German Remote Sensing Data Center

German, Germany

German Remote Sensing Data Center

German, Germany
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Dahms T.,University of Würzburg | Seissiger S.,University of Würzburg | Borg E.,German Remote Sensing Data Center | Vajen H.,German Remote Sensing Data Center | And 2 more authors.
Photogrammetrie, Fernerkundung, Geoinformation | Year: 2016

With the increasing availability of high resolution data, remote sensing is gaining importance for agricultural management. Sensor constellations such as RapidEye or Sentinel-2 have a strong potential for precision agriculture because they provide spectral information throughout the cropping season and at the subfeld level. To explore this potential, methods are required that accurately transfer the spectral information into biophysical parameters which in turn permit quantitative assessments of plant growth on the feld. Boundary condition for a successful monitoring, e.g., a repeated derivation of the biophysical parameters is to cope with the challenge of enormous data amounts, i.e. to select the input data that is most relevant. In this study, biophysical parameters of winter wheat, namely the fraction of absorbed photosynthetic active radiation (FPA R), the leaf area index (LAI) and the chlorophyll content (expressed by SPAD), were modelled with RapidEye data in Mecklenburg-West Pomerania, Germany, using Random Forest based on conditional inference trees. Focus was set at the selection of the most important information out of spectral bands and indices for parameter prediction on winter wheat. Insitu and remote sensing observations were grouped into phenological phases in order to examine the importance of single spectral bands or indices for modelling biophysical reality in the several growing stages of winter wheat. The coefficient of determination for FPAR (LAI; SPAD) ranged between 0.19 and 0.83 (0.33 and 0.66; 0.21 and 0.45). Model accuracy was linked with the phenological phase. The results showed that for each biophysical parameter, different spectral variables become important for modelling and the number of important variables depends on the phenological time span. The prediction of biophysical parameters for short phenological groups often depends only on one to three variables. The results also showed that in the phenological phase of fruit development, the model accuracy is the lowest and the determination of the importance is comparatively vague. © 2016 E. Schweizerbart'sche Verlagsbuchhandlung, Stuttgart, Germany.

Riaza A.,Geological Survey of Spain | Buzzi J.,Geological Survey of Spain | Garcia-Melendez E.,University of León | Carrere V.,University of Nantes | And 2 more authors.
Hydrological Sciences Journal | Year: 2015

Abstract: Sulphide mine waste extensively contaminates the Odiel River (southwest Spain), releasing sulphuric acid into the water body. Acidic water in this river precipitates and dissolves variably hydrated iron sulphate in a complex geological pattern controlled by climate. Local abrupt changes in the water pH in the vicinity of highly contaminated tributaries can be mapped by means of imaging spectroscopy using hyperspectral remote sensing (HyMap) data. Also, increased pH through mixing of acidic river water with marine water can be detected when the river reaches the area influenced by sea tides. Mapping the quality of water with hyperspectral data is confounded by vegetation, either dry or wet, rooted or floating. The spectral features of acidic water measured with a field spectrometer revealed the spectral influence of green vegetation, similar to the influence of the depth and transparency of water. Careful mapping of such parameters with HyMap data must therefore precede any spectral evaluation of water related to acidity in a river course. The spectral features detectable by HyMap data and associated with pH changes caused by contamination in river water by iron sulphide mine waste, and their controls, are described and references established for routine monitoring through hyperspectral image processing. © 2015 IAHS.

Landmann T.,University of Würzburg | Schramm M.,Food and Agriculture Organization of the United Nations | Huettich C.,Friedrich - Schiller University of Jena | Dech S.,German Remote Sensing Data Center
Remote Sensing Letters | Year: 2013

The mapping and characterization of wetlands in semi-arid savannas is challenging due to the large interannual and seasonal flooding variability in these important and highly vulnerable ecosystems. This study shows the possibility of using 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) metrics (from 16 day composites) in change vector analysis °CVA) to map wetland dynamics in the Linyanti wetland (Namibia) between 2001 and 2010. For each pixel, we compute the interannual°CVA intensity and the°CVA direction, as well as the cumulative change intensity and the overall direction (trend) within the observation period. Both the change vector intensities and the corresponding change directions are necessary to interpret the interannual change and assess the 9 year trend. The interannual°CVA intensities show a significant correlation with flooding magnitudes. The flooding magnitudes are derived from the Advanced Microwave Scanning Radiometer-Earth Observing System instrument (AMSR-E) radar observations for a hydrographic station located at the nearest inflow point into the Linyanti wetland. Given that long-term flooding records and satellite observations are available, the approach could be used to detect and interpret climate-induced inundation dynamics within wetlands in semi-arid Africa. © 2012 Taylor & Francis.

