sensing data via the Internet.

77 FR 39220 - Advisory Committee on Commercial Remote Sensing (ACCRES); Charter Renewal

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-02

... Commercial Remote Sensing (ACCRES); Charter Renewal AGENCY: National Oceanic and Atmospheric Administration... Committee on Commercial Remote Sensing (ACCRES) was renewed on March 14, 2012. SUPPLEMENTARY INFORMATION: In... Commercial Remote Sensing (ACCRES) is in the public interest in connection with the performance of duties...

76 FR 66042 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-25

... Commercial Remote Sensing (ACCRES); Request for Nominations ACTION: Notice requesting nominations for the Advisory Committee on Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was established to advise the Secretary of Commerce, through the Under Secretary...

An introduction to quantitative remote sensing. [data processing

NASA Technical Reports Server (NTRS)

Lindenlaub, J. C.; Russell, J.

1974-01-01

The quantitative approach to remote sensing is discussed along with the analysis of remote sensing data. Emphasis is placed on the application of pattern recognition in numerically oriented remote sensing systems. A common background and orientation for users of the LARS computer software system is provided.

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

PubMed Central

Wang, Kai; Franklin, Steven E.; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS). PMID:22163432

Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

PubMed

Wang, Kai; Franklin, Steven E; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

Remote Sensing and Reflectance Profiling in Entomology.

PubMed

Nansen, Christian; Elliott, Norman

2016-01-01

Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

Multitemporal cross-calibration of the Terra MODIS and Landsat 7 ETM+ reflective solar bands

USGS Publications Warehouse

Angal, Amit; Xiong, Xiaoxiong; Wu, Aisheng; Chander, Gyanesh; Choi, Taeyoung

2013-01-01

In recent years, there has been a significant increase in the use of remotely sensed data to address global issues. With the open data policy, the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) sensors have become a critical component of numerous applications. These two sensors have been operational for more than a decade, providing a rich archive of multispectral imagery for analysis of mutitemporal remote sensing data. This paper focuses on evaluating the radiometric calibration agreement between MODIS and ETM+ using the near-simultaneous and cloud-free image pairs over an African pseudo-invariant calibration site, Libya 4. To account for the combined uncertainties in the top-of-atmosphere (TOA) reflectance due to surface and atmospheric bidirectional reflectance distribution function (BRDF), a semiempirical BRDF model was adopted to normalize the TOA reflectance to the same illumination and viewing geometry. In addition, the spectra from the Earth Observing-1 (EO-1) Hyperion were used to compute spectral corrections between the corresponding MODIS and ETM+ spectral bands. As EO-1 Hyperion scenes were not available for all MODIS and ETM+ data pairs, MODerate resolution atmospheric TRANsmission (MODTRAN) 5.0 simulations were also used to adjust for differences due to the presence or lack of absorption features in some of the bands. A MODIS split-window algorithm provides the atmospheric water vapor column abundance during the overpasses for the MODTRAN simulations. Additionally, the column atmospheric water vapor content during the overpass was retrieved using the MODIS precipitable water vapor product. After performing these adjustments, the radiometric cross-calibration of the two sensors was consistent to within 7%. Some drifts in the response of the bands are evident, with MODIS band 3 being the largest of about 6% over 10 years, a change that will be corrected in Collection 6 MODIS processing.

Spatio-temporal Characteristics of Land Use Land Cover Change Driven by Large Scale Land Transactions in Cambodia

NASA Astrophysics Data System (ADS)

Ghosh, A.; Smith, J. C.; Hijmans, R. J.

2017-12-01

Since mid-1990s, the Cambodian government granted nearly 300 `Economic Land Concessions' (ELCs), occupying approximately 2.3 million ha to foreign and domestic organizations (primarily agribusinesses). The majority of Cambodian ELC deals have been issued in areas of both relatively low population density and low agricultural productivity, dominated by smallholder production. These regions often contain highly biodiverse areas, thereby increasing the ecological cost associated with land clearing for extractive purposes. These large-scale land transactions have also resulted in substantial and rapid changes in land-use patterns and agriculture practices by smallholder farmers. In this study, we investigated the spatio-temporal characteristics of land use change associated with large-scale land transactions across Cambodia using multi-temporal multi-reolution remote sensing data. We identified major regions of deforestation during the last two decades using Landsat archive, global forest change data (2000-2014) and georeferenced database of ELC deals. We then mapped the deforestation and land clearing within ELC boundaries as well as areas bordering or near ELCs to quantify the impact of ELCs on local communities. Using time-series from MODIS Vegetation Indices products for the study period, we also estimated the time period over which any particular ELC deal initiated its proposed activity. We found evidence of similar patterns of land use change outside the boundaries of ELC deals which may be associated with i) illegal land encroachments by ELCs and/or ii) new agricultural practices adopted by local farmers near ELC boundaries. We also detected significant time gaps between ELC deal granting dates and initiation of land clearing for ELC purposes. Interestingly, we also found that not all designated areas for ELCs were put into effect indicating the possible proliferation of speculative land deals. This study demonstrates the potential of remote sensing techniques as a tool for monitoring in areas with weak governance and lack of enforcement of land tenure.

Mapping tsunami impacts on land cover and related ecosystem service supply in Phang Nga, Thailand

NASA Astrophysics Data System (ADS)

Kaiser, G.; Burkhard, B.; Römer, H.; Sangkaew, S.; Graterol, R.; Haitook, T.; Sterr, H.; Sakuna-Schwartz, D.

2013-12-01

The 2004 Indian Ocean tsunami caused damages to coastal ecosystems and thus affected the livelihoods of the coastal communities who depend on services provided by these ecosystems. The paper presents a case study on evaluating and mapping the spatial and temporal impacts of the tsunami on land use and land cover (LULC) and related ecosystem service supply in the Phang Nga province, Thailand. The method includes local stakeholder interviews, field investigations, remote-sensing techniques, and GIS. Results provide an ecosystem services matrix with capacity scores for 18 LULC classes and 17 ecosystem functions and services as well as pre-/post-tsunami and recovery maps indicating changes in the ecosystem service supply capacities in the study area. Local stakeholder interviews revealed that mangroves, casuarina forest, mixed beach forest, coral reefs, tidal inlets, as well as wetlands (peat swamp forest) have the highest capacity to supply ecosystem services, while e.g. plantations have a lower capacity. The remote-sensing based damage and recovery analysis showed a loss of the ecosystem service supply capacities in almost all LULC classes for most of the services due to the tsunami. A fast recovery of LULC and related ecosystem service supply capacities within one year could be observed for e.g. beaches, while mangroves or casuarina forest needed several years to recover. Applying multi-temporal mapping the spatial variations of recovery could be visualised. While some patches of coastal forest were fully recovered after 3 yr, other patches were still affected and thus had a reduced capacity to supply ecosystem services. The ecosystem services maps can be used to quantify ecological values and their spatial distribution in the framework of a tsunami risk assessment. Beyond that they are considered to be a useful tool for spatial analysis in coastal risk management in Phang Nga.

Annual Forest Monitoring as part of Indonesia's National Carbon Accounting System

NASA Astrophysics Data System (ADS)

Kustiyo, K.; Roswintiarti, O.; Tjahjaningsih, A.; Dewanti, R.; Furby, S.; Wallace, J.

2015-04-01

Land use and forest change, in particular deforestation, have contributed the largest proportion of Indonesia's estimated greenhouse gas emissions. Indonesia's remaining forests store globally significant carbon stocks, as well as biodiversity values. In 2010, the Government of Indonesia entered into a REDD+ partnership. A spatially detailed monitoring and reporting system for forest change which is national and operating in Indonesia is required for participation in such programs, as well as for national policy reasons including Monitoring, Reporting, and Verification (MRV), carbon accounting, and land-use and policy information. Indonesia's National Carbon Accounting System (INCAS) has been designed to meet national and international policy requirements. The INCAS remote sensing program is producing spatially-detailed annual wall-to-wall monitoring of forest cover changes from time-series Landsat imagery for the whole of Indonesia from 2000 to the present day. Work on the program commenced in 2009, under the Indonesia-Australia Forest Carbon Partnership. A principal objective was to build an operational system in Indonesia through transfer of knowledge and experience, from Australia's National Carbon Accounting System, and adaptation of this experience to Indonesia's requirements and conditions. A semi-automated system of image pre-processing (ortho-rectification, calibration, cloud masking and mosaicing) and forest extent and change mapping (supervised classification of a 'base' year, semi-automated single-year classifications and classification within a multi-temporal probabilistic framework) was developed for Landsat 5 TM and Landsat 7 ETM+. Particular attention is paid to the accuracy of each step in the processing. With the advent of Landsat 8 data and parallel development of processing capability, capacity and international collaborations within the LAPAN Data Centre this processing is being increasingly automated. Research is continuing into improved processing methodology and integration of information from other data sources. This paper presents technical elements of the INCAS remote sensing program and some results of the 2000 - 2012 mapping.

Multitemporal Cross-Calibration of the Terra MODIS and Landsat 7 ETM+ Reflective Solar Bands

NASA Technical Reports Server (NTRS)

Angal, Amit; Xiong, Xiaoxiong; Wu, Aisheng; Changler, Gyanesh; Choi, Taeyoyung

2013-01-01

In recent years, there has been a significant increase in the use of remotely sensed data to address global issues. With the open data policy, the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) sensors have become a critical component of numerous applications. These two sensors have been operational for more than a decade, providing a rich archive of multispectral imagery for analysis of mutitemporal remote sensing data. This paper focuses on evaluating the radiometric calibration agreement between MODIS and ETM+ using the near-simultaneous and cloud-free image pairs over an African pseudo-invariant calibration site, Libya 4. To account for the combined uncertainties in the top-of-atmosphere (TOA) reflectance due to surface and atmospheric bidirectional reflectance distribution function (BRDF), a semiempirical BRDF model was adopted to normalize the TOA reflectance to the same illumination and viewing geometry. In addition, the spectra from the Earth Observing-1 (EO-1) Hyperion were used to compute spectral corrections between the corresponding MODIS and ETM+ spectral bands. As EO-1 Hyperion scenes were not available for all MODIS and ETM+ data pairs, MODerate resolution atmospheric TRANsmission (MODTRAN) 5.0 simulations were also used to adjust for differences due to the presence or lack of absorption features in some of the bands. A MODIS split-window algorithm provides the atmospheric water vapor column abundance during the overpasses for the MODTRAN simulations. Additionally, the column atmospheric water vapor content during the overpass was retrieved using the MODIS precipitable water vapor product. After performing these adjustments, the radiometric cross-calibration of the two sensors was consistent to within 7%. Some drifts in the response of the bands are evident, with MODIS band 3 being the largest of about 6% over 10 years, a change that will be corrected in Collection 6 MODIS processing.

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research.

PubMed

Liu, R; Chen, J M; Liu, J; Deng, F; Sun, R

2007-11-01

An operational system was developed for mapping the leaf area index (LAI) for carbon cycle models from the moderate resolution imaging spectroradiometer (MODIS) data. The LAI retrieval algorithm is based on Deng et al. [2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44, 2219-2229], which uses the 4-scale radiative transfer model [Chen, J.M., Leblancs, 1997. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35, 1316-1337] to simulate the relationship of LAI with vegetated surface reflectance measured from space for various spectral bands and solar and view angles. This algorithm has been integrated to the MODISoft platform, a software system designed for processing MODIS data, to generate 250 m, 500 m and 1 km resolution LAI products covering all of China from MODIS MOD02 or MOD09 products. The multi-temporal interpolation method was implemented to remove the residual cloud and other noise in the final LAI product so that it can be directly used in carbon models without further processing. The retrieval uncertainties from land cover data were evaluated using five different data sets available in China. The results showed that mean LAI discrepancies can reach 27%. The current product was also compared with the NASA MODIS MOD15 LAI product to determine the agreement and disagreement of two different product series. LAI values in the MODIS product were found to be 21% larger than those in the new product. These LAI products were compared against ground TRAC measurements in forests in Qilian Mountain and Changbaishan. On average, the new LAI product agrees with the field measurement in Changbaishan within 2%, but the MODIS product is positively biased by about 20%. In Qilian Mountain, where forests are sparse, the new product is lower than field measurements by about 38%, while the MODIS product is larger by about 65%.

Geothermal Anomaly Mapping Using Landsat ETM+ Data in Ilan Plain, Northeastern Taiwan

NASA Astrophysics Data System (ADS)

Chan, Hai-Po; Chang, Chung-Pai; Dao, Phuong D.

2018-01-01

Geothermal energy is an increasingly important component of green energy in the globe. A prerequisite for geothermal energy development is to acquire the local and regional geothermal prospects. Existing geophysical methods of estimating the geothermal potential are usually limited to the scope of prospecting because of the operation cost and site reachability in the field. Thus, explorations in a large-scale area such as the surface temperature and the thermal anomaly primarily rely on satellite thermal infrared imagery. This study aims to apply and integrate thermal infrared (TIR) remote sensing technology with existing geophysical methods for the geothermal exploration in Taiwan. Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) imagery is used to retrieve the land surface temperature (LST) in Ilan plain. Accuracy assessment of satellite-derived LST is conducted by comparing with the air temperature data from 11 permanent meteorological stations. The correlation coefficient of linear regression between air temperature and LST retrieval is 0.76. The MODIS LST product is used for the cross validation of Landsat derived LSTs. Furthermore, Landsat ETM+ multi-temporal brightness temperature imagery for the verification of the LST anomaly results were performed. LST Results indicate that thermal anomaly areas appear correlating with the development of faulted structure. Selected geothermal anomaly areas are validated in detail by field investigation of hot springs and geothermal drillings. It implies that occurrences of hot springs and geothermal drillings are in good spatial agreement with anomaly areas. In addition, the significant low-resistivity zones observed in the resistivity sections are echoed with the LST profiles when compared with in the Chingshui geothermal field. Despite limited to detecting the surficial and the shallow buried geothermal resources, this work suggests that TIR remote sensing is a valuable tool by providing an effective way of mapping and quantifying surface features to facilitate the exploration and assessment of geothermal resources in Taiwan.

