Berry Fire Vegetation Recovery

Keywords: Remote Sensing, GIS, Red-Edge Indices, Fire
Abstract Type: Virtual Poster Abstract

Abstract

The monitoring of post-fire vegetation recovery provides important information which land managers can use to formulate recovery efforts for an ecosystem. This research attempts to assess vegetation recovery using fractional vegetation cover (FVC) derived from a combination of field plots, regression fitted spectral indices and multiple endmember spectral mixture analysis (MESMA). A total of sixty field plots were collected in the summer of 2019 in each of which eight downward and eight upward hemispherical photographs were taken. The FVC was then calculated for each photograph belonging to a plot within CAN-EYE from which the average FVC was calculated. Thirty-one of these plots were then used to derive the regression fits for the spectral indices, which were implemented using raster algebra. The resulting regression fit values were then compared to the remaining plots via linear regression to determine how accurately FVC was mapped. The MESMA, derived using three forest and three herbaceous endmembers, was compared to all sixty plots using linear regression. We found that of the spectral indices, an altered form of NDVI which uses Sentinel-2 band five performed the best, achieving an R2 = 0.69. The MESMA results failed to achieve significance. Our findings are in line with similar fire-related research which compared indices generated using Sentinel-2 red-edge bands with more “traditional” indices and found that red-edge indices based on band five outperformed the other spectral indices.