Weight estimation models for commercial Pinus radiata wood in small felling stands based on UAV-LiDAR data

Abstract

Uncrewed Aerial Vehicles (UAVs) equipped with Light Detection and Ranging (LiDAR) scanners have been adopted as an effective tool for conducting forest inventories, reducing field work and associated costs. These studies are based on obtaining information on volume, height and diameter at breast height (DBH). However, in the timber sector in Galicia (Spain)the variable used as the unit of purchase/sale is "weight". Therefore, this paper sets out to test different machine learning algorithms, such as multiple linear regression (MLR), MLR log-transformed (MRL-LT), Principal Component Analysis (PCA) and Random Forest (RF) to obtain a methodology applicable in the study area and replicable in other areas, with real utility in the forestry industry for determining the weight of Pinus radiata wood. These models are based on 73 observations and 19 predictors and have been developed and applied to a total of 33 Forest Cuts (FC) divided into 5204 tesserae. The models are validated by Fold Cross-Validation, with RMSE (relative root mean square error), R2 and MAE (mean absolute error) values being calculated. Of the four models studied, the PCA model performs best (R2=0.85, RMSE=1.84, MAE=1.46), followed by the MRL-LT which gives a value of R2=0.82 (RMSE=1.84, MAE=1.46). The actual figure for total Pinus radiata timber harvested was 35,440.54 t The PCA model estimated a total of 35,913.53 t (r = 0.98, R2=0.97, εr=9.35 %), while the MRL-LT model calculated 35,847.92 t (r = 0.99, R2=0.98, εr=8.85 %). On the other hand, the weight was underestimated by the MLR model and overestimated by the RF. The plain MLR model underestimated the "weight" of the wood, while the MRL-LT model provided the best result, with errors of less than 10 % in 72 % of the FCs. In conclusion, this study provides a powerful tool that will enable stakeholders in the community timber industry to accurately estimate the weight of timber in Pinus radiata stands, enhancing a more highly automated forest inventory that can optimise the number of field operations to be performed.