Deep learning and machine learning enable broad-scale woodland height, cover, and biomass mapping

Accurate, spatially explicit quantification of vegetation structure in drylands can improve our understanding of the important role that these critical ecosystems play in the Earth system. In semiarid woodland settings, remote sensing of vegetation structure is challenging due to low tree height, cover, and greenness as well as limited spatial and temporal availability of airborne lidar data. These limitations have hindered the development of remote sensing applications in globally widespread and ecologically important dryland systems. In this study, we implement a U-Net convolutional neural network capable of predicting per-pixel, lidar-derived vegetation height in piñon-juniper woodlands using widely available, high-resolution aerial imagery. We used this imagery and modeled canopy height data to construct random forest models for predicting tree density, canopy cover, and live aboveground biomass. Trained and validated on a field dataset that spanned diverse portions of the vast range of piñon-juniper woodlands in the southwestern US, our models demonstrated high performance according to both variance explained (R²_density = 0.45; R²_cover = 0.80; R²_biomass = 0.61) and predictive error (%RMSE_density = 57; %RMSE_cover = 19; %RMSE_biomass = 42). A comparative analysis revealed that, while performance was somewhat lower than models driven solely by airborne lidar, they vastly exceeded that of models driven by aerial imagery alone or a combination of Landsat, topography, and climate data. Although the structural predictive maps featured some artifacts from illumination and perspective differences inherent to aerial imagery, this workflow represents a viable pathway for spatially exhaustive and temporally consistent vegetation structure mapping in piñon-juniper and other dry woodland ecosystems.