The collection of samples for this study was carried out at the end of the steady-state condition of 0.24 ± 0.02 kg N/m3.d nitrogen loadings. Samples of the biofilm and suspended growth biomass were taken at different depths of the reactors. The in-situ characterization followed a top-bottom approach. Fig. 1 illustrates the exact locations where the samples of suspended biomass and biofilm were obtained from the reactors.

Samples of suspended biomass were taken from port 1, port 2 and port 3 respectively. At each port, about 50 ml of reactor aliquot was wasted before sample collection to ensure that any debris or anaerobic bacteria residing in the pipeline was discarded. A 10 mL volume of aliquot was taken and immediately fixed with 1:1 absolute ethanol. Samples were then stored at -20o C.

For sampling the biofilm, the liquid was first drained from port 1 in order to allow access into the upper bed layer. Tongs were used carefully to remove the media from the upper layer. A random piece of media from the specified level was chosen. The biofilm was gently scraped off the plastic material using a sterile surgical knife before washing the media with 10 ml phosphate-buffered saline (PBS) solution. This procedure was repeated four times until all the biofilm attached to the media was completely removed. To homogenize the biofilm, the sample was sonicated for 2 minutes using an ultrasonic homogenizer (Bandelin Electronics D-1000, Germany). 10 ml of the aliquot was put in a universal bottle and fixed with 1:1 absolute ethanol before storing at -20o C. The sampling of biofilm at the second location was subsequently continued by draining the liquid from port 2. The same procedures were repeated until the media at the bottom were sampled. To detect the AOB in

the samples, the FISH technique (Coskunur 2000) was applied in order to produce the fluorescent sites in the cells, and these were detected through the use of confocal scanning laser microscopy (CSLM).

The quality assessment is necessary for each step of the pre-processing. Pfeifer et al. (2004) reported an RMSE of 57 cm for DTM in wooded areas using data point spacing about 3 m. Hyyppa et al. (1999) reported a random error of 22 cm for fluctuating forest terrain using data point density 10 pts/m2. They analyzed the effects of the date, flight attitude, pulse mode, terrain slope, and forest cover within plot variation on the DTM accuracy in the boreal forest zone. Hyyppa and Inkinen (1999) reported the CHM with an RMSE of 0.98 m and a negative bias of 0.41 m (nominal point density about 10 pts/m2). Yu et al. (2004) reported a systematic underestimation of CHM of 0.67 m for the data acquired in 2000 and

0. 54 for another acquisition in 1998. The filtering methods mentioned before are likely to fail

facing with (i) outliers in the data, (ii) complexity of the terrain, (iii) small vegetation which is completely attached to the terrain like bushes. Most of filter algorithms start with the minimum height in data. Thus the most effective error is the negative outliers which are originated from multi path errors and errors in range finder. The vegetation on the slope also produces difficulties in filter algorithms because of the reflected pulses returning from the neighbor points. Therefore, filtering methods need some initial threshold values, which

are usually defined by experience and a-priory information about the data and terrain characteristics.

Fallah Vazirabad and Karslioglu (2011) demonstrate that the quality of segmentation filter deteriorates with increasing point spacing of ALS point cloud looking at Type I and Type II errors (table 1). Large Type I error leads to a reduced DTM accuracy as a consequence, because many vegetation points will be included in DTM generation. The Type II error induces some effects resulting from the fact that measured elevation values in Lidar data are replaced by interpolated values for DTM, which cause a zig-zag pattern in the DTM modeling (figure 6).

The amount of biofilm formed on each carbon source by the flhD mutant was quantified and subjected to Duncan’s multiple range test. According to the Duncan’s grouping, the sole carbon source that formed its own group A for the flhD mutant was N-acetyl-D — glucosamine. Structurally related carbon sources that were included in the PM1 plate are D — glucosaminic acid and N-acetyl-P-D-mannosamine. Biofilm amounts formed on these three carbon sources were compared between the two strains (Table 4).

