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

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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 Presentation

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Page 1: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469

Image Indexing for Nearshore Restoration

Morgan McKenzie and Dan Allen

Geog 469

Page 2: 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

Page 3: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469

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

Page 4: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469

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?

Page 5: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469

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

Page 6: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469
Page 7: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469
Page 8: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469
Page 9: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469
Page 10: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469

Project Results• Created a image data index model

database• Discovered which attributes were

important and why• Made database searchable by

attributes

Page 11: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469

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

Page 12: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469
Page 13: Image Indexing for Nearshore Restoration Morgan McKenzie and Dan Allen Geog 469

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