Vegetative response to water availability on the San Carlos Apache Reservation

USGS Publications Warehouse

Petrakis, Roy; Wu, Zhuoting; McVay, Jason; Middleton, Barry R.; Dye, Dennis G.; Vogel, John M.

2016-01-01

On the San Carlos Apache Reservation in east-central Arizona, U.S.A., vegetation types such as ponderosa pine forests, pinyon-juniper woodlands, and grasslands have significant ecological, cultural, and economic value for the Tribe. This value extends beyond the tribal lands and across the Western United States. Vegetation across the Southwestern United States is susceptible to drought conditions and fluctuating water availability. Remotely sensed vegetation indices can be used to measure and monitor spatial and temporal vegetative response to fluctuating water availability conditions. We used the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Modified Soil Adjusted Vegetation Index II (MSAVI2) to measure the condition of three dominant vegetation types (ponderosa pine forest, woodland, and grassland) in response to two fluctuating environmental variables: precipitation and the Standardized Precipitation Evapotranspiration Index (SPEI). The study period covered 2002 through 2014 and focused on a region within the San Carlos Apache Reservation. We determined that grassland and woodland had a similar moderate to strong, year-round, positive relationship with precipitation as well as with summer SPEI. This suggests that these vegetation types respond negatively to drought conditions and are more susceptible to initial precipitation deficits. Ponderosa pine forest had a comparatively weaker relationship with monthly precipitation and summer SPEI, indicating that it is more buffered against short-term drought conditions. This research highlights the response of multiple, dominant vegetation types to seasonal and inter-annual water availability. This research demonstrates that multi-temporal remote sensing imagery can be an effective tool for the large scale detection of vegetation response to adverse impacts from climate change and support potential management practices such as increased monitoring and management of drought-affected areas. Different vegetation types displayed various responses to water availability, further highlighting the need for individual management plans for forest and woodland, especially considering the projected drier conditions in the Southwest U.S. and other arid or semi-arid regions around the world.

Remote Sensing in Geography in the New Millennium: Prospects, Challenges, and Opportunities

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Jensen, John R.; Morain, Stanley A.; Walsh, Stephen J.; Ridd, Merrill K.

1999-01-01

Remote sensing science contributes greatly to our understanding of the Earth's ecosystems and cultural landscapes. Almost all the natural and social sciences, including geography, rely heavily on remote sensing to provide quantitative, and indispensable spatial information. Many geographers have made significant contributions to remote sensing science since the 1970s, including the specification of advanced remote sensing systems, improvements in analog and digital image analysis, biophysical modeling, and terrain analysis. In fact, the Remote Sensing Specialty Group (RSSG) is one of the largest specialty groups within the AAG with over 500 members. Remote sensing in concert with a geographic information systems, offers much value to geography as both an incisive spatial-analytical tool and as a scholarly pursuit that adds to the body of geographic knowledge on the whole. The "power" of remote sensing as a research endeavor in geography lies in its capabilities for obtaining synoptic, near-real time data at many spatial and temporal scales, and in many regions of the electromagnetic spectrum - from microwave, to RADAR, to visible, and reflective and thermal infrared. In turn, these data present a vast compendium of information for assessing Earth attributes and characte6stics that are at the very core of geography. Here we revisit how remote sensing has become a fundamental and important tool for geographical research, and how with the advent of new and improved sensing systems to be launched in the near future, remote sensing will further advance geographical analysis in the approaching New Millennium.

Agricultural Production Monitoring in the Sahel Using Remote Sensing: Present Possibilities and Research Needs

DTIC Science & Technology

1993-01-01

during the agricultural season. Satellite remote sensing can contribute significantly to such a system by collecting information on crops and on...well as techniques to derive biophysical variables from remotely-sensed data. Finally, the integration of these remote - sensing techniques with crop

Method of determining forest production from remotely sensed forest parameters

DOEpatents

Corey, J.C.; Mackey, H.E. Jr.

1987-08-31

A method of determining forest production entirely from remotely sensed data in which remotely sensed multispectral scanner (MSS) data on forest 5 composition is combined with remotely sensed radar imaging data on forest stand biophysical parameters to provide a measure of forest production. A high correlation has been found to exist between the remotely sensed radar imaging data and on site measurements of biophysical 10 parameters such as stand height, diameter at breast height, total tree height, mean area per tree, and timber stand volume.

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-01

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Extracting Temporal and Spatial Distributions Information about Algal Glooms Based on Multitemporal Modis

NASA Astrophysics Data System (ADS)

Chunguang, L.; Qingjiu, T.

2012-07-01

Based on MODIS remote sensing data, method and technology to extraction the time and space distribution information of algae bloom is studied and established. The dynamic feature of time and space in Taihu Lake from 2009 to 2011 can be obtained by extracted method. Variation of cyanobacterial bloom in the Taihu Lake is analyzed and discussed. The algae bloom frequency index (AFI) and algae bloom sustainability index (ASI) is important criterion which can show the interannual and inter-monthly variation in the whole area or the subregion of Taihu Lake. Utilizing the AFI and ASI from 2009 to 2011, it found some phenomena that: the booming frequency decreased from the north and west to the East and South of Taihu Lake. The annual month algae bloom variation of AFI reflect the booming existing twin peaks in the high shock level and lag trend in general. In the subregion statistics, the IBD and ASI in 2011 show the abnormal condition in the border between the Gongshan Bay and Central Lake. The date is obvious earlier than that on the same subregion in previous years and that on others subregion in the same year.

Unsupervised change detection of multispectral images based on spatial constraint chi-squared transform and Markov random field model

NASA Astrophysics Data System (ADS)

Shi, Aiye; Wang, Chao; Shen, Shaohong; Huang, Fengchen; Ma, Zhenli

2016-10-01

Chi-squared transform (CST), as a statistical method, can describe the difference degree between vectors. The CST-based methods operate directly on information stored in the difference image and are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. However, the technique does not take spatial information into consideration, which leads to much noise in the result of change detection. An improved unsupervised change detection method is proposed based on spatial constraint CST (SCCST) in combination with a Markov random field (MRF) model. First, the mean and variance matrix of the difference image of bitemporal images are estimated by an iterative trimming method. In each iteration, spatial information is injected to reduce scattered changed points (also known as "salt and pepper" noise). To determine the key parameter confidence level in the SCCST method, a pseudotraining dataset is constructed to estimate the optimal value. Then, the result of SCCST, as an initial solution of change detection, is further improved by the MRF model. The experiments on simulated and real multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods.

Identification of dust outbreaks on infrared MSG-SEVIRI data by using a Robust Satellite Technique (RST)

NASA Astrophysics Data System (ADS)

Sannazzaro, Filomena; Filizzola, Carolina; Marchese, Francesco; Corrado, Rosita; Paciello, Rossana; Mazzeo, Giuseppe; Pergola, Nicola; Tramutoli, Valerio

2014-01-01

Dust storms are meteorological phenomena of great interest for scientific community because of their potential impact on climate changes, for the risk that may pose to human health and due to other issues as desertification processes and reduction of the agricultural production. Satellite remote sensing, thanks to global coverage, high frequency of observation and low cost data, may highly contribute in monitoring these phenomena, provided that proper detection methods are used. In this work, the known Robust Satellite Techniques (RST) multitemporal approach, used for studying and monitoring several natural/environmental hazards, is tested on some important dust events affecting Mediterranean region in May 2004 and Arabian Peninsula in February 2008. To perform this study, data provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) have been processed, comparing the generated dust maps to some independent satellite-based aerosol products. Outcomes of this work show that the RST technique can be profitably used for detecting dust outbreaks from space, providing information also about areas characterized by a different probability of dust presence. They encourage further improvements of this technique in view of its possible implementation in the framework of operational warning systems.

Automated Techniques for Quantification of Coastline Change Rates using Landsat Imagery along Caofeidian, China

NASA Astrophysics Data System (ADS)

Dong, Di; Li, Ziwei; Liu, Zhaoqin; Yu, Yang

2014-03-01

This paper focuses on automated extraction and monitoring of coastlines by remote sensing techniques using multi-temporal Landsat imagery along Caofeidian, China. Caofeidian, as one of the active economic regions in China, has experienced dramatic change due to enhanced human activities, such as land reclamation. These processes have caused morphological changes of the Caofeidian shoreline. In this study, shoreline extraction and change analysis are researched. An algorithm based on image texture and mathematical morphology is proposed to automate coastline extraction. We tested this approach and found that it's capable of extracting coastlines from TM and ETM+ images with little human modifications. Then, the detected coastline vectors are imported into Arcgis software, and the Digital Shoreline Analysis System (DSAS) is used to calculate the change rate (the end point rate and linear regression rate). The results show that in some parts of the research area, remarkable coastline changes are observed, especially the accretion rate. The abnormal accretion is mostly attributed to the large-scale land reclamation during 2003 and 2004 in Caofeidian. So we can conclude that various construction projects, especially the land reclamation project, have made Caofeidian shorelines change greatly, far above the normal.

Agricultural crop harvest progress monitoring by fully polarimetric synthetic aperture radar imagery

NASA Astrophysics Data System (ADS)

Yang, Hao; Zhao, Chunjiang; Yang, Guijun; Li, Zengyuan; Chen, Erxue; Yuan, Lin; Yang, Xiaodong; Xu, Xingang

2015-01-01

Dynamic mapping and monitoring of crop harvest on a large spatial scale will provide critical information for the formulation of optimal harvesting strategies. This study evaluates the feasibility of C-band polarimetric synthetic aperture radar (PolSAR) for monitoring the harvesting progress of oilseed rape (Brassica napus L.) fields. Five multitemporal, quad-pol Radarsat-2 images and one optical ZY-1 02C image were acquired over a farmland area in China during the 2013 growing season. Typical polarimetric signatures were obtained relying on polarimetric decomposition methods. Temporal evolutions of these signatures of harvested fields were compared with the ones of unharvested fields in the context of the entire growing cycle. Significant sensitivity was observed between the specific polarimetric parameters and the harvest status of oilseed rape fields. Based on this sensitivity, a new method that integrates two polarimetric features was devised to detect the harvest status of oilseed rape fields using a single image. The validation results are encouraging even for the harvested fields covered with high residues. This research demonstrates the capability of PolSAR remote sensing in crop harvest monitoring, which is a step toward more complex applications of PolSAR data in precision agriculture.

GIS and remote sensing techniques for the assessment of land use changes impact on flood hydrology: the case study of Yialias Basin in Cyprus

NASA Astrophysics Data System (ADS)

Alexakis, D. D.; Gryllakis, M. G.; Koutroulis, A. G.; Agapiou, A.; Themistocleous, K.; Tsanis, I. K.; Michaelides, S.; Pashiardis, S.; Demetriou, C.; Aristeidou, K.; Retalis, A.; Tymvios, F.; Hadjimitsis, D. G.

2013-09-01

Flooding is one of the most common natural disasters worldwide, leading to economic losses and loss of human lives. This paper highlights the hydrological effects of multi-temporal land use changes in flood hazard within the Yialias catchment area, located in central Cyprus. Calibrated hydrological and hydraulic models were used to describe the hydrological processes and internal basin dynamics of the three major sub-basins, in order to study the diachronic effects of land use changes. For the implementation of the hydrological model, land use, soil and hydrometeorological data were incorporated. The climatic and stream flow data were derived from rain and flow gauge stations located in the wider area of the watershed basin. In addition, the land use and soil data were extracted after the application of object oriented nearest neighbor algorithms of ASTER satellite images. Subsequently, the CA-Markov chain analysis was implemented to predict the 2020 Land use/Land cover (LULC) map and incorporate it to the hydrological impact assessment. The results denoted the increase of runoff in the catchment area due to the recorded extensive urban sprawl phenomenon of the last decade.

GIS and remote sensing techniques for the assessment of land use change impact on flood hydrology: the case study of Yialias basin in Cyprus

NASA Astrophysics Data System (ADS)

Alexakis, D. D.; Grillakis, M. G.; Koutroulis, A. G.; Agapiou, A.; Themistocleous, K.; Tsanis, I. K.; Michaelides, S.; Pashiardis, S.; Demetriou, C.; Aristeidou, K.; Retalis, A.; Tymvios, F.; Hadjimitsis, D. G.

2014-02-01

Floods are one of the most common natural disasters worldwide, leading to economic losses and loss of human lives. This paper highlights the hydrological effects of multi-temporal land use changes in flood hazard within the Yialias catchment area, located in central Cyprus. A calibrated hydrological model was firstly developed to describe the hydrological processes and internal basin dynamics of the three major subbasins, in order to study the diachronic effects of land use changes. For the implementation of the hydrological model, land use, soil and hydrometeorological data were incorporated. The climatic and stream flow data were derived from rain and flow gauge stations located in the wider area of the watershed basin. In addition, the land use and soil data were extracted after the application of object-oriented nearest neighbor algorithms of ASTER satellite images. Subsequently, the cellular automata (CA)-Markov chain analysis was implemented to predict the 2020 land use/land cover (LULC) map and incorporate it to the hydrological impact assessment. The results denoted the increase of runoff in the catchment area due to the recorded extensive urban sprawl phenomenon of the last decade.

Registration of Laser Scanning Point Clouds: A Review.

PubMed

Cheng, Liang; Chen, Song; Liu, Xiaoqiang; Xu, Hao; Wu, Yang; Li, Manchun; Chen, Yanming

2018-05-21

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

VHR satellite imagery for humanitarian crisis management: a case study

NASA Astrophysics Data System (ADS)

Bitelli, Gabriele; Eleias, Magdalena; Franci, Francesca; Mandanici, Emanuele

2017-09-01

During the last years, remote sensing data along with GIS have been largely employed for supporting emergency management activities. In this context, the use of satellite images and derived map products has become more common also in the different phases of humanitarian crisis response. In this work very high resolution satellite imagery was processed to assess the evolution of Za'atari Refugee Camp, built in Jordan in 2012 by the UN Refugee Agency to host Syrian refugees. Multispectral satellite scenes of the Za'atari area were processed by means of object-based classifications. The main aim of the present work is the development of a semiautomated procedure for multi-temporal camp monitoring with particular reference to the dwellings detection. Whilst in the emergency mapping domain automation of feature extraction is widely investigated, in the field of humanitarian missions the information is often extracted by means of photointerpretation of the satellite data. This approach requires time for the interpretation; moreover, it is not reliable enough in complex situations, where features of interest are often small, heterogeneous and inconsistent. Therefore, the present paper discusses a methodology to obtain information for assisting humanitarian crisis management, using a semi-automatic classification approach applied to satellite imagery.

