Application of Weakly Interpretable PCA-KNN Framework in Ecological Restoration Engineering Planning

Ecological restoration increasingly requires decision-support frameworks that can integrate diverse remote sensing indicators while maintaining interpretability for practical planning. Existing approaches often emphasize ecosystem assessment but provide limited guidance for selecting specific restoration projects, due to indicator redundancy and opacity of black-box deep learning models. To bridge this gap, this study proposes a weakly interpretable machine learning framework that combines principal component analysis (PCA) with k-nearest neighbors (KNN), termed PCA-KNN, for ecological restoration engineering planning. PCA fuses multi-source ecological indicators into principal components while retaining transparent loadings linked to underlying ecological processes, and KNN provides sample-based classification with traceable decision logic. Together, this structure allows ecological indicators and engineering decisions to be explicitly connected, balancing predictive performance with practical interpretability. The framework is applied to the northern slope of the Qinling Mountains, China, an ecologically significant region facing soil erosion, vegetation degradation, land-use conflict, and wetland decline. Using 13 kinds of remote sensing-derived indicators and field-verified engineering labels, the method achieves 87.6% block-level accuracy and 66.8% pixel-level accuracy, effectively mapping four restoration engineering types and reducing the planning cycle by more than half. Results demonstrate that the proposed PCA-KNN framework translates remote sensing ecological indicators into actionable engineering decisions, offering an operational and scalable pathway for data-driven restoration planning. This work advances remote sensing-based ecological restoration from post-hoc evaluation toward transparent, engineering-oriented planning support.