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.
Riaza A.,Geological Survey of Spain |
Buzzi J.,Geological Survey of Spain |
Garcia-Melendez E.,University of Leon |
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.
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.
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).
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 Wurzburg
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.