Аңдатпа
Remote sensing (RS) and geographic information systems (GIS) increasingly support ecosystem and biodiversity monitoring, yet evidence remains fragmented across sensors, indicators, analytical methods, and policy applications. This review synthesises 163 peer-reviewed studies published from 2015 to 2025 to determine how RS and GIS indicators can be selected, validated, and translated into decision-relevant evidence. The synthesis compares satellite and unmanned aerial vehicle platforms, vegetation indices, change-detection techniques, landscape metrics, machine-learning models, cloud-computing workflows, and multi-scale validation strategies. No sensor, index, or classifier is universally optimal. Landsat and Sentinel-2 provide the strongest basis for long-term and large-area monitoring, whereas commercial imagery and unmanned aerial vehicles better resolve fine habitat features and localised disturbance. Vegetation indices remain useful ecosystem proxies but cannot independently represent species composition, ecological integrity, or causal degradation processes. Reliable applications therefore require context-specific indicator selection, transparent preprocessing, representative ground reference data, uncertainty reporting, and integration of spectral, structural, ecological, and socio-economic evidence. Data fusion and machine learning improve monitoring performance when validation is rigorous, but computational sophistication cannot compensate for weak reference data. The review concludes that policy value depends on converting remotely sensed change into explicit, validated decision rules for zoning, restoration, protected-area management, and biodiversity reporting.