Biomass burning and related trace gas emissions from tropical dry

Transcription

Biomass burning and related trace gas emissions from tropical dry
int. j. remote sensing, 2002, vol. 23, no. 14, 2837 –2851
Biomass burning and related trace gas emissions from tropical dry
deciduous forests of India: A study using DMSP-OLS data and
ground-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 Ž nal form 13 June 2001)
Abstract. Biomass burning is one of the major sources of trace gas emissions in
the atmosphere. In India the major sources of biomass burning include deforestation, shifting cultivation, accidental Ž res, controlled burning, Ž re wood burning,
burning from agricultural residues and burning due to Ž re lines. Studies on
biomass burning practices gain importance due to increasing anthropogenic activities and increasing rates of deforestation. Satellite data have been widely used
over the globe to monitor the rates of deforestation and also with respect to
biomass burning studies. But, much of the polar orbiting satellites, due to their
repetitive cycle, have limitations in observing such events and in the tropics, due
to cloud cover, getting a cloud-free image during the daytime is diYcult. In this
study we used Defence Meteorological Satellite Program Operational Line
Scanner (DMSP-OLS) night-time data to study the biomass burning events over
a period of 10 years from 1987 to 1998 for the Eastern Ghats region, covering
the northern part of Andhra Pradesh, India. Two ground-based experiments were
carried out to quantify the emissions from biomass burning practices. The results
of the study with respect to trace gases suggested emission ratios for CO, CH ,
4
NO and N 0 during the burning to be about 12.3%, 1.29%, 0.29% and 0.07%
x
2
at the Ž rst site and 12.5%, 1.59%, 0.29% and 0.05% at the second site, suggesting
low inter-Ž re variability between the sites. The variation has been attributed to
the fuel load, vegetation characteristics, site conditions and local meteorological
parameters aVecting the relative amounts of combustion. Using the DMSP
OLS derived areal estimates of active Ž res, the trace gas emissions released
from the biomass burning were quantiŽ ed. The results suggested the emissions of 8.2×1010 g CO , 1.8×108 g CO, 6.0×106 g N O, 3.0×106 g NO and
2
2
x
1.2×108 g CH during March 1987. The emissions increased to 1.0×1011 g CO ,
4
2
2.3×108 g CO, 7.8×106 g N O, 3.9×107 g NO and 1.6×108 g CH , over a period
2
x
4
of 10 years. The results of the analysis suggest the possible use of monitoring
biomass burning events from DMSP-OLS night-time data.
e-mail: [email protected]
Internationa l Journal of Remote Sensing
ISSN 0143-116 1 print/ISSN 1366-590 1 online © 2002 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
DOI: 10.1080/01431160110109598
2838
1.
V. Krishna Prasad et al.
Introduction
Over the years, Ž re has in uenced the vegetation on Earth. The existence of Ž re
dates back to 350–450 million years ago. However, Ž re frequency is increasing
because of various practices, especially in tropical regions, such as biomass burning
for shifting cultivation purposes, accidental Ž res and agricultural residue burning,
leading to the evolution of large amounts of trace gases along with a complex
mixture of particulate matter in the atmosphere. The immediate eVects of biomass
burning include loss of species diversity, increased surface albedo and water run-oV,
decreased evapotranspiratio n and evolution of several greenhouse gases and aerosols
(Crutzen et al. 1985, Matson and Holben 1987, Andreae et al. 1988). Biomass burning
represents an important source of atmospheric CO, CH , H , CH Cl, NO, HCN,
4
2
3
CH CN, COS and particulate carbon (Crutzen and Carmichael 1993). Satellite
3
remote sensing can make an important contribution to the study of Ž res in the
environment and their ecological, climatic and atmospheric chemical eVects. To
develop emission source strength on a regional scale, monitoring of Ž res at regional
scale is needed. Space-borne measurements are likely to be the only viable alternative
for tracking the temporal distribution of frequency of Ž res in many areas of the world.
Current estimates of trace gas emissions from biomass burning are severely
constrained by the lack of reliable statistics on Ž re distribution and frequency, and
the lack of accurate estimates of area burned, fuel load and fuel moisture content.
