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Publisher Copyright: © 2021 GI_Forum. Europe has acknowledged the need to develop a very high precision digital model of the Earth, a Digital Twin Earth, running on cloud infrastructure to bring data and end-users closer together. We present results of an investigation of a proposed submodel of the digital twin, simulating the worlds’ forests. We focus on the architecture of the system and the key user needs on data content and access. The results are based on a user survey showing that the forest-related communities in Europe require information on contrasting forest variables and processes, with common interest in the status and forecast of forest carbon stock. We discuss the required spatial resolution, accuracies, and modelling tools required to match the needs of the different communities in data availability and simulation of the forest ecosystem. This, together with the knowledge on existing and projected future capabilities, allows us to specify a data architecture to implement the proposed system regionally, with the outlook to expand to continental and global scales. Ultimately, a system simulating the behaviour of forests, a digital twin, would connect the bottom-up and top-down approaches of computing the forest carbon balance: from tree-based accounting of forest growth to atmospheric measurements, respectively.

The forestry sector of Serbia, other users, as well as international organizations and conventions express the need for an increasing volume, specific structure and high reliability of information on the growing stock of Serbia. To meet these requirements, NFI-2 has undergone significant changes compared to NFI-1. In methodological terms, the changes are reflected in the fact that NFI-2 is a two-phase inventory. In the phase of photo-interpretation of the images, land categories are identified, changes occurred in the period 2006-2019 and field works are planned. Unlike NFI-1, all four circles on the cluster have a permanent character, the centers of the circles are better secured, which ensures their easy findings in NFI-3. The field phase is realized using high-precision instruments, digital records of measured or estimated values and online transfer to the database. The control of field works is performed through three levels, which significantly reduces the possibility of the appearance of rough and systematic errors. The scope of information has been significantly expanded, especially in the field of biodiversity assessment, nature protection, carbon storage, bioenergy, etc. Conceived in this way, NFI-2 is compatible with the inventories of most European countries, except in the area of its institutional organization in Serbia, which has not been resolved.

Airborne laser scanning (ALS) is generally known as the most accurate and primary source providing the 3D structure of forest canopy model (CHM) information; however, in many countries, it is not u...

Land-cover mapping (LCM) at a fine scale would be useful for forest management across heterogeneous natural landscapes. However, the heterogeneity of land covers at such scales results in complex spectral and textural properties that hinder the applicability of LCM. Besides, the method suffers from, e.g. inconsistent representation of different land-cover types, lack of sufficient and balanced training samples, and instability of classifiers trained by a high number of predictor variables. Even well-known object-based classification approaches are challenged with an objective evaluation of segmentation outputs. Here we classified partially ambiguous land-cover types across heterogeneous forest landscapes in the Bavarian Forest National Park (Germany) by combining metrics from airborne light detection and ranging (LiDAR) and colour infrared (CIR) imagery data and a random forest classifier implemented in an object-based paradigm. We evaluated the segmentation results by creating a global quality score based on inter- and intra-measurements of variance and the number of segments. Selected segmentation outputs were combined with balanced training samples to run the classification algorithm based on representative blocks within the national park. The entire processing chain was implemented in an open-source domain. The final segmentation consisted of LiDAR-based height, image-based Normalized Difference Vegetation Index (NDVI) and red band, with 20 cluster seeds and a minimum segment size of 40 pixels. In the classification, the most important variables included the height of the top layer, NDVI, Enhanced Vegetation Index (EVI) and Green–Red Vegetation Index (GRVI). The average values of 500 random forest runs indicated an overall accuracy of 86.6% and an estimated Cohen’s kappa coefficient of 85.2%, with different probabilities of correct classification for land-cover classes. Mature deciduous, standing deadwood, fallen deadwood, meadow, and bare soil classes were classified most accurately, whereas classification of young coniferous, intermediate-age coniferous, mature coniferous, young deciduous, and intermediate-age deciduous were associated with the highest uncertainties. Our methodology is sufficiently robust to be applied to other similarly structured sites across temperate forested landscapes. The versatility of the method is partially guaranteed by the proposed segmentation quality score, which satisfactorily corrects under- and over-segmentation.

