The expansion of open-pit mining for rare earth elements (REEs) poses a significant threat to forest ecosystems, demanding advanced remote sensing tools for effective environmental monitoring. While the standard Remote Sensing Ecological Index (RSEI) is a widely used metric, its application in mining-affected forest areas is limited by temporal instability and reduced sensitivity to complex ecological dynamics. To address this, we propose the Forest Ecological Index (FEI). It builds upon the RSEI framework by integrating optimized indicators-the Normalized Difference Fraction Index (NDFI) for greenness and the Desertification Difference Index (DDI) for dryness-specifically chosen to enhance sensitivity in forest ecosystems. FEI was tested against the standard RSEI at an REE deposit using a long-term Landsat time series. Principal Component Analysis (PCA) revealed that FEI's first principal component (PC1) captured an average of 84.1 % of the total ecological variance, outperforming RSEI's 76.3 % and demonstrating greater model stability. FEI exhibited a wider distribution of ecological grades, enhancing its ability to distinguish between severely degraded mining areas (FEI ti 0.2) and healthy forests (FEI ti 0.8), whereas RSEI showed compressed differentiation (mining ti 0.3, forest ti 0.7). Spatiotemporal trend analyses confirmed that FEI successfully tracked better landscape's known historical trajectory of degradation and subsequent rehabilitation than RSEI. Our findings establish that context-specific monitoring tools are not merely an improvement but a necessity for reliable environmental management. We present FEI as a validated, operational tool that provides nuanced and accurate data required to guide effective mine rehabilitation and promote sustainable resource futures.