Open access peer-reviewed chapter
Forest biomass fuel properties [153].
Abstract
Among the different terrestrial ecosystems, forests are the most important biomass carbon producers and the ones that store the most standing biomass carbon. Consequently, they are also the major source of biomass for energy. Forest biomass has been used as a fuel from early times, and from the late twentieth century onward, there has been a renewed interest in its use to produce heat and electricity. The interest in forest biomass as an energy source relates to some of its features, such as relative abundance and uniformity worldwide and neutrality of CO2 emissions. Nonetheless, its use is not free of risks, mostly related with the sustainability of the forest systems and their productions. This study reviews the state of the art of the forest sources of biomass for energy, their assessment, their properties as a fuel, as well as the conversion technologies used in the most common energy applications.
Keywords
- silviculture
- estimation
- remote sensing
- conversion technologies
- heat
- power
1. Introduction
Energy has been obtained from forests for thousands of years, forests being the largest contributor to the current global biomass supply [1]. Most of today’s production of solid biomass for energy occurs in non-Organization for Economic Cooperation and Development (OECD) countries (in 2015, 83.7%) [2] and is widely used for traditional cooking and residential heating. The traditional use of biomass is inefficient and characterized by severe negative impacts on human health by the combustion smoke [3]. Also, it has been linked to local deforestation and consequent environmental degradation, but the association of the demand of traditional wood for cooking and heating to large-scale deforestation is controversial [4, 5]. The modern uses of 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 in the anthropogenic CO2 emissions. This chapter reviews the forest structure (Section 2), the existing methods and techniques to evaluate biomass (Section 3), the types of biomass and biomass residues (Section 4), and the common uses of biomass for energy (Section 5).
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2. Forest structure and biomass
The role of forests in providing a large suite of products and services is well known [6, 7, 8, 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, 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]. 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, 19, 20]. The challenge is defining and separating pure and mixed stands [21].
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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, 23, 24, 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, 29, 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 (
3.1. Forest inventory
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, 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 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, 35, 36].
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, 35, 36, 54, 55, 56, 57, 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
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, 69, 70, 71, 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, 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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4. Forest biomass and forest residues
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 CO2 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). 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 the stands for energy purposes.
4.1. Energy plantations
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, 104, 105, 106, 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, rotation, harvest cycles, site, and management practices has to be considered.
The
Two main options are available for the
Other management practices include fertilization to promote yield [93, 101], though there is some controversy in the literature, with some authors stating that fertilization does not increase yield (
4.2. Stands managed for timber and other products and services
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, 133]. 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 [134, 135, 136]. The amount of forest residues depends on the species, stand structure, and stem quality, which generate a wide variability on their quantity (
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 [140, 141] and stands managed for timber and other nonwoody products and services from which forest residues can be obtained [140, 142, 143, 144]. 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 [142, 144, 145].
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5. Uses of forest biomass for energy
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 [146, 147].
5.1. Current status
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 2015, bioenergy and renewable wastes accounted for 9.4% of the world’s energy supply [2]. Among the various biomass sources, solid biofuels accounted for 63.7% of the global renewables supply (liquid biofuels, biogas, and renewable municipal waste accounted respectively for 4.3, 1.7, and 0.9% and the other renewable energy sources for the rest) [2]. 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 5.2% of the total primary energy supply in 2015 and solid biomass accounting for 36.1% of the renewable energy supply [2].
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 (
Looking at the situation in Europe, where biomass is mostly used in a modern way, the utilization of solid biomass by the energy transformation sector has a bigger prevalence. Power plants for the production of electricity have a 9% share, CHP plants both for the production of electricity and heat 16% and district heating plants 5% [148]. In total, the European energy transformation sector accounts for 30% of the solid biomass consumption, contrasting with the world average, which is around 9%.
5.2. Feedstock characterization
Biomass for energy uses comes from various sources. Generically, it can be divided into forest, agricultural, and residual biomass. From these three categories, biomass from forestry is by far the most significant source of biomass for energy production. In 2014, it generated more than 87% of the world biomass feedstock, while agriculture contributed with 10% and municipal solid wastes and landfill gas with 3% [1].
Biomass from the forest sector (
The most relevant properties in terms of energy conversion for some forest biomass fuels are presented in Table 1. Due to the variability for a specific species, they should be considered as illustrative. Untreated wood is characterized by low carbon content and high volatile matter and oxygen contents when compared to solid fossil fuels. This leads to the lower heating values of wood, which in combination with its low density results in low values of energy density. The lower heating value of oven-dry wood of different species does not have a large variation [150]. However, in practice, in many applications wood is not oven-dried and contains a certain amount of water. Typically, fresh timber has a moisture content between 50 and 60%, while timber stored for a summer and for several years have, respectively, 23–35% and 15–25% water content [150]. The lower heating value of wood fuels is very dependent on the water content of the fuel. The more water content the wood has, the lower is its energy content. The ash content of wood is typically low [151], but it can be significantly higher in bark [152]. Additionally, the harvesting process can introduce inorganic materials in the feedstock.
| Eucalyptus wood | Poplar wood | Willow wood | Beech wood | Bark (pine) | Wood chips (pine) | Pellets (wood) | |
|---|---|---|---|---|---|---|---|
| Fixed carbon | 18.80 | 13.05 | 13.73 | 14.53 | 26.60 | 19.40 | 12.65 |
| Volatile matter | 80.40 | 80.99 | 73.18 | 84.87 | 71.80 | 80.00 | 83.64 |
| Ash | 0.80 | 1.16 | 1.68 | 0.60 | 1.60 | 0.60 | 3.71 |
| Carbon | 51.20 | 47.05 | 43.06 | 49.38 | 53.90 | 51.80 | 49.12 |
| Hydrogen | 6.00 | 5.71 | 5.49 | 6.17 | 5.80 | 6.10 | 7.82 |
| Oxygen | 41.69 | 41.00 | 38.36 | 43.55 | 38.26 | 41.19 | 38.77 |
| Nitrogen | 0.20 | 0.22 | 0.44 | 0.28 | 0.40 | 0.30 | 0.56 |
| Sulfur | 0.02 | 0.05 | 0.00 | 0.01 | 0.03 | 0.01 | 0.02 |
| Moisture content (wt%, on wet base, as received) | 4.00 | 4.80 | 11.40 | 15.20 | 5.00 | 3.87 | 4.70 |
| LHV (MJ kg−1) (dry) | 18.50 | 18.19 | 18.05 | 17.97 | 20.10 | 19.56 | 17.42 |
Table 1.
