The volatile content is given by the mass loss (excluding moisture) due to thermal degradation when the biomass is heated. The degradation products are gaseous sub­stances, such as CO, CO2, NOx etc. The combustion of these volatile components results in the bright flame when wood is combusted and its colour and temperature depend on the chemical composition of the wood.

Because of its low C/H ratio, wood has a rather high volatile content — between 75 and 90 %. A high volatile content is directly proportional to a lower CV, resulting in the low energy density of biofuels compared to, e. g. coal.

To determine the volatile content, ovendry samples are placed in a ceramic crucible and the weight of each crucible and the biomass is noted. The crucibles are then placed and in the furnace at a temperature of 900 °C for 7 min (ASTM E872-82).

After cooling, the volatile content is calculated according to Eq. 8.9:

, N 100 * (m2 — m3/

Volatiles (%/ = (8.9)

m2 — m1

ml = mass of crucible, m2 = mass of ovendry sample and crucible, m3 = mass of contents and crucible after heating

Clemens C. C. von Doderer and Theo E. Kleynhans

11.1 Introduction

A variety of reasons have led to the promotion of indigenous renewable energy sources and to an entirely new energy paradigm from fossil to renewable energy resources. These include, amongst others, the need for security and diversification of energy supplies as well as for less reliance on fossil fuels, the uncertainty surrounding oil prices, and increasing concerns over environmental degradation and climate effects.

An implied aim of renewable energy production is the degree it can reduce an eventual impact on the environment associated with the use of the fossil energy that it will replace. However, as the bioenergy crop is growing, production of ancillary materials, conversion to an energy product and the use of the energy product are not necessarily free of environmental impacts. Thus it is essential that the benefits of bioenergy schemes be investigated from a life-cycle perspective. This has led to the development of a variety of methods to better comprehend the investigated system and, eventually, to reduce those environmental impacts.

There is broad agreement in the scientific community that life-cycle assessment (LCA) is one of the best methodologies for the evaluation of environmental burdens associated with the production of bioenergy and related products (Consoli et al. 1993; Davis et al. 2009; Cherubini and Str0mman 2011). It allows an identification of opportunities for environmental improvementby identifying energy and materials used as well as waste and emissions released to the environment.

The energy — and greenhouse gas balances of bioenergy systems can differ significantly. A variety of factors need to be considered, such as the type of feedstock, type of procurement system or the type of conversion technology.

C. C.C. von Doderer (H) • T. E. Kleynhans

Department of Agricultural Economics, University of Stellenbosch, Stellenbosch, South Africa e-mail: cvd@sun. ac. za

T. Seifert (ed.), Bioenergy from Wood: Sustainable Production in the Tropics, Managing Forest Ecosystems 26, DOI 10.1007/978-94-007-7448-3__11,

© Springer Science+Business Media Dordrecht 2014

Regional differences can also be significant, especially with respect to land use and biomass production patterns, as well as the reference system with which the bioenergy system is compared. In addition, bioenergy production usually results in the generation of co-products, which can replace conventional products providing further environmental benefits to the biofuel process chain (Cherubini et al. 2009).

The aim of this chapter is to provide an introduction into the LCA methodology. The usefulness and the functionality of an LCA process will be illustrated through a recent case study of a lignocellulosic bioenergy production systems.

