Swedish University of Agricultural Sciences, Department of Energy and Technology,

Sweden

1. Introduction

Our sources of energy are constantly changing. In Sweden the focus is on nuclear and hydro power for producing electricity and total Swedish energy production amounts to about 612 TWh (Anon, 2010). Since Sweden has a cold climate, there is a high demand for energy to heat homes and energy sources other than oil and coal are required. Currently, fuel systems are based on oil and electrical power but there has been an increase in the use of biomass during recent decades. The support of biomass for heating provides 19% of the total Swedish energy output, (Fig. 1).

For centuries trees have been used in a domestic context for firewood and charcoal production. In Sweden, conventional forest management combined with bioenergy production has been practiced for the last 40-50 years. Currently, for economic reasons, bioenergy harvesting is mainly based on large areas of forest land. Tops and branches are harvested from clear cut areas and this biomass contributes greatly to the production of bioenergy. Special equipment is used to harvest biomass, which is used for energy production in direct heating plants. The infrastructure is well established. Most of the harvested material goes to heating plants close to cities, although some is used by individual households.

The management of forests is mainly directed towards producing pulpwood and timber. The remaining parts of the tree — branches and tops — represent raw material for bioenergy production. Over the last twenty years there has been an increased willingness to make use of these parts of the tree.

Biomass production on former farmland, using willows, poplar and hybrid aspens, is another option for energy production. In general, the Swedish people look favorably on such land use, as well as forest biomass production. There is strict regulation of the management of forest land to minimize the risks of nutrient loss, but no such regulations exist for farmland. Farmers and some sections of the public wish to maintain farmland as an open landscape and to continue with agricultural cultivation.

The Swedish government has twice proposed a reduction in farmland available for the production of cereals, in 1969 and 1986. The plan was to reduce the area by about one million hectares, out of the total of three million hectares. Both attempts failed, although since 1968 350,000 ha have been taken out of production. Some areas of this former farmland have been planted, mostly with Norway spruce and birches, but more than 200,000 hectares which were taken out of production in the period 1970-1980 have received no subsequent management. Today these areas are covered by broadleaved trees with a range of numbers of stems per hectare (Johansson, 1999a), but they are not managed to generate forest products.

The problems which are mentioned before in first and last pulse systems for vegetated regions can be solved with full waveform technology making an important contribution to biomass estimation (Shan and Toth, 2009). The waveform is usually digitized by recording the amplitude of the return signal at fixed time intervals (figure 2). To analyze the signal of emitted short duration laser pulse with only a few ns pulse-width, higher digitizer sampling rate is required. These devices have been primarily designed for measuring vegetation properties. Extensive researches (Harding et al, 2001; Lefsky et al., 2001, 2002; Reitberger et al., 2009) have shown that waveform shape is directly related to canopy biophysical parameters including canopy height, crown size, vertical distribution of canopy, biomass, and leaf area index.

Harding et al. (2001) discussed about canopy height profile detection from full waveform raw data provided by SLICER. They studied the laser energy from the full waveform Gaussian distribution. The advantages of full waveform recording include an enhanced ability to characterize canopy structure, the ability to concisely describe canopy information over increasingly large areas, and the availability of global data sets. The examples of these data are airborne like SLICER and LVIS, and satellite data like GLAS. The other advantage of full waveform systems is that they record the entire time varying power of the return signal from all illuminated surfaces on canopy structure. It should also be stated that Lidar data, which is collected from space globally, provides only full waveform recordings (Lefsky et al., 2002).

2. Methods and models for Biomass estimation

This section is organized in terms of three subsections containing data pre-processing, methods and models, and applications.

Data pre-processing methods in turn are divided into four parts. For the filtering methods some efficient algorithms are explained. Apart from different interpolation methods the generation of the digital terrain model (DTM), digital surface model (DSM), and canopy height model (CHM) is treated. Quality assessment of laser data is carried out within another subsection. Additionally, the quality of filtering methods, interpolation methods, DTMs, DSMs, CHMs results and their performances are also evaluated. The subsection "methods and models" consider the methods and models in biomass estimation, among others single tree and tree characteristics detection. The last subsection presents applications of Lidar using the models for biomass estimation to recognize the advantages of Lidar systems in the biomass estimation.

