biomassburningandrelatedtracegasemissionsfromtropicaldry...

int. j. remote sensing, 2002, vol. 23, no. 14, 2837–2851

Biomass burning and related trace gas emissions from tropical drydeciduous forests of India: A study using DMSP-OLS data andground-based measurements

V. KRISHNA PRASAD†, YOGESH KANT†, P. K. GUPTA‡,

C. ELVIDGE§ and K. V. S. BADARINATH†

†National Remote Sensing Agency (Dept of Space-Govt of India), Balanagar,Hyderabad- 500 037, India‡National Physical Laboratory, Dr K. S. Krishnan Road, New Delhi—110 012,India§NOAA-NESDIS National Geophysical Data Center, 325 Broadway, Boulder,Colorado 80305, USA

(Received 17 April 2000; in � nal form 13 June 2001 )

Abstract. Biomass burning is one of the major sources of trace gas emissions inthe atmosphere. In India the major sources of biomass burning include deforesta-tion, shifting cultivation, accidental � res, controlled burning, � re wood burning,burning from agricultural residues and burning due to � re lines. Studies onbiomass burning practices gain importance due to increasing anthropogenic activ-ities and increasing rates of deforestation. Satellite data have been widely usedover the globe to monitor the rates of deforestation and also with respect tobiomass burning studies. But, much of the polar orbiting satellites, due to theirrepetitive cycle, have limitations in observing such events and in the tropics, dueto cloud cover, getting a cloud-free image during the daytime is diYcult. In thisstudy we used Defence Meteorological Satellite Program Operational LineScanner (DMSP-OLS) night-time data to study the biomass burning events overa period of 10 years from 1987 to 1998 for the Eastern Ghats region, coveringthe northern part of Andhra Pradesh, India. Two ground-based experiments werecarried out to quantify the emissions from biomass burning practices. The resultsof the study with respect to trace gases suggested emission ratios for CO, CH4 ,NOx and N20 during the burning to be about 12.3%, 1.29%, 0.29% and 0.07%at the � rst site and 12.5%, 1.59%, 0.29% and 0.05% at the second site, suggestinglow inter-� re variability between the sites. The variation has been attributed tothe fuel load, vegetation characteristics, site conditions and local meteorologicalparameters aVecting the relative amounts of combustion. Using the DMSPOLS derived areal estimates of active � res, the trace gas emissions releasedfrom the biomass burning were quanti� ed. The results suggested the emis-sions of 8.2×1010 g CO2 , 1.8×108 g CO, 6.0×106 g N2O, 3.0×106 g NOx and1.2×108 g CH4 during March 1987. The emissions increased to 1.0×1011 g CO2 ,2.3×108 g CO, 7.8×106 g N2O, 3.9×107 g NOx and 1.6×108 g CH4 , over a periodof 10 years. The results of the analysis suggest the possible use of monitoringbiomass burning events from DMSP-OLS night-time data.

e-mail: [email protected]

Internationa l Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-590 1 online © 2002 Taylor & Francis Ltd

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160110109598

V. Krishna Prasad et al.2838

1. IntroductionOver the years, � re has in� uenced the vegetation on Earth. The existence of � re

dates back to 350–450 million years ago. However, � re frequency is increasingbecause of various practices, especially in tropical regions, such as biomass burningfor shifting cultivation purposes, accidental � res and agricultural residue burning,leading to the evolution of large amounts of trace gases along with a complexmixture of particulate matter in the atmosphere. The immediate eVects of biomassburning include loss of species diversity, increased surface albedo and water run-oV,decreased evapotranspiration and evolution of several greenhouse gases and aerosols(Crutzen et al. 1985, Matson and Holben 1987, Andreae et al. 1988). Biomass burningrepresents an important source of atmospheric CO, CH4 , H2 , CH3Cl, NO, HCN,CH3CN, COS and particulate carbon (Crutzen and Carmichael 1993). Satelliteremote sensing can make an important contribution to the study of � res in theenvironment and their ecological, climatic and atmospheric chemical eVects. Todevelop emission source strength on a regional scale, monitoring of � res at regionalscale is needed. Space-borne measurements are likely to be the only viable alternativefor tracking the temporal distribution of frequency of � res in many areas of the world.

