Transcript Geographic Object Based Image Analysis (GeOBIA)

September 5, 2013
Tyler Jones
Research Assistant
Dept. of Geology & Geography
Auburn University
Isolated Wetlands by Ralph Tiner
U.S. Fish & Wildlife National Wetland Coordinator
 Increased interest in recent years due to Supreme Court
rulings
 There is no uniformly accepted definition of isolated
wetlands
 With current data and technology the best approach uses
geographic isolation for classification (Tiner, 2003)
 This project uses Tiner’s narrow interpretation of >40
meters from traditional non-isolated waterbodies
Geographic Object-Based Image Analysis (GeOBIA)
is a sub-discipline of GIScience devoted to partitioning remote sensing
(RS) imagery into meaningful image-objects, and assessing their
characteristics through spatial, spectral and temporal scale.
Fundamentally consisting of image segmentation, attribution, and
classification (Hay and Castilla, 2006).
Segmentation
Image Segmentation- process of partitioning a digital image into multiple segments
(sets of pixels, also known as image primitives). The goal of segmentation is to
simplify and/or change the representation of an image into meaningful image objects
that are easier to analyze (Shapiro and Stockman, 2004).
Classification
 Once created each image object can be identified and
classified based on its attributes which the user can
define.
For Example:




Spatial Extent
Linearity
Spectral Reflectance
Relationship with Image Object Primitives
Classification
Example of classification using shape, size, texture, and
height to classify buildings, trees, impervious surface and
grass on the Auburn University campus.
Differences from Traditional Raster Analysis
 Pixel based classification methods rely solely on the
reflectance values of a given pixel
 Without creating meaningful image objects each with
its own associated attributes these types of analysis are
error prone
 GeOBIA allows for hierarchical relationship
framework development that give successive levels of
image objects an association
Methodology
Analysis Data:
 National Agricultural Imagery Program (NAIP)
 1-meter spatial resolution (i.e. 1 pixel = 1meter2)
 4-band spectral resolution (red, green, blue, near
infrared)
 Imagery is flown during the 2011 growing season so
vegetation will appear “leaf on”.
 Repeated every 3 years
Methodology
Analysis Data:
 Soil Survey Geographic Dataset (SSURGO)
 vector dataset with attributed soil characteristics
 among other types of soil this dataset contains location
of all known hydric soils
 created by NRCS soil
scientists conducting soil
surveys
Methodology
Isolation Data:
 The National Hydrography Dataset (NHD)
 vector dataset delineating traditional waters such as
lakes, rivers, and streams
 built and maintained by the U.S. Geological Survey
Methodology
Isolation Data:
 Federal Emergency Management Agency’s (FEMA)
Digital Flood Rate Insurance Map (DFIRM)
 vector dataset depicting various hydrological models
 specifically the Special Flood Hazard Areas (commonly
known as the “100 year floodplain”)
Methods
 Executed using custom algorithms in eCognition
Server with parallel processing
 Developing decision-tree rulesets
 Tiling and stitching 714 NAIP DOQQs into 5,712
individual projects
 Iterative segmentations creating and shaping
meaningful objects
 Classification based on user defined thresholds.
 Measured for geographic isolation to traditional waters
 Verification (remote and field) to determine accuracy
Study Area
 Area of Alabama falling north of the 34th parallel
 Includes all or a portion of 17 Alabama counties
Geographic Isolation
Geographic Isolation
Geographic Isolation
Remote Verification
 191 areas identified as geographically isolated were
randomly selected and manually inspected using aerial
imagery
 Results showed an overall accuracy of 83.7 percent
 Errors included rooftops, shadows, and pavement
Field Verification
 Field verification was also used to assess accuracy of
classification
 57 sites were inspected and marked using TopCon GRS-1
DGPS
 Overall accuracy of 87.7%
Overall Results
 A total of 26,461 areas were identified as geographically
isolated wetlands with an overall extent of 49,139.5 acres
 Average wetland size: 1.859 acres
 County with highest number of wetlands: Cullman (4,400)
 County with lowest number of wetlands: Cherokee (355)
 County with most acreage of wetlands: Lawrence 12,668
acres
 County with least acreage of wetlands: Cherokee 346 acres
Future Work
 This project’s methodology are being extended to the rest of
the state of Alabama
 This should mean a reduction in pre-processing and
methodology construction and increase overall efficiency
 As physiography changes does this effect the accuracy of
these algorithms
 Future wetland mapping projects using GeOBIA should
investigate incorporating airborne LiDAR to increase accuracy
Questions?
Contact Information:
Tyler W. Jones
2194A Haley Center
Auburn University, AL
[email protected].