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Estimation of potential GHG emissions from net primary productivity of forests — a satellite based approach
Pergamon www.elsevier.com/locate/asr
Adv. Space Res. Vol. 29, No. 11, pp. 1793-1798.2002 Q 2002 COSPAR. Published by Elsevier Science Ltd. All rights reserved Printed in Great Britain 0273-l 177102 $22.00 + 0.00 PII: SO273-1177(02)00112-6
ESTIMATION OF POTENTIAL GHG EMISSIONS FROM NET PRIMARY PRODUCTIVITY OF FORESTS - A SATELLITE BASED APPROACH VKrishna Prasad, Yogesh Kant and K.V.S.Badarinath National Remote Sensing Agency (NRSA), Dept. of Space, Govt. of India,) Balanagar, Hyderabad - 500 037, India
ABSTRACT Solar radiation in the wavelength interval between 400 and 700 nm provides the energy for photosynthesis and this information can be used for estimating Net Primary Productivity of plants. In the present study, AVHRR coarse resolution satellite data has been used for estimating NPP and thereby potential Green House Gas (GHG) emissions by integrating satellite and ground based measurements. NPP of forests has been calculated from annual sum of daily photosynthetic absorbed radiation and the radiation use efficiency of different plant species. Fraction of absorbed photosynthetic radiation for the deciduous ecosystem has been computed from monthly AVHRR NDVI composite values and using the AVHRR simple ratio. Results of the study suggested potential productivity of 5.81 t/ha& from satellite data, when compared to actual productivity values of 5.4 t/ha/yr from girth measurements. Potential GHG emissions estimated using the NPP value, aerial to total NPP ratio, above ground biomass, burning efficiency, and emission factors from ground measurements suggested total emissions of 2.8 X IO”, 2.1 X lOlo, 2.7 X log, 9.8 X 10’ and 2.0 X 10’ gms for COa, CO, C&, NO, and N,O respectively for the study area. 0 2002 COSPAR. Published by Elsevier Science Ltd. All rights reserved.
INTRODUCTION The net flux of carbon between atmosphere and terrestrial vegetation is expressed on an annual basis in terms of net biomass accumulation, or Net Primary Production (NPP). NPP is a measure of rate at which energy is stored or incorporated into living tissues. Productivity is measured by determining photosynthetic and respiration rates either by determining the changes in biomass through time or by dimension analysis, which estimates standing crop by determining the relationship between the easily measured parameter and biomass. Traditionally, studies with respect to growth and net production are mostly based on the analysis of sequential harvest data, using exponential equations to describe changes in plant growth, as a function of time. Whilst, these type of studies have played an important role in development of quantitative understanding of growth of a plant, it has it’s own limitations. Conventionally, it would be tedious to parameterize all the variables that influence growth physiology of trees and quantification of dry matter production in ecosystems such as forests becomes more problematic. Satellite data provides valuable information with respect to quantifying various biophysical parameters of vegetation which can be used for estimating productivity of forests. Further, data on trace gas emissions at regional scales are severely constrained due to lack of information on biomass densities, productivity, combustion characteristics and emission factors. Productivity data obtained from satellite data in conjunction with combustion and emission factors can be used for estimating GHG emissions over a region. In the present study, an attempt has been made to study the productivity through conventional, empirical and satellite based approaches and relate with trace gas emissions.
1793
1794
V. K. Prasad et al.
STUDY AREA Study area forms one of the hilly northern parts of Andhra Pradesh - Eastern Ghats (17’,22, 81’,45) India. This area is mainly dominated by tropical dry deciduous forests with patches of semievergreen and evergreen forests. METHODOLOGY
Productivity Estimation from Conventional Method Girth for individual tree and shrub species of representative plots of different vegetation types are measured during three consecutive years (1995-96-97-98). These data were analyzed for estimating the productivity using the regression equations relating girth to biomass. Leaf litter estimates were not included in production estimates as the regression equations were based on the full foliage period.
