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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

  • Vindhyan Ecology and Natural History Foundation, Mirzapur,India

Abstract and Figures

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.
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Ecological Questions 29 (2018) 4: 9–22
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*
2University Department of Mathematics, MCA, Ranchi University, Ranchi, Jharkhand, India,
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.
total Orissa fire events. The vegetation type “Tropical mixed deciduous and dry deciduous forests” and “Tropical lowland forests,
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
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
Gill, 1975). Climate change of any region adversely im-
pacts on cultural, ecological values and socioeconomic
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
Dwyer et al., 2000) and have increased our comprehension
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
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contributing to carbon cycle, greenhouse gases and aerosol
der Werf et al., 2010) whereas biomass burning has a very
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 (
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-
level reveal that the pattern of collection of these minor
forest products and its significant impact on local forest
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& Mishra, 2001) due to huge dependency on local/tribal
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
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
the spatial patterns in fire events across diversified geo-
and Goparaju (2017a) identified forest fire hotspot dis-
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
2. The understanding of relationship of fire events with
various other parameters such as vegetation type,
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
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
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
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-
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://
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-
was downloaded for the present study (http://forobs.jrc. 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.
Figure 2. The fire events month wise over state of Orissa
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
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
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
Orissa fire frequency. Similarly Mayurbhanj is one of the
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-
Similar finding was observed by FAO where they found
deciduous forests are prone to fire (
The vegetation type “Cropland, irrigated, inundated or
dominated in plain with high soil fertility status widely
practiced for growing agriculture crop (paddy, wheat and
culture residues burning by farmers. Similar finding was
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
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. 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 (
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
high tribal dominated area (Fig. 7).
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
of forest fire occurred where elevation was greater than
500 meter.
The analysis of slope shows that the most of the for-
6 degree).Wind speed and temperature are characterized
by elevation is an important physiographic factor hence
affects the fire susceptibility (Rothermel, 1991) whereas
Figure 5. The MODIS based forest fire hotspot map of Orissa
Figure 6. Tribal population map (A) and Forest map (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
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
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)
Spatial analysis of fire characteristies along with various gradients of season, administrative units, vegetation
and 10). The annual precipitation decreasing trend was ob-
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
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
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.
on weather severity on fire, revealed that for an aver-
age temperature increase in 1°C, the duration of wild fire
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
( 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-
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-
The authors are grateful to the USGS, NASA Fire Infor-
mation for Resource Management System, European Com-
providing free download of various dataset used in the
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A.M.G. & Nielsen-Pincus M., 2017, Network analy-
sis of wildfire transmission and implications for risk
governance. PLoS ONE12(3): e0172867-e0172894.
Degradation, Livelihoods, and Climate Change, [in:] D.
Datt, S. Nischal (eds), Looking Back to Change Track.
TERI 219, New Delhi: 91-108.
Ahmad F. & Goparaju L., 2017a, Geospatial Assessment
of Forest Fires in Jharkhand (India). Indian Journal of
Science and Technology 10(21): 1-7. (doi: 10.17485/
Ahmad F. & Goparaju L., 2017b, Assessment of Threats
to Forest Ecosystems Using Geospatial Technology
in Jharkhand State of India. Current World Environ-
ment 12(2): 355-365. (
Ahmad F., Goparaju L., Qayum A. & Quli S.M.S., 2017,
Forest fire trend analysis and effect of environmental
parameters: A study in Jharkhand State of India using
Geospatial Technology. World Scientific News 90: 31-
Andreae M.O. & Merlet P., 2001, Emission of trace gas-
es and aerosols from biomass burning. Global Bio-
geochemical Cycles 15(4): 955-966. (https://doi.
Arjunan M., Puyravaud J.-Ph. & Davidar P., 2005, The
impact of resource collection by local communities on
serve. Tropical Ecology 46: 135-144.
%RQG:-.HHOH\ -( )LUHDV D JOREDO³KHUEL-
vore”: The ecology and evolution of flammable ecosys-
tems. Trends Ecol. Evol. 20(7): 387-394.
%RZPDQ '0 %DOFK -. $UWD[R 3 %RQG :- &DUO-
son J.M., Cochrane M.A., D’Antonio C.M., DeFries
56'R\OH -&+DUULVRQ63-RKQVWRQ )+.HHOH\
M.A., Prentice I.C., Roos C.I., Scott A.C., Swetnam
T.W., van der Werf G.R. & Pyne S.J., 2009, Fire in the
earth system. Science 324(5926): 481-484.
