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

Geographic information systems were designed largely to store, query and display static representations of the world, yet most questions in geography, ecology, epidemiology, urban studies and the environmental sciences concern processes that unfold through time. Spatiotemporal analysis and dynamic spatial modelling have consequently developed along several partly separate lines: temporal data modelling and time geography, space–time statistics and geostatistics, spatiotemporal data mining and machine learning, and simulation through cellular automata, agent-based models and process-based raster models. This critical narrative review examines how these traditions conceptualise space, time and change, what their principal techniques can and cannot infer, how simulation models are coupled with geographic information systems and evaluated, and what the application literature demonstrates about their practical value. Literature published from 1988 onwards was identified through multidisciplinary scholarly indexes and citation tracking and was appraised for relevance, methodological transparency, evaluation rigour and influence. The synthesis indicates that representational theory has advanced further than mainstream implementation, that space–time cluster detection and local regression remain sensitive to the choice of spatial and temporal units, and that the inferential basis of prospective cluster surveillance remains contested. Land-change and urban simulation models have become more flexible through learned transition rules, but validation practice has improved more slowly, with continued reliance on cell-by-cell agreement and inconsistent separation of calibration from validation. Agent-based models offer process realism at the cost of identifiability and heavy data demands. Deep learning has delivered strong short-range forecasting skill in data-rich settings, whereas its spatial explicitness, capacity to extrapolate and interpretability remain unresolved, and the appropriate validation of geospatial machine learning is itself disputed. The most defensible direction is a tighter integration of representation, analysis, simulation and evaluation, in which theory-guided learning, data assimilation and transparent multi-scale validation are treated as complementary rather than competing. Progress is likely to depend more on shared benchmarks, explicit treatment of temporal scale and reproducible model reporting than on further proliferation of model variants.