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Article

Post-Fire Vegetation Recovery Response: A Case Study of the 2020 Bobcat Fire in Los Angeles, California

by
Andrew Alamillo
1,
Jingjing Li
1,*,
Alireza Farahmand
1,
Madeleine Pascolini-Campbell
2 and
Christine Lee
2
1
Department of Geography, Geology, and Environment, California State University, Los Angeles, 5151 State University Dr, Los Angeles, CA 90032, USA
2
NASA Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(24), 4023; https://doi.org/10.3390/rs17244023 (registering DOI)
Submission received: 14 October 2025 / Revised: 10 December 2025 / Accepted: 11 December 2025 / Published: 13 December 2025
(This article belongs to the Special Issue Remote Sensing for Risk Assessment, Monitoring and Recovery of Fires)

Abstract

Wildfires can drastically alter ecological landscapes in just a few days, while it takes years of post-fire recovery for vegetation to return to its former pre-fire state. Assessing changes in vegetation can help with understanding how the hydrological components in the wildfire-affected areas contribute to potential vegetation shifts. This case study of the Los Angeles Bobcat Fire in 2020 uses Google Earth Engine (GEE) and Python 3.10.18 to access and visualize variations in Difference Normalized Burn Ratio (dNBR) area, Normalized Difference Vegetation Index (NDVI), and OpenET’s evapotranspiration (ET) across three dominant National Land Cover Database (NLCD) vegetation classes and dNBR classes via monthly time series and seasonal analysis from 2016 to 2024. Burn severity was determined based on Landsat-derived dNBR thresholds defined by the United Nations Office for Outer Space Affairs UN-Spider Knowledge Portal. Our study showed a general reduction in dNBR class area percentages, with High Severity (HS) dropping from 15% to 0% and Moderate Severity (MS) dropping from 45% to 10%. Low-Severity (LS) areas returned to 25% after increasing to 49% in May of 2022, led by vegetation growth. The remaining area was classified as Unburned and Enhanced Regrowth. Within our time series analysis, HS areas showed rapid growth compared to MS and LS areas for both ET and NDVI. Seasonal analysis showed most burn severity levels and vegetation classes increasing in median ET and NDVI values while 2024’s wet season median NDVI decreased compared to 2023’s wet season. Despite ET and NDVI continuing to increase post-fire, recent 2024 NLCD data shows most Forests and Shrubs remain as Grasslands, with small patches recovering to pre-fire vegetation. Using GEE, Python, and available satellite imagery demonstrates how accessible analytical tools and data layers enable wide-ranging wildfire vegetation studies, advancing our understanding of the impact wildfires have on ecosystems.
Keywords: vegetation; burn severity; recovery; NDVI; evapotranspiration; Remote Sensing; Landsat vegetation; burn severity; recovery; NDVI; evapotranspiration; Remote Sensing; Landsat

Share and Cite

MDPI and ACS Style

Alamillo, A.; Li, J.; Farahmand, A.; Pascolini-Campbell, M.; Lee, C. Post-Fire Vegetation Recovery Response: A Case Study of the 2020 Bobcat Fire in Los Angeles, California. Remote Sens. 2025, 17, 4023. https://doi.org/10.3390/rs17244023

AMA Style

Alamillo A, Li J, Farahmand A, Pascolini-Campbell M, Lee C. Post-Fire Vegetation Recovery Response: A Case Study of the 2020 Bobcat Fire in Los Angeles, California. Remote Sensing. 2025; 17(24):4023. https://doi.org/10.3390/rs17244023

Chicago/Turabian Style

Alamillo, Andrew, Jingjing Li, Alireza Farahmand, Madeleine Pascolini-Campbell, and Christine Lee. 2025. "Post-Fire Vegetation Recovery Response: A Case Study of the 2020 Bobcat Fire in Los Angeles, California" Remote Sensing 17, no. 24: 4023. https://doi.org/10.3390/rs17244023

APA Style

Alamillo, A., Li, J., Farahmand, A., Pascolini-Campbell, M., & Lee, C. (2025). Post-Fire Vegetation Recovery Response: A Case Study of the 2020 Bobcat Fire in Los Angeles, California. Remote Sensing, 17(24), 4023. https://doi.org/10.3390/rs17244023

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