Spatial analysis of Fire Characterization along with various gradients of Season, Administrative units, Vegetation, Socio economy, Topography and Future climate change: A case study of Orissa state in India

Abstract

Fire events are an increasing phenomenon these days due to the climate change. It is responsible for forest degradation and habitat destruction. Changes in ecosystem processes are also noticed. The livelihood of tribal population is also threatened. Geospatial technologies along with Remotely Sensed data have enormous capability to evaluate the various diversified datasets and to examine their relationship. In this analysis, we have utilized the long term fire events at district level for the Orissa state of India and forest fire hotspots were identified. The fire pattern was analyzed with respect to the existing vegetation types, tribal population and topography to understand its association/relationship. Furthermore, it was evaluated with future climate change data for better comprehension of future forest fire scenario. The study reveals that Kandhamal, Raygada and Kalahandi district have highest fire frequency representing around 38% of the total Orissa fire events. The vegetation type "Tropical mixed deciduous and dry deciduous forests" and "Tropical lowland forests, broadleaved, evergreen, <1000m" occupy the geographical area roughly 43% whereas they retain fire percent equivalent to 70%. Approximately 70% of forest fire occurred in the area where tribal population was high to very high. The 60% of forest fire occurred where elevation was greater than 500 meters whereas 48% of fire occurred on moderate slopes. Our observation of future climate change scenario for the year 2030 reflects the increase in summer temperature and irregular rainfall pattern. Therefore, forest fire intensity will be more in future in the state of Orissa whereas it's intensity will be more severe in few of the district such as Kandhamal, Raygada, Kalahandi and Koraput which have significantly high forest fire events in present scenario. The outcomes of the present study would certainly guide the policymakers to prepare more effective plan to protect the forest which is main source of livelihood to the tribal population keeping in mind of future climate change impact for prioritization of various districts of state of Orissa suffering from forest fires.

