Open Access Open Access  Restricted Access Subscription Access

Application of earth observation dataset and multi-criteria decision-making technique for forest fire risk assessment in Sikkim, India


Affiliations
1 Department of Earth Sciences, Indian Institute of Technology Kanpur, Kanpur 208 016, India; Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208 016, India, India
2 Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208 016, India, India
3 Department of Earth Sciences, Indian Institute of Technology Kanpur, Kanpur 208 016, India, India
 

Forest fire is one of the primary and recurring problems in Sikkim, India impacting the ecological heritage of the region. The article presents a fire risk model based on the identification of the major factors that contribute to forest fire, namely, vegetation type, vegetation density, land surface temperature, elevation, slope, aspect, and distance from settlements, rivers and roads, and then integrating them using a multi-criteria decision-making technique in a GIS framework. We document that more than 50% of the area of all the districts except North Sikkim falls into high to moderate risk zones. The model shows that 61% of fire information for resource management system data for the last 16 years coincide with the mapped high-risk zone of the state. Areas with low slope and with moderate vegetation density fall into very high risk, whereas areas with high slope and with high vegetation density correspond to moderate risk zones. Further, aspect and density of human intervention differentiate the very high and high-risk zones of the region. This model has provided a robust geographical representation of fire ignition probability and identification of high-risk areas at different regions for the entire state of Sikkim

Keywords

Analytic hierarchy process, forest fire risk, multi-criteria decision-making technique, remote sensing, risk map.
User
Notifications
Font Size

