45

Carbon Dioxide Emissions from Forest Biomass Burning in India
K.V.S. BADARINATH1* and Krishna PRASAD VADREVU2
1

National Remote Sensing Center,
Balanagar, Hyderabad 500 625, India
2
Department of Geography, University of Maryland
4321 Hartwick Rd, College Park, Maryland 20740, USA*email: badarikvs@yahoo.com

Abstract
Biomass burning is an important source of greenhouse gas emissions, most importantly carbon dioxide(CO2). In this study, we used burnt area estimates derived from the L3JRC product to estimate the CO2
emissions from forests. The results suggested that an average of 2,414 sq.kms. is burnt annually. Of the different years, 2000-2001 recorded the greatest area burnt followed by 2003-2004, with the least during 2002-2003.
Spatial patterns in the burnt area maps clearly revealed the greatest burnt areas were located in the Himalayan
region. Over a period of seven years, closed needleleaf evergreen forest recorded the largest burnt areas (1087
sq.kms.) of the different forest types, followed by closed broadleaf deciduous forests (937 sq.kms.) and others.
The total CO2 emissions averaged across seven years were ~6.34 CO2 Tg/yr from biomass burning of forests.
Spatial variation in CO2 emissions ranged from 2.37 to 3.85Gg CO2/m2 with a mean of 3.10 Gg CO2/m2.
Key words: burnt areas, CO2 emissions, forest fires, India

1. Introduction
Fire is considered a key driver affecting earth systemprocesses including the carbon cycle, atmosphericchemistry, the physical climate and human activities(Goldammar & Price, 1998; Crutzen & Andreae, 1990;
Marlon et al., 2009). With respect to vegetation, fires
may induce long-term changes in the floristic andphysiognomic parameters of the vegetation through theirimpact on soil nutrients and physio-chemical properties.
The response of vegetation to fires can be complex(Goldammer & Price, 1998; Nunes et al., 2005). The
regeneration process is highly variable because of thelarge number of factors involved, including plantcomposition, topography, climate, soil characteristics,
land-use history, wildfire severity, etc. (Dansereau &Bergeron, 1993; Ayanz et al., 2009). Biomass burning
releases large amounts of trace gases and particulates into
the atmosphere (Seiler & Crutzen, 1980). Sandberg et al.,(2002) estimated that fires account for approximately one
fifth of the total global CO2 emissions. Tropical biomassburning has been estimated to release ~238 Tg/yr - 420
Tg/yr (Streets et al., 2003; Van der Werf et al., 2006).
Using satellite derived fire counts together with a terrestrial biogeochemistry model, Van der Werf et al. (2006)
estimated an average biomass burning emissions ofnearly 2.5 Pg C year−1 over the 1997-2004 period. The
dominant contributors were Africa (49%), SouthAmerica (13%), equatorial Asia (11%), boreal regions
Global Environmental Research
15/2011: 45-52
printed in Japan(9%) and Australia (6%). Recently, Mieville et al.,(2010) attempted to ascertain historical biomass burningemissions. They estimated that until 1970 the emissions
were ~7,400 Tg-CO2 year−1 but since the 1980s, they
have increased rapidly and are currently about
9,950 Tg-CO2 year−1. They further attributed these differences to a decrease in fire emissions in the boreal
regions due to fire suppression policies, and an increasein tropical regions, especially in South America andIndonesia, due to deforestation as a part of agricultural
expansion. In addition to carbon emissions, a host ofother gases such as carbon monoxide, methane, hydrocarbons, nitric oxide and nitrous oxide are released in thebiomass burning process, which may contribute to global
warming and ozone layer depletion (Sieler & Crutzen,
1990). To assess the atmospheric impact of biomassburning quantitatively, accurate emissions estimates oftrace gases and aerosols are required. Crucial parameters
in biomass burning emissions estimation include burned
area, fuel consumption and the emission factor (EF) (Vander Werf et al., 2006).
With respect to the area and the amount of biomass
burnt in India, there are no clear estimates, in particularfrom forests. As per the recent state of the forest assessment report (SFR, 2009), the forest and tree cover of thecountry is 78.37 Mha (2007 year), which is 23.84% of thegeographical area. The net increase in forest and tree
cover between the latest and previous assessments(two-year data interval) is about 0.18 Mha. The decadal©2011 AIRIES46