Full text

Ecological Questions 29 (2018) 4: 9–22 http://dx.doi.org/10.12775/EQ.2018.027
Spatial analysis of fire characteristies along with various
gradients of season, administrative units, vegetation,
socio-economy, topography and future climate change:
A case study of Orissa state in India
Firoz Ahmad1, Md Meraj Uddin2, Laxmi Goparaju1*
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2University Department of Mathematics, MCA, Ranchi University, Ranchi, Jharkhand, India,
*e-mail: goparajulaxmi@yahoo.com
Received: 23 April 2018 / Accepted: 25 June 2018
Abstract. Fire events are an increasing phenomenon these days due to the climate change. It is responsible for forest degradation
and habitat destruction. Changes in ecosystem processes are also noticed. The livelihood of tribal population is also threatened. Geo-
spatial technologies along with Remotely Sensed data have enormous capability to evaluate the various diversified datasets and to
examine their relationship.
In this analysis, we have utilized the long term fire events at district level for the Orissa state of India and forest fire hotspots
were identified. The fire pattern was analyzed with respect to the existing vegetation types, tribal population and topography to un-
derstand its association/relationship. Furthermore, it was evaluated with future climate change data for better comprehension of future
forest fire scenario.
The study reveals that .DQGKDPDO5D\JDGD DQG.DODKDQGLGLVWULFWKDYHKLJKHVWILUHIUHTXHQF\UHSUHVHQWLQJDURXQG RIWKH
total Orissa fire events. The vegetation type “Tropical mixed deciduous and dry deciduous forests” and “Tropical lowland forests,
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Our observation of future climate change scenario for the year 2030 reflects the increase in summer temperature and irregular
rainfall pattern. Therefore, forest fire intensity will be more in future in the state of Orissa whereas it’s intensity will be more severe
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scenario.
The outcomes of the present study would certainly guide the policymakers to prepare more effective plan to protect the forest
which is main source of livelihood to the tribal population keeping in mind of future climate change impact for prioritization of vari-
ous districts of state of Orissa suffering from forest fires.
Keywords: forest fire events, forest fire hotspot, socio-economy, topography, climate change scenario (RCP-6), Orissa.
1. Introduction
Climate and bio-physical environment (e.g., weather, soils,
topography, and vegetation) of a region control the natu-
ral fire regimes (Gedalof, 2011). Fire regimes (frequency,
intensity, size, pattern, season, and severity) are important
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Gill, 1975). Climate change of any region adversely im-
pacts on cultural, ecological values and socioeconomic
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10 Firoz Ahmad, Md Meraj Uddin, Laxmi Goparaju
2013) by increasing the frequency and intensity of fires,
higher temperatures, drought leads to extreme changes to
their ecosystem processes, forest habitat degradation. Ris-
ing temperatures, hotter and drier summers, and wildfires
are expected to increase in the frequency, intensity, and
severity (Moritz et al., 2012). Droughts, as well as tree
mortality and vegetation stress, will result in longer fire
seasons (Flannigan et al., 2005) by increasing the fuel load
(Finney, 2001), plant disease (Sturrock et al., 2011), insect
outbreaks (Hicke et al., 2012) and the spread of invasive
species (NWF, 2011).
The enhancement of sensors in satellite remote sens-
ing has strengthened the opportunity for mapping various
natural resources, monitor/assess natural calamities and
can be successfully used to monitor fire patterns (Ager et
DO$KPDG*RSDUDMXE'Z\HUHWDO
Dwyer et al., 2000) and have increased our comprehension
RIELRPDVVEXUQLQJ.DXIPDQHWDOODQGXVHODQG
cover change (Eva & Lambin, 2000) pattern, and fire risk
and threat mapping (Chuvieco & Congalton, 1989). Satel -
lite based fire monitoring thus becomes a boon and pro-
vides a reliable source of fire events that largely overcome
the limitations of traditional fire records system (Csiszar et
DO(YD/DPELQ)ODQQLJDQ9RQGHU+DDU
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contributing to carbon cycle, greenhouse gases and aerosol
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der Werf et al., 2010) whereas biomass burning has a very
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human lives and property (Bowman et al., 2009).
India is one of the mega-biodiversity countries which
retains 173,000 forest villages largely occupied by the eth-
nic’s communities, their life fully revolves in and around
the forest for their livelihoods (Aggarwal et al., 2009).
The Orissa state has been suffering due to extreme
weather condition (Ray-Bennett, 2009). It has a rich tropi-
cal forest cover mainly dominated by deciduous forests
which retain lots of leaf litter during summer due to leaves
shedding usually in autumn, the extreme weather such as
drought dries out the vegetation, making it easier to burn
and thus becomes a better fuel source of fire (Pausas & Fer-
nandez-Munoz, 2012). Dry deciduous forests are more
vulnerable to forest fire (FAO, 2001). Orissa is one of the
highest poverty incidence states of India (Mahendra Dev &
Ravi, 2007). Several starvation deaths have been reported
from time to time especially to the tribal dominated pockets
of this state (http://www.indiatogether.org/starve-poverty).
In the diversified landscapes of Orissa, the people oc-
cupation living close and within the forests is inextricably
linked to the forest ecosystem. People depend on the for-
est for a variety of forest products for food, fodder, fuel,
agriculture, home, and a variety of commercial minor for-
est produces which can potentially deteriorate forest if uti-
OL]HGLQXQVXVWDLQDEOH PDQQHU9DULRXVVWXGLHVDWUHJLRQDO
level reveal that the pattern of collection of these minor
forest products and its significant impact on local forest
UHVXOWVLQGHJUDGDWLRQ0LVKUDHWDO$UMXQDQHWDO
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& Mishra, 2001) due to huge dependency on local/tribal
livelihood.
An analysis of temporal variation in monthly, seasonal
and annual precipitation over the state of Orissa during
the period from 1871 to 2006 revealed that a long-term
in significant declining trend of annual as well as monsoon
(June-September) rainfall and an increasing trend in post-
monsoon season (October-November) in the state manifest
the rainfall anomalies. However, the analysis also shows
that a decreasing trend in monthly rainfall in of June, July
and September, and an increasing trend in August more
predominant in the last 10 years (Patra et al., 2012) will
certainly increase forest fire period to post summer sea-
son. Similarly, analysis by Tanner et al. (2007) shows that
after 1961, the rainfall patterns are below the normal, sug-
gesting a drier spell in Orissa which will affect the forest
resources having an impact on the livelihood of the poor.
The state witnessed decreasing rainfall in some parts of
WKH\HDU0DKDSDWUD0RKDQW\3DWUDHWDO
which affected entire state badly.
The climate change in Orissa shows the possibility of
an increase in hydrologic extremes (Ghosh & Majumdar,
2006) including increasing probability of severe and ex-
treme droughts (Ghosh & Majumdar, 2007) which will
also affect the regeneration of various forest tree species
(Maithani et al., 1986) including Sal (Shorea robusta). The
forest of Orissa should be the biggest future challenge to
policy makers in preserving and conserving forest resourc-
es in tribal dominated state.
Few studies on forest fire have been carried out in India
such as Reddy et al. (2017) who quantified total burnt area
extent and CO2 HPLVVLRQV*LULUDMHWDO  LGHQWLILHG
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the spatial patterns in fire events across diversified geo-
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and Goparaju (2017a) identified forest fire hotspot dis-
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Jharkhand state fire trends, identified the forest fire hotspot
and evaluated the climate data for establishing the relation-
ship to forest fire events.
Why this study is important?
1. The study area is largely dominated by deciduous
forests which occupy a large part of Orissa. The dry
deciduous forests are more vulnerable to forest fire
(FAO, 2001) and are deteriorating at an alarming
rate.
2. The understanding of relationship of fire events with
various other parameters such as vegetation type,

