Prabhat Pramanik1, Sang Yoon Kim1 and Pil Joo Kim12* 1Division of Applied Life Science (BK21 Program), Gyeongsang

National University, Jinju, 660-701 2Division of Applied Life Sciences, Gyeongsang National University,

900 Gazwa, Jinju 660-701, 1South Korea 2Republic of Korea

1. Introduction

Agriculture is facing a challenge to develop strategies for sustainability that can conserve non-renewable natural resources, such as soil and enhance the use of renewable resources such as organic wastes. It has been estimated that more than 18 metric tons of organic sludge was generated every day in Korea in 2003 (Anonymous, 2004)while it was 105 metric tonnes per year in India (Chitdeshwari and Savithri, 2004). Among different options for recycling this sludge, application to agricultural land is probably the most reliable and cost — effective technique to supply organic matter to field crops (Coker et al., 1987). But direct application of this sludge to agricultural land might cause heavy metal contamination (McGrath, 1994). Under this perspective, industrial sludge (IS) was recycled after bioremediation involving earthworms.

Unlike several chemical methods, removal of heavy metals by biological means is more specific, eco-friendly and economical. Begum and Krishna (2010) revealed that heavy metal content in organic wastes reduced after passage through earthworm guts. Therefore, industrial sludge could be recycled through vermicomposting to produce nutrient rich plant amendment. Vermicomposting is the stabilization of organic substrates by microorganisms in presence of earthworms. Though earthworms consume fungi with organic substrates to fulfil their nitrogen requirement, the viable fungal count in earthworm casts was generally higher than that of initial waste substrates during vermicomposting (Edwards and Bohlem, 1996). Ergosterol, marker molecule of fungal cell membrane, is frequently used in microbiology to quantify fungal biomass in infected media. Madan et al. (2002) estimated fungal biomass in soil by FAME assay. Hill et al. (2000) also quantified fungal specific FAME (18:19fflc) to estimate fungal biomass in compost. Yasir et al. (2009) revealed that bacterial biomass also played important role during organic matter decomposition. Muramic acid

could be used as a marker molecule for bacterial biomass determination (King and White, 1977). The objectives of this study were to (i) standardize recycling technique of IS through vermicomposting, (ii) evaluate fungal and bacterial diversity during vermicomposting and (iii) determine plant growth promoting mechanism of vermicompost.

An electromagnetic wave scatters from two dimensional position (x, y) on the earth surface, the physical properties of the terrain cause changes in both the phase, ф(х, y), and amplitude, A(x, y), of the wave. The SAR, in fact measures the number pair (A cosф, A sinф ) in the in-phase and quadrature channels of the receiver, weighted by the SAR PSF (point sprit function). The estimates of the local reflectivity at each pixel can also be represented by the complex number Ав’ф; in this form, the SAR data are known as the complex image.

From the complex image, a variety of other products can be formed. For example, images of the real part A cosф (the in-phase component), the imaginary part A sinф (the quadrature component), the amplitude A, the phase ф, the intensity I = A2, or the log intensity log I. The use of the word ‘intensity’ is by analogy with measurements at optical wavelengths and is synonymous with power or energy.

The real and imaginary images show some structure but appear extremely noisy, the phase image is noise-like and shows no structure, while the amplitude, intensity, and log images, though noisy, are clearly easier to interpret. The noise-like quantity characteristic of these types of images is known as speckle. It must be stressed that speckle is noise-like,
but it is not noise; it is a real electromagnetic measurement, which is exploited, for example, in SAR interferometry (Oliver, and Quegan, 2004). Given that the SAR in making true measurements of the earth’s scattering properties, why do such effect arise?

As the wave interacts with the target, each scatterer contributes a backscattered wave with a phase and amplitude change, so the total returned modulation or the observed value at each pixel of the incident wave is

N

Ae’4 = £ Ake* (1)

k=1

This summation is over the number of scatters illustrated by the beam. The individual scattering amplitudes Ak and phases 4k are unobservable because the individual scatterers are on much smaller scales than the resolution of the SAR, and there are normally many such scatterers per resolution cell.

The observed intensity or power I = A2 has a negative exponential distribution (Oliver,

1991) .

P(I) = !exp(—Lj I >0 (2)

with mean value and standard deviation both equal to m, so that in this case the coefficient of variation (CV) defined as the standard deviation divided by the mean is equal CV=1. From (2) we can see that m corresponds to the average intensity.

We need to distinguish the measured value at a pixel and the parameter value m ( l is the Radar Cross Section (RCS) or backscattering coefficient). Equation (1) indicates that the observed value at each pixel is the resultant of interfering Huygens wavelets unless a single scatter completely dominates the return. Hence the value of m is specific to each pixel; the measured value is just a sample from the distribution parameterized by m.

A SAR image comprise of some variable, corresponding to local RCS, that is combined with speckle to yield the observed intensity at each pixel. The intensity is given by I = cm where n is the speckle contribution.

All the reconstruction methods for Lthat are described require estimates of the sample mean and normalized variance over the window comprising NW pixels, defined by:

Where xt denotes the pixel value. In single-stage filters, x corresponds to intensity I. The size of window depends on the application (e. g. 3 x 3,5 x 5,…).