5.3. Conversion technologies
Combustion is by far the most common way of converting forest biomass into energy [154]. It is performed in batch or continuous systems, depending on the scale, and to produce heat, power, or combined heat and power. The focus of this chapter is not on the traditional equipment to burn wood, but a review can be found, for example, in Ref. [155].
5.3.1. Heating applications
Depending of the scale, different combustion equipment can be used. In Europe, most of the biomass is burned in small-size units for
5.3.2. Power applications
The primary combustion technologies used in biomass-fired power plants are similar to that of district heating and industrial plants with indirect heating applications: fixed and fluidized bed boilers. Additionally, pulverized combustion is also used; it is used as well in industrial applications, but not so commonly [157]. Pulverized biomass-fired boilers are very efficient but require a considerable amount of fuel pretreatment [172] when the biomass is not already generated in fine particles (
5.3.3. CHP applications
Combined heat and power is the simultaneous generation of electricity and useful heat. It is a much more efficient way to burn forest biomass than biomass-fired power plants, since the overall efficiencies of CHP plants is much higher (global efficiencies above 85% can be achieved [179]). CHP biomass systems have an important application in industries that generate wood residues, such as the pulp and paper and wood industries [180, 181]. The other important CHP application is district heating plants [160]. CHP power plants for capacities above 2 MWe are dominated by burning biomass in steam turbines (Rankine cycle) [182]. Steam turbines are a mature technology and applied in a wide range of powers. However, in small decentralized plants their electrical efficiency is low [159]. In this case, CHP plants should be operated in a heat-controlled mode with low power-to-heat ratios [159]. For systems smaller than 2 MWe, the biomass CHP conversion technologies are not so well established [182]. In this power range, one of the commercial technologies available is the organic Rankine cycle (ORC). Its electric efficiency is relatively low, but the investment and maintenance costs are lower than that of the conventional Rankine cycles [183]. Another commercially available technology for small capacities is the steam piston engine [159]. Its nominal efficiency is comparable to that of steam turbines, having in efficiency little variation at partial load, contrary to steam turbines that have low part-load efficiencies [159]. Stirling engines are not commercially available yet [184]. They are a promising technology suitable for CHP plants below 100 kWe and achieve relatively high electrical efficiencies [182]. From all the commercially available technologies for sizes below 2 MWe, gasification is the one that presents higher efficiencies [182].
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6. Conclusions
The primordial source of biomass for energy is derived from stands and forests. Due to the wide range of stand structures, the amounts of biomass available for energy are also quite variable. Higher quantities per unit area are attained in energy plantations. Pure or mixed even-aged high forests managed for timber potentially originate larger amounts of forest residues when compared with the other types of stands. The renewed interest of biomass as a source of energy brought about the challenge of its estimation. Remote sensing is a useful tool that enables a more cost-efficient evaluation and monitoring when compared with the forest inventory approach. Forest biomass is a very versatile renewable energy source, yet its share on the world energy supply is relatively small. It is mainly converted to energy in combustion systems used for heat generation, but CHP and electricity production are also common. For most applications, the use of raw biomass is adequate, but it might be necessary and/or more appropriate to upgrade it.
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Acknowledgments
The work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 696140 [TrustEE - Innovative market based Trust for Energy Efficiency investments in industry] and National Funds through FCT –
The work reflects only the authors’ view and the Agency and the Commission are not responsible for any use that may be made of the information it contains.
References
- 1.
WBA. WBA global bioenergy statistics 2017. Stockholm: World Biomass Association; 2017. 79 p - 2.
IEA. Renewables information: overview. Paris: International Energy Agency; 2007. 11 p - 3.
Hanna R, Duflo E, Greenstone M. Up in smoke: The influence of household behavior on the long-run impact of improved cooking stoves. American Economic Journal: Economic Policy. 2016; 8 (1):80-114 - 4.
Arnold M, Köhlin G, Persson R, Shepherd G. Fuelwood Revisited. What Has Changed in the Last Decades? Jakarta: Center for International Forestry Research; 2003. p. 35 - 5.
Bailis R, Wang Y, Drigo R, Ghilardi A, Masera O. Getting the numbers right: Revisiting woodfuel sustainability in the developing world. Environmental Research Letters. 2017; 12 (11):115002 - 6.
Assmann E. The Principles of Forest Yield Study. Oxford: Pergamon Press; 1970. p. 506 - 7.
Smith DM, Larson BC, Kelty MJ, Ashton PMS. The Practice of Silviculture. Applied Forest Ecology. 9th ed. New York: John Wiley & Sons, Inc; 1997. p. 560 - 8.
Röser D, Asikainen A, Stupak I, Pasanen K. Forest energy resources and potentials. In: Röser D, Asikainen A, Raulund-Rasmussen K, Stupak I, editors. Sustainable Use of Forest Biomass for Energy: A Synthesis with Focus on the Baltic and Nordic Region. Managing Forest Ecosystems. Dordrecht: Springer; 2008. pp. 9-28 - 9.
Toklu E. Biomass energy potential and utilization in Turkey. Renewable Energy. 2017; 107 :235-244 - 10.
Schütz JP. Sylviculture 2. La gestion des forêts irrégulières et mélangées. Collection Gérer L’environement, n° 13. Lausanne: Presses Polytechniques et Universitaires Romandes; 1997. p. 178 [in french] - 11.
Pretzsch H. Forest Dynamics, Growth and Yield: From Measurement to Model. Berlin Heidelberg: Springer-Verlag; 2009. p. 664 - 12.
