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 , 29 , 30 ].
The evaluation on an area basis is done with extrapolation methods [ 22 , 24 ]. From the s 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. 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 ].
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.
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.
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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 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 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 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 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. It has the advantage of data acquisition being independent of the hour of the day and atmospheric conditions. 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 , 78 , 79 , 80 , 81 ]. Related to the satellite spatial resolution is the target area of estimation, which can be at regional or local scales [ 32 , 34 , 35 , 36 , 82 , 83 , 84 , 85 , 86 ] or national scales [ 87 , 88 , 89 , 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 ].
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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 neutrality in CO 2 emissions is also an important factor [ 97 , 99 ]. In general, all forests produce biomass that is mainly removed in harvests, though in smaller quantities also in silvicultural operations thinnings and prunings.
Several terms have been used to describe the forest systems whose main, and frequently the only, production is biomass for energy [ 94 , , ] 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 , , , , , , ].
The energy plantations are well represented in Europe, though to a lesser extent in the southern countries [ , ], USA [ , ], Canada [ ], and China [ ]. For the establishment of the energy plantations, the selection of species, density, rotation, harvest cycles, site, and management practices has to be considered. 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 [ , , ].
Hybrids are frequently used to increase productivity, for their adaptation to the environmental conditions and resistance to pathogens [ , , ]. From the many potential species suited for energy plantations, the three most referred in literature are: Populus spp. 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.
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 ]. Also, a dichotomy seems to exist between density and rotation [ ], frequently higher densities and shorter rotations [ , , , , ], or lower densities and longer rotations [ 97 , , , ]. Site selection is directly related to survival, growth, and yield of the tree species or clones.
To obtain high productivities, sites should be of good quality with long growing seasons [ 83 , , ], and steep slopes should be avoided when mechanization is foreseen [ 99 , , ]. Control of natural vegetation to reduce competition between spontaneous vegetation and energy plantations is better suited during site preparation [ , , ], though it might also be necessary after each harvest [ 93 , , ].
Two main options are available for the selection of planting techniques : plantation of cuttings or seedlings.
While the former is use with Salix spp. Other management practices include fertilization to promote yield [ 93 , ], though there is some controversy in the literature, with some authors stating that fertilization does not increase yield e. The control of pathogens should be primordially done by choosing resistant species or clones or by the increasing diversity e.
Irrigation should be used when water stress and growth reduction are expected [ 93 , , ]. The main goal for stands managed for timber and other products and services is not biomass for energy. The latter is a secondary production, composed of residues, which are growing stock unused parts, such as tops, limbs, stems, stumps, and that result from harvest cuttings or late thinnings or silvicultural practices noncommercial or early thinnings [ 8 , ].
Regarding forest residues, two management options can be considered: their maintenance in the stand to preserve or improve stand productivity and site fertility or their removal when negative impacts are not expected [ , , ]. The amount of forest residues depends on the species, stand structure, and stem quality, which generate a wide variability on their quantity e. Considering the different stand structures, the ones that potentially originate larger amounts of forest residues are even-aged, mixed managed stands, where some species are not well suited for timber or with timber of bad quality, and pure or mixed unmanaged stands, with high density, individuals of small diameter and bad timber quality [ 8 ].
Noteworthy are also the agroforestry systems, where the forest portfolio can include energy plantations [ , ] and stands managed for timber and other nonwoody products and services from which forest residues can be obtained [ , , , ]. The latter, frequently in rather small quantities, are mainly derived from thinnings and prunings but also from sanitary cuttings or trees that have reached the end of their lifetime cycle [ , , ]. One of the advantages of biomass over other renewable energy sources is its versatility. Biomass in general, and forest biomass in particular, can be converted into electricity, heat, or transportation fuels.
In practice, though, forest biomass is mainly used for heat and electricity production. The transformation of forest biomass into biofuels that can be used in the transport sector still faces various challenges, which have hindered its commercialization [ , ]. Despite its advantages and despite being the most used renewable energy source, the current share of bioenergy in the world is still very limited. In , bioenergy and renewable wastes accounted for 9. Among the various biomass sources, solid biofuels accounted for In OECD countries, where biomass is mostly used in modern systems, the share of biomass and renewable wastes is even lower, with these fuels accounting for 5.
Solid biofuels, which are almost entirely composed of wood, wood residues, and wood fuels, are used to produce electricity and heat. Direct heat is by far the most common application of solid biomass. In this case, biomass is used directly by the end users e. The dominance of the use of solid biomass for heating applications is mostly justified by its traditional use in the African and Asian countries for heating and cooking [ 1 ].
FAQ Policy. About this book Even though most of the biomass of the planet is in forests, we live in a world where wood as a raw material and its products are increasingly scarce.
Poplar (Populus spp.) Trees for Biofuel Production – Farm Energy
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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 , 11 , 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 , 16 , 17 ].