Automated Identification of Thermokarst Lakes Using Machine Learning in the Ice-Rich Permafrost Landscape of Central Yakutia (Eastern Siberia)

Abstract

The current rate and magnitude of temperature rise in the Arctic are disproportionately high compared to global averages. Along with other natural and anthropogenic disturbances, this warming has caused widespread permafrost degradation and soil subsidence, resulting in the formation of thermokarst (thaw) lakes in areas of ice-rich permafrost. These lakes are hotspots of greenhouse gas emissions (CO2 and CH4), but with substantial spatial and temporal heterogeneity across Arctic and sub-Arctic regions. In Central Yakutia (Eastern Siberia, Russia), nearly half of the landscape has been affected by thermokarst processes since the early Holocene, resulting in the formation of more than 10,000 partly drained lake depressions (alas lakes). It is not yet clear how recent changes in temperature and precipitation will affect existing lakes and the formation of new thermokarst lakes. A multi-decadal remote sensing analysis of lake formation and development was conducted for two large study areas (~1200 km2 each) in Central Yakutia. Mask Region-Based Convolutional Neural Networks (R-CNN) instance segmentation was used to semi-automate lake detection in Satellite pour l’Observation de la Terre (SPOT) and declassified US military (CORONA) images (1967–2019). Using these techniques, we quantified changes in lake surface area for three different lake types (unconnected alas lake, connected alas lake, and recent thermokarst lake) since the 1960s. Our results indicate that unconnected alas lakes are the dominant lake type, both in the number of lakes and total surface area coverage. Unconnected alas lakes appear to be more susceptible to changes in precipitation compared to the other two lake types. The majority of recent thermokarst lakes form within 1 km of observable human disturbance and their surface area is directly related to air temperature increases. These results suggest that climate change and human disturbances are having a strong impact on the landscape and hydrology of Central Yakutia. This will likely affect regional and global carbon cycles, with implications for positive feedback scenarios in a continued climate warming situation.

Comparing Thermal Regime Stages along a Small Yakutian Fluvial Valley with Point Scale Measurements, Thermal Modeling, and Near Surface Geophysics

“…This part of the Central Yakutian region landscape is characterized by the presence of ice-rich late Pleistocene ice complex (Yedoma) deposits of about 50 m thickness on the fluvial terraces. In some areas the permafrost is degraded by thermokarst processes, resulting in the strong occurrences of degraded ice-wedges and thermokarst lakes [16,[50][51][52]. Within this region, few narrow rivers segment the landscape, connecting larger thermokarst lakes (visible in Figure 1c as blacks dots).…”

“…Lakes are likely the most prominent surface water bodies present in continuous permafrost areas, and have a dominant influence on the ground thermal state. They are also the most well-studied features in these regions (Hughes-Allen et al [16], Mackay and Burn [17], Yoshikawa and Hinzman [18], Jorgenson and Shur [19], Plug and West [20], Rowland et al [21], Kurylyk et al [22], Johansson et al [23]). Rivers, on the other hand, have received less attention, although they can also lead to talik development and represent an important source of year-round water resources for local communities and high latitude cities [24].…”

Analysis and Prediction of Spatial and Temporal Evolution of Ecosystem Service Value on the Northern Slopes of the Kunlun Mountains Based on Land Use

“…The Kunlun Mountains are located at the northern edge of the Qinghai-Tibet Plateau [19], which is an extremely fragile ecological area in the arid zone of northwest China. As such, it is highly sensitive to climate change.…”