Martine S, Castellani V, Sala S. Carrying capacity assessment of forest resources: Enhancing environmental sustainability in energy production at local scale. Resources, Conservation and Recycling. 2015; 94 :11-20 - 13.
Boudru M. Forêt et Sylviculture. Le Traitement Des forêts [Forest and Silviculture. The Treatment of Forests]. Tome 2. Gembloux: Presses Agronomiques de Gembloux; 1989. p. 344 [in french] - 14.
Matthews JD. Silvicultural systems. Oxford: Claredon Press; 1989. p. 284 - 15.
Pommerening A, Murphy ST. A review of the history, definitions and methods of continuous cover forestry with special attention to afforestation and restocking. Forestry. 2004; 77 (1):27-24 - 16.
Ciancio O, Nocentini S. Biodiversity conservation and systemic silviculture: Concepts and applications. Plant Biosystems. 2011; 145 (2):411-418 - 17.
O’Hara KL. Multiaged Silviculture: Managing for Complex Forest Stand Structures. Oxford: Oxford University Press; 2014. p. 213 - 18.
Oliver CD, Larson BC, editors. Forest Stand Dynamics. Update Editions. New York: John Wiley & sons, Inc; 1996. p. 544 - 19.
Kelty MJ, Larson BC, Oliver CD, editors. The Ecology and Silviculture of Mixed-Species Forests. Dordrecht: Kluwer Academic Publishers; 1992. p. 287 - 20.
Forrester DI. The spatial and temporal dynamics of species interactions in mixed-species forests: From pattern to process. Forest Ecology and Management. 2014; 312 :282-292 - 21.
Gonçalves AC. Multi-species stand classification: Definition and perspectives. In: Chakravarty S, Shukla G, editors. Forest Ecology and Conservation. Rijeka. InTech; 2017. pp. 4-23 - 22.
Tomppo E, Haakana M, Katila M, Peräsaari J. Multi-Source National Forest Inventory - Methods and Applications. Managing Forest Ecosystems 18. Dordrecht: Springer Science+Business Media; 2008. p. 373 - 23.
Boutin S, Haughland DL, Schieck J, Herbers J, Bayne E. A new approach to forest biodiversity monitoring in Canada. Forest Ecology and Management. 2009; 258 :S168-S175 - 24.
McRoberts R, Tomppo E, Naesset E. Advanced and emerging issues on national forest inventories. Scandinavian Journal of Forest Research. 2010; 25 :368-381 - 25.
Corona P, Chirici G, McRoberts RE, Barbati A. Contribution of large-scale forest inventories to biodiversity assessment and monitoring. Forest Ecology and Management. 2011; 262 :2061-2069 - 26.
Eisfelder C, Kuenzer C, Dech S. Derivation of biomass information for semi-arid areas using remote-sensing data. International Journal of Remote Sensing. 2012; 33 (9):2937-2984 - 27.
Lu D, Chen Q, Wang G, Liu L, Li G, Moran EA. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth. 2016; 9 (1):63-105 - 28.
Avery TE, Burkhart HE, editors. Forest Measurements. 4th ed. New York: Macgraw-Hill Inc; 1994. p. 480 - 29.
Vidal C, Lanz A, Tomppo E, Schadauer K, Gschwantner T, di Cosmo L, Robert N. Establishing forest inventory reference definitions for forest and growing stock: A study towards common reporting. Silva Fennica. 2008; 42 (2):247-266 - 30.
Henttonen HM, Kangas A. Optimal plot design in a multipurpose forest inventory. Forest Ecosystems. 2015; 2 (31):1-14. DOI: 10.1186/s40663-015-0055-2 - 31.
Ozdemir I. Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing. 2008; 29 (19):5643-5655 - 32.
Sousa AMO, Gonçalves AC, Mesquita P, Marques da Silva JR. Biomass estimation with high resolution satellite images: A case study of Quercus rotundifolia. ISPRS Journal of Photogrametric and Remote Sensing. 2015; 101 :69-79 - 33.
Pizaña JMG, Hernández JMN, Romero NC. Remote sensing-based biomass estimation. In: Marghny M, editor. Earth and Planetary Sciences, Geology and Geophysics, Environmental Applications of Remote Sensing. Rijeka: InTech; 2016. pp. 3-40 - 34.
Gonçalves AC, Sousa AMO, Mesquita PG. Estimation and dynamics of above ground biomass with very high resolution satellite images in Pinus pinaster stands. Biomass and Bioenergy. 2017a;106 :146-154 - 35.
Gonçalves AC, Sousa AMO, Silva JRM. Pinus pinea above ground biomass estimation with very high spatial resolution satellite images. In: Carrasquinho I, Correia AC, Mutke S, editors. Mediterranean Pine Nuts from Forest and Plantations. Options Mediterranées. 2017b;122 :49-54 - 36.
Sousa AMO, Gonçalves AC, Silva JRM. Above ground biomass estimation with high spatial resolution satellite images. In: Tumuluru JS, editor. Biomass Volume Estimation and Valorization for Energy. Rijeka: InTech; 2017. pp. 47-70 - 37.
Brown S, Gillespie AJR, Lugo AE. Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science. 1989; 35 :881-902 - 38.
Ter-Mikaelian MT, Korzukhin MD. Biomass equations for sixty-five north American tree species. Forest Ecology and Management. 1997; 97 :1-24 - 39.
Eamus D, McGuinness K, William B, editors. Review of Allometric Relationships for Estimating Woody Biomass for Queensland, the Northern Territory and Western Australia, Technical report no. 5a. Australia: Australian Greenhouse; 2000. p. 56 - 40.
Keith H, Barrett D, Keenan R. Review of Allometric Relationships for Estimating Woody Biomass for New South Wales, the Australian Capital Territory, Victoria, Tasmania, and South Australia. National Carbon Accounting System Technical Report 5B. Canberra: Australian Greenhouse Office; 2000. p. 111 - 41.
Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA. National-scale biomass estimators for United States tree species. Forest Science. 2003; 49 (1):12-35 - 42.
Zianis D, Muukkonen P, Mäkipää R, Mencuccini M. Biomass and Stem Volume Equations for Tree Species in Europe. Tampere, Finland: The Finnish Society of Forest Science, The Finnish Forest Research Institute; 2005. p. 63 - 43.
Fehrmann L, Kleinn C. General considerations about the use of allometric equations for biomass estimation on the example of Norway spruce in Central Europe. Forest Ecology and Management. 2006; 236 :412-421 - 44.
de Jong J, Akselsson C, Egnell G, Löfgren S, Olsson BA. Realizing the energy potential of forest biomass in Sweden – How much is environmentally sustainable? Forest Ecology and Management. 2017; 383 :3-16 - 45.
Henry M, Picard N, Trotta C, Manlay RJ, Valentini R, Bernoux M, Saint-André L. Estimating tree biomass of sub-Saharan African forests: A review of available allometric equations. Silva Fennica. 2011; 45 (3B):477-569 - 46.
Vieilledent G, Vaudry R, Andriamanohisoa SF, Rakotonarivo OS, Randrianasolo HZ, Razafindrabe HN, Rakotoarivony CB, Ebeling J, Rasamoelina M. A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models. Ecological Applications. 2012; 22 :572-583 - 47.
Mattsson E, Ostwald M, Wallin G, Nissanka SP. Heterogeneity and assessment uncertainties in forest characteristics and biomass carbon stocks: Important considerations for climate mitigation policies. Land Use Policy. 2016; 59 :84-94 - 48.
Henry M, Besnard A, Asante WA, Eshun J, Adu-Bredu S, Valentini R, Bernoux M, Saint-André L. Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. Forest Ecology and Management. 2010; 260 :1375-1388 - 49.
Somogyi Z, Cienciala E, Mäkipää R, Muukkonrn P, Lehtonen A, Weiss P. Indirect methods of large-scale forest biomass estimation. European Journal of Forest Research. 2007; 126 :197-207 - 50.
Kangas A, Maltamo M. Forest Inventory: Methodology and Applications. Dordrecht: Springer Science & Business Media; 2006. p. 384 - 51.
Gail WB. Remote sensing in the coming decade: The vision and the reality. Journal of Application of Remote Sensing. 2007; 1 (1):012505 - 52.
Kumar L, Sinha P, Taylor S, Alqurashi AF. Review of the use of remote sensing for biomass estimation to support renewable energy generation. Journal of Applied Remote Sensing. 2015; 9 :097696-1-097696-28 - 53.
Boyd DS, Danson FM. Satellite remote sensing of forest resources: Three decades of research development. Progress in Physical Geography. 2005; 29 (1):1-26 - 54.
Phua M, Saito H. Estimation of biomass of a mountainous tropical forest using Landsat TM data. Canadian Journal of Remote Sensing. 2003; 29 :429-440 - 55.
Baccini A, Friedl MA, Woodcock CE, Warbington R. Forest biomass estimation over regional scales using multisource data. Geophysical Research Letters. 2004; 31 :L10501 - 56.
Carreiras JM, Pereira JMC, Pereira JS. Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. Forest Ecology and Management. 2006; 223 :45-53 - 57.
Lu D. The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing. 2006; 27 :1297-1328 - 58.
Muukkonen P, Heiskanen J. Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories. Remote Sensing of Environment. 2007; 107 :617-624 - 59.
Tompkins S, John FM, Carld MP, Donald WF. Optimization of endmembers mixture analysis for spectral. Remote Sensing of Environment. 1997; 59 :472-489 - 60.
Fraser RH, Li Z. Estimating fire-related parameters in boreal forest using SPOT VEGETATION. Remote Sensing of Environment. 2002; 82 :95-110 - 61.
García-Martín A, Pérez-Cabello F, de la Riva JR, Montorio R. Estimation of crown biomass of Pinus spp. from Landsat TM and its effect on burn severity in a Spanish fire scar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2008;1 :254-265 - 62.
Fernández-Manso O, Fernández-Manso A, Quintano C. Estimation pf aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images. International Journal of Applied Earth Observation and Geoinformation. 2014; 31 :45-56 - 63.
Häme T, Salli A, Andersson K, Lohi A. A new methodology for the estimation of biomass of conifer-dominated boreal forest using NOAA AVHRR data. International Journal of Remote Sensing. 1997; 18 (15):3211-3243 - 64.
Zheng D, Rademacher J, Chen J, Crow T, Bresee M, Le Moine J, Ryu S. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment. 2004; 93 :402-411 - 65.
Baccini A, Laporte N, Goetz MS, Dong H. A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environmental Research Letters. 2008; 3 :045011 - 66.
Thenkabail PS, Stucky N, Griscom BW, Ashton MS, Diels J, Meer B, Enclona E. Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data. International Journal of Remote Sensing. 2004; 25 (23):5447-5472 - 67.
Palace M, Keller M, Asner GP, Hagen S, Braswell B. Amazon forest structure from IKONOS satellite data and the automated characterization of forest canopy properties. Biotropica. 2008; 40 :141-150 - 68.
Nelson RF, Hyde P, Johnson P, Emessiene B, Imhoff ML, Campbell R, Edwards W. Investigating RaDAR-LiDAR synergy in a North Carolina pine forest. Remote Sensing of Environment. 2007; 110 :98-108 - 69.
Solberg S, Astrup R, Gobakken T, Næsset E, Weydahl DJ. Estimating spruce and pine biomass with interferometric X-band SAR. Remote Sensing of Environment. 2010; 114 :2353-2360 - 70.
Carreiras JMB, Vasconcelos MJ, Lucas RM. Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sensing of Environment. 2012; 121 :426-442 - 71.
Carreiras JMB, Melo JB, Vasconcelos MJ. Estimating the above-ground biomass in Miombo savanna woodlands (Mozambique, East Africa) using L-band synthetic aperture radar data. Remote Sensing. 2013; 5 :1524-1548 - 72.
