Biomass and productivity estimation using aerospace data and ...

KALE, SINGH & ROY

Tropical Ecology 43(1): 123-136, 2002 © International Society for Tropical Ecology

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ISSN 0564-3295

Biomass and productivity estimation using aerospace data and Geographic Information System M.P. KALE, SARNAM SINGH & P.S. ROY

Indian Institute of Remote Sensing, Dehradun 248 001 Abstract: Traditionally biomass estimation involved harvesting of the trees. As the forest cover decreased, there became the need for non-destructive methods for volume/biomass estimation. Methods were developed to relate the biomass with girth, height etc. Component-wise biomass equations were developed, which were used to estimate biomass at the plot level. In last couple of years satellite remote sensing has been successfully used for biomass and productivity estimation. The unique characteristic of plants is displayed by its reflectance in red and infrared region of electro-magnetic radiation. These have relationship with the biophysical parameters of plants. Therefore, process based models were developed to make use of the remotely sensed data available on monthly basis for estimation of Net Primary Productivity (NPP). Production efficiency model was used to estimate the NPP at the patch level, which takes Intercepted Photosynthetically Active Radiation (IPAR) and photosynthetic efficiency as input parameters to estimate NPP. Resumen: Tradicionalmente la estimación de biomasa ha involucrado la cosecha de árboles. A medida que decrece la cobertura forestal ha surgido la necesidad de contar con métodos no destructivos para estimar el volumen y la biomasa. Se desarrollaron métodos para relacionar la biomasa con el perímetro, la altura, etc., y se desarrollaron ecuaciones de biomasa en términos de los componentes, las cuales fueron utilizadas para estimar la biomasa a nivel de una parcela. En los últimos años la percepción remota satelital ha sido utilizada exitosamente para la estimación de la biomasa y la productividad. La característica única de las plantas se muestra como su reflectancia en las regiones del rojo y el infrarojo de la radiación electromagnética; éstas guardan relaciones con los parámetros biofísicos de las plantas. Se desarrollaron entonces modelos basados en procesos para hacer uso de datos de percepción remota disponibles sobre una base mensual para la estimación de la Productividad Primaria Neta (PPN). El modelo de eficiencia de producción fue usado para estimar la PPN a nivel de fragmento, tomando a la Radiación Fotosintéticamente Activa Interceptada (RFAI) y a la eficiencia fotosintética como parámetros de entrada para la estimación de la PPN. Resumo: A estimação tradicional da biomassa envolve a colheita das árvores. À medida que o coberto florestal decresce, torna-se necessário o uso de métodos não destrutivos para as estimas do volume/biomassa. Para o efeito desenvolveram-se métodos capazes de relacionar a biomassa com o perímetro, altura, etc.. Foram igualmente desenvolvidas equações de biomassa, por componente, e utilizadas para estimar a biomassa ao nível da parcela. No último par de anos a detecção remota, por satélite, tem sido utilizada com sucesso nas estimações da biomassa e da produtividade. As características únicas das plantas é representada pela sua reflectância na região da radiação electromagnética do vermelho e infravermelho. Estas estão relacionadas com os parâmetros biofísicos das plantas. Consequentemente, foram desenvolvidos modelos capazes de utilizar os dados mensais de detecção remota para estimação da Produtividade Primária Líquida (NPP). Um modelo de eficiência produtiva foi utilizado para estimação Address for Correspondence: P.S. Roy, Indian Institute of Remote Sensing, 4 Kalidas Road, P.O. Box-135, Dehradun 248001, India.

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da NPP ao nível da mancha, a qual usa como inputs a Radiação Activa Fotossintéticamente Interceptada (IPAR) e a eficiência fotosintética.

Key words: Biomass, forests, IPAR, NDVI, productivity, remote sensing.

