Integrating UAV-SfM and Airborne Lidar Point Cloud Data to Plantation Forest Feature Extraction

Abstract

A low-cost but accurate remote-sensing-based forest-monitoring tool is necessary for regularly inventorying tree-level parameters and stand-level attributes to achieve sustainable management of timber production forests. Lidar technology is precise for multi-temporal data collection but expensive. A low-cost UAV-based optical sensing method is an economical and flexible alternative for collecting high-resolution images for generating point cloud data and orthophotos for mapping but lacks height accuracy. This study proposes a protocol of integrating a UAV equipped without an RTK instrument and airborne lidar sensors (ALS) for characterizing tree parameters and stand attributes for use in plantation forest management. The proposed method primarily relies on the ALS-based digital elevation model data (ALS-DEM), UAV-based structure-from-motion technique generated digital surface model data (UAV-SfM-DSM), and their derivative canopy height model data (UAV-SfM-CHM). Following traditional forest inventory approaches, a few middle-aged and mature stands of Hinoki cypress (Chamaecyparis obtusa) plantation forests were used to investigate the performance of characterizing forest parameters via the canopy height model. Results show that the proposed method can improve UAV-SfM point cloud referencing transformation accuracy. With the derived CHM data, this method can estimate tree height with an RMSE ranging from 0.43 m to 1.65 m, equivalent to a PRMSE of 2.40–7.84%. The tree height estimates between UAV-based and ALS-based approaches are highly correlated (R2 = 0.98, p < 0.0001), similarly, the height annual growth rate (HAGR) is also significantly correlated (R2 = 0.78, p < 0.0001). The percentage HAGR of Hinoki trees behaves as an exponential decay function of the tree height over an 8-year management period. The stand-level parameters stand density, stand volume stocks, stand basal area, and relative spacing are with an error rate of less than 20% for both UAV-based and ALS-based approaches. Intensive management with regular thinning helps the plantation forests retain a clear crown shape feature, therefore, benefitting tree segmentation for deriving tree parameters and stand attributes.

Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation

“…For airborne and close-range imagery, different approaches have been proposed and proven effective, including integrating Airborne Lidar (ALS) and Unmanned Aerial Vehicles (UAVs) within a SfM framework for plantation forest management [13]; the prediction of biomass with RGB and height measurements [14]; multiplatform SfM applications for single high-resolution topography (HRT) with RGB SfM aerial and terrestrial images; and the use of a Global Navigation Satellite System (GNSS) and Terrestrial Laser Scanner (TLS) [15]. When reconstructing 3D models of static targets (e.g., buildings) using SfM algorithms, vegetation is often considered as noise and may decrease the performance significantly.…”

Effects of different ground segmentation methods on the accuracy of UAV-based canopy volume measurements

“…Mobile LiDAR scanning with a real-time kinematic (RTK) receiver was developed to improve the efficiency of canopy point cloud collection ( Karp et al., 2017 ; Wang et al., 2017 ; Gené-Mola et al., 2019 ; Mokroš et al., 2021 ). It has been shown that tree segmentation and canopy parameter extraction can also be achieved by LiDAR on UAV platforms, which reduce the effect of vibration and are easy to register ( Yoshii et al., 2022 ; Yuan et al., 2022 ; Caras et al., 2024 ).…”