Chuvieco E. & Congalton R.G., 1989, Application of re-
mote-sensing and geographic information-systems to
forest fire hazard mapping. Remote Sensing of Envi-
ronment 29(2): 147-159.
Csiszar I., Denis L., Giglio L., Justice C.O., & Hewson J.,
2005, Global fire activity from two years of MODIS
data. International Journal of Wildland Fire 14(2): 117-
Davidar P., Sahoo S., Mammen P.C., Acharya P., Puy-
ravaud J.P., Arjunan M., Garrigues J.P. & Roessingh
Degradation in India: Where do we Stand? Biological
Conservation 43(12): 2937-2944.
Dwyer E., Gregoire J.M. & Malingreau J.P., 1998, A global
analysis of vegetation fires using satellite images: Spa-
tial and temporal dynamics. Ambio 27(3): 175-181.
Dwyer E., Pinnock S., Gregoire J.M. & Pereira J.M.C.,
2000, Global spatial and temporal distribution of veg-
etation fire as determined from satellite observations.
International Journal of Remote Sensing 21(6-7): 1289-
Eva H. & Lambin E.F., 1998, Burnt area mapping in Cen-
tral Africa using ATSR data. International Journal of
Remote Sensing 19(18): 3473-3497.
Eva H. & Lambin E.F., 2000, Fires and land-cover change
in the tropics: a remote sensing analysis at the land-
scape scale. Journal of Biogeography 27(3): 765-776.
FAO, 2001, Global forest fire assessment 1990–2000. FAO
(Forest Resources Assessment), Rome.
Finney M.A., 2001, Design of Regular Landscape Fuel
Treatment Patterns for Modifying Fire Growth and Be-
havior. Forest Science 47(2): 219-229.
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Journal of Forest Research-Revue Canadienne De Re-
cherche Forestiere 16(5): 975-982.
)ODQQLJDQ 0' /RJDQ .$ $PLUR %' 6NLQQHU :5
& Stocks B.J., 2005, Future area burned in Canada.
Clim Chang 72(1-2): 1-16.
FSI, 2015,
pdf [Accessed on 15th November 2017].
Gedalof Z., 2011, Climate and spatial patterns of wild-
ILUHLQ 1RUWK $PHULFD >LQ@ ' 0F.HQ]LH& 0LOOHU
D.A. Falk (eds), The landscape ecology of fire. Spring-
Spatial analysis of fire characteristies along with various gradients of season, administrative units, vegetation
er, Dordrecht: 89–116. (
Ghosh S. & Majumdar P.P., 2007, Nonparametric methods
for modeling GCM and scenario uncertainty in drought
assessment. Water Resour. Res. 43(W07405): 1-19.
Ghosh S. & Majumdar P.P., 2006, Future rainfall scenario
over Orissa with GCM projections by statistical down-
scaling. Curr Sci. 19(6): 396–404.
Gill A.M., 1975, Fire and the Australian flora: A review.
Aust. For. 38(1): 4-25.
Giriraj A., Babar S., Jentsch A., Sudhakar S. & Murthy
M.S.R., 2010, Tracking fires in India using Advanced
Along Track Scanning Radiometer (A)ATSR data. Re-
mote Sens. 2(2): 591–610.
+LFNH- -RKQVRQ 0&+D\HV - 3UHLVOHU +. 
Effects of bark beetle-caused tree mortality on wildfire.
Forest Ecology and Management 271(2012): 81–90.
Menzel P., Pereira M.C. & Rasmussen R., 1992, Bio-
mass Burning Airborne and Spaceborne Experiment
in the Amazonas (BASE–A). Journal of Geophysi-
cal Research 97(D13): 14581-14599. (https://doi.
.ORVWHU 6 0DKRZDOG 1 5DQGHUVRQ -  /DZUHQFH 3
2012, The impacts of climate, land use, and demog-
raphy on fires during the 21st century simulated by
CLM-CN. Biogeosciences 9(1): 509-525. (https://doi.
.RURQW]L 6 0F&DUW\ - /RERGD 7 .XPDU 6  -XV-
tice C., 2006, Global distribution of agricultural
fires in croplands from 3 years of Moderate Reso-
lution Imaging Spectroradiometer (MODIS) data.
Global Biogeochemical Cycles 20(GB2021): 1-15.
.UDZFKXN 0$  0RULW] 0$  &RQVWUDLQWV RQ
global fire activity vary across a resource gradient.
Ecology 92(1): 121–132.
