Integrating UAV and Lidar Data for Retrieving Tree Volume of Hinoki Forests

A low cost but accurate forest monitoring tool with is necessary for sustainable forest management. Lidar data is quite expensive and is challenging when it comes to multitemporal data collection. Unmanned-Aerial-Vehicle (UAV) remote sensing is low cost and has flexibility in high frequency data collection with a satisfactory image resolution net. Focused on forest growth evaluation, this study investigates the potential of integrating UAV and Airborne Laser Scanner (ALS) technology in the evaluation of tree volume in high stem-density forest stands. ALS data is used to provide base state of digital surface model (DSM) and digital elevation model (DEM). The UAV aerial photographs collected in a series of inventory were used to generate multitemporal DSM of forest stands by Structure from Motion (SfM) technique. With the ALS-DEM and UAV-SfM-DSM data, this study has shown a satisfied result in retrieving tree volume of a Hinoki (Chamaecyparis obtusa) forest. The estimation accuracy was RMSE=0.695m and RMSEP=3.96%. The performance indicates that the approach of UAV-SfM-DSM with ALS-DEM has high potential within forest stock evaluation.