16 th Esri India User Conference 2015 Page 1 of 12 “Impact analysis of forest fires in tiger habitat using geospatial technology” Ameya Joshi 1 , Sayali Holankar 2 , Payal Gajbhiye 3 1 Student-M. Tech GIS, NIIT University 2 Student-M. Tech GIS, NIIT University , 3 Student-M. Tech GIS, NIIT University NH-8, Delhi-Jaipur National Highway, Neemrana, Rajasthan Word Limit of the Paper should not be more than 3000 Words = 7/8 Pages) Abstract: Forest Fires are the most underrated catastrophe of the world but has posed a severe impact on the habitats and many wild life species, thus conservation of habitats is vital. India is a land of Royal Bengal Tigers which are the top predators in the ecosystem. Their preferred habitat are swamps, grasslands, and rain forests. Forest fires, thus not only have a direct impact on the producer species in the food chain pyramid like grass or trees but also on the availability of the prey -implying middle consumers and ultimately affecting Tigers and its corridors. GIS is a leverage tool in viewing and analyzing data spatially. Our study aims at investigating the impact of fires in various Tiger Reserves from different states of India by using GIS. The study area includes six tiger reserves namely Corbett (Uttarakhand), Dampa (Mizoram), Kanha (Madhya Pradesh), Tadoba-Andhari (Maharashtra), Nagarhole (Karnataka), Anamalai (Tamilnadu) considering variation in their location, topography, climatic patterns and meteorological parameters. Here, we have used the frequency ratio method to assess the influence of causative factors like Slope, Aspect, Elevation, Land use land cover(LULC), proximity to roads (Euclidean distance) on the spread of fire and to generate fire risk potential index maps. The climatological factors like Temperature, Wind speed and precipitation have been used so as to assess area wise variations of these factors and its impact on behavior of fire. Overall, our results tries to identify important causative factors of fire, useful to address broad scale fire concerns in Tiger Reserves of India. This will also help in creating an awareness about the issue. About the Authors: Mr. Ameya Joshi He has internship experience in GIS. He has also presented on a project ‘Development of beach infrastructure using GIS’ in Indian Society of Geomatics (ISRS) conference 2013. Email ID:[email protected]Contact:+91-7610920352 Ms. Sayali Holankar She is an Environment Engineer. She has completed two months summer internship in C-DAC Pune for the period of June-July, 2015.She has worked on project “Investigating fire occurrence patterns and identification of potential fire risk zones for Kudremukh and Periyar National Parks and Tansa Wildlife Sanctuary of Western Ghats of India from 2001-2014”. Email ID:[email protected]Contact:+91-9420778555 Ms. Payal Gajbhiye She is an Environment Engineer. She has completed two months summer internship in C-DAC Pune for the period of June-July, 2015.She has worked on project “Investigating fire occurrence patterns and identification of potential fire risk zones for Kudremukh and Periyar National Parks and Tansa Wildlife Sanctuary of Western Ghats of India from 2001-2014”. Email ID:[email protected]Contact:+91-7615874733
Transcript
16th Esri India User Conference 2015
Page 1 of 12
“Impact analysis of forest fires in tiger habitat using geospatial
NH-8, Delhi-Jaipur National Highway, Neemrana, Rajasthan
Word Limit of the Paper should not be more than 3000 Words = 7/8 Pages)
Abstract:
Forest Fires are the most underrated catastrophe of the world but has posed a severe impact on the habitats and many wild life species, thus conservation of habitats is vital. India is a land of Royal Bengal Tigers which are the top predators in the ecosystem. Their preferred habitat are swamps, grasslands, and rain forests. Forest fires, thus not only have a direct impact on the producer species in the food chain pyramid like grass or trees but also on the availability of the prey -implying middle consumers and ultimately affecting Tigers and its corridors. GIS is a leverage tool in viewing and analyzing data spatially. Our study aims at investigating the impact of fires in various Tiger Reserves from different states of India by using GIS. The study area includes six tiger reserves namely Corbett (Uttarakhand), Dampa (Mizoram), Kanha (Madhya Pradesh), Tadoba-Andhari (Maharashtra), Nagarhole (Karnataka), Anamalai (Tamilnadu) considering variation in their location, topography, climatic patterns and meteorological parameters. Here, we have used the frequency ratio method to assess the influence of causative factors like Slope, Aspect, Elevation, Land use land cover(LULC), proximity to roads (Euclidean distance) on the spread of fire and to generate fire risk potential index maps. The climatological factors like Temperature, Wind speed and precipitation have been used so as to assess area wise variations of these factors and its impact on behavior of fire. Overall, our results tries to identify important causative factors of fire, useful to address broad scale fire concerns in Tiger Reserves of India. This will also help in creating an awareness about the issue.