1985 ACSM-ASPRS Fall Convention, Indianapolis, IN, September 8-13, 1985, Technical Papers

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

Not Available

1985-01-01

Papers are presented on Landsat image data quality analysis, primary data acquisition, cartography, geodesy, land surveying, and the applications of satellite remote sensing data. Topics discussed include optical scanning and interactive color graphics; the determination of astrolatitudes and astrolongitudes using x, y, z-coordinates on the celestial sphere; raster-based contour plotting from digital elevation models using minicomputers or microcomputers; the operational techniques of the GPS when utilized as a survey instrument; public land surveying and high technology; the use of multitemporal Landsat MSS data for studying forest cover types; interpretation of satellite and aircraft L-band synthetic aperture radar imagery; geological analysismore » of Landsat MSS data; and an interactive real time digital image processing system. Consideration is given to a large format reconnaissance camera; creating an optimized color balance for TM and MSS imagery; band combination selection for visual interpretation of thematic mapper data for resource management; the effect of spatial filtering on scene noise and boundary detail in thematic mapper imagery; the evaluation of the geometric quality of thematic mapper photographic data; and the analysis and correction of Landsat 4 and 5 thematic mapper sensor data.« less

Assessment and monitoring of desertification using satellite imagery of MODIS in East Asia

NASA Astrophysics Data System (ADS)

Lin, Meng-Lung; Chu, Chieh-Ming; Shih, Jyh-Yi; Wang, Qiu-Bing; Chen, Cheng-Wu; Wang, Shin; Tao, Yi-Huang; Lee, Yung-Tan

2006-12-01

The desertification in Northwestern China and Mongolia shows the result of conflicts between economic development and natural conservation. Many researches have proven the desert areas are growing in these regions. The variations of bi-weekly NDVI satellite images are used as one of the parameters to evaluate the vegetation dynamics over large scale studies. In this study, remotely sensed satellite images are conducted to provide multi-temporal vegetated and non-vegetated areas in order to assess the status of desertification in East Asia. Spatial data derived from these satellite images are applied to evaluate vegetation dynamics at regional scale to find out the hot spot areas vulnerable to desertification. The results show that the desert areas are mainly distributed over southern Mongolia, central and western Inner-Mongolia, western China (the Taklimakan desert). The desert areas were expanded from 2000 to 2002, were shrunk in 2003, and were expanded from 2003 to 2005 again. The hot spot areas of desertification are mainly distributed over southeastern Mongolia and eastern Inner-Mongolia. The results will help administrators to refine the planning processes in defining the boundaries of protected areas and will facilitate to take decision of the priority areas for conservation of desertification.

Application of Thematic Mapper data to corn and soybean development stage estimation

NASA Technical Reports Server (NTRS)

Badhwar, G. D.; Henderson, K. E.

1985-01-01

A model, utilizing direct relationship between remotely sensed spectral data and the development stage of both corn and soybeans has been proposed and published previously (Badhwar and Henderson, 1981; and Henderson and Badhwar, 1984). This model was developed using data acquired by instruments mounted on trucks over field plots of corn and soybeans as well as satellite data from Landsat. In all cases, the data was analyzed in the spectral bands equivalent to the four bands of Landsat multispectral scanner (MSS). In this study the same model has been applied to corn and soybeans using Landsat-4 Thematic Mapper (TM) data combined with simulated TM data to provide a multitemporal data set in TM band intervals. All data (five total acquisitions) were acquired over a test site in Webster County, Iowa from June to October 1982. The use of TM data for determining development state is as accurate as with Landsat MSS and field plot data in MSS bands. The maximum deviation of 0.6 development stage for corn and 0.8 development stage for soybeans is well within the uncertainty with which a field can be estimated with procedures used by observers on the ground in 1982.

Registration of Laser Scanning Point Clouds: A Review

PubMed Central

Cheng, Liang; Chen, Song; Xu, Hao; Wu, Yang; Li, Manchun

2018-01-01

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles. PMID:29883397

Field Data Collection: an Essential Element in Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Pettinger, L. R.

1971-01-01

Field data collected in support of remote sensing projects are generally used for the following purposes: (1) calibration of remote sensing systems, (2) evaluation of experimental applications of remote sensing imagery on small test sites, and (3) designing and evaluating operational regional resource studies and inventories which are conducted using the remote sensing imagery obtained. Field data may be used to help develop a technique for a particular application, or to aid in the application of that technique to a resource evaluation or inventory problem for a large area. Scientists at the Forestry Remote Sensing Laboratory have utilized field data for both purposes. How meaningful field data has been collected in each case is discussed.

Remote sensing and eLearning 2.0 for school education

NASA Astrophysics Data System (ADS)

Voss, Kerstin; Goetzke, Roland; Hodam, Henryk

2010-10-01

The "Remote Sensing in Schools" project aims at improving the integration of "Satellite remote sensing" into school teaching. Therefore, it is the project's overall objective to teach students in primary and secondary schools the basics and fields of application of remote sensing. Existing results show that many teachers are interested in remote sensing and at same time motivated to integrate it into their teaching. Despite the good intention, in the end, the implementation often fails due to the complexity and poor set-up of the information provided. Therefore, a comprehensive and well-structured learning platform on the topic of remote sensing is developed. The platform shall allow a structured introduction to the topic.

Remote sensing programs and courses in engineering and water resources

NASA Technical Reports Server (NTRS)

Kiefer, R. W.

1981-01-01

The content of typical basic and advanced remote sensing and image interpretation courses are described and typical remote sensing graduate programs of study in civil engineering and in interdisciplinary environmental remote sensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remote sensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remote sensing upon which the more specialized advanced remote sensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.

Remote sensing research in geographic education: An alternative view

NASA Technical Reports Server (NTRS)

Wilson, H.; Cary, T. K.; Goward, S. N.

1981-01-01

It is noted that within many geography departments remote sensing is viewed as a mere technique a student should learn in order to carry out true geographic research. This view inhibits both students and faculty from investigation of remotely sensed data as a new source of geographic knowledge that may alter our understanding of the Earth. The tendency is for geographers to accept these new data and analysis techniques from engineers and mathematicians without questioning the accompanying premises. This black-box approach hinders geographic applications of the new remotely sensed data and limits the geographer's contribution to further development of remote sensing observation systems. It is suggested that geographers contribute to the development of remote sensing through pursuit of basic research. This research can be encouraged, particularly among students, by demonstrating the links between geographic theory and remotely sensed observations, encouraging a healthy skepticism concerning the current understanding of these data.

Research on assessment and improvement method of remote sensing image reconstruction

NASA Astrophysics Data System (ADS)

Sun, Li; Hua, Nian; Yu, Yanbo; Zhao, Zhanping

2018-01-01

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling which can improve efficiency. A method of two-dimensional principal component analysis (2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management

USDA-ARS?s Scientific Manuscript database

Unmanned aerial vehicles (UAV) provide a unique platform for remote sensing to monitor crop fields that complements remote sensing from satellite, aircraft and ground-based platforms. The UAV-based remote sensing is versatile at ultra-low altitude to be able to provide an ultra-high-resolution imag...

Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

Treesearch

Christopher D. Lippitt; Douglas A. Stow; Philip J. Riggan

2016-01-01

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication...

75 FR 32360 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-08

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and.... Abstract NOAA has established requirements for the licensing of private operators of remote-sensing space... Land Remote- Sensing Policy Act of 1992 and with the national security and international obligations of...

78 FR 44536 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-24

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and... for the licensing of private operators of remote-sensing space systems. The information in applications and subsequent reports is needed to ensure compliance with the Land Remote- Sensing Policy Act of...

Advancement of China’s Visible Light Remote Sensing Technology In Aerospace,

DTIC Science & Technology

1996-03-19

Aerospace visible light film systems were among the earliest space remote sensing systems to be developed in China. They have been applied very well...makes China the third nation in the world to master space remote sensing technology, it also puts recoverable remote sensing satellites among the first

Polarimetric passive remote sensing of periodic surfaces

NASA Technical Reports Server (NTRS)

Veysoglu, Murat E.; Yueh, H. A.; Shin, R. T.; Kong, J. A.

1991-01-01

The concept of polarimetry in active remote sensing is extended to passive remote sensing. The potential use of the third and fourth Stokes parameters U and V, which play an important role in polarimetric active remote sensing, is demonstrated for passive remote sensing. It is shown that, by the use of the reciprocity principle, the polarimetric parameters of passive remote sensing can be obtained through the solution of the associated direct scattering problem. These ideas are applied to study polarimetric passive remote sensing of periodic surfaces. The solution of the direct scattering problem is obtained by an integral equation formulation which involves evaluation of periodic Green's functions and normal derivative of those on the surface. Rapid evaluation of the slowly convergent series associated with these functions is observed to be critical for the feasibility of the method. New formulas, which are rapidly convergent, are derived for the calculation of these series. The study has shown that the brightness temperature of the Stokes parameter U can be significant in passive remote sensing. Values as high as 50 K are observed for certain configurations.

Multidate remote sensing approaches for digital zoning of terroirs at regional scales: case studies revisited and perspectives

NASA Astrophysics Data System (ADS)

Vaudour, Emmanuelle; Carey, Victoria A.; Gilliot, Jean-Marc

2014-05-01

Geospatial technologies prove more and more useful for characterizing terroirs and this, not only at the within-field scale: amongst innovating technologies revolutionizing approaches for digitally zoning viticultural areas, be they managed by individual or cooperative grape growers, or even unions of grape growers, multispectral satellite remote sensing data have been used for 15 years already at either regional or whole-vineyard scale, starting from single date-studies to multi-temporal processings. Regional remotely-sensed approaches for terroir mapping mostly use multispectral satellite images in conjunction with a set of ancillary morphometric and/or geomorphological and/or legacy soil data and time series data on grape/wine quality and climate. Two prominent case-studies of regional terroir mapping using SPOT satellite images with medium spatial resolution (20 m) were carried out in the Southern Rhone Valley (Côtes-du-Rhône controlled Appelation of origin) in Southern France and in the Stellenbosch-Paarl region (including 5 Wine of Origin wards: Simonsberg-Stellenbosch, Simonsberg-Paarl, Jonkershoek Valley, Banghoek and Papegaaiberg and portions of two further wards, namely, Franschoek and Devon Valley) in the South Western Cape of South Africa. In addition to emphasizing their usefulness for operational land management, our objective was to develop, compare and discuss both approaches in terms of formalization, spatial data handling and processing, sampling design, validation procedures and/or availability of uncertainty information. Both approaches essentially relied on supervised image classifiers based on the selection of reference training areas. For the Southern Rhone valley, viticultural terroirs were validated using an external sample of 91 vineyards planted with Grenache Noir and Syrah for which grape composition was available over a large 17 years-period: the validation procedure highlighted a strong vintage effect for each specific terroir. The output map was appropriate at the scale of cooperative wineries and the scale of the union of grapegrowers. For the Stellenbosch-Paarl region, 55 Sauvignon Blanc vineyards previously characterized in terms of grape/vine/wine quality in several earlier studies were used to introduce expert knowledge as a basis for bootstrapped regression tree calculations, which enabled uncertainty assessment of final map results. Further perspectives related to the spatial monitoring of vine phenology according to the output terroir units and the possible characterization of both within/between terroir spatio-temporal variability of vegetative growth were initiated for the Southern Rhone terroirs considering a SPOT4-Take Five satellite time series acquired from February to June 2013 in the framework of the SPOT4-Take Five program of the French Space Agency (CNES).

Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

NASA Technical Reports Server (NTRS)

Goetz, Scott J.; Bockstael, Nancy E.; Jantz, Claire A.

2005-01-01

This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has undertaken analyses of these models. One (the CA model) is driven largely by observations on past patterns of land use change, while the other (the EC model) is driven by mechanisms of the land use change decision at the parcel level. Our project may be the first serious attempt at developing both types of models for the same area, using as much common data as possible. We have identified the strengths and weaknesses of the two approaches and plan to continue to revise each model in the light of new data and new lessons learned through continued collaboration. Questions, approaches, findings, publication and presentation lists concerning the research are also presented.

From planets to crops and back: Remote sensing makes sense

NASA Astrophysics Data System (ADS)

Mustard, John F.

2017-04-01

Remotely sensed data and the instruments that acquire them are core parts of Earth and planetary observation systems. They are used to quantify the Earth's interconnected systems, and remote sensing is the only way to get a daily, or more frequent, snapshot of the status of the Earth. It really is the Earth's stethoscope. In a similar manner remote sensing is the rock hammer of the planetary scientist and the only way comprehensive data sets can be acquired. To risk offending many remotely sensed data acquired across the electromagnetic spectrum, it is the tricorder to explore known and unknown planets. Arriving where we are today in the use of remotely sensed data in the solar system has been a continually evolving synergy between Earth observation, planetary exploration, and fundamental laboratory work.