.XVKOD -'  5LSSOH :-  7KH UROH RI WHUUDLQ
in a fire mosaic of a temperate coniferous forest. For-
est Ecology and Management 95(2): 97-107.
impacts of climate change on tribal traditional foods.
Climatic Change 120(3): 545-556. (doi: 10.1007/
Mahapatra M. & Mohanty U.C., 2006, Spatio-temporal
variability of summer monsoon rainfall over Orissa
in relation to low-pressure systems. J. Earth Syst. Sci.
115(2): 203–218.
Mahendra Dev, S. & Ravi C., 2007, Poverty and Inequal-
ity: all-India and States, 1983-2005. Economic and Po-
litical Weekly 42(6): 509-521.
0DLNKXUL 5. 1DXWL\DO 6 5DR .6  6D[HQD .*
2001, Conservation policy – people conflicts: a case
study from Nanda Devi Biosphere Reserve (a World
Heritage Site), India. Forest Policy and Economics
2(3-4): 355-365.
0DLWKDQL*3%DKXJXQD 9. /DO3 (IIHFWRI
forest fires on the ground vegetation of a moist decidu-
ous sal (Shorea robusta) forest. Indian Forester 112(8):
Socioeconomic and Socio-ecological study of Sambal-
pur Forest Division, Orissa. Journal of Human Ecology
23(2): 135-146.
9DQ 'RUQ - *DQ] '- +D\KRH . &OLPDWH
change and disruptions to global fire activity. Ecosphere
3(6): 1-22. (
National Wildlife Federation (NWF), 2011, Facing the
Storm: Indian Tribes, Climate-Induced Weather Ex-
tremes, and the Future for Indian Country. National
Wildlife Federation Rocky Mountain Research Center,
Boulder, Colorado.
NCAR GIS Program. (2012) Climate Change Scenarios,
version 2.0. Community Climate System Model, June
2004 version 3.0.
ccsm3.0/ was used to derive data products. NCAR/
UCAR. URL. ( [Ac-
cessed on 5th March 2018].
Patra J., Mishra A., Singh R. & Raghuwanshi N.S., 2012,
Detecting rainfall trends in the twentieth century
(1871–2006) over Odisha State, India. Clim. Change
111(3-4): 801–817. (doi:10.1007/s10584-011-0215-5).
Pausas J.G. & Fernandez-Munoz S., 2012, Fire regime
changes in the western Mediterranean Basin: From fu-
el-limited to drought-driven fire regime. Clim. Chang.
110(1-2): 215–226.
Ray-Bennett N.S., 2009, Multiple Disasters and Policy
Responses in Pre- and Post-independence Orissa, In-
dia. Disasters 33(2): 274-290. (doi: 10.1111/j.1467-
5HGG\ &6 $OHNK\D 99/3 6DUDQ\D .5/ $WKLUD
.-KD &6 'LZDNDU3*  'DGKZDO 9. 
Monitoring of fire incidences in vegetation types and
Protected Areas of India: Implications on carbon emis-
sions. J. Earth Syst. Sci. 126 (11): 1-15. (doi: 10.1007/
biodiversity conservation of forest ecosystems us-
ing geospatial techniques: a case study of Odisha, In-
dia. Ecological Engineering 69: 287-303. (https://doi.
5HGG\ &6 -KD &6  'DGKZDO 9.  $VVHVV-
ment and monitoring of long-termforest cover changes
22 Firoz Ahmad, Md Meraj Uddin, Laxmi Goparaju
in Odisha, India using remote sensing and GIS. Envi-
ron. Monitor. Asses. 185(5): 4399-4415.
Rothermel R.C., 1991, Predicting behavior and size of
crown fires in the northern Rocky Mountains, Res.
Pap. INT-438. U.S. Dept. of Agriculture, Forest Ser-
vice, Intermountain Research Station, Ogden, UT.
Roy D.P., Borak J.S., Devadiga S., Wolfe R.E., Zheng
M. & Descloitres J., 2002, The MODIS Land Product
Quality Assessment Approach. Remote Sensing of En-
vironment 83(1-2): 62-76.
5R\ 36 $JUDZDO 6 -RVKL 3  6KXNOD <  7KH
GLC2000 database, European Commision Joint Re-
search Centre. (
Sagar R. & Singh. J.S., 2004, Local plant species depletion
in a tropical deciduous forest of northern India. Envi-
ronmental Conservation 31(1): 55-62.
Grazing Pressure in and around the Elephant Corridors
in Mudumalai Wildlife Sanctuary, South India. Biodi-
versity and Conservation 10(12): 2181-2195.
