Paikkatietohakemisto
dataset
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FIN Aineiston tarkoituksena on: -Identifioida tie- ja rata-alueet, joiden varrella esiintyy uhanalaisia ja silmälläpidettäviä lajeja -Identifioida tie- ja rata-alueet, joiden varrella esiintyy hyviä elinvoimaisia niittyindikaattorilajeja (hyönteisten mesi- ja ravintokasveja) -Identifioida tie- ja rata-alueet, joiden varrella esiintyy suojelualueita -Identifioida tie- ja rata-alueet, joiden varrella esiintyy komealupiinia tai kurtturuusua -Identifioida tie- ja rata-alueet, joiden varrella esiintyy komealupiinia tai kurtturuusua uhanalaisten lajien lisäksi -> Löytää herkät alueet ja paikallistaa vieraslajien uhka Tieto esitetään 1 kilometrin ruuduissa. Aineistosta on julkaistu kaksi erillistä versiota. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_avoin: Avoin versio, jonka lajitietoa on karkeistettu mahdollisista herkistä lajeista johtuen. Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0) ja sitä saa käyttää lisenssiehtojen mukaisesti -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_kayttorajoitettu: Alkuperäinen karkeistamaton versio. Tämä versio on vain viranomaiskäyttöön eikä kyseistä aineistoa saa jakaa Aineistosta on tehty tarkempi menetelmäkuvaus https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_Menetelmakuvaus.pdf sekä muuttujaseloste https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_VariableDescription.xlsx ENG The purpose of the material is to: -Identify road and rail areas that have nearby observations of endangered and near threatened species -Identify road and rail areas with good meadow indicator plant species -Identify road and rail areas along which there are protected areas -Identify the road and rail areas along which there are observations of Lupinus polyphyllus or Rosa rugosa observations -Identify the road and rail areas along which there are Lupinus polyphyllus or Rosa rugosa observations in addition to sensitive species -> Finds sensitive areas and identify the overall threat of alien species The data is presented in 1-kilometer square grid cells. There are two separate versions of the data. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_avoin: Open access version, in which its species-related parts have been simplified due to data restriction issues. The material belongs to Syke's open materials (CC BY 4.0) and may be used in accordance with the license terms. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_kayttorajoitettu: Original version. This version is only for official use and the material in question may not be shared. A more precise description about the data procedures can be found from (In Finnish) https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_Menetelmakuvaus.pdf Furthermore, all the variables in the data are explained in this bilingual variable description https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_VariableDescription.xlsx This dataset was updated with the newest species observations on 10/2023 and 11/2024 Process code for this can be found from https://github.com/PossibleSolutions/VierasVayla_SpeciesUpdate
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The technical harvesting potential of small-diameter trees can be defined as the maximum potential procurement volume of small-diameter trees available from the Finnish forests based on the prevailing guidelines for harvesting of energy wood. The potentials of small-diameter trees from early thinnings have been calculated for fifteen NUTS3-based Finnish regions covering the whole country (Koljonen et al. 2017). To begin with the estimation of the region-level potentials, 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). Simulated management actions for the small-tree fraction consisted of thinnings that fulfilled the following stand criteria: • mean diameter at breast height ≥ 8 cm • number of stems ≥ 1500 ha-1 • mean height < 10.5 m (in Lapland) or mean height < 12.5 m (elsewhere). Energy wood was harvested as delimbed (i.e. including the stem only) in spruce-dominated stands and peatlands and as whole trees (i.e. including stem and branches) elsewhere. When harvested as whole trees, a total of 30% of the original crown biomass was left onsite (Koistinen et al. 2016). Energy wood thinnings could be integrated with roundwood logging or carried out independently. Second, the technical energy wood potential of small trees was operationalized in MELA by maximizing the removal of thinnings in the first period. In this way, it was possible to pick out all small tree fellings simulated in the first period despite, for example, the profitability of the operation. However, a single logging event was rejected if the energy wood removal was lower than 25 m³ha-1 or the industrial roundwood removal of pine, spruce, or birch exceeded 45 m³ha-1. The potential calculated in this way contained also timber suitable for industrial roundwood. Therefore, two estimates are given: • potential of trees below 10.5 cm in breast-height diameter • potential of trees below 14.5 cm in breast-height diameter. Subsequently, the region-level potentials were spread 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. In this study, 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) and areas protected by the State Forest Enterprise. In addition, some areas in northernmost Lapland restricted by separate agreements between the State Forest Enterprise and stakeholders were left out from the final data. Furthermore, for small trees, FAWS was further constrained by the stand criteria presented above to represent similar stand conditions for small-tree harvesting as in MELA. Finally, the region-level potentials were distributed to the grid cells by weighting with MS-NFI stem wood biomasses. 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. 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.
