Abstract
The reliability of airborne light detection and ranging (LiDAR) for delineating individual trees and estimating aboveground biomass (AGB) has been proven in a diverse range of ecosystems, but can be difficult and costly to commission. Point clouds derived from structure from motion (SfM) matching techniques obtained from unmanned aerial systems (UAS) could be a feasible low-cost alternative to airborne LiDAR scanning for canopy parameter retrieval. This study assesses the extent to which SfM three-dimensional (3D) point clouds—obtained from a light-weight mini-UAS quadcopter with an inexpensive consumer action GoPro camera—can efficiently and effectively detect individual trees, measure tree heights, and provide AGB estimates in Australian tropical savannas. Two well-established canopy maxima and watershed segmentation tree detection algorithms were tested on canopy height models (CHM) derived from SfM imagery. The influence of CHM spatial resolution on tree detection accuracy was analysed, and the results were validated against existing high-resolution airborne LiDAR data. We found that the canopy maxima and watershed segmentation routines produced similar tree detection rates (~70%) for dominant and co-dominant trees, but yielded low detection rates (<35%) for suppressed and small trees due to poor representativeness in point clouds and overstory occlusion. Although airborne LiDAR provides higher tree detection rates and more accurate estimates of tree heights, we found SfM image matching to be an adequate low-cost alternative for the detection of dominant and co-dominant tree stands.
Highlights
Accurate and reliable information about forest structure and composition is critical for forest management, biomass estimation, and the monitoring of health status [1]
Innovations in computer vision and digital photogrammetry have led to development of the structure from motion (SfM) technique for generating 3D point clouds from stereo imagery, which is similar in many aspects to light detection and ranging (LiDAR) point clouds [6]
The main aim of this study was to evaluate the efficiency of consumer light-weight and low-cost unmanned aerial systems (UAS) imagery (
Summary
Accurate and reliable information about forest structure and composition is critical for forest management, biomass estimation, and the monitoring of health status [1]. Canopy structural parameters can be extracted directly or indirectly by ground-based, airborne, or spaceborne remote sensing techniques. Advances in airborne/satellite multispectral imagery (passive optical sensors), light detection and ranging (LiDAR), and radar technologies (active sensors) over varying spectral, spatial, and temporal scales are rapidly facilitating the benefits of remote sensing use in measurements and the monitoring of forest structure. Airborne LiDAR sensing has proven to be efficient and accurate for the fine-scale estimation of forest structure parameters by indirect allometry (primarily tree height (H)) based on high-density three-dimensional (3D) point cloud canopy height models (CHM) [2,3,4,5]. The combination of UAS and modern SfM matching techniques has a wide range of applications for forest management and inventory needs with low cost, high performance, and flexibility [8,9,10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