On N-acetyl-D-glucosamine, the flhD mutant (4,900 RLU) formed a significantly larger amount of biofilm than the parent strain (1,300 RLU), while both strains grew to approximately 1 OD600. On D-glucosaminic acid, the parent strain did not grow to the cutoff OD of 0.5. The flhD mutant grew well, but the amount of biofilm biomass was poor (~600 RLU). For N-acetyl-|3-D-mannosamine, both strains grew well, the flhD mutant expressed more than twice the ability to form biofilm than its isogenic parent.

Despite the possibilities that spectral reflectance measurements offer for monitoring growth traits in plots and individual plants (e. g. in breeding programs), their use until now has been very limited. One of the main reasons is that a wide range of variability must exist for the target growth traits within the experimental units to be detected by the apparatus (Royo et al., 2003). The strongest associations between growth traits and spectral reflectance indices have been found in studies in which a wide range of variability is induced by experimental treatments, such as rates of seed or nitrogen fertilizer, varying levels of water availability or soil salinity, or the combined analysis of data recorded at different plant stages. However, when the range of variation is low, particularly when the differences are only in the genetic background, and the predictive ability of vegetation indices is tested in specific environments and growth stages, the value of spectral reflectance measurements for estimating growth traits has proven to be much more limited (Aparicio et al., 2002; Royo et al., 2003). The fact that the pattern of changes in biomass is quite similar among modern wheat varieties (Villegas et al., 2001) may be an additional obstacle to the implementation of remote sensing techniques as a screening tool in breeding programs.

Another limitation to the extensive use of spectral reflectance measurements to track changes in biomass derives from the huge number of indices reported in the literature and their misleading use (Araus et al., 2009). In addition, the lack of equipment specially designed to take measurements at individual plant level restricts the use of spectral reflectance in breeding programs, where selection in early segregating generations involves the screening of thousands of individual plants or small plots, and only reliable, fast, and cheap screening tools may be helpful. Prediction models are not of general use and need to be developed for specific situations, such as in farmer’s fields, where evidence indicates a decrease in the performance of classical and newly identified indices (Li et al., 2010b). Other great challenges are the development of functions to calculate sensor-specific spectral signal — to-noise ratios for a number of different conditions, which would allow the models to include the effects of sensor-related noise (Broge & Leblanc, 2000), and the development of new sensors more adapted to practical applications.

2. Conclusions measurements to be taken over time on the same plot or plant, so the grain produced on the measured plants is available at the end of their growth cycle. In addition, the method avoids the errors associated with destructive samplings of biomass, and is fairly quick. However, the use of canopy spectra for biomass assessment requires a thorough knowledge of the conditions of use and the constraints imposed by the measurement-related noise caused by the sensor system, the canopy structure, and the environment, which should be carefully taken into consideration in order to obtain reliable results.

3. Acknowledgements

This review was partially supported by Spanish projects CICYT AGL-2009-11187 and INIA RTA 2009-0085-00-00. Authors thank Dr. Nieves Aparicio and Dr. Fanny Alvaro for their valuable contribution to field experiments

Advances in molecular approach such as the DNA profiling through PCR-DGGE can also provide information regarding the composition of bacterial populations in soils. Cenciani et al. 2009 examined how the clearing of Amazonian rainforest for pasture and the seasonality affected the diversity of Bacteria domain. The aim of this study was to assess the extension that land use changes in Amazonia had on the structure of Bacteria domain.

According to Cenciani et al. (2009) field works were developed at Nova Vida Ranch (62o49’27»W; 10o10’5»S), in the central region of Rondonia state (Fig. 4). The predominant soil is classified as Argissolos in the Brazilian classification system (Empresa Brasileira de Pesquisa Agropecuaria — EMBRAPA, 2006) and as Ultisols (Kandiuldults) in the US soil taxonomy. It is a representative soil of Amazonian basin covering almost 22% of the Brazilian Amazonian basin. The Nova Vida Ranch covers an area of approximately 22.000 ha, consisting of a mixture of native forest and pastures of different ages. Pastures were established with no mechanical machinery nor chemical fertilization and soil acidity correction. Wood weeds were controlled by cutting the aerial part, removing the residues and burning them to reduce volume and incorporate the ashes into the soil (Feigl et al.,

2006) .

