Spectral reflectance indices were developed using formulations based on simple mathematical operations, such as ratios or differences, between the reflectance at given wavelengths. Most spectral indices use specific wavebands in the range 400 to 900 nm and their most widespread application is in the assessment of plant traits related to the photosynthetic size of the canopy, such as LAI and biomass.

The most widespread vegetation indices (VI), for measurements not only at ground level but also at aircraft and satellite level (Wiegand & Richardson, 1990) are the normalized difference vegetation index (NDVI = RniR-RREd /Rnir +RREd) and the simple ratio (SR= RNIR/RRED) (see Table 1 for their definition). The ratio between the reflectances in the near­infrared (NIR) and red (RED) wavelengths is high for dense green vegetation, but low for the soil, thus giving a contrast between the two surfaces. For wheat and barley a wavelength (A) of around 680 nm is the most commonly used for Rred, and one of 900 nm for Rnir (Penuelas et al., 1997a). These indices have been positively correlated with the absorbed photosynthetically active radiation (PAR), the photosynthetic capacity of the canopy and net primary productivity (Sellers, 1987). According to Wiegand & Richardson (1984, as cited in Wiegand et al., 1991), the fraction of the incident radiation used by the crops for photosynthesis (FPAR) may be derived from vegetation indices through their direct relationship with LAI, according to Equation (1):

FPAR(VI) = FPAR(LAI) x LAI(VI) (1)

For this reason, vegetation indices have proven to be useful for estimating the early vigor of wheat genotypes (Bellairs et al., 1996; Elliot & Regan, 1993), monitoring wheat tiller density (J. H. Wu et al., 2011), and assessing green biomass, LAI and the fraction of radiation intercepted in cereal crops (Ahlrichs & Bauer, 1983; Aparicio et al., 2000, 2002; Baret & Guyot, 1991; Elliott & Regan, 1993; Gamon et al., 1995; Penuelas et al., 1993, 1997a; Price & Bausch, 1995; Tucker 1979; Vaesen et al., 2001). They tend to minimize spectral noise caused by the soil background and atmospheric effects (Baret et al., 1992; Collins, 1978; Demetriades-Shah et al., 1990; Filella & Penuelas, 1994; Mauser & Bach, 1995).

Positive and significant correlations of SR and NDVI with LAI (Fig. 6), GAI and biomass (either on a linear or a logarithmic basis) have been reported in bread wheat and barley (Bellairs et al., 1996; Darvishzadeh et al., 2009; Fernandez et al., 1994; Field et al., 1994; Penuelas et al., 1997a). In a study conducted with 25 bread wheat genotypes, NDVI explained around 40% of the variability found in biomass (Reynolds et al., 1999). Studies involving 20-25 durum wheat genotypes have demonstrated a strong association between SR and NDVI and biomass under both rainfed and irrigated field conditions (Aparicio et al., 2000, 2002; Royo et al., 2003). Spectral reflectance measurements are also being used increasingly as a tool to detect the canopy nitrogen status and allow locally adjusted nitrogen fertilizer applications during the growing season (Mistele & Schmidhalter, 2010). Since grain yield is closely associated with crop growth and the vegetation indices are sensitive to canopy variables such as LAI and biomass that largely determine this growth, spectral data have also been proposed as suitable estimators in yield-predicting models (Aparicio et al., 2000; Das et al., 1993; Ma et al., 2001; Royo et al., 2003).

Fig. 6. Patterns of the relationships of leaf area index (LAI) with the normalized difference vegetation index (NDVI) and the simple ratio (SR). Data correspond to 7 field experiments involving 20-25 durum wheat genotypes and conducted under contrasting Mediterranean conditions for 2 years, with spectral reflectance measurements done at anthesis and milk — grain stage. Each point corresponds to the mean value of a genotype, experiment and growth stage. Adapted from Aparicio et al. (2002)

Another way to formulate the relationship between biomass and VI is to use the light use efficiency (є) model (Kumar & Monteith, 1981) based on the fact that the growth rate of a crop canopy is almost proportional to the rate of interception of radiant energy. Thus, the crop dry weight of a crop canopy at a given moment (t) may be expressed as a function of the incident radiation (Io), the fraction of the radiation intercepted by the crop canopy (FPAR), and the radiation use efficiency (є), as follows:

CDW = jlo x FPAR(LAI) x є dt (2)

0

Small increases in biomass in a small period (expressed as days or thermal units) may then be calculated as a function of LAI from the derivative of Equation (2)

= Io x FPAR(LAI) x є (3)

The incident radiation (Io) may be obtained from meteorological stations or, alternatively, it can be estimated from air temperatures (Allen et al., 1998). FPAR(LAI) may be calculated from vegetation indices on the basis of the linear relationship existing between vegetation indices and the FPAR of green canopies (Daughtry et al., 1992), and particularly between NDVI and FPAR (Bastiaansen & Ali, 2003). Radiation use efficiency (є) is assumed to be constant during the crop growing season (Casanova et al., 1998). Values of radiation use efficiency have been summarized by Russell et al. (1989) for different crops and environmental conditions; moreover, є-values can also be derived for a particular species
and environment from the slope of the relationship between total aboveground biomass and absorbed PAR energy (Liu et al., 2004; Serrano et al., 2000).

An example of use of Kumar & Monteith’s model to assess the pattern of changes in biomass from the LAI estimated from spectral reflectance measurements is shown in Fig. 7. In the example, LAI and CDW values were calculated from destructive samplings, and a comparison is made between the pattern of changes in CDW derived from the mathematical model and that assessed by destructive samplings (Fig. 7b). The model requires frequent reflectance measurements to accurately assess the pattern of changes in LAI over time (Christensen & Goudriaan, 1993), and proper estimations of the incident radiation.