Bouveta A, Mermoza S, Le Toana T, Villarda L, Mathieub R, Naidoob L, Asnerc GP. An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote Sensing of Environment. 2018; 206 :156-173 - 73.
Sarker LR, Nichol J, Iz HB, Ahmad BB, Rahman AA. Forest biomass estimation using texture measurements of high-resolution dual-polarization C-band and SAR data. IEEE Transactions on Geoscience and Remote Sensing. 2013; 51 (6):3371-2360 - 74.
Vazirabad YF, Karslioglu MO. Lidar for Biomass Estimation. In: Darko Matovic Editor. BIOMASS – Detection, Production an Usage. Rijeka: InTech; 2011. pp. 3-26 - 75.
Gleason CJ, Im J. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment. 2012; 125 :80-91 - 76.
Osama K, Hosoi F, Konishi A. 3D lidar imaging for detecting and understanding plant responses and canopy structure. Journal of Experimental Botany. 2007; 58 (4):881-898 - 77.
Dalponte M, Bruzzone L, Gianelle D. Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Transactions on Geoscience and Remote Sensing. 2008; 46 (5):1416-1427 - 78.
Sullivan AA, McGaughey RJ, Andersen HE, Schiess P. Object-oriented classification of forest structure from light detection and ranging data for stand mapping. Western Journal of Applied Forestry. 2009; 24 :198-204 - 79.
Shao Z, Zhang L. Estimating forest aboveground biomass by combining optical and SAR data: A case study in Genhe, Inner Mongolia, China. Sensors. 2016; 16 (834):1-16 - 80.
Ruiz LA, Recio JA, Crespo-Peremarch P, Sapena M. An object-based approach for mapping forest structural types based on low-density Lidar and multispectral imagery. Geocarto International. 2018; 33 (5):443-457 - 81.
Ghosh SM, Behera MD. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography. 2018; 96 :29-40 - 82.
Kinoshita T, Inoue K, Iwao K, Kagemoto H, Yamagata Y. A spatial evaluation of forest biomass usage using GIS. Applied Energy. 2008; 86 :1-8 - 83.
Fiorese G, Guariso G. A GIS-based approach to evaluate biomass potential from energy crops at regional scale. Environmental Modelling and Software. 2010; 25 :702-711 - 84.
Rørstad P, Trømborg E, Bergseng E, Solberg B. Combining GIS and forest modelling in estimating regional supply of harvest residues in Norway. Silva Fennica. 2010; 44 (3):435-451 - 85.
Viana H, Aranha J, Lopes D, Cohen WB. Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecological Modelling. 2012;226 :22-35 - 86.
Malico I, Gonçalves AC, Sousa AMO. Assessment of the availability of forest biomass for biofuels production in southwestern Portugal. Defect and Diffusion Forum. 2017; 371 :121-127 - 87.
Ranta T. Logging residues from regeneration fellings for biofuel production–a GIS-based availability analysis in Finland. Biomass and Bioenergy. 2005; 28 :171-182 - 88.
Viana H, Cohen WB, Lopes D, Aranha J. Assessment of forest biomass for use as energy. GIS-based analysis of geographical availability and locations of wood-fired power plants in Portugal. Applied Energy. 2010; 87 :2551-2560 - 89.
Lundmark R, Athanassiadis D, Wetterlund E. Supply assessment of forest biomass – A bottom-up approach for Sweden. Biomass and Bioenergy. 2015; 75 :213-226 - 90.
Halperin J, LeMay V, Chidumayo E, Verchot L, Marshall P. Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia. Forest Ecosystems. 2016; 3 :14 - 91.
Saatchi SS, Harris NL, Brown S, Lefsky M, Mitchard ET, Salas W, Zutta BR, Buermann W, Lewis SL, Hagen S, Petrova S, White L, Silman M, Morel A. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences of the United States of America. 2011; 108 (24):9899-9904 - 92.
Baccini A, Goetz SJ, Walker WS, Laporte NT, Sun M, Sulla-Menashe D, Hackler J, Beck PSA, Dubayah R, Friedl MA, Samanta S, Houghton RA. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change. 2012; 2 :182-185 - 93.
Sixto H, Hernández MJ, Barrio M, Carrasco J, Cañellas I. Plantaciones del género Populus para la producción de biomasa con fines energéticos: revisión. Investigación Agraria: Sistemas y Recursos Forestales. 2007; 16 :277-294 - 94.
Kirby KJ, Buckley GP, Mills J. Biodiversity implications of coppice decline, transformations to high forest and coppice restoration in British woodland. Folia Geobotanica. 2017; 52 :5-13 - 95.
Abbas D, Current D, Phillips M, Rossman R, Hoganson H, Brooks KN. Guidelines for harvesting forest biomass for energy: A synthesis of environmental considerations. Biomass and Bioenergy. 2011; 35 :4538-4546 - 96.
Egnell G. A review of Nordic trials studying effects of biomass harvest intensity on subsequent forest production. Forest Ecology and Management. 2017; 383 :27-36 - 97.
Pérez S, Renedo CJ, Ortiz A, Mañana M, Delgado F, Tejedor C. Energetic density of different forest species of energy crops in Cantabria (Spain). Biomass and Bioenergy. 2011; 35 :4657-4664 - 98.
Kline KL, Coleman MD. Woody energy crops in the southeastern United States: Two centuries of practitioner experience. Biomass and Bioenergy. 2010; 34 (12):1655-1666 - 99.
Dimitriou I, Rutz D. Sustainable Short Rotation Coppice a Handbook. Munich: WIP Renewable Energies; 2015. p. 104 - 100.
McKendry K. Energy production from biomass (part 1): Overview of biomass. Bioresource Technology. 2002; 83 :37-46 - 101.
Dickmann D. Silviculture and biology of short-rotation woody crops in temperate regions: Then and now. Biomass and Bioenergy. 2006; 30 :696-705 - 102.
