DEMO Paikkatietohakemisto
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The Finnish Forest Research Institute (Metla) developed a method called multi-source national forest inventory (MS-NFI). The first operative results were calculated in 1990. Small area forest resource estimates, in here municipality level estimates, and estimates of variables in map form are calculated using field data from the Finnish national forest inventory, satellite images and other digital georeferenced data, such as topographic database of the National Land Survey of Finland. Seven sets of estimates have been produced for the most part of the country until now and six sets for Lapland. The number of the map form themes in the most recent version, from year 2015, is 45. In addition to the volumes by tree species and timber assortments, the biomass by tree species groups and tree compartments have been estimated. The first country level estimates correspond to years 1990-1994. The most recent versions are from years 2005, 2007, 2009, 2011, 2013 and 2015. The maps from 2015 is the fourth set of products freely available. It is also the second set produced by the Natural Resources Institute Finland. A new set of the products will be produced annually or biannually in the future. The maps are in a raster format with a pixel size of 16m x 16m (from 2013) and in the ETRS-TM35FIN coordinate system. The products cover the combined land categories forest land, poorly productive forest land and unproductive land. The other land categories as well as water bodies have been delineated out using the elements of the topographic database of the Land Survey of Finland.
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Potential cumulative impacts on benthic habitats is based on the same method than http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/9477be37-94a9-4201-824a-f079bc27d097, but is focused on physical pressures and benthic habitats. The dataset was created based on separate analysis for potential cumulative impacts on only the benthic habitats, as these are particularly affected by physical pressures. In this case the evaluation was based on pressure layers representing http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/ea0ef0fa-0517-40a9-866a-ce22b8948c88 and http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/05e325f3-bc30-44a0-8f0b-995464011c82, combined with information on the distribution of eight broad benthic habitat types and five habitat-forming species (http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/363cb353-46da-43f4-9906-7324738fe2c3, http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/f9cc7b2c-4080-4b19-8c38-cac87955cb91, http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/264ed572-403c-43bd-9707-345de8b9503c, http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/822ddece-d96a-4036-9ad8-c4b599776eca and http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/ca327bb1-d3cb-46c2-8316-f5f62f889090). The potential cumulative impacts has been estimated based on currently best available data, but spatial and temporal gaps may occur in underlying datasets. Please scroll down to "Lineage" and visit http://stateofthebalticsea.helcom.fi/cumulative-impacts/ for more info.
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This dataset represents the Integrated biodiversity status assessment for pelagic habitats using the BEAT tool. Status is shown in five categories based on the integrated assessment scores obtained in the tool. Biological Quality Ratios (BQR) above 0.6 correspond to good status. Open sea areas were assessed based on the core indicators ‘Zooplankton mean size and total stock’ and ‘Chlorophyll-a’, as well as the pre-core indicator ‘Cyanobacterial bloom index’ . Coastal areas were assessed by national indicators. This dataset displays the result of the integrated biodiversity status in HELCOM Assessment unit Scale 4 (Division of the Baltic Sea into 17 sub-basins and further division into coastal and off-shore areas and division of the coastal areas by WFD water types or water bodies). Attribute information: "BQR" = Biological Quality Ratio "Confidence" = Confidence of the assessment "HELCOM_ID" = Code of the HELCOM assessment unit "Country" = Country of coastal assessment unit/ open sea "Level2" = HELCOM sub-basins (name of the scale 2 assessment unit) "Name" = Name of the HELCOM scale 4 assessment unit "Area_km2" = Area of assessment unit "AULEVEL" = scale of the assessment unit "HID" = assessment unit ID by country "SAUID" = ID number for the spatial assessment unit "EcosystemC" = Ecosystem component assessed "Confiden_1" = Confidence of the assessment (0-1, higher values mean higher confidence) "Total_numb" = Number of indicators used in assessment "STATUS" = Integrated status category (0-0.2 = not good (lowest score), 0.2-0.4 = not good (lower score), 0.4-0.6 = not good (low score), 0.6-0.8 = good (high score, 0.8-1.0 = good (highest score))