Studies conducted in bread wheat (Asrar et al., 1984; Serrano et al., 2000; Wiegand et al., 1992) and durum wheat (Aparicio et al., 2002) have demonstrated that SR increases linearly with increases in LAI, while NDVI shows a curvilinear response (Fig. 6). When the LAI of wheat canopies exceeds a certain level, the addition of more leaf layers to the canopy does not entail great changes in NDVI (Aparicio et al., 2000; Sellers, 1987), because the reflectance of solar radiation from the underlying soil surface or lower leaf layers is largely attenuated when the ground surface is completely obscured by the leaves (Carlson & Ripley, 1997). The consequence is that for LAI values higher than 3, NDVI becomes relatively insensitive to changes in canopy structure (Aparicio et al., 2002; Curran, 1983; Gamon et al., 1995; Serrano et al., 2000; Wiegand et al., 1992), which constitutes an important limitation for the use of NDVI to estimate LAI. In this context the linearity of the relationship between SR and LAI is not advantageous, because SR may be directly derived from NDVI as SR=(1+NDVI)/(1- NDVI), thus leading to similar statistical significances of both indices when LAI values are predicted (J. M. Chen & Cihlar, 1996). Because of the sensitivity of NDVI and SR to external factors —particularly the soil background at low LAI values—and the developments in the field of imaging spectrometry, a set of new vegetation indices have been developed in order to minimize the effect of disturbing elements in the capturing of the spectra (Baret & Guyot, 1991; Broge & Mortensen, 2002; Gilabert et al., 2002; Meza Diaz & Blackburn, 2003; Rondeaux et al., 1996).

In order to compare the suitability of the classical vegetation indices and the new ones mentioned in the literature as being appropriate for estimating growth traits in wheat and other cereals (P. Chen et al., 2009; Haboudane et al., 2004; Li et al., 2010a; Prasad et al., 2007), 83 hyperspectral vegetation indices were tested using durum wheat data from our own research. The indices were calculated from spectral reflectance measurements taken at different growth stages in 7 field experiments each involving 20-25 durum wheat genotypes, conducted under contrasting Mediterranean conditions for 2 years. Principal component analysis performed with the complete set of vegetation indices and LAI, GAI and CDW revealed that the vegetation indices most closely correlated with durum wheat growth indices were the 29 shown in Table 1. The correlation coefficients between growth traits and the selected indices are shown in Fig. 8. The results show that the majority of indices explained more than 50% of variation in LAI, GAI and CDW when determined at anthesis and milk grain stages, most correlation coefficients being statistically significant at P<0.001. However, the correlation coefficients were significant only for a small number of indices when measurements were taken at physiological maturity. From these results we can conclude that despite the large number of vegetation indices described to improve the appraisal of growth indices given by NDVI and SR, this objective was attained in only a few cases.

Fig. 8 shows that some indices changed from positive values determined at milk-grain to negative ones determined at physiological maturity, confirming that the utility of vegetation indices to assess growth traits decreases drastically when the crop starts to senesce (Aparicio et al., 2000). Young wheat plants normally absorb more photosynthetically active radiation and therefore reflect more NIR. As the plants progress in growth stage, new tissues are formed but older green tissues lose chlorophyll concentration, turning chlorotic and then necrotic. These senescent tissues increase reflectance at the visible wavelengths and decrease reflectance at the NIR wavelengths, causing a decrease in the values of the vegetation indices compared with that obtained at earlier growth stages. Aparicio et al. (2002) concluded that genotypic differences were maximized in durum wheat when growth traits were determined by spectral reflectance measurements taken at anthesis and milk-grain stage.

Mistele and Schmidhalter (2010) Xue et al. (2004)

R780/R740 R780/R740 R780/R740 RI Ratio index R810/R560

Though a large number of studies demonstrate the utility of vegetation indices for assessing growth traits in small-grain cereals when there is a wide range of variability involved in the experimental data, the results indicate that the value of the indices decreases drastically when the range of variation caused by the environment or the crop canopies is low (Aparicio et al., 2002; Royo et al., 2003). In such cases the success of the indices at tracking changes in growth traits becomes much more experiment-dependent (Babar et al., 2006; Christensen & Goudriaan, 1993). Nevertheless, as stressed above, one of the practical applications of spectral reflectance may be its use as a routine tool for screening germplasm in breeding programs, when measurements are taken on a genotype basis, usually in one or a reduced number of experiments. Moreover, vegetation indices are more appropriate for assessing LAI than for estimating biomass (Aparicio et al., 2000, 2002; Serrano et al., 2000), particularly when measurements are taken with low variability backgrounds.

The Amazon Basin covers almost 25% of South America. With about 7.5 million km2, it extends into the territory of nine countries and accounts for 70% of tropical forests around the globe. Only in Brazil the total area is 5.1 million km2 (Fearnside, 2005). Despite its great beauty and exuberance, the Amazon rainforest is found in soils of low fertility, while its maintenance depends on the cycling of nutrients from vegetation covering (Cenciani et al., 2009).

The quality and soil fertility are defined from the point of view of some essential attributes that maintain the agricultural productivity, namely as: soil ability to promote plant growth, water supply and nutrient processing, efficient gases exchange in the atmosphere-soil interface and the activity of micro and macro organisms (Dilly & Nannipieri, 2001). In this context it is highlighted the role of soil microbial biomass (SMB), defined as the living portion of soil organic matter, excluding roots and larger organisms than, approximately, 5000 pm3. The microbial biomass comprises the dormant and the metabolically active organisms in the soil; performing a primary role for maintenance and the products of microbial recycling are then absorbed by plant roots (Cenciani et al., 2009).