Introduction The terrestrial net primary productivity (NPP) is an interface between plants and other processes, as it describes the removal of CO2 from the atmosphere and potential delivery of carbon to herbivores, decomposers or humans interested in food or fiber (Field et al. 1995). The biospheric carbon pools are surface related and their size depends on the amount of carbon per surface unit in the form of biomass and extension of related ecosystems. Although only a small amount of total energy that is reaching to the earth is fixed to organic matter by green plants, it is far higher than total primary energy consumed. World per capita CO2 emissions is about 4.21 metric tons, whereas in Asia it is about 1.03 metric tons as compared to Europe where it is about 8.20 metric tons. Estimates of per capita carbon concentration are higher in North and Central America i.e. 13.59 metric tons (Anonymous 1995). These are the estimates at global and continental level, which are not sufficient to confirm that whether the forest in general and tropical dry deciduous forest in particular is actually the source or sink of CO2. This has led a worldwide debate over the role of forest in global carbon cycle. Paucity of database regarding the carbon estimates is one of the major reasons for debate to continue. The uncertainty in the carbon estimates is caused by high uncertainties in rates of land-use change, rates of forest degradation, the amount of carbon in vegetation and soil of the forests being cleared, and the allocation of carbon after clearing and burning (Brown & Iverson 1992). In Indian context as much as 86% of the forested area is under tropical forest, of which 53% is dry deciduous, 37% moist deciduous and rest is wet evergreen (Kaul & Sharma 1971). These forests are very significant in terms of carbon balance. Attempts to understand the role of terrestrial ecosystem of India have been made for biomass and productivity using ecological methods

(Chaturvedi & Singh 1987; Rawat & Singh 1988; Singh & Singh 1989; Singh & Singh 1991) and still our understanding on these forests is poor. Extensive work has been done for biomass related studies, using conventional methods but it is limited to small area only. Remote sensing is a promising tool for regional and global level estimations. Remote sensing provides a synoptic view of the object in consideration from a vantage point. The concept behind this is that different objects respond in different manner in different wavelength bands. Interaction of radiation with plant leaves is extremely complex. Gates et al. (1965) studied the spectral characteristics of leaf reflection, transmission and absorption. In Indian context Roy et al. (1986) studied grassland spectral properties and their relationship with biomass. The relationship varied among parameters like composition, leaf orientation and soil background. Among different factors affecting the reflectance from forest canopy, leaves play a major role. The changes that occur in the spectral properties of plant leaves during the growing season are significant. The very young folded compact and underdeveloped leaves exhibit lack of chlorophyll. Absorption in the visible range is due to the proto chlorophyll and anthocyanin. Gradually the leaf becomes more and more green, which decreases the reflectance. Finally a fully open leaf shows the normal spectral characteristic with the green reflectance strong and the red blue spectral regions much absorbed (Roy 1989). Reflectance from green leaves is higher in NIR (Near Infra Red) region than in visible part of the spectrum hence vegetation is clearly separable in NIR band. Sensor having different bands sense the reflectance coming from different objects and records their values in respective bands. Different statistical and spectral response modeling techniques are used for predictions at regional and global level.

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Biomass estimation (conventional vis a vis optical remote sensing methods) Several methods have been used to estimate forest biomass (Ovington 1968; Whittaker 1966). Some of the commonly employed methods can be grouped into categories viz., (a) destructive and (b) non-destructive. Destructive methods can again be categorized into (i) by harvesting of all materials in an unit area, (ii) by harvesting average tree size either for the stand or within given size (girth or height) classes, or (iii) by harvesting of individuals over a wide range in size and establishing the relationship between biomass and easily measurable plant parameters, such as diameter/girth and/or height (Roy & Shirish 1996). The height-diameter at breast height (dbh) relationship to biomass is well formulated (Kira & Ogava 1971). Conventional methods have obvious limitations of extrapolation at large level and destructive in nature. Generation of allometric equations involve felling of tree, however, this method is impractical in view of the present environmental problems (Rawat & Singh 1988). Therefore, non-destructive methods became evident with minimum or no damage to the trees. Tiwari (1992) while generating the component-wise equations for dominant species of moist deciduous forest in Shiwaliks reported high correlation (r2 = 0.90) for twigs and for foliage (r2 = 0.872) with traditionally obtained biomass and estimated through sampling of tree components like bole, branch, twig and leaves. The approach does least damage to the plants. Satellite remote sensing technique provide a synoptic view of the object in consideration from a vantage point, which can be effectively utilized for deriving valuable information at regional and global level. Remote sensing researchers have accumulated experiences on the assessment of vegetation/ physiognomic types and their ecological state (Franklin & Strahler 1988). Studies have indicated that the integrated vegetation index can be related directly to vegetation amount (above ground phytomass) and primary productivity (Goward & Dye 1987). Sellers (1985) presented a critical analysis of canopy reflectance and its role in studying photosynthesis and transpiration. Canopy reflectance of vegetation is causally related to leaf area index of the canopy and covaries with above ground biomass (Curran 1981). It is possible to use remote