  • Kramer, P. J., Carbon dioxide concentration, photosynthesis, and dry matter production. Bioscience, 1981, 31, 29–33.
  • Cochrane, M. A. and Ryan, K. C., Fire and fire ecology: concepts and Principles. In Tropical Fire Ecology (ed. Cochrane, M. A.), Springer, Switzerland AG, 2009, pp. 25–62.
  • Laurance, W. F. and Williamson, G. B., Positive feedbacks among forest fragmentation, drought, and climate change in the Amazon. Conserve. Biol., 2001, 15, 1529–1535.
  • Cochrane, M. A., Fire science for rainforests. Nature, 2003, 421, 913–919.
  • Thirgood, S., Woodroffe, R. and Rabinowitz, A., The impact of human-wildlife conflict on human lives and livelihoods. Conserv. Biol. Series, 2005, 9, 13.
  • Collins, R. D., de Neufville, R., Claro, J., Oliveira, T. and Pacheco, A. P., Forest fire management to avoid unintended consequences: a case study of portugal using system dynamics. J. Environ. Manage., 2013, 130, 1–9.
  • Lentile, L. B. et al., Remote sensing techniques to assess active fire characteristics and post-fire effects. Int. J. Wildland Fire, 2006, 15, 319–345.
  • Eugenio, F. C. et al., Applying gis to develop a model for forest fire risk: a case study in espírito santo, brazil. J. Environ. Manage., 2016, 173, 65–71.
  • Koetz, B., Morsdorf, F., Van der Linden, S., Curt, T. and Allgöwer, B., Multi-source land cover classification for forest fire management based on imaging spectrometry and lidar data. Forest Ecol. Manage., 2008, 256, 263–271.
  • You, W., Lin, L., Wu, L., Ji, Z., Zhu, J., Fan, Y. and He, D., Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability. Ecol. Indic., 2017, 77, 176–184.
  • Cocke, A. E., Fulé, P. Z. and Crouse, J. E., Comparison of burn severity assessments using differenced normalized burn ratio and ground data. Int. J. Wildland Fire, 2005, 14, 189–198.
  • Escuin, S., Navarro, R. and Fernandez, P., Fire severity assessment by using NBR (normalized burn ratio) and NDVI (normalized difference vegetation index) derived from landsat TM/ETM images. Int. J. Remote Sensing, 2008, 29, 1053–1073.
  • Wang, L., Qu, J. J. and Hao, X., Forest fire detection using the normalized multi- band drought index (NMDI) with satellite measurements. Agric. Forest Meteorol., 2008, 148, 1767–1776.
  • Guangmeng, G. and Mei, Z., Using modis land surface temperature to evaluate forest fire risk of northeast china. IEEE Geosci. Remote Sensing Lett., 2004, 1, 98–100.
  • Sharma, S. and Pant, H., Vulnerability of Indian Central Himalayan forests to fire in a warming climate and a participatory preparedness approach based on modern tools. Curr. Sci., 2017, 112, 2100–2105.
  • Cosentino, M. J., Woodcock, C. E. and Franklin, J., Scene analysis for wildland fire-fuel characteristics in a mediterranean climate. In Proceedings of the International Symposium on Remote Sensing of Environment, Environmental Research Institute of Michigan, University of Michigan, USA, 11–15 May 1981, pp. 635–646.
  • Jain, A., Ravan, S. A., Singh, R., Das, K. and Roy, P., Forest fire risk modelling using remote sensing and geographic information system. Curr. Sci., 1996, 70, 928–933.
  • Jaiswal, R. K., Mukherjee, S., Raju, K. D. and Saxena, R., Forest fire risk zone mapping from satellite imagery and gis. Int. J. Appl. Earth Observ. Geoinf., 2002, 4, 1–10.
  • Malik, T., Rabbani, G. and Farooq, M., Forest fire risk zonation using remote sensing and GIS technology in Kansrao forest range of Rajaji National Park, Uttarakhand, India. Int. J. Adv. Remote Sensing GIS, 2013, 2, 86–95.
  • Vadrevu, K. P., Eaturu, A. and Badarinath, K., Fire risk evaluation using multicriteria analysis – a case study. Environ. Monit. Assess., 2010, 166, 223–239.
  • Sharma, R., Sharma, N., Shrestha, D., Luitel, K. K., Arrawatia, M. and Pradhan, S., Study of forest fires in Sikkim Himalayas, India using remote sensing and gis-techniques. Climate Change in Sikkim: Patterns, Impacts and Initiatives, Government of Sikkim, Gangtok, India, 2012, pp. 233–244.
  • Koczkodaj, W. W. et al., Important facts and observations about pairwise comparisons (the special issue edition). Fund. Inform., 2016, 144, 291–307.
  • FEWMD, Environmental Initiatives of the State Government (1994–2016), pp. 1–124.
  • Sharma, S., Joshi, V. and Chhetri, R. K., Forest fire as a potential environmental threat in recent years in Sikkim, Eastern Himalayas, India. Climate Change Environ. Sustain., 2014, 2, 55–61.
  • Panda, A. K. and Misra, S., Health traditions of Sikkim Himalaya. J. Ayurveda Integr. Med., 2010, 1, 183.
  • Kumar, P. and Ghose, M., Estimation of pyrogenic carbon emissions from forests of Sikkim Himalaya, India: a geoinformatics approach. Curr. Sci., 2017, 113, 1864–1872.
  • Rai, S. C. and Sundriyal, R. C., Tourism and biodiversity conservation: the Sikkim Himalaya. Ambio, 1997, 26, 235–242.
  • Champion, H. G., A preliminary survey of forest types of India and Burma. Indian Forestry Record (NS) Silviculture, Government of India Press, New Delhi, 1936, vol. 1, p. 286.
  • Khawas, V. and Tamang, R. K., Conservation and management of water resource in Sikkim Himalaya: some suggestions. SpatioEcon. Develop. Rec., 2005, 12, 27–31.
  • Adab, H., Kanniah, K. D. and Solaimani, K., Modeling forest fire risk in the northeast of Iran using remote sensing and gis techniques. Nat. Hazards, 2013, 65, 1723–1743.
  • Tambe, S., Arrawatia, M. and Sharma, N., Assessing the priorities for sustainable forest management in the Sikkim Himalaya, India: a remote sensing based approach. J. Indian Soc. Remote Sensing, 2011, 39, 555–564.
  • FSI, The state of forest report 2005, Forest Survey of India, Dehradun.
  • Barsi, J. A., Schott, J. R., Hook, S. J., Raqueno, N. G., Markham, B. L. and Radocinski, R. G., Landsat-8 thermal infrared sensor (tirs) vicarious radiometric calibration. Remote Sensing, 2014, 6, 11607–11626.
  • Chuvieco, E. and Congalton, R. G., Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing Environ., 1989, 29, 147–159.
  • Saaty, T., The Analytic Hierarchy Process, Agric. Econ. Rev., McGraw Hill, New York, 1980, p. 70.
  • Saaty, T. L., A scaling method for priorities in hierarchical structures. J. Math. Psychol., 1977, 15, 234–281.
  • Alonso, J. A. and Lamata, M. T., Consistency in the analytic hierarchy process: a new approach. Int. J. Uncertain., Fuzz. Knowl. Syst., 2006, 14, 445–459.
  • Song, H.-S. and Lee, S.-H., Effects of wind and tree density on forest fire patterns in a mixed-tree species forest. Forest Sci. Technol., 2017, 13, 9–16.
  • Chandran, M., Sinha, A. and Rawat, R., Replacing controlled burning practice by alternate methods of reducing fuel load in the Himalayan long leaf pine (Pinus roxburghii sarg.) forests. In Fifth International Wildland Fire Conference, Sun City, South Africa, 2011, pp. 9–13.
  • Somashekar, R., Nagaraja, B. and Urs, K., Monitoring of fores fires in Bhadra wildlife sanctuary. J. Indian Soc. Remote Sensing, 2008, 36, 99–104.
  • Xu, D., Shao, G., Dai, L., Hao, Z., Tang, L. and Wang, H., Mapping forest fire risk zones with spatial data and principal component analysis. Sci. China Ser. E: Technol. Sci., 2006, 49, 140–149.
  • Mhawej, M., Faour, G. and Adjizian-Gerard, J., A novel method to identify likely causes of wildfire. Climate Risk Manage., 2017, 16, 120–132.
  • Somashekar, R., Ravikumar, P., Kumar, C. M., Prakash, K. and Nagaraja, B., Burnt area mapping of Bandipur National Park, India using irs 1c/1d liss iii data. J. Indian Soc. Remote Sensing, 2009, 37, 37–50.
  • Gheshlaghi, H. A., Using gis to develop a model for forest fire risk mapping. J. Indian Soc. Remote Sensing, 2019, 47, 1173–1185.
  • Chavan, M., Das, K. and Suryawanshi, R., Forest fire risk zonation using remote sensing and gis in Huynial watershed, Tehri Garhwal district, UA. Int. J. Basic Appl. Res., 2012, 2, 6–12.
  • Anitha, S., Soujanya, S. and Rajkumar, G., An approach for identifying the forest fire using land surface imagery by locating the abnormal temperature distribution. IOSR J. Comput. Eng., 2013, 14, 06–12.
  • Fang, L., Yang, J., Zu, J., Li, G. and Zhang, J., Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape. For. Ecol. Manage., 2015, 356, 2–12.
  • Wood, S. W., Murphy, B. P. and Bowman, D. M., Firescape ecology: how topography determines the contrasting distribution of fire and rain forest in the south west of the Tasmanian wilderness world heritage area. J. Biogeogr., 2011, 38, 1807–1820.
  • Weise, D. R. and Biging, G. S., Effects of wind velocity and slope on fire behavior. In Fire Safety Science – Proceedings of the Fourth International Symposium (ed. Kashiwagi, T.), International Association for Fire Safety Science, Boston, USA, 1994, pp. 1041–1051.
  • Singh, R. P. and Ajay, K., Fire risk zone assessment in Chitrakoot area, Satna MP, India. Res. J. Agric. For. Sci., 2013, 1–4.
  • Setiawan, I., Mahmud, A., Mansor, S., Shariff, A. M. and Nuruddin, A., GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia. Disaster Prev. Manag., 2004, 13(5), 379–386.