The ideal filter should eliminate the speckle so that the original signal L is retrieved. In practice, its behaviour depends on the heterogeneity of the considered area.

First, two classes can be considered: 1) the homogeneous class corresponding to the area where L is constant; 2) the heterogeneous class corresponding to the area where L varies

and includes textured areas, edges, and point targets. The filter should have the following behaviour.

1. Within the Homogeneous class: The filter should restore a. As the minimum variance unbiased estimator is the mean pixel value, the filter should assign to each pixel C the average of the pixels in a moving window centred at C for the image.

2. Within the Heterogeneous class: the filter should smooth the speckle and, at the same time, preserve edges and texture information (a variations). This supposes that: i) The filter is based on good discriminators which allow a perfect separation between speckle and textural information; and ii) the conditions assumed for the filter establishment are satisfied.

In practice, these two conditions are not always satisfied. A third class is then pointed out where the filter is no longer reliable, and original pixel values are then preserved. In the case of an isolated point target, the filter should conserve the observed value I. This is also the case when there are a few scatterers within the resolution cells.

According to above consideration, the following classes are pointed out as a function of the coefficient of variation value.

1. Class to be averaged: if C, < Cu then a = I.

2. Class to be filters: if Cu -< C, -< Cmax, than the filter should operate so that the more heterogeneous area [the larger C, ], the less it has to be smoothed.

3. Class to be preserved: If C, > Cmax ^ a = I Where C, = sqrt(Vx)

The threshold determination is given by the following consideration (Lopes, et al., 1990a). For an L-look image Cu = 1/ л/l (an area is considered homogeneous). The threshold Cmax is more difficult to determine. A theoretical and experimental study should be developed to determine exactly the Cmax value as a function of the image patterns. One of the upper

thresholds equal to — y/1 + 2 / L for an intensity image has been obtained for likelihood ratio edge detection (Touzi, et al., 1988).

Margarita Casas-Valdez1, Elisa Serviere-Zaragoza2

and Daniel Lluch-Belda1

1Centro Interdisciplinario de Ciencias Marinas-IPN (CICIMAR-IPN) 2Centro de Investigaciones Biologicas del Noroeste (CIBNOR, S. C.)

Mexico

1. Introduction

Macrocystis pyrifera (L.) C. Agardh "Sargazo gigante" is distributed along the west coast of Baja California Peninsula, from the border with the USA to Punta Prieta, Baja California Sur. This kelp forms dense submarine prairies that emerge from the sea covering areas of several hectares or square kilometers. Macrocystis has been harvested from Islas Coronado (32° 15′ N) to Bahia del Rosario (30° 30′ N) in 15 beds for 49 years, from 1956 to 2004. It was exported raw for alginate production. Recently, it has been harvested in smaller quantities to obtain extracts to be used as fertilizer (Casas-Valdez et al., 2003).

The Macrocystis seaweed was harvested by specially designed ships that cut the algae at a depth about of 1.2 m and then transported it. The ships "El Capitan" harvested from 1956 to 1966 (storage capacity of 300 t) and "El Sargacero" from 1967 to 2004 (storage capacity of 400 t). The ship operations were the same at all beds and did not change over the study period. The biomass and standing crop of Macrocystis was evaluated in summer 1982 and in an annual cycle in 1985-1986 in their natural distribution (Casas-Valdez et al., 1985; Hernandez — Carmona et al., 1989a, 1989b, 1991). The recruitment and effect of nutrient availability during the ENSO event of 1997-1998 at the southern limit of distribution of Macrocystis were studied by Lada et al. (1999), Hernandez-Carmona et al. (2001) and Edwards & Hernandez

(2004) . The relationship between environmental variables as temperature, upwelling, sea level and wind speed and the catch per unit effort (CPUE) of Macrocystis were analyzed by Casas-Valdez et al. (2003). They found an inverse correlation between temperature and harvest and concluded that temperature is the variable that best explained the variations in the Macrocystis harvest.

This is the first time that the temporal variability of harvest, effort, and harvest per unit effort (CPUE) as an indicator of the abundance in each of 15 harvested beds of Macrocystis has been analyzed.

The primary classification with respect to measuring principle is described by two techniques namely pulse ranging or time of flight (TOF) and phase measuring technique. Another classification is also available in accordance with the angular scanning technique and coverages of scanner which consist of Panorama, Hybrid, and Camera scanners (). Panorama scanners carry out distance and angular measurements providing 360° angular coverage within the horizontal plane. Types of laser scanners, which perform unrestricted scanning around the rotation axis, fall in the category of Hybrid scanners. The third category of scanners carrying out distance and angular measurements over a limited angular range and in a specific field of view is called Camera scanners (Shan and Toth, 2009). For the range measurements, it is necessary to obtain information about the exterior orientation elements (positions and orientation or attitude angles) of platforms of the terrestrial laser scanner. Precise exterior orientation elements can be detected during the calibration procedure. Sensitivity of tree volume estimates which are related to different error sources in the spatial trajectory of the terrestrial Lidar has been analyzed by (Palleja et al. ,2010). Their tests have demonstrated that the tree volume is very sensitive to the errors in the determinations of distance and the orientation angle. Cote et al. (2011) proposed to estimate the tree structure attributes by means of terrestrial Lidar. They concluded that the main limitation of the use of terrestrial system was the effect of object shading and wind. In context with the precise biomass estimation terrestrial laser scanning can be considered as a support system for airborne and space borne Lidar.