6LQKD %  6LQJK .'  $FKLHYLQJ &RQVHUYD-
tion and Livelihood : A Case Study from Orissa, In-
dia. (
dle/10535/7208/692.pdf?sequence=1). [Accessed on
25th December 2017].
Stocks B.J., Mason J.A., Todd J.B., Bosch E.M., Wotton
%0$PLUR%' )ODQQLJDQ 0' +LUVFK .* /R-
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forest fires in Canada, 1959-1997. Journal of Geo-
physical Research – Atmospheres 107(D1): 5.1-5.12.
6WXUURFN 51 )UDQNHO 6- %URZQ $9 +HQQRQ 3(
.OLHMXQDV -7 /HZLV .- :RUUDOO --  :RRGV
A.J., 2011, Climate change and forest diseases.
Plant Pathol. 60(1): 133-149. (doi:10.1111/j.1365-
7DQQHU701DLU 6 %KDWWDFKDUM\D 66ULYDVWDYD 6.
mate risk screening in DFID India. Research report.
Institute of Development Studies, Brighton. (https://
L. & Carmona-Moreno C., 2010, The influence of veg-
etation, fire spread and fire behaviour on biomass burn-
ing and trace gas emissions: Results from a process-
based model. Biogeosciences 7(6): 1991-2011.
tial patterns in vegetation fires in the Indian region.
Environ Monit Assess. 147(1-3): 1-13. (doi: 10.1007/
sis of vegetation fires and intensity in the Indian region
IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(1):
224-38. (doi: 10.1109/JSTARS.2012.2210699).
van der Werf G.R., Randerson J.T., Giglio L., Collatz G.J.,
<  YDQ/HHXZHQ77  *OREDO ILUHHPLVVLRQV
and the contribution of deforestation, savanna, forest,
agricultural, and peat fires (1997–2009). Atmospheric
Chemistry and Physics 10 (23): 11707-11735. (https://
2013, Cultural impacts to tribes from climate change
influences on forests. Clim Chang. 120(3): 615-626.
VLD>LQ@< ,]UDHO * *UX]D66HPHQRY, 1D]DURY
(eds), Proc. World Climate Change Conference, Mos-
cow. Institute of Global Climate and Ecology, Moscow:
Wells N., Goddard S. & Hayes M.J., 2004, A self-calibrat-
ing Palmer Drought Severity Index. J. Clim. 17(12):
Westerling A.L.,Gershunov A., Brown T.J., Cayan D.R.
& Dettinger M.D., 2003, Climate and wildfire in the
western United States. Bull. Am. Meteorol. Soc. 84(5):
... Due to the advancement in technology especially in the satellite sensors, it is possible to map fire patterns globally (Dwyer et al., 1998(Dwyer et al., , 2000Csiszar et al., 2005) and locally (Ahmad and Goparaju, 2018;Ahmad et al., 2018). Satellite data based fire monitoring/mapping thus offers a reliable source of fire occurrence data that can largely overcome various limitations of the traditional fire records (Flannigan and Vonder Haar, 1986;Eva and Lambin, 1998;Korontzi et al., 2006). ...
... Due to the advancement in technology especially in the satellite sensors, it is possible to map fire patterns globally (Dwyer et al., 1998(Dwyer et al., , 2000Csiszar et al., 2005) and locally (Ahmad and Goparaju, 2018;Ahmad et al., 2018). Satellite data based fire monitoring/mapping thus offers a reliable source of fire occurrence data that can largely overcome various limitations of the traditional fire records (Flannigan and Vonder Haar, 1986;Eva and Lambin, 1998;Korontzi et al., 2006). ...
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It is essential to study forest fire occurrences and the climate of any region to address the issue of forest fire vs global warming and global climate change. Studies at regional and global level help to understand the forest fire characterization microscopically. The present study has made an assessment of the long-term forest fire events in the state of Chhattisgarh, India and identified forest fire hotspot areas. We have generated a spatial pattern of climate data and made a statistical analysis. Cramer V coefficient (CVC) was calculated and its relationship with forest fire events was suggested. The study revealed that the Baster and Dantewada districts of Chhattisgarh state of India show the highest forest fire percentage equivalent to 24 and 33 %, respectively. Generally, three forest fire hotspot zones were identified. In January, February and March zone 1 received less rainfall and showed relatively high maximum temperature and potential evapotranspiration when compared with zone 2 and 3. The number of rainy days in January and February in Dantewada and Bastar district (zone 1) was 0.72 to 0.92 and was found lowest among all districts of the state. The climate parameters were more favorable to forest fire events over zone 1 compared to other zones. The evaluation of CVC value of climate data with forest fire events showed that rainfall, maximum temperature, the number of rainy days and potential evapotranspiration were in decreasing order and in the range from 0.74 to 0.32. The highest value (0.74) showed that was closely related with forest fire events. In June, these areas receive adequate rainfall (90–177 mm) which leads to an increase in the moisture content and hinders forest fuel burning capacity. Geospatial technology proved capable of analyzing thematic datasets and various modules/algorithms used in mapping, allowing to draw logical conclusions in solving various research problems.