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sensing canopy reflectance models for estimating foliage and woody biomass and productive potential (Roy 1989). Roy et al. (1986) reviewed the work on biomass estimation using remote sensing techniques. Biomass distribution in forest ecosystem is a function of vegetation type, its structure and site condition. Ground based sampling in functional homogeneous vegetation categories is an approach, which has found acceptability in the recent past (Shute & West 1982). Many studies have investigated the relationship of spectral properties of ground object with different ground parameters using statistical techniques, but this type of work has been done either in homogenous conifer forests, or grassland. The tropical dry deciduous forest which are charecterised by heterogeneity, temporal variations due to change in phenology and background reflectance possess maximum challenge for biomass and productivity estimations. In statistical sampling approach per unit biomass values of sample plots in homogenous vegetation strata was estimated, which was extrapolated to the entire area (Roy & Shirish 1996). Whereas in spectral response modeling technique analysis of spectral response was performed to investigate possible relationship between Landsat TM digital data (composite reflectance of sample plot) and biomass, to obtain the per pixel biomass values of the study area. The two techniques are comparable with the percentage error of 4.691 when total biomass of study area is investigated. Among different models, multiple regression models using brightness and wetness indices of tasseled cap transformation was used to estimate per pixel biomass. Biomass map of the study area was generated using spectral response modeling approach to know the per pixel biomass (Fig. 1). The total biomass of the study area excluding grassland was 375924.33 tons, whereas biomass in grassland was 926.36 tons. Percentage error between observed and predicted biomass was 10.46% (Fig. 2).

Biomass estimation using SAR data All weather condition SAR (Synthetic Aperture Radar) data provides an opportunity to species based forest stratification specially so in the areas with perpetual clouds. SAR data is sensitive to moisture, temperature, branch architecture, biomass, age-classes, girth, canopy density etc. In addition to this it also has higher information con-

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Fig. 1. Biomass map of Madhav National Park (Spectral Response Modeling) (Roy & Shirish 1996).

tent, multi-polarized data, more penetration etc. as compared to optical data. Longer wavelengths have poor correlation with trunk height (Moghaddam et al.1994). Selection of appropriate band(s) and polarization is quite significant for mapping as well as for quantification. Roy et al. (1994) used XBand data for mapping and found statistical parameters like standard deviation, median and variance (in a cross-section of 3×3 window) had excellent to good separability among various forest types. Since SAR is very sensitive to moisture content it would be appropriate to use in deciduous forests where phenology plays a major role in mapping and quantification of biophysical parameters. Ranson & Sun (1992) who used multifrequency data for mapping forest biomass found that backscatter from L and P bands showed high sensitivity to biomass. Other bands like X, C, S have lesser penetration hence have weaker backscatter and do not show very high correlation with biomass. However, this relationship holds good where the biomass was low because the backscatter saturates at about 10 kg m-2 in C band and 200 Mg ha-1 in L and P bands. Forests with woody biomass of 200 to 300 tons ha-1 do not follow the trend

trend and curve flattens beyond irrespective of polarization (Moghaddam et al. 1994). Crosspolarized data (HV or VH) has better relationship with biomass. However, HH data has been found highly correlated with trunk and crown biomass and VV & HV with crown biomass in P band. Similarly cross-polarized data of L band has significant relationship with biomass and stand structure. Ratios combinations of longer and shorter wavelengths have shown high correlation coefficient for standing biomass. Coefficient of determination r2=0.83 with ratio of P and C bands and r2=0.79 with L and C bands with biomass has been reported. Moghaddam et al. (1994) have analysed extensively multi-channel and multi- polarized data with various forest parameters like biomass, density, basal area and trunk height and found that P band cross-polarized (HV) and L band likepolarized (VV) data with r2 value of 0.75 and 0.76 respectively. Incidence angle also plays important role and relationship improves as the incidence angle increases from 35° to 50°, probably because of the more interaction of signal with standingstem. Combination of two or more different forest parameters (trunk, branches, basal area, soil etc.)

