Fig. 3 illustrates the percentage values of AOB concentrations with respect to VSS concentrations in biofilm samples from the full-bed reactor.

Fig. 3. Percentage values of AOB in the biofilm samples of the full-bed reactor

The highest percentage of AOB was found in a sample from the middle of the full-bed reactor (0.0829%), followed by the top part (0.0295%), whilst very little was found in the bottom part (0.0216%). A low percentage of AOB was obtained at the bottom despite the fact that the substrate and oxygen sources were supplied from here. This anomaly could best be explained by the fact that competition between heterotrophic and nitrifying bacteria for substrates (oxygen and ammonia) and space in the biofilms resulted in the fast-growing heterotrophic bacteria dominating the bottom part of the reactor. Plate 8 of biofilm sample from the bottom of the full-bed reactor show that AOB clusters are not dense as in Plates 1- 2 of the top and the middle positions.

The trend of AOB growth in the biofilm samples of the full-bed reactor was followed through for the partial-bed reactor (Fig. 4):

Fig. 4. Percentage values of AOB in the biofilm samples of the partial-bed reactor

The same argument of competition for substrates and space between heterotrophic bacteria and nitrifiers explained the lower percentage of AOB obtained in the middle (0.1019%) compared to the top part of the partial-bed reactor (0.2151%).

To validate the hypothesis made on AOB distribution in both the full and partial-bed reactors, a previous work by Wijeyekoon et al (2000) was used to investigate the effect of organic loading rates on nitrification activity. Table 5 summarizes the reactor conditions of their study.

The three reactors, packed with the same weights of anthracite, were equipped with sampling ports at depths of 6 cm (port 1), 18.5 cm (port 2) and 37.5 cm (port 3) from the top end of the filters. The specific rate of NH4+-N oxidation in the reactors was determined by the biomass extracted from those ports. It was discovered that the highest rates in filter A and B were obtained at the effluent ends of the reactors, but in filter C, the rates were comparably high from all ports. Also, among the three reactors, filter C produced the highest rates, with an average of 48.1 and 56.4 g N/ (mg protein. hr) for ports 1 and 2 respectively. The conclusion derived from the study is that at high organic carbon loadings nitrifiers are non-uniformly distributed along the length of a filter, with excessive growth of heterotrophs near the feed end and nitrifiers at the effluent end under the influence of
comparatively higher organic loading. Meanwhile, at low organic loadings, the heterotrophs and autotrophs can coexist. Filter C had the lowest organic carbon loading and consequently had the lowest biomass density. Therefore, the nitrifiers in filter C may have experienced less competitive pressure from the faster-growing heterotrophic organisms for oxygen and space. The displacement of the nitrifying population by the heterotrophs is caused by the varying ratio of carbon and nitrogen entering the reactor.

The carbon loading used in this part of study, 5.71 ±0.16 kg COD/m3.d, was much higher than the loadings used by Wijeyekoon (Table 9.4), and therefore nitrifiers were not only displaced further away from the feed source, but also buried deeper into the biofilm (Ohashi et al 1995). Fdz-Polanco et al (2000) also observed that as the amount of organic carbon entering the filter increases, the nitrification activity is displaced to the upper part of the filter in an upflow process. Quyang et al (2000) also argued that the differences in biological activity at different filter heights were due to their varying loadings.

Rowan et al (personal communication) also investigated the percent value of AOB in a full — scale BAF plant treating municipal wastewater and obtained a value of 0.65%. This value is almost three times higher than the highest percentage obtained in this study (0.2151% from Figure 9.4). The difference in values could be attributed to a number of factors including carbon loading, nitrogen loading, pH, DO, media type and size, direction of flow, backwashing regime and thus mean SRT and biofilm attachment characteristics.

4. Conclusion

The extent of comparable nitrogen removal in the two reactor configurations needs further microbiological evidence, specifically that of the existence of AOB. The formation of a dense biofilm as a result of higher turbulence would account for the higher number of AOB cells enumerated in the biofilm samples from the partial-bed reactor (4.259 x 105 ±1.881 x 105 no of AOB cells/ml sample) as compared to those from the full-bed reactor (1.523 x 105 ±7.979 x 104 no of AOB cells/ml sample). Although biomass was washed out in the treated effluent and during backwash operation, the SRT at the high organic loading of 5.71±0.16 kg COD/m3.d was still maintained at 4.2 days for the partial-bed reactor and 7.6 days for the full-bed reactor. These SRTs were still longer than the limit noted by Sastry et al (1999), who claimed that a mean cell residence time > 3 days is desirable for nitrifiers to reach a stable population for effective nitrification, and Gergeker (2002) who recorded a loss of nitrification below 2.5-2.7 days at an OLR of 5 kg COD/m3.d and a temperature of 25oC.

