|Year : 2017 | Volume
| Issue : 1 | Page : 18-23
The risk analysis of potential forest fires in a wildlife sanctuary in the western ghats (Southwest Indian Peninsula) using geospatial techniques
RS Ajin1, Ana-Maria Loghin2, PG Vinod1, Mathew K Jacob3
1 Geomatics Division, GeoVin Solutions Pvt. Ltd., Thiruvananthapuram, Kerala, India
2 Department of Civil Engineering, Faculty of Hydrotechnical Engineering, Geodesy and Environmental Engineering, Gheorghe Asachi Technical University of Iaşi, Iaşi, Romania
3 Department of Geology, Sree Narayana Guru College of Advanced Studies, Varkala; Department of Geology, Dr. Palpu College of Arts and Science, Thiruvananthapuram, Kerala, India
|Date of Web Publication||21-Mar-2017|
R S Ajin
Geomatics Division, GeoVin Solutions Pvt. Ltd., Thiruvananthapuram - 695 010, Kerala
Source of Support: None, Conflict of Interest: None
Introduction: Forest fires are potential threats to most of the wildlife sanctuaries in the world. Such areas for conservation of precious wildlife in parts of the Western Ghats in India are no exception. The present study area representing the Periyar Tiger Reserve forms a part of the Western Ghats, where during the past decade more than 200 wildfires have been recorded. Objective: The present study is designed to demarcate the fire risk zones applying principles of geospatial techniques. Materials and Methods: The technique integrates Remote Sensing and Geographic Information System. Parameters such as land cover type, surface slope, aspect, proximity to settlements, closeness to roads, and elevation have been considered. A Modified Fire Risk Index method has been used for preparing the risk zone map. Results: The prepared map shows five fire risk zones such as very high, high, moderate, low, and very low. About 27.38% of the study area, according to this study falls within the high and very high risk zones. The final result of the study is validated with the MODIS active fire (hotspot) data, which shows very good reliability. Conclusion: This study based on geospatial technology is very useful to the local authorities for identifying in advance the fire risk zones for prevention and management of forest fires in future.
Keywords: Forest fire, Modified Fire Risk Index, preventive measures, risk zones, Western Ghats
|How to cite this article:|
Ajin R S, Loghin AM, Vinod P G, Jacob MK. The risk analysis of potential forest fires in a wildlife sanctuary in the western ghats (Southwest Indian Peninsula) using geospatial techniques. Int J Health Syst Disaster Manage 2017;5:18-23
|How to cite this URL:|
Ajin R S, Loghin AM, Vinod P G, Jacob MK. The risk analysis of potential forest fires in a wildlife sanctuary in the western ghats (Southwest Indian Peninsula) using geospatial techniques. Int J Health Syst Disaster Manage [serial online] 2017 [cited 2017 Dec 17];5:18-23. Available from: http://www.ijhsdm.org/text.asp?2017/5/1/18/202653
| Introduction|| |
One of the major global environmental threats to wildlife sanctuaries is forest fire. Over the years precious and ecologically fragile forest segments have been lost due to forest fires. Loss of wildlife is indeed serious. Forest fires can be triggered by natural forces as well as anthropogenic activities. The present study area is a part of the Western Ghats, a biodiversity hotspot of Southwest Indian Peninsula. Most of the sanctuaries in the Western Ghats are prone to fire and had been affected by frequent fires in the past.,,,,,,, The present study area also had been affected by forest fires in the past. Most of such disasters could have been averted had proper measures were taken in advance. This is a study intended to characterize the causes of fires in this area and to make suggestions based on geospatial techniques to prevent/mitigate forest fires in future.
A large number of studies have been carried out by researchers on forest fire risk zone mapping. Most of the studies have used geospatial techniques to delineate the forest fire risk zones.,,,,,,,,,,,,,,,,,, Thakur and Singh  mapped forest fire risk zones in Dehradun district (India) using geospatial techniques. Factors such as vegetation moisture, slope, aspect, elevation, distance from roads, and vicinity of settlements have been considered. Dong et al. prepared forest fire risk zone maps of Baihe Forestry Bureau using satellite images and geographic information system (GIS) techniques. Variables such as vegetation type, slope, aspect, altitude, distance from roads, distance from farmlands, and distance from settlements have been selected for the study.
The objectives of this study are to demarcate the forest fire risk zones in the Periyar Tiger Reserve (PTR) using geospatial techniques and to suggest suitable preventive measures. The factors selected are land cover type, surface slope, aspect, proximity to settlements, closeness to roads, and elevation. A Modified Fire Risk Index (MFRI) method has been used for the demarcation of risk zones.
