A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles

Abstract

Groundwater level (GWL) and depth to water (DTW) are related metrics aimed at characterizing groundwater-table positions in peatlands, and two of the most common variables collected by researchers working in these ecosystems. While well-established field techniques exist for measuring GWL and DTW, they are generally difficult to scale. In this study, we present a novel workflow for mapping groundwater using orthophotography and photogrammetric point clouds acquired from unmanned aerial vehicles. Our approach takes advantage of the fact that pockets of surface water are normally abundant in peatlands, which we assume to be reflective of GWL in these porous, gently sloping environments. By first classifying surface water and then extracting a sample of water elevations, we can generate continuous models of GWL through interpolation. Estimates of DTW can then be obtained through additional efforts to characterize terrain. We demonstrate our methodology across a complex, 61-ha treed bog in northern Alberta, Canada. An independent accuracy assessment using 31 temporally coincident water-well measurements revealed accuracies (root mean square error) in the 20-cm range, though errors were concentrated in small upland pockets in the study area, and areas of dense tree covers. Model estimates in the open peatland areas were considerably better.

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 DTW surface was generated using the methods described by Rahman et al (2017). In their workflow, areas of stable open water were first classified using a decision-tree classification scheme.…”

“…The groundwater-level surface was then subtracted from the DTM to obtain DTW. The accuracy of the DTW surface was assessed as per Rahman et al (2017). Validation of the DTW surface was completed by comparing modeled values against field data collected at five wells within the study site.…”

Determining Subarctic Peatland Vegetation Using an Unmanned Aerial System (UAS)

“…High-resolution localized image collection coupled with field-based classification efforts is necessary to provide cover class and error estimates at scales useful for understanding permafrost collapse, thermokarst pond development, and vegetation change in high northern latitude ecosystems [22,23]. Because of recent developments of smaller GPS systems, gyroscopes, magnets to drive motors, miniaturization of sensors, and increases in data storage, there have been new avenues in the deployment of unmanned aerial systems (UASs) to study the environment [24].…”

Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta

“…Canopy openings occur regularly and at various spatial scales throughout a forest, due to numerous exogenous and endogenous factors. In the boreal forest of Alberta, Canada, large openings are caused by fires [1], insect manifestation [2], windthrow [3], anthropogenic disturbances [4,5], and persistent suboptimal growing conditions associated with excessive soil moisture [6], water, or rock outcroppings [7]. Smaller openings might be the outcome of individual limb fall or the death of single trees [8].…”