Abstract
Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key to meaningful VLS is a suitable 3D representation of the objects of interest. For VLS of forests, the way trees are constructed influences both the performance and the realism of the simulations. In this contribution, we analyse how well VLS can reproduce scans of individual trees in a forest. Specifically, we examine how different voxel sizes used to create a virtual forest affect point cloud metrics (e.g., height percentiles) and tree metrics (e.g., tree height and crown base height) derived from simulated point clouds. The level of detail in the voxelisation is dependent on the voxel size, which influences the number of voxel cells of the model. A smaller voxel size (i.e., more voxels) increases the computational cost of laser scanning simulations but allows for more detail in the object representation. We present a method that decouples voxel grid resolution from final voxel cube size by scaling voxels to smaller cubes, whose surface area is proportional to estimated normalised local plant area density. Voxel models are created from terrestrial laser scanning point clouds and then virtually scanned in one airborne and one UAV-borne simulation scenario. Using a comprehensive dataset of spatially overlapping terrestrial, UAV-borne and airborne laser scanning field data, we compare metrics derived from simulated point clouds and from real reference point clouds. Compared to voxel cubes of fixed size with the same base grid size, using scaled voxels greatly improves the agreement of simulated and real point cloud metrics and tree metrics. This can be largely attributed to reduced artificial occlusion effects. The scaled voxels better represent gaps in the canopy, allowing for higher and more realistic crown penetration. Similarly high accuracy in the derived metrics can be achieved using regular fixed-sized voxel models with notably finer resolution, e.g., 0.02m. But this can pose a computational limitation for running simulations over large forest plots due to the ca. 50 times higher number of filled voxels. We conclude that opaque scaled voxel models enable realistic laser scanning simulations in forests and avoid the high computational cost of small fixed-sized voxels.
Highlights
Metrics derived from airborne laser scanning (ALS) point clouds relate to important structural forest characteristics and can be used to complement and enhance time-consuming conventional forest in ventories (FIs) (Maltamo et al, 2014)
We present the agreement of metrics derived from simulated point clouds with those derived from real point clouds of the same trees, i.e., our target trees, for the different voxel modelling approaches
This study investigates different opaque voxel-based tree recon struction methods from terrestrial laser scanning (TLS) point clouds regarding their ability to create models for realistic simulation of ALS and UAV-borne laser scanning (ULS) point clouds
Summary
Metrics derived from airborne laser scanning (ALS) point clouds relate to important structural forest characteristics and can be used to complement and enhance time-consuming conventional forest in ventories (FIs) (Maltamo et al, 2014). For predicting forest and tree characteristics from point cloud data, it is important to understand the relation between ALS metrics and FI variables, and the robustness of ALS metrics to different acquisition parameters (e.g., flying altitude, scan angle, pulse repetition frequency, beam divergence) These questions have been addressed by comparing laser scanning data obtained with varying acquisition settings with in situ reference information (Chasmer et al, 2006; Hopkinson, 2007; Morsdorf et al, 2008; Næsset, 2009). Since ALS acquisitions are expensive, only a limited number of config urations can be tested, and usually, the effect of only a single or few acquisition variables is investigated This makes it difficult to disentangle the multiple and co-dependent influences of sensor and platform configurations on the point cloud characteristics. VLS data with perfect ground truth may serve as training and testing data for algorithm development and machine learning in application fields such as tree detection, tree segmentation and forest gap detection
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