1.2 Cluster size

The relative frequencies of AOB cluster diameters for all the samples investigated are presented in Fig. 2.

Fig. 2. Size distribution of cell clusters in the full — and partial-bed reactors

The results show that the majority of the clusters had diameters of 5 pm with the largest being 10 pm. These findings are quite consistent with the results obtained by Kloep et al

(2000) . Using probe Nsm 156, the majority of the hybridized clusters was found to be smaller than 10 pm and only a few were larger than 15 pm. Wagner et al (1995) also detected

clusters hybridized with probe Neu 23 having diameters between 3 pm and 20 pm from samples of municipal sewage treatment plants. Nitrifier agglomerates are therefore small, for example well below those particle sizes (>100 pm) effectively removed by conventional primary sedimentation (Kiely 1998). Their retention in the system must therefore be mainly due to interactions with the biofilm attached to the media elements in the bed.

By visual observation, yellow clusters emerge on all biofilm samples as shown on Plates 1- 4. The AOB appear yellow due to double bindings of the fluorescene-labelled probe EUB 338 (emitted as green) and Cy5-labelled probe Nso 1225 (emitted as red). The formation of cluster growths is a feature of ammonia-oxidizing bacteria, in particular Nitrosomonas sp (Wagner et al 1995; Mobarry et al 1996). The clusters were spherical to oval shaped and appeared over diameters ranging from approximately 2.5 to 12.5 pm.

Plate 3. CLSM image of a biofilm from the top of the partial-bed reactor

Plate 4. CLSM image of a biofilm from the middle of the partial-bed reactor

Plates 5 — 7 of suspended growth samples from the full-bed reactor show fewer AOB clusters than Plates 1 — 4. Layers of filamentous bacteria can be seen dominating, especially the suspended biomass samples from the top and middle parts of the reactors.

For the CLSM images of the suspended growth biomass samples from the partial-bed reactor, intense diffuse, green coloured fluorescence was often observed. This could have been due to debris, inorganic particles or the bacterial cells. A large number of coccoid structures was detected using the EUB 338 probe. They usually occurred in characteristic clumps and appeared ring shaped. MacDonald and Brozel (2000) observed the same phenomena in their study of bacterial biofilms in a simulated recirculating cooling-water

reactor and suggested that this could result from dense chromosomal material at the cell center, leading to a concentration of ribosomes at the periphery of the cells.

Plate 5. CLSM image of suspended growth biomass from the top of the full-bed reactor

Plate 7. CLSM image of suspended growth biomass from the bottom of the full-bed reactor

3.2 Enumeration of ammonia-oxidizing bacteria

The number of AOB cells per ml of biomass was calculated from the counts based on cluster diameters using an Excel spreadsheet developed by Coskunur (2000). The numbers of AOB cells obtained are given in Table 2 below:

The higher number of AOB cells present in the biofilm samples than in the suspended growth samples could be due to the fact that AOB are slow-growing bacteria that need long mean solids’ retention times to become established. Nitrifying bacteria, when compared with the heterotrophic organisms, are very much slower growing. Watson et al (1989) observed that the doubling times of these bacteria range from 8 hours to several days and that they have a tendency to attach to surfaces and to grow in cell aggregates referred to as zoogloeae or cysts (Lipponen et al 2002). In order to maintain an effective population of nitrifying bacteria within a biological reactor, a long retention time is required (Barber and Stuckey 2000). This is in accordance with the results obtained by Hidaka et al (2003), who discovered that in a biofiltration process for the advanced treatment of sewage, attached biomass contributed to most nitrification activity. Gerceker (2002) reported the loss of nitrification between SRTs of 0.9 and 2.4 days in a closely controlled jet-looped membrane bioreactor. Noguiera et al (2002) found that competition in biofilm results in a stratified biofilm structure, the fast-growing heterotrophic bacteria being drawn to the outer layers where both substrate concentration and detachment rate are high, whilst the slow-growing nitrifying bacteria stay deeper inside the biofilm. The heterotrophic layer has a positive effect on the nitrifiers by protecting them from detachment as long as the bulk oxygen concentration is high enough to preclude its depletion in the biofilm.

It is a fact that biofilm is significant in controlling long SRTs in a system. The full-bed reactor, which has a higher mass of biofilm than the partial-bed, as a result of the greater volume and surface area of the fully packed reactor, has SRTs of 21.2, 27.5 and 11.1 days at the three backwashing rates used in the study. The partial-bed reactor, on the other hand, had much shorter SRTs of 3.3, 3.9 and 2.7 days. Meanwhile, the biofilm in the partial-bed reactor was kept thin and stable, and therefore was not easily washed out during the backwash operation. Therefore, the retention time of biofilm in the partial-bed reactor is actually longer than the overall SRT of the system. Chuang et al (1997) pointed out that satisfactory nitrogen removal is achieved at SRT > 10 days.