W dniach 12–14 marca 2019 r. w Instytucie Badawczym Leśnictwa odbyła się XI Sesja Zimowej Szkoły Leśnej (ZSL), której tematem było „Zastosowanie geoinformatyki w leśnictwie”. Współorganizatorem Sesji była Dyrekcja Generalna Lasów Państwowych. Merytoryczne aspekty XI Sesji zostały przygotowane i opracowane przez Radę Programową Zimowej Szkoły Leśnej w składzie: prof. dr hab. Andrzej Grzywacz, prof. dr hab. Jacek Hilszczański, dr inż. Krzysztof Janeczko, prof. dr hab. Andrzej Klocek, mgr inż. Jacek Przypaśniak, mgr inż. Marek Roman, dr inż. Jacek Sagan, dr hab. Krzysztof Stereńczak, prof. IBL (przewodniczący), mgr inż. Adam Wasiak, prof. dr hab. Tomasz Zawiła-Niedźwiecki. Rada Programowa określiła nie tylko zakres problematyki XI Sesji, ale także propozycje tematyczne referatów zamawianych oraz wytypowania ich autorów. W trakcie XI Sesji referaty wygłosili zaproszeni goście zagraniczni reprezentujący Remote Sensing Research and Consulting Ltd. (Szwajcaria), Wyższą Szkołę Techniczną w Zurychu (Szwajcaria), Norweski Uniwersytet Przyrodniczy – NMBU, Federalny Instytut Badawczy Lasu, Śniegu i Krajobrazu (Szwajcaria), Uniwersytet we Fryburgu (Niemcy), Uniwersytet Florencki (Włochy), Instytut Geografii i Geoekologii i Instytut Technologii w Karlsruhe (Niemcy), Instytut Lasu im. Sukaczewa Syberyjskiej Filii Rosyjskiej Akademii Nauk, Federalne Centrum Badawcze «Krasnoyarsk Science Center SB RAS» i Syberyjski Uniwersytet Federalny (Rosja) oraz Uniwersytet w Cambridge (Wielka Brytania). Poza gośćmi z zagranicy na Zimowej Szkole Leśnej wystąpiło szerokie grono autorów z Polski, prezentujących wiele przykładów wykorzystania geoinformatyki w leśnictwie. W tym miejscu składam serdeczne podziękowanie autorom za przygotowanie i wygłoszenie interesujących referatów, a także za opracowanie tekstów wystąpień, które znajdą Państwo w niniejszych materiałach posesyjnych. W XI Sesji ZSL wzięło udział prawie 400 osób reprezentujących m.in.: kierownictwo Lasów Państwowych oraz jednostki organizacyjne PGL LP i Ministerstwo Środowiska. Najliczniejszą grupę słuchaczy Szkoły stanowili zarówno geomatycyreprezentujący szeroko rozumiany świat nauki, jak i firmy zajmujące się wdrożeniami i praktyką, pracownicy nadleśnictw oraz biur urządzania lasu i geodezji leśnej, na co dzień korzystających z narzędzi geoinformatycznych. Liczne grono stanowili przedstawiciele ośrodków naukowych, a także studentów polskich wydziałów leśnych.

Accurate and updated knowledge of forest tree heights is fundamental in the context of forest management. However, measuring canopy height over large forest areas using traditional inventory techniques is laborious, time-consuming and excessively expensive. In this study, image-based point clouds produced from stereo aerial photographs (AP) were used to estimate forest height, and compared to Airborne Laser Scanning (ALS) data. We generated image-based Canopy Height Models (CHM) using different image-matching algorithms (SGM: Semi-Global Matching; eATE: enhanced Automatic Terrain Extraction), which were compared with a pure ALS-derived CHM. Additionally, plot-level height and density metrics were extracted from CHMs and used as explanatory variables for predicting the Lorey’s mean height (LMH), which was measured at 296 reference points on the ground. CHMSGM and CHMALS showed similar results in predicting LMH at sample plot locations (RMSE% = 8.54 vs. 7.92, respectively), while CHMeATE had lower accuracy (RMSE% = 13.23). Similarly, CHMSGM showed a lower normalized median absolute deviation (NMAD) from CHMALS (0.68 m) compared to CHMeATE (1.1 m). Our study revealed that image-based point clouds using SGM in the presence of high-resolution ALS-derived digital terrain model (DTM) provide comparable results with ALS data, while the performance of image-based point clouds using eATE is poorer than ALS for forest height estimation. The findings of this study provide a viable and cost-effective option for assessing height-related forest structural parameters. The proposed methodology can be usefully applied in all those countries where AP are updated on a regular basis and pre-existing historical ALS-derived DTMs are available.

