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

Abstract

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.

Quantification of active layer depth at multiple scales in Interior Alaska permafrost

“…It contains a higher and steeper elevation gradient (∼107 m km −1 ). Though the region has experienced multiple major fire disturbances in the past 20 years (Zhang et al 2023) none of our study sites have evidence of major fires or floods within the last ∼50 years that would have impacted vegetation, soil, or permafrost, and necessitate the need for annual, updated images.…”

“…A comparison between the field measurements and local scale ALT estimates based on EA from table 2 can be seen in figure 4, which demonstrates an encouraging agreement between them. The combined model uncertainty is represented as the standard deviation to ensemble prediction (STDE; Zhang et al 2023).…”

Super-high-resolution aerial imagery datasets of permafrost landscapes in Alaska and northwestern Canada

“…Recent aerial campaigns within the NASA ABoVE project focused on acquiring hyperspectral, SAR, and laser altimetry data over permafrost regions in Alaska and NW Canada to observe wetlands, greenhouse gas (GHG) emissions, and active layer dynamics (Miller et al, 2019). In combination with historical aerial imagery or laser scanning datasets, modern optical airborne datasets have been used to quantify thaw subsidence following disturbances (Jones et al, 2013;Zhang et al, 2023), lake change (Jones et al, 2012), broad landscapes changes (Jorgenson et al, 2018), coastal erosion (Jones et al, 2020;Obu et al, 2017), ice-wedge degradation (Liljedahl et al, 2016;Rettelbach et al, 2021), and vegetation dynamics (Tape et al, 2006).…”