post accuracy assessment classification

Post Accuracy Assessment Soft Classification Using Virtual Globes

1. To use the high quality sampled information that accuracy assessment reveals for creating a soft classified residential lawns map.

2. To incorporate supplemental variables for aiding the segregation of residential lawns from fine- green (grassy) areas.

3. To use virtual fieldwork for validation.

Objectives

Rahul RakshitPhD CandidateClark University

Robert Gilmore Pontius Jr.Asst. ProfessorClark University

holmes, Graduate School of Geography, Clark University 1

holmes, Graduate School of Geography, Clark University 2

Supplemental VariablesVirtual

Fieldwork for Accuracy

Assessment

Soft Classified Map

Our Contribution

Satellite Image/Aerial Photo

Hard Classified Map

Accuracy Assessment

Traditional image processing methodology

Image Classification

1. To use the high quality sampled information that accuracy assessment reveals for creating a soft classified residential lawns map.

2. To incorporate supplemental variables for aiding the segregation of residential lawns from fine- green (grassy) areas.

3. To use virtual fieldwork for validation.

Objectives

Aerial Photos•4 Bands•Orthorectified•0.45 m Resolution

holmes, Graduate School of Geography, Clark University

Study Area

2

Image Courtesy: Google Earth

Land-Cover Map: Created by object oriented classification

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Residential Lawns

Residential Lawns are: grassy areas associated with a private residence

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All Fine-Greens are not Residential Lawns

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Image Courtesy: Google Earth

Fine-Green Boolean - 15% of the area

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Using Supplemental Variables

Supplemental variables are selected based on the likelihood of them containing residential lawns.

1. Building Footprints

2. Residential Zoning

3. Historic Residential Land-use

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Building Footprints

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Near Buildings Boolean

Hero Map, Graduate School of Geography, Clark University 9

Residential Zoning Boolean

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Residential Land-use 1999 Boolean

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Stratum Fine-Green

Near Buildings Res-Zoned Res -1999 Percentage of

Study Area

1 TRUE TRUE TRUE TRUE 5

2 TRUE TRUE TRUE FALSE 6

3 TRUE TRUE FALSE UN-USED 1

4 TRUE FALSE UN-USED UN -USED 6

5 FALSE TRUE TRUE TRUE 12

6 FALSE UN-USED UN-USED UN-USED 70

Total 100

Stratification

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Mutually Exclusive and Collectively Exhaustive Strata

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Sampling Tool: Stratified Random Sampling

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Sampling

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Why use Virtual Globes for Virtual Fieldwork

Better Science : We can do stratified truly random sampling that is temporally matching.

Saves time and money.

Imagery available at very high resolution aiding in easy identification of land-cover classes.

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Plotting samples on Google Earth

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Image Courtesy: Google Earth

Visiting Sample Points

Coniferous Fine-Green Fine-Green

Impervious Impervious Deciduous

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Images Courtesy: Google Earth

Google Earth

Street View

Virtual Earth 1 Virtual Earth 2 Virtual Earth 3 Virtual Earth 4

Multiple Views on Virtual Globes

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Images Courtesy: Google Earth and MS Virtual Earth

Stratum Fine-Green Near Buildings Zoned Res Res -1999 Percentage of Study Area

Upper Bound

Percentage of Lawn

Lower Bound

1 TRUE TRUE TRUE TRUE 5 64% 76 88%

2 TRUE TRUE TRUE FALSE 6 24% 38 52%

3 TRUE TRUE FALSE UN -USED 1 1% 6 13%

4 TRUE FALSE UN -USED UN -USED 6 0% 0 0%

5 FALSE TRUE TRUE TRUE 12 1% 10 19%

6 FALSE UNUSED UN -USED UN -USED 70 1% 2 6%

Total 100 5% 8 12%

Number of Samples per Strata = 50

Percentage of Fine-Green = 15%Percentage of Residential Lawns = 8%

Sampling Results

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Residential Lawn - 8% of the area

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Stratum 1

Stratum 1U2

Stratum 1U2U3

Stratum 1U2U3U4

0 2 4 6 8 10 12 14 16 18 20

Error of omission Correctly classified Error of comission

29

41

44

37

Percent of Study Area

Har

d D

efini

tion

of L

awn

Figure of Merit

Observations

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Figure of Merit: The rate at which the classification is entirely correct

Objectives

holmes, Graduate School of Geography, Clark University

1. To use the high quality sampled information that accuracy assessment reveals for creating a soft classified residential lawns map.

2. To incorporate supplemental variables for aiding the segregation of residential lawns from fine- green (grassy) areas.

3. To use virtual fieldwork for validation.

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AcknowledgementsI sincerely thank:

Prof. Robert Gilmore Pontius Jr., Clark University

Prof. Colin Polsky, Clark University

holmes team: Albert Decatur, Jenner Alpern and Nick Giner

MassGIS

Town of Ipswich

Google Earth

MS Virtual Earth

Rahul’s contact Info. : rahulbabaji@gmail.com

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This material is based upon work supported by the National Science Foundation under Grant No. 0709685Any opinions, findings, & conclusions or recommendations expressed in this material are those of the author(s) & do not necessarily reflect the views of the National Science Foundation.