Abstract Views: 451

PDF Views: 175




  • Application of earth observation dataset and multi-criteria decision-making technique for forest fire risk assessment in Sikkim, India

Abstract Views: 451  |  PDF Views: 175

Authors

Arnab Laha
Department of Earth Sciences, Indian Institute of Technology Kanpur, Kanpur 208 016, India; Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208 016, India, India
Nagarajan Balasubramanian
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208 016, India, India
Rajiv Sinha
Department of Earth Sciences, Indian Institute of Technology Kanpur, Kanpur 208 016, India, India

Abstract


Forest fire is one of the primary and recurring problems in Sikkim, India impacting the ecological heritage of the region. The article presents a fire risk model based on the identification of the major factors that contribute to forest fire, namely, vegetation type, vegetation density, land surface temperature, elevation, slope, aspect, and distance from settlements, rivers and roads, and then integrating them using a multi-criteria decision-making technique in a GIS framework. We document that more than 50% of the area of all the districts except North Sikkim falls into high to moderate risk zones. The model shows that 61% of fire information for resource management system data for the last 16 years coincide with the mapped high-risk zone of the state. Areas with low slope and with moderate vegetation density fall into very high risk, whereas areas with high slope and with high vegetation density correspond to moderate risk zones. Further, aspect and density of human intervention differentiate the very high and high-risk zones of the region. This model has provided a robust geographical representation of fire ignition probability and identification of high-risk areas at different regions for the entire state of Sikkim

Keywords


Analytic hierarchy process, forest fire risk, multi-criteria decision-making technique, remote sensing, risk map.

References





DOI: https://doi.org/10.18520/cs%2Fv121%2Fi8%2F1022-1031