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Deep Learning Technology for Automatic Burned Area Extraction Using Satellite High Spatial Resolution Images

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Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 149))

Abstract

Today, there is an acute issue of prompt provision of up-to-date and most complete spatial information for making optimal management decisions in the forestry industry. The most effective tool for solving many problems in this area is using satellite data. The advent of optical satellites of high spatial resolution (Sentinel-2, Worldview-2,3) makes it possible to use modern methods of operational mapping to solve forestry problems (in particular, identifying burnt areas after a fire). As the monitoring area increases, and the number of images increases, the need for automatic data processing. Thereof to improve the accuracy of recognition of such objects in images is to use deep learning algorithms based on convolutional neural networks. This paper presents a description of the “traditional” methods for burned area detection (vegetation indices, multi-channel, and single-channel change detection, etc.), convolutional neural networks, and their main principles, and limitations. We also present a new method for operational mapping of a burned area using convolutional neural networks and provide an increase in the accuracy of forest fire recognition by more than two times in comparison with traditional methods based on raster arithmetic. The accuracy, estimated by the F-measure, is 0.80 for Haciveliler, 0.93 - Mugla, and 0.95 - Kavaklidere.

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Acknowledgments

We thank the European Union Copernicus program for providing Sentinel data accessible via the Copernicus Open Access Hub.

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Authors and Affiliations

  1. Dnipro University of Technology, Dnipro, Ukraine

    Vita Kashtan & Volodymyr Hnatushenko

Authors
  1. Vita Kashtan
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  2. Volodymyr Hnatushenko
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Correspondence to Vita Kashtan .

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Editors and Affiliations

  1. Jan Evangelista Purkyně University in Ústi nad Labem, Ústi nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Kashtan, V., Hnatushenko, V. (2023). Deep Learning Technology for Automatic Burned Area Extraction Using Satellite High Spatial Resolution Images. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_37

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