Airborne LiDAR sensors are operated using an aircraft-mounted scanning laser altimeter, which emits short burst laser pulses. The round trip travel time between the emitted laser pulse, its interaction with a target, and its return to the sensor is measured, thus enabling a range (distance) measurement between the aircraft and the surface. Precise geographic coordinates and attitude (pitch, roll, yaw) of the aircraft are captured using a Differential Global Positioning System (DGPS) and an inertial measurement unit (IMU), respectively (Wehr and Lohr 1999). The exact location and pointing direction of the laser instrument are thus known. Each LiDAR return is represented by a (x;y;z) coordinate, where z refers to height or range; a large collection of such (x;y;z) returns is referred to as a “point cloud”. Resulting LiDAR height point clouds can be used to create detailed three-dimensional models of the area of interest, along with estimates of the vertical distribution of sub-canopy strata (Haala and Brenner 1999; Zimble et al. 2003). For example, several studies have shown that the height estimates derived from LiDAR height point clouds are as accurate and sometimes more accurate than traditional methods (Holmgren 2004). In fact, the 3D nature of LiDAR height data makes this technology especially suitable for assessing forest volume and biomass. Such LiDAR-based forest measurements are not only useful to forest inventory and canopy structure modelling (Lefsky et al. 2002a, b; Nssset 2002; Popescu et al. 2002,2004), but also

to estimation of forest fuel loads (Riano et al. 2003; Seielstad and Queen 2003), and extraction of digital elevation models (DEMs) (Popescu et al. 2002; Hodgson et al. 2003); all of these aspects are essential to forest management and site mapping.

LiDAR volume modeling has focused on both tree — and plot-based approaches (Nilsson 1996; Popescu et al. 2004), as well as stand-level assessments (van Aardt et al. 2006). These are based on either concrete surface modeling, i. e., top-of-canopy and ground, or LiDAR height distributional approaches, based on extraction of height distributional parameters (e. g., median, mode, percentiles) from all LiDAR returns within spatial units, such as grid cells or segments (stands) (Means et al. 2000; Nssset 2002; van Aardt et al. 2006, 2008). Whichever approach is selected, either tree-, plot-, or stand-level, or the use of surface height models vs. LiDAR distributions, the user should always keep in mind that the scale and geography will dictate the approach. For instance, homogenous even-aged stands are well-suited to LiDAR surface modeling (Fig. 2.5), while mixed stands with uneven ages are often best assessed by exploiting the complexity of LiDAR distributions throughout the canopy layer. Some specific examples are discussed next.

The use of locally varying window sizes, applied to height grids, is one popular approach for tree crown detection, height estimation, and determining average LiDAR values within a predefined area. Popescu et al. (2002) assessed both static and variable window size approaches for determining plot-level tree height. The authors reported that the maximum height within a predefined area returned an R2 of between 0.85 and 0.90, while an R2 of 0.85 was obtained for plot-level tree height when the local varying window size filters were applied. The local filter algorithm produced improved results in the case of co-dominant trees when compared to the maximum height approach. An alternative method includes the use of quartile-based LiDAR height metrics (Magnussen and Boudewyn 1998; Andersen et al. 2005; Nssset and Gobakken 2005), which makes use of distributional measures of LiDAR point height as opposed to absolute LiDAR height values (Holmgren et al. 2003; van Aardt et al. 2006,2008). Segmentation and regression modelling (Coops et al. 2004; van Aardt et al. 2008) and statistical analyses using field enumerated data have also been successfully employed (Nilsson 1996). van Aardt et al. (2008), for instance, showed that LiDAR distributions can be used to (i) segment a complex coniferous, deciduous, and mixed stand environment into homogenous structural stands, (ii) assess volume and biomass for these stands, and even (iii) map taxonomic types (coniferous vs. deciduous), for a wall-to-wall forest inventory by forest type. However, an estimate is of little use if not accompanied by an associated precision.

Most LiDAR-based forest inventory studies report high R2 values, except in highly variable or complex forest environments. And while most studies report root mean square error (RMSE) values that approach field inventory efforts, e. g., in the region of 10 % of the mean estimate, LiDAR is by no means a panacea. The calibration-validation approach of using a small sample of locally measured variables when transporting LiDAR models across regions or species, remains an absolute requirement. Many efforts have been made to assess LiDAR accuracy and precision when it comes to forest inventory, and while most such studies report these values, a couple of efforts or implications deserve special mention:

