Applications of Geographic Information Systems in Natural-Resource Management: A Critical Review of Methods, Evidence, Validation and Future Directions

Natural-resource management (NRM) increasingly depends on spatially explicit evidence because land, soil, water, forests and vegetation vary across space and change through time. Geographic Information Systems (GIS), often integrated with remote sensing (RS), provide a common analytical environment for resource inventory, multi-layer integration, change detection, suitability assessment and decision support. This review synthesises a curated corpus of 20 records representing 19 unique studies published between 1995 and 2026; one duplicated Kumar, Yamaç and Velmurugan record was removed before synthesis. The evidence was organised into six domains: (i) planning, decision support and participatory GIS; (ii) multi-sector RS–GIS applications; (iii) soil resources and land degradation; (iv) land-use/land-cover (LULC) change, forests and watershed programmes; (v) water and groundwater; and (vi) agriculture, climate and crop modelling. Across the corpus, overlay analysis, terrain analysis and multi-temporal change detection are the most recurrent GIS functions. Empirical evidence is strongly concentrated in Indian watershed studies. Reported LULC classification accuracies in three case-study groups range from 89.70% to approximately 95%, although these values are not directly comparable because imagery, class schemes and validation designs differ. The synthesis identifies persistent weaknesses in data quality and spatial resolution, field validation, uncertainty reporting, generalisability, subjective weighting in multi-criteria decision-making, limited temporal density, and unequal technical access. Fifteen of the 19 studies explicitly point towards artificial intelligence or machine learning as an emerging opportunity, while UAV/LiDAR data, IoT observations, cloud processing, predictive modelling and participatory web/mobile GIS remain underused. We argue that the next generation of GIS-based NRM should move from static and retrospective mapping towards dynamic, field-validated, uncertainty-aware, predictive and participatory decision systems. A validation-centred framework and a prioritised research agenda are proposed.