Aboveground Biomass and Biomass Change Estimates of Tropical Dry Forest Using Aerial and Terrestrial LiDAR

Résumé fourni par la source

Accurate estimates of carbon stocks from plant biomass are crucial for climate change mitigation. Temporal changes in biomass must also be quantified to accurately assess forest carbon emissions and absorption. LiDAR remote sensing is the primary method for mapping forest aboveground biomass (AGB) and its changes, as it can penetrate the canopy to create detailed three-dimensional point clouds. This study developed statistical models to estimate AGB and its changes in a tropical dry forest in the Yucatan Peninsula for 2013 and 2021. The models utilized biomass data from field measurements as the dependent variable and LiDAR-derived metrics as explanatory variables. Data were collected using airborne LiDAR in 2013 and ground-based LiDAR in 2021, allowing for a comparison of accuracy from different perspectives. The results indicated strong model performance, with coefficients of determination (R²) of at least 0.89 for AGB estimates in both years. Cross-validation errors ranged from 24% to 29%. For models estimating AGB increases, R² values reached approximately 0.92, with errors under 27% when differences in metrics from both years were analyzed. These results demonstrate the good performance of the models for estimating aboveground biomass and its increases using LiDAR and highlight the feasibility and importance of integrating field data and remotely sensed data in biomass estimates and its increases in tropical dry forests.

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