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Spatial and temporal dust source variability in northern China identified using advanced remote sensing analysis
Author(s) -
Taramelli A.,
Pasqui M.,
Barbour J.,
Kirschbaum D.,
Bottai L.,
Busillo C.,
Calastrini F.,
Guarnieri F.,
Small C.
Publication year - 2012
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/esp.3321
Subject(s) - environmental science , remote sensing , normalized difference vegetation index , vegetation (pathology) , land cover , endmember , scale (ratio) , soil water , image resolution , spatial ecology , physical geography , land use , geology , climate change , soil science , geography , cartography , medicine , ecology , oceanography , civil engineering , pathology , artificial intelligence , computer science , engineering , hyperspectral imaging , biology
ABSTRACT 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 primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust‐producing sources. 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. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas. Copyright © 2012 John Wiley & Sons, Ltd.