1.1 Bacterial strains and growth conditions

The bacterial strains used in this study were the E. coli parental strain AJW678, which was characterized as an efficient biofilm former (Kumari et al., 2000) and its isogenic flhD, fliA, fimA, and fimH mutants. The flhD mutant was constructed by P1 transduction, using MC1000 flhD:kan (Malakooti, 1989) as a donor and AJW678 as a recipient. This resulted in strain BP1094. AJW2145 contained a fliA::Tn5 insertion, AJW2063 a fimA::Kn mutation, and AJW2061 a fimH::kn mutation, all in AJW678 (Wolfe et al., 2003). The mutations abolish expression of FlhD/FlhC, FliA, FimA, and FimH, respectively. As a consequence, mutants in flhD and fliA are non-motile, whereas mutants in fimA are lacking the major structural subunit and mutants in fimH the mannose specific adhesive tip of the type I fimbrium. Bacterial strains were stored at -80°C in 8% dimethylsulfoxide, plated onto Luria Bertani plates (LB; 1% tryptone, 0.5% yeast extract, 0.5% NaCl, 1.5% agar) prior to use, and incubated overnight at 37°C. Bacterial strains are summarized in Table 2.

Environmental factors can cause undesired variation in the captured spectra. Light intensity, sun position, winds or nebulosity may interfere with the way in which the interaction between solar irradiation and crop is captured (Baret & Guyot, 1991; Huete 1987; Jackson 1983; Kollenkark et al., 1982). Green biomass may be overestimated when measurements are taken on cloudy days because the increased diffuse radiation improves the penetration of light into the canopy. Brief changes in canopy structure caused by winds may also induce variations in the captured spectra (Lord et al., 1985). The presence of people or objects near to the target view area should be avoided, since they can cause alterations in the measured spectra by reflecting radiation. The instruments should be painted a dark color and people should preferable wear dark clothes (Kimes et al., 1983). As a means of minimizing the variability induced by sun position, it has also been recommended that measurements be taken at about noon on rows oriented east to west.

1.1.2 Canopy attributes

The reflectivity of a crop canopy may be affected by a number of internal and external factors. The crop species, its nutritional status, the phenological stage (Fig. 4), the glaucousness, the geometry of the canopy and the spatial arrangement of its constitutive elements greatly affect the optical properties of the canopy surface. Under severe nitrogen deficiencies, chlorosis in leaves causes plants to reflect more in the red spectral region (Steven et al., 1990). The presence of non-green vegetation or non-leaf photosynthetically active organs (such as spikes and leaf sheaths of cereals) and changes in leaf erectness can also affect the spectral signature of the canopy (Aparicio et al. 2002; Bartlett et al., 1990; Van Leeuwen & Huete, 1996); for high LAI values, the reflectivity decreases with greater leaf inclination in both the VIS and the NIR wavelengths (Verhoef & Bunnik, 1981). Radiation reflected perpendicularly from plant canopies has been reported to be greater for planophile than for erectophile canopies (Jackson & Pinter, 1986; Zhao et al., 2010).

Total microbial biomass of the organic wastes was significantly (P < 0.05) increased due to vermicomposting (Fig. 1). Periodical analysis indicated an exponential nature of biomass dynamics in organic substrates during vermicomposting. Addition of PM significantly (P <

0. 05) increased microbial biomass in final vermicompost. The highest MBC content was registered within 15-30 days of vermicomposting. MBC of vermicomposts, prepared from T1 and T2 were statistically at par. Vermicompost of T3, however, recorded significantly (P <

0. 05) higher MBC as compared to other treatments.

