Browsing by Author "Lovitt, Julie"
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- ItemOpen AccessQuantifying the Impact of Seismic Lines on Methane Release in a Treed Bog Ecosystem using Unmanned Aerial Vehicles (UAVs)(2017-12-22) Lovitt, Julie; McDermid, Gregory; Strack, Maria; Bergerson, JoulePeatlands are extremely complex and sensitive ecosystems, capable of releasing vast amounts of methane in response to disturbance events. To date, little advancement has been made by researchers to quantify the impact of small-scale anthropogenic disturbances on these ecosystems, specifically seismic lines. These “low-impact” linear features present a challenge to researchers as they exist at dimensions too small for the majority of remote-sensing platforms to successfully identify and measure, even though they account for a considerable portion of land disturbance in Canada’s western Boreal, and are anticipated to have extensive, compounding environmental effects. This thesis summarizes how unmanned aerial vehicle photogrammetry can be used to address this knowledge gap by showcasing the ability to generate accurate peatland terrain models, and subsequently estimate seismic-line impacts on both physical parameters (microtopography and depth-to-water) and peatland methane emission, ultimately revealing one of the hidden impacts of seismic lines on Canada’s Boreal peatlands.
- ItemOpen AccessUAV Remote Sensing Can Reveal the Effects of Low-Impact Seismic Lines on Surface Morphology, Hydrology, and Methane (CH4) Release in a Boreal Treed Bog(Journal of Geophysical Research: Biogeosciences, 2018-02-23) Lovitt, Julie; Rahman, Mustafizur M.; Saraswati, Saraswati; McDermid, Gregory J.; Strack, Maria; Xu, BinPeatlands are globally significant stores of soil carbon, where local methane (CH4) emissions are strongly linked to water table position and microtopography. Historically, these factors have been difficult to measure in the field, constraining our capacity to observe local patterns of variability. In this paper, we show how remote sensing surveys conducted from unmanned aerial vehicle (UAV) platforms can be used to map microtopography and depth to water over large areas with good accuracy, paving the way for spatially explicit estimates of CH4 emissions. This approach enabled us to observe—for the first time—the effects of low-impact seismic lines (LIS; petroleum exploration corridors) on surface morphology and CH4 emissions in a treed-bog ecosystem in northern Alberta, Canada. Through compaction, LIS lines were found to flatten the observed range in microtopographic elevation by 46 cm and decrease mean depth to water by 15.4 cm, compared to surrounding undisturbed conditions. These alterations are projected to increase CH4 emissions by 20–120% relative to undisturbed areas in our study area, which translates to a total rise of 0.011–0.027 kg CH4 day 1 per linear kilometer of LIS (~2 m wide). The ~16 km of LIS present at our 61 ha study site were predicted to boost CH4 emissions by 20–70 kg between May and September 2016.
- ItemOpen AccessA Workflow to Minimize Shadows in UAV-based Orthomosaics(Journal of Unmanned Vehicle Systems, NRC Research Press, 2019-01-08) Rahman, Mir Mustafizur; McDermid, Gregory J.; Mckeeman, Taylor; Lovitt, JulieShadows from buildings, terrain, and other elevated features represent lost and/or impaired data values that hinder the quality of optical images acquired under all but the most diffuse illumination conditions. This is particularly problematic in high-spatial-resolution imagery acquired from unmanned aerial vehicles (UAVs), which generally operate very close to the ground. However, the flexibility and low cost of re-deployment of the platform also presents opportunities, which we capitalize on in a new workflow designed to eliminate shadows from UAV-based orthomosaics. Our straightforward, three-step procedure relies on images acquired from two different UAV flights, where illumination conditions produce diverging shadow orientations: one before solar noon and another after. From this multi-temporal image stack, we first identify and then eliminate shadows from individual orthophoto components, then construct the final orthomosaic using a feature-matching strategy with the commercial software package Photoscan. The utility of our strategy is demonstrated over a treed-wetland study site in northwestern Alberta, Canada: a complex scene containing wide variety of shadows, which our workflow effectively eliminated. While shadow-reduced orthomosaics are generally less useful for feature-identification tasks that rely on the shadow element of image interpretation, they create a superior foundation for most other image-processing routines, including classification and change-detection.