... The predicted annual temperature and precipitation anomalies data [23] the RCP 6 simulation models [49,50] for the year 2050 were used to produce the map [51] for kharif and rabi seasons of India. The temperature (°C) increase in the year 2050 during kharif and rabi seasons were found to be in the range of (0.84-1.81) and (0.75-1.97) respectively. ...
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The paper deals with various thematic parameters such as agriculture crop scenario (2000, 2010), water stress, precipitation trend and deficit, climate-induced risk towards crops, drought-prone area, suicide attributes of farmers, agro-ecological regions, prediction of future (2050) precipitation and temperature variation during kharif and rabi seasons of India and their spatial pattern were analyzed in GIS for better understanding of climate change. The analysis revealed about the need of synergic approach/strategies to address the impact of climate change. Few of the Climate-smart villages (CSVs) projects of India were discussed here based on their approach, achievement, and limitation. The CSV conceptual strategies are fully based on climate smart agriculture potentiality to achieve sustainability in food security, enhancing the livelihood, eradication of poverty and magnifying the farm household resilience. The climate-induced high and very high risk to the crops areas were found dominated in the arid and semi-arid regions which will be challenged in future due to water stress, inadequate irrigation facility, increasing trend of temperature and variation in precipitation pattern. The hotspot districts of farmer’s suicide were very significant in climate-induced very high risk zone and majority of them falls in the drought-prone areas/extremely high to high water-stressed areas which leads to crop failure. There is a need to formulate a concrete policy, legal, and institutional actions addressing the farmers problem significantly at country, state, district and village levels which will support investment/technology/guideline in and adoption of Climate-smart village (CSV) practices after seeing the socio-economic background (poverty/tribes/backward class) of them.
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We have examined the climate and forest fire data using Remote Sensing and GIS in the state of Himachal Pradesh and Uttarakhand states of India. The significant high forest fire events were observed in district of Pauri Garhwal (22.4%) followed by Naini Tal (16.4%), Tehri Garhwal (8.5%), Almora (7.7%), Chamoli (5.8%), Dehra Dun (4.6%), Uttarkashi (4.3%), Champawat (4.2%), Haridwar (3.6%), Una (3.4%), Bageshwar (3.1%), Udham Singh Nagar (2.9%), Sirmaur (2.7%), Solan (2.3%), Kangra (2.1%), Pithoragarh (1.7%) and Shimla (1.2%). The LULC forest category “Deciduous Broadleaf Forest” occupied 17.2% of total forest area and retain significantly high forest fire percent equivalent to 44.7% of total forest fire events. The study revealed that 79% of forest fire incidence was found in the month of April and May. The fire frequency was found highest in the month of April (among all months) whereas it was spread over the five grids (in the count) where the fire frequencies were greater than 100. The average monthly analysis (from January to June) for maximum temperature (°C), precipitation (mm), solar radiation (MJ/m^2), wind velocity (meter/sec.), wet-days frequency (number of days) and evapotranspiration (mm/day) were found to be in the range of (9.90 to 26.44), (26.06 to 134.71), (11738 to 24119), (1.397 to 2.237), (1.46 to 5.12) and (3.96 to 8.46) respectively. Rapid climate/weather severities which significantly enhance the forest fire events were observed in the month of April and May. The analysis of the Pearson Correlation Coefficient (PCC) values of climate parameters showed a significant correlation with forest fire events. The analysis of predicted (2050) climate anomalies data (RCP-6) for the month of April and annual precipitation manifest the significant rise in April temperature and reduction in annual precipitation observed over large part of high forest fire grids will certainly impact adversely to the future forest fire scenario.
