Abstract
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.
Highlights
Land use/land cover (LULC) changes are the most basic and prominent landscape characteristic describing the impact of anthropogenic disturbance on the surface of the Earth and play an important role in the studies of regional and global environmental changes [1]
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The study used the dense time stacking of multi-temporal Landsat images and the RF machine learning algorithm to map the land use/land cover (LULC) in Gannan Prefecture, and analyze potential driving forces based on geographic detectors
Summary
Land use/land cover (LULC) changes are the most basic and prominent landscape characteristic describing the impact of anthropogenic disturbance on the surface of the Earth and play an important role in the studies of regional and global environmental changes [1]. There are currently great uncertainties and differences among existing global LULC products due to different data sources, methods, and classification systems. High-resolution optical satellite images of the TP are affected by the high cloud cover and data gaps. It is a challenging task to use a single scene image to monitor LULC changes on the TP. Similar methods have been applied with good results for forest [10], land use [11], and impervious surface change monitoring [12]
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