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Strigul, Nikolay Connecting a cyberforest with a real forest using remote sensing data: parameterizing spatially-explicit individual-based forest models using 3D point clouds Keynote
Nikolay Strigul1
(1) Washington State University

Evolving photogrammetry and laser scanning technologies facilitate the development of complex spatially-explicit forest models. It becomes possible not only to account for traditional metrics such as crown radius and height, but also for multifaceted tree shape parameters that are critical for understanding tree spatial competition and growth. I will present an original method, named the Slice-Sector Approximation, SSA, to determining 3D shape of trees from dense point clouds. Using mean-shift algorithms that require no apriori knowledge on number of trees or clusters present, structure of outlying shape is determined around a central point at each above-ground height class. Tree shape is approximated for a variable number of azimuth sectors radiating from this center. The number of vertical slices and azimuth sectors can vary from simple, returning values for height and lower crown radius, to more complex that approximate the upper crown shape and more intricate branch arrangements. SSA is a part of a workflow for simulation of forest stands with an original spatially-explicit individual-based forest model LES parameterized with UAV-based photogrammetry and LiDAR data.