Remote sensing of on-road vehicle emissions: Mechanism, applications and a case study from Hong Kong

NASA Astrophysics Data System (ADS)

Huang, Yuhan; Organ, Bruce; Zhou, John L.; Surawski, Nic C.; Hong, Guang; Chan, Edward F. C.; Yam, Yat Shing

2018-06-01

Vehicle emissions are a major contributor to air pollution in cities and have serious health impacts to their inhabitants. On-road remote sensing is an effective and economic tool to monitor and control vehicle emissions. In this review, the mechanism, accuracy, advantages and limitations of remote sensing were introduced. Then the applications and major findings of remote sensing were critically reviewed. It was revealed that the emission distribution of on-road vehicles was highly skewed so that the dirtiest 10% vehicles accounted for over half of the total fleet emissions. Such findings highlighted the importance and effectiveness of using remote sensing for in situ identification of high-emitting vehicles for further inspection and maintenance programs. However, the accuracy and number of vehicles affected by screening programs were greatly dependent on the screening criteria. Remote sensing studies showed that the emissions of gasoline and diesel vehicles were significantly reduced in recent years, with the exception of NOx emissions of diesel vehicles in spite of greatly tightened automotive emission regulations. Thirdly, the experience and issues of using remote sensing for identifying high-emitting vehicles in Hong Kong (where remote sensing is a legislative instrument for enforcement purposes) were reported. That was followed by the first time ever identification and discussion of the issue of frequent false detection of diesel high-emitters using remote sensing. Finally, the challenges and future research directions of on-road remote sensing were elaborated.

Remote sensing of natural resources: Quarterly literature review

NASA Technical Reports Server (NTRS)

1976-01-01

A quarterly review of technical literature concerning remote sensing techniques is presented. The format contains indexed and abstracted materials with emphasis on data gathering techniques performed or obtained remotely from space, aircraft, or ground-based stations. Remote sensor applications including the remote sensing of natural resources are presented.

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

Object-Based Land Use Classification of Agricultural Land by Coupling Multi-Temporal Spectral Characteristics and Phenological Events in Germany

NASA Astrophysics Data System (ADS)

Knoefel, Patrick; Loew, Fabian; Conrad, Christopher

2015-04-01

Crop maps based on classification of remotely sensed data are of increased attendance in agricultural management. This induces a more detailed knowledge about the reliability of such spatial information. However, classification of agricultural land use is often limited by high spectral similarities of the studied crop types. More, spatially and temporally varying agro-ecological conditions can introduce confusion in crop mapping. Classification errors in crop maps in turn may have influence on model outputs, like agricultural production monitoring. One major goal of the PhenoS project ("Phenological structuring to determine optimal acquisition dates for Sentinel-2 data for field crop classification"), is the detection of optimal phenological time windows for land cover classification purposes. Since many crop species are spectrally highly similar, accurate classification requires the right selection of satellite images for a certain classification task. In the course of one growing season, phenological phases exist where crops are separable with higher accuracies. For this purpose, coupling of multi-temporal spectral characteristics and phenological events is promising. The focus of this study is set on the separation of spectrally similar cereal crops like winter wheat, barley, and rye of two test sites in Germany called "Harz/Central German Lowland" and "Demmin". However, this study uses object based random forest (RF) classification to investigate the impact of image acquisition frequency and timing on crop classification uncertainty by permuting all possible combinations of available RapidEye time series recorded on the test sites between 2010 and 2014. The permutations were applied to different segmentation parameters. Then, classification uncertainty was assessed and analysed, based on the probabilistic soft-output from the RF algorithm at the per-field basis. From this soft output, entropy was calculated as a spatial measure of classification uncertainty. The results indicate that uncertainty estimates provide a valuable addition to traditional accuracy assessments and helps the user to allocate error in crop maps.

Assessment of Fire Severity and Post-Fire Regeneration Based on Topographical Features Using Multitemporal Landsat Imagery: a Case Study in Mersin, Turkey

NASA Astrophysics Data System (ADS)

Tonbul, H.; Kavzoglu, T.; Kaya, S.

2016-06-01

Satellite based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Mersin-Gülnar wildfire, which occurred in August 2008 in Turkey, selected as study site. The fire was devastating and continued 55 days. According to Turkish General Directorate of Forestry reports, it caused two deaths and left hundreds of people homeless. The aim of this study is to determine the fire severity and monitor vegetation recovery with using multitemporal spectral indices together with topographical factors. Pre-fire and post-fire Landsat ETM+ images were obtained to assess the related fire severity with using the widely-used differenced Normalized Burn Ratio (dNBR) algorithm. Also, the Normalized Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to determine vegetation regeneration dynamics for a period of six consecutive years. In addition, aspect image derived from Aster Global Digital Elevation Model (GDEM) were used to determine vegetation regeneration regime of the study area. Results showed that 5388 ha of area burned with moderate to high severity damage. As expected, NDVI and SAVI values distinctly declined post-fire and then began to increase in the coming years. Mean NDVI value of burned area changed from 0.48 to 0.17 due to wildfire, whilst mean SAVI value changed from 0.61 to 0.26. Re-growth rates calculated for NDVI and SAVI 57% and 63% respectively, six years after the fire. Moreover, NDVI and SAVI were estimated six consecutive year period by taking into consideration east, south, north and west facing slopes. Analysis showed that north-facing and east-facing slopes have higher regeneration rates in compared to other aspects. This study serves as a window to an understanding of the process of fire severity and vegetation regeneration that is vital in wildfire management systems.

Evaluating the capacity of GF-4 satellite data for estimating fractional vegetation cover

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, Q.; Ren, H.; Zhang, T.; Sun, Y.

2016-12-01

Fractional vegetation cover (FVC) is a crucial parameter for many agricultural, environmental, meteorological and ecological applications, which is of great importance for studies on ecosystem structure and function. The Chinese GaoFen-4 (GF-4) geostationary satellite designed for the purpose of environmental and ecological observation was launched in December 29, 2015, and official use has been started by Chinese Government on June 13, 2016. Multi-spectral images with spatial resolution of 50 m and high temporal resolution, could be acquired by the sensor on GF-4 satellite on the 36000 km-altitude orbit. To take full advantage of the outstanding performance of GF-4 satellite, this study evaluated the capacity of GF-4 satellite data for monitoring FVC. To the best of our knowledge, this is the first research about estimating FVC from GF-4 satellite images. First, we developed a procedure for preprocessing GF-4 satellite data, including radiometric calibration and atmospheric correction, to acquire surface reflectance. Then single image and multi-temporal images were used for extracting the endmembers of vegetation and soil, respectively. After that, dimidiate pixel model and square model based on vegetation indices were used for estimating FVC. Finally, the estimation results were comparatively analyzed with FVC estimated by other existing sensors. The experimental results showed that satisfying accuracy of FVC estimation could be achieved from GF-4 satellite images using dimidiate pixel model and square model based on vegetation indices. What's more, the multi-temporal images increased the probability to find pure vegetation and soil endmembers, thus the characteristic of high temporal resolution of GF-4 satellite images improved the accuracy of FVC estimation. This study demonstrated the capacity of GF-4 satellite data for monitoring FVC. The conclusions reached by this study are significant for improving the accuracy and spatial-temporal resolution of existing FVC products, which provides a basis for the studies on ecosystem structure and function using remote sensing data acquired by GF-4 satellite.

Monitoring height and greenness of non-woody floodplain vegetation with UAV time series

NASA Astrophysics Data System (ADS)

van Iersel, Wimala; Straatsma, Menno; Addink, Elisabeth; Middelkoop, Hans

2018-07-01

Vegetation in river floodplains has important functions for biodiversity, but can also have a negative influence on flood safety. Floodplain vegetation is becoming increasingly heterogeneous in space and time as a result of river restoration projects. To document the spatio-temporal patterns of the floodplain vegetation, the need arises for efficient monitoring techniques. Monitoring is commonly performed by mapping floodplains based on single-epoch remote sensing data, thereby not considering seasonal dynamics of vegetation. The rising availability of unmanned airborne vehicles (UAV) increases monitoring frequency potential. Therefore, we aimed to evaluate the performance of multi-temporal high-spatial-resolution imagery, collected with a UAV, to record the dynamics in floodplain vegetation height and greenness over a growing season. Since the classification accuracy of current airborne surveys remains insufficient for low vegetation types, we focussed on seasonal variation of herbaceous and grassy vegetation with a height up to 3 m. Field reference data on vegetation height were collected six times during one year in 28 field plots within a single floodplain along the Waal River, the main distributary of the Rhine River in the Netherlands. Simultaneously with each field survey, we recorded UAV true-colour and false-colour imagery from which normalized digital surface models (nDSMs) and a consumer-grade camera vegetation index (CGCVI) were calculated. We observed that: (1) the accuracy of a UAV-derived digital terrain model (DTM) varies over the growing season and is most accurate during winter when the vegetation is dormant, (2) vegetation height can be determined from the nDSMs in leaf-on conditions via linear regression (RSME = 0.17-0.33 m), (3) the multitemporal nDSMs yielded meaningful temporal profiles of greenness and vegetation height and (4) herbaceous vegetation shows hysteresis for greenness and vegetation height, but no clear hysteresis was observed for grassland vegetation. These results show the high potential of using UAV-borne sensors for increasing the classification accuracy of low floodplain vegetation within the framework of floodplain monitoring.

Detection of Slope Instabilities Along the National Road 7, Mendoza Province, Argentina, Using Multi-Temporal InSAR

NASA Astrophysics Data System (ADS)

Michoud, Clément; Derron, Marc-Henri; Baumann, Valérie; Jaboyedoff, Michel; Rune Lauknes, Tom

2013-04-01

About 2'230 vehicles per day pass through the National Road 7 that link Buenos Aires to Santiago de Chile, crossing Andes Cordillera. This extremely important corridor, being the most important land pass between Argentina and Chile, is exposed to numerous natural hazards, such as snow avalanches, rockfalls and debris flows and remains closed by natural hazards several days per year. This goal of this study is to perform a regional mapping of geohazard susceptibilities along the Road 7 corridor, as started by Baumann et al. (2005), using modern remote sensing and numerical approaches with field checking. The area of interest is located in the Mendoza Province, between the villages Potrerillos and Las Cuevas near the Chilean border. The diversity of soil and rock conditions, the active geomorphological processes associated to post-glacial decompression, seasonal freeze and thaw and severe storms along the road corridor, increase the risk to natural hazard. With the support of the European Space Agency (ESA Category-1 Project 7154), we have in this study processed a large number of ERS and Envisat ASAR scenes, covering the period from 1995 to 2000. We applied both the small-baseline (SB) and the persistent scatterer (PSI) multi-temporal interferometric SAR (InSAR) techniques. The study area contains sparse vegetation, and the SB InSAR method is therefore well suited to map the area containing mainly distributed scatterers. Furthermore, PSI algorithms are also used for comparison for selected landslides in the inventory. Both approaches show a relatively good coherence within mountain areas, which is a good point for the landslide detections along the road. Indeed, the authors identified several large slope instabilities even active scree deposits. This inventory is finally compared with field observations and with existing susceptibility maps regarding snow avalanches, debris-flows and rockfalls. The final objective of this project is to develop a risk strategy that will help local authorities to manage the risk along this highway and also to provide guidelines.

Cropping Pattern Detection and Change Analysis in Central Luzon, Philippines Using Multi-Temporal MODIS Imagery and Artificial Neural Network Classifier

NASA Astrophysics Data System (ADS)

dela Torre, D. M.; Perez, G. J. P.

2016-12-01

Cropping practices in the Philippines has been intensifying with greater demand for food and agricultural supplies in view of an increasing population and advanced technologies for farming. This has not been monitored regularly using traditional methods but alternative methods using remote sensing has been promising yet underutilized. This study employed multi-temporal data from MODIS and neural network classifier to map annual land use in agricultural areas from 2001-2014 in Central Luzon, the primary rice growing area of the Philippines. Land use statistics derived from these maps were compared with historical El Nino events to examine how land area is affected by drought events. Fourteen maps of agricultural land use was produced, with the primary classes being single-cropping, double-cropping and perennial crops with secondary classes of forests, urban, bare, water and other classes. Primary classes were produced from the neural network classifier while secondary classes were derived from NDVI threshold masks. The overall accuracy for the 2014 map was 62.05% and a kappa statistic of 0.45. 155.56% increase in single-cropping systems from 2001 to 2014 was observed while double cropping systems decreased by 14.83%. Perennials increased by 76.21% while built-up areas decreased by 12.22% within the 14-year interval. There are several sources of error including mixed-pixels, scale-conversion problems and limited ground reference data. An analysis including El Niño events in 2004 and 2010 demonstrated that marginally irrigated areas that usually planted twice in a year resorted to single cropping, indicating that scarcity of water limited the intensification allowable in the area. Findings from this study can be used to predict future use of agricultural land in the country and also examine how farmlands have responded to climatic factors and stressors.

ALOS-PALSAR multi-temporal observation for describing land use and forest cover changes in Malaysia

NASA Astrophysics Data System (ADS)

Avtar, R.; Suzuki, R.; Ishii, R.; Kobayashi, H.; Nagai, S.; Fadaei, H.; Hirata, R.; Suhaili, A. B.

2012-12-01

The establishment of plantations in carbon rich peatland of Southeast Asia has shown an increase in the past decade. The need to support development in countries such as Malaysia has been reflected by having a higher rate of conversion of its forested areas to agricultural land use in particular oilpalm plantation. Use of optical data to monitor changes in peatland forests is difficult because of the high cloudiness in tropical region. Synthetic Aperture Radar (SAR) based remote sensing can potentially be used to monitor changes in such forested landscapes. In this study, we have demonstrated the capability of multi-temporal Fine-Beam Dual (FBD) data of Phased Array L-band Synthetic Aperture Radar (PALSAR) to detect forest cover changes in peatland to other landuse such as oilpalm plantation. Here, the backscattering properties of radar were evaluated to estimate changes in the forest cover. Temporal analysis of PALSAR FBD data shows that conversion of peatland forest to oilpalm can be detected by analyzing changes in the value of σoHH and σoHV. This is characterized by a high value of σoHH (-7.89 dB) and σoHV (-12.13 dB) for areas under peat forests. The value of σoHV decreased about 2-4 dB due to the conversion of peatland to a plantation area. There is also an increase in the value of σoHH/σoHV. Changes in σoHV is more prominent to identify the peatland conversion than in the σoHH. The results indicate the potential of PALSAR to estimate peatland forest conversion based on thresholding of σoHV or σoHH/σoHV for monitoring changes in peatland forest. This would improve our understanding of the temporal change and its effect on the peatland forest ecosystem.

Greening and browning of the Himalaya: Spatial patterns and the role of climatic change and human drivers.

