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Climate warming is altering the persistence, timing, and distribution of permafrost and snow cover across the terrestrial northern hemisphere. These cryospheric changes have numerous consequences, not least of which are positive climate feedbacks associated with lowered albedo related to declining snow cover, and greenhouse gas emissions from permafrost thaw. Given the large land areas affected, these feedbacks have the potential to impact climate on a global scale. Understanding the magnitudes and rates of changes in permafrost and snow cover is therefore integral for process understanding and quantification of climate change. However, while permafrost and snow cover are largely controlled by climate, their distributions and climate impacts are influenced by numerous interrelated ecosystem processes that also respond to climate and are highly heterogeneous in space and time. In this perspective we highlight ongoing and emerging changes in ecosystem processes that mediate how permafrost and snow cover interact with climate. We focus on larch forests in northeastern Siberia, which are expansive, ecologically unique, and studied less than other Arctic and subarctic regions. Emerging fire regime changes coupled with high ground ice have the potential to foster rapid regional changes in vegetation and permafrost thaw, with important climate feedback implications. 

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.