Anthropogenic activities and natural processes are continuously altering the mountainous environment through deforestation, forest degradation and other land-use changes. It is highly important to assess, monitor and forecast forest cover and other land-use changes for the protection and conservation of mountainous environment. The present study deals with the assessment of forest cover and other land-use changes in the mountain ranges of Dir Kohistan in northern Pakistan, using high resolution multi-temporal SPOT-5 satellite images. The SPOT-5 satellite images of years 2004, 2007, 2010 and 2013 were acquired and classified into land-cover units. In addition, forest cover and land-use change detection map was developed using the classified maps of 2004 and2013.The classified maps were verified through random field samples and Google Earth imagery (Quick birds and SPOT-5). The results showed that during the period 2004 to 2013 the area of forest land decreased by 6.4%, however, area of range land and agriculture land have increased by 22.1% and 2.9%, respectively. Similarly, barren land increased by 1.1%, whereas, area of snow cover/glacier is significantly decreased by 21.3%. The findings from the study will be useful for forestry and landscape planning and can be utilized by the local, provincial and national forest departments; and REDD+ policy makers in Pakistan.

Background: European forests are considered a crucial resource for supplying biomass to a growing bio-economy in Europe. This study aimed to assess the potential availability of forest biomass from European forests and its spatial distribution. We tried to answer the questions (i) how is the potential forest biomass availability spatially distributed across Europe and (ii) where are hotspots of potential forest biomass availability located? Methods: The spatial distribution of woody biomass potentials was assessed for 2020 for stemwood, residues (branches and harvest losses) and stumps for 39 European countries. Using the European Forest Information SCENario (EFISCEN) model and international forest statistics, we estimated the theoretical amount of biomass that could be available based on the current and future development of the forest age-structure, growing stock and increment and forest management regimes. We combined these estimates with a set of environmental (site productivity, soil and water protection and biodiversity protection) and technical (recovery rate, soil bearing capacity) constraints, which reduced the amount of woody biomass that could potentially be available. We mapped the potential biomass availability at the level of administrative units and at the 10 km × 10 km grid level to gain insight into the spatial distribution of the woody biomass potentials. Results: According to our results, the total availability of forest biomass ranges between 357 and 551 Tg dry matter per year. The largest potential supply of woody biomass per unit of land can be found in northern Europe (southern Finland and Sweden, Estonia and Latvia), central Europe (Austria, Czech Republic, and southern Germany), Slovenia, southwest France and Portugal. However, large parts of these potentials are already used to produce materials and energy. The distribution of biomass potentials that are currently unused only partially coincides with regions that currently have high levels of wood production. Conclusions: Our study shows how the forest biomass potentials are spatially distributed across the European continent, thereby providing insight into where policies could focus on an increase of the supply of woody biomass from forests. Future research on potential biomass availability from European forests should also consider to what extent forest owners would be willing to mobilise additional biomass from their forests and at what costs the estimated potentials could be mobilised.

The growth characteristics of silver fir are of high importance for selection forest management, and for the current aims laid out in Serbia’s forest management focused on increasing the share of silver firs in Serbia’s growing stock. With the objective of increasing the understanding of the growth characteristics of silver fir, the growth of two silver fir trees felled during forest site production research on Mt. Goc, located in Central Serbia, have been analyzed. Both trees showed significant differences in their growth dynamics over long periods as results of micro-site and micro-stand effects (primarily ambient light regime). The common growth characteristic of the two trees, a 450-year-old tree as the main study object (labeled Tree A) and a 270-year-old Tree B is a long stagnation stage. For Tree A the latent phase, with small interruptions, lasted 410 years; one phase lasted 330 years in continuity, which is the longest period of silver fir stagnation recorded in Europe. Tree B showed a long-lasting stagnation stage that lasted 170 years. The long stagnation stage of Tree A, characterized by an average diameter increment of 1.4 mm/year (average growth ring width of 0.7 mm) and an average height increment of 0.08 m/year, shows the extraordinary silver fir capacity for physiological survival in complete shade. This study adds to the existing knowledge of the shade tolerance of the silver fir. Therefore, the silver fir belongs to the group of extremely shade-tolerant tree species. This characteristic makes silver fir an irreplaceable tree species in the selection forest structure. It offers a wide range of silvicultural flexibility in the management of these forests, and is applicable to silver fir selection Serbia’s forests. [Projekat Ministarstva nauke Republike Srbije, br. EVBR 37008: Sustainable management of total forest potentials in the Republic of Serbia]

