Integration of Multispectral, LiDAR, and Artificial Intelligence Approaches for Characterizing Wetland Dynamics and Ecological Status

Dear Colleagues,

Wetlands are ecosystems that host high biodiversity and play essential roles as regulators of the hydrological cycle, carbon sinks, and natural water purifiers. Anthropogenic and natural pressures put wetlands at risk by decreasing their flooded surface, water depth, water bodies connectivity, or water quality. These highly variable ecosystems typically have difficult accessibility. Remote sensing techniques are essential for assessing their state at adequate spatial and temporal resolution.

Multispectral satellite imagery from sensors such as Sentinel-1, Sentinel-2, and Landsat-9, among others, enables the evaluation and monitoring of water extent using water indices or by applying machine learning and deep learning classification techniques. In shallow water bodies, spectral variability reflects changes in turbidity, chlorophyll concentration, or water depth, which complicates their analysis and highlights the need for new remote sensing approaches to better detect ecological alterations. Multispectral data also allows the analysis of spatial and temporal variations in aquatic and riparian vegetation, providing indicators of ecological integrity in wetlands. Moreover, the use of satellite imagery allows for the assessment of wetland health through water quality parameters such as chlorophyll-a concentration, suspended solids, or colored dissolved organic matter (yellow substances), which can be estimated using regression models based on multispectral information.

In addition, bathymetric LiDAR provides three-dimensional data that enables precise analysis of bottom morphology and the transition between terrestrial and aquatic environments. Its availability supports the monitoring of water volume and hydromorphological dynamics, which is valuable for studying issues such as sediment accumulation in wetlands and adjacent areas.

The integration of multispectral imagery, LiDAR data, and artificial intelligence models offers great potential for achieving a comprehensive understanding of wetland status and dynamics, serving as a key tool for their sustainable management and the conservation of these vulnerable ecosystems.

Prof. Dr. Javier Estornell
Prof. Dr. Maria-Teresa Sebastia-Frasquet
Guest Editors

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