Productivity Estimations from Models Model is a simplification of the real system and mathematical modeling in plant physiology can improve our understanding of both interdependencies of the many plant processes and their separate dependencies on plant’s external environment (Edwards, 1981). There are several empirical and mechanistic models that can be used for estimating NPP of forests. It has been well established that, on smaller scales, temperature and precipitation are dominant controlling factors on plant photosynthesis as well as soil respiration and thereby they regulate the plant productivity. Various empirical methods available in literature (Paterson, 1956, Lieth, 1973, Friedlingstein, 1992) have been used to estimate the productivity of forests of the study area.
CVP Index (Climate, Vegetation, Productivity Index) Paterson (1956) proposed the CVP index, suggesting that climate, vegetation related. The CVP index as proposed by Paterson is given by
and productivity
I = (T, / T, ) x (P x G/12) x (E/100)
are intimately
(1)
where I is the CVP index, T, is the mean temperature of the warmest month (in degree centigrade) T, is the mean annual range of temperature between coldest and warmest month, P is the mean annual precipitation (mm), G is the length of growing season in months and E is the ‘evapotranspiration reducer’ a factor based on latitude and giving the generalized total radiation received as percent of that at the equator. The correlation between the CVP index and maximum productivity for different regions of the world was significant at 95% probability according to Paterson. The variance of CVP index contributes about 80% of total variance for ideal site class. The equation for calculation of productivity thus is given by Y = 5.3 log X - 7.25
(2)
where Y is the productivity in cubic meters per ha and X is the CVP index. Since the values obtained from the Patersons’s CVP index are in m31ha, values have been converted to tons/ha by taking volume weighted average wood density and biomass expansion factors (Brown, 1997).
Miami Model Miami model developed by Lieth (1973) calculates the Net Primary Productivity as a function of climate parameters. Based on the relationships between NPP (grn~* year -‘) to mean annual temperature (‘C) and mean annual precipitation (mm yr-‘), the equations proposed to calculate NPP are 1315-0.119xT) ~PcT)=3000/(1
+
e
NPP (P)= 3000 X (l-e-
(3)
) 0000664xP)
>
where NPP (T) and NPP,, are the NPP values calculated average precipitation (P in mm yr“) respectively.
(4) using mean annual temperature
(T in ‘C) and annual
Estimation
of Potential GHG Emissions from NPP
1195
Friedlingstein’s NPP Model After evaluating the original Miami model for the global NPP, Friedlingstein et al., (1992) found that the model tends to overestimate NPP in the tropics and under estimate it in high latitudes. They modified the Miami model to tit global NPP distribution given by Fung et al., (1983). The Friedlingstein et al., (1992) NPP model is given by NPP = min (NPPt, NPP,)
(5)
NPP, = 69.1875 Tt,, Tb < 8’ C = 135O/(l+exp(1.315 - 0.119Tb)), Ti,> 8’C
(6) (7)
NPP, = 1125 (l- exp (-0.000664 P)), in tropics = 1350 (l- exp (-0.000664 P)), in other regions.
(8) (9)
where
NPP = annual net primary production (g C m2 yr-‘), Tt, = biotemperature P = annual average precipitation (mm).
(‘C, mean annual surface air temperature),
NPP Estimation from Coarse Resolution Satellite Data Solar radiation in the wavelength interval between 400 and 700 nm provides the energy that is inevitable for The capture and utilization of Photosynthetic Active Radiation (PAR) photosynthesis and primary production. represent the energetic foundation for the origin, evolution and sustained existence of biosphere (Budyoko, 1980). Information on the amount of PAR absorbed by the .phototropic biota is important in efforts to model and monitor primary production and related biospheric process (Monteith, 1977; Prince, 1991). The net flux of carbon between the atmosphere and biosphere represents the imbalance between photosynthetic carbon uptake by plants and respiratory carbon release by soils and plants. Net Primary Productivity can be calculated from the am-ma1 sum of daily Absorbed Photosynthetic Active Radiation (APAR, MJmM2day-‘) multiplied by dry matter yield of photosynthetic active radiation (E gC MJ-’ PAR-called radiation use efficiency). APAR can be estimated using NDVI (Monteith, 1977) as NPP=&,cAPAR
(10)
where NPP = Net primary productivity, E, is the energy conversion Photosynthetic Active Radiation (MJmm2day-‘) Further,
E, C AF’AR
EC
efficiency
(g C MJ’ PAR), APAR = Absorbed
can be calculated as CAPAR =
E,
(NDVI x PAR)
APAR = Ir*a x fAPAR
(11) (12)
PAR (MJ‘2) and fAPAR is a where APAR = Absorbed Photosynthetic Active Radiation, IrAR is the Intercepted unitless ratio equal to fraction of incident PAR intercepted and absorbed by photosynthetic elements in the observed landscape. IpARis given by Ip*a = SOL@, fAPAR(,,x 0.5
(13)
where SOL,,, is the total solar radiation incident on particular type of ecosystem in month, fAPAR is the fraction of the incoming PAR intercepted by green vegetation and the factor of 0.5 accounts for approximately half of the incoming total solar radiation in the PAR waveband (0.4-0.7 urn) . Coincident
values of fApAR were predicted using the relation from AVHRR Simple Ratio (SR) as, SR = ( 1 + NDVI)/ ( 1-NDVI)
(14)
1796
V. K. Prasad
etal.
