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Integration of remote sensing and GIS for evaluating soil erosion risk in northwestern Zhejiang, China / Jianqin Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 9 (September 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 9 (September 2012) . - pp 935 - 946 Titre : Integration of remote sensing and GIS for evaluating soil erosion risk in northwestern Zhejiang, China Type de document : Article/Communication Auteurs : Jianqin Huang, Auteur ; Dong Lu, Auteur ; Jin Li, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 935 - 946 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte pédologique [Termes IGN] Chine [Termes IGN] écosystème forestier [Termes IGN] érosion [Termes IGN] estimation statistique [Termes IGN] forêt tropicale [Termes IGN] gradient de pente [Termes IGN] image Landsat-TM [Termes IGN] modèle numérique de surface [Termes IGN] modèle RUSLE [Termes IGN] régression multiple [Termes IGN] risque naturel [Termes IGN] système d'information géographique Résumé : (Auteur) Estimation of soil loss using the Revised Universal Soil Loss Equation (rusle) has long been an active research topic, but its application in a large area is a challenge due to data availability and quality. In this study, the RUSLE model was used to evaluate soil erosion risk based on soil samples, a soil type map, digital elevation model (dem) data, and Landsat Thematic Mapper (tm) images. Multiple regression analysis was used to identify major factors influencing soil erosion risks. A regression model based on DEM-derived slope gradient and TM-derived fractional soil and vegetation images was developed to map soil erosion risk distribution in a forest ecosystem in Zhejiang, China. The developed method has the potential to quickly examine spatial distri-bution of soil erosion risks. This study provides a new insight for evaluating soil erosion risks in forest ecosystems with the integration of remote sensing and GIS. Numéro de notice : A2012-441 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.14358/PERS.78.9.935 En ligne : https://doi.org/10.14358/PERS.78.9.935 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31887 [article]