Simosol Oy

Riihimäki, Finland

Simosol Oy

Riihimäki, Finland
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Grant
Agency: European Commission | 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.


Pasalodos-Tato M.,Instituto Nacional Of Investigacion Y Tecnologia Agraria Y Alimentaria Inia | Makinen A.,Simosol Oy | Garcia-Gonzalo J.,University of Lisbon | Borges J.G.,University of Lisbon | And 2 more authors.
Forest Systems | Year: 2013

Aim: Since forest planning is characterized by long time horizon and it typically involves large areas of land and numerous stakeholders, uncertainty and risk should play an important role when developing forest management plans. The aim of this study is to review different methods to deal with risk and uncertainty in forest planning, listing problems that forest managers may face during the preparation of management plans and trying to give recommendations in regard to the application of each method according to the problem case. The inclusion of risk and uncertainty in decision support systems is also analyzed. Area: It covers the temporal and spatial scale of forest planning, the spatial context, the participation process, the objectives dimensions and the good and services addressed. Material and methods: Several hundreds of articles dealing with uncertainty and risk were identified regarding different forestry-related topics and approaches. Form them, around 170 articles were further reviewed, categorized and evaluated. Main results: The study presents a thorough review and classification of methods and approaches to consider risk and uncertainty in forest planning. Moreover, new approaches are introduced, showing the opportunities that their application presents in forest planning. Research highlights: The study can aid forest managers in the decision making process when designing a forest management plan considering risk and uncertainty.


Grant
Agency: European Commission | 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.


Kangas A.,University of Helsinki | Mehtatalo L.,University of Eastern Finland | Makinen A.,Simosol Oy | Vanhatalo K.,University of Helsinki
Silva Fennica | Year: 2011

In forest planning, the decision maker chooses for each stand a treatment schedule for a predefined planning period. The choice is based either on optimization calculations or on silvicultural guidelines. Schedules for individual stands are obtained using a growth simulator, where measured stand characteristics such as the basal area, mean diameter, site class and mean height are used as input variables. These characteristics include errors, however, which may lead to incorrect decisions. In this study, the aim is to study the sensitivity of harvest decisions to errors in a dataset of 157 stands. Correct schedules according to silvicultural guidelines were first determined using error-free data. Different amounts of errors were then generated to the stand-specific characteristics, and the treatment schedule was selected again using the erroneous data. The decision was defined as correct, if the type of harvest in these two schedules were similar, and if the timings deviated at maximum ±2 for thinning and ±3 years for clear-cut. The dependency of probability of correct decisions on stand characteristics and the degree of errors was then modelled. The proposed model can be used to determine the required level of measurement accuracy for each characteristics in different kinds of stands, with a given accuracy requirement for the timing of treatments. This information can further be utilized in selecting the most appropriate inventory method.


Holopainen M.,University of Helsinki | Makinen A.,University of Helsinki | Rasinmaki J.,Simosol Oy | Hyytiainen K.,Economic Research | And 2 more authors.
Forest Policy and Economics | Year: 2010

The objective of this study was to compare the relative importance of various sources of uncertainties in determining the net present value of forest stands and forested property. This was achieved by performing stand-level simulations that took into account: i) input data errors (airborne laser-scanning data vs. ocular standwise field inventory data), ii) stochastic future development of timber assortment prices and iii) errors in stand-level growth projection models. The starting point of the study was a simulated forest estate comprising 40 stands of various types sufficiently represented (e.g. with respect to species composition, development class distribution, and site quality). Stochastic timber price models were formulated, employing geometric mean-reverting principles. The results showed that sources of uncertainty all had significant effects on the probability distribution of the net present value of the stand. The relative standard deviations of stand net present values averaged 8% for stochastic timber price, 29% for errors in standwise field inventory data, 26% for errors in airborne laser-scanning data and 33% for errors in growth projection models when applying a 3% discount rate. When all three sources of uncertainty were analysed simultaneously, the highest average standard deviation was 47.4%. Interestingly, errors in the growth projections and the quality of inventory data contributed more to the variation in stand net present value than fluctuation in timber price did, although this result was based on the assumption that the forestry industry maintains its competitiveness in the long run. Our modeling approach made it possible to compare various sources of uncertainty and to set confidence intervals for net present value estimates. This approach can also result in information on which sources of uncertainty are focused. © 2010 Elsevier B.V.


Makinen A.,Simosol Oy | Kangas A.,University of Helsinki | Nurmi M.,University of Helsinki
Silva Fennica | Year: 2012

In recent years, optimal inventory accuracy has been analyzed with a cost-plus-loss methodology, where the total costs of inventory include both the measurement costs and the losses from the decisions based on the collected information. Losses occur, when the inaccuracies in the data lead to sub-optimal decisions. In almost all cases, it has been assumed that the accuracy of the once collected data remains the same throughout the planning period, and the period has been from 10 up to 100 years. In reality, the quality of the data deteriorates in time, due to errors in the predicted growth. In this study, we carried out a cost-plus-loss analysis accounting for the errors in (stand-level) growth predictions of basal area and dominant height. In addition, we included the inventory errors of these two variables with several different levels of accuracy, and costs of inventory with several different assumptions of cost structure. Using the methodology presented in this study, we could calculate the optimal inventory interval (life-span of data) minimizing the total costs of inventory and losses through the 30-year planning period. When the inventory costs only to a small extent depended on the accuracy, the optimal inventory period was 5 years and optimal accuracy RMSE 0%. When the costs more and more heavily depended on the accuracy, the optimal interval turned out to be either 10 or 15 years, and the optimal accuracy reduced from RMSE 0% to RMSE 20%. By increasing the accuracy of the growth models, it was possible to reduce the inventory accuracy or lengthen the interval, i.e. obtain clear savings in inventory costs.


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.


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.


Miettinen A.,Mtt Agrifood Research Finland | Hyytiainen K.,Mtt Agrifood Research Finland | Makinen A.,Simosol Oy
Journal of Environmental Management | Year: 2012

This paper estimates and compares the costs incurred to a private landowner from establishing and managing 25-m wide biodiversity zones on field and forest margins in southern Finland. Crop and timber prices being at their long-term averages, current agricultural support paid and the real discount rate 3%, the average annual net costs per hectare of field and forest biodiversity zones were €30 ha-1 and €108 ha-1, respectively, the field zones being the less costly alternative in 95% of cases. This result is mainly due to the poor productivity of field cultivation relative to timber production under boreal climate conditions. In addition to soil quality, the initial stand structure affects the costs of both biodiversity zone types. It is less costly for a landowner to establish biodiversity zones in forests where no final felling is imminent but which already contain some tree volume. In field biodiversity zones, costs are slightly lower on fields where forest shading is great. Uneven-aged management practiced in forest biodiversity zones was found to lead to a 3-32% reduction in the net present value of forest land compared to conventional forest management. An increase in the real discount rate increases the relative efficiency of forest biodiversity zones. © 2012 Elsevier Ltd.


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.

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