11
Spatial analysis of fire characteristies along with various gradients of season, administrative units, vegetation
forest boundary, tribal population etc. has not been
adequately manifested by research finding.
3. The states of Orissa have significant percent of popu-
lation living below poverty line whereas the tribal
population largely living in and around of the remote
areas of forest are suffering from acute poverty and
diminishing livelihood. Climate induced forest fire
is one of the reasons which leads to degradation and
reduction of the existing forest resource.
The present study has utilized the 16 years MODIS
based fire datasets for the whole of Orissa (point data of
location of forest fire) and analyzed it in GIS domain to-
wards visualization and evaluating the spatial/temporal di-
mension of fire pattern.
The objectives of the present study are as follows:
1. The month wise fire events analysis throughout the
year.
2. The fire events evaluation along all the administra-
tive units/districts.
3. Forest fire hotspots analysis.
4. Evaluation of fire events and its distribution across
the different vegetation types, topographical gradi-
ent, tribal population and future climate change sce-
nario (RCP-6).
2. Materials and methods
2.1. Study area
This study was carried out in the state of Orissa which
has total number of 30 districts which are spread over an
area of 155,707 km2, and are bounded between North lati-
tudes 17º 49’ to 22º 34’ and East longitude 81º 24’ to 87º
29’. All thirty districts were included in the study (Fig. 1).
Major forest types of total forest area of these regions are
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The degradation of forests in the Orissa state is due to
various reasons, such as forest fire, agriculture practice,
over grazing, mining, quarrying, excessive fuel wood col-
lection and over exploitation of minor forest produce. The
cause of fires is largely by anthropogenic reason which
spread accidentally due to negligence and intentionally.
The local inhabitant use to clear the land for various
purposes as per their needs. In tropical dry deciduous for-
est, the villagers start clearing the ground for collecting the
mahua (Madhuca indica) flower from the end of February
whereas people/contractor deliberately put fire to enhance
better flushes for tendu (Diospyros melanoxylon) leaves
which fetch them more money in form of revenue.
Page 5 of 22
India
Figure 1. The location of the study area

12 Firoz Ahmad, Md Meraj Uddin, Laxmi Goparaju
Agriculture practices by clearing the area largely by
the poor inhabitant for their livelihood are also one of the
reasons for forest fire in this region. The forest fire inci-
dences are noticed high during this peak summer season,
which coincides with the period of high amounts of fuel
load available on the forest floor. Agriculture residue burn-
ing is also a common practice by the farmers, a kind of
preparation of agriculture field for growing other crops.
The study of the Reddy et al. (2014) in Orissa state
reveals that the loss of dense forest and annual forest loss
from year 1975 to 2010 was equivalent to 10,679 km2 and
228 km2/year respectively.
2.2. Data and analysis
The boundary of Orissa state and districts were down-
ORDGHGIURP',9$*,6ZHEVLWHhttp://www.diva-gis.org/
Data). Moderate Resolution Imaging Spectroradiometer
(MODIS) Collection 6 Active Fire Product (MODIS C 6)
having resolution of 1-km used to quantify fire risk of the
state of Orissa, India. We utilized the fire counts data from
the year 2-11-2000 to 31-12-2016 was in the point shape
file provided as download by NASA Fire Information for
Resource Management System (FIRMS) team (https://
firms.modaps.eosdis.nasa.gov).
Fire data quality was evaluated based on specific soft-
ware by the MODIS land quality assessment team to en-
sure its high standard (Roy et al., 2002). The SPOT4 sat-
HOOLWHEDVHGYHJHWDWLRQFRYHUGHULYHGIURPµ9HJHWDWLRQLQ-
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was downloaded for the present study (http://forobs.jrc.
ec.europa.eu/products/glc2000/glc2000.php). The legends
of vegetation cover were defined by FAO. South central
Asian regional datasets (Roy et al., 2003) described vari-
ous vegetation types was utilized for this study. The fire
events in each vegetation type were evaluated. The tribal
population and forest density maps were produced from the
existing literatures. We have utilized digital elevation mod-
el GTOPO30 downloaded from USGS website. The DEM
retain grid spacing of 30 arc seconds (roughly 1 kilometer).
The fire season temperature and the annual rainfall
anomalies (climate change scenario) data for the year
2030 over the state of Orissa were downloaded using
RCP-6 scenario model (NCAR GIS Program, 2012). The
data downloaded was in point grid. The temperature and
rainfall surface were generated from the point vector file
using the kriging interpolation technique because it gives
the best linear unbiased prediction of the intermediate val-
ues. MODIS fire points data were in the form of vector
shape file applied for examining its trend and relationship
with various gradients such as month wise, administrative
boundary, vegetation classes, tribal population, forest den-
sity, slope and elevation. The ARC/GIS, Erdas Imagine
Software and Microsoft Excel were utilized significant-
ly for generation of various themes, cross evaluation to
achieve the above mentioned objectives.
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Figure 2. The fire events month wise over state of Orissa