5. Acknowledgement

This chapter of the book could not have been written without the help of my PhD supervisor Prof Tom Donnelly who not only served as my supervisor but also encouraged and challenged me throughout my academic program. He and the other faculty members, Dr. Davenport and Dr Joana of University of Newcastle upon Tyne guided me through the process, never accepting less than my best efforts. I thank them all. And last but not least the Government of Malaysia for the sponsorship of my study.

1.1 Field equipment

High spectral resolution devices have recently improved in sensitivity, decreased in cost, and increased in availability. The equipment for field measurements consists of a portable spectroradiometer, which measures the irradiance at different wavelengths with a band width of about 1-2 nm through the VIS and NIR regions of the spectrum. This unit is connected to a computer, which stores the individual scans, a fore-optics sensor for capturing the radiation, and some complements such as reference panels and supports (Fig. 5). The sensor appraises the radiation reflected by the crop canopy, delimiting the field of view to a given angle, generally between 10° and 25°, which limits the area of the crop scanned to 20-100 cm2. The angle of incident light and the angle of observation of the sensor determine the proportion of elements in the observation field. The sensor is usually mounted on a fixed or hand-held tripod, which allows all measurements to be taken at the same angle and distance from the surface of the crop —usually from 0.5 m to around 1.0 m above the canopy facing the center of the plot. A fiber optic cable transmits the captured radiation to the spectrum analyzer. To convert captured spectra to reflectance units the spectra reflected by the crop canopy must be calibrated against light reflected from a commercially available white reference panel of BaSO4 (Jackson et al., 1992). Each measurement takes around 1-2 s and between 5 and 10 scans are usually averaged per measurement.

The classical spectroradiometers measure about 250-500 bands, evenly spaced from a wavelength of 350 to 1110 nm, so a wide range of spectral reflectance indices can be calculated or the complete VIS/NIR reflectance spectra can be used. Cheaper units, such as Green SeekerTM, which give only the basic spectroradiometric indices of green biomass, such as the normalized difference vegetation index (NDVI) and the simple ratio (SR, see section 4), have been designed more recently for diagnosing nitrogen status and biomass assessment (Li et al., 2010b). The methodology allows sampling at a rate of up to 1000 samples per day.

Vermicompost was extracted with ethyl acetate (vermicompost: ethyl acetate = 1: 5, w/ v) and the extract the centrifuged at 7000 rpm for 15 minutes. The supernatant was used for radish bioassay. Five radish seeds were taken on 2mm x 2mm sterile Whatman filter paper and 750 ^l of that extract applied on radish seeds under aseptic condition and incubated at 25+1 0C for 5 days. After 5 days incubation, root and shoot length of extract applied seedlings were compared with that of control treatment.

After finding the presence of plant growth promoting compound, the ethyl acetate extract was fractionated by column chromatography using different proportions of hexane, dichloromethane and methanol to obtain 24 fractions, each of 50 ml. The fractions were then concentrated to 2-3 ml by rotary evaporator at a temperature below 40 0C. All the fractions were then tested by radish bioassay. The active three fractions (please follow the result below) were then analysed by HPLC and methanol water mixture (60: 40, v/ v) was used as mobile phase for this analysis.

Vermicompost was then extracted with sterile water (vermicompost: water = 1: 100, w/ v) under aseptic condition. The extract was then serially diluted 103 fold and incubated in broth medium with different amount of tryptophan at 300C for 7 days. After incubation, cell pellets were removed by centrifugation at 6000 rpm for 10 minutes. The supernatant was treated with Salkosky reagent and pink colour intensity was measured at 420 nm.

2. Results

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.

The harvest of Macrocystis off the Baja California Peninsula shows a seasonal pattern with minimum values in winter, and the maximum during spring and summer, then decreasing in autumn (Fig. 10). The spring and summer harvests were greater (P < 0.05) than winter and autumn, and the harvest of winter was the lowest (P < 0.05). In general the harvest of all beds had the same pattern. In the beds at Punta Mezquite (03), Salsipuedes (04), and Bahia de la Soledad (08), which were more frequently exploited, this pattern is evident, and less so the beds less harvested, such as Playas de Tijuana (02), Isla Todos Santos (05), and San Isidro (12). A similar behavior was found when the harvest obtained per hour of ship harvest (CPUE) for 1993 — 1999 was analyzed. The highest harvest/hour was during May to August (75 t/hour). These values were significantly different (P < 0.05) to both periods: February to April (62 t/hour) and September to December (54 t/hour), which were lower (Fig. 11).