Soil quality or even "soil health" can be analyzed by the activity of microbial biomass, one of few active fractions of organic matter, sensitive to tillage and that can be quantified. Overall SMB comprises about 2-3% of total organic carbon in the soil, thus indicating it to be a sensible parameter to evaluate the quality of soils submitted to different management strategies, or to pollution impacts. The development of indirect methods for measurement of SMB such as the incubation-fumigation (IF) (Jenkinson & Powlson, 1976), the substrate induced respiration (SIR) (Anderson & Domsch, 1978), the content of ATP in microbial cells (Jenkinson & Ladd, 1981) and the extraction-fumigation (EF) method (Vance et al., 1987) facilitated the assessment of the SMB compartment.

Some studies previously carried out in chronosequences forest to pasture in Amazonia have shown that SMB is reduced after 3 years of establishing pastures, but their levels are raised in older pastures, and reach similar contents in the native forest. Several studies quantified the main elements (C, N, P, S) immobilized into microbial cells at different soil depths (Feigl et al., 1995 a, b; Fernandes et al., 2002; Cenciani et al., 2009).

Overall SMB reflects the contents of total organic matter, representing an efficient and sensitive parameter in assessing the quality of soils under different management or impacts of pollution. In Brazil, some studies realized in chronosequences forest to pastures in Amazonia have shown that microbial biomass is reduced in the early years (about three to five years), but increases in older pastures reaching levels similar to those of the native forest (Feigl et al., 1995 a, b; Fernandes, 1999). The ability of SMB to increase again in older pastures, reaching values closer to the native forest suggests that the microorganisms of such soils have high resilience, or the capacity for growth and physiological activity, even after the impact of slash-and-burn of the native forest.

The stability of a system determines its ability to continue working under stress conditions, for both natural and those induced by human action (Orwin & Wardle, 2004). Since the microorganisms are the key players of the conversion of soil organic matter and the availability of nutrients, its resilience directly affects plant productivity and the stability of forest and agricultural ecosystems (Orwin & Wardle, 2005). For this reason it is essential to understand how microorganisms respond to environmental disturbances, as well as the factors involved in this response.

Biomass has been an intimate companion of humans from the dawn of civilization to the present. Its use as food, energy source, body cover and as construction material established the key areas of biomass usage that extend to this day. With the emergence of agriculture the soil productivity increased dramatically, especially with cultivation of new plant varieties and with emergence of intensive soil fertilization. In that context, the emergence and use of fossil fuels for energy and raw material in chemical industry is but a flick on the human history horizon. The amount of energy that humans used in the last two decades is roughly equal to the total amount of energy in the past. This enormous increase of energy use was made possible by extensive depletion of fossil reserves and is clearly unsustainable. Does it mean that once these reserves are depleted the amount of energy available to humans will be similar to the pre-fossil fuel era? Not necessarily. Currently, the total energy used by humanity amounts to 1/5500 fraction of the total solar energy incident on earth. In theory, significant percentage of that energy can be used for human needs, before it is let to complete the energy flow cycle (i. e. to be dissipated to space). Some of it can be harnessed and used as a direct solar energy, but other pathways uses natural photosynthesis to create biomass that can be seen as a form of chemically stored solar energy. Of course, biomass is also food and this brings about the key trade-off in biomass usage: the food vs. fuel controversy. Given these two primary uses of biomass the proper resolution of this tradeoff is essential for acceptable and beneficial biomass usage in the future. The glaring example of biomass for energy misuse is ethanol production from corn, a relatively inefficient conversion process that is also in a direct collision course with the corn as food pathway. Still, in 2009, about 15% of world corn production was converted into ethanol fuel. More subtle examples emerge when an inedible biomass is the energy source, but its production still competes with food supply chain. Recent world food price hikes, especially in 2008 have been blamed partly on diversion of food staples towards biomass fuel production. As humanity currently uses or appropriates (through deforestation and land use change) about 40% of land productive capacity, the accurate account of all existing and potential biomass usage pathways is critical for charting the way forward at the global scale, and in different regions.

Given the complexities of biomass as a source of multiple end products, food included, this volume sheds new light to the whole spectrum of biomass related topics by highlighting the new and reviewing the existing methods of its detection, production and usage. We hope that the readers will find valuable information and exciting new material in its chapters.

Since biomass means so many things to so many people, it is no wonder that the original book title, Remote Sensing of Biomass has attracted a wide range of papers, many of them very remote from the remote sensing theme. If there were few odd submissions that could not fit the theme at all, the choice would be simple. Check the quality of the paper and if it is good, suggest to the authors that it would be better to submit it elsewhere. InTech publishing is a wonderful open source publisher that published more than 180 volumes in 2010 alone, on such diverse topics as Virtual Reality, Biomedical Imaging or Globalization. Thus, an odd author who went astray could be stirred towards more suitable publication. And indeed, there were few that fell into that category. However, majority of submissions had a broad linkage to biomass, but not to its remote sensing. The wide range of themes, all related to biomass, prompted us to reconsider if the originally envisioned scope was perhaps understood by biologists and food scientists differently than by engineers? Is the simple act of examining biomass via a microscope a form of remote sensing? Is an indirect inference about details of physiological or genetic makeup of a subject biomass another form of remote sensing as well? Questions like these, and the desire to better reflect the scope and coverage of the book chapters led us to a new title, Biomass — Detection, Production and Usage. It reflects an even balance between these three areas of the biomass science and practice.

Dr. Darko Matovic

Queen’s University, Kingston, Canada

Two identical reactors were built; each reactor was 14 cm in diameter and 100 cm in height, providing an empty bed volume of 15 l. A small amount of freeboard or headspace (2.8 litres) was provided at the top of the reactor. The reactors were constructed from PVC, a non-transparent material that prevents the growth of phototrophic organisms. The columns were built with considerations for process air and influent supplies, backwashing air and water requirement and sampling outlets.