PubMed

Mishra, Niti B; Mainali, Kumar P

2017-06-01

The reliable detection and attribution of changes in vegetation greenness is a prerequisite for the development of strategies for the sustainable management of ecosystems. We conducted a robust trend analysis on remote sensing derived vegetation index time-series matrices to detect significant changes in inter-annual vegetation productivity (greening versus browning) for the entire Himalaya, a biodiverse and ecologically sensitive yet understudied region. The spatial variability in trend was assessed considering elevation, 12 dominant land cover/use types and 10 ecoregions. To assess trend causation, at local scale, we compared multi-temporal imagery, and at regional scale, referenced ecological theories of mountain vegetation dynamics and ancillary literature. Overall, 17.56% of Himalayan vegetation (71,162km 2 ) exhibited significant trend (p<0.01) and majority (94%) showed positive trend (greening). Trend distribution showed strong elevational and ecoregion dependence as greening was most dominant at lower and middle elevations whereas majority of the browning occurred at higher elevation (>3800m), with eastern high Himalaya browning more dominantly than western high Himalaya. Land cover/use based categorization confirmed dominant greening of rainfed and irrigated agricultural areas, though cropped areas in western Himalaya contained higher proportion of greening areas. While rising atmospheric CO 2 concentration and nitrogen deposition are the most likely climatic causes of detected greening, success of sustainable forestry practices (community forestry in Nepal) along with increasing agricultural fertilization and irrigation facilities could be possible human drivers. Comparison of multi-temporal imagery enabled direct attribution of some browning areas to anthropogenic land change (dam, airport and tunnel construction). Our satellite detected browning of high altitude vegetation in eastern Himalaya confirm the findings of recent dendrochronology based studies which possibly resulted from reduced pre-monsoon moisture availability in recent decades. These results have significant implications for environmental management in the context of climate change and ecosystem dynamics in the Himalaya. Copyright © 2017 Elsevier B.V. All rights reserved.

Multi-temporal Soil Erosion Modelling over the Mt Kenya Region with Multi-Sensor Earth Observation Data

NASA Astrophysics Data System (ADS)

Symeonakis, Elias; Higginbottom, Thomas

2015-04-01

Accelerated soil erosion is the principal cause of soil degradation across the world. In Africa, it is seen as a serious problem creating negative impacts on agricultural production, infrastructure and water quality. Regarding the Mt Kenya region, specifically, soil erosion is a serious threat mainly due to unplanned and unsustainable practices linked to tourism, agriculture and rapid population growth. The soil types roughly correspond with different altitudinal zones and are generally very fertile due to their volcanic origin. Some of them have been created by eroding glaciers while others are due to millions of years of fluvial erosion. The soils on the mountain are easily eroded once exposed: when vegetation is removed, the soil quickly erodes down to bedrock by either animals or humans, as tourists erode paths and local people clear large swaths of forested land for agriculture, mostly illegally. It is imperative, therefore, that a soil erosion monitoring system for the Mt Kenya region is in place in order to understand the magnitude of, and be able to respond to, the increasing number of demands on this renewable resource. In this paper, we employ a simple regional-scale soil erosion modelling framework based on the Thornes model and suggest an operational methodology for quantifying and monitoring water runoff and soil erosion using multi-sensor and multi-temporal remote sensing data in a GIS framework. We compare the estimates of this study with general data on the severity of soil erosion over Kenya and with measured rates of soil loss at different locations over the area of study. The results show that the measured and estimated rates of erosion are generally similar and within the same order of magnitude. They also show that, over the last years, erosion rates are increasing in large parts of the region at an alarming rate, and that mitigation measures are needed to reverse the negative effects of uncontrolled socio-economic practices.

Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

NASA Astrophysics Data System (ADS)

Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.

2015-04-01

To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2's continuous and manifold data stream.

Snow Water Equivalent Retrieval Using Multitemporal COSMO Skymed X-Band SAR Images To Inform Water Systems Operation

NASA Astrophysics Data System (ADS)

Denaro, S.; Del Gobbo, U.; Castelletti, A.; Tebaldini, S.; Monti Guarnieri, A.

2015-12-01

In this work, we explore the use of exogenous snow-related information for enhancing the operation of water facilities in snow dominated watersheds. Traditionally, such information is assimilated into short-to-medium term streamflow forecasts, which are then used to inform water systems operation. Here, we adopt an alternative model-free approach, where the policy is directly conditioned upon a small set of selected observational data able to surrogate the snow-pack dynamics. In snow-fed water systems, the Snow Water Equivalent (SWE) stored in the basin often represents the largest contribution to the future season streamflow. The SWE estimation process is challenged by the high temporal and spatial variability of snow-pack and snow properties. Traditional retrieval methods, based on few ground sensors and optical satellites, often fail at representing the spatial diversity of snow conditions over large basins and at producing continuous (gap-free) data at the high sample frequency (e.g. daily) required to optimally control water systems. Against this background, SWE estimates from remote sensed radar products stand out, being able to acquire spatial information with no dependence on cloud coverage. In this work, we propose a technique for retrieving SWE estimates from Synthetic Aperture Radar (SAR) Cosmo SkyMed X-band images: a regression model, calibrated on ground SWE measurements, is implemented on dry snow maps obtained through a multi-temporal approach. The unprecedented spatial scale of this application is novel w.r.t. state of the art radar analysis conducted on limited spatial domains. The operational value of the SAR retrieved SWE estimates is evaluated based on ISA, a recently developed information selection and assessment framework. The method is demonstrated on a snow-rain fed river basin in the Italian Alps. Preliminary results show SAR images have a good potential for monitoring snow conditions and for improving water management operations.

Forest mensuration with remote sensing: A retrospective and a vision for the future

Treesearch

Randolph H. Wynne

2004-01-01

Remote sensing, while occasionally oversold, has clear potential to reduce the overall cost of traditional forest inventories. Perhaps most important, some of the information needed for more intensive, rather than extensive, forest management is available from remote sensing. These new information needs may justify increased use and the increased cost of remote sensing...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Remote Sensing: Analyzing Satellite Images to Create Higher Order Thinking Skills.

ERIC Educational Resources Information Center

Marks, Steven K.; And Others

1996-01-01

Presents a unit that uses remote-sensing images from satellites and other spacecraft to provide new perspectives of the earth and generate greater global awareness. Relates the levels of Bloom's hierarchy to different aspects of the remote sensing unit to confirm that the concepts and principles of remote sensing and related images belong in…

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Annotated bibliography of remote sensing methods for monitoring desertification

USGS Publications Warehouse

Walker, A.S.; Robinove, Charles J.

1981-01-01

Remote sensing techniques are valuable for locating, assessing, and monitoring desertification. Remotely sensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remote sensing methods that may be applied to desertification studies.

Applied Remote Sensing Program (ARSP)

NASA Technical Reports Server (NTRS)

Johnson, J. D.; Foster, K. E.; Mouat, D. A.; Miller, D. A.; Conn, J. S.

1976-01-01

The activities and accomplishments of the Applied Remote Sensing Program during FY 1975-1976 are reported. The principal objective of the Applied Remote Sensing Program continues to be designed projects having specific decision-making impacts as a principal goal. These projects are carried out in cooperation and collaboration with local, state and federal agencies whose responsibilities lie with planning, zoning and environmental monitoring and/or assessment in the application of remote sensing techniques. The end result of the projects is the use by the involved agencies of remote sensing techniques in problem solving.

Communicating remote sensing concepts in an interdisciplinary environment

NASA Technical Reports Server (NTRS)

Chung, R.

1981-01-01

Although remote sensing is currently multidisciplinary in its applications, many of its terms come from the engineering sciences, particularly from the field of pattern recognition. Scholars from fields such as the social sciences, botany, and biology, may experience initial difficulty with remote sensing terminology, even though parallel concepts exist in their own fields. Some parallel concepts and terminologies from nonengineering fields, which might enhance the understanding of remote sensing concepts in an interdisciplinary situation are identified. Feedbacks which this analogue strategy might have on remote sensing itself are explored.

People, Places and Pixels: Remote Sensing in the Service of Society

NASA Technical Reports Server (NTRS)

Lulla, Kamlesh

2003-01-01

What is the role of Earth remote sensing and other geospatial technologies in our society? Recent global events have brought into focus the role of geospatial science and technology such as remote sensing, GIS, GPS in assisting the professionals who are responsible for operations such as rescue and recovery of sites after a disaster or a terrorist act. This paper reviews the use of recent remote sensing products from satellites such as IKONOS in these efforts. Aerial and satellite imagery used in land mine detection has been evaluated and the results of this evaluation will be discussed. Synopsis of current and future ISS Earth Remote Sensing capabilities will be provided. The role of future missions in humanitarian use of remote sensing will be explored.

Reclaimed surface mine terrestrial pools: Integrating remote sensing, spatial data and field work

NASA Astrophysics Data System (ADS)

Kazar, Sheila A.

This study investigated the remote sensing of aboveground biomass in reclaimed surface mine reclamation sites and the carbon (C) storage potential of these sites. The research is structured in three sections. In the first study, the potential for utilizing the tasseled cap (TC) spectral transformation to characterize multi-temporal changes of vegetation growth was investigated within nine reclaimed coal surface mines in Monongalia and Preston Counties, West Virginia. The spectral patterns of TC greenness, brightness and wetness values associated with the minesites were investigated for a multi-temporal series of Landsat Thematic Mapper (TM) images, from 1992 to 2007. In general, most of the minesites at the time of mining showed increased brightness, and decreased greenness and wetness, with a reverse of this pattern during reclamation. However, rainfall appears to be a confounding variable, at least for relatively recently reclaimed sites. Spectral change vector analysis (CVA) was found to be effective for summarizing the patterns of change in TC values before and after reclamation. In the second study, field samples were collected from reclaimed grassland minesites and used to estimate biomass and C accumulation. In general, biomass and C increased in the six years following reclamation, and then slowly declined. Three Landsat Thematic Mapper (TM) images, from April, May and September of 2007, were used to assess four vegetation indices (VIs), TC, and red and near infrared radiance for potential for mapping biomass. For the April 3 Landsat image, the vegetation indices were not statistically correlated with field-measured biomass, and nor were the regression models significant. For the May 13 image, TC greenness and EVI were most strongly correlated with biomass, with TC wetness, NDVI, TVI and SAVI all significant at the 0.05 level. A number of regression models that included age since reclamation and spectral indices for May 13 were statistically significant, with the strongest prediction obtained from EVI. For the September 18 image, the correlation of biomass and TC brightness, TM4 and TVI were all statistically significant at the 0.05 level, although regression models that included age since reclamation as a dummy variable were not significant. In the third and final study, the biophysical potential for terrestrial aboveground C storage in minelands reclaimed to grasslands was investigated at the regional and state scale. Although above-ground annual accumulation of C is low in grasslands, if the aboveground biomass were harvested annually, and stored permanently C storage over 20 years on the grasslands of reclaimed minelands in West Virginia could be 3.60-7.32 Tg C, compared to 1.60 -9.80 Tg C if those same sites were reclaimed to forests. Although there is currently only limited usage of harvested hay for purposes that would result in its long-term storage, this study points to the benefits that would accrue if such mechanisms could be developed.

The application of remote sensing techniques to the study of ophiolites

NASA Astrophysics Data System (ADS)

Khan, Shuhab D.; Mahmood, Khalid

2008-08-01

Satellite remote sensing methods are a powerful tool for detailed geologic analysis, especially in inaccessible regions of the earth's surface. Short-wave infrared (SWIR) bands are shown to provide spectral information bearing on the lithologic, structural, and geochemical character of rock bodies such as ophiolites, allowing for a more comprehensive assessment of the lithologies present, their stratigraphic relationships, and geochemical character. Most remote sensing data are widely available for little or no cost, along with user-friendly software for non-specialists. In this paper we review common remote sensing systems and methods that allow for the discrimination of solid rock (lithologic) components of ophiolite complexes and their structural relationships. Ophiolites are enigmatic rock bodies which associated with most, if not all, plate collision sutures. Ophiolites are ideal for remote sensing given their widely recognized diversity of lithologic types and structural relationships. Accordingly, as a basis for demonstrating the utility of remote sensing techniques, we briefly review typical ophiolites in the Tethyan tectonic belt. As a case study, we apply integrated remote sensing studies of a well-studied example, the Muslim Bagh ophiolite, located in Balochistan, western Pakistan. On this basis, we attempt to demonstrate how remote sensing data can validate and reconcile existing information obtained from field studies. The lithologic and geochemical diversity of Muslim Bagh are representative of Tethyan ophiolites. Despite it's remote location it has been extensively mapped and characterized by structural and geochemical studies, and is virtually free of vegetative cover. Moreover, integrating the remote sensing data with 'ground truth' information thus offers the potential of an improved template for interpreting remote sensing data sets of other ophiolites for which little or no field information is available.

Use of LANDSAT data to assess waterfowl habitat quality

NASA Technical Reports Server (NTRS)

Colwell, J. E.; Gilmer, D. S. (Principal Investigator); Work, E. A., Jr.; Rebel, D. L.; Roller, N. E. G.

1978-01-01

The author has identified the following significant results. The capability of mapping ponds over a very large area was demonstrated, with multidate, multiframe LANDSAT imagery. A small double sample of aircraft data made it possible to adjust a LANDSAT large area census. Terrain classification was improved by using multitemporal LANDSAT data. Waterfowl production was estimated, using remotely determined pond data, in conjunction with FWS estimates of breeding population. Relative waterfowl habitat quality was characterized on a section by section basis.

Remote Sensing and Remote Control Activities in Europe and America: Part 2--Remote Sensing Ground Stations in Europe,

DTIC Science & Technology

1996-04-08

Development tasks and products of remote sensing ground stations in Europe are represented by the In-Sec Corporation and the Schlumberger Industries Corporation. The article presents the main products of these two corporations.

[Estimation of desert vegetation coverage based on multi-source remote sensing data].

