Employing Aerial LiDAR Data for Forest Clustering and Timber Volume Estimation: A Case Study with Pinus radiata in Northwest Spain

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Abstract
In the case of forest inventory, heterogeneous areas are particularly challenging due to variability in vegetation structure. This is especially true in Galicia (northwest Spain), where land is highly fragmented, complicating the planning and management of single-species plantations such as Pinus radiata. This study proposes a cost-effective strategy using open-access tools and data to characterize and estimate wood volume in these plantations. Two stratification approaches—classical and cluster-based—were compared to a modeling method based on Principal Component Analysis (PCA). Data came from open-access national LiDAR point clouds, acquired using manned aerial vehicles under the Spanish National Aerial Orthophoto Plan (PNOA). Moreover, two volume estimation methods were applied: one from the Xunta de Galicia (XdG) and another from Spain’s central administration (4IFN). A Generalized Linear Model (GLM) was also fitted using PCA-derived variables with logarithmic transformation. The results show that although overall volume estimates are similar across methods, cluster-based stratification yielded significantly lower absolute errors per hectare (XdG: 28.04 m3/ha vs. 44.07 m3/ha; 4IFN: 25.64 m3/ha vs. 38.22 m3/ha), improving accuracy by 7% over classical stratification. Moreover, it does not require precise field parcel locations, unlike PCA modeling. Both official volume estimation methods tended to overestimate stock by about 10% compared to PCA. These results confirm that clustering offers a practical, low-cost alternative that improves estimation accuracy by up to 18 m3/ha in fragmented forest landscapes.
Funding Information
  • MCIN/AEI/10.13039/501100011033 (PCI2020-120705-2, PID2022-138374OA-I00)
  • MICIU/AEI/10.13039/501100011033/ (PCI2020-120705-2, PID2022-138374OA-I00)
  • Universidade de Vigo/CISUG (PCI2020-120705-2, PID2022-138374OA-I00)

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