Abstract
Coastal habitats require monitoring strategies that can capture changes in their extent, ecological characteristics, and surrounding environmental conditions. Although satellite and unmanned aerial vehicle observations offer diverse capabilities, selecting an appropriate sensor remains challenging because monitoring objectives, observation scales, and environmental constraints differ among ecosystems. This study synthesizes published evidence to develop a practical framework for selecting observation technologies for mangroves, seagrass meadows, coral reefs, and coastal environments. A structured narrative literature review was conducted using a Scopus search in September 2026, supplemented by relevant publications and technical documentation. The initial search identified 1,969 records; 95 priority candidates were screened further, and 47 publications underwent abstract-level relevance assessment. Of these, 28 were retained for the main qualitative synthesis. Evidence was organized according to monitoring objectives, sensor characteristics, environmental and operational constraints, and validation requirements. The synthesis shows that sensor suitability depends primarily on the intended monitoring product rather than nominal spatial or spectral resolution alone. Historical satellite archives support long-term habitat-change assessment, while high-resolution optical and unmanned aerial vehicle observations can provide finer spatial detail for localized applications. Radar offers complementary information where optical observations are constrained, whereas submerged habitat mapping requires particular attention to water-column effects, depth, and reference data. These findings were translated into a qualitative Sensor Selection Matrix linking monitoring objectives with candidate observation strategies and their principal limitations. The proposed framework supports context-dependent planning and transparent interpretation of monitoring outputs without establishing universal sensor rankings or transferable accuracy guarantees.
