Himalayan forest fire characterization in relation to topography, socio-economy and meteorology parameters in Arunachal Pradesh, India

Abstract

Monitoring and management of forest fire is imperative in India where 50% of forest cover is prone to the fire. The study aims for applying the geospatial technology towards forest fire characterization and evaluation of relationship with meteorological thematic layers. Spatial analysis of forest fires in the state of Arunachal Pradesh was carried out based upon the decadal (2008–2016) forest fire count datasets, which was assessed for spatial variability over the known Himalayan biodiversity hotspot in diverse geographical and socio-economic gradients. Result suggested that Kameng districts had maximum fire incidences (25.2%) whereas it has 15.2% of state forest, established the districts as ‘forest fire hotspot’ in the state. Maximum number of incidences (88%) occurred in areas of low elevation (< 1500 m). There was high correlation with socio-economy where 42.3% forest fire points falls in high poverty index areas and 73% of fire incidences in the areas having population density 6–50. All districts showed high fire incidences, therefore an urgent intervention is greatly required by the policy makers towards conservation and management of forest fire prevention and control by adopting focused intervention, strategic allocation of limited resources in potent areas in order to safeguard Himalayan region of highest biodiversity.

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References

  1. 1.

    Pew, K. L., & Larsen, C. P. S. (2001). GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rain forest of Vancouver Island, Canada. Forest Ecology and Management, 140, 1–18.

    Article  Google Scholar 

  2. 2.

    Fearnside, P. M. (2005). Deforestation in Brazilian Amazonia: History, rates, and consequences. Conservation Biology, 19(3), 680–688.

    Article  Google Scholar 

  3. 3.

    Davidenko, E. P., & Eritsov, A. (2003). The fire season in 2002 in Russia. Report of the Aerial Forest Fire Service, Avialesookhrana. International Forest Fire News, 28, 15–17.

    Google Scholar 

  4. 4.

    FAO. (2005). Global forest resources assessment. Chapter 4. Forest health and Vitality, p 60–65. http://www.fao.org/docrep/008/a0400e/a0400e00.htm. Accessed 30 Mar 2017.

  5. 5.

    Dwyer, E. J., Pereira, J. M. C., Gŕegoire, J. M., & Da Camera, C. C. (1999). Characterization of the spatio-temporal patterns of global fire activity using satellite imagery for the period April 1992–March 1993. Journal of Biogeography, 1, 57–69.

    Google Scholar 

  6. 6.

    Flannigan, M. D., & Harrington, J. B. (1988). A study of the relation of meteorlogical variables to monthly provincial area burned by Wildfire in Canada (1953–1980). Journal of Applied Meteorology, 27, 441–452.

    Article  Google Scholar 

  7. 7.

    Finney, M. A. (2001). Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. Forest Science, 47, 219–229.

    Google Scholar 

  8. 8.

    Malamud, B. D., Millington, J. D. A., & Perry, G. L. W. (2005). Characterizing wildfire regimes in the United States. Proceedings of the National Academy of Sciences in the United States of America, 102, 4694–4699.

    Article  Google Scholar 

  9. 9.

    Saha, S., & Howe, H. F. (2001). The bamboo fire cycle hypothesis: A comment. The American Naturalist, 158, 659–663.

    Article  Google Scholar 

  10. 10.

    Gadgil, M., & Meher-Homji, V. M. (1985). Ecology and management of World’s Savannas. In V. M., Tothill & J. C. Mott (Eds.), Land use and productive potential of Indian savanna (pp. 107–113). Canberra: Australian Academy of Science.

  11. 11.

    Raman, T. R. S., Rawat, G. S., & Johnsingh, A. J. T. (1998). Recovery of tropical rainforest avifauna in relation to vegetation succession following shifting cultivation in Mizoram, North-East India. Journal of Applied Ecology, 35, 214–231.

    Article  Google Scholar 

  12. 12.

    Ganesan, R., & Setty, R. S. (2004). Regeneration of amla (Phyllanthus emblica and P. indofischeri), an important NTFP from southern India. Conservation and Society, 2, 365–375.

    Google Scholar 

  13. 13.