Periodical analysis revealed the variable pattern of biomass dynamics for total microbial community, fungi and bacteria during vermicomposting of various IS and PM mixtures. Ergosterol content i. e., fungal biomass (FBC) in all the treatments was sharply increased in the first 30 days and thereafter decreased gradually till the end of the vermicomposting process (Fig. 2). However, the final fungal biomass of vermicompost was significantly (P < 0.05) higher than that of initial organic substrates. Addition of PM with IS, significantly (P < 0.05) increased fungal biomass of final vermicompost. Vermicompost prepared from T3 recorded significantly (P < 0.05) higher FBC as compared to other treatments and FBC values of vermicomposts, prepared from T and T2, were statistically at par.

Periodical analysis results revealed that total FAME content in vermicompost followed almost same of ergosterol content (Fig. 3). The highest FAME was recorded in T3 treatment and it was significantly higher than other treatments.

Muramic acid was estimated as an indicator of bacterial biomass. Periodical estimation of muramic acid in the waste mixture revealed a steady increase in the muramic acid content up to 45 days of the process and thereafter it decreased till the end of the process. The final muramic acid contents of vermicomposts, prepared from T2 and T3, were significantly (P < 0.05) higher than that of their initial waste mixtures. In case of T0 and T1 treatments, muramic acid contents of vermicomposts were statistically at par with that of initial wastes. Analysis revealed that muramic acid contents of vermicomposts, prepared from T0 and T1 treatments, did not differ statistically among them.

3.2 Plant growth promotion

Incubation of radish seeds with ethyl acetate extract of vermicomposts for 5 days significantly (P < 0.05) increased root and shoot length of radish as compared to control. Column chromatography of concentrated ethyl acetate extract of vermicomposts yielded 24 fractions. Radish bioassay with all these fractions revealed that 3 fractions (5th, 7th and 8th) out of 24 fractions were able to increase radish root and shoot length as compared to control as well as other fractions (Fig. 5). The root and shoot length of all fractions were presented in Fig. 6. Vigor index, summation of root length and shoot length, is a good indicator for plant — growth promotion and its highest value was recorded in fraction 5.

HPLC analysis of these three fractions confirmed the presence of indole acetic acid (IAA) in 5th fraction (Fig. 7). Incubation of serially diluted vermicomposts extract in tryptophan- amended broth medium revealed pink colouration after 7 days incubation. Colorimetric analysis indicated the presence of 137 ^g IAA L-1 medium after 7 days.

It is often difficult to transfer one model developed in a specific study area to other study areas because of the limitation of the model itself and the nature of remotely sensed data. Foody (Foody et al., 2003) discussed the problems encountered in model transfer. Many factors, such as uncertainties in the remotely sensed data (image preprocessing and different stages of processing), AGB calculation based on the field measurements, the disparity between remote sensing acquisition date and field data collection, and the size of sample plot compared with the spatial resolution of remotely sensed data, could affect the success of model transferability. Each model has its limitation and optimal scale for implementation. Models developed in one study area may be transferred to (1) across-scene data, which have similar environmental conditions and landscape complexity, to estimate AGB in a large area; and (2) multi-temporal data of the same study area for AGB dynamical analysis if the atmospheric calibration is accurately implemented. The spectral signatures, vegetation indices, and textures are often dependent on the image scale and environmental conditions. Caution must be taken to ensure that there is consistency between the images used in scale, atmospheric and environmental conditions. Calibration and validation of the estimated results may be necessary using reference data when using transferred models.