13
Spatial analysis of fire characteristies along with various gradients of season, administrative units, vegetation
3. Results
3.1. Fire assessment month wise/season
Spatial distribution and its evaluation of fire are important
as it helps in prevention, mitigation and control. Long term
fire data was analyzed in GIS domain to understand the
month wise pattern. The total number of fire count in Oris-
sa states was found 51,343 between the periods 2-11-2000
to 31-12-2016 and are represented in the graph. The fire
frequencies have been examined on monthly basis from
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fire events are in February, March, April and May during
the months of summer season (Fig. 2). Similar finding has
been observed by Ahmad et al. (2017) in adjacent state of
Jharkhand.
The fire events have been examined over Orissa among
the 30 districts administrative boundary. Here we have in-
tegrated forest fire percent and also the forest cover area
percent district wise based on FSI (2015) report. The total
forest area in this state of Orissa is equivalent to 50,354
square kilometer based on FSI 2015 report. The integrated
fire occurrence percent and forest cover area percent dis-
trict wise given in Figure 3.
Figure 3. Fire events and forest area percent of various districts of Orissa

14 Firoz Ahmad, Md Meraj Uddin, Laxmi Goparaju
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Orissa fire frequency. Similarly Mayurbhanj is one of the
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Mayurbhanj district villagers use community based forest
management practice that has enabled them to protect the
forest more effectively (Sinha & Singh, 2017).
3.2. Fire assessment with vegetation gradient
The derived vegetation cover was analyzed with respect to
the fire events. The map, area and fire percent of various
vegetation types are given in the Figure 4 for general under-
standing and discussion. The two vegetation type “Tropical
mixed deciduous and dry deciduous forests” and “Tropical
lowland forests, broadleaved, evergreen, <1000 m” occu-
S\WKHJHRJUDSKLFDODUHDURXJKO\ZKHUHDVWKH\UHWDLQ
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Similar finding was observed by FAO where they found
deciduous forests are prone to fire (http://www.fao.org/do-
crep/006/AD653E/ad653e50.htm#P5391_387485).
The vegetation type “Cropland, irrigated, inundated or
IORRGHG´KRZHYHURFFXS\JHRJUDSKLFDODUHDPRVWO\
dominated in plain with high soil fertility status widely
practiced for growing agriculture crop (paddy, wheat and
SXOVH HWF H[KLELW  ILUH HYHQWV PRVWO\ GXH WR DJUL-
culture residues burning by farmers. Similar finding was
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factories are located in the vicinity of the urban area
in Orissa. The chimney outlet of smoke is detected as fire
points in our analysis are the reason for more fire percent
in urban areas.
3.3. Forest fire hotspot analysis
The vegetation categories representing the forest were
merged from vegetation data to understand the fire events
in forest. The whole 16 year data of MODIS fire events
were first masked with forest area to eliminate non for-
est fire points. The remaining fire points were integrated
in ARC/GIS Software simultaneously by utilizing “point
density” sub-module for generating continuous surface of
the forest fire hotspot and its spatial pattern (Fig. 5) in the
state of Orissa (Ahmad et al., 2017). The spatial pattern
that shows most of the south western portion of Orissa state
LVDIIHFWHGE\IRUHVWILUH7KHVRXWKHUQSDUWRI.DQGKDPDO
district is highly affected by forest fire and is represented
by colour blue in Figure 5.
We have also identified four worst forest fires affect-
ed villages such as Tumudibandh, Solaguda, Gadapur and
Birikota falls on forest fire hotspot area which need high
priority for forest fire prevention and control. Similar find-
ing has been observed by Reddy et al. (2014) where they
highlighted these areas for conservation prioritization. The
availability of comprehensive and significantly accurate
