A Comparison of Stand-Level Volume Estimates from Image-Based Canopy Height Models of Different Spatial Resolutions

Abstract

Digital aerial photogrammetry has recently attracted great attention in forest inventory studies, particularly in countries where airborne laser scanning (ALS) technology is not available. Further research, however, is required to prove its practical applicability in deriving three-dimensional (3D) point clouds and canopy surface and height models (CSMs and CHMs, respectively) over different forest types. The primary aim of this study is to investigate the applicability of image-based CHMs at different spatial resolutions (1 m, 2 m, 5 m) for use in stand-level forest inventory, with a special focus on estimation of stand-level merchantable volume of even-aged pedunculate oak (Quercus robur L.) forests. CHMs are generated by subtracting digital terrain models (DTMs), derived from the national digital terrain database, from corresponding digital surface models (DSMs), derived by the process of image matching of digital aerial images. Two types of stand-level volume regression models are developed for each CHM resolution. The first model is based solely on stand-level CHM metrics, whereas in the second model, easily obtainable variables from forest management databases are included in addition to CHM metrics. The estimation accuracies of the stand volume estimates based on stand-level metrics (relative root mean square error RMSE % = 12.53%-13.28%) are similar or slightly higher than those obtained from previous studies in which stand volume estimates were based on plot-level metrics. The inclusion of stand age as an independent variable in addition to CHM metrics improves the accuracy of the stand volume estimates. Improvements are notable for young and middle-aged stands, and negligible for mature and old stands. Results show that CHMs at the three different resolutions are capable of providing reasonably accurate volume estimates at the stand level.

Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling

“…In both cases, the three-dimensional point data was of low density (1.5 and 0.82 points m −2 for ALS and DAP point clouds, respectively). Perhaps as a consequence of the data density, combined with the complexity of forest environment, the accuracy of the attribute predictions based on both sources of point cloud data were comparable or slightly lower than reported in other studies e.g., [15,16,39]. Thus, it appears that future enhanced forest inventories can rely on DAP data, with the digital terrain model required for point cloud normalization provided by ALS [42][43][44][45].…”

UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

“…Our research result was that coupling oblique photos and nadir photos can effectively improve the vertical coordinate accuracy of the point cloud. However, it is worth noting that under the premise of not using GCP, the height coordinate error provided by the scheme with nadir-photos-only reached 10.8 m (Figure 11), which is quite different from the results of other researchers [35][36][37]. The specific reason for this result is not clear, but it may be related to the camera calibration result or ground type, which can be further explored in future research.…”

Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests

“…Unlike airborne Lidar, digital aerial photographs cannot provide terrain height information, but they can provide the point cloud of the upper canopy surface [9], especially under dense forest canopy conditions. However, the relative height between the point cloud derived from digital aerial photographs and the terrain height provided by another data source, such as airborne Lidar, can be used to estimate forest biophysical properties [10][11][12][13]. Thus, periodic acquisitions of digital aerial photographs may be a practical option in areas where accurate terrain height information is available (e.g., a digital terrain model (DTM) derived from an existing Airborne Lidar dataset).…”