Regional Stem Volume Mapping: A Feasibility Assessment of Scaling Tree-Level Estimates

Abstract

Spatially detailed monitoring of forest resources is important for sustainable management but limited by a lack of field measurements. The increasing availability of multisource datasets offers the potential to characterize forest attributes at finer resolutions with regional coverage. This study aimed to assess the potential of mapping stem volume at a 30 m scale in eastern Texas using multisource datasets: airborne lidar, Landsat and LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) datasets. Gradient-boosted trees regression models relating total volume, estimated from airborne lidar measurements and allometric equations, and multitemporal Landsat and LANDFIRE predictors were developed and evaluated. The fitted models showed moderate to high correlation (R2 = 0.52–0.81) with reference stem volume estimates, with higher correlation in pine forests (R2 = 0.70–0.81) than mixed forests (R2 = 0.52–0.67). The models were also precise, with relative percent mean absolute errors (pMAE) of 13.8–21.2%. The estimated volumes also consistently agreed with volumes estimated in independent sites (R2 = 0.51, pMAE = 34.7%) and with US Forest Service Forest Inventory Analysis county-level volume estimates (R2 = 0.93, pBias = −10.3%, pMAE = 11.7%). This study shows the potential of developing regional stem volume products using airborne lidar and multisource datasets, supporting forest productivity and carbon modeling at spatially detailed scales.