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• W2954080612 abstract "Research Highlights: This study carried out a feasibility analysis on the tree height extraction of a planted coniferous forest with high canopy density by combining terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) image–based point cloud data at small and midsize tree farms. Background and Objectives: Tree height is an important factor for forest resource surveys. This information plays an important role in forest structure evaluation and forest stock estimation. The objectives of this study were to solve the problem of underestimating tree height and to guarantee the precision of tree height extraction in medium and high-density planted coniferous forests. Materials and Methods: This study developed a novel individual tree localization (ITL)-based tree height extraction method to obtain preliminary results in a planted coniferous forest plots with 107 trees (Metasequoia). Then, the final accurate results were achieved based on the canopy height model (CHM) and CHM seed points (CSP). Results: The registration accuracy of the TLS and UAV image-based point cloud data reached 6 cm. The authors optimized the precision of tree height extraction using the ITL-based method by improving CHM resolution from 0.2 m to 0.1 m. Due to the overlapping of forest canopies, the CSP method failed to delineate all individual tree crowns in medium to high-density forest stands with the matching rates of about 75%. However, the accuracy of CSP-based tree height extraction showed obvious advantages compared with the ITL-based method. Conclusion: The proposed method provided a solid foundation for dynamically monitoring forest resources in a high-accuracy and low-cost way, especially in planted tree farms." @default.
• W2954080612 created "2019-07-12" @default.
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• W2954080612 date "2019-06-27" @default.
• W2954080612 modified "2023-09-25" @default.
• W2954080612 title "A Novel Tree Height Extraction Approach for Individual Trees by Combining TLS and UAV Image-Based Point Cloud Integration" @default.
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• W2954080612 doi "https://doi.org/10.3390/f10070537" @default.
• W2954080612 hasPublicationYear "2019" @default.
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