3.2 Relation harvest-effort

During 1956 to 1999, the harvest of Macrocystis increased as a function of the level of effort (number of trips) (r = 0.98, Fig. 12) and similarly when the effort was measured as number of hours of ship harvest (r = 0.85) for 1993 — to 1999 (Fig. 13).

4. Discussion

From 1958 to 2004, the average harvest of Macrocystis was 26,000 t, which was about 50% of the standing crop estimated by Casas-Valdez et al. (1985) and Hernandez et al. (1989a, 1989b, 1991), who evaluated the biomass and standing crop of Macrocystis using aerial photography and field work along the area of the distribution of this kelp. From Islas Coronado to Bahia del Rosario they estimated a standing crop of 40,000 t in summer 1985 and 63,000 t in summer 1986. This species of seaweed has a high growth rate (13 — 21 cm/day) (Hernandez, 1996) and its regeneration rate is high.

The lowest harvest and effort recorded in category I can be related to: a) the harvest being suspended in beds 11 (1978), 06 (1985), 07 (1984), 02 (1991), and 01 (1993), b) the long distance from the beds to the base port, bed 12 (12 h 20 min), 13 (13 h), 14 (16,5 h), and 15 (20 h). The highest harvest and effort recorded in category III can be related to a) a high productivity of the bed and, b) the short distance from the bed to the base port (5 h). In relation to the previous information, Roberto Marcos (com. pers.) noted that the quantity of effort used at each bed depended on the productivity of the bed and its cost of operation, which are related principally to the distance that the ship most run from the base port to the bed. Guzman et al. (1971) and Corona (1985) mention that the more productive beds for 1956 — 1968 and 1974 — 1985 were the beds 03, 04, 08, 09, and 10 that are in categories II and III of this study. The largest harvest of Macrocystis was in spring and summer and the lowest in winter.

Along the northwest coast of the Baja California Peninsula the greatest upwellings are during spring and summer (Casas-Valdez, 2001) and have high nutrient concentrations and lower temperatures (Lynn & Sympson, 1987; Pares & O’Brien, 1989) that favor the development of Macrocystis fronds (Tegner & Dayton, 1987; Tegner et al., 1996; Lada et al., 1999). Growth studies in situ showed that the lower temperatures of spring enhance the growth rate of Macrocystis (Gonzalez et al., 1991) and also the increase of nutrients (Zimmerman & Kremer,

1986) . Casas-Valdez et al. (1985) and Hernandez-Carmona et al. (1989a, 1989b, 1991) evaluated the biomass and standing crop of Macrocystis along their natural distribution and found the largest surface and biomass of the beds in spring (45,000 t) and summer (63,000 t). They noted that these values were three times greater than those in winter (14,000).

Playa Tijuana 1958 1964 1970 1976 1982 1988 1994 2000

1958 1964 1970 1976 1982 1988 1994 2000

Fig. 3. Data series of harvest and effort of the Macrocystis pyrifera beds: Islas Coronados, Playas de Tijuana, Punta Mezquite, Salsipuedes, Isla Todos Santos and San Miguel y El Sauzal. Harvest, effort.

1958 1964 1970 1976 1982 1988 1994 2000 Year

Fig. 4. Data series of harvest and effort of the Macrocystis pyrifera beds: Punta Banda, Bahia de la Soledad, Santo Tomas, Punta China, Punta San Jose and Punta San Isidro.

Harvest, effort.

Isla San Martin

Punta San Telmo

H 1 1 1 1 1 1 1 1 1—I—г 1958 1964 1970 1976 1982 1988

1994 2000

Isla Coronado 1958 1964 1970 1976 1982 19! Year 1994 2000
Isla Todos Santos Year	San Miguel Sauzal Year

Fig. 7. Data series of harvest per unit effort (CPUE) of the Macrocystis pyrifera beds: Islas Coronados, Playas de Tijuana, Punta Mezquite, Salsipuedes, Isla Todos Santos and San Miguel y El Sauzal.

Isla San Martin	Punta San Telmo
Fig. 9. Data series of harvest and effort of the Macrocystis pyrifera beds: Punta San Telmo, Isla San Martin and Bahia del Rosario.

Fig. 10. Seasonal variation of the harvest of Macrocystis pyrifera in Baja California Peninsula. ± 2 SD.

The CPUE was used as indicator of abundance for Gelidium robustum a red seaweed that is harvested along in the west coast of the Baja California Peninsula from 1956 to the present. The unit of effort selected for this fishery was the fishing equipment (a boat with three fishermen) and the CPUE was expressed as harvest/boat (Casas-Valdez et al., 2001). They used the CPUE to determine the relationship of the abundance of Gelidium with both temperature and upwelling. As an indicator of the abundance of Macrocystis, Tegner et al.

