Harkonen S.,Finnish Forest Research Institute |
Makinen A.,University of Helsinki |
Tokola T.,University of Eastern Finland |
Rasinmaki J.,Simosol Oy |
Kalliovirta J.,Simosol Oy
Forest Ecology and Management | Year: 2010
Tree-level and stand-level forest growth simulators and their combination were evaluated using data from a large network of permanent sample plots of the National Forest Inventory covering the whole of Southern Finland. The simulators were built up with the SIMO framework. The evaluation was carried out both at the stand-level and separately for Scots pine (Pinus sylvestris), Norway spruce (Picea abies), silver birch (Betula pendula) and white birch (Betula pubescens) strata within the plots. Effects of different factors, e.g. age, soil type, stand density and geographical location on the results were also analysed. All the simulators provided relative RMSE of around 10% for stand-level tree diameter and height estimates, while the RMSE%s for basal area and the volume estimates were a little bit higher (22-25% and 24-30%, respectively). When estimating height and volume the least biased simulator was the tree-level simulator, while the diameter and basal area estimations were least biased with the combined simulator. In general the examined simulators seem to work well in circumstances where natural mortality is low or does not exist, as happens to be the case in intensively managed commercial forests. Instead, in unmanaged, unhealthy or non-homogeneous forests the estimates are less reliable. Usually the models tend to underestimate the natural mortality, but with the combined simulator and the birch strata the mortality was also often highly overestimated. It seems necessary to make both the growth and mortality models more adaptable to varying conditions in the future. Also the models for the deciduous trees require improvement. © 2009 Elsevier B.V. All rights reserved. Source
Tuomi M.,Finnish Environment Institute |
Tuomi M.,University of Helsinki |
Rasinmaki J.,Simosol Oy |
Repo A.,Finnish Environment Institute |
And 2 more authors.
Environmental Modelling and Software | Year: 2011
In this article, we present a graphical user interface software for the litter decomposition and soil carbon model Yasso07 and an overview of the principles and formulae it is based on. The software can be used to test the model and use it in simple applications. Yasso07 is applicable to upland soils of different ecosystems worldwide, because it has been developed using data covering the global climate conditions and representing various ecosystem types. As input information, Yasso07 requires data on litter input to soil, climate conditions, and land-use change if any. The model predictions are given as probability densities representing the uncertainties in the parameter values of the model and those in the input data - the user interface calculates these densities using a built-in Monte Carlo simulation. © 2011 Elsevier Ltd. Source
Agency: Cordis | Branch: FP7 | Program: CP-TP | Phase: NMP.2013.3.0-2 | Award Amount: 4.05M | Year: 2014
The forest-based value chains are one of the dominant contributors to the GDP in the rural regions of Europe. Besides the traditional value chains, novel ones are in the horizon, with significant impacts on the requirements for the raw material supply chains. A major roadblock for improving forest-based value chains is the lack of integrated control and planning mechanism. FOCUS will demonstrate how innovative sensor technologies and control methods can solve this problem, with case studies in Finland, Belgium, Switzerland,Germany,Austria, Portugal covering the main forest-based production processes in Europe biomass for bioenergy, timber and pulp wood, and cork transformation. The goal of FOCUS is to improve the individual value chain processes, and to remove the barriers for integrated planning and control for the whole value chain. The project brings together leading SMEs and organisations in the fields of environment and machine sensors, production machinery and control automation software development. The expertise is needed to address the key challenges: novel sensor development for environment, raw material and production machinery monitoring; new process specific control processes; plug-and-play composition of value chain wide control processes. The productivity and sustainability of the value chains will be enhanced by enabling the best use of the production resources, and by reducing harmful impacts like soil compaction by forest machines, and carbon footprint of the operations. Product marketability will be increased by traceability of raw material origin, and by controlling the quality of the raw material during the production process. The open source FOCUS platform will foster new business models by enabling several SMEs to jointly offer solutions. The project will be a paradigm for support of efficient and sustainable exploitation of existing and new forest-based value chains alike, and will enhance the economic development of European rural areas.
Agency: Cordis | Branch: FP7 | Program: CP-FP | Phase: SPA.2013.1.1-07 | Award Amount: 2.60M | Year: 2013
Boreal forest is the largest terrestrial biome and will be strongly affected by climate warming, which is predicted to be strongest at high latitudes, with significant impacts on the European economy. North State will demonstrate how innovative methods applied to the new Sentinel data streams can be combined with models to monitor carbon and water fluxes for pan-boreal Europe, with intensive study sites in Finland, Iceland and Russia. This will reduce the high uncertainty in current estimates of flux rates. Key model parameters will be derived from Sentinel and other EO data, in situ and ancillary data, including relevant FP7 and ESA CCI projects. Parameter types include forest characteristics such as area and species, model drivers such as incoming radiation, and indicators of the dynamic state of the forest, such as fAPAR and tree height. Many of these parameters will also be applicable for other purposes, such as operational forest management. The project brings together leading experts and organisations, including an SME experienced in developing value adding services, needed to address key research challenges that require innovative remote sensing methods: adaptation of the carbon and water cycle models for effective use of EO data; effective pre-processing and data management techniques to exploit high temporal frequency time series; assessing the potential of hyper-spectral data; developing powerful data fusion techniques; and developing an intelligent, automated framework to learn from and interpret multi-source data to address a key societal problem. It responds to the Lund declaration and the recommendations of the Space Advisory Group. It will strengthen European leadership in the provision of EO-based services and will be a paradigm for exploiting opportunities offered by the new generation of EO satellites in developing products for future GMES services.
De Meyer A.,Catholic University of Leuven |
Cattrysse D.,Catholic University of Leuven |
Rasinmaki J.,Simosol Oy |
Van Orshoven J.,Catholic University of Leuven
Renewable and Sustainable Energy Reviews | Year: 2014
Biomass supply chain optimisation is essential to overcome barriers and uncertainties that may inhibit the development of a sustainable and competitive bioenergy market. The number of research papers presenting optimisation models in the field of bioenergy systems rises exponentially. This paper gives an overview of the optimisation methods and models focussing on decisions regarding the design and management of the upstream segment of the biomass-for-bioenergy supply chain. After a general description of the supply chain and the decisions coming along with the design and management, all selected publications are classified and discussed according to (1) the mathematical optimisation methodology used, (2) the decision level and decision variables addressed and (3) the objective to be optimised. This classification allows users to identify existing optimisation methods or models that satisfy specific requirements. Moreover, the factual description of the presented optimisation methods and models points to opportunities for development of an integrated, holistic approach to optimise decisions in the field of biomass supply chain design and management. Such approach must be based on the consideration of the interrelationships and interdependence between all operations in the entire biomass-for-bioenergy supply chain. © 2014 Elsevier Ltd. Source