Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass

Abstract

Scientifically robust yet economical and efficient methods are required to gather information about larger areas of uneven-aged forest resources, particularly at the landscape level, to reduce deforestation and forest degradation and to support the sustainable management of forest resources. In this study, we examined the potential of digital aerial photogrammetry (DAP) for assessing uneven-aged forest resources. Specifically, we tested the performance of biomass estimation by varying the conditions of several factors, e.g., image downscaling, vegetation metric extraction (point cloud- and canopy height model (CHM)-derived), modeling method ((simple linear regression (SLR), multiple linear regression (MLR), and random forest (RF)), and season (leaf-on and leaf-off). We built dense point clouds and CHMs using high-resolution aerial imagery collected in leaf-on and leaf-off conditions of an uneven-aged mixed conifer⁻broadleaf forest. DAP-derived vegetation metrics were then used to predict the dominant height and living biomass (total, conifer, and broadleaf) at the plot level. Our results demonstrated that image downscaling had a negative impact on the accuracy of the dominant height and biomass estimation in leaf-on conditions. In comparison to CHM-derived vegetation metrics, point cloud-derived metrics performed better in dominant height and biomass (total and conifer) estimations. Although the SLR (%RMSE = 21.1) and MLR (%RMSE = 18.1) modeling methods produced acceptable results for total biomass estimations, RF modeling significantly improved the plot-level total biomass estimation accuracy (%RMSE of 12.0 for leaf-on data). Overall, leaf-on DAP performed better in total biomass estimation compared to leaf-off DAP (%RMSE of 15.0 using RF modeling). Nevertheless, conifer biomass estimation accuracy improved when leaf-off data were used (from a %RMSE of 32.1 leaf-on to 23.8 leaf-off using RF modeling). Leaf-off DAP had a negative impact on the broadleaf biomass estimation (%RMSE > 35% for SLR, MLR, and RF modeling). Our results demonstrated that the performance of forest biomass estimation for uneven-aged forests varied with statistical representations as well as data sources. Thus, it would be appropriate to explore different statistical approaches (e.g., parametric and nonparametric) and data sources (e.g., different image resolutions, vegetation metrics, and leaf-on and leaf-off data) to inform the interpretation of remotely sensed data for biomass estimation for uneven-aged forest resources.

Keywords

• unmanned aerial vehicle
• mixed conifer–broadleaf forest
• leaf-off imagery