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Open AccessArticle

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

1
Department of Geography, Colgate University, Hamilton, NY 13346, USA
2
Environmental Studies, Hamilton College, Clinton, NY 13323, USA
3
School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA
4
School of Environment & Sustainability, Western Colorado University, Gunnison, CO 81231, USA
5
Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Starkville, MS 39759, USA
6
North-East Science Station, Pacific Geographical Institute, Far-Eastern Branch of Russian Academy of Sciences, 678830 Cherskiy, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 2970; https://doi.org/10.3390/rs12182970
Received: 15 July 2020 / Revised: 8 September 2020 / Accepted: 10 September 2020 / Published: 12 September 2020
(This article belongs to the Special Issue She Maps)
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. View Full-Text
Keywords: Larix cajanderi; Siberia; wildfire; drones; uncrewed aerial vehicle; NDVI; GCC; normalized difference vegetation index; green chromatic coordinate Larix cajanderi; Siberia; wildfire; drones; uncrewed aerial vehicle; NDVI; GCC; normalized difference vegetation index; green chromatic coordinate
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MDPI and ACS Style

Talucci, A.C.; Forbath, E.; Kropp, H.; Alexander, H.D.; DeMarco, J.; Paulson, A.K.; Zimov, N.S.; Zimov, S.; Loranty, M.M. Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices. Remote Sens. 2020, 12, 2970. https://doi.org/10.3390/rs12182970

AMA Style

Talucci AC, Forbath E, Kropp H, Alexander HD, DeMarco J, Paulson AK, Zimov NS, Zimov S, Loranty MM. Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices. Remote Sensing. 2020; 12(18):2970. https://doi.org/10.3390/rs12182970

Chicago/Turabian Style

Talucci, Anna C.; Forbath, Elena; Kropp, Heather; Alexander, Heather D.; DeMarco, Jennie; Paulson, Alison K.; Zimov, Nikita S.; Zimov, Sergei; Loranty, Michael M. 2020. "Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices" Remote Sens. 12, no. 18: 2970. https://doi.org/10.3390/rs12182970

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