**3. Forest biomass evaluation**

Forest evaluation started with forest inventories in the Middle Ages, during wood shortage, with the aim of estimating the forest areas, stand composition, and wood volume per dimension class. The expectations, apart from wood, of forests to provide services resulted in the inclusion of a wide set of variables in the inventories, among which is biomass [22–25]; this intensified labor and increased costs [22, 24]. Forest inventories are defined by sampling design, for an assumed threshold error, which is accomplished in two sequential steps: (1) evaluation of forest area and crown cover with remote sensing [24, 26, 27] and (2) survey of field plots to measure several dendrometric variables, being the most frequent diameter at breast height and total height [22, 24, 28–30]. The evaluation on an area basis is done with extrapolation methods [22, 24]. From the 1990s of the last century onward, the development of remote sensing deployed the derivation of a set of functions to estimate several stand absolute density measures such as the number of trees, the basal area, the volume, and the biomass (*e.g.*, [31–36]). These functions enable the rationalization of forest inventory field work, facilitating also the evaluation of forest stands where field work is hard to accomplish [22, 24].

### **3.1. Forest inventory**

woody biomass are characterized by more efficient and cleaner technologies. Its utilization, though, is also linked to controversies as far as sustainability issues are concerned. In developed countries, the interest in bioenergy has been increasing mostly due to greenhouse gas mitigation policies. Wood biomass is a renewable energy source and considered to contribute to a decrease

the existing methods and techniques to evaluate biomass (Section 3), the types of biomass and

The role of forests in providing a large suite of products and services is well known [6–9]. Due to the reduction of the forest area and shortage of woody products, as well as to guarantee the sustainability of forests and ecosystems, the need to evaluate, monitor, and regulate the forest arose [10–12]. Initially, the emphasis of assessment was on the quantity per class of woody products (mainly large- and small-dimension timber), typically with the evaluation of volume [6, 13, 14]. This drove forest stands toward predominantly pure, even-aged stands, either in high forest or in coppice regime, frequently centered in one production, also due to the simpler management [6, 7, 10, 13, 14]. Later in the twentieth century, the stand and forest management were expected to include objectives other than woody products, such as services, sustainability, and conservation of the forests and ecosystems [10, 11]. This originated a shift in forest management to new approaches focused on systems of multiple productions, which have driven silviculture toward uneven-aged and mixed stands. These approaches are focused in the natural processes emulation, which originated a wide suite of methods and techniques to achieve it [10, 15–17]. The overall biomass production, as a result of the management approaches, tends to be periodical in even-aged stands with large time periods between two consecutive harvests, while multiaged stands harvest periodicity tends to be in shorter time periods and rather constant, with a quantity function of the growth, target equilibrium, and proportions of the age classes of the stand [6, 7, 10, 13, 14, 17, 18]. Stand composition, both on the quantity, variety, and quality of biomass, also derives from the management strategy. In the traditional approach, silviculture was oriented toward pure stands, while the new ones are focused on mixed stands. The latter are systems with wider complexity and consequently more difficult to manage but are considered more biodiverse and resilient, and enable risk dispersion due to their multiple productions [6, 7, 10, 14, 18–20]. The challenge is defining and separating pure and mixed stands [21].

Forest evaluation started with forest inventories in the Middle Ages, during wood shortage, with the aim of estimating the forest areas, stand composition, and wood volume per dimension class. The expectations, apart from wood, of forests to provide services resulted in the inclusion of a wide set of variables in the inventories, among which is biomass [22–25]; this intensified labor and increased costs [22, 24]. Forest inventories are defined by

biomass residues (Section 4), and the common uses of biomass for energy (Section 5).