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The Superficial deposits of Finland 1:1 000 000 is based on 'Quaternary Deposits of Finland and Northwestern Part of Russian Federation and their Resources (Sheet 1, Western part)' mapping data. This data is also based on the 1984 map 'Quaternary Deposits of Finland' (1:1 000 000) in the “Geologia” (Geology) folio of the Atlas of Finland, 5th edition (123-126, 1990). Joint scientific and technical planning for the map of the 'Quaternary Deposits of Finland and Northwestern Part of Russian Federation and their Resources' was begun in 1987 while work on the map started in 1988. No field mapping was done for the map, rather the 1:1 000 000 Quaternary map printed in 1984 was supplemented with new data partly obtained from rock aggregate studies and partly from information on superficial deposits gathered in other superficial mapping projects. The map 'Quaternary Deposits of Finland and Northwestern Part of Russian Federation and their Resources' was printed in 1993. The map shows the superficial deposits with colours and symbols and these are classed according to their mode of geological development. The deposit classes are as follows: pre-Quaternary bedrock exposures (no Quaternary layers); boulder field, physically weathered bedrock; gravelly and sandy till; silty till; clayey till; hummocky moraine ; moraine complex; esker, delta, sandur, sorted marginal formation; interlobate formation (esker); till-covered esker / other till-covered gravel and sand deposit; gravel and sand deposit peripheral to eskers; littoral gravel and sand deposit; fluvial deposit; homogeneous clay and silt deposit; layered (varved) clay and silt deposit; peat deposit. Additionally, drumlins, end moraines, aeolian deposits, and sites of gravel, sand, clay and peat extraction as well as gold panning areas have been indicated with symbols. The data set was converted to a digital format to meet the needs of the OneGeology-Europe Project for a printed map in 2009. This data did not include drumlins, end moraines, aeolian deposits, and more important sites for the exploitation of superficial deposits (sites of gravel, sand, clay and peat extraction as well as gold panning areas). In accordance with the 1984 Quaternary map (1:1 000 000) the minimum size of the deposit polygon is generally one square kilometre. In nature the size of superficial sedimentary deposits is usually smaller than one square kilometre and therefore the most common deposit type in the area is shown in the polygon. In places, deposits smaller than a square kilometre that are significant from a superficial perspective have been noted. As a rule, the narrowest point of the deposit polygon was 0.5 km and in important cases, for example on eskers, 0.3 km. As an exception, the size of small eskers has been exaggerated. Coordinate reference system of the Superficial deposits of Finland 1:1 000 000 was transformed in October 2013. The transformation from Finnish National Grid Coordinate System (Kartastokoordinaattijärjestelmä, KKJ) Uniform Coordinate Frame to ETRS-TM35FIN projection was done by using the three-dimensional transformation in accordance with the recommendations for the public administration JHS154. The water layer which were used in the OneGeology-Europe project was replaced in 2015 with the more accurate water layer of the Topographic database 1:55 0000-1:500 000 (DVE3) from ICT Agency HALTIK.