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Biomass observed

4.3 3.9 3.5 3.1 2.7 2.3 2.3

2.7

3.1

3.5

3.9

4.3

Predicted biomass Fig. 2. Plot showing observed and predicted biomass (Roy & Shirish 1996).

has been found to have better relationship with biomass. Merged data of optical (Landsat TM and IRS LISS II and III) and SAR-X band sensors offers good potential for enhancements techniques and mapping (Roy et al.1994).

Net primary productivity estimation Models: an overview Development of an appropriate model for regional/global level estimations is an iterative process. Models are developed on the basis of various dynamic features, which includes climatic, geographic, edaphic, physiological, chemical and several other factors. Output of the model developed in this way is analyzed to modify (if necessary) the model to get better results. Comparison of different models provides an insight of various strengths and weaknesses of the model, which is often useful to select or develop a comparatively better model. Cramer et al. (1995) has reviewed different NPP models presented at the workshop held at Potsdam in 1995. In general available NPP models can be categorized in three classes on the basis of required input and typical output variables. First category model generally require information about the seasonal photosynthetic activity of canopy. These models require satellite data, hence the simulation periods for this class of models are limited to that of satellite archive (Kinderman et al. 1993; Kinderman et al. 1996; Maisongrade et al. 1995; Malmstorm et al. 1995). These models in-

clude different PEMs (Production Efficiency Models), which are based on the concept of light use efficiency to convert APAR (Absorbed Photosynthetically Active Radiation) to biomass accumulated over a time period. One of the models included in this category is GLO-PEM model, which uses satellite data to estimate various climatic features like soil moisture and vapor pressure deficit. The only use of climatology in GLO-PEM was to discern the presence of C3 and C4 grasses. Second category models combine soil and vegetation characters only in order to simulate biogeochemical fluxes. This category models may use satellite data but it is generally only for calibration purpose, this is in contrast with the first category models, which use satellite data for deriving input variables. Some models in this category like CASA, (Potter et al. 1993) CENTURY, (Patron et al. 1993) and HRBM (Field et al. 1995) take into the consideration vegetation characteristics and environmental variables or indicators such as temperature, precipitation, available soil nitrogen and other fertility factors to estimate NPP as the difference between two processes viz., (a) GPP i.e. the total uptake of carbon from the atmosphere by plants, and (b) autotrophic respiration (RA: the release of carbon to the atmosphere by plant respiration). In these models GPP and/or RA could be constrained by nitrogen availability in the (a) soil profile (b) entire vegetation or (c) leaves only, which act as limiting factors. Third category model includes those models that simulate ecosystem function (biogeochemical features) and structure (vegetation type and struc-

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ture) and example of this category models are BIOME 3 (Haxeltine A. & I. Prentice 1996) DOLY (Woodward et al. 1995) and HYBRID (Friend 1995). Such models appear to be the most adequate candidates for predicting the response of vegetation to climate change because they allow dynamic coupling of the temporal changes of both structure e.g. Leaf Area Index (LAI)) and function (e.g. carbon, water, and nutrient flux). All these models have inherent strengths and weaknesses. Output derived from these models depends on various climatic, topographic, physiological, chemical, biological and geographical factors; hence it is very difficult to standardize a model to get optimum output. There may be vast amount of difference in productivity values based on geographical locations, hence it is difficult to examine whether the differences in annual estimates of global NPP were related to general differences in the growing sea-

SATELLITE SUB MODEL

son as it was estimated by the models, seasonal estimates of global NPP were compared among the models across the environmental gradients for eastern North America and Africa. Most of the models predicted increased NPP, sometimes differed up to 200% between models (Cramer et al. 1995). There are several models, which has been used for productivity estimation, like model based on only biome types (Whittaker & Likens 1975), model utilizing the meteorological parameters (Leith 1973) and Forest BGC model (Running & Coughlan 1988). The extensive data requirement on daily and annual basis of BGC (Bio-geochemical cycle) model restrict its use for regional level estimations. Information on various components like daily components includes air temperature, radiation, precipitation, humidity, LAI, evaporation, transpiration, whereas yearly components include estimation of carbon and nitrogen

