Mapping Tropical Forested Wetlands Biomass with LiDAR: A Machine Learning Comparison

Mangroves and tropical swamp forests are ecosystems that play a critical role in carbon sequestration, coastal protection, and biodiversity support. Accurately estimating aboveground biomass (AGB) in these forests is crucial for global carbon management and conservation efforts. This study evaluates the potential of LiDAR-derived metrics to model the AGB of an area with mangroves and tropical swamp forests in Southeast Mexico. The study area, located in the Pantanos de Centla Protected Area, encompasses a gradient of seasonal waterlogged conditions, from saline to freshwater. Data were collected from 25 1250-m2 plots, and three modeling approaches—linear regression, random forest, and XGBoost—were employed to estimate the AGB. The data were divided into training and test sets using an 80:20 ratio. The results indicate that the random forest model outperformed the others, achieving the lowest root mean squared error (RMSE = 20.25 Mg/ha, rRMSE = 12.25%, R2 = 0.88). The most influential variables in this model were mean height (zmean), the 35th percentile of height (zq35), and the fourth percentile of returns (p4th), all positively correlated with the AGB. The model’s robustness and uncertainty were evaluated through bootstrapping and spatial prediction across the study area, with higher AGB values concentrated near the main water channels. This study underscores the effectiveness of LiDAR-derived metrics for AGB estimation in complex forested environments.