Combining Combining Automated and Manual Automated and Manual

Techniques for Accurate Techniques for Accurate NWINWI MappingMapping

Pamela SwintPamela Swint pswint@vt.edupswint@vt.edu

Kevin McGuckinKevin McGuckin kmcguckn@vt.edukmcguckn@vt.edu

Conservation Management InstituteConservation Management InstituteBlacksburg, VABlacksburg, VA

Problem OverviewProblem Overview

• Wetland managers experience:– An increased demand for high resolution

wetland data– A need for a faster turn-around time

• We explore the efficiency of combining:1) Computer-assisted delineations2) Manual QA/QC

Photo-interpretation/AttributionImage PUBHx

PUBHx

PFO1A

PSS1B

PUBHh

PFO1E

Classified MapTraditional Approach

Image

Photo-interpretation/Attribution

Data Editing/Photo-interpretation/

Attribution

Image

Feature Analyst

PUBHx

PUBHx

PFO1A

PSS1B

PUBHh

PFO1E

Classified MapTraditional Approach

Hybrid Computer-Manual Approach

PUBHx

PUBHx

PFO1A

PSS1B

PUBHh

PFO1E

Classified Map

= AutomatedProcess

DeleteClutter

Hybrid Computer-Manual Approach

= AutomatedProcess

PUBHx

PUBHx

PFO1A

PSS1B

PUBHh

PFO1E

Classified Map

ClutterDeletion

Image Feature AnalystData Editing/

Photo-interpretation/Attribution

Feature Analyst InterfaceFeature Analyst Interface

Feature Analyst InterfaceFeature Analyst Interface

Feature Analyst InterfaceFeature Analyst Interface

Feature Analyst InterfaceFeature Analyst Interface

Example Search Patterns

Bullseye 3 (3x9) Bullseye 4 (3x13)Bullseye 1 (3x9)

Foveal Max. (3x33)

Bullseye 2 (3x17)

Circle Max. (3x97)

Manhattan Min. (3x5) Manhattan Max. (3x85)

Square Min. (3x9) Square Max. (3x81)

How does FA compare How does FA compare to hand delineations?to hand delineations?

• Pond drawn at a zoom scale of 1:2,500

Yellow = FARed = Hand Delineation

1:1,000

Case Study: Shirley PlantationCase Study: Shirley Plantation

Step 1: Create Training SamplesStep 1: Create Training Samples

• Train FA on dark water features

Step 2: Run Classification using Step 2: Run Classification using Default SettingsDefault Settings

Step 3: Create Additional Training Step 3: Create Additional Training SetSet

• To capture the remaining water features

Step 4: Classification Result using Step 4: Classification Result using Default SettingsDefault Settings

Step 5: Merge ClassificationsStep 5: Merge Classifications

Case Study: Northern AlaskaCase Study: Northern Alaska• ~ 530 square miles

Alaska Case StudyAlaska Case Study• 1 Training Set; 30,000 features identified• ~ 3 hours total (2.5 hours for trial and

error, ½ hour to train and classify data)

Final ProductFinal Product

Hybrid Computer-Manual Approach

= AutomatedProcess

PUBHx

PUBHx

PFO1A

PSS1B

PUBHh

PFO1E

Classified Map

ClutterDeletion

Image Feature AnalystData Editing/

Photo-interpretation/Attribution

Examples of ClutterExamples of Clutter

Raw FA ResultsRaw FA Results-- clipped by soils, slopeclipped by soils, slope

~ 4500 polygons 1230 polygons

Final Wetland MapFinal Wetland Map-- with manual editingwith manual editing

Hybrid Computer-Manual Approach

= AutomatedProcess

PUBHx

PUBHx

PFO1A

PSS1B

PUBHh

PFO1E

Classified Map

ClutterDeletion

Image Feature AnalystData Editing/

Photo-interpretation/Attribution

Use of Ancillary GIS DataUse of Ancillary GIS Data• Datasets involved

– SSURGO Hydric soils– NHD– USGS Topographic maps– USGS 10m NED

Comparison of Final Product Comparison of Final Product vsvsOriginal NWIOriginal NWI

Yellow = FARed = Original NWI

Yellow = FARed = Original NWI

Comparison of Final Product Comparison of Final Product vsvsOriginal NWIOriginal NWI

Minimum Mapping Unit Minimum Mapping Unit ComparisonComparison

• PUBH only; upstate New York area

LLWW AttributionLLWW Attribution

• Landscape Position, Landform, WaterflowPath, Waterbody Type (LLWW)

• LLWW attribution uses both manual and automated methods

• LLWW Modifiers are added during wetland attribution/photo-interpretation process

LLWW ModelLLWW Model• Model Inputs

– Cowardin wetlands, modifiers, flow assignments, and buffered stream network

LLWW ModelLLWW Model• Step 1

– Assign landscape position and Waterbodytype based on Cowardin classification

Lakes Rivers Ponds Expand Codes Marine Estuarine

LLWW ModelLLWW Model• Step 2

– Spatial and photo-interpreted attributes

Stream Value

Lakes Rivers

LLWW ModelLLWW Model• Step 3

– Additional Attributions

Modifiers Landform Pond Modifiers

Clean-up

How long does mapping wetlands How long does mapping wetlands this way take?this way take?

Upstate NY

Per USGS 1:24k Quad

Upstate NY

How long does mapping wetlands How long does mapping wetlands this way take?this way take?

Upstate NY

Per USGS 1:24k Quad

Upstate NY

Low Contra

st, H

igh Complex

ity

High Contrast, Little Complexity

How long does mapping wetlands How long does mapping wetlands this way take?this way take?

Per USGS 1:24k Quad

Inland VA

Upstate NY

How long does mapping wetlands How long does mapping wetlands this way take?this way take?

Per USGS 1:24k Quad

Inland VA

Upstate NY

Task BreakTask Break--downdown

ConclusionConclusion

• Combination of manual and automated techniques provide efficiency

• Processing time varies with the amount of wetland complexity involved

ConclusionsConclusions

• High contrast features One-pass Feature Analyst classification

• Low contrast features Multi-pass FA classification and/or manual delineation

• CMI is looking to asses FA in other regions (southwest, mountainous terrain...etc)

AcknowledgementsAcknowledgements

U.S. Fish and WildlifeRalph Tiner, John Swords, Herb Bergquist

Virginia TechBeth Pokorski, Staci Hudi, Tim Brown, Jason

Herman, Arvind Bhuta, Hanna Mabe

Contact InfoContact Info

Pamela Swint (pswint@vt.edu)Kevin McGuckin (kmcguckn@vt.edu)

Scott Klopfer (sklopfer@vt.edu)

Conservation Management Institute1900 Kraft Drive, Suite 250

Blacksburg, VA 24061540-231-7348