GIS and Remote Sensing for Environmental Monitoring and Natural Resource Management: Concepts, Methods and Applications

Spatial planning and natural-resource management routinely require choices among locations or land uses that must satisfy several conflicting objectives simultaneously. Geographic information system-based multi-criteria decision analysis has become the dominant analytical framework for such problems, combining geospatial data processing with formal procedures for expressing and aggregating stakeholder preferences. Its uptake has been rapid across land-use and agricultural suitability, renewable-energy siting, waste management, groundwater and flood studies, ecosystem-service planning and urban infrastructure, yet the credibility of many published applications remains uncertain. This critical narrative review examines the conceptual principles, weighting and aggregation methods, modelling approaches and principal application domains of the field, with the aim of distinguishing well-supported practice from conventions that persist without adequate justification. Literature was identified through structured searches of multidisciplinary and discipline-relevant scholarly indexes, supplemented by citation chasing, and was appraised for methodological transparency, treatment of uncertainty, validation and decision relevance. The synthesis indicates that the field rests on a sound decision-analytic foundation but that routine applications frequently depart from it. Additive aggregation of pairwise-comparison weights dominates despite well-documented concerns about the meaning of importance weights, rank reversal and dependence on criterion standardisation. Comparative studies show that the choice of weighting procedure and decision rule can alter suitability patterns substantially when the problem is evaluative, whereas sensitivity and uncertainty analysis, although methodologically mature, remains inconsistently reported. Spatially explicit, local and probabilistic formulations, participatory and web-based designs, and hybridisation with machine learning address some of these weaknesses but introduce new questions concerning interpretability, data dependence and the respective roles of expert judgement and empirical evidence. Validation against independent observations is largely confined to hazard mapping and a minority of groundwater studies, so the predictive and decision value of most suitability maps remains untested. Future progress depends on explicit problem structuring, transparent reporting, routine robustness analysis, empirically grounded validation where outcomes are observable and evaluation of whether these analyses improve real planning decisions. Used with these safeguards, the framework can support accountable spatial decisions; used mechanically, it risks lending numerical authority to largely subjective judgements.