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

Abstract

Salt marshes play a vital role in biodiversity conservation, coastal protection, and carbon sequestration. Estimating aboveground biomass (AGB) is crucial for quantifying the carbon sequestration capacity of the system. However, traditional fieldwork methods are time-consuming, demanding, and detrimental to marsh ecosystems. In this study, we propose utilizing low-altitude remote sensing techniques to collect high-resolution LiDAR and multispectral (MS) data for biomass assessment. With these datasets, we characterized salt marsh vegetation habitats through the analysis of vegetation indices (VIs) variability. LiDAR high-resolution topographic information aids in evaluating habitat distribution. Moreover, the Anthocyanin Reflectance Index 2 (ARI2), combined with the Digital Surface Model, allows for the identification and separation of the two habitats with distinct dominant species (Sarcocornia spp. and Sporobolus maritimus). The analysis of the annual cycle of VIs reveals the presence of seasonality. However, the VIs for the two vegetation classes exhibit dissimilar seasonal changes, indicating distinct growth mechanisms for each. Biomass models are created for each season in the annual cycle. Habitat-specific models exhibit higher precision (up to 99%) than models that treat species uniformly. Depending on whether the marsh is considered as a whole or separated into dominant habitats, differences in biomass estimation trends are observed. This implies that the two dominant species exhibit varying behaviours throughout the year, contributing to diverse biomass production. The study reveals a seasonal pattern in the total AGB, with the highest biomass value in summer and the lowest peak in spring, with annual variation accounting for only 9% of total output. The reduced AGB value in spring may be due to increased soil salinity and stress. The use of LiDAR and MS data from an unmanned aerial vehicle (UAV) is crucial for accurately distinguishing primary marsh habitats and creating detailed biomass models, with unprecedented accuracy. This method is user-friendly, repeatable, and cost-effective, enabling the study of salt marshes, evolutionary trends, and climate change response requiring less fieldwork.