Evaluation of methods to improve the direct estimation of standing trees volume - Mura M; Mura M; Scotti R (iForest - Biogeosciences and Forestry)
Estimating timber volume in forests is crucial for effective management and commercial purposes, particularly for forest owners and managers. Accurate volume estimates inform management decisions, growth monitoring, and silvicultural treatments. Traditional methods often involve destructive sampling, which presents logistical and economic challenges. This study evaluates the non-destructive methods of Hossfeld’s, centroid sampling, and Pressler’s to estimate the volume of 42 Pinus pinaster and 50 Pinus radiata standing trees. For each species, the estimated volume was then compared with that calculated using precise measurements on felled trees and with the volume calculated using the simplified method F = 0.5. For both species, Pressler’s method showed the highest accuracy, with RMSE values of 28 and 102 dm³ for P. pinaster and P. radiata, respectively. However, the centroid and Hossfeld’s methods also showed satisfactory results: for P. pinaster the RMSE was 40 and 47 dm³, while for P. radiata it was 100 and 118 dm3, respectively. These estimates resulted largely more accurate than those obtained using the F=0.5 method (60 dm³ for P. pinaster and 432 dm³ for P. radiata). All the quick methods tested with F=0.5 show commendable accuracy, with Pressler’s method yielding the best results. According to the Percent Relative Bias, all the three methods slightly tend to underestimate the volume. Our results support the viability of non-destructive methods for rapid volume estimation without extensive felling and measurement. However, operational application in the field and validation across different tree species and growth habits are recommended. These methods offer viable alternatives to traditional approaches, reducing resource intensity and facilitating more frequent and detailed forest surveys. Our findings support the application of non-destructive techniques in forest management, potentially streamlining volume estimation and improving the accuracy of forest inventories.