FTA INSPIRE Download Service (WFS) for Transport Networks is an INSPIRE compliant direct access Web Feature Service. It contains the following INSPIRE feature types: Road network, Rail network, Waterway network and Air transport network. The service is based on the FTA INSPIRE Transport Networks Theme Dataset. The dataset is administrated by the Finnish Transport Agency. The service is still under development and as such accessibility and full operationality or conformity with Inspire spesifications is not guaranteed.
The Arctic SDI Gazetteer Service can be used for searching place names from the arctic area and performing reverse geocoding. The Arctic SDI Gazetteer Service contains data from Canada, Denmark (including Greenland) , Finland, Iceland, Norway, Sweden and USA. It contains also data from the GEBCO Undersea feature names gazetteer.
Air traffic network-product is a link-knot routing dataset compliant with INSPIRE requirements. It includes f.ex. flight routes and aerodromes. Data shall not be used for operational flight activities or flight planning.
FTA INSPIRE View Service (WMS) for Transport Networks is an INSPIRE compliant . It contains the following INSPIRE feature types: Road network, Rail network, Waterway network and Air transport network. The service is based on the FTA INSPIRE Transport Networks Theme Dataset. The dataset is administrated by the Finnish Transport Agency. The service is still under development and as such accessibility and full operationality or conformity with Inspire spesifications is not guaranteed.
The location of the real property unit is shown on the cadastral index map. On the map, there are property and other register unit boundaries, boundary markers and property identifiers. The Land Information System of Finland contains attribute data related to property. Further information (in Finnish): http://www.maanmittauslaitos.fi/kiinteistot/asiantuntevalle-kayttajalle/kiinteistotiedot-ja-niiden-hankinta The product belongs to the open data of the National Land Survey of Finland. More information: Topographic data and how to acquire it: http://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/topographic-data-and-how-acquire-it
The technical harvesting potential of logging residues and stumps from final fellings can be defined as the maximum potential procurement volume of these available from the Finnish forests based on the prevailing guidelines for harvesting of energy wood. The potentials of logging residues and stumps have been calculated for fifteen NUTS3-based Finnish regions covering the whole country (Koljonen et al. 2017). The technical harvesting potentials were estimated using the sample plots of the eleventh national forest inventory (NFI11) measured in the years 2009–2013. First, a large number of sound and sustainable management schedules for five consecutive ten-year periods were simulated for each sample plot using a large-scale Finnish forest planning system known as MELA (Siitonen et al. 1996; Redsven et al. 2013). MELA simulations consisted of natural processes and human actions. The ingrowth, growth, and mortality of trees were predicted based on a set of distance-independent tree-level statistical models (e.g. Hynynen et al. 2002) included in MELA and the simulation of the stand (sample plot)-level management actions was based on the current Finnish silvicultural guidelines (Äijälä et al. 2014) and the guidelines for harvesting of energy wood (Koistinen et al. 2016). Final fellings consisted of clear cutting, seed tree cutting, and shelter-wood cutting, but only the clear-cutting areas were utilized for energy wood harvesting. As both logging residues and stumps are byproducts of roundwood removals, the technical potentials of chips have to be linked with removals of industrial roundwood. Future potentials were assumed to materialize when the industrial roundwood fellings followed the level of maximum sustainable removals. The maximum sustainable removals were defined such that the net present value calculated with a 4% discount rate was maximized subject to non-declining periodic industrial roundwood and energy wood removals and net incomes, and subject to the saw log removal remaining at least at the level of the first period. There were no constraints concerning tree species selection, cutting methods, age classes, or the growth/drain ratio in order to efficiently utilize the dynamics of forest structure. The felling behaviour of the forest owners was not taken into account either. For the