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[1]XU Hongfeng,WANG Yan,LIU Yungen,et al.Analysis of the Spatial and Temporal Evolution and Driving Factors of Rocky Desertification in Typical Cluster Depression Areas in the Past 30 Years Based on Google Earth Engine[J].Research of Soil and Water Conservation,2022,29(04):407-414.

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Analysis of the Spatial and Temporal Evolution and Driving Factors of Rocky Desertification in Typical Cluster Depression Areas in the Past 30 Years Based on Google Earth Engine

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 29 Number of periods: 2022 04 Page number: 407-414 Column: Public date: 2022-06-20

Title:: Analysis of the Spatial and Temporal Evolution and Driving Factors of Rocky Desertification in Typical Cluster Depression Areas in the Past 30 Years Based on Google Earth Engine

Author(s):: XU Hongfeng^1,2, WANG Yan^1,2, LIU Yungen^1,2, XIAO Yuxin^1,2, LI Zhou^1,2; (1.Southwest Forestry University, Kunming 650224, China; 2.Key Laboratory of Ecological Environment Evolution and Pollution Control in Mountainous Rural Areas of Yunnan Province, Kunming 650224, China)

Keywords:: karst; rocky desertification; GEE; geographic detector; driving factor

CLC:: P931.5

DOI:: -

Abstract:: Rocky desertification is a major problem that hinders sustainable economic development and affects the ecological environment in karst areas. With the development of rocky desertification control in recent decades, the trend of rocky desertification expansion has been greatly curbed. This study used China's typical peak cluster depression landforms in Xichou County, Wenshan Prefecture, Yunnan Province as the research area. Based on the remote sensing cloud computing platform Google Earth Engine(GEE)on the four phases of Landsat remote sensing image data sources in 1990, 2000, 2010, and 2020, the transfer matrix was used to analyze the dynamic changes of rocky desertification in Xichou County in the past 30 years, and geographic detectors was used to analyze the driving factors of rocky desertification in Xichou County. The main conclusions are as follows.(1)In 30 years, the overall evolution of rocky desertification in Xichou County showed a slight decrease, then expansion, and then a significant decrease. From a temporal perspective of 1990—2000, the type of rocky desertification mainly shifted from mild and potential rocky desertification to non-rocky desertification. In the period of 2000—2010, the type of rocky desertification mainly shifted from non-rocky desertification to potential and mild rocky desertification. From 2010 to 2020, the of rocky desertification type mainly shifted from potential, light and moderate rocky desertification to non-rocky desertification. From a spatial point of view, the distribution of rocky desertification in Xichou County was more important in the north than in the south. In terms of the effectiveness of rocky desertification control, the overall improvement had been shown. The area of rocky desertification improvement is 850.183 km². There was also sporadic deteriorated land in the northwest of Lianhuatang Township, the northeast of and Xinmajie Township, and at the junction of Jujie Township and Xisa Town, with a deteriorated land area of 51.715 km².(2)From 1990 to 2020, the control of rocky desertification in Xichou County had made a great achievement. By 2020, the area of rocky desertification-free land in Xichou County had reached up to 940.854 km², and a total of 648.476 km² of rocky desertification land had been converted into no rocky desertification land.(3)Geographic detector analysis results show that the main driving factors of rocky desertification in Xichou County are the interaction between slope and GDP(q=0.645), and the interaction between population density and GDP(q=0.639).(4)Work efficiency can be greatly improved by programming on the remote sensing computing cloud platform Google Earth Engine(GEE)to acquire and process remote sensing data online. The use of cloud computing to extract the characterizing factors of rocky desertification and solve related geological problems can provide strong technical support for subsequent related research.

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Last Update: 2022-06-20