(1996) compared data on the maximum canopy of the kelp forest and size of the annual harvest of Macrocystis for California, and they chose harvest size as the most useful data to relate to environmental variables. They pointed out that harvest size was a reflection of
changes in consumer demand, harvest productivity, and natural disturbances. They also noted that this variable has the advantage of integrating growth over a long period and has less subjectivity in its measurement.

In our study, we considered that the CPUE shows the changes in the abundance of Macrocystis better than only the harvest, because the size of the harvest varies according to the amount of effort used and not only as a function of the abundance. Furthermore, the use of the CPUE is cheaper than the use of aerial photography and field work to determine the variations in the abundance of this resource. Casas-Valdez et al. (2003) mentioned that the harvest/trip is a reasonable indicator of the Macrocystis abundance, because about 60% of the alga biomass is present in the surface canopy (North, 1968), and almost 95% of its production takes place in the first meter of the top of the water column, and the kelp is harvested at a maximum depth of 1.2 m. Furthermore the ship operations were the same at all beds and did not change over the study period. We considered that the harvest/hour is a better indicator.

The surplus production models of Schaefer and Fox were used to assess the fishery condition of Gelidium off the Baja California Peninsula from 1985 to 1997. The results have shown that the resource is not overexploited (Casas-Valdez et al., 2005). In this study we tried to use these surplus models for the data of Macrocystis, but the fit was not satisfactory. This occurred because an increased effort produced increased harvest. To fit these models, it is necessary to count, along with the catch, effort, and CPUE data, an ample range of fishing effort levels, preferably including those that correspond to the level of overexplotation in the curve (IATTC, 1999). The linear relation (correlation) found between the harvest and the effort used for the Macrocystis fishery means that the fishery was in the eumetric growth segment of the curve of the Schaefer model and therefore it is possible to conclude that there have not been negative effects of the harvest on the resource. It is considered that the effort has not been increased, due to the fact that the demand for Macrocystis has not been increased either. In fact, the harvest drastically decreased in 2005, when the principal company that was buying this kelp as raw material for the alginate production ceased buying it (Roberto Marcos com. pers.).

5. Conclusions

The Macrocystis fishery along the Mexican Pacific coast did not show signals of over exploitation due to increases in the effort corresponding to increases in the harvest, and the CPUE has been maintained almost constant since the begging of the harvesting of this resource until now (2004), with the exception of the years when "El Nino" event was present.

Along the northwest coast of the Baja California Peninsula, the highest harvest of Macrocystis was found in spring and summer, when the greatest upwellings ocurre in agreement with high nutrient concentrations and lower temperatures.

The harvest per unit of effort (CPUE) was more stable in the beds where more effort was used, as in the beds at Punta Mezquite, Salsipuedes, Bahia de La Soledad, Santo Tomas and Punta China, whereas in the beds where less effort was used the CPUE was more variable.

6. Acknowledgment

Thanks to Productos del Pacifico, S. A. de C. V. for providing the data of harvest of Macrocystis. We really appreciate the adviser of Roberto Marcos Ramirez. Thanks to Dr. Ellis Glazier for editing this English-language text. Margarita Casas Valdez and Daniel Lluch Belda are fellows of COFAA-IPN and EDI-IPN.

The capability of detecting different returns in the closely placed terrain surfaces depends on instrument parameters such as the laser pulse width (the shorter the better), detector sensitivity, response time, the system signal to noise performance, and others. In case of discrete returns more detectors are needed. With this technology the number of pulses between first pulse and last pulse can be recorded as many as the number of detectors. Thus, there are systems with second and third pulse beside first and last pulse record. In contrast to small footprint systems, large footprint systems (10-100 m) open up the possibility of recording the entire return pulse. Discrete return airborne laser systems (ALS) have the benefit of providing data over a large area, but are restricted by their laser pulse return density as points/m2 ratio. Multiple return recording capabilities of system produce point cloud density between 1 and 20 points/m2 optimistically. Often this level of point density is unsatisfactory to produce a comprehensive 3D model, especially in the vertical view (Moorthy et al. 2011).

