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.

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

“…UAV remote-sensing systems are easier to obtain crop information at the farm scale during an adequate weather condition with high temporal-spatial resolution. For example, Jayathunga et al [25] examined the potential of the digital aerial photogrammetry UAV remote-sensing system for estimating AGB of forest in Furano City, northern Japan, and provided AGB maps with a ground resolution of 14 cm. As one of the most important indicators to describe the growing status of the crop, plant height (PH) has been widely used to estimate AGB [26,27].…”

Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects

“…Motivated by the recent trends and global interest, the Remote Sensing special issue "Remote Sensing-Based Forest Inventories from Landscape to Global Scale" hosted nine peer-reviewed papers adopting various modern applications of passive and active remote sensing data for multi-scale forest inventory applications. This special issue is enriched with a series of independent, though contextually related, recent studies from diverse geographical domains of the globe, including the near-Arctic Canada [10], Northern United States [11,12], Northern Japan [13], Southern Spain [14,15], Central Italy [16], Southern Poland [17] and Western Germany [18].…”

“…In terms of forest attributes under focus, again, the published papers within this special issue covered a range of essential forest entities for inventory as well as for monitoring. Allometric tree and stand attributes were given considerable attention, with the aboveground biomass (AGB) being modelled in three of the nine studies [13][14][15], followed by growing stock [18], basal area [10] and tree height [10,11,13]. A remarkable insight was especially given by [15], who applied metrics from ALS for simultaneous estimation of soil organic carbon and the AGB, pertaining these together as essential stand characteristics that can be largely affected by thinning.…”

“…Last but not least, the studies published within this special issue addressed the use of data from various sensor types, both from passive and active as well as from mono-and multi-temporal domains. A major emphasis was put on sole or combined use of ALS point clouds [11,[13][14][15]18], which is a prudent choice when dealing with the inventory of forest structural variables on landscape to regional scales. Moreover, very high resolution, often mono-temporal satellite imagery (UAV and aerial photogrammetry, WorldView-3 data), was employed by [10,11,13], which mainly aimed at quantifying local forest structural details at high geometric resolutions.…”

“…A major emphasis was put on sole or combined use of ALS point clouds [11,[13][14][15]18], which is a prudent choice when dealing with the inventory of forest structural variables on landscape to regional scales. Moreover, very high resolution, often mono-temporal satellite imagery (UAV and aerial photogrammetry, WorldView-3 data), was employed by [10,11,13], which mainly aimed at quantifying local forest structural details at high geometric resolutions. High-resolution optical data, including RapidEye and SPOT with geometric resolutions <10 m, were applied by [18], whereas medium-resolution (Landsat and Sentinel-2 series) [12,16,17] and low-resolution [14] data were also represented, mainly due to their proven ability for monitoring forest dynamics over timespans.…”