SEASONAL STACK OF NORMALISED DIFFERENCE VEGETATION INDEX (NDVI)

IPAR SUB MODEL

SATELLITE DERIVED IPAR CALCULATIONS NET PRIMARY PRODUCTIVITY MODEL VEGETATION TYPES AND PRODUCTIVITY

FIELD BASED IPAR MEASUREMENTS

NPP = ε (Σ IPAR)

RATE OF CO2 INTAKE

ENERGY CONVERSION EFFICIENCY (ε )

GROUND SUB MODEL

NDVI = Seasonal stack of Normalised Difference Vegetation Index (NDVI) for growing season. IPAR = Intercepted Photosynthetically Active Radiation

Fig. 3. Methodology for NPP estimation (Roy & Jain 1998).

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concentration in soil, leaf, stem and root and also the soil and vegetation respiration. Whereas Production efficiency model (PEM), which takes into account most versatile parameter of vegetation i.e. IPAR (Intercepted Photosynthetically Active Radiation) seems to attract the most which faithfully represent the vegetation dynamics in tropical dry deciduous forest in terms of radiation change. PEM has been used to estimate the NPP at patch level in Madhav National Park of Shivpuri district (M.P.) using IPAR and species wise photosynthetic efficiency as input parameters (Roy & Jain 1998). Inclusion of ‘photosynthetic efficiency’ parameter in the model make the model more reliable as in estimating photosynthetic efficiency at plot level each species that is occurring into the plot was taken into the consideration. A weighted average was taken for estimating light conversion efficiency of all the individual species within the plots i.e. forest type, to calculate the average efficiencies of the sample plots. The importance value index of each species, which takes the relative basal area and the relative density into the consideration, was taken as the weight. The overall methodology of NPP estimation has been shown in Fig. 3. NPP at patch level was estimated using Miami model and Production Efficiency model and results were verified. Based on Miami model of Leith which takes mean annual temperature (ºC) and mean annual precipitation (mm year-1) into the consideration NPP of the area was estimated using following equations: 1) NPP(T) = 3000/(1 + e1.35-0.119 *T) 2) NPP(PP) = 3000*(1-e-0.000664*PP) Where, NPP(T) and NPP(PP) are the NPP values calculated on mean annual temperature and mean annual precipitation respectively. The NPP calculated by above approach was equal to 27.48 and 12.12 t ha-1 year-1 respectively. Compared to these results measured above ground productivity ranged from 1.77 – 8.98 t ha-1 year-1. The concept of calculating NPP as a product of IPAR and conversion efficiency (ε) was pioneered by Monteith (1972). The inclusion of light conversion efficiency (ε) provides the connection between light harvesting by the vegetation, and the efficiency of use of energy in assimilation of carbon. The basis for present model is the linear relationship between IPAR and NDVI. The NPP estimated using model reflected productivity as 0-49 gCm-2

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week-1 and 0-20 gCm-2 week-1 for the two phases i.e. growing phase and senescent phase respectively.

Current scenario- from patch to region Globally efforts are on to use more and more aerospace data for estimating biomass and productivity in small to large areas using Landsat TM, LISS III, IRS – WiFS and NOAA-AVHRR, etc. respectively. Regional level biomass, productivity, and carbon estimations is one of the set goals of ISROGeosphere Biosphere Programme. After conceptualization and implementation of methodology for biomass and productivity estimation at patch level i.e. Madhav National Park having area equals to 165.32 km2 we tried to extend it to the regional level i.e. entire Shivpuri district having area of 10,278 km2 (Fig. 4). We used IRS – WiFS data having resolution of 188 meters for regional level estimations. There was no significant difference in vegetation types at regional level as compared to patch level. Mixed vegetation was prominent when we considered the entire Shivpuri region, hence we stressed on increasing the number of plots in mixed forest area, in addition to this we also increased the number of sample plots also in all the other vegetation types to maintain the accuracy at regional level. We increased the number of plots from 18 to 29. This was significant as the vegetation type, and also the topography was not changing significantly from patch level to regional level. The plots were well spread in all forest areas of Shivpuri district representing all the forest types. WiFS data having resolution of 188 m. represents composite vegetaLANDSAT - TM (SPATIAL RESOLUTION 30 METERS)

MADHAV NATIONAL PARK AND A PART OF SHIVPURI DISTRICT

PATCH LEVEL IRS – WiFS (SPATIAL RESOLUTION 188 METERS)

SHIVPURI DISTRICT

REGIONAL LEVEL

Fig. 4. Hierarchy of extrapolation.