    Rodgers, W. A. (1986). The role of fire in the management of wildlife habitats: A review. Indian Forester, 112, 845–857.

    Google Scholar 

  14. 14.

    Saha, S. (2002). Anthropogenic fire regime in a deciduous forest of central India. Current Science, 82, 1144–1147.

    Google Scholar 

  15. 15.

    FSI. (2015). http://fsi.nic.in/isfr-2015/isfr-2015-forest-cover.pdf. Accessed 15 Mar 2017.

  16. 16.

    Singh, J., Borah, I. P., Barua, A., & Baruah, K. N. (1996). Shifting cultivation in north east India—an overview. Jorhat: Newsletters, Rain Forest Research Institute ‘Deovan’.

    Google Scholar 

  17. 17.

    Ramakrishnan, Ρ. S. (1993). Shifting agriculture and sustainable development: an interdisciplinary study from Northeastern India. New Delhi: Unescoand Oxford University Press.

    Google Scholar 

  18. 18.

    Daiz-Delgado, R., Lloret, F., & Pons, X. (2004). Statistical analysis of fire frequency models for catalonia (NE Spain, 1975–1998) based on fire scar maps from Landsat MSS data. International Journal of Wildland Fire, 13, 89–99.

    Article  Google Scholar 

  19. 19.

    Chuvieco, E., Englefield, P., Trishchenko, A. P., & Luo, Y. (2008). Generation of long time series of burn area maps of the boreal forest from NOAA–AVHRR composite data. Remote Sensing of Environment, 112, 2381–2396.

    Article  Google Scholar 

  20. 20.

    Ahmad, F., & Goparaju, L. (2017). Geospatial assessment of forest fires in Jharkhand. Indian Journal of Science and Technology. https://doi.org/10.17485/ijst/2017/v10i21/113215.

    Google Scholar 

  21. 21.

    Ahmad, F., & Goparaju, L. (2017). Assessment of threats to forest ecosystems using geospatial technology in Jharkhand State of India. Current World Environment, 12(2), 255–265. https://doi.org/10.12944/CWE.12.2.19.

    Article  Google Scholar 

  22. 22.

    Reddy, C. S., Harikrishna, P., Anitha, K., & Joseph, S. (2012). Mapping and inventory of forest fires in Andhra Pradesh, India: Current status and conservation needs. ISRN Forestry. https://doi.org/10.5402/2012/380412.

    Google Scholar 

  23. 23.

    Reddy, C. S., Alekhya, V. V. L. P., Saranya, K. R. L., et al. (2017). Monitoring of fire incidences in vegetation types and protected areas of India: Implications on carbon emissions. Journal of Earth System Science, 126, 11. https://doi.org/10.1007/s12040-016-0791-x.

    Article  Google Scholar 

  24. 24.

    Rahman, M. T., & Rashed, T. (2015). Urban tree damage estimation using airborne laser scanner data and geographic information systems: An example from 2007 Oklahoma ice storm. Urban Forestry and Urban Greening, 14(3), 562–572.

    Article  Google Scholar 

  25. 25.

    Saarinen, N., Vastaranta, N., Honkavaara, E., Wulder, M. A., White, J. C., Litkey, P., et al. (2015). Mapping the risk of forest wind damage using airborne scanning LiDAR. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. https://doi.org/10.5194/isprsarchives-XL-3-W2-189-2015.

    Google Scholar 

  26. 26.

    Badarinath, K. V. S., Sharma, A. R., & Kharol, S. K. (2011). Forest fire monitoring and burnt area mapping using satellite data: A study over the forest region of Kerala State, India. International Journal of Remote Sensing, 32(1), 85–102. https://doi.org/10.1080/01431160903439890.

    Article  Google Scholar 

  27. 27.

    Giriraj, A., Babar, S., Jentsch, A., Sudhakar, S., & Murthy, M. S. R. (2010). Tracking fires in India using advanced along track scanning radiometer (A) ATSR data. Remote Sensing, 2, 591–610.

    Article  Google Scholar 

  28. 28.

    Vadrevu, K. P., Csiszar, I., Ellicott, E., Giglio, L., Badarinath, K. V. S., Vermote, E., et al. (2013). Hotspot analysis of vegetation fires and intensity in the indian region. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(1), 224–238.

