Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

Abstract

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.

Siberian Ecosystems as Drivers of Cryospheric Climate Feedbacks in the Terrestrial Arctic

“…If these fire regime shifts are sustained, the potential impacts on forest cover associated with recruitment dynamics (Alexander et al, 2012(Alexander et al, , 2018Paulson et al, 2021) have important implications for cryospheric climate feedbacks. The extent to which increases in winter albedo immediately postfire (Chen et al, 2018;Stuenzi and Schaepman-Strub, 2020) are reversed during postfire succession will depend on the density of forest regrowth (Talucci et al, 2020;Paulson et al, 2021;Walker et al, 2021). Similarly, forest impacts on surface energy exchange exert a series of interrelated controls on permafrost and snow thermal dynamics (Loranty et al, 2018;Stuenzi et al, 2021).…”

Demystifying the Differences between Structure-from-MotionSoftware Packages for Pre-Processing Drone Data

“…Drone data have been captured to provide information across a range of environmental fields, predominantly to assess vegetation coverage, composition, and/or structure in the terrestrial environment (e.g., [4][5][6][7][8]). However, they have also been used to study a range of other environments, including mangroves [9][10][11], oyster reefs [12], coral reefs [13,14], coastal dunes [15,16], and seagrass beds [17].…”

UAVs Technology as a Complementary Tool in Post-Fire Vegetation Recovery Surveys in Mediterranean Fire-Prone Forests

“…Remote sensing is often based on satellite images or aerial photos, mainly with the use of false color and multispectral images to classify the severity of fires, the levels of regeneration of vegetative biomass, and the coverage rates of vegetation on the ground, at the level of small-medium scale [19][20][21]. The use of land surveying is necessary, however, to identify, at a large-scale level, the recovery process in terms of floristic composition, vegetation physiognomy, phytocoenosis structure, and species demography, which are diagnostic elements that are difficult to identify remotely.…”