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Fractional tree cover (Fcover) is an important biophysical variable for measuring forest degradation and characterizing land cover.Recently, atmospherically corrected Landsat data have become available, providing opportunities for high-resolution mapping of forestattributes at global-scale. However, topographic correction is a pre-processing step that remains to be addressed. While several methodshave been introduced for topographic correction, it is uncertain whether Fcover models based on vegetation indices are sensitive totopographic effects. Our objective was to assess the effect of topographic correction on the accuracy of Fcover modelling. The studyarea was located in the Eastern Arc Mountains of Kenya. We used C-correction as a digital elevation model (DEM) based correctionmethod. We examined if predictive models based on normalized difference vegetation index (NDVI), reduced simple ratio (RSR) andtasseled cap indices (Brightness, Greenness and Wetness) are improved if using topographically corrected data. Furthermore, weevaluated how the results depend on the DEM by correcting images using available global DEM (ASTER GDEM, SRTM) and aregional DEM. Reference Fcover was obtained from wall-to-wall airborne LiDAR data. Landsat images corresponding to minimumand maximum sun elevation were analyzed. We observed that topographic correction could only improve models based on Brightnessand had very small effect on the other models. Cosine of the solar incidence angle (<i>cos i</i>) derived from SRTM DEM showed strongerrelationship with spectral bands than other DEMs. In conclusion, our results suggest that, in tropical mountains, predictive modelsbased on common vegetation indices are not sensitive to topographic effects

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Does topographic normalization of landsat images improve fractional tree cover mapping in tropical mountains?