[Evaluation of Habitat Quality and Driving Forces Analysis in the Qarqan River Basin Based on an Enhanced Remote Sensing Ecological Index].

Authors

Jia-Cheng Zhang, Fan Gao, Kun Liu, Bing He, Jie Wu, Ying Li

Published in

Huan jing ke xue= Huanjing kexue. Volume 47. Issue 9. Pages 6237-6250. Sep 08, 2026.

Abstract

As a critical tributary within the Tarim River Basin framework （"nine tributaries and one mainstream"）， identifying spatio-temporal variations in habitat quality and their driving factors in the Qarqan River Basin is essential for regional ecological conservation and sustainable development. To address the ecological characteristics of this arid basin， this study developed an enhanced remote sensing ecological index （ERSEI）. Leveraging the Google Earth Engine （GEE） platform， we integrated Theil-Sen Median trend analysis， Mann-Kendall test， spatial autocorrelation （Moran's I）， and optimal parameters-based Geodetector to investigate habitat quality dynamics and driving factor influences （from 2000 to 2023）. Key results indicate： ① The first principal component of ERSEI contributed 76.5% of the cumulative variance， with a mean correlation coefficient of 0.79 across ecological indicators， confirming its efficacy in characterizing arid land surface features and validating its robustness. ② The annual mean ERSEI value was 0.484 from 2000 to 2023， showing a fluctuating improvement trend. The proportion of ecologically degraded areas decreased by 10.58%， while improved areas increased by 12.63%. Spatially， a polarized pattern emerged： Ecological quality was superior in high-altitude regions and oasis cores but poorer in northern desert margins and central channel-lacking arid zones. ③ Significant spatial clustering of ecological quality was observed， with high-high agglomerations concentrated in high-altitude areas and mid-altitude cultivated oases and low-low clusters predominantly distributed in mid-altitude desert belts. ④ Human activities exerted stronger driving forces on ecological evolution than natural factors over 2000 to 2023. Land use type was the dominant single factor. Interactions between land use and meteorological factors/elevation significantly enhanced the explanatory power for ERSEI （q-values： 0.854， 0.829， and 0.892）， with driving mechanisms characterized by distinct pairwise factor enhancement and nonlinear synergistic effects.

PMID:
42765241
Bibliographic data and abstract were imported from PubMed on 21 Sep 2026.

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