Forest Fire Risk Zonation Using Remote Sensing and GIS Technology in Kansrao Forest Range of Rajaji National Park, Uttarakhand, India

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1 Cloud Publications International Journal of Advanced Remote Sensing and GIS 2013, Volume 2, Issue 1, pp , Article ID Tech-56 ISSN Research Article Open Access Forest Fire Risk Zonation Using Remote Sensing and GIS Technology in Kansrao Forest Range of Rajaji National Park, Uttarakhand, India Tahir Malik 1, Ghulam Rabbani 2 and Majid Farooq 3 1, 3 Jammu & Kashmir State Remote Sensing Application Centre, Srinagar, Jammu & Kashmir, India 2 Hemwati Nandan Bahuguna Garhwal University, Uttarakhand, India Correspondence should be addressed to Tahir Malik, tahir s@ymail.com Publication Date: 8 April 2013 Article Link: Copyright 2013 Tahir Malik, Ghulam Rabbani and Majid Farooq. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited Abstract Forest fire is a major cause of changes in forest structure and function. Forest fires are as old as forests themselves. Forest fires are one of the major natural risks in the Uttarakhand forests. In such areas, fires occur frequently and there is a need for supranational approaches that analyze wide scenarios of factors involved. It is impossible to control nature, but is possible to map forest fire risk zone and thereby minimize the frequency of fire. Forest and wild land fire are considered vital natural processes initiating natural exercises of vegetation succession. However uncontrolled and misuse of fire can cause tremendous adverse impacts on the environment and the human society. A risk model for fire spreading is set up for Kansrao Forest Range of Rajaji National Park where Forest and wild land fires have been taking place historically, shaping landscape structure, pattern and ultimately the species composition of ecosystems both flora and fauna. It is based upon a combination of remote sensing and GIS data. In this study, Resourcesat P6 LISS III (spatial resolution 23.5m, 4 bands (Red) (Green) (NIR) (SWIR), Topo Sheet (SOI) no.53 J/4 on scale 1:50,000 and contour interval 20 meters, ASTER 30m and Garmin 72 GPS were used. For these analyses ArcGIS and ERDAS Imagine software was used. Land use information was obtained from the satellite images in this study. In this phase the distinction of species in the forest was determined using supervised classification. The lands that have priorities in case of fire were decided by combining the moisture of the land and slope classes that were determined by conventional approaches with the satellite images. The results of the analysis were shown by reports and graphs. The test region results should be applied all over Rajaji National Park. Keywords LISS, Forest Fire, Risk Zonation, DEM, Disaster

2 1. Introduction Wildfires are considered as a serious problem distressing many terrestrial ecosystems in the Earth system and causing economic damage for people [1] such as missing income relative to the land use, destruction and lost property, damages to agriculture, and loss of biodiversity [2, 3]. Also, it is one of the most important parts of land degradation that is caused by deforestation and desertification [4]. Information on the distribution of forest fire risk zones is essential for the effective and sound decision making process in the forest management [5]. Forest fire risk evaluation is a critical part of fire prevention, since pre-fire planning resources require objective tools to monitor when and where a fire is more prone to occur, or when will it have more negative effects [6]. Forest fire modeling involves the risk assessment and evaluation. The term risk is used to describe the probability that a fire might start, as affected by the nature and incidence of causative agents [7]. In India, about 2-3 % of the forest area is affected annually by fire and on an average over 34,000 ha forest areas are burnt by fire every year [8]. Remote sensing and GIS is important tool for mapping and management of forest fires. The first application of forest fire dates from 1960 when several aerial infrared scanners were tested for fire spot detection [9]. In addition to forest fire mapping, remote sensing has been effectively used in fire hazard rating system. In many fire hazard rating, critical factors were vegetation, slope, aspect and elevation. Deeming et al., 1978 [10] have used LANDSAT MSS image to obtain fuel oriented vegetation maps. Satellite remote sensing based forest fire detection methods have been developed and demonstrated by Cuomo et al., 2001 [11] and Salvador et al., 2000 [12]. In India, foresters have been debating the issue of forest fire control for a long time, but the paucity of information owing to the lack of qualitative and quantitative studies on forest fire and its effect has not resulted in any defined approach to controlling the forest fires. Forest fire risk zones are locations where a fire is likely to start, and from where it can easily spread to other areas. A precise evaluation of forest fire problems and decision on solutions can only be satisfactory when a fire risk zone mapping is available [13]. Understanding the behaviour of forest fire, the factors that contribute to making an environment fire prone, and the factors that influence fire behavior are essential for forest fire [9]. The GIS-based model seems to be a reasonably good approach for the conditions in India, where a major part of the forested land is being encroached upon by the population [14, 15]. In the present study, an attempt is made to prepare a forest fire risk zone map by integrating a satellite image, topographical and other ancillary data from a geographic information system(gis) for Kansrao Range which is the most forest fire sensitive area in Rajaji National Park. This study is also an attempt to exploit the capabilities of remote sensing and GIS techniques and to suggest an appropriate methodology for forest fire risk zone mapping. Such maps will help forest department officials prevent or minimize fire risk activities within the forest and take proper action when fire breaks out [16]. 2. Study Area Kansrao forest range is located in Rajaji National Park, the park extends from 29 o 52` to 30 o 15` north latitude to 78 o 57` to 78 o 23` east longitude in north India and covers an area about km 2.The study area is situated between 78 o 02` to 78 o 18` east longitude to 30 o 15` to 30 o 05` north latitude. The study area covers an area about km 2. The area is located in the Garhwal Shivaliks behind Haridwar across the Rishikesh-Haridwar road in India. The area has an uneven topography, with elevation ranging from 360 m to 860 m. The area is covered with thick green forest, mainly Sal, Teak, and other varieties of deciduous trees, along with grass and shrubs. Kansrao can be reached from International Journal of Advanced Remote Sensing and GIS 87