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tion reflectance hence micro level classification cannot be considered in WiFS. Dry deciduous forests have very distinct phenological cycle and therefore field observations need to be made in different months of the year. The satellite data in conjunction with GPS (Global Positioning System) has been used to identify homogenous vegetation stratum based on physiography, phenology and slope conditions. Random sample points in representative areas for the vegetation need to be taken under each homogeneous strata. Phenology plays a very important role in such forests therefore field observation on IPAR need to be taken in each month. In a sample plot the observation has been taken from the entire plot (0.1 ha) and averaged.

Vegetation phenology The U.S. International Biological Programme Committee has defined phenology as “The study of timing of recurring biological events, the cause of their timing with regard to biotic and abiotic factors and the interrelation among the phases of same or different species”. Tropical dry deciduous

forests have well defined phenology. Leafing has been found to be closely related with warmer months (Singh & Singh 1992) thus setting the leafing period resulting into higher LAI (Leaf Area Index) in growing period. As far as phenology is concerned the vegetation can be deciphered in two prominent phases of its life cycle, i.e. growing phase and senescent phase. Growing phase is when green leaves are there on the trees and senescent phase is when the leaves are either scanty or died. LAI dynamics is also a very important factor in tropical dry deciduous forest. In senescent phase LAI is low as there are lesser leaves on plant canopy, LAI increases with occurrence of growing season because there are considerable leaves on the canopy. During senescence phase therefore the reflectance recorded by satellite sensor is the function of different vegetation parameters viz. density, basal area and thickness of vegetation column.

Spectral profile of vegetation – case study of central India Vegetation spectral profile is unique for each vegetation type and one time, monthly or annual

Fig. 5. Spectral profile of different vegetation type (Kale et al. 2001, in press).

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data has been used widely to characterize the vegetation. There exists very good correlation between NDVI and other biophysical parameters. Seasonality is extremely important aspect in forest cover discrimination (Roy et al. 2000). Using seasonal stack of NDVI images of the year 1998, reflectance patterns of vegetation has been observed in Shivpuri district (Fig. 5). Spectral profile of the vegetation shows its seasonal behavior, which is very important to study the phenological cycle of different vegetation types. The present study is at the regional level using IRS-WiFS sensor, which have only two bands visible, and near infrared, hence it is critical to study the vegetation reflectance properties of these bands. The NIR reflectance decreases as the leaf opens. The striking decrease appears to be caused by the unfolding and expansion of the leaf and resultant loss of multitude of reflecting surfaces within a leaf. Gradually no change in the visible part of the spectrum is noticed but the reflection in NIR increases due to the development of air spaces in the mesophyll and presence of many reflecting surfaces within the leaf. A stage comes when the reflection characteristics become fairly stable throughout the visible and NIR and variation from leaf to leaf is also reduced. After this stage green reflectance increases dramatically as the blue and red absorption weakens. The characteristics progressively takes place and become more and more prominent as chlorophyll disappears. This stage is called as senescence in phenological staging (Roy 1989). It was observed that Moist deciduous forest (Mdf) show a sharp dip in the month of April, and then reflectance gradually increases till November. The reflectance values of Mdf are comparatively higher than most of the other dry deciduous vegetation types and the peak value is shown in month of November as against most of the other dry deciduous vegetation types which shows peak reflectance in the month of October. Mixed forest (miscellaneous) shows comparatively higher reflectance than any other dry deciduous vegetation. All the other vegetation types i.e. Khair forest, Salai forest, Kardhai forest show approximately similar trend in all the months of the year. Seasonal grasses show a declining trend from January to May and than gradual increase in reflectance from May to November with peak reflectance in November. Fallow/Scrub/Shrub shows gradual decline from January to June and then increase from June to December. Water and Wetland shows spe-

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cific trends based on moisture content. It was observed that maximum seperability of vegetation is in the month of October and November.