The data sources used for AGB estimation may include field-measured sample data, remotely sensed data, and ancillary data. A high-quality sample dataset is a prerequisite for developing AGB estimation models as well as for validation or assessment of the estimated results. Direct measurement of AGB in the field is very difficult. In general, AGB is calculated using the allometric equations based on measured DBH and/or height, or from the conversion of forest stocking volume. These methods generate many uncertainties and calibration or validation of the calculated AGB is necessary. Previous research has discussed the uncertainties of using the allometric equations (Brown & Gaston, 1995; Keller et al., 2001; Ketterings, 2001; Fearnside, 1992) and of conversion from stocking volume (Masters, 1993). It is important to ensure that the remote sensing data, ancillary data, and sample plots are accurately registered when ancillary data are used for AGB estimation. Understanding and identifying the sources of uncertainties and then devoting efforts to improving them are keys to a successful AGB estimation. More research is needed in the future for reducing the uncertainties from different sources in the AGB estimation procedure. Many remote sensing variables, including spectral signatures, vegetation indices, transformed images, and textures, may become potential variables for AGB estimation. However, not all variables are required because some are weakly related to AGB or they have high correlation with each other. Hence, selection of the most suitable variables is a critical step for developing an AGB estimation model. In general, vegetation indices can partially reduce the impacts on reflectance caused by environmental conditions and shadows, thus improving correlation between AGB and vegetation indices, especially in those sites with complex vegetation stand structures (LU, 2004). On the other hand, texture is an important variable for improving AGB estimation performance. One critical step is to identify suitable textures that are strongly related to AGB but are weakly related to each other. However, selection of suitable textures for AGB estimation is still a challenging task because textures vary with the characteristics of the landscape under investigation and images used. Identifying suitable textures involves the determination of appropriate texture measures, moving window sizes, image bands, and so on (Franklin & Hiernaux, 1991). Not all texture measures can effectively extract biomass information. Even for the same texture measure, selecting an appropriate window size and image band is crucial. A small window size, such as 3×3, often exaggerates the difference within the moving windows, increasing the noise content on the texture image. On the other hand, too large a window size, such as 11×11 or larger, cannot effectively extract texture information due to smoothing the textural variation too much. Also, a large window size implies more processing time. In practice, it is still difficult to identify which texture measures, window sizes, and image bands are best suited to a specific research topic and there is a lack of guidelines on how to select an appropriate texture. More research is needed to develop suitable techniques for identification of the most suitable textures for biomass estimation.

In addition to remotely sensed above ground biomass estimation in data, different soil conditions, terrain factors, and climatic conditions may influence AGB estimation because they affect AGB accumulation rates and development of forest stand structures. Incorporation of these ancillary data and remote sensing data may improve AGB estimation performance. Geographical Information System (GIS) techniques can be useful in developing advanced models through the combination of remote sensing and ancillary data.

3. Conclusion

In this chapter, we proposed a method for forest biomass estimation. One speckle noise model was used for reducing the speckle noise in SAR images. The speckle model was slightly better than the commonly used filters in terms of preserving details in forestry areas. A combination of spectral responses from optical images and textures from SAR images improved biomass estimation performance comparing pure spectral responses or textures. Intensity values of ALOS-AVNIR-2 and PRISM images and texture features of JERS-1 image were used in a multilayer perceptron neural network (MLPNN) that relates them to the forest variable measurements on the ground. We showed the biomass estimation accuracy was significantly improved when MLPNN was used in comparison to estimating the biomass by using classic method only. The RMSE values was decreased when the proposed method was used (RMSE=2.175 ton) compared the classic method (RMSE=5.34 ton).

Currently, there are standard practices for the management and harvesting of biomass from large forest stands, used in state forests and by forestry companies. It is much more challenging, however, for small-scale forest owners to utilize forest biomass for bioenergy. The amount of biomass that can be harvested from forest land or farmland depends on various factors including site condition, species and management intensity. Few practical recommendations for small-scale owners have been published, and land owners may be unaware of appropriate practice. More information would enhance the use of resources available for bioenergy production.

Herein I present examples of activities and the management of farmland and forest land demonstrating how an owner can undertake small scale biomass production for their own consumption or to supply a local market (neighbors etc.).

The examples presented are:

• ingrowth, i. e. natural establishment of broadleaved trees on former farmland via seeds, sprouts or suckers;

• direct seeding on farmland;

• management of existing mixed stands;

• harvesting tops and branches after clear cutting; and

• establishing and using fast-growing species.

Finally, some recommendations for small scale bioenergy production are presented.