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Grid net for statistics 1 km x 1 km covers whole of Finland. The grid net includes all grid cells in Finland. The location reference of a grid cell is the coordinates of the bottom left corner of each grid cell. An identifier in accordance with national conventions (consecutive numbering) has also been produced for each grid cell. The Grid net for statistics 1 km x 1 km is the area division used in the production of statistics by 1 km x 1 km grid cells. For utilizing grid data auxiliary table of regional classifications are available: https://stat.fi/en/services/statistical-data-services/geographic-data/statistical-areas/grid-net-for-statistics-1-km The general Terms of Use must be observed when using the data: https://stat.fi/en/about-us/get-to-know-statistics-finland/legislation/terms-of-use In addition to the national version, an INSPIRE information product is also available from the data.
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This assessment was part of project Baltic ForBio funded by the Interreg Baltic Sea Region Programme (https://www.slu.se/en/departments/forest-economics/forskning/research-projects/baltic-forbio/). The project was carried out in 2017-2020. The harvesting potentials in Finland were calculated for the following assortments: • Stemwood for energy from thinnings, pine • Stemwood for energy from thinnings, spruce • Stemwood for energy from thinnings, broadleaved • Stemwood for energy from thinnings (smaller than pulpwood-sized trees), pine • Stemwood for energy from thinnings (smaller than pulpwood-sized trees), spruce • Stemwood for energy from thinnings (smaller than pulpwood-sized trees), broadleaved • Logging residues, pine • Logging residues, spruce • Logging residues, deciduos • Stumps, pine • Stumps, spruce. 1.1 Decision support system used in assessment Regional energywood potentials were calculated with MELA forest planning tool (Siitonen et al. 1996; Hirvelä et al. 2017). 1.2 References and further reading Anttila P., Muinonen E., Laitila J. 2013. Nostoalueen kannoista jää viidennes maahan. [One fifth of the stumps on a stump harvesting area stays in the ground]. BioEnergia 3: 10–11. Anttila P., Nivala V., Salminen O., Hurskainen M., Kärki J., Lindroos T.J. & Asikainen A. 2018. Re-gional balance of forest chip supply and demand in Finland in 2030. Silva Fennica vol. 52 no. 2 article id 9902. 20 p. https://doi.org/10.14214/sf.9902 Hakkila, P. 1978. Pienpuun korjuu polttoaineeksi. Summary: Harvesting small-sized wood for fuel. Folia Forestalia 342. 38 p. Hirvelä, H., Härkönen, K., Lempinen, R., Salminen, O. 2017. MELA2016 Reference Manual. Natural Resources Institute Finland (Luke). 547 p. Hynynen, J., Ojansuu, R., Hökkä, H., Siipilehto, J., Salminen, H. & Haapala, P. 2002. Models for predicting stand development in MELA System. Metsäntutkimuslaitoksen tiedonantoja 835. 116 p. Koistinen A., Luiro J., Vanhatalo K. 2016. Metsänhoidon suositukset energiapuun korjuuseen, työopas. [Guidelines for sustainable harvesting of energy wood]. Metsäkustannus Oy, Helsinki. ISBN 978-952-5632-35-4. 74 p. Mäkisara, K., Katila, M., Peräsaari, J. 2019: The Multi-Source National Forest Inventory of Finland - methods and results 2015. 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 Fennica 47(4) article 1022. https://doi.org/10.14214/sf.1022. Natural Resources Institute Finland. 2019. Industrial roundwood removals by region. Available at: http://stat.luke.fi/en/industrial-roundwood-removals-by-region. Accessed 22 Nov 2019. Ruotsalainen, M. 2007. Hyvän metsänhoidon suositukset turvemaille. Metsätalouden kehittämiskeskus Tapio julkaisusarja 26. Metsäkustannus Oy, Helsinki. 51 p. ISBN 978-952-5694-16-1, ISSN 1239-6117. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O et al. 1996. MELA Handbook. 622. 951-40-1543-6. Äijälä, O., Kuusinen, M. & Koistinen, A. (eds.). 2010. Hyvän metsänhoidon suositukset: energiapuun korjuu ja kasvatus. Metsätalouden kehittämiskeskus Tapion julkaisusarja 30. 56 p. ISBN 978-952-5694-59-8, ISSN 1239-6117. Äijälä, O., Koistinen, A., Sved, J., Vanhatalo, K. & Väisänen, P. (eds). 2014. Metsänhoidon suositukset. Metsätalouden kehittämiskeskus Tapion julkaisuja. 180 p. ISBN 978-952-6612-32-4. 