Natural Resources Institute Finland and Finnish Environment Institute as producers of environmental data

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1 Natural Resources Institute Finland and Finnish Environment Institute as producers of environmental data Jussi Nikander, Kai Mäkisara, Katja Ikonen Natural Resources Institute Finland Kaisu Harju, Riitta Teiniranta, Suvi Hatunen Finnish Environment Institute

2 Contents of this lecture What are Natural Resources Institute Finland (Luke) and Finnish Environment Institute (Syke)? Examples Smart farming MVMI (the Finnish Forest Inventory) CORINE Urban and countryside areas Why is environmental data important

3 Luke and Syke Natural Resources Institute Finland and Finnish Environment Institute

4 Natural Resources Institute Finland (Luke) Natural Resources Institute Finland is a research and expert organization, which works on sustainable use of renewable resources and bioeconomy Forestry, agriculture, fisheries and wild game related research and governmental work The topics in the approximate order of importance Works under the Ministry of Agriculture and Forestry Luke was created by merger of the Finnish agricultural, forestry, and game & fisheries research institutes Luke both uses and creates environmental data in a wide range of different contexts Spatial research resources are spread around the organization, coordinated in a loose network

5 Finnish Environment Agency (Syke) Provides services, knowledge, and expertise related to the sustainable development of the society The work is related to six thematic areas Climate change prevention and mitigation Sustainable use of the Baltic sea and other water resources Sustainable production and consumption Safeguarding ecological diversity and ecosystem services Sustainable use of urban and built environment Production and use of environmental data Syke provides open environmental data services First open data sets were provided in

6 Types of spatial data production in Luke and Syke Governmental spatial data Both institutes are mandated by law to provide specific data sets Spatial services Data and related services produced to customers and partners Research-related data and services Spatial data created in research projects The categories are not mutually exclusive

7 Environmental data at Luke Luke data sets are typically related to the exploitation or monitoring of renewable resources the Finnish Forest Inventory (MVMI) Biomass-atlas Agricultural pest predictions (Kasper) Counting small game (riistakolmiot) Primary production, refinement, and use of biomass Edible biomass (both cultivated and wild) Non-edible biomass (primarily wood, also grasses, bioenergy crops) Side streams (Forestry and agricultural residue, manure, etc.)

8 Environmental data at Syke The data at Syke is related to the natural environment and its interaction with human activities CORINE land use Built environment Natura areas Floods, protected areas, ground water areas, etc

9 Luke example: smart farming

10 Smart farming and VRA Agricultural fields have variying yield potential The potential also varies within a field Smart farming in general is the application of modern ICT in farming Precision farming is farming while taking into account the within-field variation Variable Rate Application

11 Precision farming and variable rate application The goal is to execute agricultural field operations in a way that 1. The operation is applied to each location in the field exactly once All locations are treated No locations are treated multiple times 2. The amount of inputs is varied according to location in order to give optimal amount to each location The input is used fully, and there is no excess to run off Enough input is applied to each location The operations optimize the use of the whole field parcel

12 Treatment overlap and omissions

13 Within-field variation and VRA VRA is the term for varying the amount of input according to location Within-field variation is a result of multiple variables Soil Slope Humidity and rain Previous field operations Etc. When VRA is used, these variables are used in order to try and find the optimal amount of each input for each location The speed of adjustment is typically limited by the hardware. It takes certain amount of time for adjustments to be reflected in application

