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

The integration of remote sensing and geographic information systems has become the principal means by which Earth observation data are converted into spatially explicit environmental information, yet the reliability of the resulting analyses for environmental modelling and decision-making has rarely been examined across the whole chain from observation to decision. This critical narrative review evaluates developments since the opening of the Landsat archive in 2008. Peer-reviewed literature was identified through open scholarly indexes, academic search engines and citation tracking, and appraised qualitatively for relevance, methodological transparency, rigour of validation, representativeness and corroboration. Integration has moved from loosely coupled desktop systems to analysis-ready data, cloud geoprocessing platforms and data cubes, which removed most practical barriers to large-area and time-series analysis but left the handling of uncertainty largely unresolved. Object-based image analysis, machine learning, deep learning, dense time-series methods and multisource fusion have improved information extraction under tested conditions, whereas their transferability, mutual agreement and the propagation of their errors remain weakly evidenced. Independent products and models built from similar inputs frequently diverge in estimates of land cover, forest biomass, flood hazard and surface urban heat islands, largely because of differences in definitions, reference data, scale and validation design. The validation of machine learning maps is contested: random cross-validation of clustered data overstates accuracy, but the adequacy of spatial cross-validation as a remedy is disputed, and probability sampling with design-based inference remains the most defensible basis for estimating accuracy and area. Decision support through multicriteria analysis, emergency mapping and sustainability reporting is widespread, yet causal evidence of improved outcomes was found mainly for deforestation monitoring and spatially targeted enforcement, where effects depended on institutional capacity. An evidence-based framework linking observation, analysis, modelling, validation and decision stages is proposed. Priorities include decision-relevant validation standards, end-to-end propagation of uncertainty, geographically independent tests of transferability and quasi-experimental evaluation of decision impact. The credibility of integrated spatial analysis now depends more on evaluation across the whole chain than on additional sensors or algorithms.