Remote Sensing-Based Approach for the Assessing of Ecological Environmental Quality Variations Using Google Earth Engine: A Case Study in the Qilian Mountains, Northwest China

被引:13
|
作者
Wang, Hong [1 ]
Liu, Chenli [1 ]
Zang, Fei [2 ]
Liu, Youyan [2 ]
Chang, Yapeng [2 ]
Huang, Guozhu [2 ]
Fu, Guiquan [3 ]
Zhao, Chuanyan [2 ]
Liu, Xiaohuang [4 ]
机构
[1] Yunnan Univ, Sch Ecol & Environm Sci, Yunnan Key Lab Plateau Mt Ecol & Restorat Degraded, Kunming 650091, Peoples R China
[2] Lanzhou Univ, Coll Pastoral Agr Sci & Technol, State Key Lab Herbage Improvement & Grassland Agro, Engn Res Ctr Grassland Ind,Key Lab Grassland Lives, Lanzhou 730000, Peoples R China
[3] Gansu Desert Control Res Inst, Lanzhou 730070, Peoples R China
[4] Key Lab Coupling Proc & Effect Nat Resources Eleme, Beijing 100055, Peoples R China
基金
中国国家自然科学基金;
关键词
RSEI; spatial autocorrelation; geographical detector; geographically weighted regression; alpine area; GEOGRAPHICALLY WEIGHTED REGRESSION; INDEX; POPULATION; LANDSAT; REGION;
D O I
10.3390/rs15040960
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Due to climate change and human activities, the eco-environment quality (EEQ) of eco-fragile regions has undergone massive change, especially in the Tibet Plateau. The Qilian Mountains (QLM) region is an essential ecological function zone in the northeastern Tibet Plateau, which plays a vital role in northwestern China's eco-environmental balance. However, EEQ changes in the QLM during the 21st century remain poorly understood. In this study, the spatiotemporal variations of the EEQ in the QLM were analyzed from 2000 to 2020 using a remote sensing ecological index (RSEI). The EEQ driving factors are identified by the geographic detector, and the spatial influence of critical factors is represented by a geographically weighted regression model. The results show low EEQ in the QLM. From 2000 to 2020, the EEQ initially slightly improved, then deteriorated, and finally gradually recovered. Spatially, the EEQ shows an increasing trend from northwest to southeast. Moran's I of EEQ remains at around 0.95, representing high spatial aggregation. "High-High" and "Low-Low" clustering features dominate in the local spatial autocorrelation, indicating the EEQ of the QLM is polarized. Precipitation is the dominant positive factor in the EEQ, with a q statistics exceeding 0.644. Furthermore, the key factors (precipitation, distance to towns, distance to roads) affecting EEQ in different periods vary significantly in space. From results we can draw the conclusion that the natural factors mainly control the spatial patterns of EEQ, while the human factors mainly impact the temporal trend of EEQ, the EEQ in the QLM has been significantly improved since 2015. Our findings can provide theoretical support for future eco-environmental protection and restoration in the QLM.
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页数:22
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