K.V. S. BADARINATH and K.P. VADREVU

increase, i.e., the increase in forest cover between 1997
and 2007 is estimated to be 3.13 Mha. Although, theforest cover increased during the two-year survey period(2005-2007), there seem to be significant spatial differences with respect to increases/decreases in different
states and types of forests. For example, in the case ofnortheastern India, comprising seven different states, in
Arunachal Pradesh, Assam, Nagaland and Tripura,the forest cover decreased nearly 119, 66, 201 and100 sq.kms., respectively. In contrast, in the states ofManipur, Meghalaya and Mizoram the forest cover increased by nearly 328,116 and 640 sq.kms., respectively.
Overall, in northeastern India, there was an increase of
598 sq.kms. of forest cover. Although the forest coverestimates from the State of Forest Report (2009) provide
overall forest cover, it is unclear how much area wasoriginally burnt or cleared for various purposes. There
are no national level statistics available on burnt areasand carbon emissions. According to the 2001 State ofForest Report (SFR, 2001), about 50% of the forested
areas in the Indian region were fire prone (ranging from
50% in some states to 90% in others). About 6% of theforests are prone to severe fire damage (IFFN, 2002).
Considering the impacts of biomass burning on the environment, including the social, economic and environmental implications, fire statistics as well as estimates of
the amount of biomass burnt are needed. To meet theinternational reporting commitments under the United
Nations Framework Convention on Climate Change(UNFCCC), individual countries are required to estimate
annual emissions and removals of carbon and non-CO2
greenhouse gases. India is a part of UNFCCC and estimation of biomass burning emissions to date has been conducted mostly by empirical means. There is a stronger
need to use satellite-derived products for calculating
emissions from biomass burning.
In this study, we quantified the carbon released fromthe biomass burning of forests. integrating burnt area
estimates derived from satellite data with the biomass,
combustion efficiency and CO2 emission factor to arriveat the total CO2 emissions from the forest biomass burning. We also assessed the temporal variation in theamount of biomass burnt. In addition, we also compared
the CO2 emission estimates with the previously publishedestimates for relative comparison. Finally, we considered
the implications of emissions affecting local and regional
climates in addition to the need for developing highresolution burnt-area estimates from the indigenous
Indian remote sensing satellites.

2. Methodology
2.1 Forest types map
The forest vegetation in the country varies fromtropical evergreen forests on the west coast and in thenortheast to alpine forests in the Himalayas in the north.
In between the two extremes, there are semi-evergreen
forests, deciduous forests, sub-tropical broad-leaved hill
forests, sub-tropical pine forests and sub-tropical mon-tane temperate forests. Recently, the Forest Survey ofIndia (SFR, 2009) mapped the forest cover of India using
IRS-P6-LISS III data at 23.5 m resolution and 1:50,000
scale. The minimal mappable unit at this scale is 1 ha andthe classification approach used was a hybrid combining
visual as well as digital classification. In addition to the
canopy cover classes of very dense (cover of 70% ormore), moderately dense (40%-70%) and open forest(10%-40%), forest types were also mapped following theChampion and Seth (1968) classification system, at thesame scale. However, these maps were inaccessible to thegeneral public. With such a limitation, in this study, we
have used the freely available land use/cover productfrom MEdium Resolution Imaging Spectrometer(MERIS) data at a 300 m resolution for characterizing
vegetation types. Launched in 2002 onboard the
ENVISAT satellite, MERIS is a wide-field-of-view pushbroom imaging spectrometer measuring the solar radiation reflected by the Earth in 15 spectral bands fromabout 412.5 nm to 900 nm (Rast et al., 1999) and a repetitivity of three days. The land cover product is derived byclassification of a time series of MERIS full-resolution
mosaics from December 2004 - June 2006. Its 22 land
cover global classes are defined according to the United
Nations Land Cover Classification System (LCCS)(Bicheron et al., 2008). We used this product due to its
high spatial resolution (300 m) compared to the otherglobal land cover products. As we are interested inquantifying biomass burning emissions from forests, ofthe 22 classes, we have selected only six different classes
pertaining to forest classes, i.e., a) closed broadleaf
deciduous forest; b) closed needleleaf evergreen forest;c) closed to open broadleaf evergreen/semi-deciduous
forest; d) closed/ open mixed broadleaf/needleleaf forest;e) mosaic forest/ grassland/shrubland; and e) open
broadleaf deciduous forest. Specifically, we used theburnt areas derived from the L3JRC product (Tansey
et al., 2008). The L3JRC algorithm makes use of atemporal index in the near infrared (NIR) channel todetect the burnt areas. The output is then post-processed
to remove some over detections. Post-processing of thedata is largely based on the land cover information
provided by the GLC2000 product (JRC, 2011). We usedthe L3JRC derived burnt areas from 2000-2007. The data
were overlaid on the MERIS vegetation map andanalyzed for spatial variations.
2.2 Burnt areas
To assess biomass burning from forests, we used the
L3JRC product described above. L3JRC burnt areas arereported at a resolution of 1 km for seven fire years (2000
to 2007). For creating this product, a modified version ofa Global Burnt Area (GBA) 2000 algorithm was used.
Burnt areas were derived from SPOT satellite data thathas a revisit cycle of five days. A single algorithm wasused to classify the burnt areas from global, daily,
atmospheric-corrected SPOT VEGETATION reflectancedata. The main processing algorithm makes use of atemporal index in the 0.83 µm near infrared channel forCarbon Dioxide Emissions from Forest Biomass Burning in India