Current estimates of trace gas emissions from biomass burning are severelyconstrained by the lack of reliable statistics on � re distribution and frequency, andthe lack of accurate estimates of area burned, fuel load and fuel moisture content.There have been relatively few studies that demonstrate the contribution of satellite-based � re monitoring to address the current research issues relating to � re monitoringand biomass burning (Justice et al. 1993). The most practical and economicallyfeasible manner of monitoring the extent of burning associated with tropical defor-estation and grassland management is through remote sensing (Menzel et al. 1991 ).Various studies during the last decade have demonstrated the potential use of remotesensing for � re-related studies. Satellite measurements have been used to detect theoptical thickness, particle size and absorption of atmospheric aerosols (Ferrare et al.1990). Application of various geostationary satellites for monitoring � res has beenextensively discussed by Justice et al. (1993). Several onboard satellites have thepotential to monitor � res. High resolution Landsat TM includes a middle infraredchannel (2.08–2.35 mm) with a 30 m spatial resolution which permits active � res tobe detected. In addition, visible and near-infrared channels designed speci� cally forvegetation studies permit the detection of burn scars and the assessment of vegetationstate through the use of vegetation indices (Chuvieco and Congalton 1988). Theonboard National Oceanic and Atmospheric Administration Advanced Very HighResolution Radiometer (NOAA AVHRR) satellite provides daily global data at asampled resolution of 4 km (GAC) and 1 km (LAC). The middle-infrared (3.7 mm)and thermal channels (10.8 mm) provide a means to detect active � aming � res assmall as 10 m×10 m. The Along Track Scanning Radiometer (ATSR) sensor of theEuropean Resource Satellite (ERS-1) provides sensing in the middle and thermalinfrared channels and can be useful for studying the � res. The GeostationaryOperational Environmental Satellite (GOES) visible infrared Spin Scan RadiometerAtmospheric Sounder (VIS) system provides high temporal frequency coverage every30 minutes and the coarse spatial resolution of 16 km permits � res to be detected ata larger scale (Menzel et al. 1991 ).

The unique capability of the Defence Meteorological Satellite ProgramOperational Line Scanner (DMSP-OLS) when compared with the above satelliteslies in detecting visible near-infrared emission sources during the night-time. The use

Biomass burning and related trace gas emissions 2839

of DMSP-OLS data for the detection of � res was � rst noted by Croft (1973, 1979).Cahoon et al. (1992) reported the � rst systematic inventory of � res with OLS data.Elvidge et al. (1996, 1997a,b) extensively discussed the utility of DMSP-OLS dataand developed algorithms to identify and geolocate � res and city lights in digitalOLS imagery. The sensitivity of OLS is higher than NOAA AVHRR and LandsatTM and can measure the radiances down to 10 Õ 9 W cm Õ 2 sr Õ 1 cm Õ 1 . In general,with respect to the biomass burning studies, to improve the understanding of emissionestimates and the role of biomass burning in atmospheric chemistry, a combinationof accurate estimates of � re distribution, frequency and fuel loading from remotesensing, with representative ground and laboratory measurements of combustioneYciencies and emission factors for diVerent � re-vegetation regimes would lead tosuYcient improvement in understanding the biomass burning process (Kaufmanet al. 1992, Justice et al. 1993). In India, such studies with respect to monitoring of� res and data pertaining to biomass burning activities are scarce and not up to date.Further, there are no � eld-based studies for quanti� cation of combustion eYciencies,amount of biomass burnt and the emissions released due to biomass burning. TheDMSP has a broad-band panchromatic low light sensor that is useful for detectionof visible light sources at night, including � res. The scanning system has beenoperational since 1974, with digital data available since 1992, and therefore can beeVectively used for monitoring of � res and lights. In this study, we used DMSP-OLSsatellite data from diVerent time periods to study the intensity and areal extent of� res and used ground-based experimental results for quanti� cation of the amountof trace gas emissions released due to biomass burning.

