B080-0016
Assessing relationships between vegetation indices and plant community composition following wildfire in Siberian larch forests via UAV remote sensing

Monday, 14 December 2020
Poster
Elena Forbath1, Michael M Loranty2, Anna Talucci2, Jennie DeMarco3, Heather Dawn Alexander4, Alison Paulson4 and Nikita Zimov5, (1)Colgate University, Hamilton, NY, United States, (2)Colgate University, Geography, Hamilton, NY, United States, (3)Western Colorado University, Gunnison, CO, United States, (4)Mississippi State University, Forestry, Mississippi State, MS, United States, (5)Northeast Scientific Station of Pacific Institute for Geography of Russian Academy of Sciences, Cherskiy, Russia
Abstract:
Climate warming has greatly contributed to increased wildfire activity in Arctic forests and tundra. Wildfires cause significant changes in vegetation abundance and composition, both of which can be assessed through greenness indices like the Normalized Difference Vegetation Index (NDVI). NDVI can help to reveal patterns of forest recovery post-fire. Through this study, we aim to investigate the relationship between NDVI and plant community composition using high-resolution imagery acquired with Uncrewed Aerial Vehicles (UAVs).

We established plots at a field site, where a wildfire occurred about 20 years ago, in Cherskiy, Russia to investigate the effect of removing either grass, shrubs, or both vegetation types on NDVI. We also recorded percent cover of plant functional groups, which included grasses, evergreen shrubs, deciduous shrubs, lichen, and conifers, within each plot prior to vegetation removal using the point line intercept method. We collected RGB and multispectral UAV imagery of the field site on separate days, before and after vegetation removal, which were then used to generate maps of NDVI. In addition to UAV photos, we obtained coordinates of each plot center using a Real Time Kinematic (RTK) GPS. The NDVI values of each plot were extracted from the NDVI maps using the GPS plot locations. We found there was no significant difference in NDVI changes from pre- and post-vegetation removal between plots treated with grass removal, shrub removal, or removal of both vegetation types. This could be attributed to imagery being acquired at different times of day, or changes in cloud cover, affecting illumination of light onto the plants and changing the measured NDVI value. However, there are relationships between NDVI of plots and the percent cover of conifers and deciduous shrubs, which could be explained by a stronger Near Infrared reflectance than other vegetation types. This suggests that high-resolution maps of NDVI from UAVs could be used to identify fine-scale changes in vegetation composition after fire.

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