Integrating LiDAR and multispectral remote sensing data for understanding wetland-flood dynamics: a literature review

Wetlands play a crucial role in mitigating floods by attenuating peak flows, storing excess water, and regulating downstream hydrology. However, increasing pressures from urbanization, land-use change, and climate variability are degrading these vital ecosystems, underscoring the need for advanced scalable monitoring and assessment approaches. This literature review synthesizes recent advances in the integration of LiDAR and multispectral remote sensing technologies for understanding wetland-flood dynamics. LiDAR provides high-resolution elevation and structural data essential to model surface depressions, canopy height, and hydrological connectivity, while multispectral imagery captures spectral information on water extent, vegetation condition, and sediment dynamics. When combined, these datasets enable more accurate and scalable assessments of flood storage capacity, inundation extent, and seasonal variability in wetland function. Despite these advances, existing reviews largely treat LiDAR and multispectral data in isolation and rarely synthesize how their integration resolves coupled vegetation–topography–hydrology interactions, compares fusion strategies across spatial and temporal scales, or evaluates emerging AI-based integration frameworks for wetland–flood analysis. This review addresses these gaps by systematically examining data preprocessing workflows, empirical, semi-analytical, and AI-driven fusion approaches, and their applicability across diverse wetland types and observation scales. The review highlights applications ranging from global satellite missions such as PlanetScope, Copernicus Sentinel-2, Landsat, and GEDI to high-resolution UAV-based surveys.