2. Study areaThe study area is in the northern and north-eastern parts of Andhra Pradesh,

covering Adilabad, parts of Khamam, East Godavari , West Godavari , Visakhapatnam,Vizianagaram and Srikakulam districts (� gure 1). The dominant vegetation type ofthe study area is tropical dry deciduous, along with moist mixed evergreen. The forestscorrespond to southern dry mixed deciduous forests (type 5A/C3; group 5, subgroup5A) and southern tropical forests (group 6, subgroup 6A) as classi� ed by Championand Seth (1968) . Of the 30 series recognized and described by Gaussen’s holisticsystem of vegetation (Gaussen 1959, Gaussen et al. 1973), the dominant forests of thestudy area correspond to T erminalia-Anogeissus-Cleis tanthes collinus Series (series 9).The species composition includes Strychnos nuxvomica, Feronia elephantum,Pterocarpus marsupium, Ficus retusa, Pavetta indica, Canthium dicoccum, Chloroxylonswietenia, Lannea coromandelica, Macaranga peltata, Mitragyna parvi� ora, Grewiatilaefolia and Madhuca latifolia. In the study area, biomass burning for shifting cultiva-tion starts as early as February and continues until the end of May. The intensity isgreater during the months of March and April.

3. Shifting cultivation in the study areaShifting cultivation, known locally as jhumming or podu cultivation, is practised

by the local tribes of the study area. The practice consists of clearing the forestsduring the winter months of November and December and allowing the felledbiomass to dry until early March to early May. The biomass is subsequently burntto clear land for sowing. Mixed cropping is preferred and harvesting starts byOctober. After harvesting the plot is abandoned and another patch is selected tostart the cycle again.


Figure 1.

4. Datasets and methodology4.1. Datasets

In this study, DMSP-OLS data pertaining to the north-eastern part of AndhraPradesh, (� gure 1) covering Adilabad, parts of Khamam, East Godavari, WestGodavari, Visakhapatnam, Vizianagaram and Srikakulam, from 25 March 1987,1 March 1998 and 27 March 1998 were used to study areal extent of biomass burningand the amounts of trace gases released over a period of time.

4.2. DMSP-OL S sensor characteristicsDMSP-OLS is an oscillating scan radiometer designed for cloud imaging with

spectral bands (VIS and TIR) acquiring images during daytime and night-time witha swath of ~3000 km (Elvidge et al. 1997a,b) . DMSP digital data has been availablesince September 1992 and is expected to continue until 2010 (Elvidge 1996). DMSPsatellite characteristics are given in table 1. DMSP operates two satellites in sun-synchronous orbits, one in a dawn–dusk orbit and another in a day–night orbit.The platforms of DMSP are three axis stabilized with roll, pitch and yaw variations,kept within ±0.01°. The currently orbiting DMSP-OLS satellites include: F-12 withday–night overpasses at ~9:54 and 21:54 local time, and F-13, with dawn–duskoverpasses at ~6:04 and 18:04 local time. The OLS is an oscillating scan radiometerdesigned for cloud imaging with two spectral bands, visible (VIS) and thermalinfrared (TIR) and a swath of ~3000 km. With 14 orbits per day, each OLS iscapable of generating global daytime and night-time coverage of the Earth every 24hours. The data can be acquired from OLS through � ne resolution mode (0.56 km)


Table 1. DMSP OLS speci� cations.

OLS: normal daytime operationSpectral bands (mm) 0.40–1.10 10.0–13.4Nadir footprint (km)Smoothed data 2.75 km×2.75 km 2.75 km×2.75 kmReal-time data 0.55 km×0.55 km 0.55 km×2.75 kmMeasurement range 1–6—100% A 4–310 KSignal quantization 6 bit 8 bit

OLS: normal night-time operationSpectral bands (mm) 0.47–0.95 10.0–13.4Nadir footprint (km)Smoothed data 2.75 km×2.75 km 2.75 km×2.75 kmReal-time data 0.55 km×2.75 km 0.55 km×0.55 kmMeasurement range 0–64 counts 4–310 KSignal quantization 8 bit 6 bitSatellite orbit Polar, 98.8° inclination, 05:30 a.m. and 09:30 a.m.