identifying burnt areas. The output is then postprocessed to remove some over-detections. Postprocessing of the data is largely based on the land cover
information provided by the Global Land Cover-2000
product. It is assumed that a global fire year starts on the
1st of April of every year and that a surface cannot beburned more than once in the same fire season. For eachfire year (e.g., 2000-1, i.e., 1 April 2000 to 31 March
2001) a binary product is available in geographic
coordinates in addition to the ASCII text file for each fireyear representing the geographic coordinate of the centreof each pixel that has been detected as being burnt(Tansey et al., 2008).
2.3 Emissions
CO2 emissions from biomass burning were estimatedfollowing Sieler and Crutzen (1980). The biomass burning emissions from forested regions of India were calculated as:
E = A x B x β x EF
where E is the emissions (in CO2 grams), A = total landarea burned annually [m2/yr], B = the average biomass/fuel load (kg/dry matter/m2), β = the burning efficiency of the above-ground biomass, EF is the emission
factor (mass of species per mass of dry matter burned
in g/kg). The burnt areas were derived from the L3JRC
product, and the biomass data from a wide variety ofsources through a book-keeping approach. A literature
review suggested that the above-ground biomass densities in Indian forests range from 12 to 230 Mg/ha, with amean of 72.3 Mg/ha (Haripriya, 2003; Chabbra &Dadhwal, 2004; Kaul et al., 2009). For the different
forest types derived from the MERIS data, we used the47values of 3.64, 4.0, 4.76, 3.52, 2.94, and 2.7 kg dry
matter/m2 for closed broadleaf deciduous forest, closed
needleleaf evergreen forest, closed to open broadleaf
evergreen/semi-deciduous forest, closed/open mixed
broadleaf/needleleaf forest, Mosaic forest/grassland/
shrubland and open broadleaf deciduous forest respectively. These values were derived from averaging the dry
matter values for different forest types reported in theliterature (Haripriya, 2003; Chabbra et al., 2004; Manhas
et al., 2006; Kaul et al., 2009). Further, we used a combustion efficiency of 40% in this study as this value
seems to be a conservative estimate. Our previous studies
on combustion completeness for tropical secondary dry
mixed forests suggested values in the range of 28%-30%(Prasad et al., 2000; 2001). As the current study from the
MERIS vegetation data also includes broadleaf deciduous forests in addition to mixed forest types, a conservative estimate of 40% seems justified. However, we alsoinfer that more ground based studies are needed forassessing the error estimates. The CO2 emission factor
was derived from Andreae and Merlet (2001).

