新疆风电场对生态环境的影响特征及其驱动因素分析

doi:10.12118/j.issn.1000-6060.2025.743

干旱区地理 ›› 2026, Vol. 49 ›› Issue (9): 1840-1854.doi: 10.12118/j.issn.1000-6060.2025.743 cstr: 32274.14.ALG2025743

新疆风电场对生态环境的影响特征及其驱动因素分析

焦视堂¹(), 颜洋¹, 郑江华¹^,²(), 何莹¹, 巴比尔江·迪力夏提¹^,³, 魏建新³, 刘俊豪¹, 折勇睿¹

¹ 新疆大学地理与遥感科学学院，新疆乌鲁木齐 830017
² 新疆绿洲生态重点实验室，新疆乌鲁木齐 830017
³ 新疆维吾尔自治区自然资源资料档案馆（新疆维吾尔自治区自然资源数据中心），新疆乌鲁木齐 830000

收稿日期:2025-11-17 修回日期:2026-02-02 出版日期:2026-09-25 发布日期:2026-09-07
通讯作者: 郑江华（1973-），男，博士，教授，主要从事遥感与地理信息系统应用研究. E-mail: zheng.jianghua@xju.edu.cn
作者简介:焦视堂（2002-），男，硕士研究生，主要从事生态环境遥感研究. E-mail: 107552401162@stu.xju.edu.cn
基金资助:
新疆维吾尔自治区自然资源信息中心委托项目(20241219JY075)

Impact characteristics and driving factors of wind farms on the ecological environment in Xinjiang

JIAO Shitang¹(), YAN Yang¹, ZHENG Jianghua¹^,²(), HE Ying¹, Babierjiang DILIXIATI¹^,³, WEI Jianxin³, LIU Junhao¹, SHE Yongrui¹

¹ College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830017, Xinjiang, China
² Xinjiang Key Laboratory of Oasis Ecology, Urumqi 830017, Xinjiang, China
³ Xinjiang Uygur Autonomous Region Natural Resources Data Archives (Xinjiang Uygur Autonomous Region Natural Resources Data Center), Urumqi 830000, Xinjiang, China

Received:2025-11-17 Revised:2026-02-02 Published:2026-09-25 Online:2026-09-07

摘要/Abstract

摘要：

在“双碳”战略背景下，风力发电作为清洁可再生能源的代表，正快速向资源禀赋优越但生态脆弱的干旱区拓展。新疆是中国风能资源最为丰富的地区之一，其大规模风电开发对生态环境的潜在影响亟需量化评估。以新疆风电场为研究对象，基于2000—2023年中分辨率成像光谱仪（MODIS）数据与气象、地形、土地利用数据，运用遥感生态指数（RSEI）及地理探测器模型等方法，系统探究了风电场空间分布特征、生态环境变化趋势及驱动因素。结果表明：（1）新疆风电场总体呈集群化分布，集中于哈密市、吐鲁番市、阿勒泰地区等中低海拔区域，选址主要受地形平缓性、土地利用类型与风能资源约束。（2） 2000—2023年，约81.8%的风电场区域RSEI呈下降趋势，生态质量总体稳定但局地退化显著。（3）地理探测器分析显示，年均降水量（q=0.4522）与海拔（q=0.3949）是新疆风电场区域生态变化的主导因子，而年均气温、坡度与风电运行年限等因子交互作用显著，89.3%的组合呈非线性增强关系，揭示生态效应具有多因子耦合特征。研究在一定程度上可以弥补新疆风电场生态影响研究的不足，为新疆风电开发与生态保护协调管理及干旱区可再生能源生态评估提供科学依据。

关键词: 风电场, 时空演变, 遥感生态指数（RSEI）, 地理探测器, 新疆

Abstract:

Against the backdrop of the “dual carbon” strategy, wind power, a leading clean renewable energy, is rapidly expanding into arid regions with abundant resources but fragile ecosystems. Xinjiang, one of China’s most wind-resource-rich regions, urgently requires a quantitative assessment of the potential ecological effects of large-scale wind power development. Taking wind farms in Xinjiang as the study object, this work integrates moderate resolution imaging spectroradiometer data from 2000 to 2023 with meteorological, topographic, and land use data. Using the remote sensing ecological index (RSEI) and the geodetector model, we systematically investigate the spatial distribution of wind farms, temporal trends in ecological environmental changes, and their driving factors. The results indicate that (1) Wind farms in Xinjiang exhibit a clustered spatial distribution, mainly concentrated in low-to-medium altitude regions such as Hami City, Turpan City, and Altay Prefecture. Site selection is primarily constrained by terrain flatness, land use types, and wind energy resources. (2) From 2000 to 2023, approximately 81.8% of wind farm areas show a declining trend in RSEI. This suggests that although overall ecological quality remains relatively stable, localized degradation is evident. (3) Geodetector analysis reveals that mean annual precipitation (q=0.4522) and elevation (q=0.3949) are the dominant factors influencing ecological changes in wind farm areas. Interactions among factors such as mean annual temperature, slope, and wind farm operational duration are significant, with 89.3% of factor combinations exhibiting nonlinear enhancement effects, highlighting the multi-factor coupling characteristics of ecological effects. This study helps fill research gaps regarding the ecological effects of wind farms in Xinjiang. It provides a scientific basis for the coordinated management of wind power development and ecological protection, as well as for ecological assessments of renewable energy in arid regions.

Key words: wind farms, spatiotemporal evolution, remote sensing ecological index (RSEI), geographical detector, Xinjiang

焦视堂, 颜洋, 郑江华, 何莹, 巴比尔江·迪力夏提, 魏建新, 刘俊豪, 折勇睿. 新疆风电场对生态环境的影响特征及其驱动因素分析[J]. 干旱区地理, 2026, 49(9): 1840-1854.

JIAO Shitang, YAN Yang, ZHENG Jianghua, HE Ying, Babierjiang DILIXIATI, WEI Jianxin, LIU Junhao, SHE Yongrui. Impact characteristics and driving factors of wind farms on the ecological environment in Xinjiang[J]. Arid Land Geography, 2026, 49(9): 1840-1854.

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数据	名称	年份	时间分辨率	原始空间分辨率	来源
地表温度	地表温度8 d合成产品（MOD11A2）	2000—2025	8 d合成	1 km	谷歌地球引擎（GEE）（https://code.earthengine.google.com/）
植被指数	植被指数16 d合成产品（MOD13A1）	2000—2025	16 d合成	500 m	美国国家航空航天局（NASA）（https://www.earthdata.nasa.gov/）
短波红外波段	地表反射率8 d合成产品（MOD09A1）	2000—2025	8 d合成	500 m	美国国家航空航天局（NASA）（https://www.earthdata.nasa.gov/）
气温	中国1 km分辨率逐月平均气温数据集	1901—2024	逐月	1 km	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）
降水	中国1 km分辨率逐月降水量数据集	1901—2024	逐月	1 km	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）
风速	多源数据融合的中国高分辨多要素气象驱动产品（ChinaMet）	1980—2024	逐月	0.1°	国家冰川冻土沙漠科学数据中心（https://www.ncdc.ac.cn/）
海拔、坡度、地形起伏度	航天飞机雷达地形测绘任务数字高程模型（SRTM DEM）	-	-	30 m	美国国家航空航天局（NASA）（https://www.earthdata.nasa.gov/）
土地利用类型	30 m全球土地覆盖动态数据集（GLC_FCS30D）	-	-	30 m	Zenodo（https://zenodo.org/records）
Sentinel-2	哥白尼Sentinel-2地表反射率产品（COPERNICUS_S2_SR_HARMONIZED）	2023	a	10 m	谷歌地球引擎（GEE）（https://code.earthengine.google.com/）
10 m风速	多年平均风速产品	-	-	10 m	全球风能地图（https://globalwindatlas.info/）

判断依据	交互作用类型
q(x₁∩x₂)<Min[q(x₁),q(x₂)]	非线性减弱
Min[q(x₁),q(x₂)]<q(x₁∩x₂)<Max[q(x₁),q(x₂)]	单因子非线性减弱
q(x₁∩x₂)>Max[q(x₁),q(x₂)]	双因子增强
q(x₁∩x₂)=q(x₁)+q(x₂)	独立
q(x₁∩x₂)>q(x₁)+q(x₂)	非线性增强

类别	ΔRSEI范围	数量占比/%
严重退化	ΔRSEI<-0.05	3.0
轻微退化	-0.05<ΔRSEI<0.00	78.8
基本稳定	ΔRSEI=0	0.0
轻微改善	0.00<ΔRSEI<0.05	18.2
显著改善	ΔRSEI>0.05	0.0

新疆风电场对生态环境的影响特征及其驱动因素分析

Impact characteristics and driving factors of wind farms on the ecological environment in Xinjiang

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