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

1. From photos to data:
an introduction to image cataloging
as a tool for biodiversity
Claude Nozères
email: claudenozeres@gmail.com
Fisheries and Oceans Canada, Maurice Lamontagne Institute
850 route de la mer, Mont-Joli, QC, Canada G5H 3Z4

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. 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. 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. Surveys: grabs & tow
camera
shrimp often seen
in bottom photos

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. IKU Grab photos
keywords: tray, grab IKU 2008
keywords: sediment, grab, grab IKU 2008
keywords: Neoamphitrite groenlandica,
Polychaeta, Terebellidae, grab IKU 2008

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. 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. Non-commercial species in capture

11. Capture sorted by type

12. Giving an initial ID based on photos
posters produced using
files in an image catalog

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. 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. 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. 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. 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. 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. 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. 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. 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. Web photogallery: CaRMS
file export
• IML collection in LR catalog
• export key images to CaRMS

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. The Photographer’s toolbox

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. Key resources

The DAM Book by Peter Krogh, also a forum:
http://www.thedambook.com/smf/index.php

Best practices for digital photography
(website)http://www.dpbestflow.org/

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)