Preeti Sule, Shelley M. Horne and Birgit M. Prufi

North Dakota State University USA

1. Introduction

Biofilms are defined as sessile communities of bacteria that form on surfaces and are entrapped in a matrix that they themselves produce. Biofilms cause severe problems in many natural (Ferris et al., 1989; Nyholm et al., 2002), clinical (Nicolle, 2005; Rice, 2006), and industrial settings (Brink et al., 1994; McLean et al., 2001; Wood et al., 2006), while being beneficial for waste water treatment and biofuel production (Wang and Chen, 2009). In addition, the bioremediation of crude oil spills involves a biofilm of oil degrading microbes, potentially supplemented by marine flagellates and ciliates (Gertler et al., 2010). Identifying the environmental conditions that prevent or support biofilm formation, as well as understanding the regulatory pathways that signal these conditions, is a pre-requisite to both, the solving of biofilm-associated problems and the use for beneficial purposes. In a previous study by our laboratory (PruP et al., 2010), it was determined that nutrition ranked among the more important environmental factors affecting biofilm-associated biomass in Escherichia coli K-12. The key to this study was a high-throughput experiment, where biofilm biomass was determined in a collection of cell surface organelle and global regulator mutants under a variety of combinations of environmental conditions. The cell surface organelles each represented a distinct phase of biofilm formation (Sauer et al., 2002). Flagella are required for reversible attachment (phase I), curli or type I fimbriae are characteristic of irreversible attachment (phase II), and a polymeric capsule forms the matrix that permits the maturation of the biofilm (phase III). Eventually, flagellated bacteria are released from the biofilm (phase IV). Phases III and IV are particularly problematic for the disease progression. Bacteria that are located deep within the mature biofilm are particularly resistant to antibiotics and dispersed bacteria tend to serve as a reservoir that continuously feed the infection. Please, see Figure 1 for the distinction of biofilm phases.

The global regulators included in our previous study (PruP et al., 2010) are involved in the co-ordinate expression and synthesis of biofilm-associated cell surface organelles. Many of them are components of two-component systems (2CSTS), each consisting of a histidine kinase and a response regulator (for reviews on 2CSTS signaling, please, see Galperin, 2004; Parkinson, 1993; West & Stock, 2001). In response to an environmental stimulus, the sensor kinase uses ATP as a phosphodonor to auto-phosphorylate at a conserved histidine, then

transferring the phosphate to the response regulator at a conserved aspartate residue. In addition, many response regulators can be phosphorylated in a kinase independent manner by the activated acetate intermediate acetyl phosphate (for a review on acetyl phosphate as a signaling molecule, please, see Wolfe, 2005). One 2CSTS that is involved in the formation of biofilms is EnvZ/ OmpR, regulating the synthesis of flagella (Shin and Park, 1995), type I fimbriae (Oshima et al., 2002), and curli (Jubelin et al., 2005). RcsCDB is involved in the formation of biofilms, serving as an activator of colanic acid production (Gottesman et al., 1985). RcsCDB constitutes a rare phosphorelay, consisting of three proteins and four signaling domains (Appleby et al., 1996). Much of the effect of EnvZ/OmpR, and RcsCDB upon biofilm formation involves FlhD/FlhC (Prup et al., 2006), which was initially described as a flagella master regulator (Bartlett et al., 1988) and later recognized as a global regulator of bacterial gene expression (Prup & Matsumura, 1996; Prup et al., 2001, 2003).

Fig. 1. Time course of biofilm formation

An early review article (Prup et al., 2006) summarized the portion of the transcriptional network of regulation that centered around FlhD/FlhC. This partial network contained 16 global regulators, among them many 2CSTSs, such as EnvZ/OmpR, RcsCDB, and CpxR. The regulation of approximately 800 genes was affected by the network. Since many of these encoded components of the biofilm-associated cell surface organelles, it was hypothesized that the network may affect biofilm formation. This hypothesis was confirmed by the high — throughput study that led to the identification of nutrition as one of the more instrumental factors in determining biofilm biomass (Prup et al., 2010). The global regulators that were part of the network led to the mutant collection for the experiment. Among the tested environmental conditions were temperature, nutrition, inoculation density, and incubation time. Temperature and nutrition were more important in determining biofilm biomass than were inoculation density and incubation time. The mutant screen was consistent with the idea that acetate metabolism may act as a nutritional sensor, relaying information about the environment to the development of biofilms. This hypothesis was confirmed by scanning electron microscopy. A new 2CSTS, DcuS/DcuR, was identified as important in determining the amount of biofilm-associated biomass (Prup et al., 2010).

The high-throughput experiment merely determined that nutrient rich bacterial growth media are more supportive of biofilm formation than are nutrient poor media. Specific nutrients that are supportive or inhibitory to biofilm formation were not determined and are
the next logical step. This will be dependent on an assay system that quantifies biofilm biomass in the presence of an array of single nutrients. With this study, we will introduce such a system that quantifies biofilm biomass formed by Escherichia coli mutants in the presence of single nutrients by combining the Phenotype MicroArray™ technology from BioLog (Hayward, CA) with the ATP quantitative biofilm assay that was previously developed by our own lab (Sule et al., 2009), followed up by statistical analysis of the data, and metabolic modeling.

