Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies |
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| Institution: | 1. Molecular Cardiology, Department of Internal Medicine II, University of Ulm, Ulm, Germany;2. Department of Internal Medicine II, University of Ulm, Ulm, Germany;1. Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway;2. Norwegian Forest and Landscape Institute, P.O. Box 115, NO-1431 Ås, Norway;3. Northern Research Station, U.S. Forest Service, Saint Paul, MN, USA |
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| Abstract: | A computational canopy volume (CCV) based on airborne laser scanning (ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500–1000 m2 located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R2) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88–0.89, 0.89, 0.83–0.97, and 0.88–0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R2 of 0.77–0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0–1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed. |
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| Keywords: | Light Detection and Ranging (LiDAR) Forest inventory Tree allometry Delaunay triangulation Alpha shape Simplicial homomorphism Persistent homology |
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