14 Luke example: Finnish forest inventory (MVMI)

15 MVMI in a nutshell A regular inventory of the Finnish forests First conducted in 1920s MVMI 11 was finished in 2015 Contains a large number of thematic layers that cover the whole country Delivered in raster format (geotiff) Biomassa, kuusi, elävät oksat 2013 (10 kg/ha) Biomassa, kuusi, hukkapuuosa 2013 (10 kg/ha) Biomassa, kuusi, juuret, d > 1 cm 2013 (10 kg/ha) Biomassa, kuusi, kanto 2013 (10 kg/ha) Biomassa, kuusi, kuolleet oksat 2013 (10 kg/ha) Biomassa, kuusi, kuorellinen runkopuu 2013 (10 kg/ha) Biomassa, kuusi, neulaset 2013 (10 kg/ha) Biomassa, lehtipuut, elävät oksat 2013 (10 kg/ha) Biomassa, lehtipuut, hukkapuuosa 2013 (10 kg/ha) Biomassa, lehtipuut, juuret, d > 1 cm 2013 (10 kg/ha) Biomassa, lehtipuut, kanto 2013 (10 kg/ha) Biomassa, lehtipuut, kuolleet oksat 2013 (10 kg/ha) Biomassa, lehtipuut, kuorellinen runkopuu 2013 (10 kg/ha) Biomassa, lehtipuut, lehvästö 2013 (10 kg/ha) Biomassa, mänty, elävät oksat 2013 (10 kg/ha) Biomassa, mänty, hukkapuuosa 2013 (10 kg/ha) Biomassa, mänty, juuret, d > 1 cm 2013 (10 kg/ha) Biomassa, mänty, kanto 2013 (10 kg/ha) Biomassa, mänty, kuolleet oksat 2013 (10 kg/ha) Biomassa, mänty, kuorellinen runkopuu 2013 (10 kg/ha) Biomassa, mänty, neulaset 2013 (10 kg/ha) Kasvupaikan päätyyppi 2013 (1-4) Kasvupaikka 2013 (1-8) Maaluokka 2013 (1-3) Maaluokka FAO:n FRA-määritelmän mukaan 2013 (1-4) Puuston ikä 2013 (vuosi) Puuston keskiläpimitta 2013 (cm) Puuston keskipituus 2013 (dm) Puuston latvuspeittävyys, koko puusto 2013 (%) Puuston latvuspeittävyys, lehtipuut 2013 (%) Puuston pohjapinta-ala 2013 (m 3 /ha) Tietolähdeindeksi, MVMI 2013 Tilavuus, koivu 2013 (m 3 /ha) Tilavuus, koivu kuitupuu 2013 (m 3 /ha) Tilavuus, koivu tukkipuu 2013 (m 3 /ha) Tilavuus, kuusi 2013 (m 3 /ha) Tilavuus, kuusi kuitupuu 2013 (m 3 /ha) Tilavuus, kuusi tukkipuu 2013 (m 3 /ha) Tilavuus, muu lehtipuu 2013 (m 3 /ha) Tilavuus, muu lehtipuu kuitupuu 2013 (m 3 /ha) Tilavuus, muu lehtipuu tukkipuu 2013 (m 3 /ha) Tilavuus, mänty 2013 (m 3 /ha) Tilavuus, mänty kuitupuu 2013 (m 3 /ha) Tilavuus, mänty tukkipuu 2013 (m 3 /ha) Tilavuus, puusto yhteensä 2013 (m 3 /ha)

16 MVMI example: main forest type (four categories)

17 MVMI Details Delivered as a 16-bit GeoTIFF raster Value (value is missing) and (not part of the data set) and global reserved values Other values depend on the layer Pixel stands for 16m*16m area Uses the ETRS-TM35FIN coordinate system The standard coordinate system for Finnish public institutions European Terrestial Reference System 1989, (Universal) Transverse Mercantor, zone 35, Finnish variant The coordinate projection causes noticeable distortion near the projection edges

18 MVMI input and output data The input data consists of experimental field plots measured during years Landsat and Resourcesat satellite images from years In the northern part of the country the input data tends to be older than in the southern part of the country The output A large number of raster map layers The date of the maps is The actual data is typically from other dates, so this date is purely analytical