There have been relatively few studies that demonstrate the contribution of satellitebased Ž re monitoring to address the current research issues relating to Ž re monitoring
and biomass burning (Justice et al. 1993). The most practical and economically
feasible manner of monitoring the extent of burning associated with tropical deforestation and grassland management is through remote sensing (Menzel et al. 1991 ).
Various studies during the last decade have demonstrated the potential use of remote
sensing for Ž re-related studies. Satellite measurements have been used to detect the
optical thickness, particle size and absorption of atmospheric aerosols (Ferrare et al.
1990). Application of various geostationar y satellites for monitoring Ž res has been
extensively discussed by Justice et al. (1993). Several onboard satellites have the
potential to monitor Ž res. High resolution Landsat TM includes a middle infrared
channel (2.08–2.35 mm) with a 30 m spatial resolution which permits active Ž res to
be detected. In addition, visible and near-infrared channels designed speciŽ cally for
vegetation studies permit the detection of burn scars and the assessment of vegetation
state through the use of vegetation indices (Chuvieco and Congalton 1988). The
onboard National Oceanic and Atmospheric Administration Advanced Very High
Resolution Radiometer (NOAA AVHRR) satellite provides daily global data at a
sampled 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 Ž res as
small as 10 m×10 m. The Along Track Scanning Radiometer (ATSR) sensor of the
European Resource Satellite (ERS-1) provides sensing in the middle and thermal
infrared channels and can be useful for studying the Ž res. The Geostationary
Operational Environmental Satellite (GOES) visible infrared Spin Scan Radiometer
Atmospheric Sounder ( VIS) system provides high temporal frequency coverage every
30 minutes and the coarse spatial resolution of 16 km permits Ž res to be detected at
a larger scale (Menzel et al. 1991 ).
The unique capability of the Defence Meteorological Satellite Program
Operational Line Scanner (DMSP-OLS) when compared with the above satellites
lies 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 Ž res was Ž rst noted by Croft (1973, 1979).
Cahoon et al. (1992) reported the Ž rst systematic inventory of Ž res with OLS data.
Elvidge et al. (1996, 1997a,b) extensively discussed the utility of DMSP-OLS data
and developed algorithms to identify and geolocate Ž res and city lights in digital
OLS imagery. The sensitivity of OLS is higher than NOAA AVHRR and Landsat
TM and can measure the radiances down to 10Õ 9 W cmÕ 2 srÕ 1 cmÕ 1. In general,
with respect to the biomass burning studies, to improve the understanding of emission
estimates and the role of biomass burning in atmospheric chemistry, a combination
of accurate estimates of Ž re distribution, frequency and fuel loading from remote
sensing, with representative ground and laboratory measurements of combustion
eYciencies and emission factors for diVerent Ž re-vegetation regimes would lead to
suYcient improvement in understanding the biomass burning process (Kaufman
et al. 1992, Justice et al. 1993). In India, such studies with respect to monitoring of
Ž res and data pertaining to biomass burning activities are scarce and not up to date.
Further, there are no Ž eld-based studies for quantiŽ cation of combustion eYciencies,
amount of biomass burnt and the emissions released due to biomass burning. The
DMSP has a broad-band panchromatic low light sensor that is useful for detection
of visible light sources at night, including Ž res. The scanning system has been
operational since 1974, with digital data available since 1992, and therefore can be
eVectively used for monitoring of Ž res and lights. In this study, we used DMSP-OLS
satellite data from diVerent time periods to study the intensity and areal extent of
Ž res and used ground-based experimental results for quantiŽ cation of the amount
of trace gas emissions released due to biomass burning.
2.
Study area
The 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 (Ž gure 1). The dominant vegetation type of
the study area is tropical dry deciduous, along with moist mixed evergreen. The forests
correspond to southern dry mixed deciduous forests (type 5A/C3; group 5, subgroup
5A) and southern tropical forests (group 6, subgroup 6A) as classiŽ ed by Champion
and Seth (1968). Of the 30 series recognized and described by Gaussen’s holistic
system of vegetation (Gaussen 1959, Gaussen et al. 1973), the dominant forests of the
study 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, Chloroxylon
swietenia, Lannea coromandelica, Macaranga peltata, Mitragyna parvi ora, Grewia
tilaefolia and Madhuca latifolia. In the study area, biomass burning for shifting cultivation starts as early as February and continues until the end of May. The intensity is
greater during the months of March and April.