The control reactor was filled with 10.9 l cascade rings (Glitsch UK) whilst the second reactor was only partially packed with 5.5 l cascade rings. The media were stationary and held in place by a rigid polypropylene mesh with 15 mm diameter holes placed at the top and bottom of the packing. Three ports were placed along the height of the reactors for sample collection.

A synthetic waste prepared in the laboratory was used to provide a consistent organic substrate for all loadings. The basic make-up of the influent organic strength material used in the study was whey powder, glucose and meat extract (Lab Lemco powder) which contributed approximately 38%, 33% and 29% of the total soluble COD content of the substrate respectively. In order to guarantee that organic carbon was the limiting nutrient, a COD:N: P ratio of 25:5:1 was adopted. Nitrogen component of the feed came from whey powder (24.7%), meat extract (63.7%), and ammonium-dihydrogenphosphate (11.6%). 1 l of the prepared mixture produces a concentrated feed around 40000 mg/l COD.

To produce conditions that will encourage establishment of a wide range of seedlings through natural seeding, and avoid revegetation failing, an understanding of certain abiotic and biotic factors is required. The main factors that affect establishment through natural seeding are: species present, soil type, moisture, competition by grasses and herbs, available seed trees, and weather conditions (heat, dryness etc). It is important to know the timing and periodicity of seed production and dispersal. Basic knowledge about the period for the high rates of seed dispersal is necessary when practicing natural regeneration. In order to encourage natural seeding, ground preparation must be undertaken prior to seed dispersal. Specific characteristics of a species, such as number of seeds per tree, seed weight and frost resistance, greatly influence the establishment of seedlings. Seeds from some species are wind dispersed (e. g. birch and sallow (Salix caprea L.)) and others water dispersed (e. g. alder); a combination of methods may be used. Studies of wind-mediated seed dispersal for different species indicate the following order of decreasing dispersal:

birch>elm=maple>alder>hornbeam>beech>oak (Augspurger and Franson, 1987; Okubo and Levin, 1989; Willson, 1990; Karlsson, 2001). Table 1 contains data on birch and alder seed dispersal.

Distance from forest stand, m

<50———— 50-100——- 100-150——— >150 Country Reference

cut area have been found to spread at a rate of about 100 seeds m-2 up to 200 m from the tree (Fries, 1984). Most of these birch seeds were dispersed during September, although the process continued until December. In a study of sweet birch (Betula lenta L.), Matlack (1989) reported seed were dispersed 3.3 times further than the distance measured by Fries (1984). In a study of silver birch in Estonia, 21 % of the seeds were dispersed in July, 77 % in August and 2 % in September (Kohh, 1936). Heikinheimo (1932, 1937), who reported the same dispersal periods, commented that the weather during summer and autumn is the main factor affecting the period of seed dispersal. Graber and Leak (1992) presented a study on seed fall for broadleaved species in New Hampshire. The mean seed fall (million ha-1) in a study lasting 11 years was: 6.58 for yellow birch (Betula alleghaniensis Britton); 6.38 for paper birch (Betula papyrifera Marsh.); 4.11 for sugar maple (Acer saccharum Marsh.); and 0.17 for American beech (Fagus grandifolia Ehrh.). The seed viability was 30-50 %, depending on species.

Besides wind dispersal, there are some reports of secondary dispersal of seeds (Hesselman, 1934; Matlack, 1989; Greene and Johansson, 1997). The most common is by movement on snow, but for this to occur, seed fall must happen during winter months when snow is on the ground. The seeds can be damaged by friction on frozen snow, thus reducing viability. The level of seed production by alder depends on the number of hours of sunshine in the period April-September in the year before fruiting, the number of hours of sunshine in the seeding year and the level of seed production in the preceding year (MacVean, 1955). According to MacVean (1955), common alder (Alnus glutinosa (L.) Gaertner) seeds are generally dispersed within a radius of 30-60 m of the mother tree. Karlsson (2001) found that 50 % of the total number of alder seeds produced fell within 5 m and 90 % within 20 m of the stand. In a study by Johansson and Lundh (2006), 50 % of the common alder seeds were found to have fallen before December and 75 % before February. Alder seeds can also be transported by water in spring at the time of snow melt.

Seeds from European aspen (Populus tremula L.) are extremely small (low weight) with a limited growing capacity (Blumenthal, 1942, Latva-Karjanmaa et al., 2006). A large aspen growing close to Tartu city, Estonia, produced 49 kg or 54 million seeds (Reim, 1930). Only a small proportion of the aspen seeds produced will grow; success depends on site conditions, seed size and the level of competition. Aspen seeds can grow on poor sandy sites, burned areas and small patches without vegetation (Blumenthal, 1942). Seeds of sallow are also small and have a plume to aid dispersal (Grime et al., 1988). Seeds of both species can be dispersed over long distances.

The most favorable soil types for rapid establishment of seedlings are fine sand, silt and light clay, sandy-silty till and light clay till. Even peat soils can provide an ideal site, providing there is sufficient water. A mixture of mineral soil and humus is common on farmland, where the area has been cultivated for many years.