About the Authors:
Mr. Ameya Joshi He has internship experience in GIS. He has also presented on a project ‘Development of beach infrastructure using GIS’ in Indian Society of Geomatics (ISRS) conference 2013. Email ID:[email protected] Contact:+91-7610920352 Ms. Sayali Holankar She is an Environment Engineer. She has completed two months summer internship in C-DAC Pune for the period of June-July, 2015.She has worked on project “Investigating fire occurrence patterns and identification of potential fire risk zones for Kudremukh and Periyar National Parks and Tansa Wildlife Sanctuary of Western Ghats of India from 2001-2014”. Email ID:[email protected] Contact:+91-9420778555 Ms. Payal Gajbhiye She is an Environment Engineer. She has completed two months summer internship in C-DAC Pune for the period of June-July, 2015.She has worked on project “Investigating fire occurrence patterns and identification of potential fire risk zones for Kudremukh and Periyar National Parks and Tansa Wildlife Sanctuary of Western Ghats of India from 2001-2014”. Email ID:[email protected] Contact:+91-7615874733
Forest fire is a phenomenon that impacts forests as well as wildlife and their habitat. It is prevalent mostly in particular months of year called fire season. In India fire season is from months of February to mid-June (http://www.fire.uni-freiburg.de/photos/in/in.htm). As per the study of Forest Survey of India (2011), the Forest and tree cover in India is 78.29 million ha, which is 23.81% of the country’s geographical area (http://www.fsi.nic.in/). Comparing to 2009 assessment, there is decrease of 367km2 in the forest cover of India. It has been estimated that the proportion of fire prone forest area is 33% in some Indian States to over 90% in others [1] and about 3.73 million ha of forests are annually affected with fire [2-4]. The impacts of forest fires on livestock and wildlife are either immediate or secondary. Immediate impacts results in direct injury or mortality to plants and animals, animals fleeing or seeking refuge whereas secondary impacts include an alteration of forage productivity, availability and quality, and animal performance as well as creating, destroying, enhancing, or degrading various habitat attributes such as cover, shelter, structure, and natal/breeding (http://www.agf.gov.bc.ca/). Tigers are the apex predators in every ecosystem they inhibit. Until the 20th Century there were nine tiger subspecies that probably numbered over 100,000 animals. Currently there are either 5 or 6 subspecies remaining in existence and all are endangered according to the International Union of Conservation (IUCN). All tiger subspecies put together currently amount to fewer than 3,000 endangered tigers remaining in the wild. The Bengal tiger is the most numerous of endangered tiger subspecies, with probably fewer than 2000 remaining at large in India, Nepal, Bangladesh and Bhutan (http://www.allaboutwildlife.com/). As per National Tiger Conservation Authority (NTCA) there are total 47 tiger reserves in India at present. These Tiger Reserves (TR) are located in different parts of India. As climate of India varies from region to region in terms of its meteorological parameters and topographical factors, six Tiger reserves have been taken as study areas from different parts of India which are Corbett (Uttarakhand), Dampa (Mizoram), Kanha (Madhya Pradesh), Tadoba-Andhari (Maharashtra), Nagarhole (Karnataka), Anamalai (Tamilnadu). The Remote Sensing and GIS technologies could be effectively used in fire risk zonation. Satellite data in digital form provides flexibility with respect to its use as it provides broader coverage, high repeativity, variable resolutions and flexibility to investigate ground features at different scale. It facilitates the investigation of phonological patterns of forest as well as their extent and composition. Various methods and models have been used to analyze the forest fire prone areas, their dynamics and patterns. Some of these includes fire risk probability models [5], the Canadian fire weather index (FWI) system [6], and frequency ratio method to evaluate fire susceptibility index [7]. There are different satellite datasets easily and freely available for download. MODIS provides active fire locations which can be used to study the forest fire dynamics (https://firms.modaps.eosdis.nasa.gov). Landsat-8 images with 30 meter (m) resolutions have been used to generate land use land cover (LULC) maps. Tiger reserves boundaries were digitized on the basis of wildlife institute of India datasets. ESRI base maps were used for road layer generation. From ASTER 30m DEM, slope, elevation and aspect maps were procured. This forms the strong basis to investigate dynamics of forest fire in six different tiger reserves of India. The efforts have been made to investigate forest fire frequency ratio from 2001 to 2014 for the months of January to June, to ultimately identify forest fire risk potential index.
