Big geospatial datasets offer opportunities for natural-resource monitoring, but spatial dependence, unstable indicator weights, model transferability, and uncertainty complicate their integration. This study evaluates a reproducible workflow combining pressure indexing, degradation-risk modelling, spatiotemporal validation, predictor interpretation, intervention analysis, and computational scaling. A synthetic benchmark of 2,400 grid cells observed over 24 months (57,600 records) was analysed using seven normalized indicators. Indicator weights were assessed through 100 spatial block-bootstrap samples, while logistic regression and gradient boosting were evaluated under four validation schemes. Permutation analysis and Monte Carlo simulations assessed predictor contributions and intervention uncertainty. The first principal component explained 60.7% of variance, the mean pressure index was 0.502, and Moran’s I was 0.954. Logistic regression achieved AUC values of 0.804 under random validation and 0.785 under buffered spatial validation, performing comparably to gradient boosting. Hydro-ecological variables provided the strongest transferable predictive information. The integrated intervention reduced the priority-area index by 12.3% and the critical share by 29.0 percentage points. Computational scaling remained near-linear up to 2.30 million records. The workflow supports uncertainty-aware natural-resource assessment, although operational use requires local observations and independent field validation.

Keywords: cloud computing, decision support, Earth observation, machine learning, spatial cross-validation

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