The methodology used for the forest inventory is distinct according to the vegetation type. In forest areas, different parameters are measured namely: diameter at breast height (DBH), total and commercial height, crown cover percent, and location of each plots. Total height is the height from the upper branches of a tree to the ground and the commercial height is the height of the main trunk of a tree. The crown cover percent is also percent of the number of trees in a hectare. We measured the total height during the field survey and used it in the allometric equation. In addition, the identification of botanical species is also conducted.

The field work consists of collecting some bio-physical and dendrometric parameters which allowed the biomass estimation of the plots and the physiognomic-structural characterization of the different vegetation types considered. The precise geographic coordinates of each plot are obtained using a high-precision Global Positioning System (GPS), which allows the localization of each plots, in the previously geo-referenced images.

The study area is located in the northern forests of Iran around the Rezvanshahr city (Fig. 3(a)). The dominant trees of these forests are: Maple, Alder, Conifer, Beech, Hornbeam, Azedarach and Acorn. Remote sensing data also consist of: AVNIR-2 and PRISM images from ALOS and a JERS-1 image. The JERS-1 image has a spatial resolution of approximately 13m and, AVNIR-2 and PRISM images have the spatial resolutions of 10m and 2.5m respectively. According to Fig. 3(b), the ground data is collected at five plots in the study area. Each plot

N

48.975(deg)

(b)

Fig. 3. (a) Study area of the north of Iran, (b) Plots in the study area indicated with circles.

was a square with size of 50m*50m with 25 subplots with size of 10m*10m approximately. The minimum DBH considered was of 37cm. The plots were mostly covered by two classes: Acorn and Azedarach. The distribution of the classes with numbers of stands where
measured in each subplots are shown in Table 1. Table 1 summarizes some of the ground measurements and resulting calculations.

The biomass in Table 1 is modelled based on the direct DBH and the total height measurements performed during the field survey and included afterwards in the general allometric equation (15) (Brown et al., 1989).

biomass = 0.044 x ((DBH)2 x height)09719 (15)

Where: DBH is in cm, height is in m, and biomass is in kg/tree.

For speckle reduction in the SAR image, the de-speckling model apply on the JERS-1 image of the study area and then its result is compared with several of the most widely used adaptive filters including the Kuan, Gamma, Lee and Frost filters.

In order to investigate the performance of the model, we use some quantitative criteria including speckle smoothing measures and texture preservation to evaluate the performance of the model.

The ratio of the original intensity image to the filtered image enable us to determine the extent to which the reconstruction filter introduces radiometric distortion so that the reconstruction departs from the expected speckle statistics. The mean and standard deviation (SD) can then be estimated over the ratio images. When the mean value differs significantly from one, it is an indication of radiometric distortion. If the reconstruction follows the original image too closely, the standard deviation would be expected to have a lower value than predicted. It would be larger than predicted if the reconstruction fails to follow genuine RCS variations. This provides a simple test that can be applied to any form of RCS reconstruction filters. Table 2, columns 2 and 3, shows the mean and standard deviation values of the ratio images for comparison of the filters.

According to Gagnon and Jouan (1997), Equivalent number of Looks (ENL) is often used to estimate the speckle noise level in a SAR image and is equivalent to the number of independent intensity values that are used per pixel.

It is the mean-to-standard deviation ratio, which is a measure of the signal-to-noise ratio and is defined over a uniform area as follows:

{mean2 )UmformA„ (var iance)U f.

V ‘UniformArea

ENL is used to measure the degree of speckle reduction in this study. The higher the ENL value concludes the stronger the speckle reduction.

Texture preservation is another measure that is important in a SAR image for interpretation and classification. Therefore, the texture preserving capability should play an important role in measuring the performance of a speckle filter. A second-order texture, variance (Iron & Petersen, 1981), is used to measure the retention of texture information in the original and the filtered images.

The ENL and the second-order texture values of the filtered images are shown in Table 2 columns 4 and 5 respectively. Of the four commonly used filters, Enhanced Frost filter has higher speckle-smoothing capabilities than Kuan, Gamma and Enhanced Lee filters. The ENL value of the model is 26.78 that it is comparable to Enhanced Frost filter. According column 5 ,Variance Texture Operator (VTO), in Table 2, the texture preservation of the proposed filter is better than, or comparable to, those of the commonly used speckle filters. We concluded the model is slightly better than the commonly used filters in terms of preserving details in forestry areas. Furthermore, the model also affects in smoothing speckles. This improvement in the accuracy of the speckle reduction can be played an important role in the forest biomass estimation.

After reduction the speckle noise, the texture of SAR image must be measured. Of the many describing texture methods, the grey-level co-occurrence matrix (GLCM) is the most common (Marceau et al., 1990; Smith et al., 2002; Zhang et al.,2003) in remote sensing.

Nine texture measures are calculated from the GLCM for a moving window with size of 5×5 pixels that centred in pixel i, j of the de-speckled JERS-1image. After the Gram-Schmidt process, just four texture measures: contrast, correlation, maximum probability and standard — deviation are selected as the optimum measures in this area.

The PRISM image is transformed in the universal transverse Mercator (UTM) projection with a WGS84 datum based on the GPS measurements and is used as the base map. Two GPSs measured the coordinates of points along the roads of the study area. To place all data sets in a unified coordinate system, the AVNIR and JERS-1 image are registered to this map. The co-registered and geo-referenced data sets contain PRISM, AVNIR and SAR images are used to extract intensity values and texture measures respectively.