Type of resources
Available actions
Topics
Keywords
Contact for the resource
Provided by
Years
Formats
Representation types
Update frequencies
status
Service types
Scale
Resolution
From 1 - 10 / 1375
  • This dataset represents the Integrated biodiversity status assessment for benthic 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. The assessment in open sea areas was based on the core indicators ‘State of the soft-bottom macrofauna community’ and ‘Oxygen debt’. Coastal areas were assessed by national indicators, and may hence not be directly comparable with each other. This dataset displays the result of the integrated biodiverity 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" = id of the HELCOM assessment unit "country" = name of the country / opensea "level_2" = HELCOM sub-basins (name of the scale 2 assessment unit) "Name" = Name of the coastal assessment unit on scale 4 "AULEVEL" = scale of the assessment units "type_descr" = Name of the HELCOM scale 4 assessment unit "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 "Area_km2" = Area of assessment unit (km2) "Confiden_1" = Confidence level of the assessment (scores < 0.5 = low, 0.5 - 0.75 = intermediate, > 0.75 = high) "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))

  • 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)

  • The 1:250 000 data on the soft and hard areas of the seabed supplements the seabed substrate data produced by the Geological Survey of Finland (GTK) for areas for which actual survey data is unavailable. The data covers two categories; hard and soft seabed areas. The substrate types categorised as hard seabed areas cover types ranging from gravel to boulders and exposed rock, and the substrate types for soft seabed areas cover types from silt to sand. The model is based on the marine geological survey data of GTK, the substrate observations made by the Finnish Environment Institute (SYKE) and Metsähallitus, and on the environment variable data produced by the Finnish Inventory Programme for the Underwater Marine Environment, VELMU, particularly on depth and seabed openness models. The data has undergone statistical evaluation and the ultimate model is based on expert estimates and modelling. At its most precise, the data is at a scale of 1:250 000 and the areas with a size less than 0.3 square kilometres have been removed. The substrate type information pertaining to restricted areas has been removed from the final data. A permit (AK15246) for publishing data with a similar scale, i.e. the EMODnet data, was received from the Defence Command of the Finnish Defence Forces on 28 July 2014.

  • Seabed substrate maps of the European marine areas including (e.g. the Baltic Sea, the Greater North Sea, the Celtic Sea, the Iberian Coast, and the Mediterranean Sea within EU waters). The maps are collated and harmonized from seabed substrate information within the EMODnet-Geology III project. Where necessary, the existing seabed substrate classifications (of individual maps) have been translated to a scheme that is supported by EUNIS. This EMODnet reclassification scheme includes at least five seabed substrate classes. Four substrate classes are defined on the basis of the modified Folk triangle (mud to sandy mud; sand; coarse sediment; and mixed sediment) and one additional substrate class (rock and boulders) was included by the project team. If the original seabed substrate dataset has enabled more detailed substrate classification, classifications with 7 and 16 substrate classes might be available. The EMODnet-Geology III project started in 2017 with 39 marine departments of the geological surveys of Europe (from 30 countries), with an objective to assemble marine geological information from all European sea areas.

  • This dataset contains points of information describing the location and size of other discharges than illegal oil discharges observed during aerial surveillance flights by HELCOM Contracting Parties 2014-2017. Further information about illegal discharges of oil in the Baltic Sea area and HELCOM aerial surveillance activities can be found at http://www.helcom.fi/baltic-sea-trends/maritime/illegal-spills/ The dataset contains the following information: Country Year Spill_ID= Spill ID FlightType= The type of flight the detection was made during: National = "N", CEPCO = "C", Super CEPCO = "S" Date= The date of the detection (dd.mm.yyyy) Time_UTC= The time of the detection (hh:mm) Wind_speed= The wind speed at the time of the detection (m/s) Wind_direc= The wind direction at the time of the detection (degrees) Latitude= The latitude of the detection (decimal degrees) Longitude= The longitude of the detection (decimal degrees) Length__km= The length of the detection (km) Width__km= The width of the detection (km) Area__km2_= The area of the detection (km2) Spill_cat= The category of the detection: other substance = "OS", unknown substance = "UNKNOWN" EstimVol_m= Estimated volume of the detection (m3) Polluter= Polluter (rig, ship, other, unknown) Category= Category of the detection: 100m3 = "5" Casefile= The name of the casefile the detection refers to Remarks= Any additional information

  • Kauniaisten WMS-palvelu (Trimble Locus WMS Server) on WMS-rajapintapalvelu, jonka kautta on saatavilla seuraavat karttatasot (suluissa tekninen nimi): Kauniaisten opaskartta (Kauniaisten opaskartta), Kauniaisten ortokuva (Kauniaisten ortokuva), Kauniaisten osoitekartta (Kauniaisten kiinteistökartta),Kauniaisten ajantasa-asemakaava (Kauniaisten ajantasa-asemakaava), Kauniaisten karsittu kantakartta (Kauniaisten karsittu kantakartta). Palvelu perustuu Kauniaisten opaskartta-, ortokuva-, osoitekartta- ja ajantasa-asemakaavakartta-aineistoihin. Opaskartta- ja ortokuva-aineistoja hallinnoi Espoon kaupungin kaupunkimittausyksikkö , muita Kauniaisten kaupungin maankäyttöyksikkö. Palvelun käyttö on maksutonta eikä vaadi autentikointia eli tunnistautumista käyttäjätunnuksen ja salasanan avulla.

