image indexing for nearshore restoration morgan mckenzie and dan allen geog 469
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Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469. Project Goals. Create an Image Data Index Model For nearshore restoration ecologists Provide search tool for research time saving - PowerPoint PPT PresentationTRANSCRIPT
Image Indexing for Nearshore Restoration
Morgan McKenzie and Dan Allen
Geog 469
Project Goals• Create an Image Data Index Model • For nearshore restoration ecologists– Provide search tool for research time
saving– Images show important landscape
attributes: shore forms, watersheds, vegetation, structures, etc
– Index provides way to find scientific research question desired attributes
Background• Puget Sound Nearshore Restoration Project,
Research group• Client: Miles Logsdon, Restoration Ecologist– Also Matt Parsons of UW Libraries and WAGDA
• Implement restoration projects and monitor their success/failures to improve
• Examples of restoration projects: – Remove bulkheads, plant overhanging vegetation
Index of Images for Restoration
• See potential areas of restoration• Monitor areas with implemented
restoration project• Potential Scientific Research
Questions include:–How much vegetation?– Depositional or erosional shoreline?– Shoreline before/after a structure
removal?– Shoreline before/after an event?
Data Process Diagram
Download image from
WAGDA
Process images
(manually or with image processing software)
Analyze Attributes (such as %
overhanging veg, aquatic, veg, shorline length, etc)
Data Entry
For PSNRP analyze
geomorphic objects, woody
debris, % of beach armored
Project Results• Created a image data index model
database• Discovered which attributes were
important and why• Made database searchable by
attributes
Image Database
Image database
ID#NameDate
ResolutionSource
ID#Bulkhead
Over water
structure%Armored
ID#Woody Debris# of Clusters
% overhanging Vegetation
Aquatic vegetation
ID#LocationLatitude
LongitudeProjection
ID#Spit
EmbaymentErosion
DepositsWatershed
ID#Beach Length
IMAGE Structures Vegetation
Bch LengthGeomorphicObjects
Location
Conclusions• Indexing images for a specific use is
difficult because of the large unique set of content
• Completing the data entry is a huge undertaking
• Once past the data entry phase, potential for long term time saving use
• Image data index model can be used over and over again for new data