Identification and Classification of North Alabama’s Isolated Wetlands Using Geographic Object Based

Image AnalysisSeptember 5, 2013

Tyler JonesResearch Assistant

Dept. of Geology & GeographyAuburn University

Isolated Wetlands by Ralph TinerU.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)

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, andheight 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

MethodologyAnalysis 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

MethodologyAnalysis Data:

Soil Survey Geographic Dataset (SSURGO)vector dataset with attributed soil

characteristicsamong other types of soil this dataset contains

location of all known hydric soilscreated by NRCS soil scientists conducting soil surveys

MethodologyIsolation Data:

The National Hydrography Dataset (NHD)vector dataset delineating traditional waters

such as lakes, rivers, and streamsbuilt and maintained by the U.S. Geological

Survey

MethodologyIsolation Data:

Federal Emergency Management Agency’s (FEMA) Digital Flood Rate Insurance Map (DFIRM)vector dataset depicting various hydrological

modelsspecifically 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 projectsIterative segmentations creating and shaping

meaningful objects Classification based on user defined

thresholds.Measured for geographic isolation to

traditional watersVerification (remote and field) to determine

accuracy

Study AreaArea of Alabama falling north of the 34th

parallelIncludes 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 imageryResults showed an overall accuracy of 83.7

percentErrors included rooftops, shadows, and

pavement

Field VerificationField verification was also used to assess

accuracy of classification57 sites were inspected and marked using

TopCon GRS-1 DGPSOverall 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 acresCounty 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 acresCounty with least acreage of wetlands: Cherokee

346 acres

Future Work

This project’s methodology are being extended to the rest of the state of AlabamaThis should mean a reduction in pre-processing and

methodology construction and increase overall efficiencyAs physiography changes does this effect the accuracy of

these algorithmsFuture wetland mapping projects using GeOBIA should

investigate incorporating airborne LiDAR to increase accuracy

Questions?

Contact Information:

Tyler W. Jones2194A Haley Center

Auburn University, AL jonest3@auburn.edu.