The suspended growth biomass in the reactors, and especially that of the partial-bed reactor, was always subject to being washed out by the backwashing operation and lost in the effluent.

3.3 Significance of AOB Cells in the biofilm and suspended growth cultures

Tests carried out to compare the significance of AOB cells in both types of cultures were based on nonparametric methods of one-way ANOVA. Table 3 lists the results obtained.

Table 3 indicates that in both reactors there is a significant difference in the number of AOB cells in the biofilm and suspended growth samples. At 95% confidence levels, the p-value for the full-bed reactor is 0.042 whilst that of the partial-bed reactor is 0.024. Since the p — values obtained are smaller than 0.05, this means that in both reactors, specific cell concentrations of AOB were found to be significantly higher in the biofilm samples as compared to the suspended growth samples.

It was found that the AOB cells are more numerous in the biofilm samples than in the suspended growth samples of both the full — (p=0.042) and the partial-bed (p=0.024) reactors. It is therefore interesting to compare the significance of the overall AOB cells in the full — and partial-bed configurations, knowing that the mass of biofilm is lower in the partial-bed reactor due to the reduced media volume compared to the full-bed reactor.

Table 3 also indicates that there is no significant difference between the concentrations of AOB cells in the biofilm samples of the full — and partial-bed reactors (p=0.099), and also in the suspended growth samples (p=0.079). To put the overall abundance of AOB cells in the full and partial-bed reactors side-by-side, the AOB cells in the biofilm and suspended growth samples for each reactor were combined, giving total concentrations of AOB cells for that particular configuration. The p-value of specific AOB concentrations comparing the full — and partial-bed configuration is p=0.427. The value indicates an almost comparable AOB relative abundance in both the full — and partial-bed reactors. Higher mean AOB cells of the biofilm in the partial-bed reactor equate with the higher mean value of suspended growth samples in the full-bed reactor, resulting in almost equivalent mean AOB cells in both reactors.

Lazarova et al (1994) made a point that the balance between biofilm losses and growth processes on the outside of the media was dominated by shear forces, exerted by the liquid as it flowed past the media surfaces in the reactor. In a study to evaluate the essential role of hydrodynamic shear force in the formation of biofilm, Liu and Tay (2002) pointed out that biofilm density quasi-linearly increases with the increase of shear stress. Chang et al (1991) discovered that the medium concentration and the turbulence indicated by Reynolds numbers, significantly affected biofilm density and thickness of a fluidized bed biofilm reactor. In this type of reactor, increasing medium concentration can be associated with increasing attrition due to particle-to-particle contacts and increasing turbulence correlates flow fluctuations that could create forces normal to the biofilm, i. e. the shear stress. Table 4 illustrates the results obtained in their study.

In this study, since the medium is fixed, there is no attrition effect. Therefore turbulence effect could be the major factor that increases the detachment pressures, and caused the biofilm to become denser and thinner.

The growth cycle of small-grain cereals involves changes in size, form and number of plant organs. The external stages of cereal growth include germination, crop emergence, seedling growth, tillering, stem elongation, booting, inflorescence emergence, anthesis and maturity (Fig. 1). The classical monitoring of crop biomass requires destructive samplings of plants at different growth stages, counting of the number of plants contained in the sample and its weighing after oven-drying them. Crop biomass may be expressed as crop dry weight (CDW), which can be obtained from the plants sampled at a given stage as the product of average dry weight per plant (W, g) and the number of plants per unit area, and is frequently expressed as g m-2 (Villegas et al., 2001). The leaf area expansion of a cereal crop may be monitored through changes in its leaf area index (LAI, a dimensionless value), which is the ratio of leaf green area to the area of ground on which the crop is growing. LAI may be calculated as the product of the mean one-sided leaf area per plant (LAP, m2 plant-1) and the number of plants per unit area in the sample (plants m-2). Changes in total green area of the crop may be described through the green area index (GAI, a dimensionless value), which is the ratio of total green area of the plants (leaves and stems, as well as spike peduncles and spikes when applicable) to the area of ground on which the crop is growing. It can be calculated as the product of total green area per plant (GAP, m2 plant-1) and the number of plants per unit area in the sample (plants m-2) (Royo et al., 2004).

Raw data from destructive sampling can be fitted to mathematical models, usually empirically based, to describe the growth pattern during the crop cycle. The logistic model of Richards (Richards, 1959), the expolinear equation of Goudriaan & Monteith (Goudriaan & Monteith, 1990), and the asymmetric logistic peak curve first used by Royo and Tribo (Royo & Tribo, 1997), have been used to describe the growth of crops. This last model has been useful for monitoring the biomass and leaf area expansion of triticale (Royo & Blanco,

1999) and durum wheat (Royo et al., 2004; Villegas et al., 2001). The mathematical models present the variation in dry matter production, leaf area or green area expansion over time, allowing variations between species (Fig. 2), genotypes, years and environmental conditions to be assessed (Fig. 3). Similarly to the case of grain yield, variability induced by the genetic background in the growth pattern of small-grain cereals has been found to be lower than the environmental variation caused by either year or site effects (Royo et al., 2004; Villegas et al.,

2001) .