2. Output considered in assessment Valid for scenario: Maximum sustained removal Main output ☒Small-diameter trees ☒Stemwood for energy ☒Logging residues ☒Stumps ☐Bark ☐Pulpwood ☐Saw logs Additional information Stemwood for energy from thinnings. Part of this potential consists of trees smaller than pulpwood size. This part is reported as Stemwood for energy from thinnings (smaller than pulpwood-sized trees). Forecast period for the biomass supply assessment Start year: 2016 End year: 2045 Results presented for period 2026-2035 3. Description of scenarios included in the assessments Maximum sustained removal The maximum sustained removal is defined by maximizing the net present value with 4% discount rate subject to non-declining periodic total roundwood removals, energy wood removals and net incomes, further the saw log removals have to remain at least at the level of the first period. There are no sustainability constraints concerning tree species, cutting methods, age classes or the growth/drain -ratio in order to efficiently utilize the dynamics of forest structure. Energy wood removal can consist of stems, cutting residues, stumps and roots. According to the scenario the total annual harvesting potential of industrial roundwood is 79 mill. m3 (over bark) for period 2026-2035. In 2018 removals of industrial roundwood in Finland totaled 68.9 mill. m3 (Natural Resources… 2019). 4. Forest data characteristics Level of detail on forest description ☒High ☐Medium ☐Low NFI data with many and detailed variables down to tree parts. Sample plot based ☒Yes ☐No NFI sample plot data from 2014-2018. Stand based ☐Yes ☒No Grid based ☒Yes ☐No Multi-Source NFI data from 2017 (Mäkisara et al. 2019) utilized when distributing regional potentials to 1 km2 resolution. 5. Forest available for wood supply: Total forest area defined as in: FAO. 2012. FRA 2015, Terms and Definitions. Forest Resources Assessment Working Paper 180. 36 p. Available at: http://www.fao.org/3/ap862e/ap862e00.pdf. Forest and scrub land 22 812 000 ha Forest land 20 278 000 ha and scrub land 2 534 000 ha Forest area not available for wood supply Forest and scrub land 2 979 000 ha Forest land 1 849 000 ha and scrub land 1 130 000 ha Partly available for wood supply Forest and scrub land 2 553 000 ha (includes in FAWS, below) Forest land 1 149 000 ha and scrub land 1 404 000 ha. Forest Available for wood supply (FAWS) Forest and scrub land 19 833 000 ha Forest land 18 429 000 ha and scrub land 1 404 000 ha In MELA calculations all the scrub land belonging to the FAWS belongs to the category “Partly available for wood supply”, but there are no logging events on scrub land regardless or the category. 6. Temporal allocation of fellings Valid for scenario: Maximum sustained removal Allocation method ☐Optimization based without even flow constraints ☒Optimization based with even flow constraints ☐Rule based with no harvest target ☐Rule based with static harvest target ☐Rule based with dynamic harvest target See item 3 above (max NPV with 4 % discount rate). 7. Forest management Valid for scenario: Maximum sustained removal Representation of forest management ☐Rule based ☒Optimization ☐Implicit Treatments, among of the optimization makes the selections, are based on management guidelines (e.g. Äijälä etc 2014) 7.2 General assumptions on forest management Valid for scenario: Maximum sustained removal ☒Complies with current legal requirements ☐Complies with certification ☒Represents current practices ☐None of the above ☐ No information available Forest management follows science-based guidelines of sustainable forest management (Ruotsalainen 2007, Äijälä et al. 2010, Äijälä et al. 2014). 7.3 Detailed assumptions on natural processes and forest management Valid for scenario: Maximum sustainable removal Natural processes ☒Tree growth ☒Tree decay ☒Tree death ☐Other? Tree-level models (e.g. Hynynen et al., 2002). Silvicultural system ☒Even-aged ☐Uneven-aged Click here to enter text. Regeneration method ☒Artificial ☒Natural Regeneration species ☐Current distribution ☒Changed distribution Optimal distribution may differ from the current one. Genetically improved plant material ☐Yes ☒No Cleaning ☒Yes ☐No Thinning ☒Yes ☐No Fertilization ☐Yes ☒No 7.4 Detailed constraints on biomass supply Volume or area left on site at final felling ☒Yes ☐No 5 m3/ha retained trees are left in final fellings. Final fellings can be carried out only on FAWS with no restrictions for wood supply. Constraints for residues extraction ☒Yes ☐No ☐N/A Retention of 30% of logging residues onsite (Koistinen et al. 2016). Dry-matter loss 20% for logging residues, 5% for stemwood. Constraints for stump extraction ☒Yes ☐No ☐N/A Retention of 16–18% of stump biomass (Muinonen et al. 2013; Anttila et al. 2013) Dry-matter loss 5%. 8. External factors Valid for scenario: Maximum sustained removal External factors besides forest management having effect on outcomes Economy ☐Yes ☒No Climate change ☐Yes ☒No Calamities ☐Yes ☒No Other external ☐Yes ☒No
