We 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.
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.
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.
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).
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.
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.
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Vauhkonen J, Ruotsalainen R 2017. Reconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes. Ann. For. Sci. 74: 9. 10.1007/s13595-016-0598-6.