Crop growth conditions can be monitored by measuring the spectra reflected by crop canopies in the visible (VIS, A=400-700 nm) and near-infrared (NIR, A =700-1300 nm) regions of the electromagnetic spectrum (Fig. 4). Given that the amount of green area of a canopy determines the absorption of photosynthetic active radiation by photosynthetic organs, spectral reflectance measurements can provide an instantaneous quantitative assessment of
the crop’s ability to intercept radiation and photosynthesize (Ma et al., 1996). Therefore, the absorption by the crop canopy of very specific wavelengths of electromagnetic radiation is associated with certain morphological and physiological crop attributes related to the development of the total photosynthetic area of the canopy.

Days from sowing

The reflectance spectra of a healthy crop-canopy shows a relative maximum around 550 nm, a relative minimum around 680 nm and an abrupt increase around 700 nm, remaining fairly constant beyond this point (Fig. 4). The spectral reflectance in the VIS wavelengths depends on the absorption of incident radiation by leaf chlorophyll and associated pigments such as

carotenoid and anthocyanins. Crop reflectance is very low in the blue (400-500 nm) and red (600-700 nm) regions of the spectrum, because they contain the peaks of chlorophyll absorbance. Beyond 700 nm the reflectance of the NIR wavelengths is high since it is not absorbed by plant pigments and is scattered by plant tissues at different levels in the canopy (Knipling, 1970).

Visible Near-infrared

<—— X——- X——- >

Blue Green Red

Fig. 4. Variation of the reflectance spectra of a healthy wheat canopy at different growth stages compared with the bare soil spectrum. H, heading; A, anthesis; M, milk-grain stage; PM, physiological maturity. The magnitude of the increase in reflectance at around 700 nm indicates differences in biomass

Total organic carbon (TOC) of the vermicompost was estimated using the standard dichromate oxidation method of Nelson and Sommers (1982). Total Kjeldahl nitrogen (TKN) was estimated after digesting the sample with concentrated H2SO4 (1:20, w/v) followed by distillation (Bremner and Mulvaney, 1982). Total phosphorus (TP) and total potassium (TK) were analyzed from the wet digest [tri-acid (HNO3-H2SO4-HClO4) mixture was used for digestion] of vermicompost (Jackson, 1973). Total phosphorus (TP) was estimated by the colorimetric method using ammonium molybdate in hydrochloric acid and total potassium (TK) was determined by flame photometer (Bansal and Kapoor, 2000).

1.2 Microbial analysis

Microbial biomass was determined by the chloroform fumigation-extraction (FE) method (Vance et al., 1987). For fumigation, organic substrates were incubated with ethanol-free chloroform in desiccators. The TOC analyzer was used to determine total organic C (Corg) and total N in 0.5 M K2SO4 extracts of non-fumigated and fumigated soils. The microbial biomass carbon (MBC) was calculated as MBC = (Corg in fumigated soil — Corg in non — fumigated soil)/kc; where, kc = 0.33, the factor used to convert the extracted organic C to MBC (Sparling and West, 1988).

An analysis for ergosterol estimation was performed with 50 mg of lyophilized organic waste or vermicompost sample. Ergosterol was extracted from leaf litter by 30 min refluxing in alcoholic base (Gesser et al., 1991) and purified by solid-phase extraction. Final purification and quantification of ergosterol was achieved by high-performance liquid chromatography (HPLC). The system was run with HPLC grade methanol at a flow rate of 1.5 ml min-1. Ergosterol eluted after 7:11 min and detected at 282 nm; peak identity was checked on the basis of retention times of commercial ergosterol (98% purity).

The FAME analysis was performed using the modified procedure of Schutter and Dick (2000). Before analysis, fresh samples were lyophilized and three grams of lyophilized sample was treated with 10 mL of 0.2 M KOH in methanol and incubated at 37°C for 1 hr. After incubation, the pH of the system was adjusted to 7.0 with 1.0 M acetic acid, 10 mL of n-hexane was mixed and then it was vortexed. After centrifugation at 1600 rpm for 20 min., 5 mL of n-hexane layer was evaporated by N2 gas. The residue was dissolved in 170 pL of 1:1 mixture of n-hexane and methyl t-buthyl ether with 30 pL of 0.01M methyl nonadecanoate (C19:0) as internal standard for FAME and analyzed with a Hewlett-Packard 5890 Series II (Palo Alto, CA) equipped with an HP Ultra 2 capillary column (5% diphenyl — 95% dimethylpolysiloxane, 25 m by 0.2m) and a flame ionization detector. For FAME analysis, the oven temperature was raised from 170oC to 270oC at 5oC min-1 and kept at 2700C for 2 minutes.