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This dataset represents the integrated assessment of hazardous substances in the Baltic Sea in 2011-2016, assessed using the CHASE tool (https://github.com/helcomsecretariat/CHASE-integration-tool). The integration is based on hazardous substances core indicators covering concentrations of hazardous substances. This dataset displays the result of the assessment in HELCOM Assessment unit Level 3 (Division of the Baltic Sea into 17 sub-basins and further division into coastal and offshore areas). Attribute information: "HELCOM_ID" = ID of the HELCOM scale 3 assessment unit "country" = Country/ opensea "level_3" = Name of the HELCOM scale 3 assessment unit "area_km2 = Area of the HELCOM scale 3 assessment unit "AULEVEL" = Scale of the assessment units "coastal" = Code of scale 3 HELCOM assessment unit "Input" = Contamination ratio of the assessment unit (Higher score indicates higher contamination) "Confidence" = Confidence of the assessment (Low/ Moderate/ High/ Not assessed) "Status" = Status value for the assessment (= 1.0: Low contamination score, > 1.0: High contaminantion score)
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This dataset represents the Integrated biodiversity status assessment for seals (grey seal, harbour seal and ringed seal). Status is shown in five categories based on the integrated assessment scores obtained in the tool. Biological quality ratios (BQR) above 0.6 correspond to good status. The status of the seals was assessed using four core indicators: population trends and abundance of seals, distribution of Baltic seals, nutritional status of seals, and reproductive status of seals. In the latter two only grey seals are considered for the 2018 State of the Baltic Sea report. The assessment is based on the one-out-all-out approach, i.e. the species reflecting the worst status in each assessment unit. This dataset displays the result of the integrated biodiversity status in HELCOM Assessment unit Scale 2 (Division of the Baltic Sea into 17 sub-basins). Attribute information: "HELCOM_ID" = ID of the HELCOM scale 2 assessment unit "level_2" = Name of the HELCOM scale 2 assessment unit "EcosystemC" = Ecosystem component analyzed "BQR" = Biological Quality Ratio "Conf" = Confidence of the assessment "Total_indi" = Number of indicators used "% of area assessed" = Share of the total assessed area "D1CX" = MSFD descriptor 1 criteria X "conf_D1CX" = Confidence for MSFD descriptor criteria X "Confidence" = Conifdence of the assessment ("high"/ "moderate"/ "low") "STATUS" = Status of the assessment (0-0.2 = not good (lowest score), 0.2-0.4 = not good (lower score), 0.4-0.6 = not good (low score), 0.6-0.8 = good (high score, 0.8-1.0 = good (highest score))
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Datapaketet Skogsområden med högt biodiversitetsvärde i Finland består av 12 landsomfattande rasterkartor. Dessa 12 kartor är olika versioner av biodiversitetsvärden i Finlands skogar. Rasterkartornas upplösning är 96 x 96 meter. Enkla anvisningar för att läsa rasterkartorna: Ju större numeriskt värde, desto högre biodiversitetsvärde. National = Nationella analyser över biodiversitetsvärden i finska skogar (sex analyser) Regional = Regionala analyser över biodiversitetsvärden i finska skogar (ser ut som en karta men är i själva verket en samling av 13 separata analyser, region = Närings-, trafik- och miljöcentralen i Finland) (sex analyser) Sex olika prioriteringar av naturskydd gjordes med Zonation-programvaran (a) så att varje ny version innefattade allt som fanns med i tidigare, enklare analysversioner. National / Regional 1 Potentiell mängd död ved: Version 1 (V1) innefattade potentiell mängd död ved* på lokal nivå. Områden med många stora träd, många