Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

Abstract

Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such situations, we deployed an unmanned aerial system (UAS) multirotor drone in a cypress forest in a mountainous area of Japan. The structure from motion (SfM) method was used to construct a three-dimensional (3D) model of the forest (tree) structures from aerial photos. Tree height was estimated from the 3D model and compared to in situ ground data. We also analyzed the relationships between a biophysical parameter, diameter at breast height (DBH), of individual trees with canopy width and area measured from orthorectified images. Despite the constraints of ground exposure in a highly dense forest area, tree height was estimated at an accuracy of root mean square error = 1.712 m for observed tree heights ranging from 16 to 24 m. DBH was highly correlated with canopy width (R 2 = 0.7786) and canopy area (R 2 = 0.7923), where DBH ranged from 11 to 58 cm. The results of estimating forest parameters indicate that drone-based remote-sensing methods can be utilized to accurately analyze the spatial extent of forest structures.

Cooling Effects and Regulating Ecosystem Services Provided by Urban Trees—Novel Analysis Approaches Using Urban Tree Cadastre Data

“…Applying those equations to trees under different growth conditions, in dense tree stands or forests for example would lead to inaccuracies. For example, [46] did not find a power relationship, but a linear relationship between the DBH and the tree height in a forest of Japanese cypress (Chamaecyparis obtusa). Despite the high correlation coefficients and the validation that the equations give realistic results for the tree height (Table 2), we caution that for some species the sample size is rather low (e.g., Ailanthus altissima with 15 trees).…”

“…Because the growth equations were generated from urban trees in the same growth region, we assume that most impacts are considered in the equations, but they may not give the exact tree height under all urban conditions. Alternatively, tree height could also be measured by using a surface model derived from remote sensing or airborne laser scanning datasets [46]. In that case, the user should be sure that there is no time gap between the aerial survey for the surface model and the tree inspection.…”

Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

“…Ground points were first classified in the UAV SfM-derived point cloud data using a progressive morphological filter [41] in the lidR (https://CRAN.R-project.org/package=lidR) package in R. A digital terrain model (DTM) was then created from the ground points and subtracted from the point cloud Z values to calculate above ground height or Z values [34,[42][43][44][45]. The resulting point clouds contained an abnormal amount of noise points that were a few meters above the ground surface, but represented neither ground nor vegetation.…”

Extraction of Sample Plot Parameters from 3D Point Cloud Reconstruction Based on Combined RTK and CCD Continuous Photography

“…Tree-to-tree data collection is a traditional method of forestry sciences that is laborious and time-consuming. Therefore, developing cost-efficient and time-efficient alternative methods is currently necessary [4,5].…”