PubMed

Wan, Hong-Mei; Li, Xia; Dong, Dao-Rui

2012-12-01

Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remote sensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remote sensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remote sensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remote sensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remote sensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remote sensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remote sensing monitoring scheme for the ecological restoration in the study area.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

Applying Support Vector Machine in classifying satellite images for the assessment of urban sprawl

NASA Astrophysics Data System (ADS)

murgante, Beniamino; Nolè, Gabriele; Lasaponara, Rosa; Lanorte, Antonio; Calamita, Giuseppe

2013-04-01

In last decades the spreading of new buildings, road infrastructures and a scattered proliferation of houses in zones outside urban areas, produced a countryside urbanization with no rules, consuming soils and impoverishing the landscape. Such a phenomenon generated a huge environmental impact, diseconomies and a decrease in life quality. This study analyzes processes concerning land use change, paying particular attention to urban sprawl phenomenon. The application is based on the integration of Geographic Information Systems and Remote Sensing adopting open source technologies. The objective is to understand size distribution and dynamic expansion of urban areas in order to define a methodology useful to both identify and monitor the phenomenon. In order to classify "urban" pixels, over time monitoring of settlements spread, understanding trends of artificial territories, classifications of satellite images at different dates have been realized. In order to obtain these classifications, supervised classification algorithms have been adopted. More particularly, Support Vector Machine (SVM) learning algorithm has been applied to multispectral remote data. One of the more interesting features in SVM is the possibility to obtain good results also adopting few classification pixels of training areas. SVM has several interesting features, such as the capacity to obtain good results also adopting few classification pixels of training areas, a high possibility of configuration parameters and the ability to discriminate pixels with similar spectral responses. Multi-temporal ASTER satellite data at medium resolution have been adopted because are very suitable in evaluating such phenomena. The application is based on the integration of Geographic Information Systems and Remote Sensing technologies by means of open source software. Tools adopted in managing and processing data are GRASS GIS, Quantum GIS and R statistical project. The area of interest is located south of Bari, in south eastern Italy (Puglia region). Bari, one of the major cities of southern Italy, is characterized by a considerable urban sprawl. The analysis is focused on a rectangular shaped region covering the urban area of three different cities, namely Polignano a Mare and Monopoli (and Conversano minority part) which, in 2011, had a population density comprised in the range of 140-319 people per Km2(istat ). The area of interest has a surface of approximately 253 Km2 , is characterized by three urban areas (Polignano a Mare, Conversano and Monopoli) and has a coastline of almost 17 Km. References Lanorte, A., Danese M., Lasaponara R., Murgante B. (2011) "Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis" International Journal of Applied Earth Observation and Geoinformation, Elsevier, doi:10.1016/j.jag.2011.09.005 Murgante B. Danese M. (2011) "Urban versus Rural: the decrease of agricultural areas and the development of urban zones analyzed with spatial statistics" Special Issue on "Environmental and agricultural data processing for water and territory management" International Journal of Agricultural and Environmental Information Systems (IJAEIS) volume 2(2) pp. 16-28 IGI Global, ISSN 1947-3192, DOI: 10.4018/jaeis.2011070102. Murgante, B., Las Casas, G., Danese, M., (2012), "Analyzing Neighbourhoods Suitable for Urban Renewal Programs with Autocorrelation Techniques" In Burian J. (Eds.) "Advances in Spatial Planning" InTech - Open Access DOI: 10.5772/33747 ISBN:978-953-51-0377-6 Nolè G., Danese M., Murgante B., Lasaponara R., Lanorte, A., (2012) "Using Spatial Autocorrelation Techniques and Multi-temporal Satellite Data for Analyzing Urban Sprawl" Lecture Notes in Computer Science vol. 7335, pp. 512-527. Springer-Verlag, Berlin. ISSN: 0302-9743, doi: 10.1007/978-3-642-31137-6_39

An Approach of Registration between Remote Sensing Image and Electronic Chart Based on Coastal Line

NASA Astrophysics Data System (ADS)

Li, Ying; Yu, Shuiming; Li, Chuanlong

Remote sensing plays an important role marine oil spill emergency. In order to implement a timely and effective countermeasure, it is important to provide exact position of oil spills. Therefore it is necessary to match remote sensing image and electronic chart properly. Variance ordinarily exists between oil spill image and electronic chart, although geometric correction is applied to remote sensing image. It is difficult to find the steady control points on sea to make exact rectification of remote sensing image. An improved relaxation algorithm was developed for finding the control points along the coastline since oil spills occurs generally near the coast. A conversion function is created with the least square, and remote sensing image can be registered with the vector map based on this function. SAR image was used as the remote sensing data and shape format map as the electronic chart data. The results show that this approach can guarantee the precision of the registration, which is essential for oil spill monitoring.

The U.S. Geological Survey land remote sensing program

USGS Publications Warehouse

Saunders, T.; Feuquay, J.; Kelmelis, J.A.

2003-01-01

The U.S. Geological Survey has been a provider of remotely sensed information for decades. As the availability and use of satellite data has grown, USGS has placed increasing emphasis on expanding the knowledge about the science of remote sensing and on making remotely sensed data more accessible. USGS encourages widespread availability and distribution of these data and through its programs, encourages and enables a variety of research activities and the development of useful applications of the data. The science of remote sensing has great potential for assisting in the monitoring and assessment of the impacts of natural disasters, management and analysis of environmental, biological, energy, and mineral investigations, and supporting informed public policy decisions. By establishing the Land Remote Sensing Program (LRS) as a major unit of the USGS Geography Program, USGS has taken the next step to further increase support for the accessibility, understanding, and use of remotely sensed data. This article describes the LRS Program, its mission and objectives, and how the program has been structured to accomplish its goals.

Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

NASA Technical Reports Server (NTRS)

Yueh, Simon H.

2004-01-01

Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

Online catalog access and distribution of remotely sensed information

NASA Astrophysics Data System (ADS)

Lutton, Stephen M.

1997-09-01

Remote sensing is providing voluminous data and value added information products. Electronic sensors, communication electronics, computer software, hardware, and network communications technology have matured to the point where a distributed infrastructure for remotely sensed information is a reality. The amount of remotely sensed data and information is making distributed infrastructure almost a necessity. This infrastructure provides data collection, archiving, cataloging, browsing, processing, and viewing for applications from scientific research to economic, legal, and national security decision making. The remote sensing field is entering a new exciting stage of commercial growth and expansion into the mainstream of government and business decision making. This paper overviews this new distributed infrastructure and then focuses on describing a software system for on-line catalog access and distribution of remotely sensed information.

Remote Sensing and the Environment.

ERIC Educational Resources Information Center

Osmers, Karl

1991-01-01

Suggests using remote sensing technology to help students make sense of the natural world. Explains that satellite information allows observation of environmental changes over time. Identifies possible student projects based on remotely sensed data. Recommends obtaining the assistance of experts and seeking funding through effective project…

Use of remote sensing in agriculture

NASA Technical Reports Server (NTRS)

Pettry, D. E.; Powell, N. L.; Newhouse, M. E.

1974-01-01

Remote sensing studies in Virginia and Chesapeake Bay areas to investigate soil and plant conditions via remote sensing technology are reported ant the results given. Remote sensing techniques and interactions are also discussed. Specific studies on the effects of soil moisture and organic matter on energy reflection of extensively occurring Sassafras soils are discussed. Greenhouse and field studies investigating the effects of chlorophyll content of Irish potatoes on infrared reflection are presented. Selected ground truth and environmental monitoring data are shown in summary form. Practical demonstrations of remote sensing technology in agriculture are depicted and future use areas are delineated.

Applications of remote sensing to watershed management

NASA Technical Reports Server (NTRS)

Rango, A.

1975-01-01

Aircraft and satellite remote sensing systems which are capable of contributing to watershed management are described and include: the multispectral scanner subsystem on LANDSAT and the basic multispectral camera array flown on high altitude aircraft such as the U-2. Various aspects of watershed management investigated by remote sensing systems are discussed. Major areas included are: snow mapping, surface water inventories, flood management, hydrologic land use monitoring, and watershed modeling. It is indicated that technological advances in remote sensing of hydrological data must be coupled with an expansion of awareness and training in remote sensing techniques of the watershed management community.

NASA Glenn OHIOVIEW FY01/02 Project

NASA Technical Reports Server (NTRS)

2003-01-01

The results of the research performed by the university principal investigators are herein compiled. OhioView's general goals were: 1) To increase remote sensing education for Ohio s undergraduate and graduate students, and also enhancing curriculum in the mathematics and science for K-12 students using the capabilities of remote sensing; 2) To conduct advanced research to develop novel remote sensing applications, i.e. to turn data into information for more applications; 3) To maximize the use of remote sensing technology by the general public through outreach and the development of tools for more user-friendly access to remote sensing data.

The availability of conventional forms of remotely sensed data

USGS Publications Warehouse

Sturdevant, James A.; Holm, Thomas M.

1982-01-01

For decades Federal and State agencies have been collecting aerial photographs of various film types and scales over parts of the United States. More recently, worldwide Earth resources data acquired by orbiting satellites have inundated the remote sensing community. Determining the types of remotely sensed data that are publicly available can be confusing to the land-resource manager, planner, and scientist. This paper is a summary of the more commonly used types of remotely sensed data (aircraft and satellite) and their public availability. Special emphasis is placed on the National High-Altitude Photography (NHAP) program and future remote-sensing satellites.

NASA's Applied Remote Sensing Training (ARSET) Webinar Series

Atmospheric Science Data Center

2016-07-12

NASA's Applied Remote Sensing Training (ARSET) Webinar Series Tuesday, July 12, 2016 ... you of a free training opportunity: Introduction to Remote Sensing for Air Quality Applications Webinar Series Beginning in ...

Tropospheric Passive Remote Sensing

NASA Technical Reports Server (NTRS)

Keafer, L. S., Jr. (Editor)

1982-01-01

The long term role of airborne/spaceborne passive remote sensing systems for tropospheric air quality research and the identification of technology advances required to improve the performance of passive remote sensing systems were discussed.

Remote Sensing as a Demonstration of Applied Physics.

ERIC Educational Resources Information Center

Colwell, Robert N.

1980-01-01

Provides information about the field of remote sensing, including discussions of geo-synchronous and sun-synchronous remote-sensing platforms, the actual physical processes and equipment involved in sensing, the analysis of images by humans and machines, and inexpensive, small scale methods, including aerial photography. (CS)

Opportunities and problems in introducing or expanding the teaching of remote sensing in universities

NASA Technical Reports Server (NTRS)

Maxwell, E. L.

1980-01-01

The need for degree programs in remote sensing is considered. Any education program which claims to train remote sensing specialists must include expertise in the physical principles upon which remote sensing is based. These principles dictate the limits of engineering and design, computer analysis, photogrammetry, and photointerpretation. Faculty members must be hired to provide emphasis in those five areas.

Remote sensing of vegetation fires and its contribution to a fire management information system

Treesearch

Stephane P. Flasse; Simon N. Trigg; Pietro N. Ceccato; Anita H. Perryman; Andrew T. Hudak; Mark W. Thompson; Bruce H. Brockett; Moussa Drame; Tim Ntabeni; Philip E. Frost; Tobias Landmann; Johan L. le Roux

2004-01-01

In the last decade, research has proven that remote sensing can provide very useful support to fire managers. This chapter provides an overview of the types of information remote sensing can provide to the fire community. First, it considers fire management information needs in the context of a fire management information system. An introduction to remote sensing then...

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER... electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER...electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Basic Remote Sensing Investigations for Beach Reconnaissance.

DTIC Science & Technology

Progress is reported on three tasks designed to develop remote sensing beach reconnaissance techniques applicable to the benthic, beach intertidal...and beach upland zones. Task 1 is designed to develop remote sensing indicators of important beach composition and physical parameters which will...ultimately prove useful in models to predict beach conditions. Task 2 is designed to develop remote sensing techniques for survey of bottom features in

Bridging the Scales from Field to Region with Practical Tools to Couple Time- and Space-Synchronized Data from Flux Towers and Networks with Proximal and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Burba, G. G.; Avenson, T.; Burkart, A.; Gamon, J. A.; Guan, K.; Julitta, T.; Pastorello, G.; Sakowska, K.

2017-12-01

Many hundreds of flux towers are presently operational as standalone projects and as parts of regional networks. However, the vast majority of these towers do not allow straightforward coupling with remote sensing (drone, aircraft, satellite, etc.) data, and even fewer have optical sensors for validation of remote sensing products, and upscaling from field to regional levels. In 2016-2017, new tools to collect, process, and share time-synchronized flux data from multiple towers were developed and deployed globally. Originally designed to automate site and data management, and to streamline flux data analysis, these tools allow relatively easy matching of tower data with remote sensing data: GPS-driven PTP time protocol synchronizes instrumentation within the station, different stations with each other, and all of these to remote sensing data to precisely align remote sensing and flux data in time Footprint size and coordinates computed and stored with flux data help correctly align tower flux footprints and drone, aircraft or satellite motion to precisely align optical and flux data in space Full snapshot of the remote sensing pixel can then be constructed, including leaf-level, ground optical sensor, and flux tower measurements from the same footprint area, closely coupled with the remote sensing measurements to help interpret remote sensing data, validate models, and improve upscaling Additionally, current flux towers can be augmented with advanced ground optical sensors and can use standard routines to deliver continuous products (e.g. SIF, PRI, NDVI, etc.) based on automated field spectrometers (e.g., FloX and RoX, etc.) and other optical systems. Several dozens of new towers already operational globally can be readily used for the proposed workflow. Over 500 active traditional flux towers can be updated to synchronize their data with remote sensing measurements. This presentation will show how the new tools are used by major networks, and describe how this approach can be utilized for matching remote sensing and tower data to aid in ground truthing, improve scientific interactions, and promote joint grant writing and other forms of collaboration between the flux and remote sensing communities.

Monitoring Crop Phenology and Growth Stages from Space: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.; Mladenova, I. E.; Kustas, W. P.; Alfieri, J. G.