Duncanson et al. (2010) used an airborne LiDAR dataset in combination with forest inventory data to explore the relationship between their model error and canopy height, aboveground biomass (AGB), stand age, canopy rugosity, mean diameter at breast height (DBH), canopy cover, terrain slope, and dominant species type. The authors found that fusion of LiDAR and space borne imagery exhibited high associated error when applied to areas with greater than 200 Mg ha_1 of AGB; it therefore is recommended that practitioners always evaluate bias and precision using a field-based sub-sample. Generally speaking, however, more than 150 studies exist that have shown the usefulness of LiDAR sensing when it comes to forest volume or biomass estimation. The question thus becomes one of not necessarily proving the utility of the approach, but rather of constraining the estimation errors. Over and above biomass, it should be noted that there is one variable where a typical, consistent bias is often present, namely tree height. Tree height underestimation is primarily related to sensing characteristics, namely LiDAR point density and at-target beam diameter, dictated by sensor beam divergence (Baltsavias 1999). Current small-footprint discrete return LiDAR sensors have at-target beam diameters of approximately 0.5 m. These small footprints are less likely to interact with the top of the canopy, especially when the survey point density is less than the average crown size, which is the case with crown apexes of many conifer trees (Clark et al. 2004). Another reason for height underestimation is overestimations in the DTM, or digital elevation model (DEM). In old-growth high forests, where a thick understorey is usually present, it is difficult to differentiate between ground and non-ground LiDAR returns. As such, many DTM’s overestimate ground height; this results in underestimated tree height (Maltamo et al. 2004). One example of this is a universal LiDAR canopy height indicator that has been developed by Hopkinson et al. (2006). The authors predicted plot-level canopy height of various vegetation types using the standard deviation of de-trended (topographically normalised) first and second return LiDAR points (Fig. 2.5). The method returned a correlation coefficient of 0.80 when compared to field enumerated heights. However, the authors noted that when the survey was conducted over homogenous vegetation types, the local maximum LiDAR metric returned improved results.

LiDAR sensors are widely regarded as the future of forest inventory, but their application in operational environments remains limited. This is largely due to the costs associated with a LiDAR survey, the documented underestimation of tree heights (Gaveau and Hill 2003; Suarez et al. 2005), and the computational requirements of the LiDAR data processing and analysis. In addition, the application of LiDAR can be impaired by heavily undulating terrain, e. g., in mountainous areas, because of shading effects of the terrain. However, this problem applies to most airborne and satellite-based remote sensing techniques. Even given all of these caveats, LiDAR is bound to play an increasingly important role in forest inventory. It is evident that an increase in markets and vendors will drive costs down, while improved algorithms are increasingly able to address accuracy-precision concerns. These observations are borne out by the importance and investment that large international forest companies, e. g., Sappi, Mondi, Weyerhaeuser, Georgia-Pacific,

etc., are ascribing to LIDAR. This technology arguably will become more adopted, even if forest practitioners will never be able to completely circumvent field measurements for model validation purposes.

Tree growth is influenced by many factors, including genetics, climate and soils, as well as levels of disturbance of fires, diseases, slash-&-burn agriculture and charcoal production. Among the most studied and utilized species in Southern Woodlands are Brachystegia spiciformis, Pterocarpus angolensis, Julbernadia paniculata, Isober — linia angolensis, Brachystegia floribunda and Sclerocarya birrea (Shackleton 2002; Grundy 2006; Syampungani et al. 2010; Helm 2011). The majority of tree species studied have diameter increments ranging from 0.03 to 2.6 cm per annum (Helm 2011; Syampungani et al. 2010; Timberlake et al. 2010). Brachystegia spiciformis in western Zambia grew by 0.24-0.33 cm diameter per annum while in Zimbabwe, the species was reported to grow by 0.03-0.27 cm per annum (Grundy 2006; Trouet et al. 2006). A study in Zambia (Syampungani et al. 2010) revealed that there is significant difference in annual ring width between species (Table 4.3); but the increment in annual ring width of similar species did not differ significantly between disturbance factors.

