An Integrated Remote Sensing Framework for Estimating Individual Tree Attributes Using LiDAR-Derived Canopy Height Models and Orthophotos
7th International Symposium of Forest Engineering and Technologies – FETEC 2026, Brasov, Romanya, 12 - 14 Ekim 2026, (Yayınlanmadı)
- Yayın Türü: Bildiri / Yayınlanmadı
- Basıldığı Şehir: Brasov
- Basıldığı Ülke: Romanya
- Karadeniz Teknik Üniversitesi Adresli: Evet
Özet
Accurate estimation of individual tree attributes is fundamental for forest inventory, ecosystem monitoring, and precision forestry applications. Recent advances in airborne LiDAR and high-resolution aerial imagery have created new opportunities for automated extraction of tree characteristics over large forested areas. This study aims to develop an integrated remote sensing-based methodology for estimating individual tree attributes, including tree location, crown diameter, stem diameter, and tree volume, by combining LiDAR-derived Canopy Height Models (CHMs), point clouds, and orthophotos. The proposed workflow consists of four sequential stages. First, individual trees will be detected using three widely adopted algorithms-Local Maxima, Watershed, and Deepness-and their performances will be comparatively evaluated to identify the most accurate approach. Second, crown diameters will be estimated through image processing and object-based image analysis using orthophotos and point cloud data. Third, species-specific allometric equations will be employed to predict stem diameters from the estimated crown dimensions. Finally, tree volumes will be calculated using official forest management volume tables corresponding to the relevant tree species. The proposed methodology is expected to provide an accurate, automated, and transferable framework for estimating individual tree attributes from remotely sensed data, thereby supporting forest inventory, precision forestry, and sustainable forest management practices.