The BioLog Phenotype MicroArray (PM) technology has been developed for the determination of bacterial growth phenotypes (Bochner, 2009; Bochner et al., 2001, 2008). The PM technology consists of 96 well plates with 95 single nutrients dried to the base of each of 95 wells (the additional well constitutes the negative control). When used with the tetrazolium dye that is provided by the manufacturer and indicative of respiration, the PM system is used to determine growth of bacterial strains on single nutrients. Since the total system consists of 20 of such plates, the user is enabled to screen growth under close to 2,000 conditions. The plates are designated PM1 through PM20, with PM1 and PM2 containing carbon sources, PM3 containing nitrogen sources, and PM4 containing sulfur and phosphorous sources. The remaining plates can be used to determine the pH range of growth or resistance to antibiotics or other harsh conditions. Liquid growth media are supplied together with the respective plates.

With respect to bacterial growth, PMs have been used in numerous previous studies (Baba et al., 2008; Edwards et al., 2009; Mascher et al., 2007; Mukherjee et al., 2008; Zhou et al.,

2003) . However, use of this technology for the investigation of biofilms has been limited (Boehm et al., 2009). In E. coli, the use of PM technology for the quantification of biofilm biomass has not been reported. In addition, the previous use of PM technology in biofilm studies has been based on the use of the crystal violet assay for the quantification of biomass. There are, however, many more assays that have been developed for the quantification of biofilm-associated biomass, each of which serves a different purpose. The different quantitative biofilm assays are compared in Table 1.

Crystal violet is a non-specific protein dye that stains the bacterial cells and their exopolysaccharide matrix for dead and live bacteria alike. Biofilms are cultivated on 96 well plates and stained with 0.1% crystal violet in H2O. In a second step, crystal violet is solubilized with a mix of ethanol and acetone (80:20) and measured spectrophotometrically (O’Toole et al., 1999; Pratt & Kolter, 1998). The assay was developed as a high-throughput assay that is suitable for robotic instrumentation (Kugel et al., 2009; Stafslien et al., 2006,

2007) . ATP (adenosine triphosphate) (Sule et al., 2008, 2009) and XTT (4-nitro-5-sulfophenyl — 5-[(phenylamino) carbonyl]-2H-tetrazolium hydroxide) (Cerca et al., 2005) are both assays that quantify the energy metabolism of the bacteria. Therefore, only biomass of live bacteria is considered. ATP is converted by the enzyme luciferase into a bioluminescence signal, XTT is reduced by NADH to an orange colored water-soluble formazan derivative. Similar to crystal violet, fluoro-conjugated lectins quantify the biomass of live and dead bacteria alike (Burton et al., 2006). Lectins are highly-specific carbohydrate binding proteins that have been utilized to quantify different cell wall components, as well as extracellular matrix (Stoitsova et al., 2004). Specifically, wheat germ agglutinin (WGA) and soybean agglutinin (SBA) selectively complex lipooligosaccharides and colanic acid, respectively. For our experiments, we needed an assay that quantifies biofilm biomass in live bacteria that is also suitable for high-throughput experimentation, cost effective, and rapid. The ATP assay appeared as the most suitable assay among the five compared assays (Table 1).

In the past, ATP has been used as a measure of biomass (Monzon et al., 2001; Romanova et al., 2007; Takahashi et al., 2007) because its concentration is relatively constant across many growth conditions (Schneider & Gourse, 2004). For the quantification of biofilms, the BacTiter GloTM assay from Promega (Madison WI) has been used for biomass determination in Pseudomonas aeruginosa (Junker & Clardy, 2007) and E. coli (Sule et al., 2008, 2009). In E. coli, we established that a two fold increase in bioluminescence did indeed relate to a two fold increase in the ATP concentration and a 2 fold increase in the number of bacteria (Sule et al., 2008). Across eight isogenic E. coli strains (one parent strain and seven mutants), differences in biofilm biomass that were determined with the ATP assay were paralleled by observations made with scanning electron microscopy (Sule et al., 2009).

The protocol involves the formation of the biofilms on 96 well micro titer plates, incubation at the desired temperature, and washing of the biofilms with phosphate buffered saline (PBS). Special attention is needed to distinguish the pellicle that forms at the air-liquid interface from the biofilm that forms at the bottom of the wells. In particular, the AJW678 derivatives that we are working with form a solid pellicle that covers the entire surface of the culture (Wolfe et al., 2003). For users who like to include the pellicle into their study, the growth medium and the PBS will be pipetted off carefully from each well. Users who wish to discard of the pellicle can flip the entire 96 well plate over and remove the liquid this way. Eventually, 100 pl of BacTiter Glo reagent are added to each well. After 5 min of incubation, bioluminescence is measured.