3. Results
Figure 1 gives the forest type map of India derived
from the MERIS data at a 300 m resolution. Of thedifferent classes, closed to open broadleaf evergreen/semi-deciduous forest dominated with 5.23% followed
by closed broadleaf deciduous forests (3.75%), etc(Fig. 2). These four types of forests constituted 10.85%
of the total geographical area of the country. As seen in
Fig. 1, broadleaf evergreen forests dominated in thewestern, northern and northeastern parts of India, while
open broadleaf forests were confined to central India.
Figure 3 shows the burnt area estimates derived from theFig. 1 Forest type map of India derived from the MERIS (300 m) product.

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K.V. S. BADARINATH and K.P. VADREVU

Fig. 2 Forest percent coverage derived from the MERIS (300 m) product.

Fig. 3 Temporal variation in burnt forested areas derived from the L3JRC product.

L3JRC product for the seven years. An average of
2,414 sq.kms. has been estimated to be burnt annually inthe forested areas. Of the different years, 2000-2001
recorded the greatest burns, followed by 2003-2004, withthe least during the year of 2002-2003. On an annual
basis, the total area burnt for all of India derived from
L3JRC was about 30,603 sq.kms., of which forest
biomass burning constituted 2,414 sq.kms., nearly 7.8%of the total area. Figures 4 a-g show spatial and temporalvariability in burnt areas of forest classes for the sevenyears. These spatial patterns clearly suggest the greatest
burnt areas are located in the Himalayan region. Further,
considering the huge spatial and temporal variability ofburnt areas, understanding the causative factors of fires in
the Indian region need further exploration. Figure 5 gives
the amount of area burnt for each of the forest types classand Fig. 6, the average annual area. Over the seven-year
period, closed needleleaf evergreen forest recorded thegreatest burnt areas (1,087 sq.kms.) among the different
forest types, followed by closed broadleaf deciduous
forests (937 sq.kms.) and others (Fig. 6). Figure 7 shows
spatial variation in the CO2 emissions (in Gg/m2) among

the burnt areas. The values ranged from 2.37 to 3.85 witha mean of 3.10 Gg CO2/m2. A total of ~6.34 CO2 Tg/yr ofemissions has been estimated resulting from forest
biomass burning.

4. Discussion
Air quality issues in India are a matter of great concern. Emissions from forest fires may cause substantial
releases of CO2 emissions and there is a need to address
this issue through effective fire policies and management.
A variety of forest types exist in India, from the tropicalevergreen forests of Kerala and Assam to the conifers ofthe Himalayan region, and from the tropical deciduous
forests of Madhya Pradesh and Orissa to the thorny scrub
forests of Rajasthan. In India, current knowledge of thecontribution of forest fires to the total CO2 emissions is
insufficient. The current study has been undertaken to fillsuch a gap. Further, most of the studies relating to burnt
areas conducted in the Indian region are highly localized,
and national level mapping efforts on burnt areas have
yet to be undertaken. Since there are no national level

Carbon Dioxide Emissions from Forest Biomass Burning in India

49

d

a

b

e

f

c

g

Fig. 4 Burnt forested areas derived from the L3JRC product shown as a red-colored area. Fire year is (a) 2000-2001, (b) 2001-2002,(c) 2002-2003, (d) 2003-2004, (e) 2004-2005, (f) 2005-2006, and (g) 2006-2007. See the text for details.

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K.V. S. BADARINATH and K.P. VADREVU

Fig. 5 Burnt areas in different forest types derived from the L3JRC product.

Area (sq. kms)

Fig. 6 Average annual burnt areas in different forest types derived
from the L3JRC product.

