Abstract
The use of Remotely Piloted Aircraft Systems (RPAS) as well as newer automated unmanned aerial vehicles is becoming a standard method in remote sensing studies requiring high spatial resolution (<1 m) and very precise temporal data to capture phenological events. In this study we use a low cost rotorcraft to map Eriophorum vaginatum at Mer Bleue, an ombrotrophic bog located east of Ottawa, ON, Canada. We focus on E. vaginatum because this sedge plays an important role in methane (CH4) gas exchange in peatlands. Using the remote controlled rotorcraft we were able to record, process, and mosaic 11.1 hectares of 4.5 cm spatial resolution imagery extracted from individual frames of video recordings (post georegistration RMSE 4.90 ± 4.95 cm). Our results, based on a supervised classification (96% accuracy) of the red, green, blue image planes, indicate a total tussock cover of 2,417 m2. Because the basal area of the plant is more relevant for calculating its contribution to the CH4 flux, the tussock area was related to the basal area from field data (R2 = 0.88, p < 0.0001). Our final results indicate a total basal area of 1,786 ± 62.8 m2. Based on temporal measurements of CH4 flux from the peatland as a whole that vary over the growing season, we estimate the E. vaginatum contribution to range from 3.0% to 17.3% of that total. Overall, our low cost approach was an effective non-destructive way to derive E. vaginatum coverage and estimate CH4 exchange over the growing season.
Highlights
The development and application of both Remotely Piloted Aircraft Systems (RPAS)(e.g., rotorcraft, quadrocopters) and unmanned aerial vehicles (UAVs) for vegetation mapping has several advantages over conventional imaging from high altitude fixed wing aircraft or satellite platforms, for areas where high spatial resolution images are required [1]
Remote sensing techniques in general have shown potential for peatland monitoring, but most previous studies have focused on the use of relatively coarse spatial resolution imagery that often resulted in limited discrimination of cover types or biophysical characteristics [3]
Alternative techniques such as data fusion between high spatial resolution imagery and LiDAR [3], classification of pan-sharpened multispectral imagery [4], analysis of airborne hyperspectral imagery [5] and object based classification of aerial photography [6] have reduced the thematic uncertainty in peatland classifications
Summary
The development and application of both Remotely Piloted Aircraft Systems (RPAS)(e.g., rotorcraft, quadrocopters) and unmanned aerial vehicles (UAVs) for vegetation mapping has several advantages over conventional imaging from high altitude fixed wing aircraft or satellite platforms, for areas where high spatial resolution images (i.e., sub-meter pixels) are required [1]. Remote sensing techniques in general have shown potential for peatland monitoring, but most previous studies have focused on the use of relatively coarse spatial resolution imagery that often resulted in limited discrimination of cover types or biophysical characteristics [3]. Alternative techniques such as data fusion between high spatial resolution imagery and LiDAR [3], classification of pan-sharpened multispectral imagery [4], analysis of airborne hyperspectral imagery [5] and object based classification of aerial photography [6] have reduced the thematic uncertainty in peatland classifications. Small user deployable platforms (helicopters, quadracopters, UAVs) with simple imaging instrumentation such as photographic cameras or video recorders allow for flexibility and repeatability in data collection when reliance on conventional aerial imaging or satellite imagery may not be feasible
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