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<p>Understanding long-term changes in Eco-Environment Quality (EEQ) is vital for managing arid ecosystems under increasing anthropogenic pressure and climate variability. This study investigated the spatio-temporal dynamics of EEQ in the Akarçay Closed Basin from 1985 to 2020. We utilized Landsat-5 TM and Landsat-8 OLI images processed on the Google Earth Engine (GEE) platform. To assess EEQ of the basin, The Remote Sensing Ecological Index (RSEI), constructed from greenness, wetness, heat (LST), and dryness (NDBSI) indicators, was computed at five-year intervals. Principal Component Analysis (PCA) was applied to derive RSEI, and Geographical Detector analysis was used to identify key natural and anthropogenic drivers. Results indicated that NDBSI and LST had the strongest influence on RSEI, with NDBSI emerging as the dominant indicator. RSEI values were lowest in 1990 (0.267) and 2000 (0.303), and highest in 2010 (0.417). A transition was observed from fair to moderate and good ecological classes between 1985 and 2020. Spatial heterogeneity in EEQ was largely shaped by temperature, land use/land cover, and population density. Furthermore, Hotspot analysis and the Mann-Kendall trend test were employed to examine the spatio-temporal consistency of the RSEI results, revealing persistent patterns of degradation and improvement. This study demonstrates the effectiveness of RSEI for long-term environmental monitoring and provides a comprehensive scientific basis for developing sustainable management strategies in arid basins.</p>

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Assessing long-term eco-environmental quality dynamics in Akarçay River Basin (1985–2020) using Remote Sensing Ecological Index (RSEI)

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Nur Yagmur Aydin,
  • Filiz Bektas Balcik

摘要

Understanding long-term changes in Eco-Environment Quality (EEQ) is vital for managing arid ecosystems under increasing anthropogenic pressure and climate variability. This study investigated the spatio-temporal dynamics of EEQ in the Akarçay Closed Basin from 1985 to 2020. We utilized Landsat-5 TM and Landsat-8 OLI images processed on the Google Earth Engine (GEE) platform. To assess EEQ of the basin, The Remote Sensing Ecological Index (RSEI), constructed from greenness, wetness, heat (LST), and dryness (NDBSI) indicators, was computed at five-year intervals. Principal Component Analysis (PCA) was applied to derive RSEI, and Geographical Detector analysis was used to identify key natural and anthropogenic drivers. Results indicated that NDBSI and LST had the strongest influence on RSEI, with NDBSI emerging as the dominant indicator. RSEI values were lowest in 1990 (0.267) and 2000 (0.303), and highest in 2010 (0.417). A transition was observed from fair to moderate and good ecological classes between 1985 and 2020. Spatial heterogeneity in EEQ was largely shaped by temperature, land use/land cover, and population density. Furthermore, Hotspot analysis and the Mann-Kendall trend test were employed to examine the spatio-temporal consistency of the RSEI results, revealing persistent patterns of degradation and improvement. This study demonstrates the effectiveness of RSEI for long-term environmental monitoring and provides a comprehensive scientific basis for developing sustainable management strategies in arid basins.