Information pertaining to forest timber volume is crucial for sustainable forest management. Remotely-sensed data have been incorporated into operational forest inventories to serve the need for ever more diverse and detailed forest statistics and to produce spatially explicit data products. In this study, data derived from airborne laser scanning and image-based point clouds were compared using three volume estimation methods to aid wall-to-wall mapping of forest timber volume. Estimates of forest height and tree density metrics derived from remotely-sensed data are used as explanatory variables, and forest timber volumes based on sample field plots are used as response variables. When compared to data derived from image-based point clouds, airborne laser scanning produced slightly more accurate estimates of timber volume, with a root mean square error (RMSE) of 26.3% using multiple linear regression. In comparison, RMSEs for volume estimates derived from image-based point clouds were 28.3% and 29.0%, respectively, using Semi-Global Matching and enhanced Automatic Terrain Extraction methods. Multiple linear regression was the best-performing parameter estimation method when compared to k-Nearest Neighbour and Support Vector Machine. In many countries, aerial imagery is acquired and updated on regular cycles of 1–5 years when compared to more costly, once-off airborne laser scanning surveys. This study demonstrates point clouds generated from such aerial imagery can be used to enhance the estimation of forest parameters at a stand and forest compartment level-scale using small area estimation methods while at the same time achieving sampling error reduction and improving accuracy at the forest enterprise-level scale.

Research Highlights: A transparent approach to developing a forest reference emissions level (FREL) adjusted to future local developments in Southern Cameroon is demonstrated. Background and Objectives: Countries with low historical deforestation can adjust their forest reference (emission) level (FREL/FRL) upwards for REDD+ to account for likely future developments. Many countries, however, find it difficult to establish a credible adjusted reference level. This article demonstrates the establishment of a FREL for southern Cameroon adjusted to societal megatrends of strong population&mdash, and economic growth combined with rapid urbanization. It demonstrates what can be done with available information and data, but most importantly outlines pathways to further improve the quality of future FREL/FRL&rsquo, s in light of possibly accessing performance-based payments. Materials and Methods: The virtual FREL encompasses three main elements: Remotely sensed activity data, emission factors derived from the national forest inventory, and the adjustment of the reference level using a land use model of the agriculture sector. Sensitivity analysis is performed on all three elements using Monte Carlo methods. Results: Deforestation during the virtual reference period 2000&ndash, 2015 is dominated by non-industrial agriculture (comprising both smallholders and local elites) and increases over time. The land use model projections are consistent with this trend, resulting in emissions that are on average 47% higher during the virtual performance period 2020&ndash, 2030 than during the reference period 2000&ndash, 2015. Monte Carlo analysis points to the adjustment term as the main driver of uncertainty in the FREL calculation. Conclusions: The available data is suitable for constructing a FREL for periodic reporting to the UNFCCC. Enhanced coherence of input data notably for activity data and adjustment is needed to apply for a performance-based payment scheme. Expanding the accounting framework to include forest degradation and forest gain are further priorities requiring future research.

Satellite images of various spatial resolutions and different image classification techniques have been utilized for land cover (LC) mapping at local and regional scale studies. Mapping capabilities and achievable accuracies of LC classification in a mountain environment are, however, influenced by the spatial resolution of the utilized images and applied classification techniques. Hence, developing and characterizing regionally optimized methods are essential for the planning and monitoring of natural resources. In this study, the potential of four non-parametric image classification techniques, i.e., k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and neural network (NN) on the accuracy of LC classification was evaluated in the Hindu Kush mountains ranges of northern Pakistan. Moreover, we have assessed the impact of the spatial resolution of the utilized satellite imagery, i.e., SPOT-5 with 2.5 m and Landsat-8 with 30 m on the accuracy of the derived LC classification. For the classification of LC based on SPOT-5, we have achieved the highest overall classification accuracy (OCA) = 89% with kappa coefficient (KC = 0.86) using SVM followed by k-NN, RF, and NN. However, for LC classification derived from Landsat-8 imagery, we achieved the highest OCA = 71% with KC = 0.59 using RF and SVM followed by k-NN and NN. The higher accuracy derived from SPOT-5 versus Landsat-8 indicated that the results of LC classification based on SPOT-5 are more accurate and reliable than Landsat-8. The findings of the present study will be useful for the classification and mapping task of LC in a mountain environment using SPOT-5 and Landsat-8 at local and regional scale studies.