A sequence was chosen at Nova Vida: (1) a 3-ha plot of native forest, (2) a well-established pasture of 20 years (Brachiaria brizantha and Pannicum maximum), and (3) a fallow site (Fig. 1). The botanical composition of fallow includes 15-18% of woody species (Tabebuia spp., Erisma uncinatum and Vismia guianensis), 12% of Babagu palm (Orbignya phalerata Mart), herbaceous weeds 4-11%, and 63.5% of a mixture of Brachiaria brizantha and Pannicum maximum (Feigl et al., 2006). Soil samples were taken at surface layer (0-10 cm) in the rainy season and 6 months later, in the dry season.

Total soil DNA extraction and PCR products were generated according to conditions described by 0vreas et al., 1997. PCR products (300 ng) were resolved using DGGE to provide the molecular profiles of bacterial communities. The structure of similarity for Bacteria was generated from binary data. Dendrograms representing hierarchical linkage levels were constructed based on the Euclidean distance coefficient using Systat 8.0 software.

As expected PCR with specific primer sets including the forward primer coupled with a GC clamp resulted in a single 180-bp fragment. PCR products were separated by DGGE to assess the qualitative bacterial composition. Some groups of bands, exemplified as I to VI, were chosen to better compare similar and/or different band profiles (Figs. 5 and 6).

In the Figure 5a (wet season), some bands were found in all soil replicates (I, II). It means that they were present in the DNA extracted from each sample and it indicated the presence of the same bacterial community in the three sites. Pasture was characterized by the presence of band patterns concentrated in PA3 and PA4 (III), and IV is a band profile found in the fallow and in the PA5 replicate of pasture. Forest contained replicates with high variability of band patterns; therefore FO2 contained more bands than the others (V).

DGGE profiling in the dry season (Figure 6a) revealed more visible differences in the bacterial structure among the sites than in the wet season. Band patterns I and II were presented in almost all samples, except FA1 to FA4. Group III represented bands common to pasture and fallow, while IV and V were bands specific to replicates FO1 to FO4 and FO1, respectively. VI was a particular banding pattern from pasture. It was not found a band profile presented specifically in the fallow site. Independently of sampling period, similar bands were found among the sites; as well each site had its own particular bands along DGGE profile.

In the cluster analysis of PCR-DGGE products, the three sites clustered at 65% level of similarity for both wet and dry seasons. Data presented in Figure 5B shows that, in the wet season, bacterial communities were separated in three clusters, except PA2 replicate that tended to group together forest cluster; whereas PA5 replicate fell into the fallow cluster. In the Figure 6B, the effect of low water content plus history of soil use contributed to separate completely the bacterial populations from each site during the dry season. The variation in the composition of microbial community DNA between replicate soil samples was found to be as great as the variation between treatments in field based studies. The reasons for such variability are not clear, however it is likely that are attributable to the effect of soil chemical attributes plus the contents and composition of organic matter (Clayton et al., 2005; Ritz et al., 2004).

According to the authors the DGGE profiling revealed lower number of bands per area in the dry season, but differences in the genetic diversity of bacterial communities along the sequence forest to pasture was better defined than for wet season. The few research works using molecular approaches to investigate the diversity of microorganisms in Amazonia have shown that, in fact, a tiny fraction of their microbial diversity is known (Cenciani et al., 2009).

Lidar systems make use of the time of flight principle or phase-based differences to measure the distances of objects. For this, the time interval is detected between sent and return laser pulses which are backscattered from an abject. Lidar point cloud of returns generate a 3D digital representation of the vegetation structure in which each point is characterized by XYZ coordinates (Maas et al., 2008; Cote et al, 2011).

Lidar System consists of a laser ranging unit, a scanning instrument like an oscillating mirror or rotating prism and a direct geo-referencing navigation unit (using global positioning system — GPS and inertial navigation system — INS). The choice of the platform depends mainly on the application. Space-borne systems map the globe for researches and experimental purposes. Airborne systems are collecting the data for national or regional investigations. Terrestrial platforms are frequently used to produce 3D models of man-made structures or natural resources like trees. Thus, the basic principle and technical specification for a sensor installed on a platform such as Earth orbiting satellite, airplane, helicopter, tripod, or vehicles change due to the variety of the applications (Shan and Toth, 2009). Some engineering and environmental studies require information about the shallow water basin. The Bathymetric Lidar systems are capable to provide this information in the coastal zones or rivers deep to 50 meters in clear water (Bathymetric system is irrelevant to our discussions so, we will have no further dealings with it in this chapter).