NDVI values were calculated using AVHRR channel 1 and 2 data from monthly composite data set of the study area, for the period, 1998.. Estimating GHG emissions from NPP Data Since the GHG emissions from the biomass burning are mainly governed by the amount of biomass material of particular vegetation which is further dependent on the net primary productivity, GHG emissions can be estimated from the NPP of forests taking into the annual area burnt, ground based emission factors and other information with respect to vegetation (Delmas et al., 1991). GHG emissions from biomass burning in the study area has been estimated as GHG emissions = NPP x RA x Rn x RPBx REax Rar
(15)
NPP = Net Primary Productivity (From Satellite Data), RA=Aerial to total NPP ratio, Rn = Above Ground Biomass to aerial NPP ratio, Rrn = Area annually burnt (sq.km), Raa = Burning efficiency (%), REF= Emission factors (g/kg) for different trace gases. RESULTS AND DISCUSSION NDVI is used as an indicator (or surrogate measure) of vegetation condition and has been linked to several biophysical variables such as net COZ exchange rates, Absorbed Photosynthetic Active Radiation (APAR), Canopy Photosynthetic capacity etc., (Goward and Huemmrich, 1992). NDVI is sensitive to the presence of green vegetation and has been successfully used in numerous regional and global applications for studying the state of vegetation. The success of NDVI as a descriptor of vegetation lies in it’s normalization, inspite of variations in atmospheric effects (Kaufman, 1984) and radiometric degradation in the red and NIR bands (Holben et al., 1990). The most accurate measure of NPP is to first measure the net photosynthetic rates of photosynthetic tissues, then subtract the rates of non-photosynthetic tissues and finally extrapolate it to community level, using the net production per gram of biomass of each species in a community. This assessment of NPP is not possible on a large scale, since the measurement of photosynthesis and respiration for all the species in any community is not possible in reality because of wide range of conditions experienced by plants in a community. Consequently, most of the methods depend on the accumulation and disappearance of biomass through time. But the major problems in making at a precise and accurate estimates of dry matter production are errors due to measurement and the effects due to climate variation from year to year. Thus estimation of NPP of forests over a larger scale is subjected to various complications and are mostly time consuming and are point based. Compared to the conventional methods, satellite remote sensing data can be effectively used for estimating the NPP of forests. The NPP values obtained from various methods including satellite based approach are given in Table 1. As in Table 1, Paterson’s CVP index, Lieth’s Miami model and Friedlingstein’s model over estimated potential productivity when compared to actual productivity obtained from girth measurements. This is mainly attributed the coefficients that are based on the climatic inputs in the empirical models rather than the biophysical parameters. Quantification of NPP using the biophysical parameters from remote sensing data suggests that potential NPP can be estimated more accurately than the empirical models. The important parameters to estimate the productivity include incident PAR and APAR that form the basis for estimation of productivity through satellite based approach. The term FAPAR is a measure of capacity of the vegetation in the observed landscape to intercept and absorb incident PAR and is closely related to green leaf biomass and Leaf Area Index (LAI). The FAPAR and PAR determines the amount of energy directly used to primary production process and reflects the combined influence of vegetation cover and climate. Moreover, the fraction of incident PAR absorbed by the canopy (FAPAR) has been found to be an important variable in studies of energy budget and hydrology of vegetated land surfaces. Absorbed Photosynthetic Active Radiation (APAR) can be estimated from remote sensing data. In the present study, monthly maximum NDVI values corresponding to deciduous broadleaf forest have been taken for computing the APAR. Studies