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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 methane (CH 4) gas emission from peat is critical to understand CH 4 budget from natural wetlands under a climate warming scenario. Previous studies have focused on prediction and mapping of CH 4 emission flux using process-based models, while application of statistical-empirical models for upscaling spatially sparse in situ measurements is scarce. In this study, we developed an empirical remote sensing upscaling approach to estimate CH 4 emission flux in the Everglades using limited in situ point-based CH 4 emission flux measurements and Landsat data during 2013-2018. We spatially and temporally linked in situ data with Landsat surface reflectance based on temporally composite data sets and developed an object-based machine learning framework to model and map CH 4 emission flux. An ensemble analysis of two machine learning models, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM), shows that the upscaling approach is promising for predicting CH 4 emission flux with a R 2 of 0.65 and 0.87 based on a fivefold cross-validation for a dry season and wet season estimation, respectively. We generated emission flux map products that successfully revealed the spatial and temporal heterogeneity of CH 4 emission within the dominant freshwater marsh ecosystem in the Everglades. We conclude that Landsat is promising for upscaling and monitoring CH 4 emission flux and reducing the uncertainty in emission estimates from wetlands.

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