3 Cheering Crossing via Jabbarwala check post that lies 7 Kms further ahead. If this road is not motor able then Jabbarwala check post can be reached alternatively from Doiwala (3 kms from lachiwala railway bridge crossing on the highway). 3. Material and Methods 3.1. Data Figure 1: Location of Study Area The data used is Resourcesat P6 LISS III of December 2009 (spatial resolution 23.5m), Topo Sheet (SOI) No.53 J/4 on scale 1:50,000 with contour interval 20 meters, ASTER 30m and Garmin 72 GPS for defining and identifying the burned area and for estimating the vegetation loss. The data prepared for this study area were the following: forest type map, vegetation map, elevation, slope, aspect, standard topographic map and climate data (average wind, rainfall data, and temperature) Methods With the help of survey of India (SOI) website toposheet required for the study area has been identified. LISS III data was used for image analysis work. ASTER data was used to generate Digital Elevation Model (DEM) for the study area. The topographical factors like, altitude, slope and aspect layers were derived from DEM. The satellite images were corrected for the influence of atmosphere and topographic relief. These data were geo-coded with the help of rectified toposheets according to Geographic (lat. /long.) projection system with Everest ellipsoid and India Bangladesh datum was used using ERDAS Imagine software. Boundary of Kansrao forest range was digitized from toposheet in vector form with the help of ERDAS 9.1 using vector tool, an area of interest boundary generated using the vector file helped in to get the subset image of Kansro forest range from satellite data. Complete road network was digitized from toposheet and buffer was created for distance of 200m, 400m, 500m and 600m from the center of the road. The flowchart for the methodology is shown in Figure 2. International Journal of Advanced Remote Sensing and GIS 88