Lignocellulosic biomass from forests and forest industries represents a crucial resource for the bioeconomy. Many assessments have been carried out over the last decade to quantify biomass potentials from forests and forest industries. The results from these assessments are quite diverse because of varying definitions and assumptions. Here we report on a recent comprehensive assessment carried out in the frame of the S2Biom project for 37 countries in Europe. We present the methodology to estimate biomass from forests and the forest industries, with a specific effort to estimate forest biomass supply costs. A set of biomass potentials is presented, that offers flexibility to users to select biomass potentials according to specific requirements, information demands, and policy contexts. Results are presented at the regional level for different types of potentials and with associated costs to allow better insight into the composition of the potential. The associated S2Biom toolset and database allows users to select results based on own preferences and needs, thereby offering the most comprehensive assessment of forest biomass potentials currently available in Europe.

Purpose of review: This paper presents a review of the current state of the art in remote sensing based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing approaches: classical image-to-image change detection and time series analysis. Recent findings: With the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has become the most promising but also most demanding category of degradation mapping approaches. Four time series classification methods are distinguished. The methods are explained and their benefits and drawbacks are discussed. A separate chapter presents a number of recent forest degradation mapping studies for two different ecosystems: temperate forests with a geographical focus on Europe and tropical forests with a geographical focus on Africa. Summary: The review revealed that a wide variety of methods for the detection of forest degradation is already available. Today, the main challenge is to transfer these approaches to high resolution time series data from multiple sensors. Future research should also focus on the classification of disturbance types and the development of robust up-scalable methods to enable near real time disturbance mapping in support of operational reactive measures., This is the Authors' accepted version only! The final version of this paper can be located at Springer.com as part of the Current Forestry Reports (2017) 3: 32. doi:10.1007/s40725-017-0047-2

Since early 2000, several biomass assessment studies were delivered at European and global level mostly driven by the increasing demand for the development of bioenergy and biofuels, and the need to secure sustainable, continuous supply for the emerging plants. Ongoing research and development and industrial development plus increased drivers to use renewable raw materials in industrial sectors beyond energy have seen the focus of the biomass markets widen to include value chains for bio-based chemicals, pharmaceuticals, and other materials. Consequently, research is now exploring increasingly varied configurations of value chains with the aims of understanding which types and quantifying how much biomass can be extracted sustainably, generate financial returns, and help the industry achieving high-quality products for consumers. This chapter sets the scene for research on biomass supply assessments in Europe and reviews 40 studies delivered during the last 14 years. It analyzes context, key components in terms of terminology, framework conditions and assumptions, models used, and evidence provided so far for policy, research, and industry. It further discusses the main challenges, identifies gaps, and provides recommendations.

Many Eastern European countries still host landscapes with high value due to their habitat quality and size. Some of these countries are new member states of the European Union, and EU-accession is accompanied by huge investments in the development of traffic infrastructure. Environmental assessments mandatory for road constructions in the EU do not necessarily require explicit measures for the mitigation of fragmentation, and technical constructions associated with road building are frequently assumed to provide sufficient possibilities for wildlife crossings. We evaluated those technical structures at two motorway sections separating relevant subpopulations of the brown bear (Ursus arctos) in Bulgaria. Our assessment revealed that the permeability of the two motorways has been considerably overestimated. A total of just 13 out of the 77 potential crossing possibilities of the two roads together meet the requirements we defined for suitable wildlife crossings. We found that the potential for improvement of the crossing functionality of already existing technical facilities along the motorways is very limited. Given the dependence on a small number of habitat paths connecting suitable crossings with habitat on both sides of the road, connectivity between subpopulations is vulnerable to fragmentation impacts.

