Reconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes

Abstract

& Key messageWe present a data-driven technique to visualize forest landscapes and simulate their future development according to alternative management scenarios. Gentle harvesting intensities were preferred for maintaining scenic values in a test of eliciting public's preferences based on the simulated landscapes. & Context Visualizations of future forest landscapes according to alternative management scenarios are useful for eliciting stakeholders' preferences on the alternatives. However, conventional computer visualizations require laborious tree-wise measurements or simulators to generate these observations. & Aims We describe and evaluate an alternative approach, in which the visualization is based on reconstructing forest canopy from sparse density, leaf-off airborne laser scanning data. & Methods Computational geometry was employed to generate filtrations, i.e., ordered sets of simplices belonging to the three-dimensional triangulations of the point data. An appropriate degree of filtering was determined by analyzing the topological persistence of the filtrations. The topology was further utilized to simulate changes to canopy biomass, resembling harvests with varying retention levels. Relative priorities of recreational and scenic values of the harvests were estimated based on pairwise comparisons and analytic hierarchy process (AHP). & Results The canopy elements were co-located with the tree stems measured in the field, and the visualizations derived from the entire landscape showed reasonably realistic, despite a low numerical correspondence with plot-level forest attributes. The potential and limitations to improve the proposed parameterization are discussed. & Conclusion Although the criteria to evaluate the landscape visualization and simulation models were not conclusive, the results suggest that forest scenes may be feasibly reconstructed based on data already covering broad areas and readily available for practical applications.

3D Tree Reconstruction in Support of Urban Microclimate Simulation: A Comprehensive Literature Review

“…Terrestrial photogrammetry Photogrammetric point cloud [115][116][117][118][119][120][121][122][123] Aerial photogrammetry UAV sequence images [124] Photogrammetric point cloud [125][126][127][128][129] Aerial laser scanning LiDAR point clouds [130] Terrestrial laser scanning (TLS) LiDAR point clouds [39,40,43,44,[131][132][133][134][135][136][137][138][139][140][141][142] Mobile laser scanning (MLS) LiDAR point clouds [143,144] Aerial laser scanning (ALS) & aerial photography LiDAR point clouds & images identifying tree species [41] Terrestrial laser scanning (TLS) & terrestrial photography…”

Deconstructing landscape pattern: applications of remote sensing to physiognomic landscape mapping

“…ALS-based DEMs and DSMs are also very common in archaeological studies (Fryskowska et al 2017;Witharana et al 2018), allowing for the detection of historical remains and the uncovering of the historical value of the physiognomic landscape (Ode et al 2008). The role that ALS data plays in the visualisation and assessment of aesthetic properties of vegetation canopy can hardly be overestimated: one of the first attempts in this direction was made recently by Vauhkonen and Ruotsalainen (2017).…”

Disturbances of natural and cultural landscape as an element of controversies in tourism – assessment based on the analysis of stress hormones in saliva

“…The presentation of multi-sensory aspects of tourist landscape and the determination of features that impact its attractiveness can be performed using research methods described in the literature. The selected items relate to understanding how multi-sensory spatial experience in uences affective landscape image and behavioural intention (Vauhkonen, Ruotsalainen, 2017 Publications that discuss the use of eye-tracking to study the perception of landscape are particularly interesting (Scott et al, 2016;King et al, 2019;Dupont et al, 2014;Mele et al, 2014). The advantages and disadvantages of this data collection procedure are widely discussed.…”