2014-12-01

Crop growth stages in concert with weather and soil moisture conditions can have a significant impact on crop yields. In the U.S., crop growth stages and conditions are reported by farmers at the county level. These reports are somewhat subjective and fluctuate between different reporters, locations and times. Remote sensing data provide an alternative approach to monitoring crop growth over large areas in a more consistent and quantitative way. In the recent years, remote sensing data have been used to detect vegetation phenology at 1-km spatial resolution globally. However, agricultural applications at field scale require finer spatial resolution remote sensing data. Landsat (30-m) data have been successfully used for agricultural applications. There are many medium resolution sensors available today or in near future. These include Landsat, SPOT, RapidEye, ASTER and future Sentinel-2 etc. Approaches have been developed in the past several years to integrate remote sensing data from different sensors which may have different sensor characteristics, and spatial and temporal resolutions. This allows us opportunities today to map crop growth stages and conditions using dense time-series remote sensing at field scales. However, remotely sensed phenology (or phenological metrics) is normally derived based on the mathematical functions of the time-series data. The phenological metrics are determined by either identifying inflection (curvature) points or some pre-defined thresholds in the remote sensing phenology algorithms. Furthermore, physiological crop growth stages may not be directly correlated to the remotely sensed phenology. The relationship between remotely sensed phenology and crop growth stages is likely to vary for specific crop types and varieties, growing stages, conditions and even locations. In this presentation, we will examine the relationship between remotely sensed phenology and crop growth stages using in-situ measurements from Fluxnet sites and crop progress reports from USDA NASS. We will present remote sensing approaches and focus on: 1) integrating multiple sources of remote sensing data; and 2) extracting crop phenology at field scales. An example in the U.S. Corn Belt area will be presented and analyzed. Future directions for mapping crop growth stages will be discussed.

Remote Sensing: A Film Review.

ERIC Educational Resources Information Center

Carter, David J.

1986-01-01

Reviews the content of 19 films on remote sensing published between 1973 and 1980. Concludes that they are overly simplistic, notably outdated, and generally too optimistic about the potential of remote sensing from space for resource exploration and environmental problem-solving. Provides names and addresses of more current remote sensing…

On the potential of a multi-temporal AMSR-E data analysis for soil wetness monitoring

NASA Astrophysics Data System (ADS)

Lacava, T.; Coviello, I.; Calice, G.; Mazzeo, G.; Pergola, N.; Tramutoli, V.

2009-12-01

Soil moisture is a critical element for both global water and energy budget. The use of satellite remote sensing data for a characterizations of soil moisture fields at different spatial and temporal scales has more and more increased during last years, thanks also to the new generation of microwave sensors (both active and passive) orbiting around the Earth. Among microwave radiometers which could be used for soil moisture retrieval, the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E), is the one that, for its spectral characteristics, should give more reliable results. The possibility of collect information in five observational bands in the range 6.9 - 89 GHz (with dual polarization), make it currently, waiting for the next ESA Soil Moisture and Ocean Salinity Mission (SMOS - scheduled for September 2009) and the NASA Soil Moisture Active Passive Mission (SMAP - scheduled for 2013), the best radiometer for soil moisture retrieval. Unfortunately, after its launch (AMSR-E is flying aboard EOS-AQUA satellite since 2002) diffuse C-band Radio-Frequency Interferences (RFI) were discovered contaminating AMSR-E radiances over many areas in the world. For this reason, often X-band (less RFI affected) based soil moisture retrieval algorithms, instead of the original based on C-band, have been preferred. As a consequence, the sensitivity of such measurements is decreased, because of the lower penetrating capability of the X band wavelengths than C-band, as well as for their greater noisiness, due to their high sensitivity to the presence of vegetation in the sensor field of view. In order to face all these problems, in this work a general methodology for multi-temporal satellite data analysis (Robust Satellite Techniques, RST) will be used. RST approach, already successfully applied in the framework of hydro-meteorological risk mitigation, should help us in managing AMSR-E data for several purposes. In this paper, in particular, we have looked into the possible improvement, both in terms of quality and reliability, of AMSR-E C-band soil moisture retrieval which, a differential approach like RST, may produce. To reach this aim, a multi-temporal analysis of long-term historical series of AMSR-E C-band data has been performed. Preliminary results of such an analysis will be shown in this work and discussed also by a comparison with the standard AMSR-E soil moisture products, daily provided by NASA. In detail, achievements obtained investigating several flooding events happened in the past over different areas of the world will be presented.

Using in-field and remote sensing techniques for the monitoring of small-scale permafrost decline in Northern Quebec

NASA Astrophysics Data System (ADS)

May, Inga; Kim, Jun Su; Spannraft, Kati; Ludwig, Ralf; Hajnsek, Irena; Bernier, Monique; Allard, Michel

2010-05-01

Permafrost-affected soils represent about 45% of Canadian arctic and subarctic regions. Under the recently recorded changed climate conditions, the areas located in the discontinuous permafrost zones are likely to belong to the most impacted environments. Degradations of Palsas and lithalsas as being the most distinct permafrost landforms as well as an extension of wetlands have been observe during the past decades by several research teams all over the northern Arctic. These alterations, caused by longer an warmer thawing periods, are expected to become more and more frequent in the future. The effects on human beings and on the surrounding sensitive ecosystems are presumed to be momentous and of high relevance. Hence, there is a high demand for new techniques that are able to detect, and possibly even predict, the behavior of the permafrost within a changing environment. The presented study is part of an international research collaboration between LMU, INRS and UL within the framework of ArcticNet. The project intends to develop a monitoring system strongly based on remote sensing imagery and GIS-based data analysis, using a test site located in northern Quebec (Umiujaq, 56°33' N, 76°33' W). It shall be investigated to which extent the interpretation of satellite imagery is feasible to partially substitute costly and difficult geophysical point measurements, and to provide spatial knowledge about the major factors that control permafrost dynamics and ecosystem change. In a first step, these factors, mainly expected to be determined from changes in topography, vegetation cover and snow cover, are identified and validated by means of several consecutive ground truthing initiatives supporting the analysis of multi-sensoral time series of remotely sensed information. Both sources are used to generate and feed different concepts for modeling permafrost dynamics by ways of parameter retrieval and data assimilation. On this poster, the outcomes of the first project year (2009) are highlighted. The main focus during this year was to figure out whether small-scale topographical changes caused by seasonal thawing and freezing processes, are detectable by means of SAR-interferometry. For this purpose, repeat passes of interferometric products were computed from the multi-temporal image pairs of Germany's X-band SAR sensor TerraSar-X. These are then compared with in-situ measurements surveyed by high precision differential GPS, taken during the field measurements in April and August of 2009. Thus, the first methodological research question is to prove that the results from the interferogram analysis correspond to the findings of field surveys. The results are promising as topographical changes could be observed with the D-GPS as well as on the interferograms. Due to the amount of factors influencing the remote sensed data, the analysis of the information contained in the data in order to make quantitative statements still remains an effort. Nevertheless it is very likely that, after further investigation to fully understand the radar-signal, this procedure is indeed reliable and efficient, and may be applied to a long-term and interannual assessment of permafrost dynamics in the sub-arctic.

Educational activities of remote sensing archaeology (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hadjimitsis, Diofantos G.; Agapiou, Athos; Lysandrou, Vasilki; Themistocleous, Kyriacos; Cuca, Branka; Nisantzi, Argyro; Lasaponara, Rosa; Masini, Nicola; Krauss, Thomas; Cerra, Daniele; Gessner, Ursula; Schreier, Gunter

2016-10-01

Remote sensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remote sensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features. Education activities and training of remote sensing archaeology to young people is characterized of highly importance. Specific remote sensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a "remote sensing" approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remote sensing and of course to learn about history. The paper aims to present a modern didactical concept and some examples of practical implementation of remote sensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remote sensing for archaeological research in Cyprus. Through H2020 ATHENA project, the Remote Sensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.

Remote Sensing and the Earth.

ERIC Educational Resources Information Center

Brosius, Craig A.; And Others

This document is designed to help senior high school students study remote sensing technology and techniques in relation to the environmental sciences. It discusses the acquisition, analysis, and use of ecological remote data. Material is divided into three sections and an appendix. Section One is an overview of the basics of remote sensing.…

Microwave remote sensing of snowpack properties

NASA Technical Reports Server (NTRS)

Rango, A. (Editor)

1980-01-01

Topic concerning remote sensing capabilities for providing reliable snow cover data and measurement of snow water equivalents are discussed. Specific remote sensing technqiues discussed include those in the microwave region of the electromagnetic spectrum.

Commerical Remote Sensing Data Contract

USGS Publications Warehouse

,

2005-01-01

The U. S. Geological Survey's (USGS) Commercial Remote Sensing Data Contracts (CRSDCs) provide government agencies with access to a broad range of commercially available remotely sensed airborne and satellite data. These contracts were established to support The National Map partners, other Federal Civilian agency programs, and Department of Defense programs that require data for the United States and its territories. Experience shows that centralized procurement of remotely sensed data leads to considerable cost savings to the Federal government through volume discounts, reduction of redundant contract administrative costs, and avoidance of duplicate purchases. These contracts directly support the President's Commercial Remote Sensing Space Policy, signed in 2003, by providing a centralized mechanism for civil agencies to acquire commercial remote sensing products to support their mission needs in an efficient and coordinated way. CRSDC administration is provided by the USGS Mid-Continent Mapping Center in Rolla, Missouri.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Experimental Sea Slicks in the Marsen (Maritime Remote Sensing) Exercise.

DTIC Science & Technology

1980-10-30

Experimental slicks with various surface properties were generated in the North Sea as part of the MARSEN (Maritime Remote Sensing ) exercise. The one...with remote sensing instrumentation. Because of the numerous effects of surface films on air-sea interfacial processes, these experiments were designed...information was obtained on the influence of sea surface films on the interpretation of signals received by remote sensing systems. Criteria for the

SYMPOSIUM ON REMOTE SENSING IN THE POLAR REGIONS

DTIC Science & Technology

The Arctic Institute of North America long has been interested in encouraging full and specific attention to applications of remote sensing to polar...research problems. The major purpose of the symposium was to acquaint scientists and technicians concerned with remote sensing with some of the...special problems of the polar areas and, in turn, to acquaint polar scientists with the potential of the use of remote sensing . The Symposium therefore was

REMOTE SENSING IN OCEANOGRAPHY.

DTIC Science & Technology

remote sensing from satellites. Sensing of oceanographic variables from aircraft began with the photographing of waves and ice. Since then remote measurement of sea surface temperatures and wave heights have become routine. Sensors tested for oceanographic applications include multi-band color cameras, radar scatterometers, infrared spectrometers and scanners, passive microwave radiometers, and radar imagers. Remote sensing has found its greatest application in providing rapid coverage of large oceanographic areas for synoptic and analysis and

Methods of Determining Playa Surface Conditions Using Remote Sensing

DTIC Science & Technology

1987-10-08

NO. 11. TITLE (include Security Classification) METHODS OF DETERMINING PLAYA SURFACE CONDITIONS USING REMOTE SENSING 12. PERSONAL AUTHOR(S) J. PONDER...PLAYA SURFACE CONDITIONS USING REMOTE SENSING J. Ponder Henley U. S. Army Engineer Topographic Laboratories Fort Belvoir, Virginia 22060-5546 "ABSTRACT...geochemistry, hydrology and remote sensing but all of these are important to the understanding of these unique geomorphic features. There is a large body

Needs Assessment for the Use of NASA Remote Sensing Data in the Development and Implementation of Estuarine and Coastal Water Quality Standards

NASA Technical Reports Server (NTRS)

Spiering, Bruce; Underwood, Lauren; Ellis, Chris; Lehrter, John; Hagy, Jim; Schaeffer, Blake

2010-01-01

The goals of the project are to provide information from satellite remote sensing to support numeric nutrient criteria development and to determine data processing methods and data quality requirements to support nutrient criteria development and implementation. The approach is to identify water quality indicators that are used by decision makers to assess water quality and that are related to optical properties of the water; to develop remotely sensed data products based on algorithms relating remote sensing imagery to field-based observations of indicator values; to develop methods to assess estuarine water quality, including trends, spatial and temporal variability, and seasonality; and to develop tools to assist in the development and implementation of estuarine and coastal nutrient criteria. Additional slides present process, criteria development, typical data sources and analyses for criteria process, the power of remote sensing data for the process, examples from Pensacola Bay, spatial and temporal variability, pixel matchups, remote sensing validation, remote sensing in coastal waters, requirements for remotely sensed data products, and needs assessment. An additional presentation examines group engagement and information collection. Topics include needs assessment purpose and objectives, understanding water quality decision making, determining information requirements, and next steps.

Commercial use of remote sensing in agriculture: a case study

NASA Astrophysics Data System (ADS)

Gnauck, Gary E.

1999-12-01

Over 25 years of research have clearly shown that an analysis of remote sensing imagery can provide information on agricultural crops. Most of this research has been funded by and directed toward the needs of government agencies. Commercial use of agricultural remote sensing has been limited to very small-scale operations supplying remote sensing services to a few selected customers. Datron/Transco Inc. undertook an internally funded remote sensing program directed toward the California cash crop industry (strawberries, lettuce, tomatoes, other fresh vegetables and cotton). The objectives of this program were twofold: (1) to assess the need and readiness of agricultural land managers to adopt remote sensing as a management tool, and (2) determine what technical barriers exist to large-scale implementation of this technology on a commercial basis. The program was divided into three phases: Planning, Engineering Test and Evaluation, and Commercial Operations. Findings: Remote sensing technology can deliver high resolution multispectral imagery with rapid turnaround, that can provide information on crop stress insects, disease and various soil parameters. The limiting factors to the use of remote sensing in agriculture are a lack of familiarization by the land managers, difficulty in translating 'information' into increased revenue or reduced cost for the land manager, and the large economies of scale needed to make the venture commercially viable.

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2013 CFR

2013-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2011 CFR

2011-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2014 CFR

2014-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2012 CFR

2012-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2010 CFR

2010-01-01

... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and...

Advanced Remote Sensing Research

USGS Publications Warehouse

Slonecker, Terrence; Jones, John W.; Price, Susan D.; Hogan, Dianna

2008-01-01

'Remote sensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotely sensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remote sensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).