A literature review of new publications in the field of 3D data for forest applications shows that the application of airborne laser scanner data (ALS) is in the focus of research today due to its great potential for practical applications. While there is a lot of research carried out to derive forest management parameters based on laser metrics deduced from a single tree assessment or a statistical area based assessment, the delineation of stand or sub-stand units derived from laser metrics itself is a rather new approach. In order to describe stand characteristics statistical grid cell approaches or single tree approaches have been developed. The LIDAR based segmentation of stand or sub-stand units is rarely documented. This article provides information on enhanced processes to delineate stand or sub-stand units and to extract different forest information based on airborne laser derived parameters. For the stand delineation an automatic process was developed which provides a stand or sub-stand unit delineation which is according to the first results sufficiently uniform within stands and sufficiently different in species, age class, height class, structure and composition between stands in order to be distinguishable from adjacent areas. With a combined method the stand boundaries as they are established by the mapping units today, as well as sub-stand units which have in common physical characteristics indicating the same management disposition, were assessed. Finally a first validation of the forest stand unit delineation is provided, indicating the high potential of ALS data for separating stand units.

A national forest policy requires a solid information base on forests for three reasons. First, to fulfils international standards for the monitoring of forests, especially for carbon storage and biodiversity (Helsinki criteria, Kyoto protocol); second, to identify areas that require political action, including monitoring of the success of measures taken; and third, to provide a sound and reliable information base for private decision-makers about the expected wood supply. Presently, the Turkish national forest service is launching an initiative towards a national forest inventory (NFI) initiated by Prof. Asan, University of Istanbul. During an exchange programme, some initial considerations for an NFI system an NFI were worked up and will be presented in this paper. These initial considerations focus on sampling design, the use of remote sensing and the use of the available information on Turkish forests. First, an overview on the present inventory methods on the forest management level will be given in order to analyse (i) our present knowledge and deficiencies of the information on Turkish forests and (ii) what use could be made of this information in the framework of a National Forest Inventory concept. Furthermore, natural conditions and the possibility of using remote sensing will be analysed. Initial concepts to use the regional forest maps (generalised maps in the scale 1:100,000, based on maps from forest management inventories) and remote sensing will be presented. The regional forest maps and mapping based on remote sensing using optical high-reso.lution satellite data (TM/SPOT/IRS1/ASTER) are to be used with the statistical pre-stratification method to enable different sampling densities and identify non-forest sampling plots. A forest map based on remote sensing is included in the concept to supplement the statistical information. The use of very high-resolution satellite data (e.g. IKONOS) will be discussed. Finally, open questions for preparatory studies will be identified.

This paper is based on results of a study in a state forest enterprise in Nordrhein-Westfalen, Germany. This study was motivated from both introducing new technological options, and the necessity to adapt and change the inventory and monitoring system within forest management due to developments and changes in the silvicultural concepts and changing management objectives (ecology, bio-diversity, stability, effective cost management, organisational changes). The paper focuses on the aspect of silvicultural concepts. The developed and evaluated system, can be used both in age class forest, in forests in transition and in near-natural forests. The system was tested together with some options with respect to remote sensing data and analysis methods. The finally proposed concept consists of five information levels, that are useful for long-term-planning and ongoing management: (i) an ortho-image-map based on colour infrared photos, (ii) mapping of single objects of high interest, (iii) stand mapping and description, (v) reliable overall and structural data for groups of stands (iv) reliable overall and structural data for the whole enterprise with (iv) and (v) based on a sample based continuos forest inventory. For the estimating procedure within the sample based continuos forest inventory a stratification estimation technique, that reduces the estimation errors compared to simple estimation, is developed. It is demonstrated, that the system is highly cost efficient and of high information quality at the same time.

Sustainably managed forests provide renewable raw material that can be used for primary/secondary conversion products and as biomass for energy generation. The potentially available amounts of timber, which are still lower than annual increments, have been published earlier. Access to this timber can be challenging for small-dimensioned assortments; however, technologically improved value chains can make them accessible while fulfilling economic and environment criteria. This paper evaluates the economic, environmental and social sustainability impacts of making the potentially available timber available with current and technologically improved value chains. This paper focuses on increasing the biomass feedstock supply for energy generation. Quantified impact assessments show which improvements – in terms of costs, employment, fuel and energy use, and reduced greenhouse gas emissions – can be expected if better mechanized machines are provided. Using three different methods – Sustainability Impacts Assessment (SIA), Life Cycle Assessment (LCA), and Emission Saving Criteria (ESC) – we calculated current and innovative machine solutions in terms of fuel use, energy use, and greenhouse gas emissions, to quantify the impact of the technology choice and also the effect of the choice of assessment method. Absolute stand-alone values can be misleading in analyses, and the use of different impact calculation approaches in parallel is clarifying the limits of using LCA-based approaches. The ESC has been discussed for the recast of the Renewable Energy Directive. Potential EU-wide results are presented.