Spatiotemporal Evolution and Associated Factors of Ecological Quality in the Poyang Lake Basin from 2000 to 2024 Based on Remote Sensing

Daohong Gong; Shuhui Lai; Letian Yang; Xiang Hou

doi:10.53941/rem.2026.100011

Abstract

Based on the Google Earth Engine (GEE) platform, this study constructed a Remote Sensing Ecological Index (RSEI) time series for the Poyang Lake Basin from 2000 to 2024. The analysis integrated Theil-Sen median trend estimation, the Mann-Kendall test, the Hurst exponent, and an interpretable machine-learning framework combining extreme gradient boosting (XGBoost) with Shapley additive explanations (SHAP) to examine the spatiotemporal evolution of ecological quality and the factors associated with its spatial heterogeneity. The basin-average RSEI increased slightly from 0.693 in 2000 to 0.710 in 2024, indicating that the basin generally remained at a good ecological level, although interannual fluctuations were evident. Spatially, the RSEI showed a stable pattern of high values in the surrounding mountainous forest belts and lower values in the central plain, urbanized areas, and some lake and wetland zones. The ecological grade structure improved, with the proportion of areas classified as “excellent” increasing from 14.42% to 22.93%. Theil-Sen and Mann-Kendall results showed that areas with historical ecological improvement were more extensive than areas of degradation. However, the Hurst exponent indicated that many pixels exhibited anti-persistent characteristics (H < 0.5), suggesting that future ecological trajectories may be uncertain rather than a simple continuation of historical improvement. XGBoost-SHAP analysis showed that land surface temperature (LST) and NDVI made the largest contributions to RSEI prediction. Because both variables are components of the RSEI, these results should be interpreted as index sensitivity to heat and vegetation conditions rather than independent causal effects. External variables, including GDP, population density, nighttime light intensity, elevation, and slope, showed additional predictive associations with spatial heterogeneity, conditional on the fitted model. These findings not only support differentiated watershed management but also provide a remote sensing perspective on long-term ecological quality changes associated with climate variability and human activities.

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