PAUWES Data Portal
remote sensing
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This document is a map of land and vegetation cover of the North Bank Region of the Gambia produced by the National Environment Agency of the Gambia (NEA). It is reproduced as a background map in 2015 by Constantine Kouevi, student WASCAL MRP-The Gambia, in her Master thesis. This study shows that there is a densification of human activities in this geographical space, which explains a high human concentration. Land management policies depend on the quality of natural resource management. This can lead to potential conflicts.
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These datasets show the leaf area index (LAI), the one-sided area of green leaves per unit ground area for West Africa from 2007 to 2012. The regionally adapted, continuous, and gap free time series has been created by data fusion methods based on LAI products of LSA-SAF (http://landsaf.meteo.pt/) and geoland 2 (BioPar, http://www.gmes-geoland.info/service-portfolio/biophysical-parameter-products.html). Additional information: - 10-day timesteps (3 datasets per month) - spatial resolution: ~1km (Lat/Lon WGS-84, 0.00892857 deg) - coverage: 2007-2012 - data scaling: LAI[%]= pixelvalue/30 - valid data range: 0-210 (=LAI of 0-7), 255=no data - number of samples: 2130, number of lines: 1345
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Land use/cover classification of West Africa based on multi-sensor earth observation data. The regional land cover/use map for West Africa has a spatial resolution of 250 m. It is based on earth observation data from three optical and radar sensors. Annual time series of the optical Moderate Resolution Imaging Spectroradiometer (MODIS) are used for discriminating semi-natural vegetation classes and agricultural classes. Envisat Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS) time series, in combination with MODIS data, are used for delineating permanent and seasonal water bodies. Built-up areas of two density classes are mapped based on radar imagery collected by the satellites TanDEM-X and TerraSAR-X. The accuracy assessment of the multi-sensor land cover map yields an overall accuracy of 80% at legend level 1 (9 classes) and 73% at the more detailed legend level 2 (14 classes). For further details on the mapping approach and a discussion of strengths and limitations on the approach and dataset, we refer to the following publication: Gessner, Ursula; Machwitz, Miriam; Esch, Thomas; Tillack, Adina; Naeimi, Vahid; Kuenzer, Claudia; Dech, Stefan (2015): Multi-sensor mapping of West African land cover using MODIS, ASAR and TanDEM-X/TerraSAR-X data. Remote Sensing of Environment, accepted.
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These LULC maps were created through automatic digital classification of RapidEye imagery acquired during the cropping season of 2012. Two monthly time-steps (June and October) were analyzed.Reference (or field) data on which the classification was based were acquired through a field campaign that lasted from May to October 2012. Standard image pre-processing techniques such as geometric and radiometric correction were conducted on the data prior to classification. The Random Forest classification algorithm was used for classification. Two levels of classification were conducted: (1) a level 1 classification which includes four broad LULC classes and (2) a level 2 classification which comprises of nine LULC classes. The poor temporal coverage of the RapidEye imagery made the accurate delineation of certain crop classes (e.g. groundnuts) very challenging. Nonetheless, an overall accuracy of 79% was obtained
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Land use / land cover classification of the districts Bolgatanga and Bongo in Ghana based on very high resolution remote sensing data (5m). Focus of the classification is on the agricultural class, where single crop types are discriminated. Remote sensing base: RapidEye, TerraSAR-X, Landsat.