Digital Terrestrial Photogrammetry to Enhance Field-Based Forest Inventory across Stand Conditions

Abstract

Forest inventories in uncertain future economic and environmental conditions require the development of cost-effective measurement techniques to provide robust and accurate information on forests across regional and global scales. Digital terrestrial photogrammetry (DTP) can be used to detect and measure trees on sample plots. In this study, a method was developed which used spherical images taken strategically within plots, and under varying acquisition conditions, to derive forest inventory attributes. Using a set of 102 photos on 400 m2 circular plots achieved a mean detection rate of 72.3% and estimated diameter to an RMSE of 19.0%. This study also explored the sensitivity of detection and estimation accuracy to different field and acquisition conditions. Detection of individual trees was significantly influenced by the tree size and species (p < 0.05 in a regression analysis), while plot-level detection was influenced by size and stem density. Tree size and the distance to the camera significantly influenced the accuracy of estimated attributes. These results are comparable to those of other DTP and terrestrial laser scanning studies in similar forest types while using fewer photos and less time, demonstrating the value of cost-effective methods for DTP estimation of forest attributes.