Amino sugars in biomass suspensions, chloroform-fumigation-extraction (CFE) extracts and in incubated organic wastes were determined following standard method of Zhang and Amelung (1996). Sample aliquots corresponding to a about 50 mg microbial biomass, with 100 pg myo-inositol added as internal standard, were hydrolyzed with 10 ml of 6M HCl at 105 °C for 8 h. The CFE extracts were freeze-dried prior to hydrolysis. The released amino sugars were separated from impurities by neutralization with 0.4M KOH. Prior to derivatization, 100pg of methylglucamine was added as recovery standard. Derivatization was carried out according to (Guerrant and Moss, 1984). In brief, aldononitrile derivatives of the amino sugars were prepared by heating the samples in 0.3 ml of a derivatization reagent (32 mg hydroxylamine hydrochloride ml-1 and 40 mg 4-(dimethylamino) pyridine ml-1 in pyridine-methanol 4/1) at 75 °C for 30 min. After acetylation with 1 ml of acetic anhydride at 75-80 °C for 20 min, dichloromethane was added, and excess derivatization reagents were removed by washing with 1 ml of 1 M HCl and 1 ml of water two times each. The remaining organic phase was dried under an air stream at 45 °C and dissolved in 0.3 ml ethyl acetate — hexane (1/1). The amino sugar derivatives were separated on a HP 6890 GC equipped with a HP-5 fused silica column (30 m*0.25 mm ID with 0.33 ^m film thickness) and a flame ionization detector. Amino sugars were quantified using inositol as the internal standard and methylglucamine as recovery standard.

The approach of this chapter for reconstruction of backscattering coefficient ( a ) is based on Bayes criterion relating the observed intensity! to the a such that

PAr(a | !) = P(Ha)Pa(a)/P,(I) (4)

Where PAP (a 11) is the a posterior conditional probability of a, which has a particular value given I, and P(11 a) is the likelihood function, which describes the effect of speckle during imaging. This is given by (Oliver, and Quegan, 2004).

specifically in most instances. Generally we wish to provide an estimate of — that represents it’s most likely value given an observed I. This is equivalent to minimizing the log likelihood X = ln PAP— 11) with respect to — . Two types of maximum will be considered. If there is not prior knowledge of the form of P——) we can only optimize with respect to the likelihood function in (4) leading the Maximum Likelihood Estimate (MLE). However, if the form of the a priori PDF is known, the optimum is referred to as the maximum a posterior (MAP) estimate. The latter is more precisely determined since it is based on more specific prior knowledge about the properties of the complete process.

The simplest approach to de-speckling is to average the intensity over several pixels within a window centred on a specific pixel. This is tantamount to assuming that the RCS is constant over the filter window. If this assumption is incorrect, the method is fundamentally flawed. The joint probability that all N pixels have this mean value is given by

for L-look SAR, where pixels are assumed independent, The MLE for — is then given by —ML = I which is the average intensity over all the pixels in the window, corresponding to the multi-looking. Note that if this is applied to a single pixel the MLE is equal to the intensity of that pixel. Different values for the MLE in the de-speckling filters depend on constraints introduced by the model.

Multi-look de-speckling fails where the assumption of constant RCS within the window breaks down. The filter should then adapt to model the excess fluctuations compared with speckle within the window.

In this chapter, the approach that we developed for de-speckling is based on the least square method. If the original intensity of the centre pixel in a window is I, then its corrected value can be obtained by performing a first-order expansion in Taylor saris about the local mean I such that

-ls = I + k(I — I) + e

Where

e: is the error that must be optimized; k: is selected to minimized e; — LS: is the backscattering

_ 1 N

coefficient and I = —VI

Nj-1 j

But a better estimate for — can be obtained, if we have a prior knowledge about the PDF of the RCS. The Bayes rule in (4) shows how this priori PDF can be used to provide a MAP reconstruction when combined with the likelihood function. The RCS of natural clutter can be well represented by a Gamma distribution of the form

Where p and v are the mean RCS and order parameter, respectively. These parameters cannot be measured directly and must be estimated from the data. Hence, estimates for p and v are obtained by passing a window over the original image and setting

ju = I and v = 1/V= (1+1/L)/(V — 1/L)

The PDF of a given intensity I when both likelihood and a priori PDF are available is given

by

Hence, the log likelihood is given by

X = ln P(I | a) + ln Pa(a) = LlnL — Llna + (L — 1)lnI — lnT(L) — LI / a

+ v ln v — v ln u + (v — 1)lna — ln r(v) — AA (10)

u

and the corresponding Gamma MAP solution for RCS (Kuan, at al., 1987; Oliver, 1991) is given by the quadratic:

In regions of pure speckle, we would expect Vt «1/L so that, v = <x> and aMAP «I. However, statistical fluctuations cause the estimate for Vp to be less than 1/L, so v becomes negative. Again, the reconstruction can be improved when this occurs by setting v =®so that aMAP = I . In the opposite limit of small v, provided that /и/1 ^ 4vL /(L +1)2, the solution becomes aMAP = I /(1 +1/ L).