Birch seeds establish well on undisturbed sites with a high level of moisture (Mork, 1948; Fries, 1982). During the first part of the growing season in Nordic countries (April-May) soil moisture tends to be low. The lack of rain combined with the sunshine during this period results in a dry soil. Therefore any soil treatment (plowing, harrowing or screefing) should be undertaken in autumn or very early in spring. Studies to determine the best soil treatment to ensure limited cover of competitive vegetation indicate that removal of topsoil is preferable (Karlsson, 1996).

growing individual stems. In studies of dormant buds on birch, most have been found close to the ground: 0-10 cm above or 0-5 cm below ground level (Kauppi, 1989; Kauppi et al., 1987; 1988 Johansson, 1992a). The number of sprouts per living birch stump has been found to vary between 1 and 52, mean 10±8, decreasing to 3-8 sprouts per stump after five years (Johansson, 1992 b, c). Rydberg (2000) found the number of birch sprouts had decreased by >40 % of the initial number two years after stump creation nine years after cutting, Johansson (2008) found that the initial number of sprouting birch stumps had decreased to 61 and 55 % respectively for downy and silver birch stumps. In a study of downy birch growing in central Finland, the number of sprouts decreased from an average of 9.5 one year after cutting to 5 after three years and 3 after seven years. The sprouting abilities of red oak (Quercus rubra L.), white oak (Quercus alba L.), black cherry (Prunus serotina Ehrh.), sugar maple and yellow poplar (Liriodendron tulipifera L.) growing in West Virginia were studied by Wendel (1974). After ten years the number of sprouts per living stump was 15-20 % of the initial number produced. In another study of yellow poplar, the average number of sprouts recorded six years after cutting was 7.0 per stump (Beck, 1977). Sprouting capacity is highest when a tree is young (Johansson, 1992c). Kauppi et al. (1988) reported the poorest sprouting results from old (40 year) downy birch stumps. Older trees have thicker stem bark, so the buds cannot penetrate the bark and develop into sprouts (Mikola, 1942). Sprouting capacity may depend on carbohydrates in the roots. However, Johansson (1993) found no pronounced peaks in the carbohydrate content in birch roots during the year. Sprouting capacity may also depend on the cutting date. Johansson (1992b) found the highest number of living birch stumps producing sprouts cut in all months but June-October. Etholen (1974) found no effect of cutting time on the sprouting ability of young downy birch stumps.

In southeastern New York, Kays and Canham (1991) studied the sprouting ability of four hardwood species: red maple (Acer rubrum L.), gray birch (Betula populifolia Marsh.), white ash (Fraxinus Americana L.) and black cherry (Prunus serotina Ehrh.). They reported that gray birch had the highest mortality (87 %) of stumps after cutting in May but the other species only had mortalities of 10-20 % depending on cutting date. In a study of the suckering capacity of parent trees of American beech, a mean of 41,365 (3,924-89,765) suckers ha-1 was found (Jones and Raynal, 1986).

European aspen and trembling aspen (Populus tremuloides Michx.) are two Populus species with a high capacity for sucker production. The number of suckers after cutting the mother tree differs depending on the cutting date (Johansson, 1993) and on site, stand and management factors (Frey et al., 2003). The age of the mother tree also influences the suckering ability (Brinkman and Roe, 1975). A trembling aspen stand was found to produce 8000 suckers ha-1 after cutting (Tew, 1970). In a study by Alban et al. (1994) of trembling aspen growing in Minnesota, the number of suckers the first year after disturbance was >250,000 per hectare. The number had decreased to 40,000 after five years (Stone and Elioff,

1998) . Trembling aspen stands growing on similar soils in Minnesota and British Columbia produced 50,000 suckers ha-1 after five years and the mean sucker height was 2.1 m (Stone and Kabzems, 2002). The root system of an individual aspen is widely spread, with root lengths up to 20 m (Reim, 1930). In a Swedish study, about 70 % of the suckers occurred within 10 m of the parent aspen tree (Barring, 1988). In a study by Johansson (1993) the content of starch in roots of European aspens fluctuated during the year with the lowest levels in May-July. The same pattern has been reported for trembling aspen by Baker (1925), Zehngraff (1946), Tew (1970) and Brinkman and Roe (1975).The lowest content has been recorded in late May and early June. When aspen is cut in the winter the highest numbers of suckers are produced (Stoeckler and Macon, 1956; Steneker, 1976; Peterson and Peterson,

1992) . In other studies (Shier and Zasada, 1973; Fraser et al., 2002) on trembling aspen, no relationships have been identified between carbohydrate content in roots and the number of suckers initiated.

Alder regenerate vegetatively by sprouts or suckers depending on species. In a study of red alder (Alnus rubra Bong.), the number of sprouts per living stump ranged between 5 and 9 (Harrington, 1989). In another study of the same species, the number of sprouts was in the range 9-13 (DeBell and Turpin, 1989). According to Rytter (1996), young grey alders (Alnus incana (L.) Moench) produce sprouts after cutting, but the old trees produce suckers. In a Finnish study, grey alder stumps sprouted within three weeks of cutting (Paukkkonen and Kauppi, 1992). Sucker production by grey alder is the main means of vegetative regeneration when the trees are more than 25-30 years old (Schrotter, 1983). In a study of seasonal variation of carbohydrates in the roots of common and grey alders, levels were found to be highest during September-November (Johansson, 1998). In a study of the influence of felling time on sprout and sucker production by common and grey alder, the carbohydrate content in the roots was found to influence biomass production (Johansson, 2009). The highest number of sprouts from common alder stumps was produced after cutting in August-October (23-24 sprouts stump-1). Ten years later, the number of sprouts had decreased to 1.3-2.3 sprouts stump-1. The average number of sprouts on living grey alder stumps was highest after cutting in March (3.0), August (3.4) and September (3.4), with a reduction to an average of 2.0 after five years. The number of grey alder suckers per m2 was highest, 21.0, after cutting in September with a reduction to 1.5 after five years. The recommendation, therefore, is to cut grey alder in August and September ad common alder in August-October when the largest number of sprouts and suckers will result. In a study on the initial sprouting of 4-year-old red alders, the percentage of sprouting stumps was highest when the alders were cut in January (Harrington, 1984).

