Skip to main content

Advertisement

SpringerLink
Log in

A Comparative Analysis of Forest Fire Risk Zone Mapping Methods with Expert Knowledge

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Despite repeated occurrences of forest fire, very less scientific studies have been reported in the Indian context especially in Kudremukh region to mitigate and suppress the fire. The objective of this article was to pool the expert knowledge on forest fire triggering factors from officials working in wildlife division in the Western Ghats of India through a questionnaire and to validate the risk zones obtained from three popular fire risk zone mapping methods namely logistic regression, multi-criteria decision analysis, and weighted overlay. Based on the earlier studies and expert knowledge, fire ignition parameters considered are elevation, slope, and aspect, proximity to roads, water bodies and area of human activities, normalized difference vegetation index (NDVI), land surface temperature (LST), and vegetation type. The regression model was based on previous fire occurrences and the other two based on expert’s opinion. The three models were validated and compared using past fire occurrence events. The logistic regression model gave 88.89% of accuracy and that of multi-criteria decision analysis with 74.6% accuracy, and that of weighted overlay method with an accuracy of 68.24% for the specific study area. The logistic regression model is useful in the presence of historical data, whereas expert knowledge is helpful for mapping risk zones using multi-criteria decision analysis and weighted overlay analysis when historical data are scarce or not available for mapping risk zones. The obtained risk maps can be used for deciding watchtower locations, installation of sensors, cameras, etc. In every forest division, it is recommended to prepare a standard questionnaire form and document their experiences on forest fire in the region under their supervision before they are getting transferred to another location.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  • Abhineet, J., Roy, P. S., Ravan, S. A., Singh, R. K., & Das, K. K. (1996). Fire risk modelling using remote sensing and geographic information system. Current Science,70(10), 928–933.

    Google Scholar 

  • Ajin, R. S., Loghin, A. M., Vinod, P. G., & Jacob, M. K. (2017). Forest fire risk zone mapping using RS and GIS techniques a study in Achankovil Forest Division, Kerala, India. Journal of Earth, Environment and Health Sciences,23, 109–115.

    Google Scholar 

  • Akay, A. E., & Erdogan, A. (2017). GIS-based multi-criteria decision analysis for forest fire risk mapping. Remote Sensing and Spatial Information Sciences,4, 25–30.

    Google Scholar 

  • Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment,29, 147–159.

    Article  Google Scholar 

  • Chuvieco, E., Salas, F. J., Carvacho, L., & Silva, F. R. (1999). Integrated fire risk mapping remote sensing of large wildfires in the European Mediterranean Basin (pp. 61–85). Berlin: Springer.

    Book  Google Scholar 

  • Deng, O., Su, G., Huang, Q. Y., & Li, Y. Q. (2013). Forest fire risk mapping based on the spatial logistic model of Northeastern China Forest Zone. In International conference on geo-informatics in resource management and sustainable ecosystem, (pp 181–192).

  • Eugenio, F. C., Dos Santos, A. R., Fiedler, N. C., Ribeiro, G. A., da Silva, A. G., Dos Santos, Á. B., et al. (2016). Applying GIS to develop a model for forest fire risk a case study in Espírito Santo, Brazil. Journal of Environmental Management,173, 65–71.

    Article  Google Scholar 

  • Gholamreza, J. G., Bahram, G., & Osman Majidi, D. (2012). Forest risk zone mapping from GIS in Northern forests of Iran. International Journal of Agriculture and Crop Sciences,4(12), 818–824.

    Google Scholar 

  • Jaiswal, R. K., Mukherjee, S., Kumaran, D. Raju, & Saxena, R. (2002). Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geo-information,4, 1–10.

    Article  Google Scholar 

  • Jose, S. K. (2012). Geospatial characterization and conservation potential for Agasthyamala Biosphere Reserve, Western ghats, India.

  • Jose, M., Silva, S., Tome, M., Alvim, M., & Pereira, J. M. C. (2001). Spatial prediction of fire ignition probabilities comparing logistic regression and neural networks. Photogrammetric Engineering Remote Sensing,67(1), 73–81.

    Google Scholar 

  • Kanga, S., Tripathi, G., & Singh, S. K. (2017). “Forest fire hazards vulnerability and risk assessment in Bhajji forest range of Himachal Pradesh (India) a geospatial approach. Journal of Remote Sensing & GIS,8(1), 1–16.

    Google Scholar 

  • Martin, M. P., Ceccato, P., Flasse, F, & Downey I. (1999). Fire detection and fire growth monitoring using satellite data. Remote Sensing of Wildfire (pp. 101–122). Berlin: Springer.

    Chapter  Google Scholar 

  • Pourghasemia, H. R. (2015). GIS-based forest fire susceptibility mapping in Iran a comparison between evidential belief function and binary logistic regression models. Scandinavian Journal of Forest Research,9(3), 155–165.

    Google Scholar 

  • Roy, P. S. (2002). Forest fire and degradation assessment using satellite remote sensing and geographic information system. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology,15, 361–400.

    Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process (Vol. 4, pp. 23–157). New York: Mc Graw Hill.

    Google Scholar 

  • Sharma, L. K., Kanga, S., Nathawat, M. S., Sinha, S., & Pandey, P. C. (2012). Fuzzy-AHP for forest fire risk modeling. Disaster Prevention and Management,21(2), 160–171.

    Article  Google Scholar 

  • Shruti, K., Sharma, L., Nathawat, M. S., & Sharma, S. K. (2011). Geospatial approach for forest fire risk modeling—A case study of Taradevi range of Shimla forest division in Himachal Pradesh. The Indian Forester,137(3), 296–303.

    Google Scholar 

  • Sowmya, S. V., & Somashekar, R. K. (2010). Application of remote sensing and GIS in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. Journal of Environmental Biology,31(6), 969–974.

    Google Scholar 

  • Suryabhagavan, K. V., Alemu, M., & Balakrishnan, M. (2016). GIS-based multi-criteria decision analysis for forest fire susceptibility mapping a case study in Harenna forest, southwestern Ethiopia. International Society for Tropical Ecology,57(1), 33–43.

    Google Scholar 

  • Tien Bui, D., Thi, K.-T., Nguyen, V. C., Le, H. D., & Revhaug, I. (2016). Tropical forest fire susceptibility mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, using GIS-based kernel logistic regression. Remote Sensing,8(3), 347–355.

    Article  Google Scholar 

  • Tripathi, D. K., Sahdev, S., & Kumar, M. (2017). Forest fire risk zone mapping using GIS—A case study. International Journal of Computer Science,5(7), 9–15.

    Google Scholar 

  • Yakubu, I., Mireku Gyimah, D., & Duker, A. A. (2015). Review of methods for modeling forest fire risk and hazard. International Journal of Science and Technology,9(3), 155–165.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Technology Karnataka, Surathkal, Karnataka, India

    H. Yathish, K. V. Athira, K. Preethi, U. Pruthviraj & Amba Shetty

Authors
  1. H. Yathish
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. V. Athira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Preethi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. U. Pruthviraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amba Shetty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Preethi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yathish, H., Athira, K.V., Preethi, K. et al. A Comparative Analysis of Forest Fire Risk Zone Mapping Methods with Expert Knowledge. J Indian Soc Remote Sens 47, 2047–2060 (2019). https://doi.org/10.1007/s12524-019-01047-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-019-01047-w

Keywords

Search

Navigation