loading page

Case Study of Camp Fire Employing Novel Metric for Time Series Analysis of Vegetation Recovery
  • +2
  • Alissa Sherbatov,
  • Evan Hsiang,
  • Cassie Kilburn,
  • Joseph Ortiz,
  • Benjamin Koppe
Alissa Sherbatov
GLOBE Program

Corresponding Author:[email protected]

Author Profile
Evan Hsiang
GLOBE Program
Author Profile
Cassie Kilburn
GLOBE Program
Author Profile
Joseph Ortiz
GLOBE Program
Author Profile
Benjamin Koppe
GLOBE Program
Author Profile

Abstract

Abstract—Wildfires are a major global issue, costing the United States 71.1 to 347.8 billion dollars annually (Graham, 2020). Rising global temperatures have increased the frequency and intensity of wildfires (Cuevas-Gonzales, 2009). Climate change has thus created a need for new methods to examine the effects of wildfires. In this research, we evaluated how the 2018 Camp Fire environmentally impacted land cover in California, and the extent to which the area’s land cover suffered from long-term damage. Our hypothesis was that the affected land was damaged significantly, but recovered partially by the end of the investigated period. In order to assess the healing of a patch of land burnt in the Camp Fire, corrected reflectance images of the patch collected from NASA Worldview were analyzed using Python to return each image’s Pixel Greenness Value (PGI)–an original metric developed by our team that analyzes an image’s color content to return a numerical value corresponding to vegetation health. These values were then plotted. Over the course of 26 weeks after the Camp Fire, the patch of land partially regained its PGI. Major recovery occurred between weeks 7 and 15. We concluded that the area burnt in the Camp Fire only partially recovered, as the moving average of the PGI value only reached 81% of the baseline value by the end of the investigated period. Our findings demonstrate the environmental damage that wildfires can cause and the potential of PGI as a useful metric for assessing the impact of wildfires. Future studies could compare PGI results with those of the Normalized Difference Vegetation Index (NDVI), known for its usage in such vegetation recovery analyses. The case study could also repeat the experiment with Landsat data taken from the United States Geological Survey website, to account for the lack of atmospheric correction in NASA Worldview data. Key words: extreme event, wildfire, land cover, environmental science, programming