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

Remote Sensing

Masayuki Itoh¹  Kotaro Iizuka²  Taichiro Yonehara³  Yoshiko Kosugi³ 

[1] Center for Southeast Asian Studies, Kyoto University, Kyoto 606-8502, Japan;Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan;Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan;

关键词: UAV;    UAS;    drone;    structure from motion;    forest inventory;    biophysical parameter;    aerial survey;   

DOI  :  10.3390/rs10010013

来源: DOAJ

【 摘 要 】

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 (R2 = 0.7786) and canopy area (R2 = 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.

【 授权许可】

Unknown