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Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469

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

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Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469

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  1. Image Indexing for Nearshore RestorationMorgan McKenzie and Dan AllenGeog 469

  2. 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

  3. 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

  4. 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?

  5. Data Process Diagram Analyze Attributes (such as % overhanging veg, aquatic, veg, shorline length, etc) Process images (manually or with image processing software) Download image from WAGDA Data Entry For PSNRP analyze geomorphic objects, woody debris, % of beach armored

  6. Project Results • Created a image data index model database • Discovered which attributes were important and why • Made database searchable by attributes

  7. IMAGE Structures Vegetation ID# Name Date Resolution Source ID# Bulkhead Over water structure %Armored ID# Woody Debris # of Clusters % overhanging Vegetation Aquatic vegetation Image Database Image database Location Geomorphic Objects Bch Length ID# Location Latitude Longitude Projection ID# Spit Embayment Erosion Deposits Watershed ID# Beach Length

  8. 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

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