In a study of the spouting ability of Eucalyptus in plantations, the number of sprouts per living stump varied, but the highest number was 5-6 sprouts stump-1 (Sims, 1999). The stumps have the capacity to resprout several times, depending on their vigor.

The terrain model function z = f(x, y) is computed from 3D points, p; = (xl, yl, zi),i = 1, …,n, where n is the number of points (Shan and Toth, 2009). Heights are stored at discrete, regularly aligned points, and the interpolated height as the height of the grid has to be given within a grid mesh. These grid heights are obtained by interpolation methods explained before in the subsection 3.1.2. These methods consist of nearest neighbor, IDW, kriging, spline, and least square fitting.

An alternative method to the interpolations is so called triangular irregular network (TIN) data structure. The original points are used for reconstructing the surface in the form of TIN. For large point sets, triangular networks are more effective than the time consuming methods which are mentioned before. Digital surface model (DSM) is generated from noise removed Lidar data and represents the canopy top model. Digital terrain model (DTM) is basically produced by the laser pulse returns which are assumed to be on the terrain. (van Aardt et al., 2008). By subtracting DTM from DSM, CHM can be obtained which is presented in figure 5. Hence, CHM is a digital description of the difference between tree canopy points and the corresponding terrain points.

Biofilms that formed on the PM1 plates were quantified with the ATP assay and compared between the two strains with the f-test. The analysis did not yield any carbon sources that supported more biofilm in the parent strain than in the mutant. The 25 carbon sources that yielded significantly higher amounts of biofilm in the flhD mutant are demonstrated in Figure 4. Since the carbon sources that supported biofilm formation by the mutant more so than by the parent are numerous, we decided to analyze each strain statistically first and focus the comparison between the strains to specific structural categories of carbon sources. These are designated ‘nutrient categories’ throughout this manuscript.

2.1.1 Carbon sources that formed their own duncan’s group for the parent strain

The normalized data set from the parent strain was subjected to Duncan’s multiple range test. According to this test, the two carbon sources that were the best biofilm supporters for the parent E. coli strain, maltotriose and maltose, formed exclusive groups A and B. Without

forming its own Duncan group, ribose was the carbon source that supported the smallest amount of biofilm among all carbon sources tested, while still supporting growth. The parent strain also formed good amounts of biofilm on the remaining C6-sugars. Interestingly, the amount of biofilm that formed on maltotriose (trisaccharide of glucose) was roughly three times the amount of biofilm that formed on glucose. The amount of biofilm that formed on maltose (disaccharide of glucose) was about twice the amount that formed on glucose. The C5-sugars xylose and lyxose did not support growth of the parental strain to the cutoff of 0.5 OD600. For all these carbon sources, biofilm amounts formed by the flhD mutant were compared to the parent strain (Table 3). In contrast to the parental strain, the flhD mutant did not grow well on C6-sugars and their oligosaccharides. Unlike the parental strain, the mutant did not grow well on ribose, but grew to the cut off of 0.5 OD600 on lyxose and xylose. Still, the amount of biofilm formed by this strain on C5-sugars was low (<1,000 RLU). An interesting phenomenon was observed for sugar phosphates and sugar acids. Sugar phosphates supported biofilm production by the mutant more so (>1,200 RLU) than for the parent strain (<600 RLU). Likewise, sugar acids were found to be good supporters of biofilm for the flhD mutant strain (1,500 to 2,500 RLU), but not for the parent (500 to 800 RLU). This was even more remarkable, considering the fact that the parental strain (OD600 ~ 1.0) grew better on sugar acids than the flhD mutant (OD600 of 0.2 to 0.8).

Biomass assessment of individual plants by conventional methodologies involves destructive sampling, which is inappropriate for studies aiming to monitor the growth of specific individuals during their growth cycle, or when the grain produced by the plant has to be harvested at ripening, as in breeding programs. In such cases growth traits such as dry weight per plant (W), green area per plant (GAP) and leaf area per plant (LAP) may be properly estimated through vegetation indices.

Since the devices commercially available at present only allow measurements at canopy level, spectral reflectance measurements of individual plants require some adaptation of common equipment to avoid background effects. In studies conducted with wheat by Casadesus et al. (2000) and with four cereal species by Alvaro et al. (2007), the plants were covered by a tube of reflecting walls provided by an artificial source of light (Fig. 5). In order to provide a homogeneous background, aluminum foil was placed around the base of each plant, covering the entire tube base. The spectroradiometer was fitted to a receptor for diffuse spectral irradiance, centered at the top of the tube. The spectra obtained were standardized with the spectrum previously sampled in the empty tube with the soil covered

with a homogeneous white reflecting surface. This method allows measurements to be taken at any time of the day, regardless of the environmental conditions (sun light angle and intensity, weather conditions, etc.), while avoiding background disturbances such as soil color. In this case each spectral reflectance measurement takes 20-30 s and five scans per plant are sufficient to obtain reliable results.

Consistent associations of NDVI and SR with W (R2=0.91, P<0.001), GAI (R2=0.88-0.89, P<0.001) and LAP (R2=0.66-0.69, P<0.001) measured on spaced plants (Alvaro et al., 2007) have been reported. The accuracy of reflectance measurements to detect differences between individual plants seems to be comparable to that obtained by destructive measurements of growth traits (Alvaro et al., 2007), so this methodology is a promising tool for assessing growth traits in spaced individual plants. However, the time needed to prepare the plants and to take measurements may constrain its extensive use.

Amazonian tropical forest soils are supposed to hold high microbial biodiversity, since they support by litter recycling one of the most luxuriant ecosystems. However anthropogenic practices of slash-and-burn, mainly for pasture establishment, induce deep changes in the biogeochemical cycles, and possibly in the composition and function of microbial species (Cenciani et al., 2009).