3.
Shifting cultivation in the study area
Shifting cultivation, known locally as jhumming or podu cultivation, is practised
by the local tribes of the study area. The practice consists of clearing the forests
during the winter months of November and December and allowing the felled
biomass to dry until early March to early May. The biomass is subsequently burnt
to clear land for sowing. Mixed cropping is preferred and harvesting starts by
October. After harvesting the plot is abandoned and another patch is selected to
start the cycle again.
2840
V. Krishna Prasad et al.
Figure 1.
4. Datasets and methodology
4.1. Datasets
In this study, DMSP-OLS data pertaining to the north-eastern part of Andhra
Pradesh, (Ž gure 1) covering Adilabad, parts of Khamam, East Godavari, West
Godavari, Visakhapatnam, Vizianagaram and Srikakulam, from 25 March 1987,
1 March 1998 and 27 March 1998 were used to study areal extent of biomass burning
and the amounts of trace gases released over a period of time.
4.2. DMSP-OL S sensor characteristics
DMSP-OLS is an oscillating scan radiometer designed for cloud imaging with
spectral bands ( VIS and TIR) acquiring images during daytime and night-time with
a swath of ~3000 km (Elvidge et al. 1997a,b). DMSP digital data has been available
since September 1992 and is expected to continue until 2010 (Elvidge 1996). DMSP
satellite characteristics are given in table 1. DMSP operates two satellites in sunsynchronous 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 with
day–night overpasses at ~9:54 and 21:54 local time, and F-13, with dawn–dusk
overpasses at ~6:04 and 18:04 local time. The OLS is an oscillating scan radiometer
designed for cloud imaging with two spectral bands, visible ( VIS) and thermal
infrared (TIR) and a swath of ~3000 km. With 14 orbits per day, each OLS is
capable of generating global daytime and night-time coverage of the Earth every 24
hours. The data can be acquired from OLS through Ž ne resolution mode (0.56 km)
Biomass burning and related trace gas emissions
Table 1.
OLS: normal daytime operation
Spectral bands (mm)
Nadir footprint ( km)
Smoothed data
Real-time data
Measurement range
Signal quantization
OLS: normal night-time operation
Spectral bands (mm)
Nadir footprint ( km)
Smoothed data
Real-time data
Measurement range
Signal quantization
Satellite orbit
2841
DMSP OLS speciŽ cations.
0.40–1.10
10.0–13.4
2.75 km×2.75 km
0.55 km×0.55 km
1–6— 100% A
6 bit
2.75 km×2.75 km
0.55 km×2.75 km
4–310 K
8 bit
0.47–0.95
10.0–13.4
2.75 km×2.75 km
2.75 km×2.75 km
0.55 km×2.75 km
0.55 km×0.55 km
0–64 counts
4–310 K
8 bit
6 bit
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 the
VIS 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 TIR
band data are calibrated using an onboard blackbody source and views of deep
space to provide 8-bit data with a temperature range of 190–3100 K, ideal for
detecting and characterizing clouds.
4.3. Fire detection
During 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 band
signal was intensiŽ ed using a photo multiplier tube (PMT) making it possible to
detect even faint VNIR sources. The thermal band saturates at 310 K and typical
surface temperature background s are in the 270–290 K range. Though there are
advantage s with respect to the visible band, due to substantial overlap between
adjacent visible band Ž re pixels, a variable amount of ‘double counting’ of Ž res was
observed during the night-time. In contrast, due to the smaller Instantaneou s Field of
View (IFOV) of the thermal infrared Ž ne pixels, there was little overlap between the
adjacent pixels, and the subpixel Ž res were observed only once. The extensive details
of the sub-component s of the night-time Ž re detection algorithm are given by Elvidge
et al. (1996). The process includes sub-orbiting, glare removal, identiŽ cation of VNIR
emission sources, lightning removal and geolocation. The sub-orbiting process involves
visual inspection to identify usable orbital segments over land areas and exclusion of
features such as auroras, which are not relevant to the detection of Ž res. In the process
of light intensiŽ cation, under certain geometric conditions, the OLS telescope is
illuminated by sunlight. The scattering of sunlight oV the end of telescope into the
optical path results in visible band detector saturation, leading to glare. The OLS
images were pre-processed to remove glare. For identiŽ cation of VNIR emission
sources, a ‘light picking algorithm’ was used. A detecting block of 20 pixels×20 pixels
with saturated digital counts with digital number counts 45 or greater were set to
zero. Lights were identiŽ ed in the central 20 pixel×20 pixel block, inside the 50
pixel×50 pixel block to generate the background statistics. The distribution of DN