Woody biomass is said to increase with increase in mean annual rainfall across the Southern African region (Frost 1996). Aboveground biomass in old — growth, uneven aged stands has been reported to be 55 Mg ha_1 in dry miombo woodland of Zambia and Zimbabwe on average (Chidumayo 1991; Guys 1981) while the average biomass in wet miombo woodland has been observed to be about 90 Mg ha_1 (Table 4.2). Additionally, lower values have been observed in young re-growth stands of miombo woodlands (1.5 Mg ha_1) in a 3-6 year old coppice stands. Similar variations have also been observed in Mopane woodland namely; 1.1 Mg ha_1 in south eastern Zimbabwe to 79 Mg ha_1 in northern Botswana (Tietema 1993). Shackleton and Scholes (2011) also observed an increase in basal area and biomass from an arid locality to mesic one. They attributed this to differences in stocking between the mesic locality, and the arid and semi-arid localities. This also suggests that moisture availability has an influence on biomass accumulation across ecosystems.

Root biomass studies have been limited in Southern African woodlands. How­ever, what has been documented clearly is that the Zambezian woodland species have horizontally and vertically extensive root systems. Maximum recorded lateral distance ranges from 15 to 27 m in dominant miombo species namely Julbernadia paniculata and Brachystegia spiciformis (Strang 1965; Savory 1963). In dry

Table 4.3 Growth rates of key Miornbo species under different disturbances

miombo woodland of Central Zambia, an average of 32.7 Mg ha_1 as root biomass was observed by Chidumayo (1993). In the Transvaal region of South Africa, Roux et al. (1994) recorded total root biomass of 29.79 t Mg ha_1 he in dense Mopane woodland.

An advantage of chipping at the landing is that the harvesting/extraction and the chipping operations are not directly interlinked, and can be separated by hours or even months. This allows for a large feedstock to be built up, facilitating the use of high capacity chippers (>100 m3h_1) capable of filling a waiting truck within an acceptable time and thereby eliminating chip storage problems. Chipping material at roadside landing is the most common production method in biomass to energy chains in Europe. It can also involve chipping onto the ground or into containers. For chipping onto the ground, suitable preparation of the landing should be carried out beforehand (i. e., a clean and level site), while chipping into containers requires detailed logistics planning that synchronises container arrivals.

For chipping into waiting trucks, the challenge lies in balancing chipper produc­tivity with truck waiting time. Chip transport trucks have loose volume capacities of 85-120 m3 and should be filled quickly. High performance chippers capable of doing so represent large capital investments that incur expensive waiting time between truck arrivals, while low performance chippers shift the waiting time to the trucks, which can result in queuing at the landing or poor truck utilisation.

Chippers also need to be relocated from site to site. A solution to the challenges of getting this balance right is the use of chipper-trucks, with on board chippers that both chip and transport the material to the plant. The obvious benefit being that they are self-contained and highly mobile, with the drawback being the loss of payload both in terms of mass and volume due the presence of the chipper (Bjorheden 2008).

Recent research findings (Thorsen et al. 2011) show that self-contained chipping trucks perform well, especially in situations where their high mobility can be utilised to the full.

While gains are made in chipper productivity, the extraction of uncomminuted material (FT, tops, branches) is the least robust link in the chain. Efficient extraction of smaller trees or tree sections requires that they are pre-bunched and well presented for grapple-skidding or forwarding. Pre-bunching almost always implies mechanised felling while grapple-skidding results in higher levels of contamination with mineral soil (high ash levels) and forwarding requires that the trees have been laid in the stand and not in the strip row. FT or tree sections need to be compacted on the forwarder loadbed in order to improve the payload and maximize returns on the time cost of driving in and out of the stand.

Several innovative technologies for the thermochemical or biochemical conversion of woody biomass into useful bioenergy products are presently under development, although few of these have reached commercialization, as discussed below. A com­plete list of lignocellulose processing facilities for bioenergy production, including types of feedstocks used, processing scale and operational status, is available at http://www. bioenergy2020.eu/flles/publications/pdf/2010-bericht-demoplants. pdf.

