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A Causal Remote Sensing Framework to Disentangle Climate and Anthropogenic Drivers of Grassland Recovery on the Qinghai-Tibet Plateau

文献类型：期刊论文

作者Liu, Zhenghe2; Dai, Erfu1,3; Xing, Shuo1,3; Zhou, Liang2; Gao, Hong2
刊名REMOTE SENSING
出版日期2026-02-04
卷号18期号:3页码:504
关键词Qinghai-Tibet Plateau ecosystem services causal inference ecological restoration interpretative machine learning sand fixation adaptive management
DOI10.3390/rs18030504
产权排序2
文献子类Article
英文摘要Highlights What are the main findings? Developed a causal remote sensing framework integrating machine learning with counterfactual analysis to rigorously correct for observational selection bias. Identified the restoration program as the dominant driver for sand fixation, delivering a positive net ecological benefit (+6.02 t hm-2) despite the confounding effects of climate change. What are the implications of the main findings? Long-term earth observation confirms that restoration functions primarily to mitigate degradation, significantly retarding degradation rates driven by adverse climatic shifts. Identifying abiotic thresholds via satellite monitoring necessitates a shift from uniform expansion to precision management guided by remote sensing-based spatial zoning.Highlights What are the main findings? Developed a causal remote sensing framework integrating machine learning with counterfactual analysis to rigorously correct for observational selection bias. Identified the restoration program as the dominant driver for sand fixation, delivering a positive net ecological benefit (+6.02 t hm-2) despite the confounding effects of climate change. What are the implications of the main findings? Long-term earth observation confirms that restoration functions primarily to mitigate degradation, significantly retarding degradation rates driven by adverse climatic shifts. Identifying abiotic thresholds via satellite monitoring necessitates a shift from uniform expansion to precision management guided by remote sensing-based spatial zoning.Abstract Disentangling the impacts of ecological restoration from climate change is an ongoing challenge in remote sensing since the traditional correlative approaches often cannot elucidate causal mechanisms. To overcome this, we introduce a Causal Remote Sensing Framework that uses multi-source satellite data (2000-2020), machine learning (XGBoost, SHAP) and causal inference (T-Learner) to build pixel-level counterfactuals. Using this framework, we assessed the Return Grazing to Grassland Program (RGGP) on the Qinghai-Tibet Plateau. Our results demonstrate that a warming and wetting climate improved Water yield (WY) while at the same time decreasing sand fixation (SF) in 83.6% of the region. Notably, the restoration project became the main factor that slowed this decline. After controlling for observational selection bias, the program had a net positive effect of (+6.02 t hm-2), reducing degradation in 64.6% of treated areas. This framework provides a practical way for the remote sensing community to go beyond change monitoring to allow the diagnosis of the causal mechanisms in complex human-environment systems.
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WOS关键词CARBON ; CHINA
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001688211500001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/221001]  
专题拉萨站高原生态系统研究中心_外文论文
通讯作者Dai, Erfu
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
2.Lanzhou Jiaotong Univ, Sch Geomat & Geog Informat, Lanzhou 730070, Peoples R China;
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Zhenghe,Dai, Erfu,Xing, Shuo,et al. A Causal Remote Sensing Framework to Disentangle Climate and Anthropogenic Drivers of Grassland Recovery on the Qinghai-Tibet Plateau[J]. REMOTE SENSING,2026,18(3):504.
APA Liu, Zhenghe,Dai, Erfu,Xing, Shuo,Zhou, Liang,&Gao, Hong.(2026).A Causal Remote Sensing Framework to Disentangle Climate and Anthropogenic Drivers of Grassland Recovery on the Qinghai-Tibet Plateau.REMOTE SENSING,18(3),504.
MLA Liu, Zhenghe,et al."A Causal Remote Sensing Framework to Disentangle Climate and Anthropogenic Drivers of Grassland Recovery on the Qinghai-Tibet Plateau".REMOTE SENSING 18.3(2026):504.

入库方式： OAI收割

来源：地理科学与资源研究所

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