Suchomel C, Pyttel P, Becker G, Bauhus J. Biomass equations for sessile oak ( Quercus petraea (Matt.) Liebl.) and hornbeam (Carpinus betulus L.) in aged coppiced forests in Southwest Germany. Biomass and Bioenergy. 2012;46 :722-730 - 103.
Yemshanov D, McKenney D. Fast-growing poplar plantations as a bioenergy supply source for Canada. Biomass and Bioenergy. 2008; 32 :185-197 - 104.
Guidi W, Pitre F, Labrecque M. Short-rotation coppice of willows for the production of biomass in eastern Canada. In: Matovic MD, editor. Biomass Now—Sustainable Growth and Use. Rijeka: InTech; 2013. pp. 421-448 - 105.
Verwijst T, Lundkvist A, Edelfeldt S, Albertsson J. Development of sustainable willow short rotation forestry in northern europe. In: Matovic MD, editor. Biomass Now - Sustainable Growth and Use. Rijeka: InTech; 2013. pp. 479-502 - 106.
Hauk S, Gandorfer M, Wittkopf S, Müller UK, Knoke T. Ecological diversification is risk reducing and economically profitable–the case of biomass production with short rotation woody crops in south German land-use portfolios. Biomass and Bioenergy. 2017; 98 :142-152 - 107.
Vávrová K, Knápek J, Weger J. Short-term boosting of biomass energy sources – Determination of biomass potential for prevention of regional crisis situations. Renewable and Sustainable Energy Reviews. 2017; 67 :426-436 - 108.
DeBell DS, Harrington CA. Deploying genotypes in short-rotation plantations: Mixtures and pure cultures of clones and species. The Forestry Chronicle. 1993; 69 :705-713 - 109.
Updegraff K, Baughman MJ, Taf SJ. Environmental benefits of cropland conversion to hybrid poplar: Economic and policy considerations. Biomass and Bioenergy. 2004; 27 :411-428 - 110.
Wetttman GF. Silvicultural systems for biomass production in Canada. New Zealand Journal of Forestry Science. 2000; 30 (1-2):5-15 - 111.
Liang WJ, Hu HQ, Liu FJ, Zhang DM. Research advance of biomass and carbon storage of poplar in China. Journal of Forest Research. 2006; 17 (1):75-79 - 112.
Ceulemans R, McDonald AJS, Pereira JS. A comparison among eucalypt, poplar and willow characteristics with particular reference to a coppice, growth-modelling approach. Biomass and Bioenergy. 1996; 11 :215-231 - 113.
Hinchee M, Rottmann W, Mullinax L, Zhang C, Chang S, Cunningham M, Pearson L, Nehra N. Short-rotation woody crops for bioenergy and biofuels applications. In Vitro Cellular & Developmental Biology. Plant. 2009; 45 (6):619-629 - 114.
Labrecque M, Teodorescu TI. Field performance and biomass production of 12 willow and poplar clones in short-rotation coppice in southern Quebec (Canada). Biomass and Bioenergy. 2005; 29 :1-9 - 115.
Fischer M, Kelley AM, Ward EJ, Boone JD, Ashley EM, Domec J-C, Williamson JC, King JS. A critical analysis of species selection and high vs. low-input silviculture on establishment success and early productivity of model short-rotation wood-energy cropping systems. Biomass and Bioenergy. 2017; 98 :214-227 - 116.
Proe MF, Griffiths J, Craig J. Effects of spacing, species and coppicing on leaf area, light interception and photosynthesis in short rotation forestry. Biomass and Bioenergy. 2002; 23 :315-326 - 117.
Geyer WA. Biomass production in the central Great Plains USA under various coppice regimes. Biomass and Bioenergy. 2006; 30 :778-783 - 118.
Vande Walle I, Van Camp N, Van de Casteele L, Verheyen K, Lemeur R. Short-rotation forestry of birch, maple, poplar and willow in Flanders (Belgium) II. Energy production and CO2 emission reduction potential. Biomass and Bioenergy. 2007; 31 :276-283 - 119.
Sims REH, Senelwa K, Maiava T, Bullock BT. Eucalyptus species for biomass energy in New Zealand: Part II: Coppice performance. Biomass and Bioenergy. 1999; 17 :333-343 - 120.
Eufrade HJ Jr, de Melo RX, Sartori MMP, Guerra SPS, Ballarin AW. Sustainable use of eucalypt biomass grown on short rotation coppice for bioenergy. Biomass and Bioenergy. 2016; 90 :15-21 - 121.
Laureysens I, Bogaert J, Blust R, Ceulemans R. Biomass production of 17 poplar clones in a short-rotation coppice culture on a waste disposal site and its relation to soil characteristics. Forest Ecology and Management. 2004; 187 :295-309 - 122.
Laureysens I, Pellis A, Willems J, Ceulemans R. Growth and production of a short rotation coppice culture of poplar. III. Second rotation results. Biomass and Bioenergy. 2005; 29 :10-21 - 123.
Buhler DD, Netzer DA, Riemenschneider DE, Hartzler RG. Weed management in short rotation poplar and herbaceous perennial crops grown for biofuel production. Biomass and Bioenergy. 1998; 14 (4):385-394 - 124.
Telenius BF. Stand growth of deciduous pioneer tree species on fertile agricultural land in southern Sweden. Biomass and Bioenergy. 1999; 16 :13-23 - 125.
Kopp RF, Abrahamson LP, White EH, Volk TA, Nowak CA, Fillhart RC. Willow biomass production during ten successive annual harvests. Biomass and Bioenergy. 2001; 20 :1-7 - 126.
Deckmyn G, Laureysens I, Garcia J, Muys B, Ceulemans R. Poplar growth and yield in short rotation coppice: Model simulations using the process model SECRETS. Biomass and Bioenergy. 2004; 26 :221-227 - 127.
Ceulemans R, Deraedt W. Production physiology and growth potential of poplars under short-rotation forestry culture. Forest Ecology and Management. 1999; 121 :9-23 - 128.
