Quantifying mangrove carbon assimilation rates using UAV imagery

Javier Blanco-Sacristán; Kasper Johansen; Mariana Elías-Lara; Yu-Hsuan Tu; Carlos M Duarte; Matthew F McCabe

doi:10.1038/s41598-024-55090-w

Quantifying mangrove carbon assimilation rates using UAV imagery

Sci Rep. 2024 Feb 26;14(1):4648. doi: 10.1038/s41598-024-55090-w.

Authors

Javier Blanco-Sacristán¹, Kasper Johansen², Mariana Elías-Lara², Yu-Hsuan Tu², Carlos M Duarte³, Matthew F McCabe²

Affiliations

¹ Climate and Livability Initiative, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia. javier.blancosacristan@kaust.edu.sa.
² Climate and Livability Initiative, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia.
³ Red Sea Research Center and Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Abstract

Mangrove forests are recognized as one of the most effective ecosystems for storing carbon. In drylands, mangroves operate at the extremes of environmental gradients and, in many instances, offer one of the few opportunities for vegetation-based sequestering of carbon. Developing accurate and reproducible methods to map carbon assimilation in mangroves not only serves to inform efforts related to natural capital accounting, but can help to motivate their protection and preservation. Remote sensing offers a means to retrieve numerous vegetation traits, many of which can be related to plant biophysical or biochemical responses. The leaf area index (LAI) is routinely employed as a biophysical indicator of health and condition. Here, we apply a linear regression model to UAV-derived multispectral data to retrieve LAI across three mangrove sites located along the coastline of the Red Sea, with estimates producing an R² of 0.72 when compared against ground-sampled LiCOR LAI-2200C LAI data. To explore the potential of monitoring carbon assimilation within these mangrove stands, the UAV-derived LAI estimates were combined with field-measured net photosynthesis rates from a LiCOR 6400/XT, providing a first estimate of carbon assimilation in dryland mangrove systems of approximately 3000 ton C km^-2 yr^-1. Overall, these results advance our understanding of carbon assimilation in dryland mangroves and provide a mechanism to quantify the carbon mitigation potential of mangrove reforestation efforts.