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

Geographic information systems (GIS) have moved from principally organising, overlaying and analysing spatial data towards supporting large-scale prediction, multimodal learning and increasingly automated decisions. This transition has been enabled by geospatial big data, cloud computation, machine learning, deep learning and emerging geospatial foundation models. Yet the expansion of predictive capacity has not resolved persistent questions concerning geographical representation, spatial dependence, scale, data provenance, out-of-area generalisation, uncertainty and the legitimacy of decisions based on model outputs. This critical narrative review examines how classical GIScience principles interact with newer artificial intelligence methods across the pathway from data acquisition to operational decision support. The literature was selected through targeted scholarly searching, source verification and thematic appraisal of foundational and contemporary studies. Four arguments emerge. First, spatial context and the choice of analytical support remain constitutive modelling decisions rather than preprocessing details. Second, gains reported under random validation can be misleading when geographical or temporal dependence is ignored. Third, deep learning and foundation models can extract useful representations from heterogeneous geospatial inputs, but their comparative value depends on task, training geography, benchmark design and deployment conditions. Fourth, credible decision support requires an explicit distinction between prediction, explanation and causal intervention, together with traceable evidence, communicated uncertainty and institutional oversight. The review integrates methodological evidence from environmental mapping, remote sensing, urban mobility and socioeconomic estimation, while identifying where apparent advances remain qualified by weak transferability, uneven geographical coverage and incomplete evaluation. It proposes a decision-centred evaluation framework that links data fitness, geographical theory, model validation and accountable use. Future research should prioritise externally replicated spatial benchmarks, process-aware models, uncertainty-aware deployment and assessments of whose interests geospatial predictions serve. The principal implication is not that advanced models replace spatial analysis, but that dependable GeoAI depends on preserving and testing the geographical assumptions on which its predictions rest.