Stanturf JA, Van Oosten C, Netzer DA, Coleman MD, Portwood CJ. Ecology and silviculture of poplar plantations. In: Dickmans DI, Isebrands JG, Eckenwalder JE, Richardson J, editors. Poplar Culture in North America. Ottawa: NRC Research Press, National Research Press, National Research Council of Canada; 2001. pp. 13-206 - 129.
Coleman M, Tolsted D, Nichols T, Johnson W, Wene E, Houghtaling T. Post-establishment fertilization of Minnesota hybrid poplar plantations. Biomass and Bioenergy. 2006; 30 :740-749 - 130.
Pei MH, Ruiz C, Bayon C, Hunter T, Lonsdale D. Pathogenic variation in poplar rust Melampsora larici-populina from England. European Journal of Plant Pathology. 2005;111 (2):147-155 - 131.
Allen SJ, Hall RL, Rosier PTW. Transpiration by two poplar varieties grown as coppice for biomass production. Tree Physiology. 1999; 19 (8):493-501 - 132.
Petzold R, Schwaerzel K, Feger K-H. Transpiration of a hybrid poplar plantation in Saxony (Germany) in response to climate and soil conditions. European Journal of Forest Research. 2011; 130 (5):695-706 - 133.
Smith WB, Miles PD, Perry CH, Pugh SA. Forest Resources of the United States, 2007. Gen. Tech. Rep. WO-78. Washington, DC: U.S. Department of Agriculture, Forest Service; 2009. p. 336 - 134.
Belleau A, Brais S, Paré D. Soil nutrient dynamics after harvesting and slash treatments in boreal aspen stands. Soil Science Society of America Journal. 2006; 70 :1189-1199 - 135.
Jonsell M. The effects of forest biomass harvesting on biodiversity. In: Röser D, Asikainen A, Raulund-Rasmussen K, Stupak I, editors. Sustainable Use of Forest Biomass for Energy: A Synthesis with Focus on the Baltic and Nordic Region. Managing Forest Ecosystems. Dordrecht: Springer; 2008. pp. 129-154 - 136.
Pedroli B, Elbersen B, Frederiksen P, Grandin U, Heikkilä R, Krogh PH, Izakovičová Z, Johansen A, Meiresonne L, Spijker J. Is energy cropping in Europe compatible with biodiversity? – Opportunities and threats to biodiversity from land-based production of biomass for bioenergy purposes. Biomass and Bioenergy. 2013; 55 :73-86 - 137.
Castellano PJ, Volk TA, Herrington LP. Estimates of technically available woody biomass feedstock from natural forests and willow biomass crops for two locations in New York state. Biomass and Bioenergy. 2009; 33 :393-406 - 138.
López-Rodríguez F, Atanet CP, Blázquez FC, Celma AR. Spatial assessment of the bioenergy potential of forest residues in the western province of Spain, Caceres. Biomass and Bioenergy. 2009; 33 :1358-1366 - 139.
Rothe A, Moroni M, Neyland M, Wilnhammer M. Current and potential use of forest biomass for energy in Tasmania. Biomass and Bioenergy. 2015; 80 :162-172 - 140.
Jose S, Bardhan S. Agroforestry for biomass production and carbon sequestration: An overview. Agroforestry Systems. 2012; 86 :105-111 - 141.
Holzmueller EJ, Jose S. Bioenergy crops in agroforestry systems: Potential for the US north central region. Agroforestry Systems. 2012; 85 :305-314 - 142.
Dupraz C, Newman SM. Temperate agroforestry: The European way. In: Gordon AM, Newman SM, editors. Temperate Agroforestry Systems. Oxon: CAB International; 1999. pp. 181-236 - 143.
Moore RW, Bird PR. Agroforestry systems in temperate Australia. In: Gordon AM, Newman SM, editors. Temperate Agroforestry Systems. Oxon: CAB International; 1999. pp. 119-148 - 144.
Martín D, Vázquez-Piqué J, Alejano R. Effect of pruning and soil treatments on stem growth of holm oak in open woodland forests. Agroforestry Systems. 2015; 89 :599-609 - 145.
Alejano R, Vázquez-Piqué J, Carevic F, Fernández M. Do ecological and silvicultural factors influence acorn mass in holm oak (southwestern Spain)? Agroforestry Systems. 2011; 83 :25-39 - 146.
Brown RC. Introduction to thermochemical processing of biomass into fuels, chemicals, and power. In: Brown RC, editor. Thermochemical Processing of Biomass. Conversion into Fuels, Chemicals and Power. Chichester: Wiley; 2011. pp. 1-12 - 147.
Ge X, Fuqing X, Yebo L. Solid-state anaerobic digestion of lignocellulosic biomass: Recent progress and perspectives. Bioresource Technology. 2016; 205 :239-249 - 148.
Eurostat. Available from: http://ec.europa.eu/eurostat/data/database [Accessed: 2018-05-02] - 149.
Hoefnagels R, Resch G, Junginger M, Faaij A. International and domestic uses of solid biofuels under different renewable energy support scenarios in the European Union. Applied Energy. 2014; 131 :139-157 - 150.
Krajnc N. Wood Fuels Handbook. Pristina: Food and Agriculture Organization of the United Nations; 2015. p. 31 - 151.
Strezov V. Properties of biomass fuels. In: Strezov V, Evans TJ, editors. Biomass Processing Technologies. Boca Raton: CRC Press; 2014. pp. 1-32 - 152.
Ragland KW, Aerts DJ, Baker AJ. Properties of wood for combustion analysis. Bioresource Technology. 1991; 37 (2):161-168 - 153.
ECN. Phyllis2, database for biomass and waste. Available from: https://www.ecn.nl/phyllis2 . Energy Research Centre of the Netherlands [Accessed: 2018-05-02] - 154.
Pisupati SV, Tchapda AH. Thermochemical processing of biomass. In: Ravindra P, editor. Advances in Bioprocess Technology. Springer International Publishing; 2015. pp. 277-314 - 155.
de Carvalho RL. Wood-burning stoves worldwide: technology, innovation and policy [thesis]. Aalborg: Aalborg University; 2016. DOI: 10.5278/VBN.PHD.ENGSCI.00122 - 156.