2842
V. Krishna Prasad et al.
values in each 50 pixel×50 pixel cell was analysed to identify the set of pixels for use
as the local background. The upper limit of the backgroun d was selected and the
mean and standard deviation of the backgroun d pixel set was calculated. The pixels
containing visible band emission sources were identiŽ ed using a threshold set at the
DN value of the background mean plus standard deviation. The lightening removal
was done by a ‘light picking algorithm’, by testing the length versus width of all lights
detected using cloud areas. A geolocation algorithm was used to estimate the latitude
and 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 Ž res
For 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 were
processed, classifying pixels into either of the three categories, (1) cloud, (2) cloudfree with no VNIR emissions, and (3) cloud-free with a VNIR emission source. The
pixels were geolocated and re-sampled into the reference grid. Time series analysis
was accomplished by running a counter for each of the three classes for each cell in
the reference grid. After the light picking algorithm was applied, lightening was
removed from the incoming data. Stable lights were masked out of the incoming
data after the lights were geolocated and re-sampled to the same reference grid. The
algorithm that removes the stable lights identiŽ es the pixels that occur in or directly
adjacent to the known stable light locations and sets their DN to zero. The VNIR
sources that are not associated with either lightening or stable light sources were
identiŽ ed as Ž res (Elvidge et al. 1996 ).
4.5. Ground-based measurements: trace gases
Two ground-base d experiments were conducted jointly by the National Remote
Sensing Agency (NRSA) and the National Physical Laboratory (NPL) under the
ISRO-GBP programme in the biomass burning areas to quantify trace gas emissions.
The experiment was planned in such a way that the local environmental conditions
were clear and the dates coincided with the onset of biomass burning practices. The
experiments 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 forests
of the study area are mainly tropical dry deciduous forests. The sites have similar
elevation, topography and type of vegetation material. The experimental setup
designed to quantify the total gases emitted from the biomass burning measured,
simultaneously, in the same volume of smoke plume, diVerent trace gases along with
CO , collected from the probe tied to a pole 5–8 m above the  ames. Gaseous species
2
measured from the ground included CO , CO, NO and NO (NO ). For the
2
2
x
determination of the ambient background concentrations, measurements were taken
before the onset of Ž re for the two dates. Continuous measurements were made for
the above species. NO were measured with an API USA chemiluminescent NO/NO
x
2
analyser (model 200A), CO with an infrared gas Ž lter correlation analyser (model
300, Advanced Pollution Instruments Inc., USA) and CO with an infrared gas
2
analyser (Li-COR model 6252). Calibration of the CO and CO instruments was
2
carried out prior to the experiment using cylinders of calibration gas standards
(512 ppmv CO /28 ppmv CO) in air. All the continuous measurements were recorded
2
using a data logger and stored on an online personal computer for further computations. Also, nearly 30 canister samples were collected for analysis of CH and N O.
4
2
Biomass burning and related trace gas emissions
Table 2.
2843
Biomass characteristics.
Site 1
Site 2
Site Name
Date
Area burned ( ha)
Biomass prior to burning (t haÕ 1)
~biomass burnt (t haÕ 1)
Damanapalli
20 Feb 1999
1.5
12–14
4.7
Velagapalli
22 Feb 1999
1.0
13.5–15.3
3.43
Moisture content (%)
Litter
Grass
Wood
Leaves
Air temperature
RH (%)
Wind speed (m sÕ 1)
Wind direction
Rate of spread (m sÕ 1)
Fire intensity ( kcal sÕ 1 mÕ 1)
Flame length (m)
Flame height (m)
2.0
2.0
20–22
2.3
36.0
35
0–1
NW
0.3
3207
3
2.5
2.0
1.0
20–24
2
36.1
41
0–0.77
NE
0.2
2882
2.5
2–3
CH analysis was carried out with a gas chromatograph y system equipped with
4
a  ame ionization detector (FID) and Porapak Q (80–100 mesh) in a 3.2 mm
o.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 was
maintained at 150°C and detector temperature at 375°C. For N O, the analysis was
2
performed using a gas chromatographi c system equipped with an electron capture
detector (ECD) and a Porapak Q (80–100 mesh) in a 3.2 mm o.d.×152 mm stainless
column and supplied with a carrier gas of 10% methane in argon, with a  ow rate
of 15 cc minÕ 1. The injector temperature was maintained at 150°C and detector
temperature at 350°C.
