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Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture
by
Zhu, Gaofeng
, Zhou, Huakun
, Liu, Chenli
, Xue, Pengfei
, Li, Wenlong
, Yan, Hepiao
in
accuracy
/ Algorithms
/ Application programming interface
/ area
/ Biodiversity
/ case studies
/ China
/ Classification
/ Climate change
/ Cloud cover
/ Clouds
/ Decision trees
/ Detectors
/ Economic development
/ Elevation
/ forest land
/ forests
/ Gannan Prefecture
/ geographical detector
/ Google Earth Engine (GEE)
/ Grasslands
/ humans
/ Internet
/ issues and policy
/ Land cover
/ Land management
/ Land use
/ land use degree index
/ Landsat
/ Landsat satellites
/ Livestock
/ Population density
/ Population growth
/ Principal components analysis
/ random forest
/ Remote monitoring
/ Remote sensing
/ Satellite imagery
/ Sensors
/ Statistical methods
/ Sustainability management
/ Sustainable development
/ sustainable land management
/ Vegetation
/ Water shortages
2020
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Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture
by
Zhu, Gaofeng
, Zhou, Huakun
, Liu, Chenli
, Xue, Pengfei
, Li, Wenlong
, Yan, Hepiao
in
accuracy
/ Algorithms
/ Application programming interface
/ area
/ Biodiversity
/ case studies
/ China
/ Classification
/ Climate change
/ Cloud cover
/ Clouds
/ Decision trees
/ Detectors
/ Economic development
/ Elevation
/ forest land
/ forests
/ Gannan Prefecture
/ geographical detector
/ Google Earth Engine (GEE)
/ Grasslands
/ humans
/ Internet
/ issues and policy
/ Land cover
/ Land management
/ Land use
/ land use degree index
/ Landsat
/ Landsat satellites
/ Livestock
/ Population density
/ Population growth
/ Principal components analysis
/ random forest
/ Remote monitoring
/ Remote sensing
/ Satellite imagery
/ Sensors
/ Statistical methods
/ Sustainability management
/ Sustainable development
/ sustainable land management
/ Vegetation
/ Water shortages
2020
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Do you wish to request the book?
Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture
by
Zhu, Gaofeng
, Zhou, Huakun
, Liu, Chenli
, Xue, Pengfei
, Li, Wenlong
, Yan, Hepiao
in
accuracy
/ Algorithms
/ Application programming interface
/ area
/ Biodiversity
/ case studies
/ China
/ Classification
/ Climate change
/ Cloud cover
/ Clouds
/ Decision trees
/ Detectors
/ Economic development
/ Elevation
/ forest land
/ forests
/ Gannan Prefecture
/ geographical detector
/ Google Earth Engine (GEE)
/ Grasslands
/ humans
/ Internet
/ issues and policy
/ Land cover
/ Land management
/ Land use
/ land use degree index
/ Landsat
/ Landsat satellites
/ Livestock
/ Population density
/ Population growth
/ Principal components analysis
/ random forest
/ Remote monitoring
/ Remote sensing
/ Satellite imagery
/ Sensors
/ Statistical methods
/ Sustainability management
/ Sustainable development
/ sustainable land management
/ Vegetation
/ Water shortages
2020
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Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture
Journal Article
Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture
2020
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Overview
As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities and continuous climate change. However, extensive cloud cover limits the ability of optical remote sensing satellites to monitor accurately LULC changes in this area. To overcome this problem in LULC mapping in the Ganan Prefecture, 2000–2018, we used the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform. The dynamic trends of LULC changes were analyzed, and geographical detectors quantitatively evaluated the key driving factors of these changes. The results showed that (1) the overall classification accuracy varied between 89.14% and 91.41%, and the kappa values were greater than 86.55%, indicating that the classification results were reliably accurate. (2) The major LULC types in the study area were grassland and forest, and their area accounted for 50% and 25%, respectively. During the study period, the grassland area decreased, while the area of forest land and construction land increased to varying degrees. The land-use intensity presents multi-level intensity, and it was higher in the northeast than that in the southwest. (3) Elevation and population density were the major driving factors of LULC changes, and economic development has also significantly affected LULC. These findings revealed the main factors driving LULC changes in Gannan Prefecture and provided a reference for assisting in the development of sustainable land management and ecological protection policy decisions.
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