Combustion: Systems that employ direct combustion to convert biomass and charcoal into energy for heat, power, and CHP (Combined heat & Power) are widely utilized and commercially available for small-, medium — and large-scale applications. Large scale co-flring of bio-oil has been carried out at Manitowac and Red Arrow, but few other cases of application exist.

Pyrolysis: Different pyrolytic technologies, including torrefaction (Brownsort 2009), pressurized pyrolytic reactor processing (Antal and Gr0nli 2003), slow pyrolysis (Brownsort 2009), vacuum pyrolysis (Bridgwater and Peacocke 2000) and fast pyrolysis (Dahmen et al. 2012; Bridgwater and Peacocke 2000) are all considered to be mature technologies. Slow pyrolysis technologies for charcoal and biochar production are commercially available (Bioenergy Ltd., Yury Yudkevitch, Biogreen Energy, Enecon, Pty Ltd, ICM Inc., Pacific Pyrolysis (formerly BEST Energies)), while fast pyrolysis is on the verge of commercialisation (Dynamotive, Ensyn, BTG, Biomass Eng., KIT/Lurgi, Pytec, ARBI-Tech, ROI, Agri-Therm, Anhui Yineng, Metso Consortium).

Gasification-synthesis: Biomass gasification technologies have been sufficiently developed to be considered as a significant contributor to global sustainable energy production. Nevertheless, there are still some issues with biomass processing (pre­treatment, gas cleaning, reforming efficiency, etc.) to be addressed before successful large-scale commercial introduction of biomass gasification takes place (Bridgwater et al. 2002; Tijmensen et al. 2002). Commercial-scale technology for Fischer — Tropsch synthesis using syngas has been in operation for several decades in South Africa, Malaysia and elsewhere.

Direct liquefaction: Commercialisation of hydrothermal technologies suffers from difficulties that arise with the conversion of batch reactors to continuously processing systems, as it is difficult to pump fluids at high pressures and low flow rates (Peterson and Haase 2009). Nevertheless, the supercritical water gasification process appears to be a suitable technique for hydrogen production from biomass at the commercial scale (Calzavara et al. 2005).

Alcoholic fermentation from lignocellulose: The biochemical conversion of lig — nocellulose into cellulosic ethanol by fermentation has been substantially developed in the past few decades, comparatively more than thermochemical technologies for liquid transportation fuel production (Anex et al. 2010). The first commercial projects for cellulosic ethanol production using woody biomass and/or forest residues as feedstocks are presently under development, including several efforts by Borregaard Industries (Norway), Mascoma Corp (USA), KL Energy Corporation (USA) and SEKAB (Sweden). Butanol fermentation from lignocellulose requires further development prior to commercialisation.

Anaerobic digestion of lignocellulose: Anaerobic digestion (AD) for the pro­duction of biogas is a well-established commercial technology. However, the limitations in terms of conversion efficiency and productivity of lignocellulose conversion requires either the co-digestion of limited amounts of lignocellulose with readily digestible, high-nitrogen content substrates (such as sewage sludge) or alternatively further development of pretreatments applied to lignocellulose to improve digestibility. Pretreatment development needs to facilitate a greater degree of pure lignocellulose digestion as well as higher tolerance of AD to inhibitors formed by sugar degradation during pretreatment.

The rise in biofuel production has had large demand effects on agricultural markets, especially grains and oilseeds. In 2007 it was estimated that biofuel production used 5 % of world cereal production, 9 % of world oilseed production and 10 % of sugar cane production. About half of the global increase in world grain consumption (about 80 million tonnes) was used for biofuels (FAO 2009).