on NPP estimation from satellite based approaches suggests that most dominant factor is IPAR. One evidence is the constancy of kc’ corn many experimental studies of unstressed plants. And also results from several studies indicate that nutrient stress and water stress have much larger effects on IPAR than on ‘sC’(Garcia et al., 1988). Running and Hunt (1993) found that measured values of ‘E,’ for woody vegetation is significantly lower than those of herbaceouos vegetation. They speculated that the cause for lower kc’ may be due to maintenance respiration by woody biomass. Based on the photosynthetic Quantum requirement of about 20mol photons.mol* COP (including photorespiration), it is suggested that there is a maximum E, of about 5.5g.MS1, which is reduced by uneven illumination, nutrient deficiency and stomata1 closure (Prince, 1991).
Estimation
of Potential GHG Emissions from NPP
Table 1. NPP Estimation Methods Actual NPP from conventional method Paterson’s CVP index Lieth’s Miami Model Friedlingstein’s NPP Model Satellite derived approach
1791
from Different Methods Estimated Productivity 5.4
Values (t/ha&r)
36.4 26.07 11.73 5.81
Further, ‘Ed’ is a net after maintenance and growth respiration carbon losses are accounted. APAR is a function of LAI, so ‘8,’ and CAPAR can be considered analogous to growth analysis terms net assimilation rate and leaf area duration respectively. Thus, a constant of 5.5g.M.I’ has been used to estimate the potential productivity_of the forests of the study area. Estimation of potential NPP over the forests of the study area from the satellite derived methodology suggested potential productivity of 5.81 t ha-lyre1 which is slightly more than the actual productivity of 5.4 t ha-’ yr’. Using this value, the potential GHG emissions are estimated using the aerial to total NPP ratio, above ground biomass, burning efficiency, and emission factors from ground measurements. Total emissions for the study area suggested 2.8 X lo”, 2.1 X lOLo,2.7 X 109, 9.8 X lo8 and 2.0 X 10’ gms for COz, CO,CH4, NO, and N20 respectively. ACKNOWLEDGEMENTS Authors are thankful to Dr. D.P. Rao, N.R.S.A, for providing Kant thank ISRO-GBP committee for providing fellowship. REFERENCES Brown, S., Estimating
all the facilities.
VKrishna
Prasad and Yogesh
biomass and biomass change of tropical forests: a primer, FAO Forestry paper No.134., Food and Agriculture Organization, Rome, 55-62, 1997. Budyko, M.L., Global Ecology. Progresspublishers, Moscow, 34-41, 1980 Delmas, R.A., P. Loudjani, A. Podaire, and J. C. Menaut, Biomass burning in Africa : An assessment of annually burned biomass, In Global Biomass Burning, Atmospheric, Climatic and Biosnheric Implications, Edited by J.S. Levine, The MIT Press, Cambridge, 199 1. Charles-Edwards, D.A., The Mathematics of Photosynthesis and Productivity, Academic Press, New York, 198 1. Friedlingstein, P., C. Delire, J.F. Muller, and J.C. Gerard, The climate induced variation of the continental biosphere - a model simulation of the Last glacial maximum, Geophys. Res. Lett., 19, 897-900, 1992. Fung, I.Y., K. Prentice, E. Matthews, J.Lemer, and G.Russell, Three dimensional tracer model study of atmospheric COz. response to seasonal exchanges with the terrestrial biosphere. J.Geophys. Res., 88, 1281-1294, 1983. Garcia, R., E.T. Kanemasu, B.L. Blad, A.J.L. Hatfield, D.J. Major, R.J. Reginato, and K.G. Hubbard, Interception and use efficiency of light in winter wheat under different nitrogen regions, Agri. For. Meteorol., 44,175-186, 1988. Goward, S.N., and K.F. Huemmrich, Vegetation canopy PAR absorptance and the normalized difference vegetation index: An assessment using the SAIL Model, Remote Sensing of Environment, 39,119- 114, 1992. Holben, B.N., Y.J. Kaufman, and J.D. Kendall, NOAA-11 AVHRR visible and near-IR inflight calibration, International Journal of Remote Sensing, 1 l(8), 15 1 l-l 5 19, 1990. Kaufman, Y.J , Atmospheric effects on remote sensing of surface reflectance, SPIE Remote Sensing, 475, 20-33, 1984. Lieth, H ., Primary Production: Terrestrial ecosystems, Human Ecology, 1,303-322, 1973. Monteith, J.L., Climate and the efficiency of crop production in Britain, Philosophical Transactions of Royal Society, 281,277-283, 1977.
