Mots-clés
AVHRR ; Chine ; Domaine aride ; Désert ; Indice de végétation ; LANDSAT ; MODIS ; Modèle ; Nei Menggu ; Poussière ; Propriétés du sol ; Série chronologique ; Transport éolien ; Télédétection ; VégétationAVHRR ; Aeolian transport ; Arid area ; China ; Desert ; Dust ; Inner Mongolia ; LANDSAT ; Model ; Remote sensing ; Soil properties ; Time series ; Vegetation ; Vegetation indexAVHRR ; Campo árido ; China ; Desierto ; LANDSAT ; Modelo ; Polvo ; Propiedades del suelo ; Serie cronológica ; Teledetección ; Transporte eólico ; Vegetación ; Índice de vegetaciónSpatial and temporal dust source variability in northern China identified using advanced remote sensing analysis
Auteur(s) et Affiliation(s)
TARAMELLI, A.
ISPRA, Inst. for Environmental Research, Rome, Italie
PASQUI, M.
Inst. of Biometeorology and National Research Council/IBIMET-CNR, Florence, Italie
BARBOUR, J.
Lamont Doherty Earth Observatory, Columbia University, New York, Etats-Unis
KIRSCHBAUM, D.
Lamont Doherty Earth Observatory, Columbia University, New York, Etats-Unis
BOTTAI, L.
Inst. of Biometeorology and National Research Council/IBIMET-CNR, Florence, Italie
Consortium LaMMa - Lab. for Meteorology and Environmental Modelling, Sesto Fiorentino, Italie
BUSILLO, C.
Inst. of Biometeorology and National Research Council/IBIMET-CNR, Florence, Italie
Consortium LaMMa - Lab. for Meteorology and Environmental Modelling, Sesto Fiorentino, Italie
CALASTRINI, F.
Inst. of Biometeorology and National Research Council/IBIMET-CNR, Florence, Italie
Consortium LaMMa - Lab. for Meteorology and Environmental Modelling, Sesto Fiorentino, Italie
GUARNIERI, F.
Inst. of Biometeorology and National Research Council/IBIMET-CNR, Florence, Italie
Consortium LaMMa - Lab. for Meteorology and Environmental Modelling, Sesto Fiorentino, Italie
SMALL, C.
Lamont Doherty Earth Observatory, Columbia University, New York, Etats-Unis
Description :
The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi-scale remote sensing analysis of vegetation changes. The representation of intra-annual and inter-annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter-annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend.
Type de document :
Article de périodique
Source :
Earth surface processes and landforms, issn : 0197-9337, 2013, vol. 38, n°. 8, p. 793-809, nombre de pages : 17, Références bibliographiques : 3 p.
Date :
2013
Editeur :
Pays édition : Royaume-Uni, Chichester, Wiley
Langue :
Anglais
Anglais
Droits :
Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI)
Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI)