生态环境质量会直接影响人类的生存和健康。在国家资源战略西移的背景下，煤炭富集且生态敏感脆弱的新疆地区将成为我国矿产资源的接替区和战略储备区。随着煤矿开采规模的扩大以及开采时间的增加，采矿过程中的露天采掘、倾卸固体废石、尾矿坝和地面沉陷等会使得脆弱的生态环境进一步恶化。因此，亟需对研究区开展生态环境质量评估研究，以期为矿区环境监测治理和科学控制人类活动提供资料依据，为矿区的可持续发展和合理开采提供技术参考。目前关于干旱矿区生态环境的评估指数很少，而适用于戈壁荒漠较多、植被稀疏且干旱少雨的新疆矿区的生态指数更少。因此，本研究以新疆红沙泉露天煤矿为研究区，基于多源遥感数据构建适合评估干旱荒漠区生态环境的评价指数，并揭示红沙泉煤矿生态环境的时空演变规律，借助多元线性回归模型、随机森林回归模型与双重差分法定量评价采矿对荒漠区生态环境的影响。主要研究内容及结果如下：

（1）本研究利用归一化植被指数（Normalized Difference Vegetation Index, NDVI）、湿度指标（wet, WET）、地表温度（Land Surface Temperature, LST）、干度指标NDBSI、裸岩石砾地指标（New Gravel Land Index, NGLI）、盐度指标（Salinity Index, SI-T）以及土地退化指标（Land Deterioration, LD）7个指标构建了基于熵权法的生态环境评价指数（Entropy Weight Method-Remote Sensing Ecological Index, EVM-RSEI）和基于主成分分析法的干旱生态环境指数（Arid Ecological Environment Index, AEEI）用来评估研究区的生态环境情况，并利用主成分的贡献率、平均相关度以及与生态环境公报提供的EI进行对比等方法对其进行了精度验证，验证结果表明AEEI更适合评估研究区的生态环境情况。

（2）本研究利用AEEI评估了研究区1988~2022年的生态环境状况，结果表明：①1988~2022年研究区的AEEI均值呈下降趋势；空间上，研究区西南部到东北部的生态环境呈逐步变差趋势，研究区的中部及东北部的生态环境质量AEEI为差和较差等级，西部则以良和优等级为主；②研究区1988~2022年生态环境质量差、较差等级的面积占比呈上升趋势，中、良和优等级的面积占比均呈下降趋势，表明随着采矿活动推进研究区AEEI呈下降趋势；③1988~2022年研究区的生态环境以退化和变化不显著为主，退化区面积（23.36%）高于改善区（2.14%），退化区域主要集中在研究区的西北部、矿区内以及矿区周围。

（3）利用多种方法定量评价采矿活动的影响：①基于多元线性回归模型和随机森林回归模型模拟的气候条件下的AEEI′均呈现出西南优、东北差的空间格局；基于随机森林回归模型计算的AEEI′与AEEI的R和均方误差（Mean Squared Error, MSE）优于多元线性回归模型，表明随机森林回归模型能更好的进行基于气候条件的AEEI′模拟；②基于随机森林回归模型计算的研究区2000~2022年的δ的均值为-0.0473，矿区的平均δ远大于研究区的δ值，表明采矿活动对研究区的生态环境产生了明显的负影响，尤其是矿区；③双重差分模型计算的2000~2011年和2012~2022年平均δ分别为-0.0820和-0.2098。④多元线性回归模型、随机森林回归模型、双重差分模型的结果均表明采矿活动对生态环境产生了负影响，且这三种计算结果在趋势上基本一致，证明了随机森林模型和双重差分法在量化采矿活动对生态环境影响方面的可行性。采矿活动对矿区的生态环境影响最严重，因此，在对矿区生态环境重度受损区进行修复时应优先考虑人工修复且在对植被进行浇水时应注意尽量避免蒸发量较大的时间段，轻度和中度生态环境受损区则应以自然修复为主，必要时辅以人工修复。

The quality of the ecological environment will directly affect the survival and health of human beings. Under the background of the national resource strategy moving west, Xinjiang, which is rich in coal and sensitive and fragile in ecology, will become the substitute area and strategic reserve area of China's mineral resources. With the expansion of coal mining scale and the increase of mining time, open-pit mining, dumping of solid waste rocks, tailings dams, and ground subsidence during the mining process will further deteriorate the fragile ecological environment. Therefore, it is urgent to conduct ecological environment quality assessment research in the study area, in order to provide data basis for environmental monitoring and scientific control of human activities in mining areas, and to provide technical references for sustainable development and rational mining in mining areas. At present, there are few ecological environment assessment indexes in arid mining areas, and even fewer ecological indexes are applicable to Xinjiang mining areas where there are more Gobi deserts, sparse vegetation, and less drought and rainfall. Therefore, this study takes the Hongshaquan open-pit coal mine in Xinjiang as the research area, constructs an evaluation index suitable for evaluating the ecological environment of arid desert areas based on multi-source remote sensing data, and reveals the spatio-temporal evolution law of the ecological environment of Hongshaquan coal mine. Using multiple linear regression models, random forest regression models, and Differences-in-Differences, to quantitatively evaluated the impact of mining on the ecological environment of desert areas. The main research content and results are as follows:

(1) This study used seven indicators, namely Normalized Difference Vegetation Index (NDVI), Humidity Index (WET), Land Surface Temperature (LST), Dryness Index (NDBSI), New Gravel Land Index (NGLI), Salinity Index (SI-T), and Land Degradation Index (LD), to build the Entropy Weight Method-Remote Sensing Ecological Index, EVM-RSEI and Arid Ecological Environment Index (AEEI) based on principal component analysis were used to assess the ecological environment of the study area. The accuracy of AEEI was verified by the contribution rate of principal components, the average correlation degree and the comparison with EI provided in the ecological environment bulletin. The verification results showed that AEEI was more suitable for assessing the ecological environment in the study area.

(2) This study used AEEI to evaluate the ecological environment of the study area from 1988 to 2022, and the results showed that: ① The average AEEI of the study area showed a downward trend from 1988 to 2022; In terms of space, the ecological environment in the southwest to northeast of the study area shows a gradually deteriorating trend. The AEEI of the ecological environment quality in the central and northeast of the study area is poor and fair, while in the west, it is mainly good and excellent. ②The proportion of areas with poor and fair ecological environment quality in the study area showed an increasing trend from 1988 to 2022, while the proportion of areas with medium, good, and excellent grades showed a downward trend, indicating that the AEEI of the study area showed a decreasing trend with the advancement of mining activities. ③From 1988 to 2022, the ecological environment in the study area was mainly characterized by degradation and insignificant changes. The area of degradation (23.36%) was higher than that of improvement (2.14%). The degradation areas were mainly concentrated in the northwest of the study area, within the mining area, and around the mining area.

(3) A variety of methods were used to quantitatively evaluate the impact of mining activities: ①The AEEI′ under climate conditions simulated based on multiple linear regression models and random forest regression models both exhibit a spatial pattern of southwest superiority and northeast inferiority; The R and MSE of AEEI 'and AEEI calculated based on the random forest regression model are superior to the multiple linear regression model, indicating that the random forest regression model can better simulate AEEI′ based on climate conditions; ② Based on the rand forest regression model, the average δ value from 2000 to 2022 in the study area is -0.0473, and the average δ value in the mining area is much higher than that in the study area, indicating that mining activities have a significant negative impact on the ecological environment in the study area, especially in the mining area; ③ The average δ from 2000 to 2011 and 2012 to 2022 calculated by the Differences-in-Differences is -0.0820 and -0.2098, respectively; ④ The results of the multiple linear regression model, random forest regression model, and Differences-in-Differences all indicate that mining activities have a negative impact on the ecological environment, and these three calculation results are basically consistent in trend, proving the feasibility of the random forest model and Differences-in-Differences in quantifying the impact of mining activities on the ecological environment. Mining activities have the most serious impact on the ecological environment of mining areas. Therefore, when repairing areas with severe ecological damage in mining areas, priority should be given to manual restoration. When watering vegetation, attention should be paid to avoiding periods of high evaporation as much as possible. For areas with mild and moderate ecological damage, natural restoration should be the main approach, supplemented by manual restoration if necessary.

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