Bayer A.,German Remote Sensing Data Center | Bachmann M.,German Remote Sensing Data Center | Mssller A.,German Remote Sensing Data Center
34th International Symposium on Remote Sensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring | Year: 2011

Land degradation processes in the subtropical Thicket Biome in the Eastern Cape Province, South Africa, are observed and monitored. As a result, a significant loss of soil quality on such sites has been recorded. This study focuses on the determination of fundamental soil parameters like organic carbon, iron, and clay in order to assess ecosystem degradation. The test site in South Africa is surveyed for ground truth and hyperspectral image data are obtained. We take advantage of spectral mixture analysis to approximate the 'pure' soil signal from mixing pixel signatures. For a subsequent quantification of soil parameters, spectral feature analysis is linked with multiple linear regression techniques. For organic carbon and iron, calibrations of high accuracy are used for the prediction of image data. The results highly correlate with measured contents. In contrast, the quantification of clay content is still problematic mostly due to the existence of soil structural crusts.

Oney B.,University of Bayreuth | Shapiro A.,World Wildlife Fund | Wegmann M.,University of Würzburg | Wegmann M.,German Remote Sensing Data Center | And 2 more authors.
34th International Symposium on Remote Sensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring | Year: 2011

Environmental change usually occurs at centennial or greater time scales, except when driven by human activities. Sediment input into freshwater and marine ecosystems is a result of natural processes. However, mining, deforestation, unsustainable agricultural practices can greatly increase sediment input into marine and aquatic systems. This study analyzed the particular year-toyear changes of the water characteristics in Borneo's proximity using MERIS water color data from 2003-2010 with a semi-automatic approach by combining BEAM, GRASS and R. According to this time series analysis of the coastal waters around Borneo, water quality shows spatial heterogeneity but corresponds to land development patterns in the last 8 years. Land cover was found to explain downstream water quality, with dominance of variables related to land development. Further development on the island of Borneo, without proper planning. could continue to worsen water quality around Borneo and deteriorate conditions for coral reefs and other coastal ecosystems.

Cord A.F.,Helmholtz Center for Environmental Research | Klein D.,German Remote Sensing Data Center | Mora F.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Dech S.,German Remote Sensing Data Center | Dech S.,University of Würzburg
iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society | Year: 2012

Current changes of biodiversity result almost exclusively from human activities. As a consequence, spatially continuous estimates of species distributions are needed to support biodiversity evaluation and management. In the last two decades, species distribution models (SDMs) have been established as important tools for extrapolating in situ (point) observations. To account for current habitat loss, climate data used as predictors in SDMs need to be complemented by measures of current land surface characteristics. For this purpose, two alternative data sources are available, namely categorical land cover and continuous remote sensing data, each with their advantages and drawbacks. The objective of this study was therefore to directly compare the suitability of an existing land cover classification and remote sensing time series for the delineation of current biotope availability. The analysis used the Maximum Entropy algorithm to model the distributions of twelve tree species representative of the major Mexican forest types. Model results were evaluated based on AUC (area under curve) and statistical model deviance and revealed that land cover-based models overestimated species distributions and that the suitability of land cover data was dependent on species characteristics. The findings of this study support the selection of predictors in species distribution modelling in the future.

Nguyen L.-D.,HCMC Institute of Resources Geography | Thuy L.-T.,CNRS Center for the Study of the Biosphere from Space | Claudia K.,German Remote Sensing Data Center | Viet P.-B.,HCMC Institute of Resources Geography
31st Asian Conference on Remote Sensing 2010, ACRS 2010 | Year: 2010

The Mekong Delta is one of the most endangered regions in the world under the effects of global warming. Such effects include ocean warming, a rise in sea level, heat waves and periods of unusually warm weather, intense precipitations, typhoons, high tides and storm surges. Those effects result in coastal flooding, river flooding, inland flooding, salt water intrusion, coastal erosion, coastal sedimentation and cause degradation of biodiversity, spread of disease, changes in the population and habitat of plants and animals (e.g. birds and fish). Studies need to be conducted to quantify the changes observed by satellites in land use / land cover, in coastline, river bank, in flood extent and duration, and in cultural practices. The role of Earth Observation data is significant to provide both large view on the Mekong delta and high resolution observations in the regions where significant impacts of global change are being observed. Several sources of remote sensing data dating back from few decades can be used to quantify the changes. The paper presents the preliminary results of remote sensing applications in the Mekong Delta for change detection such as land use / land cover and inundation (WISDOM project); mangrove and rice/agriculture (Planet Action project); coastal line and river bank erosion and the further research works (WISDOM and RICEMAN projects).