The study area
The study area that includes the PTR forms a part of the Western Ghats of the southwest Indian Peninsula and lies between 76°55'0” and 77°25'0” E longitudes and 9°16'0” and 9°38'0” N latitudes. This protected area is bordered by Kottayam and Ranni forest divisions of Kerala state along the West, Northwest and South, Southwest, respectively and the state of Tamil Nadu to the North, East, and Southeast. The PTR covers an area of 777 km 2. The study area is shown in [Figure 1].
| Materials and Methods|| |
The PTR is represented within Survey of India topographic maps numbered 58 C/15, 58 G/2, 58 G/3, 58 G/6, and 58 G/7 in 1:50,000 scale. The thematic map layers were prepared using ArcGIS 9.3 and ERDAS IMAGINE 9.2 software tools. The land cover type map was derived from the IRS-P6 LISS-III image of 23.5 m resolution. The ERDAS IMAGINE software was used for the supervised classification of the LISS-III image. The road networks and human settlements were digitized from the topographic maps and Google Earth. The closeness to roads and proximity to settlements map layers were prepared from the digitized data using ArcGIS spatial analyst tools. The contour data were generated from the SRTM DEM of 30 m resolution. ArcGIS spatial analyst and three-dimensional analyst tools were used to prepare the slope, elevation, and aspect map layers from the 20 m interval contour data. A MFRI model developed during this study was used for the demarcation of forest fire risk zones. These thematic map layers were reclassified using the Equal Interval method. Index was assigned to each class of the thematic map layers according to their capacity on fire ignition and spreading. The Index was calculated from the weight and rank (Index = Weight × Rank) and is shown in [Table 1]. The forest fire risk zone map was prepared by overlaying the index map layers using ArcGIS tools. Finally, the risk zone map was validated using the MODIS active fire data (MODIS Collection 5 Standard Quality) for the past 10 years.
| Results and Discussion|| |
Land cover type
Forest land cover may be sparsely vegetated or luxuriously vegetated. Moisture and water availability is a major factor that controls the type of land cover. The areas with dry and dense vegetations are more prone to fires. The land cover types existing in this area are deciduous forest, grassland, forest plantation, evergreen forest, built-up area, wetland, and water body. The present study recognizes that the deciduous forests and grasslands are prone to higher incidence of forest fires. The land cover type map is shown in [Figure 2].
In the present study area, geomorphological feature like natural slope is very critical. Steep slopes in the leeward side of approaching orographic wind accelerate the upward migration of blazing forest fire. Trees and other vegetation are exposed to end to end burning producing enormous heat. The loss of natural vegetation is severe, which subsequently exposes the land vulnerable to rapid denudation. The surface slope (in degrees) of this area ranges from 0 to 45.25 and is shown in [Figure 3].
The direction of the slope of a terrain, that is, its exposure in relation to the sun's rays is taken as its aspect. It is related to the rate of fuel drying and the movement of the fire. In the Northern hemisphere, usually South-facing slopes receive maximum sunlight and become warmer, suitable for deciduous vegetation. Areas receiving direct sunlight are at higher degree of fire risk. Further such areas correspond to the direction of the prevalent wind. It is reported that, Eastern aspects receive early heating from the sun, along with early slope winds. Western aspects receive late heating with higher intensity due to post noon high energy heat transfer. The aspect of this area has been grouped into nine classes such as Flat, North, Northeast, East, Southeast, South, Southwest, West, and Northwest. The aspect map is shown in [Figure 4].
Closeness to roads
This has been proved to be an important factor of accidental forest fires. Motorable roads as well as forest paths used by travelers, tourists and inhabitants of the forests can cause accidental forest fires. Careless disposal of burning match sticks, cigar butts, and leaving behind unextinguished fire woods after campfires and way side cooking etc., are potential reasons for causing forest fires. Furthermore, the pilgrims visiting Sabarimala, a Hindu pilgrimage center located within the PTR is also one of the reasons. Millions of devotees are visiting Sabarimala every year. The devotees traveling through the forest roads sometimes engage in road side cooking and create camp fires to drive away wild animals and to keep themselves warm during cold nights. Such activities may result in accidental forest fires. On the basis of closeness to roads, the area has been grouped into five classes' such as 0–1711 m, 1711–3422 m, 3422–5134 m, 5134–6845 m, and 6845–8557 m. The closeness to roads map is shown in [Figure 5].
