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Mapping multiple variables for predicting soil loss by geostatistical methods with TM images and a slop map / G. Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 8 (August 2003)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 8 (August 2003) . - pp 889 - 898 Titre : Mapping multiple variables for predicting soil loss by geostatistical methods with TM images and a slop map Type de document : Article/Communication Auteurs : G. Wang, Auteur ; G. Gertner, Auteur ; S. Fangbe, Auteur ; A.B. Anderson, Auteur Année de publication : 2003 Article en page(s) : pp 889 - 898 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie thématique [Termes IGN] érosion [Termes IGN] estimation statistique [Termes IGN] géostatistique [Termes IGN] krigeage [Termes IGN] modélisation [Termes IGN] terrain Résumé : (Auteur) Soil erosion is widely predicted as a function of six input factors, including rainfall erosivity, soil erodibility, slope length, slope steepness, cover management, and support practice. Because of the multiple factors, their interactions, and their spatial and temporal variability, accurately mapping the factors and further soil loss is very difficult. This paper compares two geostatistical methods and a traditional stratification to map the factors and to estimate soil loss. Soil loss is estimated by integrating a sample ground data set, TM images, and a slope map. The geostatistical methods include collocated cokriging and a joint sequential cosimulation model. With both geostatistical methods, local estimates and variances at any location where the factors and soil loss are unknown can be computed. The results showed that the two geostatistical methods performed significantly better than traditional stratification in terms of overall and spatially explicit estimates. Furthermore, the cokriging led to higher accuracy of mean estimates than did the cosimulation, while the latter provided decision makers with reliable uncertainties of the local estimates as useful information to assess risk when making decisions based on the prediction maps. Numéro de notice : A2003-169 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.69.8.889 En ligne : https://doi.org/10.14358/PERS.69.8.889 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22465 [article]

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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