  • Categories  

    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. Six sets of estimates have been produced for the most part of the country until now and five sets for Lapland. The number of the map form themes in the most recent version, from year 2011, 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 and 2011. The maps from 2011 is the second set of products freely available. The 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 20mx20m 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 topographic database of the Land Survey of Finland.

  • Categories  

    Lack of spatial soil data in digital form has been a primary obstacle in establishing European policies on land use and environmental protection. Abundant data on soil characteristics exist in Finland but have been scattered among various sources, making it difficult for authorities to make country-wide presentations and predictions.The objective of the project was to create georeferenced soil map and database according to the instructions of the European Soil Bureau using data from existing databases and collecting some new data. The basis of the work was a geological map of quaternary deposits, which describes the soil at a depth of 1 metrem (parent material) according to the Finnish classification based on the concentration of organic matter and the texture of mineral material. Primary research topics included generalization methodology of soil polygons with GIS technology, calculation of soil characteristics needed in the database and computerizing the existing non-digital soil information. It was proved that aerial geophysics can be used for separation of shallow peats from deep peat soils and muddy soils and other wet areas can be identified. Soil names according to the FAO/Unesco system and the World Reference Base for Soil Resources (WRB-2014) were derived from the soil names of the Finnish soil classification system and geophysical data. Soilscape (Soil Mapping Units) of Finland with WRB-2014 soil classification, intented to be used in European scale e.g to delineate risk areas mentioned in soil framework directive proposal.

  • Categories  

    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 1st thinnings, pine • Stemwood for energy from 1st thinnings, spruce • Stemwood for energy from 1st thinnings, broadleaved • Stemwood for energy from 1st thinnings (smaller than pulpwood-sized trees), pine • Stemwood for energy from 1st thinnings (smaller than pulpwood-sized trees), spruce • Stemwood for energy from 1st 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 sustainable removal Main output ☒Small-diameter trees ☒Stemwood for energy ☒Logging residues ☒Stumps ☐Bark ☐Pulpwood ☐Saw logs Additional information Stemwood for energy from 1st thinnings. Part of this potential consists of trees smaller than pulpwood size. This part is reported as Small-diameter trees. Forecast period for the biomass supply assessment Start year: 2015 End year: 2044 Results presented for period 2025-2034 3. Description of scenarios included in the assessments Maximum sustainable removal The maximum sustainable 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 80.7 mill. m3 (over bark) for period 2025-2034. 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 2013-2017. Stand based ☐Yes ☒No Grid based ☒Yes ☐No Multi-Source NFI data from 2015 (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 sustainable 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 sustainable 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 sustainable 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) Constraints for stump extraction ☒Yes ☐No ☐N/A Retention of 16–18% of stump biomass (Muinonen et al. 2013; Anttila et al. 2013) 8. External factors Valid for scenario: Maximum sustainable removal External factors besides forest management having effect on outcomes Economy ☐Yes ☒No Climate change ☐Yes ☒No Calamities ☐Yes ☒No Other external ☐Yes ☒No

  • The marine habitat type data concerns the modelling work carried out within the Finnish Inventory Programme for the Underwater Marine Environment (VELMU) in spring 2015. The task was done in cooperation between the Geological Survey of Finland (GTK) and Åbo Academi University (ÅA). The work included the modelling of the marine habitats included in the Annex 1 of the Habitats Directive: reefs (1170) and sandbanks, which are slightly covered by sea water all the time (1110). The aforementioned marine habitat types are specified on the basis of seabed substrate type and topographic form and they can overlap one another. The objective was to produce comprehensive maps of the occurrences of reefs and sandbanks throughout the entire marine area of Finland based on the best data available. The criteria to determine the marine habitats were discussed with the responsible bodies and the instructions (version 5.1), which include more precise criteria for determining marine habitat types than the Natura 2000 Habitats Manual (Airaksinen & Karttunen 2001), for a Natura 2000 inventory were utilised. On the basis of different criteria and test analyses, a decision was made to model the following entireties: - Potential rocky reefs - detail-scale sites that are likely to have reef occurrences. - Potential rocky reef environments - larger sites that are likely to have reef occurrences. - Potential sandbanks - detail-scale sites that are likely to have sandbank occurrences. - Potential sandbank environments - larger sites that are likely to have sandbank occurrences. The data concerning the marine habitats of restricted areas has been removed.