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Visualization and quantification of significant anthropogenic drivers influencing rangeland degradation trends using Landsat imagery and GIS spatial dependence models: A case study in Northeast Iran 被引量:1
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作者 OMID Abdi ZEINAB Shirvani MANFRED F.Buchroithner 《Journal of Geographical Sciences》 SCIE CSCD 2018年第12期1933-1952,共20页
Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main ... Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main steps:(1) conducting a trend analysis of rangeland degradation and anthropogenic dynamics in 1986–2000 and 2000–2015,(2) visualizing the effects of anthropogenic drivers on rangeland degradation using bivariate local spatial autocorrelation(BiLISA), and(3) quantifying spatial dependence between anthropogenic driving forces and rangeland degradation using spatial regression approaches. The results show that 0.77% and 0.56% of rangelands are degraded annually during the first and second periods. The BiLISA results indicate that dry-farming, irrigated farming and construction areas were significant drivers in both periods and grazing intensity was a significant driver in the second period. The spatial lag(SL) model(wi=0.3943, Ei=1.4139) with two drivers of dry-farming and irrigated farming in the first period and the spatial error(SE) model(wi=0.4853, Ei=1.515) with livestock density, dry-farming and irrigated farming in the second period showed robust performance in quantifying the driving forces of rangeland degradation. To conclude, the BiLISA maps and spatial models indicate a serious intensification of the anthropogenic impacts of ongoing conditions on the rangelands of northeast Iran in the future. 展开更多
关键词 rangeland degradation Landsat GIS anthropogenic driving forces bilisa spatial regression
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