emissions. This chapter reviews the forest structure (Section 2),

in the anthropogenic CO<sup>2</sup>

24 Renewable Resources and Biorefineries

**2. Forest structure and biomass**

**3. Forest biomass evaluation**

Biomass was not traditionally assessed in the forest inventories. It was only from the late twentieth century onward that it was included, compelled by the need to evaluate carbon stocks, sequestration and losses, and biomass for bioenergy. The methods to evaluate biomass can be grouped in two broad classes [22, 24]: the direct methods and the indirect methods. The former, though very accurate, are destructive and frequently used to derive data sets for modeling. The latter are mathematical functions that use as explanatory variables dendrometric variables, frequently diameter at breast height and/or total height. These functions are frequently developed for each biomass component (stem, bark, leaves, branches, and crown), and total tree biomass is obtained by summing all the components. Similarly, biomass per plot is the sum of the biomass of all the trees, and normally referred to a standard area unit, typically the hectare. The functions are species-specific, site-specific, and regime-specific, due to the tree species habit and growth pattern per site and regeneration method (seed for high forest and vegetative for coppice). As a result, a wide range of functions is found in literature [37–45]. The advantage of these functions is their accuracy [27]. The shortcomings are related to the selection of the best function for the stand location, species, and stand structure [46, 47]. The choice might encompass some difficulties when no functions exist or those that exist are not adequate, thus resulting in large estimation bias [48]; and with the extrapolation methods in the evaluation of the forest areas [24], decreasing the accuracy with the increase of the area evaluated due to the variation in stand structure, topography, soil, and climate [49]. The estimation errors with this method are assumed to be between 15 and 40%, with the standard threshold of 25% [50].

## **3.2. Remote sensing**

The major advantage of remote sensing is related to the wide range of working scales, associated with the spectral, spatial, radioactive, and temporal resolutions, as well as to their technology [51, 52], which allow the evaluation of the distribution of the forest area, species, and their physical and biochemical properties [53]. The advantages of biomass estimation with remote sensing methods when compared with those using forest inventory are: (1) can be applied regardless of the area dimension [26, 27], (2) does not need field work, therefore being interesting in areas where it is difficult to implement it or where many field plots are needed to attain the threshold error [24, 27]; (3) short time cycles can be used for data collection, contrary to forest inventory, where cycles shorter than 5–10 years are unfeasible [24, 26]; (4) different scales can be used as function of imagery spatial resolution [26, 27]; and (5) it applies to all the area, thus extrapolation methods are not required [32, 34–36].

**4. Forest biomass and forest residues**

neutrality in CO<sup>2</sup>

the stands for energy purposes.

**4.1. Energy plantations**

spp. [97, 101, 112, 119, 120].

Forests are the terrestrial ecosystems that produce and store the most biomass, which explains why biomass for energy has been derived mainly from forests for a long time [8, 13, 14, 93, 94]. The forest biomass varies according to site, stand structure, topography, climate, management system, and disturbances [91, 95, 96]. The two features that make biomass a primordial source for energy are their availability and uniformity at a global level [8, 97, 98]; more recently, the

Solid Biomass from Forest Trees to Energy: A Review http://dx.doi.org/10.5772/intechopen.79303 27

In general, all forests produce biomass that is mainly removed in harvests, though in smaller quantities also in silvicultural operations (thinnings and prunings). Forests can be grouped in two broad types considering the biomass removal for energy purposes [95, 100]: energy plantations, where all biomass is harvested for energy and forest systems managed for timber and/or other products and services, where all or part of forest residues can be removed from

Several terms have been used to describe the forest systems whose main, and frequently the only, production is biomass for energy [94, 101, 102] and that are characterized by specific spatial and temporal features [93, 99]. The most important features of these systems, when compared with agricultural crops or other forest systems, are their low risks, high economic viability, harvest flexibility, availability worldwide, biodiversity enhancement (especially if incorporated in agricultural crops portfolio), and the possibility of use for phytoremediation purposes [97, 100, 103–107]. The energy plantations are well represented in Europe, though to a lesser extent in the southern countries [103, 105], USA [108, 109], Canada [110], and China [111]. For the establishment of the energy plantations, the selection of species, density, rota-