Proximity to settlements
Most hamlets of human settlements are close to water sources. However, in the peripheral zones of human encroachment within the forests, the chance of human induced forest fire is very high. It is in such zones that forest product gatherers and poachers make accidental fires. On the basis of proximity to settlements, the area has been grouped into five classes' such as 0–3655 m, 3655–7310 m, 7310–10965 m, 10965–14620 m, and 14620–18275 m. The proximity to settlements map is shown in [Figure 6].
Higher the elevation, the topography becomes steeper. In higher elevation, wind power is more and relatively continuous. Thus, in higher elevations, conditions are much favorable for nurturing localized natural or induced forest fires to disasters of alarming dimensions. In higher elevations, frequency of lightning strikes is much more compared to the plains. This factor contributes to a higher frequency of fires in forest zones of higher elevation. The elevation of the study area ranges from 80 to 1981 meters and is shown in [Figure 7].
Forest fire risk zones
The forest fire risk zone map of the PTR is prepared by overlaying the index map layers of land cover type, surface slope, aspect, proximity to settlements, closeness to roads, and elevation using GIS tools. The area of the prepared map is grouped into five risk zones such as very low, low, moderate, high, and very high. The forest fire risk zone map is shown in [Figure 8]. The result is validated using the MODIS active fire (hotspot) data. In order to validate the result, the MODIS hotspots have been overlaid on forest fire risk zones. A total of 222 hotspots have been recorded from January 2007 to May 2016 (10 years). The study shows that 92.79% (206 hotspots) of the forest fires are recorded in the high and very high risk zones alone. This shows that the methodology is reliable and can be employed in other areas of similar condition. Most of the hotspots spatially fall close to the roads and human settlements. This confirms the influence of humans in its occurrence intentionally or unintentionally. About 27.38% of the study areas, according to this study falls within the high and very high risk zones. The area and percentage of each fire risk zones is shown in [Table 2].
Based on the forest fire risk zone mapping, some preventive measures are suggested as follows:
- Construct watch towers on high and very high risk zones and appoint adequate number of trained and well equipped fire watchers during the fire season
- Conduct training programs for the forest planners and officials
- Conduct awareness campaign on the causes and consequences of forest fires for the tribal villagers and tourists
- Promote controlled burning to remove the dry leaves (fuel) on the forest floor and make fire belts on either side of forest roads
- Create fire lines (firebreaks) to slow down or prevent the advancement of forest fire
- Develop and maintain effective communication system among the forest guards and forest administrative system.
| Conclusion|| |
Several studies have shown that forest fires are major threat to sanctuaries of wildlife across the world. The present study has found that the PTR area has also been affected by frequent forest fires in the past. The study could not find significant evidences of precautionary measures taken to avert forest fires in this area. Therefore, the risk analysis of potential forest fires in this forest segment has great significance. This study has delineated the fire risk zones in the PTR using Remote Sensing (RS) and GIS techniques. The study concludes that 92.79% (206 hotspots) of the forest fires in this area are recorded in the high and very high risk zones alone. Thus, it is concluded that the methodology adopted in this study is reliable. This study has also suggested a few plausible preventive measures. The study has concluded that majority of the forest fires in this area were human induced, especially the irresponsible intervention of travelers, pilgrims, tourists and the tribal people residing here. The present study has shown that techniques based on RS and GIS can be used to take steps in advance for preventing forest fires in future. This study based on geospatial technology is very useful to the forest authorities for identifying in advance the fire risk zones for the prevention and management of forest fires in future.
We acknowledge the use of MODIS active fire data from LANCE FIRMS operated by the NASA/Goddard Space Flight Center (GSFC)/Earth Science Data and Information System with funding provided by NASA/HQ. Discussions with Diane Davies, Operations Manager, LANCE, NASA GSFC is also acknowledged.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Adab H, Kanniah KD, Solaimani K. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards 2013;65:1723-43.
Ajin RS, Loghin AM, Jacob MK, Vinod PG, Krishnamurthy RR. The risk assessment of potential forest fire in Idukki Wildlife Sanctuary using RS and GIS techniques. Int J Adv Earth Sci Eng 2016;5:308-18.
Ajin RS, Loghin AM, Vinod PG, Jacob MK. RS and GIS based forest fire risk zone mapping in Periyar Tiger Reserve, Kerala, India. J Wetl Biodivers 2016;6:175-84.
Ajin RS, Loghin AM, Vinod PG, Jacob MK. Forest fire risk zone mapping in Chinnar Wildlife Sanctuary, Kerala, India: A study using geospatial tools. J Glob Resour 2016;3:16-26.
