Enhancing salt marshes monitoring: Estimating biomass with drone-derived habitat-specific models.

To quantify the amount of carbon sequestered by salt marshes, it is essential to estimate their aboveground biomass (AGB). In this study, we propose utilizing low-altitude remote sensing techniques to collect high-resolution LiDAR and multispectral (MS) data for biomass assessment. We characterized salt marsh vegetation habitats by examining vegetation indices (VIs), and the high-resolution topographic information from LiDAR helped assess habitat distribution. Specifically, the Anthocyanin Reflectance Index 2 (ARI2), combined with the Digital Surface Model (DSM), allows for the identification and separation of the two habitats with distinct dominant species (Sarcocornia spp. and Sporobolus maritimus). The VIs for the two vegetation classes exhibit different seasonal changes throughout the annual cycle, suggesting distinct growth mechanisms for each. Biomass models for the specific seasons are created, showing higher precision (up to 99%) from habitat-specific models compared to those treating species uniformly. Differences are observed in biomass estimation patterns depending on whether the marsh is assessed as a whole or separated into dominating habitats, indicating that the two dominant species exhibit different behaviours that influence biomass production differently throughout the year. Seasonal variations in AGB indicate a peak in summer and a decline in spring, with annual variation accounting for just 9% of the total output, possibly influenced by increased soil salinity and stress in spring. Using LiDAR and MS data from an unmanned aerial vehicle (UAV) is essential for precise identification of primary marsh habitats, facilitating the creation of highly accurate biomass models. This user-friendly, repeatable, and cost-effective method enables the study of salt marshes, evolutionary trends, and climate change response requiring less fieldwork.