4.5.1. Biomass assessment
Non-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 m
subplots representing average situations. The above ground vegetation was separated
into trunk material (<50 cm in diameter), small wood (20–50 cm in diameter), leaves
and fruit; trees, lianas, shrubs and herbs with leguminous and non-leguminous
vegetation were recorded separately. Fresh Ž eld weights of each component were
taken before drying and subsequent nutrient analysis. The combustion factor for the
sites was calculated using the post-Ž re biomass values and pre-Ž re fuel biomass.
4.5.2. L itter sample collection
Sample 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. QuantiŽ cation of combustion eY ciency, emission ratios and emission factors
As 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
2844
V. Krishna Prasad et al.
(Andreae et al. 1996), in the study, emission ratios and emission factors were used
to quantify the biomass burning process.
Usually, after a few minutes of Ž re, the emissions from the  aming and smouldering 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 diVerentiate
diVerent phases of combustion such as the  aming and smouldering stages (Ward
et al. 1992). Combustion eYciency (CE) is deŽ ned as the fraction of carbon emitted
as carbon dioxide (C-CO ) relative to total gaseous carbon emitted by the Ž re. Since
2
the carbon emissions tend to be dominated by CO and CO, CE can be approximated
2
as,
CE=CO /(CO +CO )
2-C
2-C
-C
(1)
Combustion eYciency can be calculated for experimental Ž res by measuring the
relative increase of CO and CO in the atmosphere, integrated over the duration of
2
the Ž 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 are
deŽ ned as the above background-mixin g ratio of the compound studied, divided by
the above background mixing ratio of a reference compound. CO is generally taken
2
as a reference compound and, in the study, emission ratios for CO and CH were
4
computed relative to CO .
2
Emission factor is deŽ ned as the amount of compound released per amount of
fuel consumed (g kgÕ 1 dry matter). Calculation of the emission factor requires
knowledge of the carbon content of the Ž re, expressed as combustion eYciency (Hao
and Ward 1993).
4.5.4. Fire behaviour
The rate of spread of the Ž re front,  ame length and height during the burning
was estimated for the surface Ž res by noting the start time and the spread rate
manually, by stop watch. The Ž re intensity deŽ ned by Byram (1959) was used to
describe the intensity of Ž res, expressed as kcal sÕ 1 mÕ 1 of Ž re front (Trollope 1981).
The Ž re intensity was calculated as the numerical product of the available heat
energy and the forward rate of spread of the Ž re front using the equation:
I=Hwr
(2)
where, I=Ž re intensity (kcal sÕ 1 mÕ 1), H=heat yield (kcal kgÕ 1), w=mass of fuel
consumed (kg mÕ 2) and r=rate of spread of the Ž re front (m sÕ 1). The values for
diVerent heat yields developed for diVerent plant species, available in the literature
for tropical dry deciduous species (Vimal and Tyagi 1984), were averaged to calculate
the Ž re intensity. DiVerent heat yields were used by Trollope et al. (1996) for head
and backŽ res. In our study, for computing the Ž re intensity, heat yields for diVerent
species were averaged. The release of heat energy during the Ž res as represented by
the  ame height was estimated visually and through Ž eld photographs . The duration
of the experiment from ignition was approximately 2–2.5 h until the smoke emissions
from the fuel bed had almost disappeared.
