Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. (June 2015)

• Record Type: Journal Article
• Authors: Zahawi, Rakan A.; Dandois, Jonathan P.; Holl, Karen D.; Nadwodny, Dana; Reid, J. Leighton; Ellis, Erle C.
• Is Part Of: Biological conservation. Volume 186(2015)
• Page Start: 287
• Page End: 295
• Publication Date: 2015-06
• Subjects: Canopy structure -- Costa Rica -- Drone -- Ecosynth -- Hexacopter -- LiDAR -- Point cloud model; Conservation of natural resources -- Periodicals; Nature conservation -- Periodicals; Ecology -- Periodicals; Environment -- Periodicals; Environmental Pollution -- Periodicals; Electronic journals; 333.9516
• DOI: 10.1016/j.biocon.2015.03.031
• Languages: English
• ISSNs: 0006-3207

Abstract

Highlights: We compared ground-based to a remote-sensing (Ecosynth) monitoring method. Ecosynth is a viable alternative to quantify forest structure metrics. Ecosynth height can estimate above-ground biomass and predict frugivorous bird abundance. The methodology can generate additional metrics not readily quantifiable in the field. Abstract: Large areas of tropical lands are being removed from agriculture and restored to address conservation goals. However, monitoring the ecological value of these efforts at the individual land-owner scale is rare, owing largely to issues of cost and accessibility. Traditional field-based measures for assessing forest recovery and habitat quality can be labour intensive and costly. Here we assess whether remote sensing measurements from lightweight unmanned aerial vehicles (UAV) are a cost-effective substitute for traditional field measures. An inexpensive UAV-based remote sensing methodology, "Ecosynth", was applied to measure forest canopy structure across field plots in a 7–9-yr tropical forest restoration study in southern Costa Rica. Ecosynth methods combine aerial images from consumer-grade digital cameras with computer vision software to generate 3D 'point cloud' models of vegetation at high spatial resolutions. Ecosynth canopy structure measurements were compared to field-based measures and their ability to predict the abundance of frugivorous birds; key seed dispersers that are sensitive to canopy structure. Ecosynth canopy height measurements were highly correlated with field-based measurements ( R 2 ⩾ 0.85), a result comparable in precision to LiDAR-based remote sensing measurements. Ecosynth parameters were also strongly correlated with above-ground biomass ( R 2 ⩾ 0.81) and percent canopy openness ( R 2 = 0.82). Correlations were weaker with proportion-based measures such as canopy roughness ( R 2 = 0.53). Several Ecosynth metrics (e.g., canopy openness and height) predicted frugivore presence and abundance at levels of accuracy similar to those of field-based measurements. Ecosynth UAV remote-sensing provides an effective alternate methodology to traditional field-based measures of evaluating forest structure and complexity across landscapes. Furthermore, given the volume of data that can be generated in a single flight plan, as well as the ability to use the technology in remote areas, these methods could expand the scope of studies on forest dynamics and recovery when combined with field-based calibration plots.