Assessment of Spatio-Temporal Landscape Changes from VHR Images in Three Different Permafrost Areas in the Western Russian Arctic

Abstract

Our study highlights the usefulness of very high resolution (VHR) images to detect various types of disturbances over permafrost areas using three example regions in different permafrost zones. The study focuses on detecting subtle changes in land cover classes, thermokarst water bodies, river dynamics, retrogressive thaw slumps (RTS) and infrastructure in the Yamal Peninsula, Urengoy and Pechora regions. Very high-resolution optical imagery (sub-meter) derived from WorldView, QuickBird and GeoEye in conjunction with declassified Corona images were involved in the analyses. The comparison of very high-resolution images acquired in 2003/2004 and 2016/2017 indicates a pronounced increase in the extent of tundra and a slight increase of land covered by water. The number of water bodies increased in all three regions, especially in discontinuous permafrost, where 14.86% of new lakes and ponds were initiated between 2003 and 2017. The analysis of the evolution of two river channels in Yamal and Urengoy indicates the dominance of erosion during the last two decades. An increase of both rivers’ lengths and a significant widening of the river channels were also observed. The number and total surface of RTS in the Yamal Peninsula strongly increased between 2004 and 2016. A mean annual headwall retreat rate of 1.86 m/year was calculated. Extensive networks of infrastructure occurred in the Yamal Peninsula in the last two decades, stimulating the initiation of new thermokarst features. The significant warming and seasonal variations of the hydrologic cycle, in particular, increased snow water equivalent acted in favor of deepening of the active layer; thus, an increasing number of thermokarst lake formations.

46-Year (1973–2019) Permafrost Landscape Changes in the Hola Basin, Northeast China Using Machine Learning and Object-Oriented Classification

“…In the Bovanenkovo gas field, Yamal Peninsula, the affected areas substantially increased from 70 km 2 in 1984 to 836 km 2 in 2011 in association with the oil exploration and development activities [65]. The industrial transformation continued at this site where the length of gas pipelines increased by 7.4 times between 2004 and 2016 [66]. Similarly, in northern Alaska the oil field development had affected 34% of the mapped area from 1949 to 2011 [67].…”

Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm

“…Often real-time surveillance applications [35,36] have urgent constraints that require fast data analytics in analyzing the situations quickly and accurately. Land scanning by high resolution imaging of satellite generates a large amount of real-time data [37,38], which is continuous and dynamic-situations and landscapes change.…”

Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr

“…RTS are abrupt permafrost disturbances that result from slope failure after thawing of ice-rich permafrost which is found either in ice-rich Yedoma regions (Strauss et al, 2017) or formerly glaciated areas that still contain permafrost-preserved buried glacial ice (Kokelj et al, 2017). Initiated by fluvial processes, thermo-erosion or mass wasting following heavy precipitation events and the exposure of ice-rich permafrost, RTS expand successively into the landscape with retrogressive growth of a steep headwall and the increase of a slump floor, rapidly and irreversibly changing the landscape (Ardelean et al, 2020;Kokelj and Jorgenson, 2013;Séjourné et al, 2015). RTS vary in size, ranging from under 0.15 ha to mega slumps of 52 ha and more (Ramage et al, 2017;Kokelj et al, 2015;Lacelle et al, 2015;Günther et al, 2015).…”

“…Previous RTS studies covered a range of local to regional spatial extents and annual to decadal temporal resolutions. Commonly, RTS dynamics were estimated using very high resolution remote sensing imagery for limited local spatial extents from a selected number of points in time, restricted by availability of very high resolution imagery, allowing assessment of individual or a cluster of few RTS (Ardelean et al, 2020;Balser et al, 2014;Pollard, 2005, 2008;Lantz and Kokelj, 2008;Luo et al, 2019;Segal et al, 2016;Séjourné et al, 2015;Mu et al, 2020). Based on manually digitised RTS extents and limited fieldwork, these studies found increasing rates of RTS activity for varying time periods.…”