For this study, we will use the ATP assay to quantify biofilm biomass that forms on the PM1 plate of BioLog’s PM system. The PM1 plate contains 95 single carbon sources in addition to the negative control. Besides the fact that the use of PM technology for the determination of the nutritional requirements of biofilm has not been reported in E. coli yet, the combination of PM technology with the ATP assay is novel. The combination of both, PM technology and ATP assay, together with the statistical analysis and metabolic modeling, enables the rapid screening of thousands of nutrients for their ability to support or inhibit growth and biofilm formation in one experimental setup. The described technique is not only cost-efficient and easy to perform, but also high-throughput in nature, providing valuable insight into the nutritional requirements during biofilm formation.

Measurements of the reflectance spectra of crop canopies are affected by both sampling conditions and canopy features. The most important are detailed in the following sections.

1.1.1 Sensor position

The angles between sun, sensor and canopy surface may lead to the appearance of shadow or soil background in the field of view of the apparatus, causing disturbing effects in the spectra measured (Aparicio et al., 2004; Baret and Guyot, 1991; Eaton & Dirmhirn, 1979). The angle of the sun is more important in canopies with low LAI (Kollenkark et al., 1982; Ranson et al., 1985). Variability in reflectance due to variation in the sensor view angle has been reported to depend on the stage of development of the crop (J. A. Smith et al., 1975), the structure of the vegetative canopy (Colwell, 1974) and the leaf area index (Aparicio et al.,

2004) . Angles between the sensor azimuth and the sun azimuth of between 0° and 90° minimize the variability caused by changes in the elevation of the sensor or the sun (Wardley, 1984). However, when off-nadir view angles are used, the analysis of the remote sensing data could be complicated due to the non-Lambertian characteristics of vegetation (unequal reflection of incident light in all directions and reflection depending on the wavelength) (Ranson et al., 1985). The degree of canopy cover captured by the sensor is minimum at nadir position, and increases with the angle of observation. The effect of angle is particularly important in crops arranged in rows, which may have different orientations in relation to the solar angle and the observation angle (Ranson et al., 1985; Wanjura & Hatfield, 1987). The nadir position of the sensor (sensor looking vertically downward) is the most widely used, because it has a low interaction with sun position and row orientation and delays the time at which spectra become saturated by LAI (Araus et al., 2001).

Chemical analysis revealed that total concentrations of nitrogen, phosphorus and potassium of all the treatments were increased due to vermicomposting. Addition of poultry manure (PM) significantly (P < 0.05) increased nitrogen content in final vermicompost as compared to control treatment (Table 2). Data revealed that total nitrogen and phosphorus content of final vermicompost was increased with increasing PM proportion in initial waste mixtures. Addition of PM with IS significantly (P < 0.05) increased total potassium content after vermicomposting, however, its values in T2 and T3 treatments were statistically at per.

Total heavy metal content of the organic substrates decreased due to vermicomposting (Table 2). The extent of decrease in heavy metal content was proportionately increased with the amount of PM added to IS. Among different heavy metals, zinc recorded the maximum decrease in total concentration after vermicomposting followed by Cr, Cu and Pb. Though vermicomposting significantly (P < 0.05) reduced total content of different heavy metals, the values were not significantly affected by different PM proportions.

Intensity value and texture measures from the co-registered and geo-referenced data sets are used in the algorithm to estimate the forest biomass. The data sets are related to the forest biomass through a classification analysis. The correspondence between the data sets and ground plots is made using PCI Geomatica software, where the ground plot GPS locations are superimposed on the data set. For each selected pixel (or point) from data set, a window
with size of 5×5 pixels around the point is used and the average intensity values for the PRISM and three channels of the AVNIR images with four texture values of the JERS-1 image are calculated. Thus each selected point contains a vector with eight attributes where the first four elements are the average intensity values and the second four elements are the texture measures values. These vectors of data set construct the feature space. The vectors belong to the pixels of the ground plots and subplots are used as training patterns in the classification process.

The classification analysis is done with a MLPNN. A multi layers neural network is made up of sets of neurons assembled in a logical way and constituting several layers. Three distinct types of layers are present in the MLPNN. The input layer is not itself a processing layer but is simply a set of neurons acting as source nodes which supply input feature vector components to the second layer. Typically, the number of neurons in the input layer is equal to the dimensionality of the input feature vector. Then there is one or more hidden layers, each of these layers comprising a given number of neurons called hidden neurons. Finally, the output layer provides the response of neural network to the pattern vector submitted in the input layer. The number of neurons in this layer corresponds to the number of classes that the neural network should differentiate (Haykin, 1999; Miller et al., 1995; .

