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

Abstract

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

CLC Number: X87;TP79

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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Construction and application of improved remote sensing ecological index for pollution

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