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

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...

Full description

Saved in:

Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Switzerland), 2023-03, Vol.15 (5), p.1226
Main Authors:	Hughes-Allen, Lara, Bouchard, Frédéric, Séjourné, Antoine, Fougeron, Gabriel, Léger, Emmanuel
Format:	Article
Language:	English
Subjects:	Accuracy Air temperature anthropogenic activities Anthropogenic factors Arctic region Arctic zone Artificial neural networks Automation carbon Carbon cycle Carbon dioxide climate Climate change Cold regions Deep learning Disturbances Emissions Global temperature changes Global warming Grasslands Greenhouse effect Greenhouse gas emissions Greenhouse gases Heterogeneity Holocene Holocene epoch Human impact Human influences humans Hydrology Ice environments Image segmentation Lakes Landscape landscapes Machine learning Mask R-CNN Neural networks Ocean, Atmosphere Permafrost Positive feedback Precipitation Remote sensing Satellite observation satellites Sciences of the Universe Siberia Soil degradation subsidence Surface area Technology application thermokarst Trends Yedoma permafrost
Citations:	Items that this one cites Items that cite this one
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!