Equatorial crossings

or through smooth resolution mode (2.7 km). The visible band pass straddles theVIS and near-infrared VNIR portions of the spectrum with a full width half maximum(FWHM) of 0.58–0.91 mm. The TIR band has a FWHM of 0.58–0.91 mm. The TIRband data are calibrated using an onboard blackbody source and views of deepspace to provide 8-bit data with a temperature range of 190–3100 K, ideal fordetecting and characterizing clouds.

4.3. Fire detectionDuring the night-time, when the solar illumination is nil, areas of active visible

and near-infrared emissions were detected. During the night-time, the visible bandsignal was intensi� ed using a photo multiplier tube (PMT) making it possible todetect even faint VNIR sources. The thermal band saturates at 310 K and typicalsurface temperature backgrounds are in the 270–290 K range. Though there areadvantages with respect to the visible band, due to substantial overlap betweenadjacent visible band � re pixels, a variable amount of ‘double counting’ of � res wasobserved during the night-time. In contrast, due to the smaller Instantaneous Field ofView (IFOV) of the thermal infrared � ne pixels, there was little overlap between theadjacent pixels, and the subpixel � res were observed only once. The extensive detailsof the sub-components of the night-time � re detection algorithm are given by Elvidgeet al. (1996) . The process includes sub-orbiting, glare removal, identi� cation of VNIRemission sources, lightning removal and geolocation. The sub-orbiting process involvesvisual inspection to identify usable orbital segments over land areas and exclusion offeatures such as auroras, which are not relevant to the detection of � res. In the processof light intensi� cation, under certain geometric conditions, the OLS telescope isilluminated by sunlight. The scattering of sunlight oV the end of telescope into theoptical path results in visible band detector saturation, leading to glare. The OLSimages were pre-processed to remove glare. For identi� cation of VNIR emissionsources, a ‘light picking algorithm’ was used. A detecting block of 20 pixels×20 pixelswith saturated digital counts with digital number counts 45 or greater were set tozero. Lights were identi� ed in the central 20 pixel×20 pixel block, inside the 50pixel×50 pixel block to generate the background statistics. The distribution of DN


values in each 50 pixel×50 pixel cell was analysed to identify the set of pixels for useas the local background. The upper limit of the background was selected and themean and standard deviation of the background pixel set was calculated. The pixelscontaining visible band emission sources were identi� ed using a threshold set at theDN value of the background mean plus standard deviation. The lightening removalwas done by a ‘light picking algorithm’, by testing the length versus width of all lightsdetected using cloud areas. A geolocation algorithm was used to estimate the latitudeand longitude of the pixel centre based on the geodetic subtrack of the satellite orbit,satellite altitude, sensor model, an Earth sea level model and digital terrain data.

4.4. Distinction between lights and � resFor detecting the stable lights, time series image analysis was used. A dataset

with respect to the reference grid was established and a large number of orbits wereprocessed, classifying pixels into either of the three categories, (1) cloud, (2) cloud-free with no VNIR emissions, and (3) cloud-free with a VNIR emission source. Thepixels were geolocated and re-sampled into the reference grid. Time series analysiswas accomplished by running a counter for each of the three classes for each cell inthe reference grid. After the light picking algorithm was applied, lightening wasremoved from the incoming data. Stable lights were masked out of the incomingdata after the lights were geolocated and re-sampled to the same reference grid. Thealgorithm that removes the stable lights identi� es the pixels that occur in or directlyadjacent to the known stable light locations and sets their DN to zero. The VNIRsources that are not associated with either lightening or stable light sources wereidenti� ed as � res (Elvidge et al. 1996 ).