Objective Study aims at investigating the patterns of forest fire for six tiger reserves from the year 2001 to 2014 and from the month of January to June. Specific objectives are as follows:
(i) Investigating the impact of different parameters on forest fire occurrence.
(ii) Identifying forest fire risk potential zones in Tiger Reserves.
Methodology
The grids of 500×500m were made on the study area boundaries so as to bring all the datasets into same resolution of 500m pixel size. All the datasets were projected into GCS_WGS_1984 coordinate system. Fire locations from year 2001 to 2014 were used to extract the elevation, slope, aspect, lulc and Euclidean class values for each of the fire location after reclassification. In case of Euclidean distance sum values were taken to generate 500×500m grids whereas in case of slope, elevation, aspect mean values were taken for grid generation. LULC maps were gridded into 500 meter resolution by taking majority values in order to assign fire location to respective LULC class.
The frequency ratio method is used for the generation of forest fire risk potential index maps, so as to assess the probabilistic value of each considered factor that impact the forest fire event. This method is widely used in risk analysis studies [7], [8], [9], [11]. From, all the reclassified datasets the class wise grid count and fire events were obtained from year 2001-2014. After that, %grid count and %fire events were estimated to calculate the frequency ratios for all the years and for all the factors under consideration. The formula used for calculating the frequency ratio is as follows:
Frequency Ratio = (%Fire count) / (%Grid Count) [7]
The frequency ratio (FR) estimate is a quantitative indicator which indicates strength of the relationship between the fire risk event occurrence and the specific class values of the causative factor [10]. All the maps of one particular year, containing frequency ratios as one of their attribute were summed up to make the final forest fire risk potential index map. The formula for fire susceptibility index (FSI) is as follows:
𝑭𝑺𝑰 = ∑ 𝑭𝑹 [10]
Where, FR= Frequency Ratio
A higher FSI indicates higher fire risk and vice versa. Similarly this procedure is followed for generation of monthly (January, February, March, April, May, June) forest fire risk potential index maps.
Software Used: ArcGIS 10.3.1
Tools Used: Grid Index Feature, Slope, Aspect, Euclidean Distance, Zonal statistics, Reclass, Model Builder
16th Esri India User Conference 2015
Page 5 of 12
Fig.2- Methodology
Study Area Boundary
MODIS Fire Product
(2001-2014)
Satellite Data ESRI Base Map
Aster DEM Landsat-8 imageries
Roads (2001-2014)
Fire locations (2001-2014)
Slope Map
LULC Maps
Aspect Map
Euclidean Distance
Conversion to 500×500m
Homogenizations to 500m grids
Reclassification
Classification of fire locations for all the input data
Frequency Ratio Estimation
Forest fire risk potential index maps
Mean Mean Sum
Majority
16th Esri India User Conference 2015
Page 6 of 12
Results and Discussions
The yearly trend analysis with respect to average wind speed, precipitation and temperature and how these factors have influenced the behavior of fire distribution for each of the tiger reserves have been shown in fig.2. Dampa TR has highest fire events in 2009 and is justifying as it also experienced highest temperature and low rainfall. Fire count seem to be low in 2002 wherein rainfall is moderate. In case of Corbett TR, fire events are highest in 2008 wherein temperature is high and rainfall is low. Kanha TR experienced highest temperature and fire events in 2009, but lowest events are observed in 2014 even though temperature was high and rainfall was moderate. It seems Fire events in Kanha TR may be more human induced compared to other tiger reserves. Same may be the case with Anamalai TR, where lowest events have occurred in 2001 and 2011 in presence of moderate rainfall and high temperature. Wind Speed must have played an important role in 2004 as it was the highest and may have influenced to highest fire counts compared to other years. Temperature, Wind Speed and precipitation are higher in 2004 with highest fire counts in Nagarhole TR. Tadoba TR has highest fire counts in the year 2007 with moderate rainfall and wind speed. Fire events show lowest trend in 2014 although the rainfall seem to be critically low with high temperature and wind speed.
