Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8845095 | Ecological Indicators | 2018 | 9 Pages |
Abstract
Understanding human and climate-induced vegetation changes could benefit regional ecological management. In this paper, a framework was established to quantify the relative contribution rates of human and climate factors to vegetation changes in Chongqing, China, from 2000 to 2015. MODIS NDVI time series data were collected, and land use data were produced using Landsat TM/OLI images. A combined analysis of land use and vegetation changes was conducted, and the residual trends method (RESTREND) was explored in the framework. The results showed that Chongqing experienced a significant vegetation increase from 2000 to 2015. The relative contribution rates of human activities and climate to the vegetation changes were 90.96% and 9.04%, respectively. These results indicate that human activities had a dominant role in vegetation restoration in Chongqing in 2000-2015. Throughout the study period, extensive land use changes occurred in Chongqing. During the study period, persistent forested land, farmland-forested land (a land use conversion type), persistent farmland, and grassland-forested land were the four most important land use (changed and unchanged) types attributed to the growing season NDVI (GSN) increases in Chongqing from 2000 to 2015. The contributions of these four land use types were 45.03%, 21.19%, 16.79% and 12.60%, respectively. The farmland-forested land was characterized as the most effective land use conversion type for vegetation restoration. The proposed framework allows for human and climate-induced vegetation changes to be quantitatively distinguished at the regional scale and provides the contribution rates of each changed and unchanged land use type. The framework is expected to be useful for regional ecological management and research.
Keywords
Related Topics
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Agricultural and Biological Sciences
Ecology, Evolution, Behavior and Systematics
Authors
Rui Liu, Linlin Xiao, Zhe Liu, Jicai Dai,