Biomass burning and related trace gas emissions
2845
4.5.5. Non-CO trace gas emissions from burning
2
The trace gas emissions were calculated as follows:
CH emissions=(carbon released)×(emission ratio)×16/12
4
CO emissions=(carbon released)×(emission ratio)×28/12
NO emissions=(carbon released)×(N/C ratio)×(emission ratio)×44/28
NO emissions=(carbon released )×(N/C ratio)×(emission ratio)×46/14
x
4.5.6. T race gas emission estimation
In this study, we used the Ž re observations of the DMSP-OLS data to estimate
trace gas emissions from biomass burning. Since the DMSP-OLS data obtained
were not calibrated to subpixel level, we assume that the entire area of each Ž re
pixel was burned. The biomass burning in the study area is attributed mostly to
shifting cultivation, and the dominant forest type is represented by tropical dry
deciduous vegetation. The amount of trace gas (T) emitted from biomass burning
from shifting cultivation areas was estimated as (Elvidge et al. 1996 ):
T=M(EFX )=A B a{(FEX )Pf+(EFX )P ]
(3)
a
f
s s
where T=amount of X produced from Ž res per unit time, M=amount of biomass
burned per unit time, EFX =weighted average emission factor of X, A=area burned
a
per unit time, B=above ground biomass density, a=fraction of above ground biomass
burned, EFX =emission factor of X during the  aming phase, EFX =emission
f
s
factor of X during the smouldering phase, P =fraction of biomass burned during the
f
 aming phase, P =fraction of biomass burned during the smouldering phase.
s
In the study, the emission factors obtained from the ground-based experiments
conducted during February 1999, for biomass burning for shifting cultivation purposes in tropical deciduous forests, were used. The combustion factor for biomass
burning for shifting cultivation purposes was taken as 30% for the burns obtained
from the Ž eld-based measurements. Detailed discussion with respect to biomass and
combustion characteristics is given elsewhere (Krishna Prasad et al. 2000a, 2000b) .
The above ground biomass estimated from Ž eld measurements was taken as
20 t haÕ 1. The emissions for smouldering and  aming were averaged and presented
as total biomass burning emissions.
5.
Results and discussion
The DMSP-OLS night-time images of the three periods, 25 March 1987, 1 March
1998 and 27 March 1998, were used to identify the Ž res in the study area. Analysis
of the data suggests that the intensity of the Ž res in the study area increased to a
large extent during 1998 compared with 1987, over a period of 10 years. The data
more or less pertained to a similar period (i.e. March) when the biomass burning
takes place intensively, particularly in the lower districts of the study area. The bright
Ž re areas, which appeared as random spots during 1987, appeared as regular patches
during 1998 (Ž gure 2(a, b, c)). The increase in the intensity of the Ž res is also evident
from the constantly reducing forest cover in the study area. During the 1989 assessment by the Forest Survey of India (FSI), using the 1985–1987 satellite imagery, the
forest cover was estimated to be 47 270 km2, compared with 47 112 km2 during the
1995 assessment using 1991–1993 satellite data. A further decrease in forest cover
was noticed during the 1997 assessment from 1993–1995 satellite data, accounting
V. Krishna Prasad et al.
2846
(a)
(b)
(c)
Figure 2.
DMSP OLS nighttime data of (a) 1 March 1998, ( b) 27 March 1998, (c) 20 April
1998.
for about 43 290 km2. Calculation of area estimates from the DMSP-OLS data
suggests that nearly 450 km2 of the northern part of Andhra Pradesh was aVected
by Ž res during the March season. The area under Ž res increased to nearly 700 km2
over a period of 10 years. Further, comparison of early March data with late March
data suggests that the intensity gradually increased from 427 km2 to 700 km2, indicating a gradual increase in biomass burning practices in the study area. Since most of
the study area is occupied by forests, and from collecting local information, we have
attributed the Ž res to biomass burning practices for shifting cultivation purposes.
In order to quantify the biomass burning process, two ground-base d measurements
were conducted. In the study, the in plume mixing ratios obtained for CO , CO, NO,
2
Biomass burning and related trace gas emissions
2847
NO (online data loggers) CH and N O (grab sampling ) from the ground-base d
2
4
2
sampling 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 conditions at the two sites are given in table 2. The individual emission ratios determined for
each of the samples (during 1-min intervals) were averaged for diVerent phases of
combustion and reported as a single burning event. The temporal variation in the
evolution of diVerent trace gases, CO , CO and NO , versus time is shown in Ž gure 3(a,
2
x
b, c) (20 February, site 1) and Ž gure 4(a, b, c) (22 February, site 2). From Ž gures 3 and
4 it is evident that, for the two sites, there is less variability in evolution of trace gases
during diVerent phases of combustion . As the combustion progressed from  aming to
smouldering, the emission ratios were found to increase for both of the sites. This
increase can be attribute d to the type of biomass consumed and the local site conditions
aVecting the burning during the diVerent phases of the combustion process. The biomass
at both sites consisted of mostly dried leaves together with small amounts of grass and
litter varying from 3 to 4 t haÕ 1.