While the diversity of microorganisms in the soil is immense, only a very low percentage is cultivable (around 1%) under laboratory conditions. The limited range between the bacteria species, for example, hampers the detection by microscopy techniques. Additionally the methods of obtaining bacteria in culture medium are not very effective for its quantification, due to difficulties in reproducing the conditions that every species or groups require in their natural habitats (Felski & Akkermans, 1998). Estimates of the global diversity of fungi indicate that a small percentage is described in the literature, especially due to limitations found in techniques of cultivation to assess the diversity of fungi. Apart from this the lack of taxonomic knowledge hinders the identification of bacterial and fungal species found in the soil (Kirk et al., 2004).

The study of prokaryote diversity is extremely complex because the definition of species for these organisms is a question still open. Currently a prokaryotic species is regarded as a group of strains including the standard strain, characterized by some degree of phenotypic consistency showing 70% or more DNA-DNA homology and more than 95% similarity between the 16S rRNA gene sequences. In this context we highlight the importance of polyphasic taxonomy, which aims to integrate different datasets and phenotypic, genetic and phylogenetic information about the microorganisms (Gevers et al., 2005).

With the advance of molecular biology it became possible to identify bacteria, fungi and other microorganisms in the soil and plants without need to isolate them. One of cultivation — independent molecular tool that has often been used to analyze the diversity and dynamics of microbial populations in the environment is the polyacrylamide gel electrophoresis in denaturing gradient (DGGE). The DNA is extracted and purified and only a fragment of the rRNA gene is amplified by the polymerase chain reaction (PCR). The amplification products are analyzed by gel electrophoresis, which allows the separation of small PCR products, commonly up to 400 bp according to their contents of guanine plus cytosine (G+C) Consequently, the fingerprinting pattern is distributed along a linear denaturing gradient (Muyzer & Ramsing, 1995; Courtois et al., 2001; Cenciani et al., 2009).

1.1 Fungi diversity assessed by PCR-DGGE

The role of fungi in the soil is complex and fundamental to maintain the functionality of the biome. Fungi play an active role in nutrient cycling and develop pathogenic or symbiotic associations with plants and animals, besides interacting with other microorganisms (Anderson & Cairney, 2004).

Working with soils in the Amazonia, Monteiro et al. (2007) described the changes in the genetic profiling of soil fungal communities caused by different land use systems (LUS): primary forest, secondary forest, agroforestry, agriculture and pasture. The author conducted her study in the following sequence: DNA extraction — total DNA was extracted using the Fast DNA kit (Qbiogene, Irvine, CA, USA), according to the manufacturer’s instructions; PCR — a fragment of the 18S rRNA gene (1700 bp) of fungi was amplified by PCR according to Oros-Sichler et al. 2006; DGGE — amplicons were separated on an acrylamide gel containing bisacrilamide and a linear gradient of urea and formamide (Fig. 2).

Diversity Database program (BioRad) was used to determine the richness of amplicons. The non-metric multidimensional scaling (NMDS) tool was used to determine the effect of land use changes under the fungi communities through the PRIMER 5 program (PRIMER-E Ltd., 2001).

The DGGE of the 18S rRNA gene combined with NMDS statistic tool showed the presence of distinct communities in each of the areas analyzed, with the presence of single bands. Results indicated the dominance of specific fungal groups in every treatment, especially in the area converted to pasture, distant from the other systems of land use (Fig. 2).

Following this pattern the authors asserted that the banding profile generated by DGGE represent fungi communities from different soils, and were shown to be more similar among samples from the same system of land use than among samples of different systems of land use. However the clustering of samples through NMDS showed that there is a tendency for samples from pasture be different of the other sites, which are closest relatives among them ( Fig. 3). Finally the results obtained by the authors show that changes in the land use affected the community structure of soil fungi; as well it is also possible that the type of vegetation covering has a key role in such changes (Monteiro et al., 2007).

Although molecular fingerprinting approaches such as cloning and sequencing are being used increasingly for evaluation of fungal communities, there are scarce studies reaching the diversity of fungi in soils of native forests, and in the same soils but impacted by agricultural management. Within this context changes in the genetic profile of fungi according to each system of land use, and the environmental stress can provide valuable information for the sustainable management of forest soils (Monteiro et al., 2007).

Yashar Fallah Vazirabad and Mahmut Onur Karslioglu

Middle East Technical University

Turkey

1. Introduction

Great attention has been paid to biomass estimation in recent years because biomass can simply be converted to carbon storage which is very important to understand the carbon cycle in the environment. Biomass is typically defined as the oven-dry mass of the above ground portion of a group of trees in forestry (Brown, 1997, 2002; Bartolot and Wynne, 2005; Momba and Bux, 2010). However there are a few studies about below ground biomass estimation. Conventionally, it is estimated using measurements which are recorded on the ground. On the other hand, the large number of studies have confirmed that Lidar as a kind of active remote sensing system is able to estimate biomass properly (Popescu, 2007). Hence time-consuming field works can be avoided and unavailable regions become accessible using a relatively low cost and automated Lidar system. (Nelson et al., 2004; Drake et al., 2002, 2003; Popescu et al., 2003, 2004).