Generally, commercial systems are designed to receive data from small-footprint (0.20­3.00m diameter, depending on flying height and beam divergence) with higher repetition frequency (Mallet and Bretar, 2009). These systems acquire a high point density and an accurate height determination. However, small-footprint systems often miss tree tops which cause under estimation in tree height. Therefore, it is hard to define whether the ground has been detected under dense vegetation or not. Consequently, ground and tree heights cannot be well estimated (Dubayah and Blair, 2000). Large-footprint systems (10-70 m diameter) increase the chance to both hit the ground and the tree top and eliminate the biases of small — footprint systems. Thus, the return waveform gives a record of vertical distribution of the captured surface within a wider area which provides important information for biomass estimation. First experimental full waveform topographic systems were large-footprint systems and mostly carried by satellite platforms. With a higher flying height, pulses must be fired at a lower frequency and with a higher energy to penetrate into the forest canopy as much as possible (Mallet and Bretar, 2009).

This method was applied to determine the presence of ammonia oxidizing bacteria (AOB) and to quantify them in the reactors. The steps involved fixation of the samples, permeabilization and hybridisation with probes, and finally detection with confocal laser scanning microscope (CLSM).

2.2.1Paraformaldehyde Fixation and Permeabilization

Generally, the samples used for this technique have undergone short term fixation where absolute ethanol was added in a volume ratio of 1 sample: 1 ethanol in sterile universal bottles and stored at -20o C.

A 1 ml volume of the stored sample was transferred to a 1.5 ml eppendorf tube and centrifuged at 13000 x g for 3 minutes. The supernatant was removed and the sample was washed with phosphate buffered saline (PBS) by adding 1 ml of the solution, mixing using vortex and centrifuging at 13000 x g for 3 minutes before removing the supernatant again. The resulting pellet was resuspended in 0.25 ml PBS and 0.75 ml PFA fixative and vortexed. A 4 % paraformaldehyde fixative solution was prepared fresh for every time of use, the procedure of which tabulated in Appendix 4.1. The suspension was incubated for at least 3 hours, or overnight, at 4oC.

After fixation, the cells were washed by centrifuging at 13000 x g for 3 minutes, removing the supernatant, adding 1 ml PBS and mixing. The samples were centrifuged again at 13000 x g for 3 minutes. The supernatant was removed and the sample was kept with PBS and absolute ethanol at 1:1 (v/v) and mixed. It was then stored at -20oC.

Extracting the forest characteristics from Lidar data for biomass estimation is classified into two categories, height distribution with its statistical analysis, and single tree detection containing its location and characteristics.

2.1.2 Methods and models used in biomass estimation

A conventional model of biomass estimation is introduced by Thomas et al. (2006), which is given as: b X dbh2 X height, where b is the coefficient. This equation was developed for the whole tree as well as the components of the stem wood, stem bark, branches, and foliage. As soon as the metrics (dbh and height) are measured for each plot, the equation can be established to estimate biomass and biomass components. The coefficient b is a variable which is related to the species of trees. Measurements for the deriving forest biomass are destructive sampling which is the input of regression modeling. For this, sample trees are measured and then cut and weighted (Popescu et al, 2004). The mass of components of each tree is regressed to one or more dimensions of the standing tree. As discussed in the introduction section, biomass has also been estimated by means of previously developed models using Lidar which relies on tree characteristics extraction like height, dbh, and crown size. Crown size is not used directly in the estimation procedure but it is useful for extracting the tree species. All developed models and their parameters for biomass

estimation must be calibrated on the basis of tree characteristics. For this, four models were studied by Salmaca (2007). These are power function, Log transformed model, fractional power transformation, and explanatory function. The Power function is developed for North of USA, the Log transformed model is described by a linear function, the fractional power transformation is referred to linearized curvilinear model, and the explanatory function is constituted by a polynomial model. Under these models the Log transformed model is recommended which delivers the results with the unit of kilogram per every tree (Fallah Vazirabad, 2007). Consequently, tree characteristics extraction by Lidar data plays an important role in the biomass estimation model.

