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


 TThe 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 km2). 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 km2 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.