trädslag och ovanliga skogsmiljöer får högt lokalt värde. National / Regional 2 Potentiell mängd död ved och straff: Version 2 = V1 + straff för åtgärder som har negativ inverkan på biodiversiteten. De lokala värdena stämde bättre överens med verkligheten när man tog hänsyn till verkliga förändringar i skogar. National / Regional 3 Potentiell mängd död ved – straff + skogskonnektivitet: Version 3 = V2 + konnektivitet utifrån ekologisk likhet, avstånd och kvalitet mellan skogsområden (genomsnittlig försvagning 400 m). Ofragmenterade skogsområden av hög kvalitet framkommer. National / Regional 4 Potentiell mängd död ved – straff + skogskonnektivitet + RL-arter: Version 4 = V3 + observationer av rödlistade skogsarter. Habitat med rödlistade skogsarter framkommer. National / Regional 5 Potentiell mängd död ved – straff + skogskonnektivitet + RL-arter + skogslagen 10 §: Version 5 = V4 + konnektivitet till särskilt viktiga livsmiljöer enligt skogslagens 10 § (genomsnittlig försvagning 200 m). Värdefulla skogsområden och landskap i närheten av skyddade skogsområden med högt biodiversitetvärde framkommer. National / Regional 6 Potentiell mängd död ved – straff + skogskonnektivitet + RL-arter + skogslagen 10 § + PN-konnektivitet: Version 6 = V5 + konnektivitet till permanenta naturskyddsområden (genomsnittlig försvagning 2 km). Värdefulla skogsområden och landskap i närheten av skyddade områden med högt biodiversitetvärde framkommer. *Uträkning av potentiell mängd död ved (PMDV) PMDV beräknades i två skeden för varje skikt träslag i varje trädskikt: 1) Index för potentiell mängd död ved (PMDVi) togs fram med MOTTI-programmet (b, c, d). • 168 trädslag, fertilitetsklass och latitudkombinationer 2) PMDVi användes för att omvandla diameter och volym till potentiell mängd död ved • Genererades för hela Finland enligt bestånd med en upplösning på 16 x 16 m • Kombinerades sedan i 20 trädslag och fertilitetsklasser och förenades till 96 x 96 m upplösning. Inmatade data Den potentiella mängden död ved beräknades från beståndsdata (trädslag, medeldiameter, volym, vegetationsklass) vilket omfattade hela landet. Bästa möjliga data användes för varje område. - 24 % av Finland täcks av statligt ägda skogs- och naturskyddsområden och privata naturskyddsområden. o Forststyrelsens Naturtjänster: data om fält- och bestånd (5/2015) o Forststyrelsens Skogsbruk: data om fält- och bestånd (5/2015) o Privatägda naturskyddsområden: data om fält- och bestånd (5/2015) - 37 % av Finland täcks av privatägd skog som inte är naturskyddsområden: Skogscentralen, skogsdata (6.5.2005–6.5.2015) - 39 % av Finland täcks av o Naturresursinstitutet: Nationella skogsinventariedata som är tillverkat med skogsinventeringsmetod som utnyttjar information om riksskogstaxeringens provytor och satellitbilder 2013 (volym, trädslag, vegetationsklass och medeldiameter) Spatiella data om skogsbruk med negativ effekt på biodiversitet (till exempel fällning, gallring och dikning) (uppdaterades 10/2017) - Lantmäterivärket och Finlands miljöcentral SYKE: dikning i finsk torvmark (SOJT_09b1) - Forststyrelsens Skogsbruk: utförda anmälningar om användning av skog och dikningsfigurer - Skogscentralen: anmälningar om användning av skog och dikningsfigurer - University of Maryland/Dept. of Geographica Sciences: Global Forest Change/Forest Cover Loss 2000-2014 Observationer av skogsarter som har rödlistats av IUCN (sedan 1990): Finländska miljödatabasen HERTTA Spatiella data om särskilt viktiga livsmiljöer enligt skogslagens 10 § (uppdaterades 10/2017) - Forststyrelsens Skogsbruk och Skogscentralen Spatiella data om permanenta naturskyddsområden (uppdaterades 2/2018) - Forststyrelsens Naturtjänster: databas över naturskyddsområden SATJ Bakgrund Områden som är viktiga för skogens biodiversitet identifierades runt om i Finland för att främja hållbar markanvändning genom planering och naturskydd på lokal, regional och nationell nivå genom att informera markägare, ministerier och skogtjästemän. Vikten av sådana analyser beror på ökad användning av naturresurser och skadliga effekter på biodiversiteten tillsammans med begränsade naturskyddsresurser. Dessa betonar vikten av att utveckla kostnadseffektiv, ekologiskt hållbar markanvändning som dessa spatiella prioriteringar av naturskydd för skogar som görs för första gången för hela Finland. Prioriteringsmetoden Zonation användes för att hitta