4.5. Ground-based measurements: trace gasesTwo ground-based experiments were conducted jointly by the National Remote

Sensing Agency (NRSA) and the National Physical Laboratory (NPL) under theISRO-GBP programme in the biomass burning areas to quantify trace gas emissions.The experiment was planned in such a way that the local environmental conditionswere clear and the dates coincided with the onset of biomass burning practices. Theexperiments were conducted on 20 February (Site 1) and 22 February 1999 (Site 2)(table 2) at the study area of the Rampa Forests, Eastern Ghats, India. The forestsof the study area are mainly tropical dry deciduous forests. The sites have similarelevation, topography and type of vegetation material. The experimental setupdesigned to quantify the total gases emitted from the biomass burning measured,simultaneously, in the same volume of smoke plume, diVerent trace gases along withCO

2, collected from the probe tied to a pole 5–8 m above the � ames. Gaseous species

measured from the ground included CO2 , CO, NO and NO2 (NOx ). For thedetermination of the ambient background concentrations, measurements were takenbefore the onset of � re for the two dates. Continuous measurements were made forthe above species. NOx were measured with an API USA chemiluminescent NO/NO2analyser (model 200A), CO with an infrared gas � lter correlation analyser (model300, Advanced Pollution Instruments Inc., USA) and CO

2with an infrared gas

analyser (Li-COR model 6252). Calibration of the CO and CO2 instruments wascarried out prior to the experiment using cylinders of calibration gas standards(512 ppmv CO2/28 ppmv CO) in air. All the continuous measurements were recordedusing a data logger and stored on an online personal computer for further computa-tions. Also, nearly 30 canister samples were collected for analysis of CH

4and N

2O.


Table 2. Biomass characteristics.

Site 1 Site 2

Site Name Damanapalli VelagapalliDate 20 Feb 1999 22 Feb 1999Area burned (ha) 1.5 1.0Biomass prior to burning (t haÕ 1 ) 12–14 13.5–15.3~biomass burnt (t haÕ 1 ) 4.7 3.43

Moisture content (%)Litter 2.0 2.0Grass 2.0 1.0Wood 20–22 20–24Leaves 2.3 2Air temperature 36.0 36.1RH (%) 35 41Wind speed (m sÕ 1 ) 0–1 0–0.77Wind direction NW NERate of spread (m s Õ 1 ) 0.3 0.2Fire intensity (kcal sÕ 1 m Õ 1 ) 3207 2882Flame length (m) 3 2.5Flame height (m) 2.5 2–3

CH4 analysis was carried out with a gas chromatography system equipped witha � ame ionization detector (FID) and Porapak Q (80–100 mesh) in a 3.2 mmo.d.×152 mm stainless column and supplied with a high purity nitrogen carrier gas(IOLAR-1 Grade) with a � ow rate of 20 cc minÕ 1 . The injector temperature wasmaintained at 150°C and detector temperature at 375°C. For N2O, the analysis wasperformed using a gas chromatographic system equipped with an electron capturedetector (ECD) and a Porapak Q (80–100 mesh) in a 3.2 mm o.d.×152 mm stainlesscolumn and supplied with a carrier gas of 10% methane in argon, with a � ow rateof 15 cc min Õ 1 . The injector temperature was maintained at 150°C and detectortemperature at 350°C.

4.5.1. Biomass assessmentNon-living, above ground biomass that was clear felled for burning purposes at

the shifting cultivation sites, was sampled by quantitative methods in 20 m×10 msubplots representing average situations. The above ground vegetation was separatedinto trunk material (<50 cm in diameter) , small wood (20–50 cm in diameter), leavesand fruit; trees, lianas, shrubs and herbs with leguminous and non-leguminousvegetation were recorded separately. Fresh � eld weights of each component weretaken before drying and subsequent nutrient analysis. The combustion factor for thesites was calculated using the post-� re biomass values and pre-� re fuel biomass.

4.5.2. L itter sample collectionSample plots of 1 m2 were laid down at diVerent sub-plots at the two sites for

analysis of moisture content and nutrients in the litter.

4.5.3. Quanti� cation of combustion e Y ciency, emission ratios and emission factorsAs the absolute concentrations of trace gases in the sample plume have little

meaning because of the various degrees of dilution of � ame gases with ambient air


(Andreae et al. 1996), in the study, emission ratios and emission factors were usedto quantify the biomass burning process.

