GEOINFORMATION MODELING OF FOREST RESOURCES BASED ON MOBILE LASER SCANNING DATA
DOI:
https://doi.org/10.33042/2522-1809-2025-3-191-453-458Keywords:
point cloud, forest resources, geoinformation modeling, CloudCompare, forest monitoringAbstract
This article presents a practical approach to the geoinformation modeling of forest resources using mobile laser scanning technology. The study focuses on the application of the STONEX X120GO mobile laser scanner, which operates on the SLAM (Simultaneous Localization and Mapping) algorithm. This technology enables accurate three-dimensional spatial data acquisition without dependence on satellite signals, making it particularly effective in densely vegetated areas with complex topography, such as mixed forests in the Kharkiv region of Ukraine.
Over 41 million spatial points were collected during the survey and processed using the open-source software CloudCompare. The methodology involved a multi-stage workflow: classification of ground points using the Cloth Simulation Filter (CSF), normalization of elevation data, and segmentation of individual trees using the TreeIso plugin. These steps enabled the identification of tree crowns, calculation of tree height, and detection of forest structure anomalies.
A detailed analysis of crown density and vegetation distribution was conducted, and areas with potential tree dieback or structural degradation were identified. These insights formed the basis for generating thematic GIS layers, including forest canopy height models and risk maps, which are essential for effective forest resource management and biodiversity conservation.
One of the major findings of the study is the importance of parameter optimization in the segmentation algorithms. Factors such as vegetation density, vertical relief variation, and point cloud noise significantly influence the accuracy of tree classification. The integration of LiDAR-derived models with geospatial analysis tools proves highly effective in addressing the growing demand for sustainable and data-driven forest management.
The paper also outlines prospects for future research, particularly the application of artificial intelligence and machine learning algorithms for automatic classification and monitoring of forest ecosystems based on LiDAR data. The presented approach provides a scalable and cost-efficient tool for environmental monitoring and planning.
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