1798
V. K. Prasad er al.
Paterson, S.S., The Forest Area of the World and it’s Potential Productivitv, The Royal University of Gotenborg, Sweden, 215-222, 1956. Prince, S.D., A model of regional primary production for use with coarse resolution satellite data, International Journal ofRemote Sensing, 12,1313-1330, 1991. Running, S.W., and E.R. Hunt, Generalization of a forest ecosystem process model for other Biomes, BIOME-BGC and an application for Global scale models, ScalinP Phvsiolonical Processes: Leaf to Globe, Academic Press, Inc., New York, 1993.
Adv. Space Res. Vol. 29, No. 11, pp. 1793-1798.2002 Q 2002 COSPAR. Published by Elsevier Science Ltd. All rights reserved Printed in Great Britain 0273-l 177102 $22.00 + 0.00 PII: SO273-1177(02)00112-6
ESTIMATION OF POTENTIAL GHG EMISSIONS FROM NET PRIMARY PRODUCTIVITY OF FORESTS - A SATELLITE BASED APPROACH VKrishna Prasad, Yogesh Kant and K.V.S.Badarinath National Remote Sensing Agency (NRSA), Dept. of Space, Govt. of India,) Balanagar, Hyderabad - 500 037, India
ABSTRACT Solar radiation in the wavelength interval between 400 and 700 nm provides the energy for photosynthesis and this information can be used for estimating Net Primary Productivity of plants. In the present study, AVHRR coarse resolution satellite data has been used for estimating NPP and thereby potential Green House Gas (GHG) emissions by integrating satellite and ground based measurements. NPP of forests has been calculated from annual sum of daily photosynthetic absorbed radiation and the radiation use efficiency of different plant species. Fraction of absorbed photosynthetic radiation for the deciduous ecosystem has been computed from monthly AVHRR NDVI composite values and using the AVHRR simple ratio. Results of the study suggested potential productivity of 5.81 t/ha& from satellite data, when compared to actual productivity values of 5.4 t/ha/yr from girth measurements. Potential GHG emissions estimated using the NPP value, aerial to total NPP ratio, above ground biomass, burning efficiency, and emission factors from ground measurements suggested total emissions of 2.8 X IO”, 2.1 X lOlo, 2.7 X log, 9.8 X 10’ and 2.0 X 10’ gms for COa, CO, C&, NO, and N,O respectively for the study area. 0 2002 COSPAR. Published by Elsevier Science Ltd. All rights reserved.
INTRODUCTION The net flux of carbon between atmosphere and terrestrial vegetation is expressed on an annual basis in terms of net biomass accumulation, or Net Primary Production (NPP). NPP is a measure of rate at which energy is stored or incorporated into living tissues. Productivity is measured by determining photosynthetic and respiration rates either by determining the changes in biomass through time or by dimension analysis, which estimates standing crop by determining the relationship between the easily measured parameter and biomass. Traditionally, studies with respect to growth and net production are mostly based on the analysis of sequential harvest data, using exponential equations to describe changes in plant growth, as a function of time. Whilst, these type of studies have played an important role in development of quantitative understanding of growth of a plant, it has it’s own limitations. Conventionally, it would be tedious to parameterize all the variables that influence growth physiology of trees and quantification of dry matter production in ecosystems such as forests becomes more problematic. Satellite data provides valuable information with respect to quantifying various biophysical parameters of vegetation which can be used for estimating productivity of forests. Further, data on trace gas emissions at regional scales are severely constrained due to lack of information on biomass densities, productivity, combustion characteristics and emission factors. Productivity data obtained from satellite data in conjunction with combustion and emission factors can be used for estimating GHG emissions over a region. In the present study, an attempt has been made to study the productivity through conventional, empirical and satellite based approaches and relate with trace gas emissions.