Usually, after a few minutes of � re, the emissions from the � aming and smoul-dering combustion processes become intermixed, and it becomes diYcult to assess

which process (� aming or smouldering) is dominating the emissions being measured(Ward and Radke 1993). Combustion eYciency is a useful parameter to diVerentiatediVerent phases of combustion such as the � aming and smouldering stages (Ward

et al. 1992). Combustion eYciency (CE) is de� ned as the fraction of carbon emittedas carbon dioxide (C-CO

2) relative to total gaseous carbon emitted by the � re. Since

the carbon emissions tend to be dominated by CO2 and CO, CE can be approximatedas,

CE=CO2-C

/(CO2-C+CO

-C) (1)

Combustion eYciency can be calculated for experimental � res by measuring the

relative increase of CO2 and CO in the atmosphere, integrated over the duration ofthe � re. Fine dry fuels, such as savannah grasses, burn with high eYciency (>0.95%),

whereas large diameter fuels such as logs and dung tend to smoulder (CE<0.70 )(Ward et al. 1992 ).

Emission ratios are used for relative comparison of diVerent emissions and arede� ned as the above background-mixing ratio of the compound studied, divided by

the above background mixing ratio of a reference compound. CO2 is generally takenas a reference compound and, in the study, emission ratios for CO and CH4 werecomputed relative to CO

2.

Emission factor is de� ned as the amount of compound released per amount offuel consumed (g kg Õ 1 dry matter) . Calculation of the emission factor requires

knowledge of the carbon content of the � re, expressed as combustion eYciency (Hao

and Ward 1993).

4.5.4. Fire behaviour

The rate of spread of the � re front, � ame length and height during the burningwas estimated for the surface � res by noting the start time and the spread rate

manually, by stop watch. The � re intensity de� ned by Byram (1959) was used to

describe the intensity of � res, expressed as kcal s Õ 1 m Õ 1 of � re front (Trollope 1981).The � re intensity was calculated as the numerical product of the available heat

energy and the forward rate of spread of the � re front using the equation:

I=Hwr (2)

where, I=� re intensity (kcal s Õ 1 m Õ 1 ), H=heat yield (kcal kg Õ 1 ), w=mass of fuel

consumed (kg m Õ 2 ) and r=rate of spread of the � re front (m s Õ 1 ). The values for

diVerent heat yields developed for diVerent plant species, available in the literaturefor tropical dry deciduous species (Vimal and Tyagi 1984), were averaged to calculate

the � re intensity. DiVerent heat yields were used by Trollope et al. (1996) for head

and back� res. In our study, for computing the � re intensity, heat yields for diVerentspecies were averaged. The release of heat energy during the � res as represented by

the � ame height was estimated visually and through � eld photographs . The duration

of the experiment from ignition was approximately 2–2.5 h until the smoke emissionsfrom the fuel bed had almost disappeared.


4.5.5. Non-CO2

trace gas emissions from burningThe trace gas emissions were calculated as follows:

CH4 emissions=(carbon released)×(emission ratio)×16/12

CO emissions=(carbon released)×(emission ratio)×28/12

NO emissions=(carbon released)×(N/C ratio)×(emission ratio)×44/28

NOx emissions=(carbon released)×(N/C ratio)×(emission ratio)×46/14

4.5.6. T race gas emission estimationIn this study, we used the � re observations of the DMSP-OLS data to estimate

trace gas emissions from biomass burning. Since the DMSP-OLS data obtainedwere not calibrated to subpixel level, we assume that the entire area of each � repixel was burned. The biomass burning in the study area is attributed mostly toshifting cultivation, and the dominant forest type is represented by tropical drydeciduous vegetation. The amount of trace gas (T) emitted from biomass burningfrom shifting cultivation areas was estimated as (Elvidge et al. 1996 ):