Fig. 7 CO2 (Gg/m2) released through biomass burning of forests.
The results represent averaged amount from 2000-2007.

burnt area products, we have had to rely on the global
L3JRC product for assessing the burnt areas and theresulting CO2 emissions from forests. Regarding the
L3JRC algorithm and burnt areas, Tansey et al., (2008)
reported that the algorithm performs reasonably well fora number of different vegetation types, in particular,
areas that have a relatively higher vegetation percent ascompared to low vegetation cover areas (such as herbaceous and sparse herbaceous or shrub cover classes)
where significant underestimation of burnt areas canoccur (Tansey et al., 2008). Since our study is focused on
the forested regions, we have used only the major forest
type classes from the Globcover product. Thus, the burnt
area estimates derived from the forested areas may havefewer errors, but validation is required to account for
uncertainties. In addition to area estimates, fuel type, fuelload and combustion efficiency can also effect emissions
calculations. In our case, we have used the MERIS 300 m
Globcover map for delineating forested areas. The spatial
patterns in the vegetation types (Fig. 1) reveal that the
map represents broad vegetation types very well, although a better map with different floristic types andphysiognomy would have helped to refine our current
estimates. The biomass data have been obtained througha book-keeping approach. Overall, we infer that uncertainties in our emissions calculations resulting from theforest-type map as well as biomass were considerably
less compared to the burning efficiency value of 40%
used in this study. Our earlier studies, focusing on tropical dry deciduous and secondary mixed deciduous forests,
suggested that the combustion factor could vary from20%-30% during the initial phase of slash and burn agriculture (Prasad et al., 2000; 2001). Thus, there is astronger need to validate the combustion factors fromdiverse forests of the Indian region. Despite theselimitations, we have estimated annual CO2 emissions ofCarbon Dioxide Emissions from Forest Biomass Burning in India

nearly 6.34 CO2 Tg/yr from biomass burning of forests.
This estimate is far less than the earlier estimate of102-353 Tg/yr CO2 from forests of India (Venkataraman
et al., 2006). The discrepancies are mainly attributed tousage of MODIS-derived active fires for estimating theburnt areas. Since each active fire pixel with a spatial
resolution of 1 km is considered a burnt pixel, the area
estimates derived from such an approach may not be
representative of the actual area burnt in the field.
Relatively, the use of burnt area products for emissions
estimations compared to active fires is considered highly
effective (Tansey et al., 2008; Roy et al., 2008). We alsoinfer the need for collecting data on fire-causative factors(both anthropogenic and biophysical data) at a variety of
scales and geographic gradients. Accurate information onfire causes would help to reliably predict where, when,
why and how many fires are likely to occur, information
which could allow fire and landscape management totake on more proactive planning than a reactive process(Leone et al., 2003). Regarding emissions, although
wildfires can be limited to a particular forest type or area,
the emissions can be transported beyond the physicalboundaries of the forested area through long-range
transport. Depending on the meteorological conditions,the smoke plumes and haze layers can persist in theatmosphere for long periods of time with conditions
prevailing (Badarinath et al., 2007; 2009). Thus, there isa stronger need to address fire management issues at a
local scale.

Acknowledgements
We are grateful to Dr. Haruo Tsuruta of the Universityof Tokyo, Japan, for inviting us to contribute to thespecial issue on Biomass Burning Focussing on the AsianRegion. We are also thankful to Dr. Kevin Tansey of the
University of Leicester, UK, for the L3JRC data. We
thank the Globcarbon Project Team, European Space
Agency, for the land cover datasets.
References
Andreae, M.O. and P. Merlet (2001) Emission of trace gases andaerosols from biomass burning. Global Biogeochemical Cycles
15, 955-966.
Ayanz, J.S.M, J.M.C. Pereira, R.Boca, P. Strobl, J. Kucera and A.
Pekkarinen (2009) Mapping and analysis of burned areas.
In: E. Chuvieco, ed., Forest Fires in the European
Mediterranean Region, pp.189-204 Springer-Verlag, Berlin
Heidelberg.
Badarinath, K.V.S., S.K. Kharol, T.R.K. Chand, Y.G. Parvathi, T.
Anasuya and A.N. Jyothsna (2007) Variations in Black Carbon
aerosol, carbon monoxide and ozone over an urban area ofHyderabad, India during the forest fire season. AtmosphericResearch, 85: 18-26.
Badarinath, K.V.S., S.K. Kharol, A.R. Sharma and V.K. Prasad(2009) Analysis of aerosol and carbon monoxide characteristics
over Arabian Sea during crop residue burning period in the
Indo-Gangetic Plains using multi-satellite remote sensing datasets, Journal of Atmospheric and Solar-Terrestrial Physics(JASTP), 71: 1267-1276, doi: 10.1016/j.jastp.2009.04.004.