Land use/land cover classification Temporally dynamic nature of tropical vegetation makes it critical from ecological study point of view. Seasonal variations in different forest types throughout the year make it essential to study the forest ecosystem by taking phenological variations into the consideration. The phenological cycle rotates between two prominent seasons i.e. (1) Growing Season (from mid August to mid December) (2) Senescent season (from mid December to mid April). From mid April forests remained in the defoliated state till the occurrence of rainy season, as the rainy season started leaf sprouting started. By mid August almost all the vegetation types have fully grown leaves. Ground investigations were made in different seasons of the year to study the phenological patterns of major vegetation types. Accurate classification of tropical dry environment is a complex task due to the temporally dynamic nature of the ecosystem. There is a greater possibility of intermixing of spectral signatures of background soil and vegetation types during the dry seasons when there are no leaves on the trees and hence the forest floor is exposed. Satellite data offers immense possibilities and combinations for generating data sets for further analysis. Of the several data sets usually either raw data and/or derived data set like NDVI are used for the classification. Data available from sensors like NOAA, WiFS etc. have frequent data acquisition. Seasonal stack of FCCs and NDVIs were used for classification, which takes seasonal variations into account. This gives immense opportunities to consider phenological characteristics of the vegetation. Multitemporal data set of raw as well NDVI were used for deriving land use/Land cover classified map of the area (Fig. 6). Overall classification accuracy achieved was 81.03%

Intercepted photosynthetically active radiation The green plants in the presence of sunlight convert carbon dioxide to energy. The fraction of the radiation which is used for this purpose is called Photosynthetically Active Radiation (PAR),

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Fig. 6. Land use/Land cover map of Shivpuri district (Kale et al. 2001 in press).

which ranges from 400 nm to 700 nm, hence it can be said that Intercepted Photosynthetically Active Radiation (IPAR) is that part of electromagnetic radiation which is utilized by the plants for the process of photosynthesis. The measurement of radiation within the canopy was made with the instrument (Decagon’s ceptometer) using the method described by Norman & Jarvis (1975). APAR = (Io+Rc)-(Tc+Rs) where APAR is the incident PAR absorbed by the canopy (µ mol m-2 photon units), Io is the incident PAR flux density, Rc is the PAR reflected from the canopy, Tc is the PAR transmitted through the canopy and Rs is the PAR reflected from the soil. Due to difficulties in measuring the reflected components i.e. Rc and Rs, IPAR was estimated instead of APAR. Very little error was observed due to this, because most of the energy that is incident on the canopy is absorbed in PAR range, and secondly, reflected components balance each other. Hence IPAR can be defined as the difference between the energy of the incident radiation and energy that is transmitted through the canopy. IPAR = Io - Tc Seasonal variations in APAR are mainly due to: 1. Seasonal sun movement and hence change in solar Zenith angle. 2. Change in plant phenology.

The term intercepted PAR is commonly used interchangeably with APAR. A distinction should be made between these two terms. IPAR is purely a statistical quantity and represent only the probability of photon that are intercepted by plant elements as compared to APAR in which photons passing through the gaps inside the canopy without the interaction with the plant elements are also considered. If all the photons are intercepted by the elements as in the case of complete canopy cover, than IPAR=1. If all the photons pass through the canopy gaps without being intercepted then IPAR =0. This definition does not include the process of energy absorption. This distinction is very important, especially in the NIR and SWIR regions of the spectrum but in the PAR region, APAR and IPAR can be used interchangeably due to negligible scattering and strong absorption of photons by green plant elements. IPAR measurements were taken in the permanent sample plots in November, January, February and March 2000-2001 using Decagon’s ceptometer. First total incident radiation upon the canopy was estimated by taking measurements in open area outside the plot where direct sunlight is available. Moving diagonally inside the sample plot and taking readings at regular intervals esti-