This increase in biofuel production has become one of the major issues in the debate over climate change and global food security. On the one hand, biofuels are criticized for promoting food shortages, utilise much needed agricultural subsidies, offer little or no greenhouse gas mitigation and drive deforestation in developing countries. On the other hand they are promoted as a tool for economic development that could increase production and revitalise many countries’ agricultural sectors. The cultivation of biofuels as cash crops may lead to increased investment in infrastructure that is needed to support thriving or emergent agricultural markets (Gamborg et al. 2011).

The substantial increases in agricultural commodity prices between 2005 and 2008 have partly been blamed on the conversion of food crops to biofuel. These increases have had negative implications for food security in the short term but also for net food buying countries and particularly for low income food deficit countries (FAO 2009) which raised fundamental questions about food sovereignty and government priorities. It could for instance be asked if countries dependent on food aid such as Kenya and Ethiopia should be selling fertile land to developers for biofuel production (Friends of the Earth 2010). In the long run, however, growing demand for biofuels and the rise in agricultural commodity prices may present an opportunity for promoting agricultural growth and rural development in developing countries. Furthermore the development of environmentally friendly biofuels could promote access to cheaper and safer energy supplies in rural areas which could further stimulate economic growth (FAO 2009).

Food security may be compromised if high yield agricultural lands are used for bioenergy production, pushing agriculture into more vulnerable, lower quality lands (Cushion et al. 2010). Such situations could occur when food growing farmers are forced off their land to make way for biofuel plantations. In Ghana for instance, where 50 % of the population work on the land and grow food for local consumption, Jatropha plantations have forced small farmers and particularly women farmers off their land. Valuable food sources such as shea nut trees have been cleared for bioenergy plantations. Small farmers in Ghana have expressed fears that they will not be able to afford to farm the land or buy food for their families. In Tanzania a similar situation occurred when thousands of rice and maize farmers were forced off their land in 2009 to make way for sugarcane plantations (Friends of the Earth 2010).

Converting forests into bioenergy plantations could also increase the food insecurity of forest-dependent communities. These impacts are, however, often short term and there is potential for biofuel production to have less impact on food security over the longer term. Biofuel production can for instance be beneficial to small producers when they are located far from markets and cannot sell their produce at competitive prices due to transport costs. Food production could be uncompetitive under such conditions making biofuels a better option (Cushion et al. 2010). It is impossible to make broad scale policy decisions about food security and biofuel production that will favour either the one or the other. Such decisions have to be made on a case by case, crop by crop, region by region and even location by location basis (Practical Action Consulting 2009).

With increasing maturity of the LCA approach, the numbers of studies using LCA have increased. A variety of LCA studies deal with forestry, forestry products, or different forestry production phases, such as harvesting, primary or secondary transport. The increasing interest in short-rotation coppice (SRC) systems is also reflected by the increasing number of LCA studies investigating the environmental impact of these bioenergy initiatives. A selection of LCA studies focussing on forestry operations and related products, as well as SRC plantings, is listed in Table 11.1, below.

The study outlines a methodology to extrapolate LiDAR-measured tree height to areas sampled using only multispectral IKONOS imagery in support of forest management activities. This cost reducing sampling scheme was developed using geostatistical procedures and expert knowledge in order to address the high cost of LiDAR surveys. The scheme sampled 2.25 ha of all LiDAR canopy returns in an area covering approximately 1,000 ha, which equates to 0.25 % of the study area. The premise of the study was that the transect type sampling scheme would reduce the cost of the LiDAR survey and thus make incorporation of the sensor more cost effective within an operational scenario. However, statistical results did not fully support the extrapolation approach proposed here, i. e., using spectral reflectance and age as predictor variables. While the LiDAR accurately measures tree heights, the RMSE values associated with validation data were outside the acceptable error margins employed in operational scenarios (±5 %). Over-prediction of tree heights suggested that the proposed methodology was not perfectly attuned to accurate estimation of tree height for areas external to the LiDAR collection framework. However, given