51

Bicheron, P., V. Amberg, L. Bourg, D. Petit, M. Huc, B. Miras,
C. Brockmann, S. Delwart, F., Ranéra, O. Hagolle, M. Leroy
and O. Arino (2008)<http://postel.mediasfrance.org/IMG/pdf/Bicheron_etal_MERI
SColloque_sept2008.pdf>
Champion, H.G. and S.K. Seth (1968) A Revised Survey of theForest Types of India, Government of India Publications, New
Delthi.
Chhabra, A. and V.K. Dadhwal (2004) Assessment of Major Pools
and Fluxes of Carbon in Indian Forests. Climatic Change, 64:
341-360.
Crutzen, P.J. and M.O. Andreae (1990) Biomass burning in thetropics: image on atmospheric chemistry and biogeochemical
cycles. Science, 250: 1669-1678.
Dansereau, P.R. and Y. Bergeron (1993) Fire history in thesouthern boreal forest of Quebec. Canadian Journal of Forest
Research, 23: 25-32.
IFFN (2002) Fire situation in India. International Forest Fire News,
26: 23-27.
<http://www.fire.uni-freiburg.de/iffn/country/in/in_5.htm>
Goldammer, J.G. and C. Price (1998) Potential impacts of climatechange on fire regimes in the tropics based on MAGICC and a
GISS GCM-derived lightning model. Climate Change, 39:
273-296.
Haripriya, G.S. (2003) Carbon budget of the Indian forest ecosystem. Climatic Change, 56: 291-319.
Kaul, M., VK. Dadhwal and G.M.J. Mohren (2009) Land usechange and net carbon flux in Indian forests. Forest Ecology andManagement, 258: 100-108.
Leone, V., N. Koutsias, J. Martinez, C. Vega-Garcia and B.
Allgower (2003) The human factor in fire danger assessment. In:
E. Chuvieco, ed., Wildland Fire Danger Estimation and
Mapping: The Role of Remote Sensing Data, World Scientific,
New Jersey.
Manhas, R.K., J.D.S. Negi, R. Kumar and P.S. Chauhan (2006)
Temporal assessment of growing stock, biomass and carbon
stock of Indian forests. Climatic Change, 74: 191-221.
Marlon J.R., P.J. Bartlein, M.K. Walsh, S.P. Harrison, K.J. Brown,
M.E. Edwards, P.E. Higuera, M.J. Power, R.S. Anderson, C.
Briles, A. Brunelle, C. Carcaillet, M. Daniels, F.S. Hu, M.
Lavoie, C. Long, T. Minckley, P.J.H. Richard, A.C. Scott, D.S.
Shafer, W. Tinner, C.E. Umbanhowar and C. Whitlock (2009)
Wildfire responses to abrupt climate change in North America.
Proceedings of the National Academy of Sciences, 106:2519-2524.
Mieville, A., C. Granier, B. Liousse, F. Guillaume, J.-F. Mouillot,
M. Lamarque, J.-M. Gr´egoire and G. Petron (2010) Emissionsof gases and particles from biomass burning during the 20th
century using satellite data and an historical reconstruction,
Atmospheric Environment, 44: 1469-1477.
Nunes, M.C.S., M.J. Vasconcelos, J.M.C. Pereira, N., Dasgupta,
R.J. Alldredge and F.C. Rego (2005) Land cover type and fire in
Portugal: do fires burn land cover selectively? LandscapeEcology, 20: 661-673.
Prasad, V.K., P.K. Gupta, C. Sharma, A. Sarkar, Y. Kant, K.V.S.
Badarinath, T. Rajagopal and A.P. Mitra (2000) NOx emissions
from biomass burning of shifting cultivation areas from tropicaldeciduous forests of India – estimates from ground-based
measurements. Atmospheric Environment, 34 (20): 3271-3280.
Prasad, V.K., Y. Kant, P.K. Gupta, C. Sharma, A.P. Mitra and K.V.S.
Badarinath (2001) Biomass and combustion characteristics of
secondary mixed deciduous forests of India. Atmospheric
Environment, 35: 3085-3095.
Rast, M., J. L. Bézy and S. Bruzzi (1999) The ESA Medium
Resolution Imaging Spectrometer MERIS – a review of the
instrument and its mission. International Journal of Remote
Sensing, 20: 1681-1702.
Roy, D.P., L. Boschetti, C.O. Justice and J. Ju (2008) The collection 5 MODIS burned area product-Global evaluation by