19 The analytical date in MVMI MVMI data is dated on July 31st, 2013 The data was (mostly) gathered before this Forest growth models were used to adjust the data to the correct date The models are based on statistical methods such as regression The growing season was from may to august The models were designed for coniferous trees Broad-leaves used the model for pinewood When using satellite imagery, the model was calibrated from experimental data gathered on the ground The accuracy of the model was improved by using several input data sets Oldest input sets were from the year

20 The analysis methods for different themes Experimental input data sets (point data) were turned into surfaces using the k-nearest neighbors method For each pixel, the k nearest data points were used to interpolate the pixel value Amount of biomass, volume of wood, etc. were estimated using a number of different models Area covered by trunks was taken directly from experimental data The error on a single pixels can be large, but the decreases as the area being used increases For example, the average error in volyme layers is 86 m 3 /ha in southern Finland

21 Other Luke environment data sets The Biomass-atlas is an open data service that attempts to bring all Finnish biomass-related data sets together Currently a work in progress, even if there are several datasets already publicly available Riistakolmiot (game triangles): calculation of the number of small game observations (mostly tracks) in specific areas Public service,

22 Other Luke environmental data A large amount of data regarding wild animal and fish populations Large number of forestry-related data sets for various purposes Agricultural services: farming, horticulture, diseases, greenhouses Greenhouse-related spatial data sets can be quite different from the two other applications Statistical data from various Finnish farms Etc. Data sets can be public, open to research, or confidential

23 Syke example: CORINE land cover

24 CORINE Coordination of Information on the Environment EU-wide program for gathering and managing important land cover data The program was started in 1985 and the first CORINE data set was delivered in 1990 Finland started participating in 2000 The program is older than EU in its current form CORINE contains over 40 different land cover classes

25 CORINE

26 CORINE in Finland Kerrostaloalueet Pientaloalueet Palveluiden alueet Teollisuuden alueet Liikennealueet Satama-alueet Lentokenttäalueet Maa-ainesten ottoalueet Kaivokset Kaatopaikat Rakennustyöalueet Vapaa-ajan asunnot Muut urheilu- ja vapaa-ajan toiminta alueet Golfkentät Raviradat Pellot Hedelmäpuu- ja marjapensasviljelmät Laidunmaat Luonnon laidunmaat Käytöstä poistunut maatalousmaa Puustoiset pelto- ja laidunmaat Lehtimetsät kivennäismaalla Lehtimetsät turvemaalla Havumetsät kivennäismaalla Havumetsät turvemaalla Havumetsät kalliomaalla Sekametsät kivennäismaalla Sekametsät turvemaalla Sekametsät kalliomaalla Luonnonniityt Varvikot ja nummet Harvapuustoiset alueet, cc <10% Harvapuustoiset alueet, cc 10-30%, kivennäismaalla Harvapuustoiset alueet, cc 10-30%, turvemaalla Harvapuustoiset alueet, cc 10-30%, kalliomaalla Harvapuustoiset alueet, sähkölinjan alla Rantahietikot ja dyynialueet Kalliomaat Niukkakasvustoiset kangasmaat Sisämaan kosteikot maalla Sisämaan kosteikot vedessä Avosuot Turvetuotantoalueet Merenrantakosteikot maalla Merenrantakosteikot vedessä Joet Järvet Meri Luokkia, joita ei Suomesta löydy ovat mm. Viinitarhat Oliivitarhat Jokien suistot Vuorovesialueet

27 CORINE map example: a Finnish city

28 CORINE details Created as 20m * 20m GeoTIFF files Data delivered to EU as 25 hectare (500m*500m) shapefiles created from the raster data Each data set contains age-related metadata files The metadata contains information about the age of the input data and thus establishes the temporal range for the actual data Each new CORINE data set also contains data that summarizes the changes between this and previous data set Current CORINE in Finland is from 2012 Earlier data sets are from 2000 and 2006, so the next publication might be in