Bortlot et al. (2005) proposed to locate trees by image processing module assuming that the tree crown is circular, trees are taller than surroundings, and tree tops tend to be convex. They used the data of small footprint Lidar system. The algorithm starts by generating a CHM and works by shadow search method to find the crown boundaries which is related to tree tops. After defining a threshold and fitting the circles to the smoothed and generalized CHM, the circles should present the top of actual trees. The algorithm eliminates the small trees which are close to tall ones, because it searches for related high point neighboring. They conclude that tree heights are associated with canopy volume and therefore should be related to the biomass. They used the tree heights detected from image processing as variables for a stepwise multiple linear regression to find an equation for biomass prediction. They evaluated the results with highly significant (>95%) carrying out an efficient field measurement to calibrate the number of trees which are detected by an algorithm based on their height. Small trees are not included in this evaluation.

Lefsky et al. (1999) developed equations relating height indices to canopy area and biomass. They indicated that there are some differences in the predictive ability of the height indices; these differences are small, and statistically nonsignificant. However, the canopy structure information which is summarized in the median, mean, and quadratic mean canopy height indices, improved the stand canopy estimation related to the maximum canopy height. They defined the relation between tree height, H and dbh as: dbh = (H/19.1)21. They concluded that the result of the model using stepwise multiple regressions causes a higher variance value than those from the simple linear regression referring to the CHM. But, the predictions of the stand attributes were less applicable to the CHM than the height indices. Stepwise multiple regressions of basal area and biomass using the canopy height profile vector as independent variables increase the importance of the field measured regression equations.

Fallah Vazirabad and Karslioglu (2009) investigated the biomass estimation with the method of single tree detection. Lidar data segmentation filtering method is applied to point clouds to distinguish canopy points from the terrain points which are used for the generation of a DTM. The CHM is obtained by subtracting the DSM (from original data) from DTM. A single tree detection method is employed to locate trees and detect the height of each tree top. Diameter at breast height (at 1.37 m from ground) is extracted from the close relation with the tree height which is defined by field measurements for the evaluation. A Log transformed model is applied for biomass estimation on the basis of the dbh variable.

types. Nelson et al (1988) used discrete Lidar data to collect forest canopy height data. Two logarithmic equations were tested to find the best model. They used a height distribution method and analyzed a statistical approach. Falkowski et al (2006) described and evaluated spatial wavelet analysis techniques to estimate the location, height, and crown diameter of individual trees from Lidar data. Two dimensional hat wavelets were convolved with a CHM to identify local maxima within the wavelet transformation image. Maltamo et al.

(2004) examined the CHM local maxima search method for high dense forest regions to detect individual trees. Because of the dense understory tree layer in most area, about 40% of all trees were detected. However, the detected tree heights were obtained with an accuracy of ±50 cm.

Anderson et al. (2006) developed a methodology for acquiring accurate individual tree height field measurements within 2 cm accuracy using a total station instrument. They utilized these measurements to establish the expected accuracy of tree height derived from small and large footprint Lidar data. It turned out that the accuracy of small footprint Lidar data changes according to the tree species. The comparison has shown that tree heights which are retrieved from small footprint Lidar are more accurate than the result of large footprint data. Hopkinson (2007) investigated the influence of flight altitude, beam divergence, and pulse repetition frequency on laser pulse return intensities and vertical frequency distributions within a vegetated environment. The investigation showed that the reduction in the pulse power concentration by widening the beam, increasing the flight altitude, or increasing the pulse repetition frequency results in (i) slightly reduced penetration into short canopy foliage and (ii) increased penetration into tall canopy foliage, while reducing the maximum canopy return heights.

Yu et al. (2004) demonstrated the applicability of small footprint, multi return Lidar data for forest change detection like forest growth or harvested trees. An object oriented algorithm was used for tree detections referred to the tree to tree matching method and statistical analysis. The small trees could not be detected by the algorithm. The forest growth is estimated about 5 cm in canopy crown and 10-15 cm in tree height.