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The Finnish Uniform Coordinate System (in Finnish Yhtenäiskoordinaatisto, YKJ) has been used in biological observation mapping since the 1970s. Based on YKJ, Finland is divided in square-shaped areas, the size of which are determined according to the needs of the study. The area division used in national biomonitoring is 10 km x 10 km squares, but in some cases 1 km x 1 km and 100 m x 100 m YKJ squares are also used. This data set includes XY-lines that form square grid in four scales according to Unified Coordinate System (100 m - 100 km), with identifiers describing each square.
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The EMODnet (European Marine Observation and Data network) Geology project collects and harmonizes marine geological data from the European sea areas to support decision making and sustainable marine spatial planning. The partnership includes 39 marine organizations from 30 countries. The partners, mainly from the marine departments of the geological surveys of Europe (through the Association of European Geological Surveys-EuroGeoSurveys), have assembled marine geological information at various scales from all European sea areas (e.g. the White Sea, Baltic Sea, Barents Sea, the Iberian Coast, and the Mediterranean Sea within EU waters). This dataset includes EMODnet seabed substrate maps at a scale of 1:10 000 from the European marine areas. Traditionally, European countries have conducted their marine geological surveys according to their own national standards and classified substrates on the grounds of their national classification schemes. These national classifications are harmonised into a shared EMODnet schema using Folk's sediment triangle with a hierarchy of 16, 7 and 5 substrate classes. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. Further information about the EMODnet Geology project is available on the portal (http://www.emodnet-geology.eu/).
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This dataset contains the ship accidents in the Baltic Sea during the period 1989 to end of 2023. It is constructed from the annual data collected by HELCOM Contracting Parties on ship accidents in the Baltic Sea and starting from 2019 from EMSA EMCIP Database extraction (for those Contracting Parties that are member of the EU). The accident data has been compiled by the HELCOM Secretariat and EMSA. According to the decision of the HELCOM SEA 2/2001 shipping accident data compilation will include only so-called conventional ships according to the Regulation 5, Annex I of MARPOL 73/78 - any oil tanker of 150 GT and above and any other ships of 400 GT and above which are engaged in voyages to ports or offshore terminals under the jurisdiction of other Parties to the Convention. According to the agreed procedure all accidents (including but not limited to grounding, collision with other vessel or contact with fixed structures (offshore installations, wrecks, etc.), disabled vessel (e.g. machinery and/or structure failure), fire, explosions, etc.), which took place in territorial seas or EEZ of the Contracting Party irrespectively if there was pollution or not, are reported. The dataset contains the following information: Unique_ID = An unique identifier consisting of 4 digit running number and the year of the accident Country Year Date = Date (dd/mm/yyyy) Time = Time of the accident (hh:mm) Location = Location of the accident (open sea / port / port approach, from 2019 -> open sea / port) Acc_Type = Type of accident Colli_Type = Type of collision / contact (with vessel / object) Acc_Detail = More information on the accident CauseDetai = Details on the