1793
1794
V. K. Prasad et al.
STUDY AREA Study area forms one of the hilly northern parts of Andhra Pradesh - Eastern Ghats (17’,22, 81’,45) India. This area is mainly dominated by tropical dry deciduous forests with patches of semievergreen and evergreen forests. METHODOLOGY
Productivity Estimation from Conventional Method Girth for individual tree and shrub species of representative plots of different vegetation types are measured during three consecutive years (1995-96-97-98). These data were analyzed for estimating the productivity using the regression equations relating girth to biomass. Leaf litter estimates were not included in production estimates as the regression equations were based on the full foliage period.
Productivity Estimations from Models Model is a simplification of the real system and mathematical modeling in plant physiology can improve our understanding of both interdependencies of the many plant processes and their separate dependencies on plant’s external environment (Edwards, 1981). There are several empirical and mechanistic models that can be used for estimating NPP of forests. It has been well established that, on smaller scales, temperature and precipitation are dominant controlling factors on plant photosynthesis as well as soil respiration and thereby they regulate the plant productivity. Various empirical methods available in literature (Paterson, 1956, Lieth, 1973, Friedlingstein, 1992) have been used to estimate the productivity of forests of the study area.
CVP Index (Climate, Vegetation, Productivity Index) Paterson (1956) proposed the CVP index, suggesting that climate, vegetation related. The CVP index as proposed by Paterson is given by
and productivity
I = (T, / T, ) x (P x G/12) x (E/100)
are intimately
(1)
where I is the CVP index, T, is the mean temperature of the warmest month (in degree centigrade) T, is the mean annual range of temperature between coldest and warmest month, P is the mean annual precipitation (mm), G is the length of growing season in months and E is the ‘evapotranspiration reducer’ a factor based on latitude and giving the generalized total radiation received as percent of that at the equator. The correlation between the CVP index and maximum productivity for different regions of the world was significant at 95% probability according to Paterson. The variance of CVP index contributes about 80% of total variance for ideal site class. The equation for calculation of productivity thus is given by Y = 5.3 log X - 7.25
(2)
where Y is the productivity in cubic meters per ha and X is the CVP index. Since the values obtained from the Patersons’s CVP index are in m31ha, values have been converted to tons/ha by taking volume weighted average wood density and biomass expansion factors (Brown, 1997).
Miami Model Miami model developed by Lieth (1973) calculates the Net Primary Productivity as a function of climate parameters. Based on the relationships between NPP (grn~* year -‘) to mean annual temperature (‘C) and mean annual precipitation (mm yr-‘), the equations proposed to calculate NPP are 1315-0.119xT) ~PcT)=3000/(1
+
e
NPP (P)= 3000 X (l-e-
(3)
) 0000664xP)
>
where NPP (T) and NPP,, are the NPP values calculated average precipitation (P in mm yr“) respectively.
(4) using mean annual temperature
(T in ‘C) and annual
Estimation
of Potential GHG Emissions from NPP
1195
Friedlingstein’s NPP Model After evaluating the original Miami model for the global NPP, Friedlingstein et al., (1992) found that the model tends to overestimate NPP in the tropics and under estimate it in high latitudes. They modified the Miami model to tit global NPP distribution given by Fung et al., (1983). The Friedlingstein et al., (1992) NPP model is given by NPP = min (NPPt, NPP,)
(5)
NPP, = 69.1875 Tt,, Tb < 8’ C = 135O/(l+exp(1.315 - 0.119Tb)), Ti,> 8’C
(6) (7)
NPP, = 1125 (l- exp (-0.000664 P)), in tropics = 1350 (l- exp (-0.000664 P)), in other regions.
