Scaled biomass estimation in woodland ecosystems: Testing the individual and combined capacities of satellite multispectral and lidar data

• Published in: Remote sensing of environment 2021-09, Vol.262, p.112511, Article 112511
• Main Authors: Campbell, Michael J., Dennison, Philip E., Kerr, Kelly L., Brewer, Simon C., Anderegg, William R.L.
• Format: Article
• Language: English
• Subjects: Aboveground biomass; Airborne lasers; Airborne lidar; Arid zones; Biomass; Biometrics; Canopies; Coverage; Data collection; Data integration; Decision analysis; Decision trees; Ecosystems; Environmental accounting; GEDI; Landsat; Landsat satellites; Leaf area; Lidar; Mapping; Model accuracy; Prediction models; Regional-scale models; Remote sensing; Root-mean-square errors; Satellite data; Satellites; Scale models; Scaled modeling; Spaceborne lidar; Spatial discrimination; Spatial resolution; Uncertainty; Vegetation; Waveforms; Woodland ecosystems; Woodlands
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2021.112511

Airborne laser scanning (ALS) data enable accurate modeling and mapping of aboveground biomass (AGB), but the limited spatial and temporal extents of ALS data collection limit the capacity for broad-scale carbon accounting. Conversely, while space-based remote sensing instruments provide increased spatial and temporal coverage, it can be difficult to directly link field-level vegetation biometrics to satellite data due to coarser spatial resolution and positional uncertainty. The combined use of ALS and satellite remote sensing data may offer a solution to efficient, accurate, and consistent AGB mapping across time and space. Such airborne-spaceborne data fusion has been demonstrated successfully in high-biomass settings; however, the unique structural conditions of dryland woodland ecosystems, with open canopies and low leaf area indices, pose mapping challenges that require further study. These challenges are particularly acute with large footprint spaceborne lidar, where short, widely-spaced trees may limit the capacity for accurate AGB estimation. In this study, we present a scaled methodological framework for linking field-measured woodland AGB to ALS data and, in turn, linking ALS-modeled AGB to satellite data, using piñon-juniper woodlands in southeastern Utah as a case study. We compare the effectiveness of this scaling approach using two satellite sensors, Landsat 8 OLI (multispectral) and GEDI (lidar). Since the predicted outputs of our local-scale model are being used as inputs to our regional-scale model, we also demonstrate an approach for propagating uncertainty throughout this nested, multiscale analytical framework, leveraging the inherent variability within a random forest's decision trees. Given the positional uncertainty of GEDI footprints, we test a range of different footprint sizes for their relative effects on ALS-GEDI AGB model accuracy. Our local-scale (field-ALS) predictive model was able to account for 74% of variance in AGB, and estimate AGB with a root mean squared error (RMSE) of 14 Mg/ha, a mean absolute error (MAE) of 11.09 Mg/ha. Our regional-scale (ALS-Landsat/GEDI) analysis with propagated uncertainty revealed that the combined use of Landsat and GEDI metrics produced the best predictive model (R2 = 0.68; RMSE = 12.71 Mg/ha; MAE = 9.40 Mg/ha), followed by Landsat-only metrics (R2 = 0.66, RMSE = 13.08 Mg/ha; MAE = 9.71 Mg/ha), and GEDI-only metrics (R2 = 0.49, RMSE = 16.01 Mg/ha; MAE = 12.14 Mg/ha). These results suggest t