0
2
4
6
8
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14A
vg.
win
d S
pe
ed
Year
Yearly average wind Speed
Corbett Dampa AnamalaiNagarhole Tadoba Kanha
02468
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
Avg
. P
rece
pit
atio
n
Year
Yearly Average Precepitation
Corbett Dampa Anamalai
Nagarhole Tadoba Kanha
010203040
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
Avg
. Te
mp
ratu
re
Year
Yearly Average Temperature
Corbett Dampa Anamalai
Nagarhole Tadoba Kanha
0100200300400
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
Fire
Co
un
t
Year
Yearly Fire Count
Corbett Dampa Anamalai
Nagarhole Tadoba Kanha
2a. 2b.
2c. 2d.
Fig.2-Yearly Average wind Speed, Precipitation, Temperature and Fire Count Distribution for Tiger Reserves
16th Esri India User Conference 2015
Page 7 of 12
As shown in fig.3, the highest fire counts for all tiger reserves have occurred in the month of March. Temperature shows a positive correlation with the occurrence of fire events in the month of March. Precipitation is low in March which may be one of the influential factor why occurrence of fire is more. In case of Kanha and Corbett, forest fires are also highest in April. Compared to all, Dampa has the highest precipitation in the month of April, May and June.
0
1
2
3
4
5
6
7
Jan Feb Mar Apr May Jun
Avg
. W
ind
Sp
ee
d
Months
Monthly Average Wind speed
Corbett Dampa Anamalai Nagarhole Tadoba Kanha
0
5
10
15
20
25
Jan Feb Mar Apr May Jun
Avg
. P
rece
pit
atio
n
Months
Monthly Average Precepitation
Corbett Dampa Anamalai Nagarhole Tadoba Kanha
0.00
10.00
20.00
30.00
40.00
Jan Feb Mar Apr May Jun
Avg
. Te
mp
era
ture
Months
Monthly Average Temperature
Corbett Dampa Anamalai Nagarhole Tadoba Kanha
0.00
100.00
200.00
300.00
400.00
500.00
600.00
Jan Feb Mar Apr May Jun
Fire
Co
un
t
Months
Monthly Fire Count
Corbett Dampa Anamalai Nagarhole Tadoba Kanha
3a. 3b.
3c. 3d.
Fig.3-Monthly Average wind Speed, Precipitation, Temperature and Fire Count Distribution for Tiger Reserves
16th Esri India User Conference 2015
Page 8 of 12
Fig. 4 displays the spatial analysis of fire events considering factors such as elevation, slope, LULC, and Euclidean distance showing very interesting patterns. All the tiger reserves experience maximum fire events in proximity to the roads and as the distance from roads increases, fire counts decreases. Most fire counts have occurred in dense forest in Tadoba, Corbett, Dampa, and Nagarhole. But in Anamalai, open forest experiences maximum fire events. In Kanha forest fires have occurred mostly in semi-dense forest. The overall trend of fire events in all tiger reserves with respect to its slope is more between gentle to moderate whereas decreasing towards steeper slopes. When it comes to elevation, moderate elevation ranges experience highest fire incidences in Corbett, Tadoba, Nagarhole, Anamalai. In case of Dampa at lower elevations, fire events are high. However, Kanha shows an exception with most of the fire incidences at higher altitudes.
0
20
40
60
80
100
120
140
160
180
200
1 2 3 4 5 6 7 8 9
Fire
Co
un
t
Euclidean Distance Class Total
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9
Fire
Co
un
t
Slope Class Total
0
50
100
150
200
250
Fire
Co
un
t
Elevation(meters) Total
0100200300400500600700800900
Agriculture DenseForest
OpenForest
Semi DenseForest
Fire
Co
un
t
LULC Class Total
Fig.-4 Sample graphs of Tadoba-Andhari TR displaying fire events in each causative factors
4a. (Class 1-3 lower distance, 4-6 moderate distance and 7-9 higher distance from road)
4c. Elevation class vs fire events 4d. LULC Class vs fire events
16th Esri India User Conference 2015
Page 9 of 12
Sample Fire Risk Potential Index Maps for Tadoba- Andhari TR from year 2001-2014
0-5 6-10 11-15 >15 Legend
Fire Location
2001 2002
2004
2005
2006
2008
2009
5a. 5b. 5c.
5d. 5e. 5f.
5g. 5h. 5i.
