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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]

AVHRR-based spectral vegetation Index for quantitative assessment of vegetation state and productivity: calibration and validation / F. Kogan 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 899 - 906 Titre : AVHRR-based spectral vegetation Index for quantitative assessment of vegetation state and productivity: calibration and validation Type de document : Article/Communication Auteurs : F. Kogan, Auteur ; A. Gitelson, Auteur ; E. Zakarin, Auteur ; L. Spivak, Auteur ; L. Lebed, Auteur Année de publication : 2003 Article en page(s) : pp 899 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Advanced Very High Resolution Radiometer [Termes IGN] étalonnage en vol [Termes IGN] gestion des ressources [Termes IGN] indice de végétation [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] tronc Résumé : (Auteur) The goal of the work was to estimate, quantitatively, vegetation state and productivity using AVHRR based Vegetation Condition Index (VCI). The VCI algorithm includes application of postlaunch calibration to visible channels, calculation of NDVI from channels' reflectance, removal of high frequency noise from NDVI's annual time series, stratification of ecosystem resources, and separation of ecosystem and weather components in the NDVI value. The weather component was calculated by normalizing the NDVI to the difference of the extreme NDVI fluctuations (maximum and minimum), derived from multiyear data for each week and land pixel. The VCI was compared with wheat density measured in Kazakhstan. Six test fields were located in different climatic (annual precipitation 150 to 700 mm) and ecological (semi desert to steppe forest) zones with elevations from 200 to 700 m and a wide range of NDVI variation over space and season from 0.05 to 0.47. Plant density (PD) was measured in wheat fields by calculating the number of stems per unit area. PD deviation from year to year (PDD) was expressed as a deviation from median density calculated from multiyear data. The correlation between PDD and VCI all stations was positive and quite strong (r2 > 0.75) with the Standard Errors of Estimates (SEE) of PDD less than 16 percent ; for individual stations, the SEE was less than 11 percent. The results indicate that VCI is an appropriate index for monitoring weather impact on vegetation and for assessment of pasture and crop productivity in Kazakhstan. Because satellite observations provide better spatial and temporal coverage, the VCI based system will provide efficient tools for management of water resources and the improvement of agricultural planning. This system will serve as a prototype in the other parts of the world where ground observations are limited or not available. Numéro de notice : A2003-170 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.69.8.899 En ligne : https://doi.org/10.14358/PERS.69.8.899 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22466 [article]