nya skogsområden med potentiellt högt skyddsvärde. Det övergripande målet var att tillämpa rikstäckande prioriteringsanalyser utifrån skogsdata relaterade till biodiversitet och markanvändningsdata som hade samlats in på beståndsnivå. De data som primärt tillämpades på skogsstruktur och -kvalitet (vegetationsklass, trädslag, volym och diameter) gav ekologiskt användbara ersättningar för skyddsvärde i barrskog. Resultaten visar att en betydande andel skog med högt biodiversitetsvärde finns utanför det aktuella nätverket för finska naturskyddsområden. Eftersom största delen av det finska skogsområdet är kommersiellt kan nätverket för naturskyddsområden inte stoppa den pågående nedgången av biodiversitet i skogarna. Nyckelord: biodiversitet, död ved, GIS, Handlingsplanen för den biologiska mångfalden i skogarna i södra Finland METSO, markanvändning, värdering, prioritering, skogar, skogarnas biodiversitet, skogsbruk, skogsskydd, spatiell prioritering av naturskydd,Zonation-programvara Datapaketet innefattar 12 rasterkartor och en .lyr-fil. .lyr-filen innehåller färgade symboler och beskrivningar av olika analysversioner. .lyr-filen är troligen endast genomförbar med GIS-programmet som tillhandahålls av ESRI Inc. Datapaketet kan hämtas från: http://www.syke.fi/en-US/Open_information/Spatial_datasets High Biodiversity Value Forests 2018 (Zonation) nationwide High Biodiversity Value Forests 2018 (Zonation) regional Detailjerad poster på engelska: http://www.syke.fi/en-US/Research__Development/Ecosystem_services/Specialist_work/Zonation_in_Finland/Zonation_materials/Posters eller http://www.syke.fi/download/noname/%7B771FF5A4-DAB6-45EE-8246-F38FC0090CAD%7D/138289 Detailjerad rapport på finska: http://hdl.handle.net/10138/234359 Mikkonen et al. 2018. Suomen ympäristökeskuksen raportteja 9/2018. Monimuotoisuudelle tärkeät metsäalueet Suomessa - Puustoisten elinympäristöjen monimuotoisuusarvojen Zonation-analyysien loppuraportti. Andra källor: a) Moilanen et al. 2014. Zonation–Spatial Conservation Planning Methods and Software. Version 4. User Manual. See also www.syke.fi/Zonation/en b) Hynynen et al. 2015. Eur. J. For. Res. 134/3. Long-term impacts of forest management on biomass supply and forest resource development: a scenario analysis for Finland. c) Hynynen et al. 2014. Metlan työraportteja 302. Scenario analysis for the biomass supply potential and the future development of Finnish forest resources. d) Salminen et al. 2005. Comput. electron. agr. 49/1. Reusing legacy FORTRAN in the MOTTI growth and yield simulator. Användar lisens: Creative Commons 4.0. © SYKE Datasources: Finnish Forest Centre, Metsähallitus, Natural Resources Institute Finland, National Land Survey of Finland, Hansen/UMD/Google/USGS/NASA
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The Finnish Forest Research Institute (Metla) developed a method called multi-source national forest inventory (MS-NFI). The first operative results were calculated in 1990. Small area forest resource estimates, in here municipality level estimates, and estimates of variables in map form are calculated using field data from the Finnish national forest inventory, satellite images and other digital georeferenced data, such as topographic database of the National Land Survey of Finland. Seven sets of estimates have been produced for the most part of the country until now and six sets for Lapland. The number of the map form themes in the most recent version, from year 2015, is 45. In addition to the volumes by tree species and timber assortments, the biomass by tree species groups and tree compartments have been estimated. The first country level estimates correspond to years 1990-1994. The most recent versions are from years 2005, 2007, 2009, 2011, 2013 and 2015. The maps from 2015 is the fourth set of products freely available. It is also the second set produced by the Natural Resources Institute Finland. A new set of the products will be produced annually or biannually in the future. The maps are in a raster format with a pixel size of 16m x 16m (from 2013) and in the ETRS-TM35FIN coordinate system. The products cover the combined land categories forest land, poorly productive forest land and unproductive land. The other land categories as well as water bodies have been delineated out using the elements of the topographic database of the Land Survey of Finland.
