Seppo Huurinainen’s scientific contributions

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Fig. 24.1 Example of the forest inventory pipeline established in the pilot, with reference to the generic DataBio pipeline concept
Fig. 24.2 Visualization of species-wise volumes generated using the Forestry TEP platform at Hippala. Shown is the estimated stem volume of the dominating tree species in each 10 m by 10 m area (red = broadleaved, blue = pine, green = spruce). The darker the color, the higher the volume (range around 0-300 m 3 /ha). Forest stands are outlined by red lines
Fig. 24.3 Forestry TEP is an online platform for efficient exploitation of Copernicus Sentinel and other satellite data in forest monitoring and analysis. Along with the data, the platform offers processing services and tools and allows to develop and share new services
Fig. 24.4 EO Regions! platform provides access to various remote sensing services based on earth observation data, in particular the Copernicus data, allowing automated processing and connection with other platforms
Fig. 24.5 Pilot site in Galicia populated with forest estate data in Wuudis Service. The user can browse through information such as ID, area, stem count, volume, and tree value for each forest stand, and visualize supporting material such as field photos

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Forest Variable Estimation and Change Monitoring Solutions Based on Remote Sensing Big Data
  • Chapter
  • Full-text available

August 2021

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77 Reads

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Stéphanie Bonnet

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Allan A. Nielsen

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In this pilot, we demonstrate the usability of online platforms to provide forest inventory systems for exploiting the benefits of big data. The pilot highlights the technical transferability of online platform based forest inventory services. All of the services tested in the piloting sites were technically implemented successfully. However, in new geographical areas, strong user involvement in service definition and field data provision will be needed to provide reliable and meaningful results for the users. Overall, the pilot demonstrated well the benefits of technology use in forest monitoring through a range of forest inventory applications utilizing online big data processing approaches and inter-platform connections.

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FIGURE 1. THE EXPONENTIAL GROWTH OF DATA [REF-01]. ............................................................................................ 15 FIGURE 2. KEY CHARACTERISTICS OF BIG DATA (BASED [REF-02])..................................................................................... 16 FIGURE 3. THE DATA-INFORMATION-KNOWLEDGE-WISDOM HIERARCHY OF ACKOFF [REF-03]. ............................................. 17 FIGURE 4. BIG DATA MANAGEMENT PROCESS [REF-08] ................................................................................................. 21 FIGURE 5. BAR CHART (A) AND PIE CHART (B)............................................................................................................... 28 FIGURE 6. HISTOGRAMS (A) AND LINE GRAPH (B). ......................................................................................................... 29 FIGURE 7. SCATTERPLOT. ........................................................................................................................................ 29 FIGURE 8. PCA (A) AND PARALLEL COORDINATES VISUALIZATION (B). ................................................................................ 30 FIGURE 9. PCA (A) AND PARALLEL COORDINATES VISUALIZATION (B). ................................................................................ 30 FIGURE 10. VISUALISATION OF SENSOR DATA. .............................................................................................................. 32 FIGURE 11. BRUSHING. THE ROUNDED AREA IS HIGHLIGHTED IN THE HISTOGRAM AND ON THE MAP. ........................................ 33 FIGURE 12. BDVA REFERENCE ARCHITECTURE WITH NUMBERS OF DATABIO COMPONENTS. .................................................. 34 FIGURE 13. NIST BIG DATA REFERENCE ARCHITECTURE................................................................................................. 35 FIGURE 14. YIELD POTENTIAL APPLICATION.................................................................................................................. 38 FIGURE 15. DATA COLLECTED BY FOREST MACHINES HELP TO EVALUATE HARVESTING CONDITIONS, FOR EXAMPLE. PHOTO: ERKKI OKSANEN. .................................................................................................................................................. 42 FIGURE 16. FOREST BIG DATA PLATFORM WITH FOREST BIG DATA AND APPLICATION COMPONENTS (HTTP://WWW.DATATOINTELLIGENCE.FI/FOREST-BIG-DATA.HTML). ........................................................................ 43 FIGURE 17. METSÄÄN.FI SERVICE WITH RELATED OPERATIONS AND USER GROUPS. ............................................................... 45 FIGURE 18. ENTITY OF FOREST DATA DEVELOPMENT IN METSÄÄN.FI SERVICE. SPECIFIC FOCUS ON IMPROVEMENT OF DATA MOBILITY AND DATA QUALITY, AND E-SERVICE PROMOTION. (METSÄTIETO 2020-KEHITTÄMISSUUNNITELMA). ............................. 46 FIGURE 19. TRAGSA DRONES USED IN FORESTRY PILOT. ............................................................................................... 47 FIGURE 20. GENERATED PRODUCTS (IMAGERIES) IN FORESTRY PILOT. ................................................................................ 47 FIGURE 21. GENERATED INDEXES (IMAGES) IN FORESTRY PILOT. ....................................................................................... 48 FIGURE 22. FOREST VALUE CHAIN AND THE EXPECTED BENEFITS OF 'WUUDIS DATA' TO ALL SEGMENTS OF THE VALUE CHAIN. ......... 50 FIGURE 23. CONCEPT OF WUUDIS DATA. ................................................................................................................... 51 FIGURE 24. THE CONCEPT OF NEW SENOP HYPERSPECTRAL CAMERA, RELEASED ÍN 2018. ...................................................... 52 FIGURE 25. EXAMPLE OF CLOUD-FREE REFLECTANCE IMAGE OF THE FORESTS OF CZECH REPUBLIC GENERATED USING BIG DATA SPATIALTEMPORAL ANALYSIS UTILIZING ALL-AVAILABLE SENTINEL-2 OBSERVATIONS BETWEEN JUNE AND AUGUST 2016................. 53 FIGURE 26. EXAMPLE OF SATELLITE-DERIVED PRODUCT DESCRIBING FOREST HEALTH STATUS-AMOUNT OF CHLOROPHYLLS IN FOREST CANOPIES. RED AREAS ARE IDENTIFIED AS FORESTS WITH LOW CHLOROPHYLL CONTENT. CLOUD-FREE IMAGE MOSAIC GENERATED ABOVE SENTINEL-2 BIG DATA WAS USED AS AN INPUT IN THE ALGORITHM. ................................................................. 54 FIGURE 27. ILLUSTRATION OF VMS (FROM EC COMMISSION, FISHERIES POLICY-CONTROL TECHNOLOGIES). ............................. 56 FIGURE 28. MARINETRAFFIC INFORMATION PORTAL SHOWING VESSEL TRAFFIC IN NORTHERN EUROPE BASED ON AIS DATA (FROM WWW.MARINETRAFFIC.COM). ........................................................................................................................ 59 
Table 2 . Open data providers relevant for fisheries.
DataBio - D6.3 – State of the Art

December 2017

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82 Reads

Big data technologies have shown significant benefits in many sectors of society, as diverse as manufacturing, business management and health science. This report looks at the state of the art of big data technologies and their application in bioeconomy, i.e. the parts of the economy that use renewable biological resources from land and sea – such as crops, forests, fish, animals and micro-organisms – to produce food, materials and energy. The DataBio project in particular, addresses agriculture, forestry and fishery, where it aims to advance the use of big data technologies by implementing several pilot demonstrations. The purpose of the document is to provide an overview for the general public and non-expert readers of recent developments in big data and highlight opportunities of how it could serve the bioeconomy sector in the near future.