Fallah Vazirabad and Karslioglu (2010) used a technique based on the searching for the local maximum canopy height to detect individual tree with variable window size and shape. the method detects tree location, number of trees, and the height of each single tree. The variable window size and shape solved the problems of small tree detection and not detectable CHM margin regions. The importance of field measurements and reference information (like orthophoto) are emphasized for evaluation. Popescu and Zhao (2008) developed a method for assessing crown base height for individual tree using Lidar data in forest to detect single tree crown. They also investigated the Fourier and wavelet filtering, polynomial fit, and percentile analysis for characterizing the vertical structure of individual tree crowns. Fourier filtering used for smoothing the vertical crown profile. The investigation resulted in the detection of 80% of tree crown correctly.

Moorthy et al. (2011) utilized terrestrial laser scanning to investigate the individual tree crown. From the observed 3D laser pulse returns, quantitative retrievals of tree crown structure and foliage were obtained. Robust methodologies were developed to characterize diagnostic architectural parameters, such as tree height (Й2 = 0.97, rmse = 0.21m), crown width (Й2 = 0.97, rmse = 0.13 m), crown height ((Й2 = 0.86, rmse = 0.14 m), crown volume (Й2 = 0.99, rmse = 2.6 m3). It seems that the first pulse return from the upside view of an individual tree in terrestrial laser scanning brought about the low performance in crown height while the other characteristics are detected well.

Riano et al. (2004) estimated leaf area index (LAI) and crown size using Lidar data. They concluded that LAI was better estimated using larger search windows while the crown size was better estimated using small window size. They generated the vegetation height above the ground for each laser pulse using interpolated values extracted from DTM. DTM was produced using the bisection principle. They also applied spline function interpolation in order to obtain the height above the ground. But in this work it is not obvious whether first or last return has been used to extract the canopy height, effecting the result significantly.

Altogether, we present an assay that builds upon two previous assays, the PM technology and the ATP assay. Both assays have been used in much different contexts previously. PM plates have been commonly used to discover various bacterial characteristics based on phenotypic changes (Bochner et al., 2008). Studies involving PM plates include the evaluation of the alkaline stress response induced changes in the metabolism of Desulfovibrio vulgaris (Stolyar et al., 2007). PMs have also been used for the identification of bacterial species (Al-Khaldi & Mossoba, 2004). The use of PM technology in biofilm research is

limited to a study of the ability of E. coli to form biofilm upon ribosomal stress (Boehm et al., 2009). That study used the crystal violet assay as a detection tool for the amount of biofilm. Here we report for the first time a combination of the established ATP assay along with the PM technology to assess nutritional dependence of E. coli during biofilm formation. Since the statistics approach alone (f-test) yielded no more than a list of data that were difficult to interpret, we decided for a combined statistics/metabolism approach to analyze the complex data. The combination of the two experimental parts of the assay together with the two analysis parts enables the user to rapidly screen hundreds and thousands of single nutrients for their ability to inhibit growth and biofilm formation in one experimental setup. Integrating different mutants into the study will yield valuable insight into the regulatory mechanisms that are involved in the signaling of these nutrients. The described technique is not only cost-efficient and easy to perform, but also high-throughput in nature. It is ideally suited to provide valuable insight into the nutritional requirements that determine biofilm biomass, as well as the respective signaling pathways.

Jalal Amini1 and Josaphat Tetuko Sri Sumantyo2

University of Tehran, Tehran, 2Chiba University, Chiba, 1Iran 2Japan

1. Introduction

Biomass, in general, includes the above-ground and below-ground living mass, such as trees, shrubs, vines, roots, and the dead mass of fine and coarse litter associated with the soil. Due to the difficulty in collecting field data of below-ground biomass, most previous researches on biomass estimation have been focused on the above-ground biomass (AGB). Different approaches have been applied for above ground biomass (AGB) estimation, where traditional techniques based on field measurement are the most accurate ways for collecting biomass data. A sufficient number of field measurements are a prerequisite for developing AGB estimation models and for evaluating its results. However, these approaches are often time consuming, labour intensive, and difficult to implement, especially in remote areas; also, they cannot provide the spatial distribution of biomass in large areas.

