Land use/cover and land degradation across the Eurasian steppe: Dynamics, patterns and driving factors

Du, Zhenrong; Yu, Le; Chen, Xin; Gao, Bingbo; Yang, Jianyu; Fu, Haohuan; Gong, Peng

doi:10.1016/j.scitotenv.2023.168593

Cited by 8 publications

(2 citation statements)

References 67 publications

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“…The geographical detector is a powerful tool for conducting driver and factor analyses [50,51]. The model comprises four detectors: risk detector, factor detector, ecological detector, and interaction detector.…”

Section: Geographical Detectormentioning

confidence: 99%

A 20-Year Analysis of the Dynamics and Driving Factors of Grassland Desertification in Xilingol, China

Li,

Cao,

et al. 2023

Remote Sensing

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Grassland desertification stands as an ecological concern globally. It is crucial for desertification prevention and control to comprehend the variation in area and severity of desertified grassland (DGL), clarify the intensities of conversion among DGLs of different desertification levels, and explore the spatial and temporal driving factors of desertification. In this study, a Desertification Difference Index (DDI) model was constructed based on albedo-EVI to extract desertification information. Subsequently, intensity analysis, the Geo-detector model, and correlation analysis were applied to analyze the dynamics and driving factors of desertification. The results showed the following: (1) Spatially, the DGL in Xilingol exhibited a zonal distribution. Temporally, the degree of DGL decreased, with the proportion of severely and moderately desertified areas decreasing from 51.77% in 2000 to 37.23% in 2020, while the proportion of nondesertified and healthy areas increased from 17.85% in 2000 to 37.40% in 2020; (2) Transition intensities among different desertification levels were more intense during 2000–2012, stabilizing during 2012–2020; (3) Meteorological factors and soil conditions primarily drive the spatial distribution of DDI, with evapotranspiration exhibiting the most significant influence (q-value of 0.83), while human activities dominate interannual DDI variations. This study provides insights into the conversion patterns among different desertification levels and the divergent driving forces shaping desertification in both spatial and temporal dimensions in Xilingol.

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Section: Geographical Detectormentioning

confidence: 99%

A 20-Year Analysis of the Dynamics and Driving Factors of Grassland Desertification in Xilingol, China

Li,

Cao,

et al. 2023

Remote Sensing

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“…Land is closely related to human production, life, ecology and other human activities [1]. The spatial patterns and functions of land evolve because of the interaction of human behavior with the land [2].…”

Section: Introductionmentioning

confidence: 99%

Research on Sustainable Land Use in Alpine Meadow Region Based on Coupled Coordination Degree Model—From Production–Living–Ecology Perspective

Zhang,

Wang

et al. 2024

Sustainability

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Changes in land use types in alpine meadow areas have significant impacts on the ecological environment in alpine areas. Exploring land use change is crucial for land use management and optimization in alpine regions. Thus, it is necessary to analyze land use evolution and its drivers in alpine meadow regions from a production–living–ecology space (PLES) perspective by using remote sensing data. We first constructed the PLES evaluation system for Gannan. Then, we analyzed the spatial and temporal evolution characteristics and coupling degree of PLES in the study area. Finally, the driving factors affecting PLES were explored with geodetector. The conclusions of the study reveal that the distribution of productive and ecological spaces is large and concentrated, while the distribution of living spaces is more decentralized. The PLES was mainly concentrated in the area above 2500 m but below 4000 m and with a slope of 40° or less. During the study period, the area of production space showed a decreasing trend, while the areas of living and ecological space both showed increasing trends, primarily occurring at the expense of production space. DEM and GDP were the main factors affecting the distribution of PLES. The coupling level and the degree of coupling coordination were relatively stable in general, showing a pattern of “high in the east and low in the west”. The study provides technical support and a theoretical basis for the future planning of land space and ecological environment optimization in the alpine meadow regions.

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Boundary migration between zonal vegetation types in Inner Mongolia over the past two decades

Li,

Guo,

Wang

et al. 2024

CATENA

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Land use/cover and land degradation across the Eurasian steppe: Dynamics, patterns and driving factors

Cited by 8 publications

References 67 publications

A 20-Year Analysis of the Dynamics and Driving Factors of Grassland Desertification in Xilingol, China

A 20-Year Analysis of the Dynamics and Driving Factors of Grassland Desertification in Xilingol, China

Research on Sustainable Land Use in Alpine Meadow Region Based on Coupled Coordination Degree Model—From Production–Living–Ecology Perspective

Boundary migration between zonal vegetation types in Inner Mongolia over the past two decades

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