The critical step in using Lidar data is the data pre-processing. Choosing the proper filtering method plays an important role in the quality of results. Actually, it cannot be expected that the quality of the result should be better than the data accuracy itself. On the other side, all interpolation methods have no difficulties to generate precise 3D models since dense enough Lidar data is available.

2.1.1 Filtering

The purpose of filtering is to remove the vegetation points. Figure 3 shows all points before
filtering (figure 3, left) and terrain points after filtering (figure 3, right).

The terrain points extracted from the point cloud of Lidar data set are used as an input to generate a DTM. The first pulse data sets contain vegetation points and terrain points in the forest area. Numerous kinds of filtering methods are developed to classify the terrain and vegetation points in the point cloud (Pfeifer et. al., 2004; Tovari and Pfeifer, 2005). Different concepts for filtering, with different complexity and performance characteristics have been proposed in mainly four categories such as morphological, progressive densification, surface based, segmentation based filter. There are also developments, extensions, and variants for these filter methods.

The morphological filter was derived by Vosselman (2000) from the mathematical morphology definition. It works in such a way that the smaller are the distances between a ground point and its neighboring points, the lesser is the height difference. Based on this criterion the method can properly eliminate the outliers. The progressive densification filter is developed by Axelsson (Axelsson, 2000). This filter works progressively by classifying points which belong to the ground. Surface based filters assume at the beginning that all points lying on the ground form a surface. Then a fitting procedure is applied to extract the points which do not belong to the ground. This method goes back to Pfeifer et al. (2001). Segmentation filters are developed as the fourth category. Segment is a group of points which are located within defined thresholds such as the distance and height difference between neighbor points. Sithole (2005) introduced a segment classification method by performing region growing techniques referring to Tovari and Pfeifer (2005). It works by classifying segments into as many classes as possible (Filin and Pfeifer, 2006).

The experimental comparison of filtering algorithms with manual methods for DTM extraction is introduced by Sithole and Vosselman (2004) to show the suitability of filters with the terrain shape. In comparison with other filtering methods, segment base filter is turned out to be a more reliable method in steep slope terrain extraction using a surface growing method (Sithole and Vosselman 2005).

Fig. 4. Segmentation method, point cloud from vertical view

The most important part in this method is the accuracy assessment and parameter tuning. These processes for the segmentation method are performed by Vazirabad and Karslioglu (2009) as shown in figure 4. Segmented terrain points are coloured as brown and green while white points are assumed to be the vegetation points in forest area.

For this study, a mutation was needed that would abolish one of the early cell surface organelles that contribute to the biofilm, while still permitting the formation of biofilms. We performed scanning electron microscopy (SEM) to determine the ability of the five bacterial strains (parental strain, flhD mutant, fliA mutant, fimA mutant, fimH mutant) to form biofilms. Biofilms were grown for 38 h at 37oC on glass cover slips with tryptone broth (TB; 1% tryptone, 0.5% NaCl) as a growth medium. Biofilms were fixed in 2.5% glutaraldehyde and prepared for SEM as described (Sule et al., 2009). Images were obtained with a JEOL JSM-6490 LV scanning electron microscopy (SEOL Ltd., Tokyo, Japan) at 3,000 fold magnification. 10 to 15 images were obtained per bacterial strain from at least three independent biological samples. One representative image is shown per bacterial strain.

1.2 Biofilm quantification with PM technology and the ATP assay

We used the PM1 plate of the BioLog PM system that contains 95 single carbon sources. When used with the tetrazolium dye that is provided by the manufacturer and indicative of respiration (Bochner et al., 2001), the PM system can be used for measuring growth of bacterial strains on single nutrients. We here describe a protocol for the determination of biofilm amounts (Figure 2).