4 Fire Risk Zonation Model IJARSG An Open Access Journal (ISSN ) Forest type Map Forest Cover/ Type Density Fuel type index Remote Sensing Data Forest Density Map Elevation Map Elevation Index ASTER DEM Contour Map Digital Terrain Model (DTM) Slope Map Slope Index Roads Road Buffer Map Aspect map Aspect Index Accessibility Index Settlement Settlement Buffer Map Settlement Index Figure 2: Methodology Flowchart Settlement areas were digitized in point vector form, as the settlement was outside the Kansrao forest range the settlement buffer were created for 1000m, 2000m and 3000 m distance. The FCC of LISS III December 2009 image was used in the study. FCC was rectified with toposheet using first order nearest neighbor rules. A total of 20 ground points were used to register the image with the rectification error of less than 1 pixel, with the help of ERDAS Generation of Thematic Layers For inputting spatial data in GIS, it is necessary that the resource information is in the form of map; hence the mapping of the thematic layers is one of the primary requirements. Remote sensing, coupled with limited ground checks, is the most ideal way for generating resource maps. A. Generation of Forest Type and Density Type Layers In this study density mapping employed the use of LISS III data, which was free from cloud cover. Unsupervised classification approach was used for forest density mapping using ERDAS. The study area was classified into 50 spectral classes using unsupervised image classification approach. Eventually the forest cover of study area was stratified into four major types on the basis of density viz; VDF (very dense forest), MDF (moderately dense forest), OF (open forest) NF (non forest), as per the fundamental criteria of FSI for forest cover mapping (FCM). Supervised approach was used for forest type classification in which 20 training sets were taken from Google earth as ground control points. Based on the field visit the study area was classified into Moist Sal forest, Deciduous forest, Plantation and Degraded forest. B. Generation of Slope, Aspect and Elevation from DEM (Digital Elevation Model) A subset of the ASTER 30 m DEM of study area was clipped with the help of boundary vector layer. Elevation, Aspect and Slope were generated from ASTER 30m DEM with the help of ERDAS EMAGINE 9.1 using topographic analysis tool. International Journal of Advanced Remote Sensing and GIS 89

5 3.4 Generating Index Value Maps In the present study the map layers generated above viz. forest density (Figure 3), vegetation type map (Figure 4), fuel type map (Figure 5), slope map (Figure 6), aspect map (Figure 7), elevation map (Figure 8), road map (Figure 9) and settlement map (Figure 10) and were reclassified for assigning weightage Weights were given to each factor according to their influence on fire behavior by having experience and the opinion of the experts in the field (Tables 1 to 6). Table 1: Elevation Index Map and Weights Elevation in Meters Weights Table 2: Slope Classes and Weights Slope % (degrees) Weights Above 45 % 4 Table 3: Aspect Classes and Weights Aspect Weights North 1 East 2 West 3 South 4 Table 4: Road Buffer Distance and Weights Roads Buffers (m) Weights 200 m m m m 1 Table 5: Settlement Buffer Distance and Weights Settlement Buffer (m) Weights 1000 m m m 3 International Journal of Advanced Remote Sensing and GIS 90

6 Table 6: Fuel Type Index Type Density Weights Deciduous forest Degraded forest High 4 Medium 4 Low 3 High 3 Medium 2 low 1 Plantation High 2 Medium 2 Low 1 Moist Sal forest High 2 Medium 1 Low 1 4. Fire Risk Zonation Index/Fire Risk Modeling In this study spatial modeling has been done to obtain the combined effect of fuel type index, elevation index, slope index, aspect index, road index and settlement index. Different weights have been assigned as per the importance of the particular variables in relation to the area under study. Highest weight of nine have been given to the fuel type index, because, fuel contributes to the maximum extent due to inflammability factor. The second highest weights has been given to slope and aspect, because, sun facing aspects receive direct sun rays and make the fuel drier and highly inflammable; higher slopes contribute to convectional preheating and easy ignition and spreading of fire. Besides on steep slopes, the dry biomass is more close to fire flames causing the fire to spread more speedily. Spatial analysis using a function in model maker tool of ERDAS was carried out which revealed that on a 36% slope, the rate of fire spread is twice as compared to fire on a slope of 18%. The following equation was used in the map calculation: Where FRZI = Fire Risk Zonation Index FUI SLI ASI RDI STI ELI = Fuel Type Index = Slope Index = Aspect Index = Road Index = Settlement Index = Elevation Index As different weights were tried for different variables and the weights given in the equations were used to generate fire risk zonation map. The fire risk index values in this map (FRZI) were ranging from 14 to 135. Based on the statistics of different weight classes, the map was reclassified into five classes as very low, low, moderate, high and very high to generate fire risk area map. International Journal of Advanced Remote Sensing and GIS 91

7 Figure 3: Forest Density Map Figure 4: Vegetation Type Map Figure 5: Fuel Type Map Figure 6: Slope Map Figure 7: Aspect Map Figure 2: Elevation Map Figure 9: Road Buffers Figure 2: Settlement Buffers International Journal of Advanced Remote Sensing and GIS 92

