Multi-temporal fine-scale modelling of Larix decidua forest plots using terrestrial LiDAR and hemispherical photographs

Abstract

Fine-scale architectural tree models serve as an effective representation of three-dimensional plant material distributions. They can help to quantify wood volume and biomass, to estimate leaf area distributions on a detailed scale, and can be exploited for physically based modelling approaches. If architectural tree models can be derived for multiple acquisition dates, they permit the detailed investigation of phenological effects. Although promising approaches for the generation of architectural tree/forest models from terrestrial LiDAR data are available, they are often non-trivial and their application to forest plots is often difficult. This is restricting the flexibility of these reconstruction approaches especially for multi-temporal analyses. In this paper, forest models of two Larix decidua forest plots are reconstructed by making use of terrestrial LiDAR data and digital hemispherical photographs (DHP). Recent modelling strategies are enhanced and developed further in order to improve the robustness and usability of the architectural tree model reconstruction process.Raw point cloud data are directly used as input to solve both tree delineation and tree reconstruction in a single processing pipeline. This includes terrain filtering, intensity filtering, and trunk extraction. These steps are followed by a hierarchical and iterative multi-tree branch and twig reconstruction. Based on multi-temporal DHPs, various foliage states are documented. These DHPs and the reconstructed branching architectures are used to flexibly generate and update multi-temporal 3D models of foliage. In order to quantify the modelling performance with respect to various forest characteristics, a test setup based on simulated forest and acquisition geometries is build up. It can be shown, that typical sources of error in the tree reconstruction process are minimized by the proposed approach. It is possible to estimate wood volume distributions, trunk tapering and leaf area distributions with an error of only 10–14%. Except for strongly overlapping tree crowns, the overall accuracy of the single tree delineation in interlinked tree crowns is higher than 80%. Considering these error margins, we apply the modelling strategy to two forest plots and derive architectural models for three dates during the growing season. Using DHPs as reference data, it can be shown, that the estimated gap fraction values derived from the generated models show an error of only 10–15%.

Plant phenotyping relevance

LiDARと全天空写真から樹木の3D構造・葉面積・木材体積などの植物形質を抽出する再構成パイプラインを開発し、シミュレーションで性能検証しているため、フェノタイピング手法が中心である。

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