datasets of fires (Stocks et al., 2002) helps us to identify
forest fire spatial pattern (Gedalof, 2011) and assist in the
management and prevention of forest fire.
3.4. Overall assessment forest fire
on tribal population and forest density
In this analysis we have used the existing map (http://www.
orissalinks.com/orissagrowth/archives/5015) for generat-
ing the tribal population map, which was brought in GIS
domain (Fig. 6A). The forest map of the area generated
using the existing vegetation types map by merging vegeta-
tion class representing the forest is given in the Figure 6B.
We have also analyzed the fire events with the existing for-
est density map (http://www.orissalinks.com/orissagrowth/
topics/others/orissa-forests7KHUHVXOWVUHYHDOWKDWRI
ILUHLV IRXQGLQWKHGHQVHIRUHVWDUHD ZKHUHDVRIILUH
was in the open forest area. This is also one of the major
threats to the forest of Orissa (Reddy et al., 2014) which is
at a great risk of diminishing livelihood among the tribal
community (Aggarwal et al., 2009).
The fire counts were masked using the forest map
which means it only represents the fire count in forest area.
The tribal population percent map categorized into four
classes based on their population percent viz. low (<20),
medium (20-35), high (35-50) and very high (>50). The
VWXG\ UHYHDOV WKDW  IRUHVW ILUH FRXQWV IDOOV LQ YHU\
high tribal dominated area (Fig. 7).
5RXJKO\ RIIRUHVW ILUH RFFXUV LQ WKH DUHD ZKHUH
tribal population was high to very high. This affects the
critical linkage of livelihood-forest of a huge forest de-
pendent tribal population (Davidar et al., 2010) including
traditional foods important to them (Lynn et al., 2013).
3.5. Topographical data analysis
The thematic layer such as elevation and slope were ex-
tracted from the digital elevation model. These themes
were examined in GIS domain with existing fire incidenc-
es data. The analysis of fire events based on the eleva-
tion and slope range is given in the Figure 8A and Figure
8B. These results suggest that the maximum percent of
IRUHVWILUH  RFFXUUHG LQ WKH KLJKHU HOHYDWLRQ!
PHWHUHTXLYDOHQWWRWKHDUHDZKHUHDVURXJKO\
of forest fire occurred where elevation was greater than
500 meter.
The analysis of slope shows that the most of the for-
HVW ILUH  RFFXUV WR PRGHUDWH VORSH JUDGLHQW  WR
6 degree).Wind speed and temperature are characterized
by elevation is an important physiographic factor hence
affects the fire susceptibility (Rothermel, 1991) whereas

[15]
)LJXUH9HJHWDWLRQPDSLQVWDWHRI2ULVVD$DQG)LUHHYHQWVDORQJYDULRXVYHJHWDWLRQW\SHVLQVWDWHRI2ULVVD%
A
B

[16]
Figure 5. The MODIS based forest fire hotspot map of Orissa
A B
Figure 6. Tribal population map (A) and Forest map (B)

[17]
)LJXUH2YHUDOOVFHQDULRRIWULEDOGRPLQDWLRQIRUHVWILUHIRUHVWDUHDLQ2ULVVD
A B
Figure 8. Fire events in elevation gradient (A) and Fire events in slope gradient (B)

18 Firoz Ahmad, Md Meraj Uddin, Laxmi Goparaju
forest fire usually spreads faster in uphill than downhill
WKXVVWHHSHUWKHVORSHIDVWHUWKHILUHPRYHPHQW.XVKOD
Ripple, 1997).
4. Discussion
4.1. Climate anomalies and its impact
Here we have analyzed the future climate change scenario
for the year 2030. The produced temperature anomalies
map showing the prediction for the year 2030 (RCP-6 sce-
nario model) in the month of February, March, April, May
and June are given in Figure 9, Figure 10, Figure 11, Fig-
ure 12 and Figure 13 respectively. Similarly the Figure
14 shows the annual precipitation anomaly for the year
2030(RCP-6 scenario model).
These maps were further examined to understand the
future forest fire scenario. The climate anomalies map for
the year 2030 show the temperature will increase during
the fire season over the all districts of Orissa. The increase
in temperature is found to be highest in the districts of
.DQGKDPDO 5D\JDGD DQG .DODKDQGL KLJKHVW IRUHVW ILUH
districts) in the month of February and March (Figs 9
Figure 9. Temperature anomaly (February) Figure 10. Temperature anomaly (March)
Figure 11. Temperature anomaly (April) Figure 12. Temperature anomaly (May)