(8) (9)
where
NPP = annual net primary production (g C m2 yr-‘), Tt, = biotemperature P = annual average precipitation (mm).
(‘C, mean annual surface air temperature),
NPP Estimation from Coarse Resolution Satellite Data Solar radiation in the wavelength interval between 400 and 700 nm provides the energy that is inevitable for The capture and utilization of Photosynthetic Active Radiation (PAR) photosynthesis and primary production. represent the energetic foundation for the origin, evolution and sustained existence of biosphere (Budyoko, 1980). Information on the amount of PAR absorbed by the .phototropic biota is important in efforts to model and monitor primary production and related biospheric process (Monteith, 1977; Prince, 1991). The net flux of carbon between the atmosphere and biosphere represents the imbalance between photosynthetic carbon uptake by plants and respiratory carbon release by soils and plants. Net Primary Productivity can be calculated from the am-ma1 sum of daily Absorbed Photosynthetic Active Radiation (APAR, MJmM2day-‘) multiplied by dry matter yield of photosynthetic active radiation (E gC MJ-’ PAR-called radiation use efficiency). APAR can be estimated using NDVI (Monteith, 1977) as NPP=&,cAPAR
(10)
where NPP = Net primary productivity, E, is the energy conversion Photosynthetic Active Radiation (MJmm2day-‘) Further,
E, C AF’AR
EC
efficiency
(g C MJ’ PAR), APAR = Absorbed
can be calculated as CAPAR =
E,
(NDVI x PAR)
APAR = Ir*a x fAPAR
(11) (12)
PAR (MJ‘2) and fAPAR is a where APAR = Absorbed Photosynthetic Active Radiation, IrAR is the Intercepted unitless ratio equal to fraction of incident PAR intercepted and absorbed by photosynthetic elements in the observed landscape. IpARis given by Ip*a = SOL@, fAPAR(,,x 0.5
(13)
where SOL,,, is the total solar radiation incident on particular type of ecosystem in month, fAPAR is the fraction of the incoming PAR intercepted by green vegetation and the factor of 0.5 accounts for approximately half of the incoming total solar radiation in the PAR waveband (0.4-0.7 urn) . Coincident
values of fApAR were predicted using the relation from AVHRR Simple Ratio (SR) as, SR = ( 1 + NDVI)/ ( 1-NDVI)
(14)
1796
V. K. Prasad
etal.
NDVI values were calculated using AVHRR channel 1 and 2 data from monthly composite data set of the study area, for the period, 1998.. Estimating GHG emissions from NPP Data Since the GHG emissions from the biomass burning are mainly governed by the amount of biomass material of particular vegetation which is further dependent on the net primary productivity, GHG emissions can be estimated from the NPP of forests taking into the annual area burnt, ground based emission factors and other information with respect to vegetation (Delmas et al., 1991). GHG emissions from biomass burning in the study area has been estimated as GHG emissions = NPP x RA x Rn x RPBx REax Rar
(15)
NPP = Net Primary Productivity (From Satellite Data), RA=Aerial to total NPP ratio, Rn = Above Ground Biomass to aerial NPP ratio, Rrn = Area annually burnt (sq.km), Raa = Burning efficiency (%), REF= Emission factors (g/kg) for different trace gases. RESULTS AND DISCUSSION NDVI is used as an indicator (or surrogate measure) of vegetation condition and has been linked to several biophysical variables such as net COZ exchange rates, Absorbed Photosynthetic Active Radiation (APAR), Canopy Photosynthetic capacity etc., (Goward and Huemmrich, 1992). NDVI is sensitive to the presence of green vegetation and has been successfully used in numerous regional and global applications for studying the state of vegetation. The success of NDVI as a descriptor of vegetation lies in it’s normalization, inspite of variations in atmospheric effects (Kaufman, 1984) and radiometric degradation in the red and NIR bands (Holben et al., 1990). The most accurate measure of NPP is to first measure the net photosynthetic rates of photosynthetic tissues, then subtract the rates of non-photosynthetic tissues and finally extrapolate it to community level, using the net production per gram of biomass of each species in a community. This assessment of NPP is not possible on a large scale, since the measurement of photosynthesis and respiration for all the species in any community is not possible in reality because of wide range of conditions experienced by plants in a community. Consequently, most of the