In this chapter, we improve the Gamma-MAP filter by introducing an algorithm that detects and adapts to structural features, such as edges, lines, and points using lease square method. The Gamma-MAP filter appears to give limited de-specking performance. Large windows yield good speckle reduction over homogeneous regions but lead to artifacts over a distance equal to the filter dimension in the presence of strong features. This means that background clutter has excess variations in the precisely those areas where one would like to accurately defined. Small windows are largely free of these artifacts but give inadequate speckle reduction. In our algorithm, iteration leads to a considerable reduction in the speckle.

In principal, it should be possible to base the iteration process on updating the current pixel value, denoted by x, rather than the original intensity I. However, this demands knowledge of the conditional probability P(x | a ) relating the current pixel value x to the RCS a. For residual speckle, this PDF would be expected to be gamma-distributed. Also any degradation in reconstruction will be retained, and probably exacerbated, during subsequent iterations. Thus it seems preferable to insist that the estimated RCS during each iteration should be consistent with the original intensity image described by the speckle conditional probability P(I| a ). Though convergence is slower, there is less chance of progressively increasing radiometric distortion. Thus, we hope that x converges to a and PDF for x converge to equation (8).

The equation (11) is nonlinear with respect to aMAP, we linearize equation (11) by Taylor series about the initial value for aMIAP (a°tMP) as follows:

2

f (ГМАР ) = ‘VjMAL + (L + 1 — ФмАТ — Lx = 0

M

f (ашг) = f (*l, P) + (f )0 da, MAF + e = 0 (12)

®ГМАР

f (Г) = {+ (L +1 — V)a°mp — Lx} + {+ (L +1 — v)Jdvmp + e = 0

Where x is the current pixel value, m and v are estimated from the current iteration, so that M = x and v = 1/ Vx.

Thus, we can write N observation equations for pixels with intensity xt (i=1, 2^ N) in the current iteration within the moving window with size of N=wxw (here w =3) that centred on a specific pixel as follows:

+(L+1 — )&Lp — Lxi r +

+ (L +1 — v„)^daMAP + e = 0; i = 1,2,…,N Mw

Fig 1 shows the process of the de-speckling model.

According to the diagram of Fig 1, a moving window, W, is placed in the top left centre of the SAR image to be filtered (Fig 2) and the mean and the standard deviation values of the pixels within the moving window centred on a specific pixel are computed. Based on the pixels in the window, a linear observation equation system is performed for all pixels in the window using the observation equation (13). The system is solved by using the least square method (LSM) to determine the correction da1MP. This correction is added to the value, crI;MIAP, and the new value, tr1^, is replaced in the output image (filtered image) at the point that is corresponding to the location of the specific pixel(see Fig 2).

The proposed algorithm in Fig 1 proceeds as following steps:

Step 1: Initialization stage

1. Set the parameters and consider the 1th pixel with intensity It Step 2: Perform intensity update (Filtered image)

1. Compute the mean and the standard deviation values of the moving window W centred in the 1th pixel

2. Perform the linear observation equation system based on the equation (13) for all elements in the window W

3. Using the least square method to determine the correction drMAP

4. Compute the new value a^p (г1ар = Гмар + drmap ) for 1th pixel

5. Increment 1 and go to step 2 until 1 = M x N, (M x N is the size of the image)

О Г tm im ‘ im im O’

Step 3: Acceptance/ Rejection stage

1. Evaluation of the ratio of the original intensity image, I, to the derived RCS image, x2, (Ratio image)

2. Estimate the mean, r, and standard deviation, SD[r], for the Ratio image as follows

1 NimxMim 1 NimxMim

r = У r and SD[r] = У (r, — l)2 (14)

Ntm x Mtm il ‘ Ntm x Mm il 1

Where rt = Ij(x2), is the ratio of the pixel intensity I, to the derived RCS (x2), at pixel ‘.

3. IF { r and SD[r] values are remained almost the same in the previous iteration} THEN {stop the algorithm}

ELSE {continue and go to step 1}.

harvest size (wet weight). Total data were extracted from 3 230 daily records. For the period 1993 to 1999, additional information was obtained of the time of harvest from 638 records. With these data we estimated the monthly, seasonal, and annual values of harvest, effort and harvest per unit of effort for each bed and for the full region. The difference in the storage capacity of the two ships was weighted according with Casas-Valdez et al. (2003).

We selected as units of effort: a) the number of trips and b) the time of harvest. The harvest per unit effort (CPUE) (volume of harvest per trip made by each ship or volume of harvest per hour of harvest), was calculated with the equation-

CPUE = C/f (1)

Where: C = volume of Macrocystis harvested; f = effort

Seasonal harvest, and harvest and effort per category were compared using an ANOVA analysis with the software Statistic 7.0. The significant difference among treatments was determined using the Tuckey test. The relationship between the harvest of Macrocystis and the effort was determined through correlation analysis (Anderson, 1972).

2. Results

2.1 Harvest, effort, and CPUE in Macrocystis beds

Macrocystis was harvested from Islas Coronado (32° 15′ N) to Bahia del Rosario (30° 30′ N) from 1956 to 2004 at 15 beds: Islas Coronados (01), Playas de Tijuana (02), Punta Mezquite (03), Salsipuedes (04), Isla Todos Santos (05), San Miguel y Sauzal (06), Punta Banda (07), Bahia de La Soledad (08), Santo Tomas (09), Punta China (10), Punta San Jose (11), Punta San Isidro (12), Punta San Telmo (13), Punta San Martin (14) and Bahia del Rosario (15) (Fig. 1). These beds are located at a distance of 1-5 km of the coast.