T=M(EFXa)=A B a{(FEX

f)Pf+(EFX

s)P

s] (3)

where T=amount of X produced from � res per unit time, M=amount of biomassburned per unit time, EFXa=weighted average emission factor of X, A=area burnedper unit time, B=above ground biomass density, a=fraction of above ground biomassburned, EFXf=emission factor of X during the � aming phase, EFXs=emissionfactor of X during the smouldering phase, P

f=fraction of biomass burned during the

� aming phase, Ps=

fraction of biomass burned during the smouldering phase.In the study, the emission factors obtained from the ground-based experiments

conducted during February 1999, for biomass burning for shifting cultivation pur-poses in tropical deciduous forests, were used. The combustion factor for biomassburning for shifting cultivation purposes was taken as 30% for the burns obtainedfrom the � eld-based measurements. Detailed discussion with respect to biomass andcombustion characteristics is given elsewhere (Krishna Prasad et al. 2000a, 2000b) .The above ground biomass estimated from � eld measurements was taken as20 t haÕ 1 . The emissions for smouldering and � aming were averaged and presentedas total biomass burning emissions.

5. Results and discussionThe DMSP-OLS night-time images of the three periods, 25 March 1987, 1 March

1998 and 27 March 1998, were used to identify the � res in the study area. Analysisof the data suggests that the intensity of the � res in the study area increased to alarge extent during 1998 compared with 1987, over a period of 10 years. The datamore or less pertained to a similar period (i.e. March) when the biomass burningtakes place intensively, particularly in the lower districts of the study area. The bright� re areas, which appeared as random spots during 1987, appeared as regular patchesduring 1998 (� gure 2(a, b, c)). The increase in the intensity of the � res is also evidentfrom the constantly reducing forest cover in the study area. During the 1989 assess-ment by the Forest Survey of India (FSI), using the 1985–1987 satellite imagery, theforest cover was estimated to be 47 270 km2 , compared with 47 112 km2 during the1995 assessment using 1991–1993 satellite data. A further decrease in forest coverwas noticed during the 1997 assessment from 1993–1995 satellite data, accounting


(a)

(c)

(b)

Figure 2. DMSP OLS nighttime data of (a) 1 March 1998, (b) 27 March 1998, (c) 20 April1998.

for about 43 290 km2 . Calculation of area estimates from the DMSP-OLS datasuggests that nearly 450 km2 of the northern part of Andhra Pradesh was aVectedby � res during the March season. The area under � res increased to nearly 700 km2over a period of 10 years. Further, comparison of early March data with late Marchdata suggests that the intensity gradually increased from 427 km2 to 700 km2 , indicat-ing a gradual increase in biomass burning practices in the study area. Since most ofthe study area is occupied by forests, and from collecting local information, we haveattributed the � res to biomass burning practices for shifting cultivation purposes.

In order to quantify the biomass burning process, two ground-based measurementswere conducted. In the study, the in plume mixing ratios obtained for CO

2, CO, NO,


NO2 (online data loggers) CH4 and N2O (grab sampling) from the ground-base dsampling at two sites were used to compute emission ratios relative to carbon dioxide.Data with respect to biomass, amount of biomass burnt and the meteorologica l condi-tions at the two sites are given in table 2. The individual emission ratios determined foreach of the samples (during 1-min intervals) were averaged for diVerent phases ofcombustion and reported as a single burning event. The temporal variation in theevolution of diVerent trace gases, CO2 , CO and NOx , versus time is shown in � gure 3(a,b, c) (20 February, site 1) and � gure 4(a, b, c) (22 February, site 2). From � gures 3 and4 it is evident that, for the two sites, there is less variability in evolution of trace gasesduring diVerent phases of combustion . As the combustion progressed from � aming tosmouldering, the emission ratios were found to increase for both of the sites. Thisincrease can be attributed to the type of biomass consumed and the local site conditionsaVecting the burning during the diVerent phases of the combustion process. The biomassat both sites consisted of mostly dried leaves together with small amounts of grass andlitter varying from 3 to 4 t ha Õ 1 .