19
Spatial analysis of fire characteristies along with various gradients of season, administrative units, vegetation
and 10). The annual precipitation decreasing trend was ob-
VHUYHGLQWKHGLVWULFWRI.DQGKDPDO5D\JDGDDQG.RUDSXW
Our observation of future climate change scenario for the
year 2030 reflects the forest fire will be more in the state of
Orissa whereas it is more severe in few of the district such
DV .DQGKDPDO 5D\JDGD .DODKDQGL DQG .RUDSXW ZKLFK
have significantly high forest fire events.
A recent study by Ahmad et al. (2017) reveals that the
weather and climate parameter has strong association with
forest fire events in deciduous forests. Studies in the past
reveal that the climate of a region has a major control on
ILUHDFWLYLW\.UDZFKXN0RULW] :HVWHUOLQJHW al.,
2003), seasonal trends in maximum temperature, precipita-
tion, and drought severity (Wells et al., 2004) is a major
player it wildfire frequency and the extent of damage.
6WXG\ FRQGXFWHG E\ 9RURE\RY  RI WKH LPSDFWV
on weather severity on fire, revealed that for an aver-
age temperature increase in 1°C, the duration of wild fire
VHDVRQ LQFUHDVH E\  &OLPDWH YDULDELOLW\ DQG FKDQJH
are likely to adversely affect the livelihood of poor and
tribal people in forest dominated area of Orissa due to
degradation of forest.
5. Conclusion
The above study had utilized 16 years fire datasets and
analyzed it in GIS domain towards further visualizing the
spatial dimension of fire trend, pattern analysis and iden-
tifying the forest fire hotspots in Orissa and to understand
their interrelationship with other parameters.
There is a need to monitor the forest fire hot spot zone
because these areas are dominated by the tribal people and
their livelihood is significantly reduced. The government
should initiate more robust programme in high forest fire
districts in the line of Joint Forest Management (JFM) by
meaningfully involving village/ tribal people and encour-
age them to combat the forest fires. This can be achieved
by involving the village level committees monitored by
elected representatives.
In the above study, it was found that high to very high
tribal population areas had suffered from the aftermath of
forest fire and their livelihood is threatened. These are-
as need some long term special strategies must be jointly
tackled (politically, socially and administratively) to revive
the livelihood and to mitigate the poverty significantly. An
alternative livelihood and forest conservation programmes
are required to enhance the income of tribal people suffer-
ing with poverty based on sustainable livelihood approach
(http://www.fao.org/3/a-ah252e.pdf) by utilizing local ad-
ministrative unit and NGOs.
The evaluation of future climate data (RCP-6 scenario
model) for the year 2030 show the temperature will in-
crease during the fire season over the all districts of Orissa
whereas it will be more crucial for some of the highest for-
est fire districts because of the significant increase in tem-
perature in the month of February and March. The decreas-
ing trend in annual precipitation was observed in few of
the highest forest fire districts.
There is a need to formulate and implement the robust
forest fire policy based on the evaluation of socio eco-
nomic condition of ethnic tribes and the future climate
Figure 13. Temperature anomaly (June) Figure 14. Annual precipitation anomaly

20 Firoz Ahmad, Md Meraj Uddin, Laxmi Goparaju
change scenario. Furthermore, the state government should
increase their effectiveness to prevent and control forest
fire by mean of manpower, funds, equipment and technol-
ogy.
Regular scientific monitoring and qualitative research
studies are solicited by utilizing the fire events, climate,
socioeconomic, tribal population, livelihood and other de-
pendent parameters to understand the phenomenon at close
quarters which will further enhance the policy related is-
sues.
Acknowledgements
The authors are grateful to the USGS, NASA Fire Infor-
mation for Resource Management System, European Com-
PLVVLRQ¶VVFLHQFH DQGNQRZOHGJH VHUYLFH',9$*,6 for
providing free download of various dataset used in the
analysis.
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