Remote sensing in the coastal and marine environment. Proceedings of the US North Atlantic Regional Workshop

NASA Technical Reports Server (NTRS)

Zaitzeff, J. B. (Editor); Cornillon, P. (Editor); Aubrey, D. A. (Editor)

1980-01-01

Presentations were grouped in the following categories: (1) a technical orientation of Earth resources remote sensing including data sources and processing; (2) a review of the present status of remote sensing technology applicable to the coastal and marine environment; (3) a description of data and information needs of selected coastal and marine activities; and (4) an outline of plans for marine monitoring systems for the east coast and a concept for an east coast remote sensing facility. Also discussed were user needs and remote sensing potentials in the areas of coastal processes and management, commercial and recreational fisheries, and marine physical processes.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1992-01-01

Research findings are summarized for projects dealing with the following: application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated Mie scatterers with size distribution and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; theoretical modeling for passive microwave remote sensing of earth terrain; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Brazil's remote sensing activities in the Eighties

NASA Technical Reports Server (NTRS)

Raupp, M. A.; Pereiradacunha, R.; Novaes, R. A.

1985-01-01

Most of the remote sensing activities in Brazil have been conducted by the Institute for Space Research (INPE). This report describes briefly INPE's activities in remote sensing in the last years. INPE has been engaged in research (e.g., radiance studies), development (e.g., CCD-scanners, image processing devices) and applications (e.g., crop survey, land use, mineral resources, etc.) of remote sensing. INPE is also responsible for the operation (data reception and processing) of the LANDSATs and meteorological satellites. Data acquisition activities include the development of CCD-Camera to be deployed on board the space shuttle and the construction of a remote sensing satellite.

Application of remote sensing to state and regional problems. [for Mississippi

NASA Technical Reports Server (NTRS)

Miller, W. F.; Bouchillon, C. W.; Harris, J. C.; Carter, B.; Whisler, F. D.; Robinette, R.

1974-01-01

The primary purpose of the remote sensing applications program is for various members of the university community to participate in activities that improve the effective communication between the scientific community engaged in remote sensing research and development and the potential users of modern remote sensing technology. Activities of this program are assisting the State of Mississippi in recognizing and solving its environmental, resource and socio-economic problems through inventory, analysis, and monitoring by appropriate remote sensing systems. Objectives, accomplishments, and current status of the following individual projects are reported: (1) bark beetle project; (2) state park location planning; and (3) waste source location and stream channel geometry monitoring.

Physics teaching by infrared remote sensing of vegetation

NASA Astrophysics Data System (ADS)

Schüttler, Tobias; Maman, Shimrit; Girwidz, Raimund

2018-05-01

Context- and project-based teaching has proven to foster different affective and cognitive aspects of learning. As a versatile and multidisciplinary scientific research area with diverse applications for everyday life, satellite remote sensing is an interesting context for physics education. In this paper we give a brief overview of satellite remote sensing of vegetation and how to obtain your own, individual infrared remote sensing data with affordable converted digital cameras. This novel technique provides the opportunity to conduct individual remote sensing measurement projects with students in their respective environment. The data can be compared to real satellite data and is of sufficient accuracy for educational purposes.

Application of remote sensing to water resources problems

NASA Technical Reports Server (NTRS)

Clapp, J. L.

1972-01-01

The following conclusions were reached concerning the applications of remote sensing to water resources problems: (1) Remote sensing methods provide the most practical method of obtaining data for many water resources problems; (2) the multi-disciplinary approach is essential to the effective application of remote sensing to water resource problems; (3) there is a correlation between the amount of suspended solids in an effluent discharged into a water body and reflected energy; (4) remote sensing provides for more effective and accurate monitoring, discovery and characterization of the mixing zone of effluent discharged into a receiving water body; and (5) it is possible to differentiate between blue and blue-green algae.

SUPERFUND REMOTE SENSING SUPPORT

EPA Science Inventory

This task provides remote sensing technical support to the Superfund program. Support includes the collection, processing, and analysis of remote sensing data to characterize hazardous waste disposal sites and their history. Image analysis reports, aerial photographs, and assoc...

Remote Sensing and the Earth

NASA Technical Reports Server (NTRS)

Brosius, C. A.; Gervin, J. C.; Ragusa, J. M.

1977-01-01

A text book on remote sensing, as part of the earth resources Skylab programs, is presented. The fundamentals of remote sensing and its application to agriculture, land use, geology, water and marine resources, and environmental monitoring are summarized.

Operational Use of Remote Sensing within USDA

NASA Technical Reports Server (NTRS)

Bethel, Glenn R.

2007-01-01

A viewgraph presentation of remote sensing imagery within the USDA is shown. USDA Aerial Photography, Digital Sensors, Hurricane imagery, Remote Sensing Sources, Satellites used by Foreign Agricultural Service, Landsat Acquisitions, and Aerial Acquisitions are also shown.

Investigation related to multispectral imaging systems

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Erickson, J. D.

1974-01-01

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community.

An object-based storage model for distributed remote sensing images

NASA Astrophysics Data System (ADS)

Yu, Zhanwu; Li, Zhongmin; Zheng, Sheng

2006-10-01

It is very difficult to design an integrated storage solution for distributed remote sensing images to offer high performance network storage services and secure data sharing across platforms using current network storage models such as direct attached storage, network attached storage and storage area network. Object-based storage, as new generation network storage technology emerged recently, separates the data path, the control path and the management path, which solves the bottleneck problem of metadata existed in traditional storage models, and has the characteristics of parallel data access, data sharing across platforms, intelligence of storage devices and security of data access. We use the object-based storage in the storage management of remote sensing images to construct an object-based storage model for distributed remote sensing images. In the storage model, remote sensing images are organized as remote sensing objects stored in the object-based storage devices. According to the storage model, we present the architecture of a distributed remote sensing images application system based on object-based storage, and give some test results about the write performance comparison of traditional network storage model and object-based storage model.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

NASA Astrophysics Data System (ADS)

Jessup, A.; Holman, R. A.; Chickadel, C.; Elgar, S.; Farquharson, G.; Haller, M. C.; Kurapov, A. L.; Özkan-Haller, H. T.; Raubenheimer, B.; Thomson, J. M.

2012-12-01

DARLA is 5-year collaborative project that couples state-of-the-art remote sensing and in situ measurements with advanced data assimilation (DA) modeling to (a) evaluate and improve remote sensing retrieval algorithms for environmental parameters, (b) determine the extent to which remote sensing data can be used in place of in situ data in models, and (c) infer bathymetry for littoral environments by combining remotely-sensed parameters and data assimilation models. The project uses microwave, electro-optical, and infrared techniques to characterize the littoral ocean with a focus on wave and current parameters required for DA modeling. In conjunction with the RIVET (River and Inlets) Project, extensive in situ measurements provide ground truth for both the remote sensing retrieval algorithms and the DA modeling. Our goal is to use remote sensing to constrain data assimilation models of wave and circulation dynamics in a tidal inlet and surrounding beaches. We seek to improve environmental parameter estimation via remote sensing fusion, determine the success of using remote sensing data to drive DA models, and produce a dynamically consistent representation of the wave, circulation, and bathymetry fields in complex environments. The objectives are to test the following three hypotheses: 1. Environmental parameter estimation using remote sensing techniques can be significantly improved by fusion of multiple sensor products. 2. Data assimilation models can be adequately constrained (i.e., forced or guided) with environmental parameters derived from remote sensing measurements. 3. Bathymetry on open beaches, river mouths, and at tidal inlets can be inferred from a combination of remotely-sensed parameters and data assimilation models. Our approach is to conduct a series of field experiments combining remote sensing and in situ measurements to investigate signature physics and to gather data for developing and testing DA models. A preliminary experiment conducted at the Field Research Facility at Duck, NC in September 2010 focused on assimilation of tower-based electo-optical, infrared, and radar measurements in predictions of longshore currents. Here we provide an overview of our contribution to the RIVET I experiment at New River Inlet, NC in May 2012. During the course of the 3-week measurement period, continuous tower-based remote sensing measurements were made using electro-optical, infrared, and radar techniques covering the nearshore zone and the inlet mouth. A total of 50 hours of airborne measurements were made using high-resolution infrared imagers and a customized along track interferometric synthetic aperture radar (ATI SAR). The airborne IR imagery provides kilometer-scale mapping of frontal features that evolve as the inlet flow interacts with the oceanic wave and current fields. The ATI SAR provides maps of the two-dimensional surface currents. Near-surface measurements of turbulent velocities and surface waves using SWIFT drifters, designed to measures near-surface properties relevant to remote sensing, complimented the extensive in situ measurements by RIVET investigators.

A remote sensing and GIS-enabled asset management system (RS-GAMS).

DOT National Transportation Integrated Search

2013-04-01

Under U.S. Department of Transportation (DOT) Commercial Remote Sensing and : Spatial Information (CRS&SI) Technology Initiative 2 of the Transportation : Infrastructure Construction and Condition Assessment, an intelligent Remote Sensing and : GIS-b...

Remote Sensing.

ERIC Educational Resources Information Center

Williams, Richard S., Jr.; Southworth, C. Scott

1983-01-01

The Landsat Program became the major event of 1982 in geological remote sensing with the successful launch of Landsat 4. Other 1982 remote sensing accomplishments, research, publications, (including a set of Landsat worldwide reference system index maps), and conferences are highlighted. (JN)

Remote sensing utility in a disaster struck urban environment

NASA Technical Reports Server (NTRS)

Rush, M.; Holguin, A.; Vernon, S.

1974-01-01

A project to determine the ways in which remote sensing can contribute to solutions of urban public health problems in time of natural disaster is discussed. The objectives of the project are to determine and describe remote sensing standard operating procedures for public health assistance during disaster relief operations which will aid the agencies and organizations involved in disaster intervention. Proposed tests to determine the validity of the remote sensing system are reported.

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with...results compared with those from remote - sensing models and from direct measurements. The agreement from different determinations suggests that...reasonable results for remote sensing reflectance of clear blue water to turbid brown water are obtainable from above-surface measurements, even under conditions of high waves.

Bibliography of Remote Sensing Techniques Used in Wetland Research

DTIC Science & Technology

1993-01-01

8217 is investigating the application of remote sensing technology for detecting changes in wetland environments. This report documents a bibliographic...search conducted as part of that work unit on applications of remote sensing techniques in wetland research. Results were used to guide research...efforts on the use of remote sensing technology for wetland change detection and assessment. The citations are presented in three appendixes, organized by wetland type, sensor type, and author.

Use of Openly Available Satellite Images for Remote Sensing Education

NASA Astrophysics Data System (ADS)

Wang, C.-K.

2011-09-01

With the advent of Google Earth, Google Maps, and Microsoft Bing Maps, high resolution satellite imagery are becoming more easily accessible than ever. It have been the case that the college students may already have wealth experiences with the high resolution satellite imagery by using these software and web services prior to any formal remote sensing education. It is obvious that the remote sensing education should be adjusted to the fact that the audience are already the customers of remote sensing products (through the use of the above mentioned services). This paper reports the use of openly available satellite imagery in an introductory-level remote sensing course in the Department of Geomatics of National Cheng Kung University as a term project. From the experience learned from the fall of 2009 and 2010, it shows that this term project has effectively aroused the students' enthusiastic toward Remote Sensing.

Strategies for using remotely sensed data in hydrologic models

NASA Technical Reports Server (NTRS)

Peck, E. L.; Keefer, T. N.; Johnson, E. R. (Principal Investigator)

1981-01-01

Present and planned remote sensing capabilities were evaluated. The usefulness of six remote sensing capabilities (soil moisture, land cover, impervious area, areal extent of snow cover, areal extent of frozen ground, and water equivalent of the snow cover) with seven hydrologic models (API, CREAMS, NWSRFS, STORM, STANFORD, SSARR, and NWSRFS Snowmelt) were reviewed. The results indicate remote sensing information has only limited value for use with the hydrologic models in their present form. With minor modifications to the models the usefulness would be enhanced. Specific recommendations are made for incorporating snow covered area measurements in the NWSRFS Snowmelt model. Recommendations are also made for incorporating soil moisture measurements in NWSRFS. Suggestions are made for incorporating snow covered area, soil moisture, and others in STORM and SSARR. General characteristics of a hydrologic model needed to make maximum use of remotely sensed data are discussed. Suggested goals for improvements in remote sensing for use in models are also established.

A preliminary study of the statistical analyses and sampling strategies associated with the integration of remote sensing capabilities into the current agricultural crop forecasting system

NASA Technical Reports Server (NTRS)

Sand, F.; Christie, R.

1975-01-01

Extending the crop survey application of remote sensing from small experimental regions to state and national levels requires that a sample of agricultural fields be chosen for remote sensing of crop acreage, and that a statistical estimate be formulated with measurable characteristics. The critical requirements for the success of the application are reviewed in this report. The problem of sampling in the presence of cloud cover is discussed. Integration of remotely sensed information about crops into current agricultural crop forecasting systems is treated on the basis of the USDA multiple frame survey concepts, with an assumed addition of a new frame derived from remote sensing. Evolution of a crop forecasting system which utilizes LANDSAT and future remote sensing systems is projected for the 1975-1990 time frame.

Archimedean Witness: The Application of Remote Sensing as an Aid to Human Rights Prosecutions

NASA Astrophysics Data System (ADS)

Walker, James Robin

The 21st century has seen a significant increase in the use of remote sensing technology in the international human rights arena for the purposes of documenting crimes against humanity. The nexus between remote sensing, human rights activism, and international criminal prosecutions sits at a significant crossroads within geographic thought, calling attention to the epistemological and geopolitical implications that stem from the "view from nowhere" afforded by satellite imagery. Therefore, this thesis is divided into three sections. The first looks at the geographical questions raised by the expansion of remote sensing use in the context of international activism. The second explores the complications inherent in the presentation of remote sensing data as evidence of war crimes. Building upon the first two, the third section is a case study in alternate forms of analysis, aimed at expanding the utility of remote sensing data in international criminal prosecutions.