As recommended by the manufacturer for the determination of growth phenotypes, the bacterial cultures were streaked from LB plates onto R2A plates (to deplete nutrient stores) and incubated at 37°C for 48 hours. Bacteria were removed from the plates with a flocked

swab (Copan, Murrieta CA), resuspended and then further diluted with IF-0a GN/ GP Base (BioLog, Hayward CA) inoculation fluid to an optical density (OD600) of 0.1. Leucine, methionine, threonine and thiamine were added at a final concentration of 20 pg/ml, the redox dye that is used for the determination of growth phenotypes was omitted for biofilm quantification. 100 pl of the inoculum was then dispensed into each of the 96 wells of the PM1 plates. The inoculated plates were wrapped with parafilm to minimize evaporation and incubated at 37°C for 48 hours. Biofilm amounts were quantified using the previously described ATP based technique (Sule et al., 2008, 2009). Briefly, the growth medium was carefully aspirated out of each well, minimizing loss of biofilm at the air liquid interface. The biofilms were then washed twice with phosphate buffered saline (PBS) in order to remove any residual media components. The biofilms were air dried and quantified using 100 pl BacTiter Glo™ reagent (Promega, Wisconsin, WI). The biofilms were incubated with the reagent for 10 min at room temperature and the bioluminescence was recorded using a TD 20/20 luminometer from Turner Design (Sunnyvale, CA). The bioluminescence was reported as relative lux units (RLU).

The determination of biofilm amounts in the presence of single nutrients was performed four times for each strain. In addition, growth on these carbon sources was determined in three independent replicate experiments, following the protocol that is described for the determination of growth phenotypes and including the redox dye (Bochner et al., 2001). Carbon sources on which both strains grew to an average OD600 of 0.5 or more were selected for the f-test analysis and carbon sources on which each strain grew to an average OD600 of

0.

5 or more were selected for the ANOVA/Duncan analysis of biofilm amounts (see below).

the fold variation was calculated, using the lowest experiment as a norm (1 fold). Data points in each experiment were divided by the respective fold variation. The normalized experimental data sets were subjected to two independent types of statistical analysis, all done statistically significant differences between the amounts of biofilm that were formed on a given carbon source between the two strains. Since this analysis yielded more carbon sources than we could comprehend on a physiological level, we then analyzed each strain individually and then compared biofilm amounts on individual carbon sources for specific nutrient categories of structurally related carbon sources. For this analysis, the normalized biofilm data from each strain were subjected to separate one way ANOVAs, followed up with Duncan’s multiple range tests. The tests compared the means of the amount of biofilm formed in the presence of each carbon source to all the other carbon sources within each strain. Carbon sources whose mean was different from the means of all the other carbon sources with statistical significance formed their own group in the Duncan’s test. Carbon sources whose mean difference from the other carbon sources was not statistically significant formed overlapping groups.

Performing Duncan’s test on the parent strain, two carbon sources formed groups A and B. Among the remaining carbon sources, we determined those that were structurally related to group A and B carbon sources. This was done after a determination of the respective chemical structures with the Kyoto Encyclopedia of Genes and Genomes (KEGG; Kanehisa & Goto, 2000; KEGG, 2006). Biofilm amounts formed by the flhD mutant were compared to the parent strain for all these carbon sources. In a second analysis, one carbon source formed group A in the Duncan’s test for the flhD mutant. Among the remaining carbon sources, we identified two carbon sources that were structurally related. Biofilm amounts for these three carbon sources were compared between the two strains. For both analyses, data were summarized in a Table (3 and 4).

When the crop canopy does not cover the entire soil surface, the target view area may include measurements of soil background, which may disturb the spectra measurements. Soil reflectances in the red and NIR wavelengths are usually linearly related (Hallik et al., 2009). As shown in Fig. 4, reflectance of bare soil differs from that of the crop canopy, because green vegetation reduces the values of red reflectance and increases the values of NIR reflectance when compared with those of the soil background. A number of studies on the effect of the soil reflectivity on the crop reflectance (Colwell, 1974; Huete et al., 1985), concluded that the most important factors are the chemical composition and water content of the soil. Greater discrimination power between wheat plots differing in biomass has been found on dark soils than on light soils (Bellairs et al., 1996).

In an attempt to minimize the variability induced by external factors, reflectance values recorded by the spectroradiometer are seldom taken directly but rather used to calculate different indices —usually formulas based on simple operations between reflectances at given wavelengths.