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R2 = 0.76

2 1 .5

ΣNDVI

1 0 .5 0 0

2000

4000

6000

ΣIPAR

Fig. 7. Plot showing correlation between Σ NDVI and Σ IPAR values (Kale et al. 2001 in press).

mated average transmitted radiation through canopy. Finally IPAR was calculated by the following formula: IPAR = Io – Tc It was observed that Interception was gradually decreased from November to March. This was due to the onset of senescent phase (Roy & Jain 1998). In order to estimate IPAR, statistical analysis was performed to see the correlation of seasonal

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IPAR values with respective NDVI values. The study carried out by Roy & Jain (1998) using LANDSAT TM data in a part of Shivpuri district (M.P.) showed that IPAR shows good correlation with sum NDVI. (Coefficient of determination, R2 = 87.40 %). A correlation was established between monthly NDVI values with respective IPAR values for different seasons; the correlation found this way was not significant. Significant correlation (R2 = 0.76) was observed between Σ NDVI (Sum value of seasonal NDVI stack) and Σ IPAR (sum value of seasonal IPAR stack) (Fig. 7). Linear regression was established between ΣNDVI and Σ IPAR values, to derive the coefficients. Model Y = a + bX was used to predict the IPAR values and to finally generate the IPAR image. NDVI values are the function of the vegetation vigor, hence higher NDVI values show healthy vegetation, this is because healthy vegetation absorbs most of the visible light that hits on it and reflects large portion of NIR light. Unhealthy or sparse vegetation reflects more visible light and less NIR light, hence shows lower NDVI values.

Fig. 8. IPAR Image of Shivpuri district (M.P.) (IRS WiFS multi temporal data) showing Scaled IPAR values (Kale et al. 2001, in press).

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Correlation was established between monthly NDVI values with respective IPAR value but it didn’t show good correlation this is because during the senescent phase the reflectance is mostly due to the died plant parts and scanty leaves which leads to enhanced background reflectance in red part of the spectrum, hence the NDVI values represents reflectance due to the mixed components. Sum values of the seasonal NDVI stack take into the consideration all the monthly NDVI values and hence the resultant values better represents reflectance due to canopy. IPAR is an important parameter in determining the productivity using ‘production efficiency model’, where productivity is a function of IPAR and photosynthetic efficiency of vegetation. Interception of PAR is a function of amount of leaves present on the canopy, if leaves are more, then interception is also high, hence IPAR image shows that wherever IPAR values are high comparatively more leaves are present on the canopy, which may be attributed to soil and topographic conditions. IPAR values were classified in four classes (Fig. 8). (1) 3000-4000 µ mol m-2s-1 (2) 2000-3000 µ mol m-2s1 (3) 1000-2000 µ mol m-2s-1 (4) below 1000 µ mol m2s-1. Forest areas showed high IPAR i.e. above 2000 µ mol m-2. Higher IPAR values i.e. above 3000 µ mol m-2s-1 may be attributed to topographic conditions and moisture availability. Predicted IPAR accuracy assessment was performed by making “line fit plot” and by calculating residual values (difference between predicted and observed IPAR values). Percent accuracy of predicted points was calculated. It was observed that accuracy of predicted values fluctuates between 77.32% to 99.07%. Only three points showed low accuracy (below 77.76%) this may be attributed to high background soil and stone reflectance and high deciduous condition.

Conclusion The quantification of biomass is required as the primary inventory data to understand pool changes and productivity of tropical forest (Esser 1984; Whittaker & Woodwell 1971). Looking into socio-economic point of view, forest biomass is the only renewable energy source easily available to human being. Over the years, due to anthropogenic actions the size of the carbon pool has diminished (Bolin 1977). There is a worldwide debate

over the role of forest in global carbon cycle, there are basically two schools of thought, some scientists believe that forest are the source of carbon dioxide, while others are of the opinion that forest are actually the sink of the atmospheric carbon. Paucity of database world over is one of the main reasons for this debate to continue; hence unless the accurate information about the source-sink relationship is available, mitigation strategies cannot be formulated. Biomass and productivity information is one of the very important inputs as far as climate studies are concerned. If this information is available it could provide valuable inputs to actually know the global carbon pattern related to forest ecosystem.

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