The harvest of Macrocystis increased from 9,900 t in 1956 to 41,500 t in 1976-1977. The average harvest from 1978 to 1982 was 30,000 t, from 1984 to 1997 it was 32,000 t and from 1999 to 2004 was 28,000 t (Fig. 2). In the years 1958, 1983, and 1998 the harvest underwent drastic reductions due the high temperatures presented due to ENSO phenomena. The historical series of CPUE accordingly shows considerable decreases during 1958, 1983, and 1998. In all the other years it was almost a constant level at an average of 342 t/trip.

The historical series of harvest and effort of the 15 beds of Macroystis (Figs. 3, 4, and 5) show that there is ample variability among them. For example, the Punta Mezquite (03) bed was harvested for 40 years, with an effort of 741 trips (Fig. 6) and a total harvest of 257,000 t. The Punta Banda (07) bed, however, was only harvested for 5 years, with an effort of 5 trips (Fig. 6) and a total harvest of 1,800 t.

Considering the average harvest and the effort applied during 49 years the Macrocystis beds were grouped into three categories; I) with an average harvest of 2,160 t (1,800 — 76,450 t) and an effort of 6 trips/year (01, 02, 05, 06, 07, 11, 12, 13, 14 and 15); II) with an average harvest of 3,600 t (90,800 — 176,150 t) and an effort of 12 trips/year (04, 08, 09 and 10); III) with an average harvest of 6,400 t (257,000 t) and an effort of 19 trips/year (03). There are significant differences (P < 0.05) among categories. The variation of the CPUE of Macrocystis beds is shown in figures 7, 8 and 9. The CPUE was more stable in the beds where more effort was used (03, 04, 08, 09 and 10).

Fig. 1. Distribution of Macrocystis pyrifera beds harvested off the Baja California Peninsula (Taken from Casas-Valdez et al., 2003).

Measurement process of laser scanner can be represented by the frequency, intensity, phase and the travel time of the sent and returned signal. The transmitted and received energy are formulated similar to the Radar (radio detection and ranging) equation (Shan and Toth,

2009) . This can be expressed as an integral (Mallet and Bretar, 2009) and the range is measured in pulsed systems as R = c. t/2 , where c is the speed of light, t is two way laser light travel time, R is the distance to be measured (Shan and Toth, 2009). The equation of the continuous waveform is R = 0.5 (ф/2п)Х, where ф is the phase difference and X is the wavelength which is operationally between 600 and 1000 nm (Electromagnetic infrared range). This interval is not eye-safe. Therefore, the optimum performance has to be balanced against safety considerations.

In addition to positional data, each Lidar observation must also contain the scan angle for each shot together with the measurement of reflectance from the target. Since the calculation of range for the detected pulse involves the elapsed time the precision of time measurement is of vital importance considering that 7 ns sensivitiy is needed to distinguish 1 m object. This plays in turn a decisive role in the scanning of vegetated areas. In some methods they use a fraction which is a constant in the sent and return pulse. But, in others, they take the centroid of the pulses as a time reference.

The characteristics of forest inventory from both discrete return (first, last, multi returns) and full waveform recordings are extensively studied by different Lidar approaches such as tree crown detection and biomass estimation (Harding et al., 2001; Coopes et al., 2004; Jang et al., 2008; Brantberg et al., 2003).

100 cm are often called small footprint systems typically at frequencies around 15 kHz (Heritage and Large, 2009). Early small footprint systems recorded the range only up to the first reflecting object or the first pulse in discrete returns. In principle, the map of all first pulses results in such a model showing only the height of all surface objects. This requires to record the last reflecting object in each return signal if there is more than one reflectance, which is often referred to the last pulse. Although the last pulse data has clearly the potential to penetrate vegetation canopies, it can never be guaranteed that the last pulse can reach the ground and is not reflected from the higher point of canopy. Furthermore, where low vegetation is involved, the first and last pulse may be too close together to generate a reliable range and leads consequently to over estimation of the terrain height.

Coopes et al. (2004) used airborne discrete returns to indicate canopy crown and height. Lim and Treitz (2004) collected the airborne discrete first and last returns for above ground biomass estimation. In Jang et al. (2008) the apple tree inventory are extracted from discrete return without explaining their effect on the results. First and last returns are used by Thomas et al. (2006) but the effects of which are not explained on the results of canopy height models.

Fallah Vazirabad and Karslioglu (2010) extracted the tree tops empirically from the first pulse data because it contains more canopy returns than the ground ones. In discrete return systems, the small diameter of footprints and the high repetition rates of these systems made possible to have high spatial resolution, which can yield dense distributions of sampled points. Thus, discrete return systems are preferred for detailed mapping of ground and canopy surface. Finally, these data are readily and widely available, with ongoing and rapid development in forestry.