Assessing the Value of UAV Photogrammetry for Characterizing Terrain in Complex Peatlands

Abstract

Microtopographic variability in peatlands has a strong influence on greenhouse gas fluxes, but we lack the ability to characterize terrain in these environments efficiently over large areas. To address this, we assessed the capacity of photogrammetric data acquired from an unmanned aerial vehicle (UAV or drone) to reproduce ground elevations measured in the field. In particular, we set out to evaluate the role of (i) vegetation/surface complexity and (ii) supplementary LiDAR data on results. We compared remote-sensing observations to reference measurements acquired with survey grade GPS equipment at 678 sample points, distributed across a 61-hectare treed bog in northwestern Alberta, Canada. UAV photogrammetric data were found to capture elevation with accuracies, by root mean squares error, ranging from 14-42 cm, depending on the state of vegetation/surface complexity. We judge the technology to perform well under all but the most-complex conditions, where ground visibility is hindered by thick vegetation. Supplementary LiDAR data did not improve results significantly, nor did it perform well as a stand-alone technology at the low densities typically available to researchers.

UAV Remote Sensing Can Reveal the Effects of Low‐Impact Seismic Lines on Surface Morphology, Hydrology, and Methane (CH4) Release in a Boreal Treed Bog

“…As described in Lovitt et al (2017) and Rahman et al (2017) numerous external factors, such as weather conditions during UAV operations and input model accuracies (i.e., DTM, water level surface), may have affected the accuracy of our results. These factors were addressed in their respective manuscripts and will not be discussed further.…”

“…The workflow used to generate a classified MT surface is provided in the supporting information associated with this publication ( Figure S1). From the aerial data, a dense point cloud comprised only of ground points, which we call the ground dense point cloud (gDPC), was generated as per Lovitt et al (2017). Accuracies of the gDPC were estimated by PhotoScan (Version: 1.2.4) as~0 cm (x,y) and 21 cm (z).…”

“…Using ENVI (Version 5.1), we then applied a mask, buffered by 18 pixels, over treed areas, followed by a lowpass filter (window size 2 m) to generate a reference surface (Ref) representing general site slope. The purpose of buffering the tree mask was to ensure that vegetation edge points (i.e., points falling on the boundary between terrain and tree, as per Lovitt et al, 2017) were excluded from the low-pass filter calculation. The purpose of excluding these points was to avoid artificially increasing ground-surface elevations in areas where points represented vegetation rather than ground.…”

Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation

“…Northern peatlands have fine-scale spatial variation in vegetation, land cover and topography (Lovitt, Rahman, & McDermid, 2017;Middleton et al, 2012;Palace et al, 2018). This variation is also reflected in ecosystem functioning and responses (Lehmann et al, 2016).…”

“…Ultra-high spatial resolution (UHSR) remote sensing, which provides data with cm-level pixel size, can reveal such patterns in vegetation composition that are lost in coarser resolution (Díaz-Varela, Calvo Iglesias, Cillero Castro, & Díaz Varela, 2018;Gonçalves et al, 2016;Lehmann et al, 2016;Mora, Vieira, Pina, Lousada, & Christiansen, 2015). In particular, the benefits of UHSR are evident in fragmented landscapes such as peatlands (Arroyo- Mora, Kalacska, Lucanus, Soffer, & Leblanc, 2017;Lehmann et al, 2016;Lovitt et al, 2017;Palace et al, 2018).…”