Linking repeat lidar with Landsat products for large scale quantification of fire-induced permafrost thaw settlement in interior Alaska

• Published in: Environmental Research Letters
• Main Authors: Caiyun Zhang, Thomas A Douglas, David Brodylo, M Torre Jorgenson
• Format: Article in Journal/Newspaper
• Language: English
• Published: IOP Publishing 2023
• Subjects: repeat lidar; permafrost; wildfires; machine learning modeling; thaw settlement; Environmental technology. Sanitary engineering; TD1-1066; Environmental sciences; GE1-350; Science; Q; Physics; QC1-999; Ice; Alaska
• Online Access: https://doi.org/10.1088/1748-9326/acabd6; https://doaj.org/article/e8590ec80e254b88bfe19e965fe6da16

The permafrost–fire–climate system has been a hotspot in research for decades under a warming climate scenario. Surface vegetation plays a dominant role in protecting permafrost from summer warmth, thus, any alteration of vegetation structure, particularly following severe wildfires, can cause dramatic top–down thaw. A challenge in understanding this is to quantify fire-induced thaw settlement at large scales (>1000 km ^2 ). In this study, we explored the potential of using Landsat products for a large-scale estimation of fire-induced thaw settlement across a well-studied area representative of ice-rich lowland permafrost in interior Alaska. Six large fires have affected ∼1250 km ^2 of the area since 2000. We first identified the linkage of fires, burn severity, and land cover response, and then developed an object-based machine learning ensemble approach to estimate fire-induced thaw settlement by relating airborne repeat lidar data to Landsat products. The model delineated thaw settlement patterns across the six fire scars and explained ∼65% of the variance in lidar-detected elevation change. Our results indicate a combined application of airborne repeat lidar and Landsat products is a valuable tool for large scale quantification of fire-induced thaw settlement.