Journal article
Estimating Mangrove Forest Volume Using Terrestrial Laser Scanning and UAV-Derived Structure-from-Motion
Drones, Vol.3, 32
2019
Abstract
Mangroves provide a variety of ecosystem services, which can be related to their structural complexity and ability to store carbon in the above ground biomass (AGB). Quantifying AGB in mangroves has traditionally been conducted using destructive, time-consuming, and costly methods, however, Structure-from-Motion Multi-View Stereo (SfM-MVS) combined with unmanned aerial vehicle (UAV) imagery may provide an alternative. Here, we compared the ability of SfM-MVS with terrestrial laser scanning (TLS) to capture forest structure and volume in three mangrove sites of differing stand age and species composition. We describe forest structure in terms of point density, while forest volume is estimated as a proxy for AGB using the surface differencing method. In general, SfM-MVS poorly captured mangrove forest structure, but was efficient in capturing the canopy height for volume estimations. The differences in volume estimations between TLS and SfM-MVS were higher in the juvenile age site (42.95%) than the mixed (28.23%) or mature (12.72%) age sites, with a higher stem density affecting point capture in both methods. These results can be used to inform non-destructive, cost-effective, and timely assessments of forest structure or AGB in mangroves in the future.
Details
- Title
- Estimating Mangrove Forest Volume Using Terrestrial Laser Scanning and UAV-Derived Structure-from-Motion
- Authors
- Angus D Warfield (Author) - University of the Sunshine Coast, Queensland, School of Science and Engineering - LegacyJavier X Leon (Author) - University of the Sunshine Coast - School of Science & Engineering
- Publication details
- Drones, Vol.3, 32; 18
- Publisher
- MDPI AG
- Date published
- 2019
- DOI
- 10.3390/drones3020032
- ISSN
- 2504-446X
- Copyright note
- Copyright © 2019 The Authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99451370102621
- Output Type
- Journal article
Metrics
37 File views/ downloads
418 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Web Of Science research areas
- Remote Sensing
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites