Northwestern Arctic Canada is one of the most rapidly warming regions in the Arctic (Serreze et al., 2009). Retrogressive thaw slumps (RTS) are one of the most dramatic thermokarst features in permafrost terrain (Kokelj et al., 2013). Many studies have focused on describing the distribution of thermokarst landscapes (i.e., Olefeldt et al., 2016), as well as change in thermokarst terrain over the historical record (i.e., Kokelj and Jorgenson, 2013). However, improved high temporal and spatial resolution monitoring of thaw slump activity is required to enhance our understanding of factors governing their growth. Recent advances in aerial and ground-based Structure from Motion (SfM), a photogrammetry application, allow for temporal and spatial high-resolution characterization of landscape changes. This thesis explores two methods in SfM photogrammetry: 1) aerial imaging using an unmanned aerial vehicle (UAV) and 2) ground-based imaging using stationary multi-camera time-lapse installations, to derive high-resolution temporal and spatial data for change detection. A trend in mean elevation change was produced, and agrees with the RTS behaviour over the study period, which supports the viability of the proposed capture method. The lack of congruency in data range suggests need for further development in terms of analyses and differencing algorithms employed. The proposed method may be feasible for employment in other fields of science in which high temporal resolution change detection is desired. This proof of concept study was conducted at a small slump on the Peel Plateau, NWT, Canada, and aims to enhance understanding of the development and perpetuation of thaw slumps, to better anticipate landscape and ecosystem responses to future climate change.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/36885 |
| Date | January 2017 |
| Creators | Armstrong, Lindsay Faye |
| Contributors | Lacelle, Denis |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.002 seconds