Abstract
The capability of microwave energy to penetrate forest vegetation makes possible the extraction of information on both the crown and trunk components from radar data. At C-band, the backscattered energy is correlated mainly with the crown constituents such as the leaves, twigs and small branches. Information on the other components beneath the canopy can be sensed through the use of bands with longer wavelengths such as the L-or P-band. The sensitivity of co-polarized and cross-polarized waves to the shapes and orientation of different tree constituents provide an added advantage in the information extraction procedure. The Trunk-Canopy Biomass Index (TCBI), which is the sum of the L-HH and C-HV backscatter, can be a measure of the total aboveground biomass as both the crown and trunk layers are taken into consideration. Owing to possible morphological variation, the relationship between TCBI and biomass is however not expected to be unique for a whole forest vegetation. It is important therefore that stand structure be first considered to allow a more accurate biomass assessment by the TCBI. An index of the relative proportions of the crown and trunk may be indicative of the approximate tree morphology. It is believed that the Trunk-Canopy Morpholoyg Index (TCMI), which is the ratio of the L-HH to C-HV backscatter, provides a measure of tree structure. In this study, two categories are used to classify stands according to structure: the needle-leaved pines/conifers and the broad-leaved deciduous/evergreen trees. A two stage procedure of forest biomass estimation is therefore proposed. The first stage involves the determination of the stand structure category based on the TCMI. Once the structure is known, a specific structure-dependent TCBI could then be applied for the biomass estimation process. The effectiveness of these indices is assessed by applying them to actual and modeled data interpolated from published works of other investigators. Stand structure and total aboveground biomass were found to be highly correlated with TCMI and TCMI and TCBI, respectively. Comparison of the results is made difficult by the limitations in the amount of data available from the published studies and the possible errors introduced during the interpolation process. In order to verify the validity of these results, further application of these indices using AIRSAR images and adequate amount of actual and measured values from an independent study site in the Blue Mountains area in New South Wales, Australia will be conducted.

1. Introduction
The importance of quantifying and monitoring forest vegetation, given the vital productive, protective, and regenerative functions of this natural resource, is undeniable. Information on the amount and extent of vegetation provides an insight on what or how much to expect from a specific forest area in terms of these functions. From another perspective, the information can be useful in determining whether or not forest rehabiliation or other appropriate actions are needed in consideration of these three major functions.

Forest aboveground biomass, or the quantity of vegetative material per unit area, is one of the parameters recognized as a good indicator of forest condition. The use of radar remote sensing to

Provide estimates of this parameter has been receiving increasing over recent years. This is mainly due to ability of the radar to provide data independent of solar illumination and weather conditions. In addition, radar waves, depending n the wavelength/frequency, can be employed to scope different sections of the vegetation profile. Information on crown layer components (e.g. foliage) can be inferred from the radar backscatter at high frequency bands such as C-and X-while information pertaining to the trunks and lower branches can be obtained through the longer and more penetrative wavelengths of L-and P-bands.

Since the backscattering coefficient is influenced not only by the quantity of biomass but by how the individual components are oriented and distributed throughout the entire tree length, it is also possible to determine the general tree/stand structure based on the radar data. In fact, due to possible morphological variations between stands within a given forest area, it is recommended that the determination of the general tree/stand structure must precede biomass estimation and that radar data-based biomass equations should be formulated based on the structure. This is to avoid erroneous results will most likely be produced as it is possible to obtain different backscatter readings from two stands containing the same amount of biomass but are structurally-different.

2. Synergism of Radar Parameters for Optimum Information Extraction
The amount and quality of information which can be inferred from radar data depend on the characteristics of the target and the radar system. Important factors under the former relate to the roughness, geometric and dielectric properties of the imaged surface while under the latter are the microwave frequency/wavelength, polarization and incidence angle used in the data acquisition process. Accurate inference of vegetation properties results from the formulation of a well-established relationship between the target and the radar parameters. Being well-established implies present and a clear understanding of how these mechanisms relate to the radar parameters.

The advent of multi-parameter imaging radars extricated investigators from the limitations of using mono-band radar data in carrying out forest-related research. The opportunity lies in the simultaneous use of the radar parameters for optimum extraction of information from radar imagery. It is believed that a better assessment of forest resources could be achieved by utilizing a combination of the parameters. The theoretical basis for this premise and the different combinations will be presented in the following topics. Focus will be on the more controllable radar system-based components such as wavelength and polarization with forest aboveground biomass and stand structure assessments as the particular areas of application.

2.1 Forest aboveground biomass assessment

2.1.1 Combined wavelength and polarization estimation
The total forest aboveground biomass is the summation of the biomass of the crown and bole components of all the trees in the area under consideration. Obviously, to derive an estimate of this quantity from radar data, information on both the crown and the bole should be available. A simple model of the total biomass could then be given as
B_total = B_t + B_c           (1)
Where B_total represents the total aboveground biomass, B_t the trunk biomass, and B_c the crown biomass.

The C-band, with its relatively shorter wavelength, is sensitive to the upper layer of the vegetation such as the leaves, twigs and small branches of the crown. The more penetrative wavelength of L-band can pass through a greater volume of the canopy and interacts with bigger structures at the lowermost canopy portion and more regularly, with the trunk and big branches. Most of the returns from the latter is from tree-ground double bounce backscattering which occurs mainly due to the Vertical orientation of the trunk with respect to the ground hence forming a corner a corner reflector-like structure. Thus,

where is the backscattering coefficient sum correlated with the total aboveground biomass;

i and j are the unit vectors in x and y axes in the xy coordinate system corresponding to the backscattering coefficients of the L-and C-band, respectively.

The type of polarization employed determines the radar response to the various shapes and orientations of the scattering mechanisms within the canopy. Backscatter from cross-polarized waves tends to be related to the canopy volume rather than the lower components such as the soil and as such may be an indicator of crown biomass (ESA, 1995). Incoming vertically-polarized waves readily interact with the vertical components of the canopy. Due to the nature of its orientation, horizontally-polarized radiation tends to have a deeper degree of penetration and is less likely to be affected by canopy attenuation. The highest correlations between radar backscatter and total biomass are obtained using waves with HV-and HH- polarization: VV- waves are more sensitive to the components of the crown and tend to saturate at lower total biomass levels (Dobson et al., 1995a).

Integrating the theories pertaining to the sensitivities of the different radar wavelengths and polarizations, we can deduce the following relationships:
s^o (B_t+ib) " s^o _L-HH           (3)
s^o (B_L+b) " s^o _C-HV          (4)
where B_t+ib refers to the biomass of the trunk and lower branches, B_L+b is the biomass of the leaves and most of the brances, while soL-HH and s C-HV are the L-HH and C-HV backscattering coefficients, respectively. Considering the above relationships, equation (2) could then be written as

Where is^o_L-HH + js^o_C-HV is called the Trunk-Canopy Biomass Index (TCBI). To avoid possible overestimation of total aboveground biomass, other polarization combinations of the L-and C-bands wee not included in the equation as the biomass values they represent are already included in the s^o_L-HH and s^o_C-HV backscatter.