RT journal article
T1 Enhancing salt marshes monitoring: Estimating biomass with drone-derived habitat-specific models
A1 Curcio, Andrea Celeste
A1 Barbero González, Luis Carlos
A1 Peralta González, Gloria
A2 Biología
A2 Ciencias de la Tierra
K1 Unmanned aerial vehicle (UAV)
K1 LiDAR Multispectral (MS)
K1 Aboveground biomass (AGB)
K1 Vegetation indices (VIs)
K1 Blue carbon
AB Salt marshes play a vital role in biodiversity conservation, coastal protection, and carbonsequestration. Estimating aboveground biomass (AGB) is crucial for quantifying the carbonsequestration capacity of the system. However, traditional fieldwork methods are timeconsuming,demanding, and detrimental to marsh ecosystems. In this study, we propose utilizinglow-altitude remote sensing techniques to collect high-resolution LiDAR and multispectral(MS) data for biomass assessment. With these datasets, we characterized salt marsh vegetationhabitats through the analysis of vegetation indices (VIs) variability. LiDAR high-resolutiontopographic information aids in evaluating habitat distribution. Moreover, the AnthocyaninReflectance Index 2 (ARI2), combined with the Digital Surface Model, allows for the identificationand separation of the two habitats with distinct dominant species (Sarcocornia spp. and Sporobolusmaritimus). 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 distinctgrowth 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 speciesuniformly. Depending on whether the marsh is considered as a whole or separated into dominanthabitats, differences in biomass estimation trends are observed. This implies that the twodominant species exhibit varying behaviours throughout the year, contributing to diverse biomassproduction. The study reveals a seasonal pattern in the total AGB, with the highest biomass valuein summer and the lowest peak in spring, with annual variation accounting for only 9% of totaloutput. The reduced AGB value in spring may be due to increased soil salinity and stress. The useof LiDAR and MS data from an unmanned aerial vehicle (UAV) is crucial for accurately distinguishingprimary marsh habitats and creating detailed biomass models, with unprecedentedaccuracy. This method is user-friendly, repeatable, and cost-effective, enabling the study of saltmarshes, evolutionary trends, and climate change response requiring less fieldwork.
SN 2352-9385
YR 2024
FD 2024-05
LK http://hdl.handle.net/10498/32879
UL http://hdl.handle.net/10498/32879
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026