(i) the fact that goodness-of-flt statistics were promising,

(ii) the documented problems associated with field enumeration techniques,

(iii) the exacerbating factors related the spherical shape of Eucalyptus crowns, it is proposed that future research address the sampling design itself, while also investigating the use of off-nadir (multi-angular) imagery. Such multi-angular imagery has been shown to be more amenable to extraction of vegetation struc­ture than traditional nadir-only imagery. The study concluded that, even though the precision metrics reported here were unsatisfactory for truly operational purposes, specific metrics related to the correlation between LiDAR-derived tree height and extrapolated tree height (multi-spectral imagery) were encour­aging. We believe that studies such as this are necessary if we are to eventually provide large scale, operational structural assessments of our forest resources using currently expensive, but accurate LiDAR surveying approaches.

2.6 Conclusions

We believe that future research on biomass inventory should focus on multi­phase inventory with sensor fusion. Research is progressing on methodologies that combine LiDAR and high resolution UAV photography, as well as many other sensor combinations. The synergy of combining sensors like multispectral, LiDAR, and RADAR likely will hold the key to containing cost and errors, as well as covering much larger areas of standing biomass. Treuhaft et al. (2004), for instance, provide a useful summary of RADAR use for forest biomass estimation, as well as fusion of RADAR and optical remote sensing sensor data.

Multi-phase inventories combine the different strengths of different methods and sensors in localisation, as well as quantifying not only the biomass, but also the components of the biomass in terms of leaves, branch wood, and stems above ground. No direct measure of belowground biomass (BGB) is currently possible. Estimates of BGB are possible once proper models exist for an ecotype or species, most typically through an established AGB-BGB conversion factor.

Finally, a table is provided to summarize current forest biomass (inventory) technologies in terms of the level of training required to implement, the cost of execution, and the scale for which it is suitable (Table 2.1). Both the lower limit as well as upper limit of the method is indicated, where small (S) indicate community and stand level, medium (M) indicate farm to catchment level, and large (L) indicates regional to global scale inventory. An almost invariable truth holds: The larger the area required to assess with terrestrial sampling, the higher the cost, while with remote sensing methods, the inverse is true in that the unit cost of assessing a small area is higher than assessment of catchment and larger areas.

Remote sensing is not a golden solution or panacea to all of our forest inventory challenges, whether those be inventory related or focused on the condition (nutrient, moisture) of the resource. But the technologies described above do offer viable solutions to operational assessment needs in the forestry and ecological communities. Some of these technologies are closer to operational implementation

than other, e. g., airborne LiDAR vs. either space borne or ground-based LiDAR, but in all cases certain commonalities should be observed:

1. Field work will always form an essential component of any inventory, whether for calibration of biomass models developed elsewhere, or for validation of existing models for different sites or seasons.

2. Implementation costs will decrease as new markets develop, vendors’ numbers increase, and practitioners adopt novel approaches. However, we should always strive to balance these costs with associated accuracy and precision trade-offs.

3. Finally, fusion of multiple approaches or modalities likely will be key to a successful and comprehensive inventory system — some sensors are best suited to structural assessment (inventory) and others to spectral assessment (species, nutrients, moisture status), while ground-based efforts will always occupy a necessary component in the inventory chain.

There may be other such essential truths, but we trust that this chapter has provided the reader with an overview of what is possible using remote sensing. Research in this field is ongoing, and there is ample evidence that the future of technologically advanced approaches to forest resource assessment is bright.

