An automated framework for RSEI-based spatiotemporal assessment of ecological quality: a case study in Qinghai Province, China

This is the authors' abstract. We don't add key points for this paper.

Long-term ecological-quality assessment increasingly relies on remote sensing, but reproducible workflows are still needed to connect satellite preprocessing, ecological-index construction, spatial pattern analysis, trend diagnosis, trajectory characterization, and driver association in a transparent way. In this study, we implemented an automated and modular workflow for remote-sensing ecological index (RSEI)-based spatiotemporal assessment and applied it to Qinghai Province, China, from 2000 to 2024. The workflow integrates annual compositing of satellite observations, principal component analysis (PCA)-based RSEI construction, Global and Local Moran’s I analysis, trend significance detection, Hurst persistence diagnosis, nonlinear trajectory modeling, and GeoDetector-based multi-driver association analysis within a unified workflow. The application showed that ecological quality in Qinghai improved overall, but the recovery was strongly nonlinear and stage dependent. Four phases were identified: stable adjustment (2000–2005), sustained improvement (2005–2010), fluctuating degradation (2010–2015), and recovery enhancement (2015–2024). Positive trajectories accounted for 94.91% of the province and were dominated by abrupt and curvilinear forms, indicating improvement was more often step-like or nonlinear than smoothly linear. Ecological quality also exhibited persistent positive spatial autocorrelation and a stable northwest–southeast gradient. Precipitation showed the highest explanatory power of both improvement and degradation, followed by potential evapotranspiration, temperature, soils, and human pressure. Most areas were classified as showing historically persistent slight or significant improvement. Rather than proposing a new ecological index or statistical algorithm, this study demonstrates how established RSEI-related methods can be organized into an automated, reproducible, and transferable workflow for long-term ecological-quality monitoring.