The inter-� re variability in the emission ratios at both of the sites is attributed tothe local site conditions, topography , environmenta l parameters and fuel characterist ics.In site 1, the burning lasted for 2h 15 min (� aming: 23 min; mixed: 9 min; smouldering:103 min) while in site 2 burning lasted for 2 h 38 min (� aming: 11 min; mixed: 4 min;smouldering: 143 min). The diVerences in the � re behaviour were related to local meteoro-logical conditions, fuel load and topography of the site. For example, due to relativelyhigh humidity (41%), � aming at the second site lasted only for 11 min whereas it lastedup to 23 min at the � rst site. The � re behaviour for the two sites was variable in natureand the data represent only the average � re behaviour noticed during the experiment.There was considerable overlapping of diVerent phases of combustion during the � amingprocess of head � res and back � res. The larger the dCO/dCO2 ratio, the less eYcientthe combustion (Cofer et al. 1996). Signi� cant increase in the CO/CO

2ratios were

observed as the eYciency of the combustion decreased. The results of this study indicatecomparativel y high emission ratio for CO/CO2 during the smouldering phase of combus-tion. This can be attributed to the duration of � aming and mixed phase combustionwhich are comparatively shorter than the smouldering phase. For the � rst and secondsites, the emission ratio for CO with respect to CO

2, was found to be 12.3% at the � rst

site and 12.5% at the second site. The small diVerences in inter-� re variability wereattributed to relative diVerences in timings of the diVerent phases of combustion. In thecase of tropical forest � res, the emission ratios reported by diVerent authors for CH4are very close to a value of ER(CH

4)=1.2±0.5%. The value is considered to be the

most accurate average value for tropical forest � res (Andreae et al. 1996). Also, bycomparing diVerent studies Delmas et al. (1991), concluded that sampling from groundlevel and aircraft sampling gave approximatel y the same results.

The emission ratios obtained for CH4 in our study (averaged for all combustionstages) of 1.29% at the � rst site and 1.59% at the second site, are nearer to the estimatesof emission ratios obtained for tropical forests elsewhere. The emission ratio for NOxwas found to be 0.29% at both sites during the burning. The value is comparativel ylow compared with � eld-based measurements (2–8%) and laboratory studies (0.7–1.6%)reported in the literature (Andreae et al. 1988). The N

2O emission ratio during the

diVerent phases of combustion, when compared with other types of ecosystems, showedrelatively high values. Andreae (1991 ) reported emission ratios (%) of 0.18–2.2 and0.01–0.05 for � eld measurements and lab studies, respectively, with a best guess of 0.1.Thus the mean values for burning obtained in our study of 0.05% for the � rst site and0.07% for the second site are nearer to the above estimates of Andreae et al. (1996).


(a)

(b)

(c)

Figure 3.

Using the ground data for above ground biomass, combustion eYciency, emissionfactors and area aVected by � res, and the approach of Elvidge et al. (1996 ) anattempt was made to calculate the trace gas emissions released from biomass burning,detected through active � res during diVerent time periods. The study suggested


(a)

(c)

(b)

Figure 4.

emissions of 8.2×1010 g CO2 , 1.8×108 g CO, 6.0×106 g N2O, 3.0×106 g NOx and1.2×108 g CH4 during March 1987. The emissions increased to 1.0×1011 g CO2 ,2.3×108 g CO, 7.8×106 g N2O, 3.9×107 g NOx and 1.6×108 g CH4 , over a period often years. Since the biomass burning process continues until the end of May in the


study area, it is expected that the intensity of � res would increase further, therebyincreasing the rate of release of trace gas emissions. Thus this study highlights theutility of DMSP-OLS data for monitoring � res and estimating trace gas emissions.

AcknowledgmentsWe are grateful to Dr D. P. Rao, Director, NRSA, Professor S. K. Bhan, Deputy

Director, NRSA, C. Sharma, A. K. Sarkar and Professor A. P. Mitra of NPL, NewDelhi, for their encouragement. The help provided by Dr P. Sivarama Krishna,Director, SAKTI, NGO, during the � eld experiment is gratefully acknowledged.

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