Analysis of the Distribution Pattern and Driving Factors of Bald Patches in Black Soil Beach Degraded Grasslands in the Three-River-Source Region

Weitao Jing, Zhou Wang, Guowei Pang (), Yongqing Long, Lei Wang, Qinke Yang and Jinxi Song
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Weitao Jing: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Zhou Wang: Key Laboratory of Ecological Hydrology and Disaster Prevention in Arid Regions, State Forestry and Grassland Administration, Xi’an 710127, China
Guowei Pang: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Yongqing Long: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Lei Wang: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Qinke Yang: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Jinxi Song: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China

Land, 2025, vol. 14, issue 5, 1-18

Abstract: The degradation of ‘black soil beach’ (BSB) ecosystems in the Three-River-Source region, characterized by widespread bald patches and severe soil erosion, poses a critical threat to regional ecological security and sustainable pastoralism. This study aims to elucidate the spatial distribution patterns and driving factors of bald patches in BSB degraded grasslands within the Guoluo Tibetan Autonomous Prefecture, providing a scientific basis for targeted restoration strategies. Utilizing multi-source remote sensing data (Landsat 8–9 OLI, UAV imagery, and Google Earth), we employed the Multiple Endmember Spectral Mixture Analysis (MESMA) method to identify bald patches, combined with the landscape pattern index and spatial autocorrelation to quantify their spatial heterogeneity. Geographical detector analysis was applied to assess the influence of natural and anthropogenic factors. The results indicate the following: (1) The patches are bounded by the Yellow River, showing a distribution pattern of ‘high in the west and low in the east’. The total area of patches reached 32,222.11 km 2 , accounting for 43.43% of the total area of Guoluo Prefecture, among which Maduo County and Dari County had the highest degradation rate. (2) With the aggravation of degradation, the patch density of each county increased first and then decreased, while the aggregation index and landscape shape index continued to decrease. (3) Spatial autocorrelation of bare patches strengthens with degradation severity (Moran’s I index 0.6543→0.7999). LISA identified two clusters: the high–high agglomeration area in the north of Maduo–Dari and the low–low agglomeration area in the southeast of Jiuzhi–Banma, revealing the spatial heterogeneity of the degradation process. (4) The spatial distribution pattern of bare patches was mainly affected by the annual average precipitation and actual stocking capacity, and the synergistic effect was significantly higher than that of a single factor. The combination of a 4491–4708 m high altitude area, 0–5° gentle slope zone, and soil texture (clay 27–31%, silt 43–100%) has the highest degradation risk. This multi-factor coupling effect explains the limitations of traditional single factor analysis and provides a new perspective for accurate repair.

Keywords: black soil beach; MESMA; landscape pattern index; spatial autocorrelation; geographical detector; Guoluo Tibetan Autonomous Prefecture (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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