2003
2007
2009
16th Esri India User Conference 2015
Page 10 of 12
The fig.5 highlights the forest fire risk potential index maps from year 2001 to 2014 for Tadoba-Andhari Tiger Reserve representing the darker color areas as highest fire prone whereas lighter color areas as lowest fire prone areas. In similar manner month wise (January to June) and year-wise (2001-2014) maps have been created for other considered tiger reserves. The sample frequency ratio table of year 2001 for Tadoba-Andhari based on Euclidean distance have been displayed in Table.1. Similar procedure has been followed for other considered factors to generate the month wise and year wise forest fire risk potential index maps.
2010
2011
2012
2014
5j. 5k. 5l.
5m. 5n.
0-5 6-10 11-15 >15 Legend
Fire Location
Fig. 5-Sample Forest fire risk potential index maps For Tadoba-Andhari Tiger Reserve From 2001-14
2013
16th Esri India User Conference 2015
Page 11 of 12
Table.1 Sample Frequency Ratio Table of Euclidean distance for Tadoba- Andheri Tiger Reserve 2001
Conclusion Based on our investigations, we conclude that fire events are mostly governed by topography, temperature, precipitation, wind speed, local climate and anthropogenic disturbances. There may be other factors which may influence the fire events like wind velocity and direction, soil moisture etc. but they have not been considered in present study. In present work study the frequency ratio method seems to be promising as it has the potential to accurately depict the forest fire potential areas. The proposed future work includes identification of reasons of occurrences of fire and investigate more drivers which govern the incidences and spread of the fire and implement the same on other tiger reserves in India. Secondly, the data of mortality rates of animals due to forest fire will be fruitful in correlating our present study to determine interesting patterns.
Tadoba Year- 2001 ( Euclidean Distance)
Class Grid Count
% Grid Count
Fire Count
% Fire Count Frequency Ratio
1 159660 22.99297509 12 25 1.087288613
2 118091 17.00653527 8 16.66666667 0.980015412
3 108552 15.63280366 8 16.66666667 1.066134203
4 91262 13.1428341 8 16.66666667 1.268118165
5 79749 11.48482256 3 6.25 0.544196479
6 58185 8.379345206 4 8.333333333 0.994508894
7 38732 5.577877434 0 0 0
8 29072 4.186720354 3 6.25 1.492815252
9 11083 1.596086327 2 4.166666667 2.610552197
694386 48
16th Esri India User Conference 2015
Page 12 of 12
References 1. Roy PS. (2004) Forest Fire and Degradation Assessment using Satellite Remote Sensing and Geographic
Information System. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, pp. 362-
363.
2. FSI (Forest Survey of India) (1997). State of Forest Report. Ministry of Environment and Forest. Govt. of
India.
3. Bahuguna VK. (1999) Forest fire prevention and control strategies in India. International Forest fires
News, 20: 5-9.
4. Srivastava R, Chidambaram KK, Kumaravelu G. (1998) Impact of forest fire and biotic interference on the
biodiversity of Eastern Ghats. Indian Forester.125 (5).
5. Li Xiaowei, Fu Guobin; Zeppel, Melanie JB, Yu Xiubo, Zhao Gang, Eamus, Derek, Yu Qiang. Probability
Models of Fire Risk Based on Forest Fire Indices in Contrasting Climates over China. Journal of Resources
and Ecology; Jun2012, Vol. 3 Issue 2, p105.
6. Lehtonen, Ilari, Ruosteenoja, Kimmo, Venäläinen, Ari, Gregow, Hilppa. The projected 21st century forest-
fire risk in Finland under different greenhouse gas scenarios. Boreal Environment Research; Apr2014,
Vol. 19 Issue 2, p127
7. Biswas S, Vadrevu K. P, Lwin Z. M, Lasko K, Justice C. O. Factors Controlling Vegetation Fires in Protected
and Non-Protected Areas of Myanmar. PLOS ONE | DOI:10.1371/journal.pone.0124346 April 24, 2015 8. Manap MA, Nampak H, Pradhan B, Lee S, Sulaiman WNA, Ramli MF. Application of probabilistic based
frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arab J Geosci. 2014; 7(2):711–724.
9. Lee S, Sambath T. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol. 2006 Apr 4; 50(6):847–855.
10. Nepstad DC, Verssimo A, Alencar A, Nobre C, Lima E, Lefebvre P, et al. Large-scale impoverishment of Amazonian forests by logging and fire. Nature. 1999; 398:505–508. DOI: 10.1038/19066
11. Saro Lee, Biswajeet Pradhan. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides (2007) 4:33–24 May 2006 published online: 7 July 2006 | DOI: 10.1007/s10346-006-0047-y
