Burn Severity Estimation in Northern Australia Tropical Savannas Using Radiative Transfer Model and Sentinel-2 Data

Changming Yin, Binbin He, Marta Yebra, Xingwen Quan, Andrew C. Edwards, Xiangzhuo Liu, Zhanmang Liao, Kaiwei Luo

    Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

    1 Citation (Scopus)

    Abstract

    In this study, the burn severity of several wildfires ignited at northern Australian tropical savannas area were estimated using the Forest Reflectance and Transmittance (FRT) radiative transfer model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) satellite data. To alleviate the spectral confusion between severe (SV) and not-severe (NSV) burnt levels caused by sparse tree distribution, the MODIS Vegetation Continuous Fields (VCF) tree cover percentage data was used to constrain the inversion. The results showed that the accuracy of burn severity estimation significantly improves when considering the tree coverage, with overall accuracy for two study sites increasing from 65% to 81% and kappa coefficient from 0.35 to 0.55. Future work will focus on extending the methodology to other ecosystems.

    Original languageEnglish
    Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
    Place of PublicationPiscataway, NJ
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages6712-6715
    Number of pages4
    Edition1
    ISBN (Electronic)9781538691540
    DOIs
    Publication statusPublished - 14 Nov 2019
    Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
    Duration: 28 Jul 20192 Aug 2019

    Publication series

    NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

    Conference

    Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
    Country/TerritoryJapan
    CityYokohama
    Period28/07/192/08/19

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