Bochow M.,Helmholtz Center Potsdam | Taubenbock H.,German Remote Sensing Data Center | Segl K.,Helmholtz Center Potsdam | Kaufmann H.,Helmholtz Center Potsdam
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2010

Recently a growing number of investigations is dealing with the characterization and partitioning of urban agglomerations into urban structure types (USTs) based on remote sensing data. Since the USTs of interest are usually chosen with respect to the research question, application and type of urban agglomeration there is a need for a flexible and adaptable approach for automatic UST classification. In this study we identify the commonalities of published approaches and derive requirements and tasks to deal with m UST classification. Based on this, we focus on the development of a UST classification system that is highly automated, flexible and adaptable to enable a wide applicability. © 2010 IEEE.

Rogge D.,German Remote Sensing Data Center | Rivard B.,University of Alberta | Segl K.,Helmholtz Center Potsdam | Grant B.,PO Box 8076 | Feng J.,University of Alberta
Remote Sensing of Environment | Year: 2014

This study first investigates using AISA airborne hyperspectral imagery (2. m spatial resolution) to produce detailed lithologic maps in a subarctic region (Nunavik, Canada) where ultramafic rock units associated with Ni-Cu-(PGE) mineralization are exposed in the presence of lichen coatings. Twenty AISA flight-lines were radiometrically leveled and merged to form a 10. ×. 20. km mosaic, which then served to generate a simulated spaceborne EnMAP scene (30. m spatial resolution) using the End-to-End Simulation Tool to assess the sensors mapping capabilities. Spatial Spectral Endmember Extraction was used to derive spectral endmembers for the AISA and EnMAP data. Image endmembers were compared with spectral measurements of field samples to assess how well key rock types were represented. Results show that the AISA imagery provided a better representation of mafic and ultramafic rock types compared with the EnMAP simulation. Endmembers were then used to map the distribution of geological materials using Iterative Spectral Mixture Analysis. Results indicate the airborne data provided more detailed maps compared with EnMAP simulated data. However, EnMAP data could still discriminate and map broad scale lithological units, specifically mafic and ultramafic rocks. This study demonstrates the feasibility of EnMAP to provide large scale reconnaissance mapping capability of geologic materials over vast subarctic and arctic regions (potentially 30. ×. 5000. km of imagery per day) using expert knowledge combined with automated spectral analysis methods. © 2014 Elsevier Inc.

Malec S.,University of Bayreuth | Rogge D.,German Remote Sensing Data Center | Heiden U.,German Remote Sensing Data Center | Sanchez-Azofeifa A.,University of Alberta | And 2 more authors.
Remote Sensing | Year: 2015

Soil erosion can be linked to relative fractional cover of photosynthetic-active vegetation (PV), non-photosynthetic-active vegetation (NPV) and bare soil (BS), which can be integrated into erosion models as the cover-management C-factor. This study investigates the capability of EnMAP imagery to map fractional cover in a region near San Jose, Costa Rica, characterized by spatially extensive coffee plantations and grazing in a mountainous terrain. Simulated EnMAP imagery is based on airborne hyperspectral HyMap data. Fractional cover estimates are derived in an automated fashion by extracting image endmembers to be used with a Multiple End-member Spectral Mixture Analysis approach. The C-factor is calculated based on the fractional cover estimates determined independently for EnMAP and HyMap. Results demonstrate that with EnMAP imagery it is possible to extract quality endmember classes with important spectral features related to PV, NPV and soil, and be able to estimate relative cover fractions. This spectral information is critical to separate BS and NPV which greatly can impact the C-factor derivation. From a regional perspective, we can use EnMAP to provide good fractional cover estimates that can be integrated into soil erosion modeling. © 2015 by the authors.

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