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Spatial prediction and uncertainty assessment of topographic factor for revised universal soil loss equation using digital elevation models / G. Wang in ISPRS Journal of photogrammetry and remote sensing, vol 56 n° 1 (May - June 2001)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 56 n° 1 (May - June 2001) . - pp 65 - 80 Titre : Spatial prediction and uncertainty assessment of topographic factor for revised universal soil loss equation using digital elevation models Type de document : Article/Communication Auteurs : G. Wang, Auteur ; G. Gertner, Auteur ; P. Parysow, Auteur ; A. Anderson, Auteur Année de publication : 2001 Article en page(s) : pp 65 - 80 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] érosion [Termes IGN] géostatistique [Termes IGN] incertitude des données [Termes IGN] modèle numérique de surface [Termes IGN] modèle RUSLE [Termes IGN] prédiction Résumé : (Auteur) Revised Universal Soil Loss Equation (RUSLE) is a model to predict longtime average annual soil loss, related to rainfall-runoff, soil erodibility, slope length and steepness, cover management, and support practice. The product of slope length L and steepness S is called topographic factor LS, implying the topographic effect on soil loss. This study focuses on (a) spatially predicting the topographic factor LS for RUSLE using a Digital Elevation Model (DEM), (b) selecting the appropriate DEM spacing for predicting the LS factor, and (c) modeling the loss of spatial variability of the predicted LS factor due to DEM resampling. The results show that using the physically based topographical factor LS equation and DEMs led to a higher correlation of predicted LS values with topographical features, compared to a spatial simulation method based on LS empirical models and sample data. The appropriate DEM spacing required to achieve prediction precision and detailed spatial variability of the LS factor was not identical for both requirements and a compromise may be made depending on the application aims. By modeling the spatial variability of predicted LS values for different DEM spacing, a new method to directly measure loss of spatial variability due to data resampling was developed. Compared to measures of entropy and global variance, the new method can reveal the different losses of spatial variability in different directions when the spatial variability is anisotropic. Copyright ISPRS Numéro de notice : A2001-221 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/S0924-2716(01)00035-1 En ligne : https://doi.org/10.1016/S0924-2716(01)00035-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21915 [article]

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