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Forest fires are a major threat to the existence of forests these days due to climate change and global warming scenario. The present study utilizes geospatial techniques to analyze the incidences of forest fires events from the year 2005 to 2016 in the Jharkhand state of India. Forest fire hotspot areas within the state were identified. The analysis of the datasets reveals that approximately 89% West part of Jharkhand in Palamu district which it continues along with Paschim (west) Singhbhum district till the end of April. Three major locations were identified in Jharkhand forest as forest fire hotspot. Statistical analysis (Cramer's V coefficient) was performed to test the scale / magnitude of association of forest fire with driving factor (meteorological parameters). The range of CVC value varied between 0.74 to 0.32 whereas rainfall retain the highest value 0.74 means it is one of the strongest driving factor among all other environmental parameter contribute to forest fire events. The study of forest fire event analysis, its correlation of trend and its interrelationship with environmental/meteorological parameters gives better comprehension for forest fire events thus helps in mitigation, control and prevention to safeguard our precious forest and the environment. World Scientific News 90 (2017) 31-50-32
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Conservation of forest biodiversity is vital for mankind as it provides enormous benefits such as biological resources and ecosystem services. Of late, the forests are facing risk and threats such as fragmentation, degradation and forest fires which are responsible for the deteriorating condition. The progress in the field of science and technology like satellite remote sensing and GIS since the past few decades in India and the world provide an opportunity to track and monitor the changes taking place on the Earth’s surface. Besides, analysis of large spatial data in GIS can also provide insight into the various driving factors which lead to the loss of biodiversity in the threatened ecosystems i.e forests. This study has attempted to obtain information about the spatial extent of the three forest ecosystem degradation indicators viz. deforestation, fragmentation of forest and forest fires using methodical approach in the Jharkhand state of India. The satellite remote sensing data sets belonging to Landsat-8 were used to analyse the forest cover of Jharkhand state.To identify the areas of threat, grid cells (5KmX5Km) were generated in GIS domain. Analysis of deforestation was conducted using multi source data of the time periods 1935 and 2015. Evaluation of deforestation spanning over a time period reveals that vital changes have occurred in the forests of Jharkhand and determined 1224 extinct, 248 critically endangered, 318 endangered and 396 vulnerable ecosystem grid cells. The fragmentation analysis has determined 148 critically endangered, 296 endangered and 402 vulnerable ecosystem grid cells. Forest fire point’s data from the year 2005 to 2016 were utilized and analysis was executed. Further frequency of forest fires for each grid was noted. The result indicates that 67.3% of grid cell of Jharkhand forest was affected with forest fire. Conservation status has been evaluated based on the value of threat for each grid which was the fundamental criteria for conservation priority hotspot. About 2.1% of Jharkhand forest ecosystem grids are defined as extremely high ecosystem risk stage and have been designated in the category of conservation priority hotspot-1 followed by 19.7% conservation priority hotspot-2, 41.3% conservation priority hotspot-3, 27.8% conservation priority hotspot-4 and 9.1% lowest conservation priority hotspot-5. This study highlights the capability of integrating remote sensing and GIS data for mapping the forest degradation, which can be useful in formulating the strategies and policies for protection and conservation of forests.
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Background: The forests in India and worldwide are threatened by many factors, one among them being the increasing frequency of forest fires. It damages the forest ecosystem and the environment thus altering the global climate. A proper monitoring and understanding of forest fires both spatially and temporally would assist in management of forest and help in protecting the biodiversity and wildlife habitat. Satellite remote sensing and GIS help in visualizing the extent and damage of forest fire at various scales and time periods. Objectives: To analyze the incidences of forest fires in Jharkhand state of India. Forest fire hotspot district were identified and analyzed. Methods/Statistical Analysis: The forest fire point data from the year 2005 to 2016 was analyzed in GIS domain for Jharkhand. The Landsat-8 data was utilized to obtain the forest cover of 2015 for Paschim Singhbhum district. The 2km*2km grid was generated to evaluate each grid with reference to forest fire incidence. Findings: Analysis of the datasets revealed that highest forest fire district of Jharkhand state is Paschim Singhbhum, it retain 30% of total forest fire whereas it contain approximately 17 % of the state forest cover. The study reveals very high frequency of forest fire grids in Paschim Singhbhum district falls in north-west of Pansuan dam of Porhat forest division. It provides a spatial view of forest fire occurrence, spread over duration of time which can be incorporated in management objectives to deal with the adverse effect of forest fire. Application/Improvements: Appropriate measures can focus on the particular very high to medium forest fire grid to minimize the effect of forest fire impact.