The *selection of species* is of primordial importance. The species better suited for energy plantations are those that have high biomass production in dry weight, good sprouting ability, fast juvenile growth, narrow crowns or large-sized leaves in the upper crown, biomass with high specific energy and quality, adaptability to a wide range of sites, and resistance to biotic and abiotic agents [100, 112, 113]. Hybrids are frequently used to increase productivity, for their adaptation to the environmental conditions and resistance to pathogens [104, 114, 115]. From the many potential species suited for energy plantations, the three most referred in literature are: *Populus* spp. [101, 111, 112, 115–118], *Salix* spp. [101, 112, 114, 116, 118], and *Eucalyptus*

*Density*, *rotation,* and *harvest cycles* are strictly linked, since the main goal of energy plantations is to attain the highest production in the shortest time (*e.g.*, [104, 116, 117]). Thus, three principles regulate density and rotation; namely the law of final constant yield, the development of social classes in a stand, and self-thinning law [93]. However, there is a large variability of densities from 1000 stems ha−1 to 310,000 stems ha−1 [99, 108, 114, 116, 118, 121, 122] and rotation lengths between 1 and 20 years [99, 108, 114, 116, 118, 121, 122]. Also, a dichotomy

emissions is also an important factor [97, 99].

tion, harvest cycles, site, and management practices has to be considered.

The biomass functions that use satellite image data are mathematical functions that use data derived from satellite optical sensors for the explanatory variable [33], such as spectral reflectance, crown diameter, crown horizontal projection, crown cover, original bands and/ or vegetation indices [32, 34–36, 54–58]. The statistical methods and techniques used to fit the functions are varied. Examples are linear and nonlinear regression, regression k-nearest neighbor, neural networks, regression tree, random forest, and support vector machine [27, 52]. Remote sensing data is derived from passive or active sensors.

For an optical sensor (passive sensor), the spatial resolution is the main distinctive feature of the satellite images and can be grouped in three broad classes: coarse, medium, and high. The *coarse spatial resolution* satellite imagery (>100 m) comprises: National Oceanic and Atmosphere Administration (NOAA) with the Advanced Very High Resolution Radiometer (AVHRR) sensor, Moderate Resolution Imaging Spectroradiometer (MODIS), and Satellite *Pour l'Observation de la Terre* (SPOT) Vegetation [55, 59–62]. The *medium spatial resolution* satellite imagery (10 to 100 m) includes Landsat, Sentinel, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Wide Field Sensors (WiFS) [55, 63–65], as well as recently Landsat 8 and Sentinel, free global-scale remote sensing data. The *high spatial resolution* satellite imagery consists of: IKONOS, QuickBird, WordView, and GeoEye satellites, with a pixel size smaller than 5 m [33–36, 66, 67].

The active sensors, Radio Detection and Ranging (RADAR) and Light Detection and Ranging (LiDAR), have gained relevance for biomass estimation in the last years [68–72]. The RADAR use microwaves to obtain information of surface target. It has the advantage of data acquisition being independent of the hour of the day and atmospheric conditions. More recently, the synthetic aperture radar (SAR) sensor, C-band RADARSAT-2, and X-band TerraSAR provide more accurate biomass estimation due to the spatial resolution variability, polarization, and incidence angles [73]. LiDAR systems allow to obtain detailed information about the structure of vegetation (horizontal and vertical tree dimension), considering the distances measured to the object surface [74, 75]. It can be supported by spaceborne, airborne, and terrestrial platforms that create a very precise 3D-point cloud data from vegetation [76] and are used to develop models for several vegetation biophysical parameters, such as tree height, crown dimensions, volume, and canopy density [52]. The statistical methods most frequently used to develop biomass functions are linear and multilinear regression [52] and machine learning algorithms [70, 71].

Some studies used a combination of LiDAR and multispectral or hyperspectral data to identify the different forest areas where the spectral response is similar, to improve the biomass estimation [77–81]. Related to the satellite spatial resolution is the target area of estimation, which can be at regional or local scales [32, 34–36, 82–86] or national scales [87–90]. However, some difficulties in the estimation of biomass with accuracy may arise due to the variability of the stands and forests, especially in the tropical forests [91, 92].