accident cause Assistance = Assistance after the accident Offence = Offence against Rule Damage = Damage to the ship HumanEleme = Occurrence / Reason of human error IceCondit = Ice conditions CrewIceTra = Crew trained for ice conditions Pollution = Pollution (Yes/No) Pollu_m3 = Pollution in m3 Pollu_t = Pollution in tonnes Pollu_Type = Type of pollution RespAction = Response actions after the accident Cargo_Type = Type of cargo Ship1_Name = Ship 1 identification (Not published after 2018) Sh1_Categ = Ship 1 type (according to AIS category) Sh1_Type = Ship 1 more detail ship type category Sh1_Hull = Ship 1 hull construction Sh1Size_gt = Ship 1 GT Sh1Sizedwt = Ship 1 DWT Sh1Draug_m = Ship 1 draught in meters / category Cause_Sh1 = Cause of accidents from ship 1 Pilot_Sh1 = Presence of pilot on ship 1 Ship2_Name = Ship 2 identification (Not published after 2018) Sh2_Categ = Ship 2 type (according to AIS category) Sh2_Type = Ship 2 more detail ship type category Sh2_Hull = Ship 2 hull construction Sh2Size_gt = Ship 2 GT Sh2Sizedwt = Ship 2 DWT Sh2Draug_m = Ship 2 draught in meters / category Cause_Sh2 = Cause of accidents from ship 2 Pilot_Sh2 = Presence of pilot on ship 2 Add_Info = Additional information Latitude = Latitude (decimal degrees) Longitude = Longitude (decimal degrees) For more information about shipping accidents in the Baltic Sea, see the HELCOM annual reports: https://helcom.fi/helcom-at-work/publications/ https://helcom.fi/media/publications/HELCOM-report-on-Shipping-accidents-in-the-Baltic-Sea-2019-211207-FINAL.pdf
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Seabed substrate 1:250 000 is one of the products produced in the EMODnet (European Marine Observation and Data network) Geology EU project. Project provided seabed geological material from the European maritime areas. The EMODnet Geology project (http://www.emodnet-geology.eu/) collects and harmonizes geological data from the European sea areas to support decision-making and sustainable marine spatial planning. The EMODnet Geology partnership has included 36 marine organizations from 30 countries. This data includes the EMODnet seabed substrate map at a scale of 1:250 000 from the Finnish marine areas. It is based on the data produced on a scale of 1:20 000 by the Geological Survey of Finland (GTK), which does not cover the whole Finnish marine area yet. The seabed substrate data will be updated with a new interpreted data on a yearly basis.The data has been harmonized and reclassified into five Folk substrate classes (mud, sandy clays, clayey sands, coarse sediments, mixed sediments) and bedrock. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. The data have been generalized into a target scale (1:250 000). The smallest smallest cartographic unit within the data is 0.3 km2 (30 hectares). Further information about the EMODnet-Geology project is available on the portal (http://www.emodnet-geology.eu/). Permission (AK15246) to publish the material was obtained from the Finnish Defence Office 28.07.2014
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Agricultural parcel containing spatial data The agricultural parcels are a year-by-year data set containing the agricultural parcels in the annual aid application. A agricultural parcel is defined as a continuous area of a single reference parcel where a single crop, a mixture of crops or a mixture of crops is grown, set aside or used for a specific purpose. A reference parcel may have one or more agricultural parcels. A agricultural parcel may belong to only one reference parcel. The boundaries of the agricultural parcels, and hence their areas, may vary from year to year within the reference plot. The aim of the definition of valuable data sets (EU) 2023/138 has been to ensure that the most socially potential public data are reusable. The Food Authority is concerned with the theme Spatial data: reference parcels and agricultural parcels From the 2023 data onwards, the Food Authority will add attribute information to the published data as follows: Agricultural parcel containing spatial data - Organic production New field in the data set LUOMUVILJELY = Organic production: 1=absent or 0=no More information: https://www.ruokavirasto.fi/tuet/maatalous/hakuoppaat-ja-ohjeet-viljelijoille/peltotukien-hakuopas/peltotukien-hakuopas-2025/