Ecological health assessment of Tibetan alpine grasslands in Gannan using remote sensed ecological indicators

Du, Zeyu; Ji, Xibin; Liu, Jane; Zhao, Wenyue; He, Zhibin; Jiang, Jiachang; Yang, Qiyue; Zhao, Liwen; Gao, Jinlong

doi:10.1080/10095020.2024.2311862

Geo-spatial Information Science

2024

DOI: 10.1080/10095020.2024.2311862

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Ecological health assessment of Tibetan alpine grasslands in Gannan using remote sensed ecological indicators

Zeyu Du,

Xibin Ji,

Jane Liu

et al.

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2024

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Cited by 5 publications

References 86 publications

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Analysis of ecological environment quality heterogeneity across different landform types in Myanmar and its driving forces

Shi,

Peng,

Lin

et al. 2024

Ecological Indicators

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Analysis of ecological environment quality heterogeneity across different landform types in Myanmar and its driving forces

Shi,

Peng,

Lin

et al. 2024

Ecological Indicators

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Transport pathway identification and meteorological driving force spatial-temporal analysis of an extreme dust storm event on South Mongolian Plateau

Wang,

2024

Geo-spatial Information Science

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Exploring Ecological Quality and Its Driving Factors in Diqing Prefecture, China, Based on Annual Remote Sensing Ecological Index and Multi-Source Data

Wang,

Sheng,

Zhu

2024

Land

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The interaction between the natural environmental and socioeconomic factors is crucial for assessing the dynamics of plateau ecosystems. Therefore, the remote sensing ecological index (RSEI) and CatBoost-SHAP model were employed to investigate changes in the ecological quality and their driving factors in the Diqing Tibetan Autonomous Prefecture, China, from 2001 to 2021. The results showed an increase from 0.44 in 2001 to 0.71 in 2021 in the average RSEI for the Diqing Prefecture, indicating an overall upward trend in the ecological quality. Spatial analysis shows the percentage of the area covered by different levels of RSEI and their temporal changes. The results revealed that “good” ecological quality accounted for the largest proportion of the study area, at 42.77%, followed by “moderate” at 21.93%, and “excellent” at 16.62%. “Fair” quality areas accounted for 16.11% and “poor” quality areas only 2.57%. The study of ecological and socioeconomic drivers based on the CatBoost-SHAP framework also indicated that natural climate factors have a greater impact on ecological quality than socioeconomic factors; however, this effect differed significantly with altitude. The findings suggest that, in addition to strengthening climate monitoring, further advancements in ecological engineering are required to ensure the sustainable development of the ecosystem and the continuous improvement of the environmental quality in the Diqing Prefecture.

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Ecological health assessment of Tibetan alpine grasslands in Gannan using remote sensed ecological indicators

Cited by 5 publications

References 86 publications

Analysis of ecological environment quality heterogeneity across different landform types in Myanmar and its driving forces

Analysis of ecological environment quality heterogeneity across different landform types in Myanmar and its driving forces

Transport pathway identification and meteorological driving force spatial-temporal analysis of an extreme dust storm event on South Mongolian Plateau

Exploring Ecological Quality and Its Driving Factors in Diqing Prefecture, China, Based on Annual Remote Sensing Ecological Index and Multi-Source Data

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