29 CORINE input data sets Earlier CORINE land cover data sets 2000, 2006 National land survey topographic database (Maastotietokanta) Building information (rakennus ja huoneistorekisteri) Digiroad Shore data Satellite data The Finnish forest inventory Field parcel register Fell (tunturi) data The data is combined in various ways Other input is also used (e.g. for mining sites)

30 Syke example: Urban and countryside areas

31 Urban and countryside areas in Finland Finnish population density varies significantly In Torkkelinmäki, Helsinki, there are over residents / km 2 (Comparable to Manhattan) There are large uninhabited areas, especially in the Northern and Eastern parts of the country Differences can be large even inside municipalities The goal of the urban and countryside classification is to characterize the different areas of Finland without using municipal borders The outcome is a classification of all Finnish land area into seven categories

32 The categorization Urban environments: population centers with over residents. Further divided into three categories Inner urban area Outer urban area Surrounding urban area The rest of the country is divided into four different categories of countryside Population centers in the countryside area Countryside near urban area General countryside area Sparsely populated countryside area

33 An example of urban and countryside categorization

34 Details of the categorization The data is in vector format (shapefiles) Calculated without taking municipality borders into account using 250m * 250m pixel size The different categories defined using different criteria Urban areas are calculated using amount of built area compared to the whole area Surrounding area defined using distance from urban area Countryside population areas are smaller centers General countryside based on land use Sparsely populated countryside is the rest of the area

35 Other Syke environmental data sets Zoned areas, populated areas, population centers, city centers, commercial areas, etc. Flood risk areas, flooded areas Natura 2000 Beaches (for swimming), rivers, river depths The diversity of under-surface natural sea environment Vedenalaisen meriluonnon monimuotoisuus VELMU)

36 What use is environmental information from the natural environment?

37 Bioeconomy and biosociety Forest is the most important renewable resource Finland has Forest is the livehood of Finland All Finnish bioresources are spread over a large area Bioeconomy requires well-designed logistics Fresh water may be the second most important renewable resource, especially in the future Sustainable use of bioresources We must be able to maintain our resources for the future The load capacity of the natural environment, sustainable intensification Many data sets are mandated by a degree (MVMI, CORINE, etc.) Spatial data is an important tool for planning

38 The importance of environmental data is increasing Number of downloads from Syke Download TOP 5 v Pohjavesialueet 2. Natura Valuma-aluejako 4. Luonnonsuojelualueet 5. Corine maanpeite

39 Environmental data through web services: WMS Palvelun nimi INSPIRE_SYKE_Korkeus - Järvien syvyyskäyrät INSPIRE_SYKE_SuojellutAlueet - Luonnonsuojelualueet ja Natura-aineistot INSPIRE_SYKE_Hydrografia - Valuma-aluejako ja uomaverkosto INSPIRE_SYKE_Geologia - Pohjavesialueet INSPIRE_SYKE_Ortoilmakuvat - Image-satelliittikuvamosaiikit INSPIRE_SYKE_AlueidenHallintaJaRajoitukset1 - Vesipuitedirektiivin mukaiset vesimuodostumat (2. suunnittelukausi) ja vesienhoitoalueet INSPIRE_SYKE_Luonnonriskialueet * - Tulvariskialueet ja tulvavaaravyöhykkeet INSPIRE_SYKE_YmparistontilanSeuranta * - Hydrologiset havaintopaikat, vesienhoitoalueiden pintavesien ja pohjavesien seurantapaikat INSPIRE_SYKE_Maanpeite - Corine Land Cover INSPIRE_SYKE_EliomaantieteellisetAlueet - Metsäkasvillisuusvyöhykkeet ja suokasvillisuusvyöhykkeet INSPIRE_SYKE_AlueidenHallintaJaRajoitukset2 - Uimavesidirektiivin mukaiset uimarannat sekä maasto- ja vesiliikenteen rajoitusalueet -aineistot

40 40 Teppo Tutkija