The inter-Ž re variability in the emission ratios at both of the sites is attributed to
the local site conditions, topography , environmenta l parameters and fuel characterist ics.
In site 1, the burning lasted for 2 h 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 Ž re behaviour were related to local meteorological conditions, fuel load and topograph y of the site. For example, due to relatively
high humidity (41%),  aming at the second site lasted only for 11 min whereas it lasted
up to 23 min at the Ž rst site. The Ž re behaviou r for the two sites was variable in nature
and the data represent only the average Ž re behaviour noticed during the experiment.
There was considerable overlapping of diVerent phases of combustion during the  aming
process of head Ž res and back Ž res. The larger the dCO/dCO ratio, the less eYcient
2
the combustion (Cofer et al. 1996). SigniŽ cant increase in the CO/CO ratios were
2
observed as the eYciency of the combustion decreased. The results of this study indicate
comparativel y high emission ratio for CO/CO during the smouldering phase of combus2
tion. This can be attribute d to the duration of  aming and mixed phase combustion
which are comparativel y shorter than the smouldering phase. For the Ž rst and second
sites, the emission ratio for CO with respect to CO , was found to be 12.3% at the Ž rst
2
site and 12.5% at the second site. The small diVerences in inter-Ž re variability were
attributed to relative diVerences in timings of the diVerent phases of combustion. In the
case of tropical forest Ž res, the emission ratios reported by diVerent authors for CH
4
are very close to a value of ER(CH )=1.2±0.5%. The value is considered to be the
4
most accurate average value for tropical forest Ž res (Andreae et al. 1996). Also, by
comparing diVerent studies Delmas et al. (1991), concluded that sampling from ground
level and aircraft sampling gave approximatel y the same results.
The emission ratios obtained for CH in our study (averaged for all combustion
4
stages) of 1.29% at the Ž rst site and 1.59% at the second site, are nearer to the estimates
of emission ratios obtained for tropical forests elsewhere. The emission ratio for NO
x
was found to be 0.29% at both sites during the burning. The value is comparativel y
low compared with Ž eld-based measurements (2–8%) and laboratory studies (0.7–1.6%)
reported in the literature (Andreae et al. 1988). The N O emission ratio during the
2
diVerent phases of combustion, when compared with other types of ecosystems, showed
relatively high values. Andreae (1991 ) reported emission ratios (%) of 0.18–2.2 and
0.01–0.05 for Ž 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 Ž rst site and
0.07% for the second site are nearer to the above estimates of Andreae et al. (1996).
2848
V. Krishna Prasad et al.
(a)
(b)
(c)
Figure 3.
Using the ground data for above ground biomass, combustion eYciency, emission
factors and area aVected by Ž res, and the approach of Elvidge et al. (1996 ) an
attempt was made to calculate the trace gas emissions released from biomass burning,
detected through active Ž res during diVerent time periods. The study suggested
Biomass burning and related trace gas emissions
2849
(a)
(b)
(c)
Figure 4.
emissions of 8.2×1010 g CO , 1.8×108 g CO, 6.0×106 g N O, 3.0×106 g NO and
2
2
x
1.2×108 g CH during March 1987. The emissions increased to 1.0×1011 g CO ,
4
2
2.3×108 g CO, 7.8×106 g N O, 3.9×107 g NO and 1.6×108 g CH , over a period of
2
x
4
ten years. Since the biomass burning process continues until the end of May in the
2850
V. Krishna Prasad et al.
study area, it is expected that the intensity of Ž res would increase further, thereby
increasing the rate of release of trace gas emissions. Thus this study highlights the
utility of DMSP-OLS data for monitoring Ž res and estimating trace gas emissions.
Acknowledgments
We 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, New
Delhi, for their encouragement. The help provided by Dr P. Sivarama Krishna,
Director, SAKTI, NGO, during the Ž eld experiment is gratefully acknowledged.
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