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Remote Sens. 2016, 8(8), 615; doi:10.3390/rs8080615

Rapid Assessments of Amazon Forest Structure and Biomass Using Small Unmanned Aerial Systems

1
Department of Biology, Wake Forest University, 1834 Wake Forest Rd, Winston Hall, Winston-Salem, NC 27109, USA
2
Center for Energy, Environment, and Sustainability, Wake Forest University, 1834 Wake Forest Rd., Winston-Salem, NC 27109, USA
3
Department of Global Ecology, Carnegie Institution for Science, 260 Panama St, Stanford, CA 94305, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly, Compton Tucker, Lars T. Waser and Prasad S. Thenkabail
Received: 14 May 2016 / Revised: 15 July 2016 / Accepted: 21 July 2016 / Published: 25 July 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
View Full-Text   |   Download PDF [6589 KB, uploaded 25 July 2016]   |  

Abstract

Unmanned aerial vehicles (UAVs) can provide new ways to measure forests and supplement expensive or labor-intensive inventory methods. Forest carbon, a key uncertainty in the global carbon cycle and also important for carbon conservation programs, is typically monitored using manned aircraft or extensive forest plot networks to estimate aboveground carbon density (ACD). Manned aircraft are only cost-effective when applied to large areas (>100,000 ha), while plot networks are most effective for total C stock estimation across large areas, not for quantifying spatially-explicit variation. We sought to develop an effective method for frequent and accurate ACD estimation at intermediate scales (100–100,000 ha) that would be sensitive to small-scale disturbance. Using small UAVs, we collected imagery of 516 ha of lowland forest in the Peruvian Amazon. We then used a structure-from-motion (SFM) approach to create a 3D model of forest canopy. Comparing SFM- and airborne Light Detection and Ranging (LiDAR)-derived estimates of canopy height and ACD, we found that SFM estimates of top-of-canopy height (TCH) and ACD were highly correlated with previous LiDAR estimates (r = 0.86–0.93 and r = 0.73–0.94 for TCH and ACD, respectively, at 0.1–4 ha grain sizes), with r = 0.92 for ACD determination at the 1 ha scale, despite SFM and LiDAR measurements being separated by two years in a dynamic forest. SFM and LiDAR estimates of mean TCH and mean ACD were highly similar, differing by only 0.4% and 0.04%, respectively, within mature forest. The technique allows inexpensive, near-real-time monitoring of ACD for ecological studies, payment for ecosystem services (PES) ventures, such as reducing emissions from deforestation and forest degradation (REDD+), forestry enterprises, and governance. View Full-Text
Keywords: unmanned aircraft; structure-from-motion; SFM-LiDAR fusion; forest carbon monitoring; light detection and ranging (LiDAR); SUAS unmanned aircraft; structure-from-motion; SFM-LiDAR fusion; forest carbon monitoring; light detection and ranging (LiDAR); SUAS
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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).

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MDPI and ACS Style

Messinger, M.; Asner, G.P.; Silman, M. Rapid Assessments of Amazon Forest Structure and Biomass Using Small Unmanned Aerial Systems. Remote Sens. 2016, 8, 615.

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