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Image cataloging as a tool for marine biodiversity discovery


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Introduction to image cataloging and metadata tagging as a complement for fisheries surveys with biodiversity analyses.

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Image cataloging as a tool for marine biodiversity discovery

  1. 1. From photos to data: an introduction to image cataloging as a tool for biodiversity Claude Nozères email: Fisheries and Oceans Canada, Maurice Lamontagne Institute 850 route de la mer, Mont-Joli, QC, Canada G5H 3Z4
  2. 2. This is not a software review  Default programs on PCs:  MS PhotoEditor  Windows Live Photo Gallery  Apple iPhoto  Other software  Google Picasa (free, basic)  IrfanView (free, basic)  Adobe Photoshop (expensive, complicated)
  3. 3. This is about work examples recent cases with biodiversity projects: using catalogs to turn photos into reliable data  all examples done with Adobe Lightroom 
  4. 4. Case study 1 Conservation zones Several areas in the St. Lawrence are of special interest for marine life  high biological productivity  diversity of benthic habitats  Q: how to document the biodiversity?
  5. 5. Surveys: grabs & tow camera shrimp often seen in bottom photos
  6. 6. Benthic sled photo transects • photo of sea bottom every 10 s. • identify and count epifauna in view keywords: photo 2006, Pandalus borealis, Ophiura sarsii, individual photos by transect (yellow thumbnails are endpoints) keywords: photo 2007, Leptasterias polaris...
  7. 7. IKU Grab photos keywords: tray, grab IKU 2008 keywords: sediment, grab, grab IKU 2008 keywords: Neoamphitrite groenlandica, Polychaeta, Terebellidae, grab IKU 2008
  8. 8. The problem: many images  At first, a solution was sought for organizing the underwater photos  selecting good shots for analysis  adding or correcting dates and locations  However, the sampling surveys also had photos (field samples, lab examination)  these were very useful to consult for questions about species and stations
  9. 9. Case Study 2 Trawl bycatch  Capture a large diversity of organisms  Taxonomic expertise not available while at sea  Q: how to record the diversity in bycatch?
  10. 10. Non-commercial species in capture
  11. 11. Capture sorted by type
  12. 12. Giving an initial ID based on photos posters produced using files in an image catalog
  13. 13. Individual images by type do not need a photo for every specimen, but useful for new or uncertain species keywords: unknown, Ascidiacea, TE-008
  14. 14. The problem: effort for species ID no time to identify all taxa while at sea  BUT need a name & a weight to record   sort by types, give names, take photos  save some specimens for validation later  later in the lab, can give correct names  photos show original appearance & colour  photos can help to correct data, e.g., counts
  15. 15. Solutions: image management  Digital Asset Management (DAM) for images & their data  browsing (sorting & comparing)  organizing (grouping)  editing (image properties)  DAM makes use of standardized types of metadata  intended for commercial photography  can exploit these data fields for marine projects
  16. 16. Image metadata—info. on photos Data fields are useful for sorting and comparing: capture date (EXIF) – original camera clock date  keyword (IPTC)– subject tags for species, scenes  location (IPTC) – tag for station name  GPS (EXIF) latitude and longitude coordinates  example of filtering for metadata in a Lightroom image catalog
  17. 17. Why an image catalog?  browser: metadata in images read as they are being examined  e.g., Windows Explorer, Photoshop (Bridge)  catalog: metadata in images is also stored in a database, or catalog, file  can do more with organization & tagging  can work with files offline (files not present)  NB: all work here is with Lightroom catalogs
  18. 18. Image catalogs: added value It takes work to tag & organize photos in a catalog! Benefits include:  rapid browsing and comparison of photos  quick confirmation of correct names or values, such as date, species, station, or GPS coordinates  bulk export of image data into database archives and for use in data analyses  easier to generate graphic products on-demand, e.g., posters and photo galleries
  19. 19. Benefits: ‘Discovered species’ By comparing image results across surveys, several lesser-known species were discovered or corrected from the established records and literature for the region Notable examples in St. Lawrence Estuary • • • • • • bivalves: Mya pseudoarenaria; Panomya vs. Mya truncata large deepwater amphipod: Neohela monstrosa sea cucumber: Pentamera calcigera brittlestars: Ophiacantha, Stegophiura, Amphiura sea anemones: Actinostola callosa (not Actinauge) sea pens: Anthoptilum grandiflorum (not Pennatula) revealed in 2011
  20. 20. Discovering from catalogs Actinostola (not Actinauge) Anthoptilum (not Pennatula) photo 2006 Actinostola trawl 2006 Actinauge Difficult to ID sea anemones in underwater photos - finally validated with trawl photos Easy to return to catalog and update records, species keyword photo 2006 Gulf 8/2011 DFO surveys in 2011 revealed errors in sea pens–was easy to confirm in the image catalog Confusion with sea pens dates back to 1919
  21. 21. Discovering from public images  Blog: BIO’s Offshore Benthic Ecology Group did a cruise blog in June 2011  posted images while at sea—became inspiration to confirm Anthoptilum from captures in the St. Lawrence  Article (Belley et al. 2010) contained images – revealed errors in ID  errors would have continued if kept to internal reports or publications without images  Photogallery: export images to a web gallery  when errors are discovered, update catalog & re-export
  22. 22. Web photogallery: CaRMS file export • IML collection in LR catalog • export key images to CaRMS
  23. 23. Exporting data for analyses  Lightroom uses a sqlite3 database engine  provides a graphical interface for easy to do queries & edits  built-in data tools are limited, but the user community has produced shareware plugins (donation/small fee)  Photographer’s Toolbox  jfriedl (Jeffrey’s Lightroom Goodies)  in current projects, exported text (csv) is put to use in Excel, Access, Oracle (w/Spatial Database Engine), and Primer (multivariate analyses)
  24. 24. The Photographer’s toolbox
  25. 25. Summary  standard metadata was used to tag images in a Lightroom catalog  browsing catalog images across surveys resulted in more species being identified  exporting images for public viewing provided opportunities for verification  exporting image metadata enabled their use in external databases for analyses
  26. 26. Key resources  The DAM Book by Peter Krogh, also a forum:  Best practices for digital photography (website)  Adobe Photoshop Lightroom 3: The Missing FAQ by Victoria Bampton (paper & PDF)  CaRMS photogallery user guide, Kennedy et al. 2011. DFO Tech. Rep. 2933. (paper & PDF)  NIDM image data guide (report to be published)