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Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining

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Abstract

Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.

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Acknowledgements

The authors would like to thank the Commonwealth Scholarship Commission, British Council, United Kingdom and Ministry of Human Resource Development, Government of India for providing the necessary support and funding for this research. Authors gratefully acknowledge as well the United Stated Geological Survey (USGS) for the free access to the Landsat satellite images. Dr. Petropoulos’s contribution to this work has been supported by the EU Marie Curie Project ENViSIon-EO (Project contract ID 752094) and the NERC’s Newton Fund RCUK project Towards a Fire Early Warning System for Indonesia (ToFEWSI). The views expressed here are those of the authors solely and do not constitute a statement of policy, decision, or position on behalf of NASA or the authors' affiliated institutions.

Funding

This research received funding from the Commonwealth Scholarship Commission, British Council, United Kingdom and Ministry of Human Resource Development, Government of India.

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Authors and Affiliations

  1. Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India

    Prashant K. Srivastava

  2. Department of Geography and Earth Sciences, University of Aberystwyth, Aberystwyth, Wales, UK

    George P. Petropoulos

  3. Department of Soil & Water Resources, Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “Demeter” (Former NAGREF), Directorate General of Agricultural Research, 1 Theofrastou St., 41335, Larisa, Greece

    George P. Petropoulos & Dimitra Loka

  4. Department of Geology, University of Delhi, Delhi, India

    Manika Gupta

  5. K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Allahabad, 211002, India

    Sudhir K. Singh

  6. NASA Jet Propulsion Laboratory, Pasadena, CA, USA

    Tanvir Islam

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  1. Prashant K. Srivastava
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Contributions

Conceptualization, PKS; methodology, PKS, KS, MG, GP and SKS; software, KS, MG and SKS; formal analysis and validation, MG, SKS and TI; X.X.; writing-original draft preparation, all co-authors; writing-review and editing, all co-authors.

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Correspondence to Prashant K. Srivastava.

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Srivastava, P.K., Petropoulos, G.P., Gupta, M. et al. Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining. Model. Earth Syst. Environ. 5, 627–643 (2019). https://doi.org/10.1007/s40808-018-0555-5

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