Integrating UAV and Lidar Data for Retrieving Tree Volume of Hinoki Forests

Yoshii, Tatsuki; Matsumura, Naoto; Lin, Chinsu

doi:10.1109/igarss39084.2020.9323219

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…The wide application of UAV in modern forest resource-monitoring makes the available remote-sensing data more diversified and abundant [25][26][27]. The emergence of airborne LiDAR data and ultrahigh-resolution RGB images made it possible to monitor a forest at the individual tree scale [28,29]. In this context, it is highly possible to directly extract the crown border from UAV data and then obtain the FCC information by the statistical analysis of the crown proportion within the unit area.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data

Gao

Sun

et al. 2022

Remote Sensing

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Forest-canopy closure (FCC) reflects the coverage of the forest tree canopy, which is one of the most important indicators of forest structure and a core parameter in forest resources investigation. In recent years, the rapid development of UAV LiDAR and photogrammetry technology has provided effective support for FCC estimation. However, affected by factors such as different tree species and different stand densities, it is difficult to estimate FCC accurately based on the single-tree canopy-contour method in complex forest regions. Thus, this study proposes a method for estimating FCC accurately using algorithm integration with an optimal window size for treetop detection and an optimal algorithm for crown-boundary extraction using UAV LiDAR data in various scenes. The research results show that: (1) The FCC estimation accuracy was improved using the method proposed in this study. The accuracy of FCC in a camphor pine forest (Pinus sylvestris var. mongolica Litv.) was 89.11%, with an improvement of 6.77–11.25% compared to the results obtained from other combined conditions. The FCC accuracy for white birch (White birch platyphylla Suk) was about 87.53%, with an increase of 3.25–8.42%. (2) The size of the window used for treetop detection is closely related to tree species and stand density. With the same forest-stand density, the treetop-detection window size of camphor pine was larger than that of white birch. The optimal window size of camphor pine was between 5 × 5~11 × 11 (corresponding 2.5~5.5 m), while that of white birch was between 3 × 3~7 × 7 (corresponding 1.5~3.5 m). (3) There are significant differences in the optimal-canopy-outline extraction algorithms for different scenarios. With a medium forest-stand density, the marker-controlled watershed (MCW) algorithm has the best tree-crown extraction effect. The region-growing (RG) method has better extraction results in the sparse areas of camphor pine and the dense areas of white birch. The Voronoi tessellation (VT) algorithm is more suitable for the dense areas of camphor pine and the sparse regions of white birch. The method proposed in this study provides a reference for FCC estimation using high-resolution remote-sensing images in complex forest areas containing various scenes.

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Section: Introductionmentioning

confidence: 99%

An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data

Gao

Sun

et al. 2022

Remote Sensing

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Segmentation and Classification of UAV-based Orthophoto of Watermelon Field Using Support Vector Machine Technique

Lin

Huang

Lin

2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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Path Planning Method for UAVs Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph

Majeed

Hwang

2021

Applied Sciences

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Finding an optimal/quasi-optimal path for Unmanned Aerial Vehicles (UAVs) utilizing full map information yields time performance degradation in large and complex three-dimensional (3D) urban environments populated by various obstacles. A major portion of the computing time is usually wasted on modeling and exploration of spaces that have a very low possibility of providing optimal/sub-optimal paths. However, computing time can be significantly reduced by searching for paths solely in the spaces that have the highest priority of providing an optimal/sub-optimal path. Many Path Planning (PP) techniques have been proposed, but a majority of the existing techniques equally evaluate many spaces of the maps, including unlikely ones, thereby creating time performance issues. Ignoring high-probability spaces and instead exploring too many spaces on maps while searching for a path yields extensive computing-time overhead. This paper presents a new PP method that finds optimal/quasi-optimal and safe (e.g., collision-free) working paths for UAVs in a 3D urban environment encompassing substantial obstacles. By using Constrained Polygonal Space (CPS) and an Extremely Sparse Waypoint Graph (ESWG) while searching for a path, the proposed PP method significantly lowers pathfinding time complexity without degrading the length of the path by much. We suggest an intelligent method exploiting obstacle geometry information to constrain the search space in a 3D polygon form from which a quasi-optimal flyable path can be found quickly. Furthermore, we perform task modeling with an ESWG using as few nodes and edges from the CPS as possible, and we find an abstract path that is subsequently improved. The results achieved from extensive experiments, and comparison with prior methods certify the efficacy of the proposed method and verify the above assertions.

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Integrating UAV and Lidar Data for Retrieving Tree Volume of Hinoki Forests

Cited by 4 publications

References 11 publications

An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data

An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data

Segmentation and Classification of UAV-based Orthophoto of Watermelon Field Using Support Vector Machine Technique

Path Planning Method for UAVs Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph

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