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

Hughes-Allen, Lara ; Bouchard, Frédéric ; Séjourné, Antoine ; Fougeron, Gabriel ; Léger, Emmanuel

Remote sensing (Basel, Switzerland), 2023-03, Vol.15 (5), p.1226 [Peer Reviewed Journal]

Basel: MDPI AG

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Title:
Automated Identification of Thermokarst Lakes Using Machine Learning in the Ice-Rich Permafrost Landscape of Central Yakutia (Eastern Siberia)
Author: Hughes-Allen, Lara ; Bouchard, Frédéric ; Séjourné, Antoine ; Fougeron, Gabriel ; Léger, Emmanuel
Subjects: Accuracy ; Air temperature ; Anthropogenic factors ; Arctic zone ; Artificial neural networks ; Automation ; Carbon cycle ; Carbon dioxide ; Climate change ; Deep learning ; Disturbances ; Emissions ; Global temperature changes ; Global warming ; Grasslands ; Greenhouse effect ; greenhouse gas emissions ; Greenhouse gases ; Heterogeneity ; Holocene ; Human impact ; Human influences ; Hydrology ; Ice environments ; Image segmentation ; Lakes ; Landscape ; Machine learning ; Mask R-CNN ; Neural networks ; Ocean, Atmosphere ; Permafrost ; Polar environments ; Positive feedback ; Precipitation ; Remote sensing ; Satellite observation ; Sciences of the Universe ; Soil degradation ; Surface area ; Technology application ; thermokarst ; Trends ; Yedoma permafrost
Is Part Of: Remote sensing (Basel, Switzerland), 2023-03, Vol.15 (5), p.1226
Description: 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.
Publisher: Basel: MDPI AG
Language: English

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

Hughes-Allen, Lara ; Bouchard, Frédéric ; Séjourné, Antoine ; Fougeron, Gabriel ; Léger, Emmanuel

Basel: MDPI AG

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