Strategic planning for the most appropriate silvicultural and harvesting system for bio-energy crops should be done simultaneously for maximum economic benefit. The reasons are: (1) The profitability of plantation systems are often strongly influenced by harvesting and transport costs, the latter commonly constituting the biggest share of all expenses in the value chain from plant to mill, and (2) Different harvesting systems are designed to work optimally within specific ranges of individual tree volumes (Ackerman and Pulkki 2004), for example, (a) mechanised conventional timber harvesting with individual tree volumes from approximately 0.1 to 0.9 m3, (b) clearfelling with chainsaws from 0.01 to 0.1 m3, and (c) modified agricultural harvester <0.01 m3. We will explain this relationship with data from Eucalyptus grandis crops grown in South Africa, where the aforementioned volume ranges would translate into diameters at breast height (dbh) classes of approximately 16-32 cm; 8-15 cm and <8 cm, respectively. From an economic perspective, it is thus imperative to design the silvicultural system in such a way that it could deliver mature crops falling within a specific range, and to match this with the capabilities of the chosen harvesting system. To a large degree, this can be achieved by manipulating the relationship between stand density and rotation length in short — rotation crops. However, (Coetzee 1999) has shown that this relationship is strongly dependent on the site index (or similar measure of site production potential). An example of mean annual increment development in South African E. grandis crops, grown on various stand densities across three different site indices are shown in Fig. 5.1, based on the data produced by Coetzee et al. 1996; Coetzee and Naicker 1998; Coetzee 1999, with key data points summarised in Table 5.1. The site index in this study is defined as the mean height of trees per compartment that fall into the 80th percentile with respect to dbh, at a reference age of 5 years (hereafter SI5).

From Fig. 5.1 and Table 5.1, it is clear that the peak MAI on a site with SI5 = 26 can be achieved (a) as early as 3.6 years with 2,000 stems ha (possibly even at 3 years if more than 2,000 sph had been tested), however, (b) it will take up to 5.0 years if only 800 stems were established per hectare. The quadratic mean dbh of scenario (a) in the aforementioned text would be 13.3 cm; while that of scenario (b) would be 19.6 cm. On a low productivity site (SI5 = 15.5) the MAI will culminate at 7.0 years with 2,000 stems (scenario c) and will only culminate beyond 12 years with 800 stems per hectare (scenario d). These data sets clearly show that MAI and individual tree size are strongly related to the interactive effects of rotation length, stand density and site index. It follows that site-specific management regimes should be developed for rotation length by stand density combinations. Scenario (b) lends itself to harvesting with a mechanised system, whereas scenario’s (a) and (c) are more suited to a chainsaw system. The data of Coetzee (1999) did not test very dense stocking levels, but it appears that systems with between 3,000 and 4,000 sph could yield slightly higher peak MAI’s, with the volume carried on small stems which lend themselves to harvesting with a modified agricultural harvester. Sochacki et al. 2007, working on a low productivity site in Australia, showed that stand densities of up to 4,000 sph yielded the largest volume production at age 3 years in that study. Stand densities of 3,000-4,000 sph could thus be considered if harvesting with modified agricultural harvesting equipment is envisaged.

If the silviculturalist opts for very high stand densities (more than 2,000 sph) with the aim to utilise a modified agricultural harvesting system, there will be additional factors that have to be considered when deciding on the optimum stand density by rotation length combinations across a range of site indices. These considerations will include the following:

• Increased cost of establishment because more trees have to be planted

• Increased tree stress due to intraspecific competition with high stand densities (in Sochacki et al. (2007) study, tree mortality was an important factor affecting final biomass production on some treatments).

• Less flexibility around the felling age (especially on higher site indices), because productivity may decline sharply if the rotation over-matures (see Fig. 5.1).

• Early canopy closure, leading to lower weed management costs.

• Lower levels of inter-specific competition (i. e. between competing vegetation and trees), which will improve tree uniformity and an increasing fraction of NPP being partitioned to above-ground tissues (Little et al. 2003; Stape et al. 2010).

• Changes in wood characteristics such as density and fibre properties. Shorter rotations will have an increased proportion of juvenile wood in the final volume of biomass harvested.

• Increases in nutrient depletion from the site due to intensive biomass harvesting.

800 s/ha

♦ Site index 15.5 — Є— Site index 21 —*— Site index 26

1400 s/ha

Site index 15.5 Site index 21 Site index 26

2000 s/ha

Site index 15.5 Site index 21 Site index 26