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ORIGINAL RESEARCH article

Front. Remote Sens.
Sec. Multi- and Hyper-Spectral Imaging
Volume 5 - 2024 | doi: 10.3389/frsen.2024.1446641
This article is part of the Research Topic Advancements in Fire Management Through Remote Sensing Technologies View all articles

Comparative Study of Machine Learning Methods for Mapping Forest Fire Areas Using Sentinel-1B and 2A imagery

Provisionally accepted
Xinbao Chen Xinbao Chen *Yaohui Zhang Yaohui Zhang *Zecheng Zhao Zecheng Zhao *Chang Liu Chang Liu *
  • Hunan University of Science and Technology, Xiangtan, China

The final, formatted version of the article will be published soon.

    To investigate the adaptability of machine learning methods in various scenarios for mapping forest fire areas, this study presents a comparative study on the recognition and mapping accuracy of three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN), based on Sentinel-1B and 2A imagery. The study focuses on identifying fire-burning and burnt areas in a large-scale forest fire that occurred in Xintian County, China, in October 2022. Initially, three sets of pre-fire, during-fire, and post-fire remote sensing data were preprocessed. Various feature parameters from Sentinel-1B and 2A imagery were combined to identify fire-related land cover types. The experimental results revealed that: (i) During the pre-fire period, the SVM method demonstrated superior accuracy compared to the other two methods. The combination of spectral and Normalized Difference Vegetation Index (NDVI) features achieved an optimal accuracy for identifying forest areas with an overall accuracy (OA) of 93.52%. (ii) In the during-fire period, RF method exhibited higher accuracy compared to the other two methods with peak fire identification accuracy reached by combining spectral and Normalized Burn Ratio (NBR) index features at an OA of 95.43%. (iii) In the post-fire period, SVM demonstrated superior accuracy compared to other methods. The highest accuracy of 94.97% was achieved when combining spectral and radar features from Sentinel-1B imagery, highlighting the effectiveness of using spectral and radar backward scattering coefficients as feature parameters to enhance forest fire recognition accuracy for burnt areas. These findings suggest that appropriate machine learning algorithms should be employed under different conditions to obtain more precise identification of forest fire areas.This study provides technical support and empirical evidence for extracting and mapping forest fire areas while assessing damage caused by fires.

    Keywords: comparative study, forest fire, machine learning, Sentinel-1B/2A, Classification

    Received: 10 Jun 2024; Accepted: 18 Nov 2024.

    Copyright: © 2024 Chen, Zhang, Zhao and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xinbao Chen, Hunan University of Science and Technology, Xiangtan, China
    Yaohui Zhang, Hunan University of Science and Technology, Xiangtan, China
    Zecheng Zhao, Hunan University of Science and Technology, Xiangtan, China
    Chang Liu, Hunan University of Science and Technology, Xiangtan, China

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