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Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

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  • Fiona Ngadze
  • Kudzai Shaun Mpakairi
  • Blessing Kavhu
  • Henry Ndaimani
  • Monalisa Shingirayi Maremba

Abstract

When wildfires are controlled, they are integral to the existence of savannah ecosystems and play an intrinsic role in maintaining their structure and function. Ample studies on wildfire detection and severity mapping are available but what remains a challenge is the accurate mapping of burnt areas in heterogenous landscapes. In this study, we tested which spectral bands contributed most to burnt area detection when using Sentinel-2 and Landsat 8 multispectral sensors in two study sites. Post-fire Sentinel 2A and Landsat 8 images were classified using the Random Forest (RF) classifier. We found out that, the NIR, Red, Red-edge and Blue spectral bands contributed most to burned area detection when using Landsat 8 and Sentinel 2A. We found out that, Landsat 8 had a higher classification accuracy (OA = 0.92, Kappa = 0.85 and TSS = 0.84)) in study site 1 as compared to Sentinel-2 (OA = 0.86, Kappa = 0.74 and TSS = 0.76). In study site 2, Sentinel-2 had a slightly higher classification accuracy (OA = 0.89, Kappa = 0.67 and TSS = 0.64) which was comparable to that of Landsat 8 (OA = 0.85, Kappa = 0.50 and TSS = 0.41). Our study adds rudimentary knowledge on the most reliable sensor allowing reliable estimation of burnt areas and improved post-fire ecological evaluations on ecosystem damage and carbon emission.

Suggested Citation

  • Fiona Ngadze & Kudzai Shaun Mpakairi & Blessing Kavhu & Henry Ndaimani & Monalisa Shingirayi Maremba, 2020. "Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-13, May.
    • Handle: RePEc:plo:pone00:0232962
    DOI: 10.1371/journal.pone.0232962
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    References listed on IDEAS

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    1. Niels Andela & Guido R. van der Werf, 2014. "Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition," Nature Climate Change, Nature, vol. 4(9), pages 791-795, September.
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    1. Paolo De Fioravante & Tania Luti & Alice Cavalli & Chiara Giuliani & Pasquale Dichicco & Marco Marchetti & Gherardo Chirici & Luca Congedo & Michele Munafò, 2021. "Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification," Land, MDPI, vol. 10(6), pages 1-35, June.

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