Spatiotemporal Dynamics of Bare Sand Patches in the Mu Us Sandy Land, China

Bare sand patches are extensively distributed in dryland ecosystems, and their spatiotemporal evolution provides critical insights into regional eco-environmental changes. The Mu Us Sandy Land, a typical dryland region, exemplifies a distinctive mosaic distribution of bare sand and vegetation patches. Based on the Google Earth Engine (GEE) platform and Landsat time-series imagery (1986–2023), this study extracted multi-temporal bare sand patches using the random forest algorithm. We quantified their spatiotemporal dynamics and identified driving mechanisms through integration with natural/socioeconomic datasets. Key findings include the following: (1) The total area of bare sand patches decreased significantly after 2000, with an average annual reduction of 530.08 km2 (p < 0.01), a rate markedly exceeding pre-2000 rates. (2) Before 2000, bare sand patches were widespread across the entire region; however, by 2023, only residual patches persisted in the northwestern regions. (3) The most significant reduction in bare sand patch area is attributable to the shrinkage of giant patches (>10 km2). (4) The spatial distribution of bare sand patches is primarily controlled by a combination of natural factors, including stream, precipitation, topography, and wind regime. (5) The principal drivers of the reduction in bare sand patch area are anthropogenic activities, such as the implementation of ecological restoration projects, advancements in agricultural technology, and transformations in breeding patterns. These findings provide a scientific foundation for desertification control and ecosystem management strategies in drylands.

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Publication Details

Journal
Remote Sensing
Published
2025-09-19
DOI
https://doi.org/10.3390/rs17183244
Primary Topic
Geology and Paleoclimatology Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Spatiotemporal Dynamics of Bare Sand Patches in the Mu Us Sandy Land, China

Yingjun Pang, Yanping Cao, Kang Yang
Remote Sensing
Geology and Paleoclimatology Research
article

Spatiotemporal Dynamics of Bare Sand Patches in the Mu Us Sandy Land, China

Yingjun Pang, Yanping Cao, Kang Yang
article en

Abstract

Bare sand patches are extensively distributed in dryland ecosystems, and their spatiotemporal evolution provides critical insights into regional eco-environmental changes. The Mu Us Sandy Land, a typical dryland region, exemplifies a distinctive mosaic distribution of bare sand and vegetation patches. Based on the Google Earth Engine (GEE) platform and Landsat time-series imagery (1986–2023), this study extracted multi-temporal bare sand patches using the random forest algorithm. We quantified their spatiotemporal dynamics and identified driving mechanisms through integration with natural/socioeconomic datasets. Key findings include the following: (1) The total area of bare sand patches decreased significantly after 2000, with an average annual reduction of 530.08 km2 (p < 0.01), a rate markedly exceeding pre-2000 rates. (2) Before 2000, bare sand patches were widespread across the entire region; however, by 2023, only residual patches persisted in the northwestern regions. (3) The most significant reduction in bare sand patch area is attributable to the shrinkage of giant patches (>10 km2). (4) The spatial distribution of bare sand patches is primarily controlled by a combination of natural factors, including stream, precipitation, topography, and wind regime. (5) The principal drivers of the reduction in bare sand patch area are anthropogenic activities, such as the implementation of ecological restoration projects, advancements in agricultural technology, and transformations in breeding patterns. These findings provide a scientific foundation for desertification control and ecosystem management strategies in drylands.

Remote SensingVol. 17(18)
Henan University (CN), Chinese Academy of Forestry (CN), State Forestry and Grassland Administration (CN)
Openalex Percentile: Top 70%
Geology and Paleoclimatology Research
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