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The Baltic Sea Pressure Index is a calculation of quantity and spatial distribution of potential cumulative impacts on the Baltic Sea. The BSII is based on georeferenced datasets of human activities (36 datasets), pressures (18 datasets) and ecosystem components (36 datasets), and on sensitivity estimates of ecosystem components (so-called sensitivity scores) that combine the pressure and ecosystem component layers, created in <a href="http://www.helcom.fi/helcom-at-work/projects/holas-ii" target="_blank">HOLAS II</a> project. The assessment can be applied with a focus on pressures only by using the Baltic Sea Pressure Index (BSPI) which shows the anthropogenic pressures/human activities in the defined assessment units without including ecosystem components. The BSPI however includes a weighting component in order to grade the effect of the pressures on the ecosystem in a generalized perspective. Cumulative impacts are calculated for each assessment unit (1 km2 grid cells) by summing all impacts occurring in the unit. All datasets and methodologies used in the index calculations are approved by all HELCOM Contracting Parties in review and acceptance processes. This dataset covers the time period 2011-2016. Please scroll down to "Lineage" and visit <a href="http://stateofthebalticsea.helcom.fi/cumulative-impacts/" target="_blank">State of the Baltic Sea website</a> for more info.
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This dataset represents the Integrated biodiversity status assessment for fish used in State of the Baltic Sea – Second HELCOM holistic assessment 2011-2016. Status is shown in five categories based on the integrated assessment scores obtained in the BEAT tool. Biological Quality ratios (BQR) above 0.6 correspond to good status. The assessment is based on core indicators of coastal fish in coastal areas, and on internationally assessed commercial fish in the open sea. The open sea assessment includes fishing mortality and spawning stock biomass as an average over 2011–2016. Open sea results are given by ICES subdivisions, and are not shown where they overlap with coastal areas. Coastal areas results are given in HELCOM Assessment unit Scale 3 (Division of the Baltic Sea into 17 sub-basins and further division into coastal and off-shore areas) Attribute information: "COUNTRY" = name of the country / opensea "Name" = Name of the coastal assessment unit, scale 3 (empty for ICES open sea units) "HELCOM_ID" = ID of the HELCOM scale 3 assessment unit (empty for ICES open sea units) "EcoystemC" = Ecosystem component analyzed "BQR" = Biological Quality Ratio "Conf" = Confidence (0-1, higher values mean higher confidence) "Total_indi" = Number of HELCOM core indicators included (coastal assessment units) "F__of_area = % of area assessed "D1C2" = MSFD descriptor 1 criteria 2 "Number_of" = Number of open sea species included "Confidence" = Confidence of the assessment "BQR_Demer" = Demersal Biological Quality Ratio "F_spec_Deme" = Number of demersal species included "Conf_Demer" = Confidence for demersal species "BQR_Pelagi" = Pelagic Biological Quality Ratio "F_specPela" = Number of pelagic species included "Conf_Pelag" = Confidence for pelagic species "ICES_SD" = ICES Subdivision number "STATUS" = Integrated status category (0-0.2 = not good (lowest score), 0.2-0.4 = not good (lower score), 0.4-0.6 = not good (low score), 0.6-0.8 = good (high score, 0.8-1.0 = good (highest score))
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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