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We characterized wildfire transmission and exposure within a matrix of large land tenures (federal, state, and private) surrounding 56 communities within a 3.3 million ha fire prone region of central Oregon US. Wildfire simulation and network analysis were used to quantify the exchange of fire among land tenures and communities and analyze the relative contributions of human versus natural ignitions to wildfire exposure. Among the land tenures examined, the area burned by incoming fires averaged 57% of the total burned area. Community exposure from incoming fires ignited on surrounding land tenures accounted for 67% of the total area burned. The number of land tenures contributing wildfire to individual communities and surrounding wildland urban interface (WUI) varied from 3 to 20. Community firesheds, i.e. the area where ignitions can spawn fires that can burn into the WUI, covered 40% of the landscape, and were 5.5 times larger than the combined area of the community core and WUI. For the major land tenures within the study area, the amount of incoming versus outgoing fire was relatively constant, with some exceptions. The study provides a multi-scale characterization of wildfire networks within a large, mixed tenure and fire prone landscape, and illustrates the connectivity of risk between communities and the surrounding wildlands. We use the findings to discuss how scale mismatches in local wildfire governance result from disconnected planning systems and disparate fire management objectives among the large landowners (federal, state, private) and local communities. Local and regional risk planning processes can adopt our concepts and methods to better define and map the scale of wildfire risk from large fire events and incorporate wildfire network and connectivity concepts into risk assessments.
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Carbon emissions released from forest fires have been identified as an environmental issue in the context of global warming. This study provides data on spatial and temporal patterns of fire incidences, burnt area and carbon emissions covering natural vegetation types (forest, scrub and grassland) and Protected Areas of India. The total area affected by fire in the forest, scrub and grasslands have been estimated as 48765.45, 6540.97 and 1821.33 km ², respectively, in 2014 using Resourcesat-2 AWiFS data. The total CO 2 emissions from fires of these vegetation types in India were estimated to be 98.11 Tg during 2014. The highest emissions were caused by dry deciduous forests, followed by moist deciduous forests. The fire season typically occurs in February, March, April and May in different parts of India. Monthly CO 2 emissions from fires for different vegetation types have been calculated for February, March, April and May and estimated as 2.26, 33.53, 32.15 and 30.17 Tg, respectively. Protected Areas represent 11.46% of the total natural vegetation cover of India. Analysis of fire occurrences over a 10-year period with two types of sensor data, i.e., AWiFS and MODIS, have found fires in 281 (out of 614) Protected Areas of India. About 16.78 Tg of CO 2 emissions were estimated in Protected Areas in 2014. The natural vegetation types of Protected Areas have contributed for burnt area of 17.3% and CO 2 emissions of 17.1% as compared to total natural vegetation burnt area and emissions in India in 2014. 9.4% of the total vegetation in the Protected Areas was burnt in 2014. Our results suggest that Protected Areas have to be considered for strict fire management as an effective strategy for mitigating climate change and biodiversity conservation.
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Landscape fires during the 21st century are expected to change in response to multiple agents of global change. Important controlling factors include climate controls on the length and intensity of the fire season, fuel availability, and fire management, which are already anthropogenically perturbed today and are predicted to change further in the future. An improved understanding of future fires will contribute to an improved ability to project future anthropogenic climate change, as changes in fire activity will in turn impact climate. In the present study we used a coupled-carbon-fire model to investigate how changes in climate, demography, and land use may alter fire emissions. We used climate projections following the SRES A1B scenario from two different climate models (ECHAM5/MPI-OM and CCSM) and changes in population. Land use and harvest rates were prescribed according to the RCP 45 scenario. In response to the combined effect of all these drivers, our model estimated, depending on our choice of climate projection, an increase in future (2075–2099) fire carbon emissions by 17 and 62% compared to present day (1985–2009). The largest increase in fire emissions was predicted for Southern Hemisphere South America for both climate projections. For Northern Hemisphere Africa, a region that contributed significantly to the global total fire carbon emissions, the response varied between a decrease and an increase depending on the climate projection. We disentangled the contribution of the single forcing factors to the overall response by conducting an additional set of simulations in which each factor was individually held constant at pre-industrial levels. The two different projections of future climate change evaluated in this study led to increases in global fire carbon emissions by 22% (CCSM) and 66% (ECHAM5/MPI-OM). The RCP 45 projection of harvest and land use led to a decrease in fire carbon emissions by −5%. The RCP 26 and RCP 60 harvest and landuse projections caused decreases around −20%. Changes in human ignition led to an increase of 20%. When we also included changes in fire management efforts to suppress fires in densely populated areas, global fire carbon emission decreased by −6% in response to changes in population density. We concluded from this study that changes in fire emissions in the future are controlled by multiple interacting factors. Although changes in climate led to an increase in future fire emissions this could be globally counterbalanced by coupled changes in land use, harvest, and demography.
