Overview of methods and tools for evaluating future woody biomass availability in European countries

Barreiro, Susana; Schelhaas, M.J.; Kändler, Gerald; Antón-Fernández, Clara; Colin, Antoine; Bontemps, Jean Daniel; Alberdi, Iciar; Condés, Sonia; Dumitru, M.; Ferezliev, Angel; Fischer, Christoph; Gasparini, Patrizia; Gschwantner, Thomas; Kindermann, Georg; Kjartansson, Bjarki; Kovácsevics, Pál; Kučera, Miloš; Lundström, Anders; Marin, Gheorghe; Mozgeris, Gintautas; Nord‐Larsen, Thomas; Packalen, Tuula; Redmond, John; Sacchelli, Sandro; Sims, Allan; Snorrason, Arnór; Stoyanov, Nickola; Thürig, Esther; Wikberg, Per Erik

doi:10.1007/s13595-016-0564-3

Cited by 51 publications

(45 citation statements)

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“…The availability of robust, spatially-explicit information on woody biomass supply is therefore relevant to plan and monitor the implementation of climate mitigation, renewable energy, and wood mobilization policies at the national and international scale (Verkerk et al 2011), as well as to support the optimal planning of biomass plants and assess the quantity and localization of their source material at the regional or local scale (Cozzi et al 2013, Sacchelli et al 2013, Kraxner et al 2015. Moreover, mapping the spatial pattern of large-scale forest biomass can provide initial values for processbased models to simulate forest and carbon dynamics (Vacchiano et al 2012), and offers a scientifically and politically relevant picture of the carbon stocks within a region (Du et al 2014, Barreiro et al 2016). …”

Section: Introductionmentioning

confidence: 99%

Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery

Vacchiano¹,

Berretti²,

Motta³

et al. 2018

iForest

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Forest biomass is a renewable energy source, more climate-friendly than fossil fuels and widely available in Europe. The wood energy chain has been suggested as a means to re-activate forest management and improve the value of forest stands in marginalized rural areas. However, wall-to-wall estimates of forest biomass, needed to design the location and size of power and heat biomass plants in any given territory, are notoriously difficult to obtain. This paper tests an algorithm to predict forest biomass using publicly available Landsat satellite imagery in the Liguria region, northern Italy. We used regional forest inventory data to train and validate an artificial neural network (ANN) classifier that uses remotely-sensed information such as three principal components of Landsat-5 TM spectral bands, the Enhanced Vegetation Index (EVI), and topography, to retrieve aboveground live tree volume. Percent root mean square error was -9% and -23% for conifers and broadleaves respectively in the calibration dataset, and -27% and -24% in the validation dataset. The reconstructed volume map was updated to present day using current volume increment rates reported by the Italian National Forest Inventory. A wall-to-wall map of forest biomass from harvest residues was finally produced based on species-specific wood density, biomass expansion factors, volume logged for timber assortments, forest accessibility, and topography. . This analysis workflow can be replicated in all mountain regions with a predominant broadleaved coppice component.

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Section: Introductionmentioning

confidence: 99%

Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery

Vacchiano¹,

Berretti²,

Motta³

et al. 2018

iForest

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“…Forests and wood products play a key role in international climate policy, as they can store carbon, and, in addition, wood-based products can be used to replace materials and energy from non-renewable sources. Carbon credits and increased demand for bioenergy (European Commission 2009;2014) have again arisen concern about the availability of wood recourses (Hänninen and Kallio 2007;Nabuurs et al 2007;Alberdi et al 2016;Barreiro et al 2016;Packalen et al 2016). At the same time, political decisions have been made to preserve forest biodiversity (United Nations 1992;European Commission 2006;2011) and, consequently, to increase areas setaside for conservation and encourage ecologically oriented forest management.…”

Section: Information Needs For Policy Supportmentioning

confidence: 99%

“…Many countries have projection systems based on tree-level growth models and the simulation of specific management activities and natural events, such as thinnings, regeneration fellings and mortality (e.g. Siitonen et al 1996;Kaufmann 2001;Wikström et al 2011;Packalen et al 2014;Barreiro et al 2016). These forestry scenario models are mainly developed for large-scale timber production analyses at the strategic level of forest management to assist policy makers.…”

Section: Information Needs For Policy Supportmentioning

confidence: 99%

Multi-source forest inventory data for forest production and utilization analyses at different levels

Haakana¹

2017

Diss. For.

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National forest inventory (NFI) data are commonly used in national and regional scenario analyses on forest production and utilization possibilities. There is an increased demand for similar analyses at the sub-regional level, and further, to incorporate spatially explicit data into the analyses. However, the fairly sparse network of NFI sample plots allows analyses only for large areas. The present dissertation explored whether satellite imagery, NFI sample plot data and the k nearest neighbour estimation method can be employed in generating spatial forest data for scenario analyses at the local level. The method was first applied in the area of two villages in Eastern Finland to quantify the effects of administrative land use and technical land-form constraints on timber production. Secondly, the impacts of three alternative regional felling strategies on suitable habitat for the Siberian flying squirrel (Pteromys volans) were assessed.As a scenario analysis tool, the Finnish forestry dynamics model MELA was used. Management units for simulations of forest development and management activities were delineated by means of image segmentation and digital maps on restriction areas, and new weights for NFI sample plots, that is, the representativeness in these units, were estimated by means of satellite image data. The performance of different segmentation methods and different spectral features in the estimation were examined. Image segments corresponding to forest stands enabled the use of patch-and landscape-level models in the prediction of suitable habitat.Satellite image-based estimation of new NFI sample plot weights was found to be a feasible method for generating forest data for scenario analyses in areas smaller than is possible with the plot data only, for example, for municipalities. Satellite imagery with large geographic coverage and continuous NFI field measurements provide cost-efficient data sources for versatile impact and scenario analyses at the local level.

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“…In recent years, the supply of wood-based biomass has been estimated at national [10] and European levels [11][12][13]. The national level estimates are usually based on detailed forest inventory data, while the European level studies have often relied on aggregated statistical data [11].…”

Section: Introductionmentioning

confidence: 99%

A Markov Chain Model for Simulating Wood Supply from Any-Aged Forest Management Based on National Forest Inventory (NFI) Data

Packalen

2017

Forests

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Markov chain models have been applied for a long time to simulate forest dynamics based on transitions in matrices of tree diameter classes or areas of forest size and structure types. To date, area-based matrix models have been applied assuming either even-aged or uneven-aged forest management. However, both management systems may be applied simultaneously due to land-use constraints or the rationality of combining the systems, which is called any-aged management. We integrated two different Markov chain models, one for even-aged and another for uneven-aged forest management, in an area-based approach to analyze wood supply from any-aged forest management. We evaluate the impacts of parameterizing the model based on available data sets, namely permanent and temporary Finnish National Forest Inventory (NFI) sample plots and a plot-level simulator to determine transitions due to different types of thinning treatments, and present recommendations for the related methodological choices. Our overall observation is that the combined modelling chain simulated the development of both the even-and uneven-aged forest structures realistically. Due to the flexibility of the implementation, the approach is very well suited for situations where scenario assumptions need to be varied according to expected changes in silvicultural practices or land-use constraints, for example.

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Overview of methods and tools for evaluating future woody biomass availability in European countries

Cited by 51 publications

References 50 publications

Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery

Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery

Multi-source forest inventory data for forest production and utilization analyses at different levels

A Markov Chain Model for Simulating Wood Supply from Any-Aged Forest Management Based on National Forest Inventory (NFI) Data

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