52

K.V. S. BADARINATH and K.P. VADREVU

comparison with the MODIS active fire product. Remote Sensing of Environment, 112(9): 3690-3707.
Sandberg, D.V., R.D. Ottmar, J.L. Peterson and J. Core (2002)
Wildland fire on ecosystems: Effects of fire on air. General
Technical Report RMRS-GTR-42, Vol. 5, USDA Forest Service,
Rocky Mountain Research Station, Ft. Collins, CO.
Seiler, W. and J.P. Crutzen (1980) Estimates of the gross and net
fluxes of carbon between the biosphere and the atmosphere frombiomass burning. Climatic Change, 2: 207-247.
State of the Forest Report (2001) India-State of the Forest Report,
Forest Survey of India, Ministry of Environment and Forests,
Government of India, Dehradun, India.
State of the Forest Report (2009) India-State of the Forest Report,
Forest Survey of India, Ministry of Environment and Forests,
Government of India, Dehradun, India.
Streets, D.G., K.F. Yarber, J.H. Woo and G.R. Carmichael (2003)
Biomass burning in Asia: Annual and seasonal estimates andatmospheric emissions. Global Biogeochemical Cycles, 17(4):1099, doi: 10.1029/2003GB002040.
Tansey, K., J.M. Gre´goire, P. Defourny, R. Leigh, J-F. Pekel, E.V.
Bogaert and E. Bartholome (2008) A new, global, multi-annual(2000–2007) burnt area product at 1 km Resolution. Geophysical Research Letters, 35: L01401, doi:10.1029/2007GL031567.
Van der Werf, G.R., J.T. Randerson, L. Giglio, G.J. Collatz, P.S.
Kasibhatla and A.F. Arellano, Jr. (2006) Interannual variability
on global biomass burning emissions from 1997 to 2004.
Atmospheric Chemistry and Physics, 6: 3423-3441.
Venkataraman, C., G. Habib, D. Kadamba, M. Shrivastava, J.F.
Leon, B. Crouzille, O. Boucher and D.G. Streets (2006) Emissions from open biomass burning in India: integrating theinventory approach with the higher resolution Moderate
Resolution Imaging Spectroradiometer (MODIS) active fire andland count data. Global Biogeochemical Cycles, 20: GB2013,
doi: 10, 1029/2005GB002547.

K.V.S. BADARINATH
K.V.S. BADARINATH, who heads the Atmospheric Science Section, National Remote Sensing
Agency, Hyderabad, India, obtained his Ph.D. in
Physics from the Indian Institute of Technology
in 1984. He has been working in the areas ofRemote Sensing Applications, Atmospheric
Aerosols and Forest Fires for over two decades. He is a Fellow of the Indian
Geophysical Union and Andhra Pradesh Academy of Sciences. He has
published over 180 research papers in international/national journals andhas provided guidance to nine Ph.D. students.

Krishna PRASAD VADREVU
Dr. Krishna PRASAD VADREVU is working as an
Associate Research Professor at the Geography
Department, University of Maryland, College
Park, USA. His research interests are in satellite
remote sensing, biomass burning, greenhouse gas
emission inventories, and similar fields. His
Ph.D. work focused on biodiversity and forest cover studies involving
remote sensing techniques. At the Geography Department, University of
Maryland College Park, USA, he is currently focusing on fire research. He
is also the Fire Implementation Team officer for the GOFC-GOLD
program, which is global in scope. He has published over 60 research
papers in international/national journals.

(Received 20 May 2011, Accepted 9 September 2011)