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tUp-scaling the evaluation of threat status of biodiversity from species to ecosystem level has remained for long a research challenge in global conservation science. To meet this challenge, the present study makes an attempt toward actionable conservation prescription and assigning a threat category scheme for forest ecosystems. The scheme sets the quantitative criteria for evaluation of cumulative anthropogenic threats in grid cells, such as deforestation, degradation, fragmentation, forest fires and biological invasions.Adopting the convention of IUCN, five conservation status categories (i.e. Critically Endangered, Endangered, Vulnerable, Near Threatened, Least Concern) have been similarly adopted for the forest ecosystems facing these threats. The operational success of this scheme of threat categories at ecosystem level has been strengthened by remote sensing and field data generated for the forest ecosystems of Odisha, India.The threat category status of the forest ecosystems were identified by creating grids (5 km × 5 km) in GIS and assigned the degree of the threats for each grid. The database on deforestation was generated using topographical maps of 1935 and remote sensing data of 1975 and 2010. The degradation in forest ecosystems have been assessed based on the change in forest canopy closure, fragmentation pattern, forest fire distribution and impact of biological invasions. The analysis for conservation priority hotspots complements an assessment of the threatened ecosystems undergoing remarkable level of multiple threats. Areas under the danger of cumulative anthropogenic threats would have a higher priority. 5.8% grids of existing forest had included under the category of conservation priority hotspot-I, followed by 12.4% in conservation priority hotspot-II, and 12.5% in conservation priority hotspot-III. An integrated approach involving the cumulative anthropogenic threat indicators have been found to be the most appropriate tool to empirically evaluate the threat status of the forest ecosystems. Finally, identification of ecosystems specially those facing increasing extinction risks, as attempted in the present study, can help in devising an appropriate policy and management agenda for the conservation and sustainable use of biodiversity.
Fire is a natural environmental variable over most of Australia. It is a unique environmental variable in that it: tends to be self propagating; occurs for extremely limited periods in any one locality; may have devastating effects; occurs over a wide range of environments and plant communities.In many ecosystems fire is a normal environmental variable. Its immediate effects on vegetation depend on fire intensity but longer-term effects depend also on fire frequency and season of occurrence. Using these three variables, various fire regimes may be defined. Species may be adapted to these fire regimes but not to fire per se. Interaction between fire and an adaptive trait may facilitate survival or reproduction of a species but this effect alone does not guarantee that the species is adapted to a certain fire regime—this depends on many characteristics of the life cycle.Much of the relevant Australian literature is concerned with adaptive traits while relatively little considers adaptations of species. A knowledge of species' adaptation is necessary if we are to predict species' behaviour under various natural or imposed fire regimes.
The present study on socio-economic and socio-ecological aspects of Sambalpur Forest Division of Orissa reveals that forests play an important role in the economy of the State in terms of contribution to state revenue, State Domestic Product as well as dependence of people for livelihood. The people living adjacent to the reserved forests of Sambalpur Forest Division (pre-reorganised) are heavily dependent on these forests. Although the nominal forest cover of the state continues to remain unchanged one notices a gradual decline in the effective forest cover of the state. In terms of socio-economic profile ,it was found that majority of the sample respondents (71.83) lived in kacha houses with only 4.83% having own toilet facilities,92.7% depending on outside dug well and tube well for drinking water and 90.5% depending on wood litter collected from forest for fuel requirement. Cultivation is the primary occupation among Other Backward Class (OBC) group where as daily wage labours largely belong to Scheduled Caste and Scheduled Tribe population. Per capita annual income stands at Rs.3684/-which is significantly low and indicates the degree of deprivation of the people.The study on extent and nature of dependence on forest and people's perceptions about the causes of degradation and methods of conservation reveals that the people living adjacent to the reserved forest areas are highly dependent on the forest for medicinal plants(about 51 species).Regarding cultural practices of the people in the sacrifice of trees and animals, only 2.3% of respondents indicated sacrifice of trees and majority of respondents resorting to sacrifice of domestic animals but not wild life. Regarding loss of forest species 92.35% of the respondents stated about 28 different species lost from the forests. Most important causes of degradation as perceived by the respondents are domestic use by villagers, business/trading, fuel wood crisis in the region, illegal forest produce sale etc. Important methods of forest conservation are Village committee, VSS, Vana Mahostava, Social forestry etc. A very high proportion of the respondents suggested that there should be more forest personnel, formation and effective role of village committees and recruitment of more village volunteers. Regarding the magnitude of exploitation, about 29% visualized that there is frequent exploitation by outsiders.