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

Spatially explicit simulation of land use and land cover (LULC) change within geographic information systems (GIS) has become a routine component of environmental assessment, urban and regional planning, and climate-related research. The rapid diffusion of hybrid cellular automata, patch-generating and machine-learning models has, however, outpaced critical evaluation of their assumptions, predictive skill and suitability for informing decisions. This critical narrative review appraises the complete GIS-based modelling chain, from the observational basis of multi-temporal land cover maps through quantity estimation, spatial allocation, predictive techniques, calibration and validation, scenario development and coupling with environmental assessment models. Peer-reviewed literature published from 2000 onwards was identified through multidisciplinary and environmental scholarly databases supplemented by citation searching, and was synthesised thematically using an explicit qualitative appraisal of transparency, validation adequacy, analytical contribution and generalisability. The synthesis indicates that the principal modelling paradigms differ less in their capacity to identify where change is likely than in how they treat the quantity of change, neighbourhood interaction, patch formation and human decision-making. Predictive gains attributed to machine learning and deep learning are often modest when judged against change-sensitive metrics, and they remain constrained by short time series, class imbalance and limited transferability. Validation practice is the weakest link in the chain: agreement statistics dominated by persistence continue to be reported as evidence of accuracy, whereas three-map comparison, the components of the figure of merit and null-model benchmarks are applied inconsistently. Scenario studies frequently represent alternative futures as parametric adjustments of trend-based transition probabilities, and downscaled global pathways introduce further uncertainty that is seldom quantified. Environmental applications, particularly carbon storage and ecosystem service assessments, commonly convert simulated land configurations into indicators through fixed coefficients without propagating map or model uncertainty. An integrative framework is proposed that separates established from hypothesised error pathways, and priorities are identified for standardised change-focused validation, multi-model benchmarking, uncertainty propagation, richer process representation and co-produced scenarios. On the present evidence, GIS-based LULC change models are best regarded as structured tools for exploring plausible futures rather than as precise forecasts of future landscapes.