面向污染的改进型遥感生态指数构建及应用

doi:10.16088/j.issn.1001-6600.2025091003

摘要/Abstract

摘要： 在全球城市化与环境污染加剧的背景下,揭示生态质量演变特征对生态治理具有重要意义。本文以四川省宜宾市为研究区,基于Google Earth Engine平台,利用Landsat遥感影像构建绿度、湿度、热度指标,并引入不透水面干旱指数(integrated drought index,IDI)和遥感空气质量指数(remote sensing air quality index,RAQI),提出基于城市化与大气污染的改进型遥感生态指数(pollution-urbanization-based improved remote sensing ecological index,PBEI)。在权重确定方法上,对比主成分分析法(PCA)与熵权法(EWM)结果发现,PCA对高方差指标依赖较强,易受噪声干扰;EWM依据指标信息熵自适应赋权,能有效提升模型稳定性与可解释性。与遥感生态指数(remote sensing ecological index,RSEI)相比,EWM构建的PBEI多年平均信息熵整体提升约0.26%,PCA构建的PBEI第一主成分贡献率平均提高7.41个百分点,显著增强对不透水面干旱纹理及生态过渡区的识别能力。在空间表现上,PCA更擅长突出污染与人工干扰显著的重点区域,EWM则强化生态梯度的连续型表达。综合 Hurst指数、空间自相关与Mann-Kendall趋势检验,结果表明宜宾市生态质量在2014—2024年呈“先降后升”演变特征,近年生态改善显著。本研究验证了PBEI在城市生态监测中的适用性,为区域生态评估与可持续发展提供了科学参考。

关键词: 生态质量变化, 遥感生态指数, 信息熵, 熵权法, 主成分分析法, 时空变化分析

Abstract: Against the backdrop of accelerating urbanization and worsening environmental pollution, understanding the spatiotemporal evolution of ecological quality is crucial for effective ecosystem governance. Taking Yibin City as the study area, this research employs the Google Earth Engine (GEE) platform and landsat imagery to construct greenness, wetness, and heat indicators, while introducing the integrated drought index (IDI) and the remote sensing air quality index (RAQI). An improved remote sensing ecological index, namely thepollution-urbanization-based improved remote sensing ecological index (PBEI), is proposed to better capture the impacts of urbanization and air pollution. To determine index weights, both principal component analysis (PCA) and the entropy weight method (EWM) were compared. The results indicate that PCA is more sensitive to high-variance indicators and susceptible to noise, whereas EWM adaptively assigns weights based on information entropy, effectively enhancing model stability and interpretability. Compared with the traditional remote sensing ecological index (RSEI), themulti-year averageinformation entropy of the EWM-based PBEI increased byapproximately 0.26%, and the contribution rate of the first principal component in the PCA-based PBEI increased by an average of 7.41%, demonstrating its superior ability to characterize impervious surface drought patterns and transitional ecological zones. Spatially, the PCA-based PBEI better highlights areas with intensive pollution and anthropogenic disturbance, while the EWM-based PBEI strengthens the continuous expression of ecological gradients. Comprehensive analyses using the Hurst index, spatial autocorrelation, and Mann-Kendall trend test reveal that Yibin’s ecological quality exhibited a “decline-rise” pattern during 2014-2024, with significant improvement in recent years. These findings validate the applicability of the PBEI in urban ecological monitoring and provide a robust scientific reference for regional ecological assessment and sustainable urban development.

Key words: ecological quality changes, remote sensing ecological index, information entropy, entropy weight method, principal component analysis, spatiotemporal change analysis

中图分类号: X87;TP79

罗通, 赵良军, 王银清, 李宪鹏, 刘茂. 面向污染的改进型遥感生态指数构建及应用[J]. 广西师范大学学报（自然科学版）, 2026, 44(5): 179-193.

Luo Tong, Zhao Liangjun, Wang Yinqing, Li Xianpeng, Liu Mao. Construction and application of improved remote sensing ecological index for pollution[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(5): 179-193.

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