    Article  Google Scholar 

  29. 29.

    Nair, M., Ravindranath, N. H., Sharma, N., et al. (2013). Poverty index as a tool for adaptation intervention to climate change in northeast India. Climate and Development, 5(1), 14–32. https://doi.org/10.1080/17565529.2012.751337.

    Article  Google Scholar 

  30. 30.

    Liebetrau, A. M. (1983). Measures of association; quantitative applications in the social sciences series (Vol. 32, pp. 15–16). Newbury Park, CA: Sage Publications.

    Google Scholar 

  31. 31.

    Stocks, B. J., Mason, J. A., Todd, J. B., Bosch, E. M., Wotton, B. M., Amiro, B. D., et al. (2002). Large forest fires in Canada, 1959–1997. Journal of Geophysical Research—Atmospheres, 108, 5.1–5.12.

    Article  Google Scholar 

  32. 32.

    Vázquez, A., & Moreno, J. M. (1998). Patterns of lightning and people-caused fires in peninsular Spain. International Journal of Wildland Fire, 8(2), 103–115.

    Article  Google Scholar 

  33. 33.

    Leblon, B., Kasischke, E., Alexander, M., Doyle, M., & Abbott, M. (2002). Fire danger monitoring using ERS-1 SAR images in the case of Northern Boreal Forests. Natural Hazards, 27, 231–255.

    Article  Google Scholar 

  34. 34.

    Leone, V., Koutsias, N., Martinez, J., Vega-Garcia, C., & Allgower, B. (2003). The human factor in fire danger assessment. In E. Chuvieco (Ed.), Wildland fire danger estimation and mapping. The role of remote sensing data (pp. 143–196). New Jersey: World Scientific.

    Google Scholar 

  35. 35.

    Wang, W., Zhang, C., Allen, J. M., Li, W., Boyer, M. A., Segerson, K., et al. (2016). Analysis and prediction of land use changes related to invasive species and major driving forces in the state of Connecticut. Land, 5, 25. https://doi.org/10.3390/l&5030025.

    Article  Google Scholar 

  36. 36.

    Ahmad, F., Goparaju, L., Qayum, A., & Quli, S. M. S. (2017). Forest fire trend analysis and effect of environmental parameters: A study in Jharkhand State of India using Geospatial Technology. World Scientific News, 90, 31–50.

    Google Scholar 

  37. 37.

    Rothermel, R. C. (1991). Predicting behavior and size of crown fires in the northern Rocky Mountains. Researcg Paper INT-438. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station, p 46.

  38. 38.

    Kushla, J. D., & Ripple, W. J. (1997). The role of terrain in a fire mosaic of a temperate coniferous forest. Forest Ecology and Management, 95, 97–107.

    Article  Google Scholar 

  39. 39.

    Mercer, D. E., & Prestemon, J. P. (2005). Comparing production function models for wildfire risk analysis in the wildland–urban interface. Forest Policy and Economics, 7, 782–795.

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to the USGS for free download of Landsat and DEM (ASTER) data which was used in the analysis. We are also greatful to the Forest survey of India (FSI), DIVA GIS, National Center for Environmental Prediction (NCEP) for providing free download of various dataset used in the analysis. And, also to the Department of Environment and Forests, Govt. of Arunachal Pradesh Itanagar for this opportunity of carrying out the research work.

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FA proposed the idea and analyzed the satellite and ancillary data in GIS domain, LG supervised the analysis, and added dimensions of metrological factors and drafted the manuscript. AQ made critical evaluation regarding GIS analysis and provided continuous feedbacks. All authors read and approved the final manuscript.

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Correspondence to Abdul Qayum.

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Ahmad, F., Goparaju, L. & Qayum, A. Himalayan forest fire characterization in relation to topography, socio-economy and meteorology parameters in Arunachal Pradesh, India. Spat. Inf. Res. 26, 305–315 (2018). https://doi.org/10.1007/s41324-018-0175-1

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Keywords

  • Arunachal Pradesh
  • Cramer’s V coefficient
  • Forest fire incidences
  • Meteorological data
  • Socio-economy
  • Topographical data