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A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data

Theoretical and Applied Climatology volume 137, pages 637–653 (2019)Cite this article

Abstract

A reliable forest fire susceptibility map is a necessity for disaster management and a primary reference source in land use planning. We set out to evaluate the use of the LogitBoost ensemble-based decision tree (LEDT) machine learning method for forest fire susceptibility mapping through a comparative case study at the Lao Cai region of Vietnam. A thorough literature search would indicate the method has not previously been applied to forest fires. Support vector machine (SVM), random forest (RF), and Kernel logistic regression (KLR) were used as benchmarks in the comparative evaluation. A fire inventory database for the study area was constructed based on data of previous forest fire occurrences, and related conditioning factors were generated from a number of sources. Thereafter, forest fire probability indices were computed through each of the four modeling techniques, and performances were compared using the area under the curve (AUC), Kappa index, overall accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). The LEDT model produced the best performance, both on the training and on validation datasets, demonstrating a 92% prediction capability. Its overall superiority over the benchmarking models suggests that it has the potential to be used as an efficient new tool for forest fire susceptibility mapping. Fire prevention is a critical concern for local forestry authorities in tropical Lao Cai region, and based on the evidence of our study, the method has a potential application in forestry conservation management.

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Acknowledgements

We would like to greatly thank the following institutions for providing the data for this research: (1) Ministry of Agriculture and Rural Development (Vietnam), (2) Ministry of Natural Resources and Environment (Vietnam), (3) NOAA’s National Centers for Environmental Information (USA), and (4) U.S. Geological Survey.

Funding

This research was supported by the Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

Author information

Authors and Affiliations

  1. Geospatial Science, School of Science, RMIT University, Melbourne, VIC, 3000, Australia

    Mahyat Shafapour Tehrany & Simon Jones

  2. ARC Centre of Excellence for Australian Biodiversity and Heritage, Global Ecology, College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, South Australia, Australia

    Farzin Shabani

  3. Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia

    Farzin Shabani

  4. Division of Computer Science, Pablo de Olavide University of Seville, Seville, Spain

    Francisco Martínez-Álvarez

  5. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  6. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

Authors
  1. Mahyat Shafapour Tehrany
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  2. Simon Jones
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  3. Farzin Shabani
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  4. Francisco Martínez-Álvarez
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  5. Dieu Tien Bui
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Correspondence to Dieu Tien Bui.

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Tehrany, M.S., Jones, S., Shabani, F. et al. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data. Theor Appl Climatol 137, 637–653 (2019). https://doi.org/10.1007/s00704-018-2628-9

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  • DOI: https://doi.org/10.1007/s00704-018-2628-9