Abstract
Large-scale, long time-series, and high-precision land-use mapping is the basis for assessing the evolution and sustainability of ecosystems in Xilingol, the Inner Mongolia Autonomous Region, China. Based on Google Earth Engine (GEE) and Landsat satellite remote-sensing images, the random forest (RF) classification algorithm was applied to create a yearly land-use/land-cover change (LULC) dataset in Xilingol during the past 20 years (2000–2020) and to examine the spatiotemporal characteristics, dynamic changes, and driving mechanisms of LULC using principal component analysis and multiple linear stepwise regression methods. The main findings are summarized as follows. (1) The RF classification algorithm supported by the GEE platform enables fast and accurate acquisition of the LULC dataset, and the overall accuracy is 0.88 ± 0.01. (2) The ecological condition across Xilingol has improved significantly in the last 20 years (2000–2020), and the area of vegetation (grassland and woodland) has increased. Specifically, the area of high-coverage grass and woodland increases (+13.26%, +1.19%), while the area of water and moderate- and low-coverage grass decreases (−15.96%, −7.23%, and −3.27%). Cropland increases first and then decreases (−34.85%) and is mainly distributed in the southeast. The area of deserted land decreases in the south and increases in the center and north, but the total area still decreases (−13.74%). The built-up land expands rapidly (+108.45%). (3) In addition, our results suggest that regional socioeconomic development factors are the primary causes of changes in built-up land, and climate-related factors are the primary causes of water changes, but the correlations between other land-use types and relevant factors are not significant (cropland and grassland). We conclude that the GEE+RF method is capable of automated, long time-series, and high-accuracy land-use mapping, and further changes in climatic, environmental, and socioeconomic development factors, i.e., climate warming and rotational grazing, might have significant implications on regional land surface morphology and landscape dynamics.
Highlights
The general landscape of the Xilingol region consists of gently undulating hills, plateaus, and dunes; the elevation gradually decreases from south to north, with an average altitude of 800–1800 m; it is bordered by the northern foothills of Yin Mountain in the south and the Gobi of the Mongolian Plateau in the north; it is a zone of transition from the arid northwest to the humid east and a sensitive area in responding to global change [33]
The study shows that using the random forest (RF) algorithm for land-use/land-cover change (LULC) in Xilingol leads to better results
We produce an annual LULC dataset for 2000–2020 in Xilingol, and the results indicate that the ecological state of the study area improves significantly [35,62], with the area of natural vegetation expanding steadily, especially high-coverage grass
Summary
LULC is a direct consequence of human and nature interactions [4] and is influenced by multiple behavioral and structural factors, i.e., demand, technical capacity, social relationships, and natural environment [5]. 2021, 13, 5134 temperature, precipitation, etc.) is the underlying element that determines the spatial distribution patterns of LULC and significantly influences changes in surface patterns and landscape dynamics at long time scales [6,7]. Various human-imposed land-development activities, such as agricultural exploitation and urbanization, change the regional land-use structure and landscape ecological patterns at the local scale and accelerate global warming through increased greenhouse gas emissions [8,9,10]
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