Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory

Abstract

In this study, we estimate the forest stock volume by multiplying the number of trees detected remotely by the estimated mean individual volume of the population (individual approach). A comparison was made with the conventional inventory method (area approach), which included 100 simulations of a simple random sampling process and a Bootstrap resampling. The study area included three stands: stand 1, 16-year-old pine; stand 2, 7-year-old pine; and stand 3, 5-year-old eucalyptus. A census was carried out in each stand for the variables diameter and total height. Individual volume was estimated by a ratio estimator, and the sum of all volumes was considered as the total parametric volume. The area approach presented parametric values within the confidence interval for 91%, 94%, and 98% of the simulations for the three stands, respectively. The mean relative errors for the area approach were −3.5% for stand 1, 0.3% for stand 2, and −0.9% for stand 3. The errors in stands 1 and 3 were associated with the spatial distribution of the volume. The individual approach proved to be efficient for all stands, and their respective parametric values were within the confidence interval. The relative errors were 1% for stand 1, −0.7% for stand 2, and 1.8% for stand 3. For stand 1 and 3, this approach yielded better results than the mean values obtained by the area approach simulations (Bootstrap resampling). Future research should evaluate other remote sources of data and other forest conditions.

Keywords

• RPA (remotely piloted aircraft)
• CHM (canopy height model)
• tree detection