Detection of dryland degradation using Landsat spectral unmixing remote sensing with syndrome concept in Minqin County,China |
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Institution: | 1. College of Grassland Agriculture, Northwest A&F University, Yangling, China;2. Institute of Soil and Water Conservation, Northwest A&F University, Yangling, China;3. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, China;4. International Center for Climate and Global Change Research, College of Forestry & Wildlife Sciences, Auburn University, Auburn, United States;5. Department of Environmental Sciences, Emory University, Atlanta, United States |
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Abstract: | This study was to detect dryland degradation coupling linear spectral unmixing model of Landsat images with syndrome concept in temperate dryland system, Minqin, China. The phenological contrast and complementation between green vegetation fraction in summer, sandland fraction and saline land fraction in spring, was firstly structured to quantify degradation characteristics by simple correlation analysis with ground data. The spatiotemporal patterns of the three degradation indicators were interpreted with the help “dust bowl” syndrome, qualitatively deciphered the degradation causal clusters, loops and important consequences in the study area. The results indicate water-using and distribution pattern was changed, agricultural intensity and productivity increased, salinization lessened in oasis, whereas sandification risk heightened. This approach developed in this study, has the potentially broad applicability, for dryland system monitoring and modelling. |
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Keywords: | Linear spectral mixture analysis Change detection Syndrome Dryland system Western China |
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