A comparison of multiple methods for mapping local-scale mesquite tree aboveground biomass with remotely sensed data

Highlights: • The machine learning method, random forest, was introduced to build the local-scale mesquite tree aboveground biomass map. • Very high spatial resolution aerial photos and lidar data were applied in the study. • The results showed that machine learning method was the best for estimating mesquite tree aboveground biomass. • Mapping rangeland biomass was important for carbon studies in rangeland ecosystems. -- Abstract: A local-scale mesquite tree (Prosopis glandulosa Torr.) aboveground biomass map contribute to our understanding of the spatial distribution of woody plant aboveground biomass, and carbon stocks and fluxes in rangeland ecosystems. The objective of the study was examining a methodological approach to use airborne lidar data and multispectral imagery to create very high spatial resolution local-scale mesquite tree aboveground biomass maps by comparing three statistical methods and identifying significant prediction variables. The three statistical methods were the stepwise regression, the least absolute shrinkage and selection operator (LASSO), and the random forests. These methods were applied to establish the mesquite tree aboveground biomass equations and model from the in-situ mesquite tree aboveground biomass with the lidar metrics and multispectral data. The results showed the stepwise regression and LASSO had limited adj-R² and MSE. However, the random forests method with combined multispectral imagery and lidar data presented acceptable MSE and R² (1.08 Mg ha⁻¹ and 0.37). In summary, the random forests method with combined multispectral imagery and lidar data offered the most reliable and reasonable combination to generate a very high spatial resolution local-scale mesquite tree aboveground biomass map.