Vinod PG, Ajin RS, Jacob MK. RS and GIS based spatial mapping of forest fires in Wayanad Wildlife Sanctuary, Wayanad, North Kerala, India. Int J Earth Sci Eng 2016;9:498-502.
Ajin RS, Jacob MK, Menon AR, Vinod PG. Forest Fire Risk Analysis Using Geo-Information Technology: A Study of Peppara Wildlife Sanctuary, Thiruvananthapuram, Kerala, India. Proceedings of the 2nd
Disaster Risk Vulnerability Conference, India; 2014. p. 160-5.
Sringeswara AN, Shivanna MB, Gowda B. Forest Fire and Its Management in Kudremukh National Park, Western Ghats, India Using Remote Sensing and GIS. Proceedings of the 13th
ESRI India User Conference, India; 2012. p. 1-9.
Sowmya SV, Somashekar RK. Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. J Environ Biol 2010;31:969-74.
Somashekar RK, Ravikumar P, Mohankumar CN, Prakash KL, Nagaraja BC. Burnt area mapping of Bandipur National Park, India using IRS 1C/1D LISS III data. J Indian Soc Remote Sens 2009;37:37-50.
Ajin RS, Loghin AM, Karki A, Vinod PG, Jacob MK. Delineation of forest fire risk zones in Thenmala forest division, Kollam, Kerala, India: A study using geospatial tools. J Wetl Biodivers 2016;6:139-48.
Suryabhagavan KV, Alemu M, Balakrishnan M. GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: A case study in Harenna forest, Southwestern Ethiopia. Trop Ecol 2016;57:33-43.
Ajin RS, Ciobotaru AM, Vinod PG, Jacob MK. Forest and wildland fire risk assessment using geospatial techniques: A case study of Nemmara forest division, Kerala, India. J Wetl Biodivers 2015;5:29-37.
Ajin RS, Vinod PG, Menon AR. Forest Fire Risk Analysis Using GIS and RS Techniques: An Approach in Idukki Wildlife Sanctuary, Kerala, India. Proceedings of the 24th
Swadeshi Science Congress, India; 2014. p. 406-13.
Singh D. Historical fire frequency based forest fire risk zonation relating role of topographical and forest biophysical factors with geospatial technology in Raipur and Chilla range. SSARSC Int J Geo Sci Geo Inform 2014;1:1-9.
Sivrikaya F, Sağlam B, Akay AE, Bozali N. Evaluation of forest fire risk with GIS. Pol J Environ Stud 2014;23:187-94.
Veeraanarayanaa B, Ravikumar SK. Assessing fire risk in forest ranges of Guntur district, Andhra Pradesh: Using integrated remote sensing and GIS. Int J Sci Res 2014;3:1328-32.
Eskandari S, Ghadikolaei JO, Jalilvand H, Saradjian MR. Detection of fire high-risk areas in northern forests of Iran using Dong model. World Appl Sci J 2013;27:770-3.
Rajabi M, Alesheikh A, Chehreghan A, Gazmeh H. An innovative method for forest fire risk zoning map using fuzzy inference system and GIS. Int J Sci Technol Res 2013;2:57-64.
Singh RP, Ajay K. Fire risk assessment in Chitrakoot area, Satna MP, India. Res J Agric Forestry Sci 2013;1:1-4.
Chavan ME, Das KK, Suryawanshi RS. 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.
Ghobadi GJ, Gholizadeh B, Dashliburun OM. Forest fire risk zone mapping from geographic information system in Northern forests of Iran (Case study, Golestan province). Int J Agric Crop Sci 2012;4:818-24.
Mahdavi A, Shamsi SR, Nazari R. Forests and rangelands' wildfire risk zoning using GIS and AHP techniques. Caspian J Environ Sci 2012;10:43-52.
Thakur AK, Singh D. Forest fire risk zonation using geospatial techniques and analytic hierarchy process in Dehradun district, Uttarakhand, India. Univers J Environ Res Technol 2014;4:82-9.
Dong X, Li-Min D, Guo-Fan S, Lei T, Hui W. Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. J Forestry Res 2005;16:169-74.
Setiawan I, Mahmud AR, Mansor S, Shariff AR, Nuruddin AA. GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia. Disaster Prev Manage 2004;13:379-86.
Chuvieco E, Congalton RG. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ 1989;29:147-59.
Måren IE, Karki S, Prajapati C, Yadav RK, Shrestha BB. Facing north or south: Does slope aspect impact forest stand characteristics and soil properties in a semiarid trans-Himalayan valley? J Arid Environ 2015;121:112-23.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8]
[Table 1], [Table 2]