The advantages of remotely sensed data, such as in repetitively of data collection, a synoptic view, a digital format that allows fast processing of large quantities of data, and the high correlations between spectral bands and vegetation parameters, make it the primary source for large area AGB estimation, especially in areas of difficult access. Therefore, remote sensing-based AGB estimation has increasingly attracted scientific interest (Nelson et al., 1988; Sader et al., 1989; Franklin & Hiernaux, 1991; Steininger, 2000; Foody et al., 2003; Zheng et al., 2004; Lu, 2005). There are also other papers including (Dobson et al., 1992; Rignot et al., 1995; Rignot et al., 1994; Quinones & Hoekman, 2004) with SAR-based methods in above ground biomass estimation.

One strategy that can be used for AGB estimation is to combine synthetic aperture radar (SAR) image texture with optical images based on the classification analysis. Limitation on the used only optical data is the insensitivity of reflectance to the change in biomass and different stands. The use of the SAR data has the potential to overcome this limitation. But presence of the speckle in SAR data is also a barrier to the exploitation of image texture. Reducing the speckle would improve the discrimination among different land use types, and would make the textual classifiers more efficient in radar images. Ideally, the filters will reduce speckle without loss of information.

Many adaptive filters that preserve the radiometric and texture information have been developed for speckle reduction. Adaptive filters based upon the spatial domain are more widely used than frequency domain filters. The most frequently used adaptive filters

include Lee, Frost, Lee-Sigma and Gamma-Map. The Lee filter is based on the multiplicative speckle model, and it can use local statistics to effectively preserve edges and features (Lee, 1980). The Frost filter is also based on the multiplicative speckle model and the local statistics, and it has similar performance to the Lee filter (Frost, 1982). The Lee-Sigma filter is a conceptually simple but effective alternative to the Lee filter, and Lee-Sigma is based on the sigma probability of the Gaussian distribution of image noise (Lee, 1980). Lopes (Lopes et al., 1990) developed the Gamma-Map filter, which is adapted from the Maximum a Posterior (MAP) filter (Kuan, 1987). Lee, Frost and Lee-Sigma filters assume a Gaussian distribution for the speckle noise, whereas Gamma-Map filter assumes a Gamma distribution of speckle (Lopes et al., 1990a; Lopes et al, 1990b). Modified versions of Gamma — Map have also been proposed (Nezry et al., 1991; Baraldi & Parmiggiani, 1995). Nezry (Nezry et al., 1991) combined the ratio edge detector and the Gamma-Map filter into the refined Gamma-Map algorithm. Baraldi and Parmiggiani (1995) proposed a refined Gamma — Map filter with improved geometrical adaptively. Walessa and Datcu combined the edge detection and region growing to segment the SAR image and then applied speckle filtering within each segment under stationary conditions. Dong et al. (2001) proposed an algorithm for synthetic aperture radar speckle reduction and edge sharpening. The proposed algorithm was functions of an adaptive-mean filter. Achim et al. (2006) proposed a novel adaptive de-speckling filter using the introduced heavy-tailed Rayleigh density function and derived a maximum a posterior (MAP) estimator for the radar cross section (RCS). The authors (Sumantyo & Amini, 2008) proposed a filter based on the least square method for speckle reduction in SAR images.

In this chapter, we develop a method for the forest biomass estimation based on (Amini & Sumantyo, 2009). Both SAR and optical images are used in a multilayer perceptron neural network (MLPNN) that relates them to the forest measurements on the ground. We use a speckle noise model that proposed by the authors in 2008 (Sumantyo & Amini, 2008) for reducing the speckle noise in the SAR image. Reducing the speckle would improve the discrimination among different land use types, and would make the textual classifiers more efficient in SAR images. We investigate both quantitative and qualitative criteria in speckle reduction and texture preservation to evaluate the performance of the proposed filter on the forest biomass estimation.

In summary, the objectives of this chapter are:

1. The efficiency of the de-speckling filter on forest biomass estimation and,

2. Improved the accuracy of forest biomass estimation when using both SAR images texture and optical images in a non-linear classifier method (MLPNN).

In the rest of the chapter, we will have a survey on de-speckling filters and then we will describe a method for the forest biomass estimation and we finally give the experimental results for the study area.