03224nam a2200253 a 450000100080000000500110000800800410001902400440006010000170010424501570012126001770027852023260045565000150278165000190279665000170281565000140283265300230284665300260286965300100289570000160290570000160292170000160293770000170295319812132022-03-30       2013    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1117/12.20286402DOI1 aMULIANGA, B.  aEstimating potential soil erosion for environmental services in a sugarcane growing área ussing multisource remote sensing data.h[electronic resource]  aIn: SPIE REMOTE SENSING, 4., 2013, Dresden. Remote sensing for agriculture, ecosystems, and hydrology XV: proceedings... Bellingham: SPIE, 2013. v. 8887. Ref. 88871W.c8887  aCharacterization of landscapes is crucial in modelling potential soil erosion to ascertain environmental services that are provided by the main land use in the ecosystem. Remote sensing techniques have proved successful in characterization of landscapes. In this study area of a rain-fed Kibos-Miwani sugar zone of Kenya, we used Normalized Difference Vegetation Index (NDVI) data extracted from satellite imagery to characterize the spatial and temporal heterogeneity of the vegetation conditions, and to model potential soil erosion. Data used included Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m NDVI acquired in the period 2000 to 2012; 30 m Landsat5 time series images acquired between November 2010 and June 2011; a 30 m digital elevation model (DEM); and ground observations (land cover and soil characteristics). Ground observations were cross tabulated and analysed under ISO 17025 laboratory procedures. Temporal NDVI was extracted directly from MODIS 250 m images to study the changes in seasonal vegetation at the region scale, while spatial NDVI was extracted by analysing Landsat 5 images at the field scale. NDVI extracted from Landsat images for a specific date, represented vegetation conditions for that simulation period. To compute potential soil erosion, we ran three simulations using the spatially explicit Fuzzy-based dynamic soil erosion model (FuDSEM) based on identified vegetative conditions, thanks to MODIS data. Input datasets included Landsat 5 NDVI, the slope, aspect, curvature and soil physical properties. Results of land cover presented sugarcane as the main land use, occupying 76% of the land scape. Results of NDVI analysis were consistent with crop management practices, illustrating a spatially heterogeneous land scape with varied vegetation conditions throughout the year. Results of the simulations were not significantly different for the different periods of the year. Out of simulations, we noted a homogeneous low erosion risk in areas with natural land cover with a global mean of 0.42. Medium to intense erosion risk in cropped areas was evident, with erosion risk varying from one pixel to the other. Simulation results suggest that crop management practices (planting and harvesting processes) are the drivers of erosion in sugar cane cultivated areas.  aland cover  aremote sensing  asoil erosion  asugarcane  aCropping practices  aEnvironmental service  aSlope1 aBÉGUÉ, A.1 aSIMÕES, M.1 aCLOUVEL, P.1 aTODOROFF, P.