The network that is used in this study arrange in layers as following. The number of neurons in the output layer is taken to be equal to the number of classes desired for the classification. Here, the output layer of the network used to categorize the image in five classes should contain five neurons. The input layer contains eight neurons corresponding to the number of attributes in the input vectors. The input vector to the network for pixel i of the data sets is the form vios = {vil, vi2,… viB}. Where the first four elements belong to the intensity values of PRISM and AVNIR images and the second four elements belong to the texture measures of JERS-1 image for a window with size of 5×5 around pixel i of the geo — referenced data sets. After the determination of the input layer, the number of hidden layers required as well as the number of neurons in these layers still needs to be decided upon. An important result, established by the Russian mathematician Kolmogorov in the 1950s, states that any discriminate function can be derived by a three-layer feed-forward neural network (Duda, 2001). Increasing the number of hidden layers can then improve the accuracy of the classification, pick up some special requirements of the recognition procedure during the training or enable a practical implementation of the network. However, a network with more than one hidden layer is more prone to be poorly trained than one with only one hidden layer.

Thus, a three-layer neural network with the structure 8-10-5 (eight input neurons, ten hidden neurons and five output neurons) is used to classify the data sets into five classes. Training the neural network involves tuning all the synaptic weights so that the network learns to recognize given patterns or classes of samples sharing similar properties. The learning stage is critical for effective classification and the success of an approach by neural networks depends mainly on this phase. The network is trained by using back-propagation rule (Paola & Schowengerdt, 1995). After training the network, the parameters are selected as: Momentum value 0.9, Learning rate 0.1, and the number of iteration 2000. The numbers of training data are 200 patterns of the subplots that are selected randomly from the classes, in which each class is represented with at least 40 patterns. The set of training patterns is presented repeatedly to the neural network until it has learnt to recognize them. A training pattern is said to have been learnt when the absolute difference between the output of each output neuron and its desired value is less than a given threshold. Indeed, it is pointless to train the network to reach the target outputs 0 or 1 since the sigmoid function never attains its minimum and maximum (Masters, 1993). For classification of data sets into five classes, the threshold is set to 0.4. The network is trained when all training patterns have been learnt. Once the network is trained, the weights of the network are applied on the data sets to classify into five classes: class1 Azedarach, class2 Acorn, class3 Beech, class4 Grassland and class5 None. The result of the classified image is shown in Fig. 4.

After classification, it is needed to determine the degree of classification accuracy. The most commonly used method of representing the degree of accuracy of a classification is to build confusion matrix.

The confusion matrix is usually constructed by a test sample of patterns for each of the five classes. A set of test sample with 105 patterns based on the ground truth collection were randomly selected in the classified image for accuracy assessment. The values 70% and 65% are achieved for overall accuracy and kappa coefficient respectively. One reason for misclassification can be due to poor selection of training areas, so that some training patterns don’t accurately reflect the characteristics of the classes used. Another reason can be due to poor selection of land cover categories, resulting in correct classification of areas from the point of view of the network, but not from that of the user. Thus the classification accuracy can be improved by redefining the training patterns and land cover categories.

In order to show the texture of SAR image and the neural network classifier improve the accuracy of the classification and then forest biomass estimation, we employ the Maximum Likelihood (ML) classifier method using only the intensity values of the PRISM and AVNIR images. The overall classification accuracy of 57% is achieved with ML classifier. The accuracy of 70% with the neural network is significantly better than the accuracy of 57% with ML.

In comparison between the MLPNN and ML classifiers, the advantages of MLPNN that is used in this study are:

i. It can accept all kind of numerical inputs whether or not these conform to statistical distribution or not.

ii. It can recognize inputs that are similar to those which have been used to train them. Because the network consists of a number of layers of neurons, it is tolerant to noise present in the training patterns.

Thus, we can estimate the forest biomass of the classes in the classified image which has been classified based on the SAR image texture and the MLPNN classifier. We also evaluate the biomass for two classes based on the allometric equation (15) for the classic method based on the ML classifier and the proposed method. The results are shown in Table 3, where the classic method and the proposed method have been applied in the classified image to estimate the biomass for two classes.

For the accuracy assessment of the proposed method, Table 4 shows how well the results agree with the ground measurements results from Table 1, when the classic method and the proposed method are used for biomass estimation. Table 4 shows the estimated biomass when both methods are used. The root mean square error (RMSE) of estimated biomass with both methods is indicated in the table. The RMSE values is decreased when the model is used (RMSE=2.175 ton) compared the classic method (RMSE=5.34 ton).

optical image and SAR image texture in a non-linear classifier method, neural network, significantly improve the accuracy of the forest biomass estimation.