Reliable assessment of landslide susceptibility and geoecological risk requires an integrated workflow that moves coherently from a conceptual factor system to a reproducible, scalable implementation. This study presents an integrated geoinformation framework that unifies five sequential stages — data preparation, factor processing, landslide susceptibility (LSA) modelling, validation, and risk modelling - within a single pipeline on the Google Earth Engine cloud platform. Within this framework, forest cover is parameterized as a self-contained structural block of six quantitative indicators rather than a binary attribute. Each factor is calibrated by the frequency ratio method, and weight coefficients are determined by Random Forest Feature Importance; the calibrated layers are integrated into a logistically transformed susceptibility index and classified into five classes. The framework was implemented and validated at two independent test sites in the Ukrainian Carpathians - a base site on the Tereblia River (236 landslide occurrences) and a control site in the Verkhovyna district (190 occurrences). In the resulting ten-factor model the thematic-block contributions are morphometric ≈ 49.7%, forest ≈ 25.9%, geological ≈ 14.2%, and hydrological ≈ 10.3%; under spatial-block validation 88.1% of independent test landslides fell into the medium-to-very-high classes (AUC 0.72). At the output stage the framework yields two parallel risk indicators — a nature-conservation priority index based on the Nature Protection Status (NPS) and risk to population and infrastructure. The proposed framework is fully interpretable, reproducible, and scalable to other landslide-prone regions.
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