methods depend on the accumulation and disappearance of biomass through time. But the major problems in making at a precise and accurate estimates of dry matter production are errors due to measurement and the effects due to climate variation from year to year. Thus estimation of NPP of forests over a larger scale is subjected to various complications and are mostly time consuming and are point based. Compared to the conventional methods, satellite remote sensing data can be effectively used for estimating the NPP of forests. The NPP values obtained from various methods including satellite based approach are given in Table 1. As in Table 1, Paterson’s CVP index, Lieth’s Miami model and Friedlingstein’s model over estimated potential productivity when compared to actual productivity obtained from girth measurements. This is mainly attributed the coefficients that are based on the climatic inputs in the empirical models rather than the biophysical parameters. Quantification of NPP using the biophysical parameters from remote sensing data suggests that potential NPP can be estimated more accurately than the empirical models. The important parameters to estimate the productivity include incident PAR and APAR that form the basis for estimation of productivity through satellite based approach. The term FAPAR is a measure of capacity of the vegetation in the observed landscape to intercept and absorb incident PAR and is closely related to green leaf biomass and Leaf Area Index (LAI). The FAPAR and PAR determines the amount of energy directly used to primary production process and reflects the combined influence of vegetation cover and climate. Moreover, the fraction of incident PAR absorbed by the canopy (FAPAR) has been found to be an important variable in studies of energy budget and hydrology of vegetated land surfaces. Absorbed Photosynthetic Active Radiation (APAR) can be estimated from remote sensing data. In the present study, monthly maximum NDVI values corresponding to deciduous broadleaf forest have been taken for computing the APAR. Studies on NPP estimation from satellite based approaches suggests that most dominant factor is IPAR. One evidence is the constancy of kc’ corn many experimental studies of unstressed plants. And also results from several studies indicate that nutrient stress and water stress have much larger effects on IPAR than on ‘sC’(Garcia et al., 1988). Running and Hunt (1993) found that measured values of ‘E,’ for woody vegetation is significantly lower than those of herbaceouos vegetation. They speculated that the cause for lower kc’ may be due to maintenance respiration by woody biomass. Based on the photosynthetic Quantum requirement of about 20mol photons.mol* COP (including photorespiration), it is suggested that there is a maximum E, of about 5.5g.MS1, which is reduced by uneven illumination, nutrient deficiency and stomata1 closure (Prince, 1991).
Estimation
of Potential GHG Emissions from NPP
Table 1. NPP Estimation Methods Actual NPP from conventional method Paterson’s CVP index Lieth’s Miami Model Friedlingstein’s NPP Model Satellite derived approach
1791
from Different Methods Estimated Productivity 5.4
Values (t/ha&r)
36.4 26.07 11.73 5.81
Further, ‘Ed’ is a net after maintenance and growth respiration carbon losses are accounted. APAR is a function of LAI, so ‘8,’ and CAPAR can be considered analogous to growth analysis terms net assimilation rate and leaf area duration respectively. Thus, a constant of 5.5g.M.I’ has been used to estimate the potential productivity_of the forests of the study area. Estimation of potential NPP over the forests of the study area from the satellite derived methodology suggested potential productivity of 5.81 t ha-lyre1 which is slightly more than the actual productivity of 5.4 t ha-’ yr’. Using this value, the potential GHG emissions are estimated using the aerial to total NPP ratio, above ground biomass, burning efficiency, and emission factors from ground measurements. Total emissions for the study area suggested 2.8 X lo”, 2.1 X lOLo,2.7 X 109, 9.8 X lo8 and 2.0 X 10’ gms for COz, CO,CH4, NO, and N20 respectively. ACKNOWLEDGEMENTS Authors are thankful to Dr. D.P. Rao, N.R.S.A, for providing Kant thank ISRO-GBP committee for providing fellowship. REFERENCES Brown, S., Estimating
all the facilities.
VKrishna
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