Application of UAV LiDAR Data to Assess the Relationship Between Forest Canopy Structure and Understory Light Environment in the Son Tra Nature Reserve, Da Nang
DOI:
https://doi.org/10.5281/zenodo.18186613Keywords:
LiDAR, UAV, Canopy Height Model (CHM), Forest Canopy Cover (FCC))Abstract
The three-dimensional (3D) structure of forest canopies plays a critical role in determining habitat conditions for living organisms, in which the heterogeneous distribution of understory light is a key factor directly influencing forest regeneration and biodiversity. However, conventional field surveys and ground-based imaging methods are limited in spatial coverage, posing challenges for comprehensive ecosystem monitoring. This study introduces a methodological framework utilizing LiDAR data acquired from unmanned aerial vehicles (UAVs) to quantify canopy structural heterogeneity and model the relationship between 3D canopy structure and light distribution. The workflow includes collecting high-density LiDAR point clouds, generating digital terrain, surface, and canopy height models (DTM, DSM, CHM), and calculating canopy height and canopy cover metrics. Statistical analyses are then conducted to evaluate the relationships between canopy height, canopy surface structure, and understory light conditions. The results demonstrate that UAV-based LiDAR is an effective tool for high-resolution forest structural mapping, providing valuable potential for applications in sustainable forest management.
Downloads
References
[1] Helbach, J., Frey, J., Messier, C., Mörsdorf, M., & Scherer-Lorenzen, M. (2022). Light heterogeneity affects understory plant species richness in temperate forests supporting the heterogeneity–diversity hypothesis. Ecology and Evolution, 12(2), e8534.
[2] McGaughey, R.J. FUSION/LDV: Software for LiDAR Data Analysis and Visualization; USDA Forest Service Pacific Northwest Research Station: Seattle, WA, USA, 2018.
[3] Brüllhardt, M., Rotach, P., Schleppi, P., & Bugmann, H. (2020). Vertical light transmission profiles in structured mixed deciduous forest canopies assessed by UAV-based hemispherical photography and photogrammetric vegetation height models. Agricultural and Forest Meteorology, 281, 107843.
[4] Gao, T.; Gao, Z.; Sun, B.;Qin, P.; Li, Y.; Yan, Z. An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data. Remote Sens. 2022, 14, 4317.
[5] Moe, K.T.; Owari, T.; Furuya, N.; Hiroshima, T.; Morimoto, J. Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests. Remote Sens. 2020, 12, 2865.
[6] Wang, B., Proctor, C., Yao, Z., Li, N., Chen, Q., Liu, W., Ma, S., Jing, C., Zhou, Z., Liu, W., Ma, Y., Wang, Z., Zhang, Z., & Lin, L. (2024). FLApy: A Python package for evaluating the 3D light availability heterogeneity within forest communities. Methods in Ecology and Evolution, 15, 1540–1552.
[7] Shi, Y.; Wang, T.; Skidmore, A.K.; Heurich, M. Improving LiDAR-based tree species mapping in Central European mixed forests using multi-temporal digital aerial colour-infrared photographs. Int. J. Appl. Earth Obs. Geoinf. 2020, 84, 101970.
[8] Báo cáo Tổng kết kỹ thuật Sản phẩm bay quét UAV LiDAR, chụp ảnh số, xử lý dữ liệu khu vực Sơn Trà, Tp Đà Nẵng, Xí nghiệp Chụp ảnh Hàng không - Công ty TNHH MTV Trắc địa Bản đồ - Cục Tác chiến, năm 2025.
[9] Zhou, M., Li, C. & Li, Z. Extraction of individual tree attributes using ultra-high-density point clouds acquired by low-cost UAV-LiDAR in Eucalyptus plantations. Annals of Forest Science 82, 20 (2025).
[10] Watt, M.S.; Jayathunga, S.; Hartley, R.J.L.; Pearse, G.D.; Massam, P.D.; Cajes, D.; Steer, B.S.C.; Estarija, H.J.C. Use of a Consumer-Grade UAV Laser Scanner to Identify Trees and Estimate Key Tree Attributes across a Point Density Range. Forests 2024.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
CC 4.0