8 5. Results and Discussion Settlement, accessibility and forest types had played an important role in fire risk zonation modeling. The other variables elevation and slope have comparatively less impacting estimation of fire risk zonation. The area under different fire risk zones is summarized in Table 7. Table 7: Table Showing Fire Risk Percentage Fire Risk Percentage Area in km 2 Very high 4% 3.0 High 29% Moderate 45% Low 21% Very low 1% 0.88 Figure 11: Showing Fire Risk Percentage 5.1. Fire Risk Zonation Map A further study of risk zonation map (Figure 12) with forest type map showed that deciduous and degraded forest types having high fuel content were falling on very high and high risk areas where as moist sal and plantation were falling on low and very low fire risk areas. Very high and high fire risk areas were mostly lying in southern and western aspect having warmer and dry conditions, whereas northern and eastern slopes were falling on low and very low fire risk areas. This may be attributed to the fact that southern and western aspects receive high amount of sun insulation for the major part of the day and accordingly are warmer than other aspects. Fire could thus certainly be averted by taking precautionary measures. Hence, despite the fact that no fire prone areas can be demarcated where fire occurs due to natural or intentional human causes, it is advantageous to have a fire risk map to avert possible disasters caused by fire due to human activities. It should prove to be helpful to the Forest Department, as this type of fire risk zone map would enable the department to set up an appropriate fire-fighting infrastructure for the areas more prone to fire damage. Such a map would help in planning the main roads, subsidiary roads, inspection paths, etc. and may lead to a reliable communication and transport system to efficiently fight small and large forest fires. Figure 12: Forest Fire Risk Zonation Map International Journal of Advanced Remote Sensing and GIS 93

9 5.2. Validation of Forest Fire Risk Model The final forest fire risk model was validated with past fire incidences data that was collected from field visits and fire points were taken from Forest Survey of India website. The results of the study showed that out of 27 fire incidences 20 incidences had occurred in very high and high risk areas. 6. Conclusion Fire risk modeling using multi criteria analysis and integrating different layers resulted in developing fire risk assessment of study area. Fire risk index map can be used to prioritize for taking forest fire prevention initiatives at management level. Forest type, density maps and other parameters can be helpful in installation of suitable watch towers for prevention of fire. Layers generation for slope altitude and forest density can be used for calculating response time for the disaster. Digital elevation model can be effectively used for studying terrain characteristics and for generating a view shed. The precision in the modeling could be increased by adding more number of variables in the analysis. However, the selection of variables should be based on knowledge base of the area. The areas shown under very high, high and moderate fire risk zones are those areas where fire can be unintentionally caused by human activities, and where fire could thus certainly be averted by taking precautionary measures. Hence, despite the fact that no fire prone areas can be demarcated where fire occurs due to natural or intentional human causes, it is advantageous to have a fire risk map to avert possible disasters caused by fire due to human activities. It should prove to be helpful to the Forest Department, as this type of fire risk zone map would enable the department to set up an appropriate fire-fighting infrastructure for the areas more prone to fire damage. Such a map would help in planning the main roads, subsidiary roads, inspection paths, etc. and may lead to a reliable communication and transport system to efficiently fight small and large forest fires. Acknowledgment We would like to express our special thanks to Mr. Porwal (I.F.S.), for his guidance to this research paper and sharing his knowledge and experience with us, for his contributions. References [1] Butry D.T., et al. What is the Price of Catastrophic Wildfire? Journal of Forestry ; [2] Burke T.E., et al. An Internet Based Forest Fire Information System. Unasylva (2) [3] Pettenella D., et al., 2009: Proposal for a Harmonized Methodology to Assess Socio-Economic Damages from Forest Fires in Europe. Final Reports of Studies on Forest Fires Done Under Forest Focus Regulation. Economic Damages Study. Joint Research Centre (JRC) and the Directorate General for Environment (DG ENV) of the European Commission (EC). [4] Hernandez-Leal P.A., et al. Fire Risk Assessment Using Satellite Data. Advances in Space Research ; [5] Hussin Y.A., et al. 2008: The Applications of Remote Sensing and GIS in Modeling Forest Fire Hazard in Mongolia. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Archives -Volume XXXVII, Part B5, Beijing, China, International Journal of Advanced Remote Sensing and GIS 94

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