Traditional remote sensing systems detect vegetation cover using active and passive optical imaging sensors (Moorthy et al., 2011). Passive systems depend on the variability in vegetation spectral responses from the visible and near-infrared spectral regions. Widely accepted algorithms such as the Normalized Difference Vegetation Index (NDVI) have been empirically correlated to structural parameters (Jonckheere et al., 2006; Solberg et al., 2009; Morsdorf et al., 2004, 2006) such as Leaf Area Index (LAI) of canopy-level. On the contrary to passive optical imaging sensors, which are only capable of providing detailed measurements of horizontal distributions in vegetation canopies, Lidar systems can produce more accurate data in both the horizontal and vertical dimensions (Lim et al., 2003). Lidar — based instruments from space-borne, airborne, and terrestrial platforms provide a direct means of measuring forest characteristics which were unachievable previously by passive remote sensing imagery.

Developments in remote sensing technologies, in particular laser scanning techniques, have led to innovative methods and models in the estimation of forest inventories in terms of efficiency and scales (Hudak et al., 2008; Tomppo et al., 2002; Tomppo and Halme, 2004; Zhao et al., 2009; Koch, 2010; Yu et al., 2011). Lidar experiments and researches within the remote sensing community are now focusing to develop robust methodologies. These methods and models employ very precise 3D point cloud data (Omasa et al., 2007) to direct process and retrieve vegetation structural attributes which are validated by in situ measurements of vegetation biophysical parameters (Maas et al., 2008; Cote et al., 2011). Laser scanning systems have been used to extract various kinds of parameters, such as tree height, crown size, diameter at breast height (dbh), canopy density, crown volume, and tree

species (Donoghue et al., 2007; Means et al., 1999, 2000; Magnussen et al. 1999). Most authors concentrate on the above-ground biomass while there are a few known studies focusing on the below-ground biomass (Kock, 2010; Nasset, 2004).

Bortlot and Wynne (2005) used Lidar data to generate canopy height models. Tree heights detected from image processing are entered as variables in a stepwise multiple linear regression to find an equation for biomass estimation. The method skips detecting small trees. They are not included in the process of estimation. A previous work by Lefsky et al.

(1999) presented the prediction of two forest structure attributes, crown size and aboveground biomass from Lidar data. They analyzed the full waveform of the return pulses to define the beginning of canopy return. Linear regression was used to develop biomass estimation equation based on a defined canopy height index. Finally, they proposed stepwise multiple regression model to predict canopy volume and relatively biomass. They concluded that tree height is highly correlated with dbh in a square power function.

Van Aardt et al. (2008) evaluated the potential of an object oriented approach to forest classification as well as volume and biomass estimation using small footprint, multiple return Lidar data. A hierarchical segmentation method was applied to a canopy height model (CHM). An empirical model is employed to estimate the canopy volume and biomass. They performed stepwise discriminant analysis as a part of classification steps for variable reduction. Fallah Vazirabad and Karslioglu (2009) investigated the biomass estimation based on single tree detection method. This method is used to locate trees and detect the height of each tree top. Diameter at breast height is extracted from the close relation to the tree height which is defined by field measurements. A Log transformed model is applied for biomass estimation taking into account the dbh variable.

Airborne lidar is confirmed as the most ideal technology to obtain accurate CHM over large forested areas because of its high precision and its ability to receive ground returns over vegetated areas. Spaceborne geoscience laser altimeter system (GLAS) data on the other hand are intended to use mainly for scientific studies of sea ice elevation (Zwally et al., 2002; Kurtz et al., 2008; Xing et al., 2010), but it is also suitable for the estimation of the canopy height map (Lefsky et al., 2005; Simard et al., 2008; Chen, 2010; Duncanson et al., 2010).

The reason for the applications of GLAS data to canopy height mapping is to estimate the dynamic global carbon stock. Xing et al. (2010) analyzed the deforestation and forest degradation as a carbon source estimation model. They also investigated the forest growth model for afforestation and reforestation. Forest carbon stocks, fluxes, and biomass are directly related to each other (Garcia-Gonzalo et al., 2001; Widlowski et al., 2004). Therefore, accurate estimation of biomass of stocks and fluxes is essential for terrestrial carbon content and greenhouse gas inventories (Muukkonen and Heiskanan, 2007; Xing et al, 2010).

A general overview of forest applications is provided by recent studies (Hyyppa et al., 2009; Dees and Koch, 2008; Mallet and Bretar, 2009; Koch, 2010). They show that the information related to the height or structure of forests can be extracted with high quality.

Apart from the land cover classification Lidar intensity data can be used to differentiate materials such as asphalt, grass, roof, and trees (Hasegawa, 2006; Donoghue et al., 2007; Kim, 2009; Song et al., 2002). To identify the position and diameter of tree stems within a forest the intensity of Lidar returns has been successfully used (Lovell et al., 2011). Hopkinson and Chasmer (2009) compared four lidar-based models of canopy fractional cover and found that those models which included the intensity of the returns were less affected by differences in canopy structure and sensor configuration. This is because the intensity measurements provide some quantification of the surface areas interacting with the laser beam. Reitberger et al (2008) used a waveform decomposition method to extract intensity and concluded that detection of small trees below the main canopy was improved. The ability to acquire laser pulse echoes from the bottom part of vegetation canopies is restricted in the spaceborne and airborne Lidar system. This is reffered to the system properties such as laser footprint size, recording frequency, as well as the natural placement of the crown elements, for example dense or open canopies. But to provide detailed specification of canopy and individual tree crowns characterization it is logical to introduce a terrestrial platform which has a much higher resolution laser pulse records than others. However, terrestrial data for tree 3D models have some problems such as overlapping crowns and under-story vegetation which cause shadowing effects.

Deriving forest data from Lidar data to model the canopy height distribution and its statistical analysis was proposed by (Holmgren and Persson, 2004; Lim et al., 2003, 2004; Nfesset, 2002). The single tree detection, its location and characteristics on the basis of statistical analysis have been studied by (Hyyppa and Inkinen, 1999; Fallah Vazirabad and Karslioglu, 2010; Yu et al. 2011).