present situation in 2015, the removal of industrial roundwood was assumed to be the same as the average level in 2008–2012. Fourth, the technical harvesting potentials were derived by retention of 30% of the logging residues onsite (Koistinen et al. 2016) and respectively by retention of 16–18% of stump biomass (Muinonen et al. 2013). Next, the regional potentials were allocated to municipalities proportionally to their share of mature forests (MetINFO 2014). Subsequently, the municipality-level potentials were spread evenly on a raster grid at 1 km × 1 km resolution. Only grid cells on Forests Available for Wood Supply (FAWS) were considered in this operation. Here, FAWS was defined as follows: First, forest land was extracted from the Finnish Multi-Source National Forest Inventory (MS-NFI) 2013 data (Mäkisara et al. 2016). Second, restricted areas were excluded from forest land. The restricted areas consisted of nationally protected areas (e.g. nature parks, national parks, protection programme areas). References Äijälä O, Koistinen A, Sved J, Vanhatalo K, Väisänen P (2014) Metsänhoidon suositukset [Guidelines for sustainable forest management]. Metsätalouden kehittämiskeskus Tapion julkaisuja. Hynynen J, Ojansuu R, Hökkä H, Salminen H, Siipilehto J, Haapala P (2002) Models for predicting the stand development – description of biological processes in MELA system. The Finnish Forest Research Institute Research Papers 835. Koistinen A, Luiro J, Vanhatalo K (2016) Metsänhoidon suositukset energiapuun korjuuseen, työopas [Guidelines for sustainable harvesting of energy wood]. Metsäkustannus Oy, Helsinki. Koljonen T, Soimakallio S, Asikainen A, Lanki T, Anttila P, Hildén M, Honkatukia J, Karvosenoja N, Lehtilä A, Lehtonen H, Lindroos TJ, Regina K, Salminen O, Savolahti M, Siljander R (2017) Energia ja ilmastostrategian vaikutusarviot: Yhteenvetoraportti. [Impact assessments of the Energy and Climate strategy: The summary report.] Publications of the Government´s analysis, assessment and research activities 21/2017. Mäkisara K, Katila M, Peräsaari J, Tomppo E (2016) The Multi-Source National Forest Inventory of Finland – methods and results 2013. Natural resources and bioeconomy studies 10/2016. Muinonen E, Anttila P, Heinonen J, Mustonen J (2013) Estimating the bioenergy potential of forest chips from final fellings in Central Finland based on biomass maps and spatially explicit constraints. Silva Fenn 47. Redsven V, Hirvelä H, Härkönen K, Salminen O, Siitonen M (2013) MELA2012 Reference Manual. Finnish Forest Research Institute. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O, Teuri M (1996) MELA Handbook. Metsäntutkimuslaitoksen tiedonantoja 622. ISBN 951-40-1543-6.
Lorry parking areas at E18 road in Finland. Data is in Esri shapefiles, in ETRS-TM35FIN coordinates. Data is maintained and provided by FTA (Finnish Transport Agency).
Elevation zones is a raster dataset that visualises elevation of the terrain. The product covers the whole of Finland. There are four product versions available in which the pixel sizes are 32, 64, 128 and 512 metres. The dataset does not contain elevation values; it is a colour image that visualises the height of the terrain above sea level as zones. The product Elevation zones is available as a version that covers the whole country and as versions that cover a certain area. The product belongs to the open data of the National Land Survey of Finland. More information: Topographic data and how to acquire it http://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/topographic-data-and-how-acquire-it.
NLS-FI INSPIRE Download Service (WFS) for Buildings/Point is an INSPIRE compliant direct access Web Feature Service. It contains the following INSPIRE feature types: Building The service is based on the NLS-FI INSPIRE Buildings Theme Dataset. The dataset is administrated by the National Land Survey of Finland. The service contains all features from the dataset that are modelled as points.
FTA INSPIRE Transport Networks Theme Dataset is a dataset depicting the Transport Networks covering the whole of Finland. It contains the following INSPIRE feature types: Road network, Rail network, Waterway network and Air transport network. The elements are updated weekly. The dataset is based on transport network data from the Finnish Transport Agency and Finavia. The dataset is available via the FTA INSPIRE Download Service (WFS) for Transport Networks and it can be viewed via the FTA INSPIRE View Service (WMS) for Transport Networks.