Míguez JL, Morán JC, Granada E, Porteiro J. Review of technology in small-scale biomass combustion systems in the European market. Renewable and Sustainable Energy Reviews. 2012; 16 (6):3867-3875 - 157.
van Loo S, Koppejan J, editors. The Handbook of Biomass Combustion and Co-Firing. London: Earthscan; 2012. p. 464 - 158.
Martinopoulos G, Papakostas KT, Papadopoulos AM. A comparative review of heating systems in EU countries, based on efficiency and fuel cost. Renewable and Sustainable Energy Reviews. 2018; 90 :687-699 - 159.
Carlon E, Schwarz M, Golicza L, Verma VK, Prada A, Baratieri M, Haslinger W, Schmidl C. Efficiency and operational behaviour of small-scale pellet boilers installed in residential buildings. Applied Energy. 2015; 155 :854-865 - 160.
Lake A, Rezaie B, Beyerlein S. Review of district heating and cooling systems for a sustainable future. Renewable and Sustainable Energy Reviews. 2017; 67 :417-425 - 161.
Ericsson K, Werner S. The introduction and expansion of biomass use in Swedish district heating systems. Biomass and Bioenergy. 2016; 94 :57-65 - 162.
Koornneef J, Junginger M, Faaij A. Development of fluidized bed combustion – An overview of trends, performance and cost. Progress in Energy and Combustion Science. 2007; 33 (1):19-55 - 163.
Yin C, Rosendahl LA, Kær SK. Grate-firing of biomass for heat and power production. Progress in Energy and Combustion Science. 2008; 34 (6):725-754 - 164.
S2Biom. Biomass conversion technologies database. Available from: http://s2biom.alterra.wur.nl/ [Accessed on 2018-04-20] - 165.
Nussbaumer T, Thalmann S. Status Report on District Heating Systems in IEA Countries. Zürich: Swiss Federal Office of Energy, and Verenum; 2014. p. 48 - 166.
Taibi E, Gielen D, Bazilian M. The potential for renewable energy in industrial applications. Renewable and Sustainable Energy Reviews. 2012; 16 (1):735-744 - 167.
Saygin D, Gielen DJ, Draeck M, Worrell E, Patel MK. Assessment of the technical and economic potentials of biomass use for the production of steam, chemicals and polymers. Renewable and Sustainable Energy Reviews. 2014; 40 :1153-1167 - 168.
Remus R, Aguado-Monsonet MA, Roudier S, Sancho LD. Best Available Techniques (BAT) Reference Document for Iron and Steel Production. Luxembourg: Publications Office of the European Union; 2013. p. 621 - 169.
Mousa E, Wang C, Riesbeck J, Larsson M. Biomass applications in iron and steel industry: An overview of challenges and opportunities. Renewable and Sustainable Energy Reviews. 2016; 65 :1247-1266 - 170.
Schorcht F, Kourti J, Scalet BM, Roudier S, Delgado Sancho L. Best Available Techniques in the Cement, Lime and Magnesium Oxide Manufacturing Industries. Reference Document. Luxembourg: Publications Office of the European Union; 2013. p. 480 - 171.
Rahman A, Rasul MG, Khan MMK, Sharma S. Recent development on the uses of alternative fuels in cement manufacturing process. Fuel. 2015; 145 :84-99 - 172.
van den Broek R, Faaij A, van Wijk A. Biomass combustion for power generation. Biomass and Bioenergy. 1996; 11 (4):271-281 - 173.
Bilgili M, Ozbek A, Sahin B, Kahraman A. An overview of renewable electric power capacity and progress in new technologies in the world. Renewable and Sustainable Energy Reviews. 2015; 49 :323-334 - 174.
IEA. Technology Roadmap. Bioenergy for Heat and Power. Paris: IEA Publications; 2012. p. 62 - 175.
Demirbaş A. Sustainable cofiring of biomass with coal. Energy Conversion and Management. 2003; 44 (9):1465-1479 - 176.
Itai Y, Santos R, Branquinho M, Malico I, Ghesti GF, Brasil AM. Numerical and experimental assessment of a downdraft gasifier for electric power in Amazon using açaí seed (Euterpe oleracea Mart.) as a fuel. Renewable Energy. 2014; 66 :662-669 - 177.
Kirkels AF, Verbong GPJ. Biomass gasification: Still promising? A 30-year global overview. Renewable and Sustainable Energy Reviews. 2011; 15 :471-481 - 178.
EPA. Biomass Combined Heat and Power Catalog of Technologies. V.1.1. U. S. Environmental Protection Agency; 2007. p. 112 - 179.
BASIS. Report on conversion efficiency of biomass. BASIS–Biomass Availability and Sustainability Information System. 2015:20 - 180.
Berglin N, Berntsson T. CHP in the pulp industry using black liquor gasification: Thermodynamic analysis. Applied Thermal Engineering. 1998; 18 (11):947-961 - 181.
Stubdrup KR, Karlis P, Roudier S, Sancho LD. Best Available Techniques (BAT) Reference Document for the Production of Wood-Based Panels. EUR27732EN. Luxembourg: European Commission; 2016. p. 257 - 182.
Strzalka R, Schneider D, Eicker U. Current status of bioenergy technologies in Germany. Renewable and Sustainable Energy Reviews. 2017; 72 :801-820 - 183.
Algieri A, Morrone P. Energetic analysis of biomass-fired ORC systems for micro-scale combined heat and power (CHP) generation. A possible application to the Italian residential sector. Applied Thermal Engineering. 2014; 71 (2):751-759 - 184.
Obernberger I, Hammerschmid A, Forstinger M. Techno-Economic Evaluation of Selected Decentralised CHP Applications Based on Biomass Combustion with Steam Turbine and ORC Processes. IEA Bioenergy Task 32 Project. BIOS Bioenergiesysteme: Graz; 2015. p. 66