ramesh gautam, jean woods, simon eching, mohammad mostafavi, scott hayes, tom hawkins, jeff milliken...

Ramesh Gautam, Jean Woods, Simon Eching, Mohammad Mostafavi, Scott Hayes, tom Hawkins, Jeff milliken

Division of Statewide Integrated Water Management

Land and Water Use Section

California Department of Water Resources, Sacramento, California

Rice Classification Using Remote Sensing and GIS

Overview of Presentation

Challenges as well as Solution Approach

Algorithm Development & Testing

Results & Discussion

Summary & Conclusion

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Challenges & Our solution Approach

Timing of flooding: Timing of field flooding ranges from April through June.Approach: Decision tree based algorithm was developed to capture all fields that undergo flooding—Potentially Rice Fields

Spectral similarity: Spectral patterns of rice, ponds, reservoirs and wetlands may be similar.Solution: Condition based methodology was applied to filter out spectrally similar fields.

Image availability: Cloud-free and suitable temporal resolution images may not be available for the specified date.Image Substitution: Nearest date (before or after) image was considered to replace the targeted date image

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Pre-processing & field visit

Cloud-free LANDSAT-5 satellite images were obtained from April to September, 2010

The field border was updated using NAIP and LANDSAT images.

Crop type, field condition, percent cover and irrigation type were collected through field survey.

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Algorithm Development

LANDSAT-5 Satellite Image

Stack all Bands in Erdas Imagine

Erdas Imagine

Subset all images to Stanislaus CountyConvert all digital numbers into radiance

eCognition Developer

Import all images and thematic vector layersCompute EVI, LSWI, & NDVI

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Vegetation Indices: NDVI, EVI & LSWI

Normalized Difference Vegetation Index (NDVI)

NDVI = (NIR –RED)/(NIR +RED)

Enhanced Vegetation Index (EVI)

EVI = 2.5*(NIR-RED)/(NIR+6*RED-7.5*Blue+1)

Land Surface Water Index (LSWI)

LSWI = (NIR-SWIR)/(NIR+SWIR)

Algorithm Development

LSWI+0.05>EVI or NDVI of AprilLSWI+0.05>EVI or NDVI of JuneLSWI+0.05> EVI or NDVI of May

April, May or June Flooded Field

Grain/Corn Indices: EVI and LSWI0 2 4 6 8

10

12

02

46

810

12

Orchards Indices: EVI and LSWI

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0 2 4 6 8

10

12

02

46

810

12

Alfalfa Indices: EVI and LSWI

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0 2 4 6 8

10

12

02

46

810

12

Rice Indices: EVI and LSWI

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0 2 4 6 8

10

12

02

46

810

12

Reservoir or Wetland Indices: EVI and LSWI

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0 2 4 6 8

10

12

02

46

810

12

Algorithm Development Cont’d…

A field flooded in April

Was this field also flooded in May and/or June ?

A field flooded in May

Was this field also flooded in April and/or June?

A field flooded in June

Was this field also flooded in April and/or May?

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Algorithm Development Cont’d…

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Field A:Initially

Flooded in April

Flooded in May? Yes

No

Flooded in June?

No

Yes

Calculate EVI after 40 days

from May Image dateCalculate EVI

after 40 days from June

Image date

Calculate EVI after 40 days

from April Image date

[EVI>(MAX

EVI/2)]?No

Yes

Potential Rice field

Algorithm Development Cont’d…

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What if the fields are flooded in April, May,

June, July, August, September?

Reservoir, Pond, River or

Lake

Remove all of them

Algorithm developmentcont’d…

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Group all potential rice

fields

Remove all fields which have NDVI higher than 0.4 in April and May

Remove all fields that have high length to

width ratio (L/W)>2000

Evaluate the

remaining fields

Classified map: Stanislaus county

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Results & Discussions

Accurately Classified Error of Ommission Error of commission

Field ID (Acres)

Field ID (Acres)

Field ID (Acres)

119430 13.34 120340 0.31 136300 19.35

119600 15.34 120370 1.12 134900 14.46

137930 169.68     131710 38.25

137960 16.23     132740 14.23

139000 36.02     130840 29.36

141000 11.56        

155130 38.47        

157390 24.46        

157640 22.23        

158030 41.81        

Total 389.14   1.43   115.64

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ERROR ANALYSIS, LANDSAT 5(April 17, 2010)

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ERROR ANALYSIS CONT’D…(May 19, 2010)

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ERROR ANALYSIS CONT’D…(June 20, 2010)

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ERROR ANALYSIS CONT’D…(July 6, 2010)

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ERROR ANALYSIS CONT’D…(August 7, 2010)

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ERROR ANALYSIS CONT’D…(September 24, 2010)

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RICE FIELD AND CONFUSED FIELD: EVI PLOT

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AREA MAPPED AS PASTURE IN THE LAND USE SURVEY, BUT FLOODED(June 13, 2010)

additional study Identifying Rice Fields in Glenn and Colusa

Counties

LANDSAT 5 satellite images of Glenn and Colusa Counties were obtained from NASA .

All images were geometrically, radiometrically, and atmospherically corrected using the algorithm developed at NASA.

Spectral band layers were stacked and clipped to Glenn and Colusa Counties.

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Error Matrix-Rice Classification

Rice Others Total %Accuracy

Rice 157,394.00 2,259.63 159,653.63 99%

Others 5,226.00 353,899.50 359,125.50 99%

Total 162,620.00 356,159.13 518,779.13

%Accuracy 97% 99% 99%11/19/14 DWR-DSIWM-Land & Water Use

Glenn and Colusa Counties Surveyed and Classified Rice Fields in 2003

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Rice Classification-Error Analysis

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Rice Classification-Error Analysis

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Rice Classification-Error Analysis Cont’d…

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Rice Classification-Error Analysis Cont’d…

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Rice Classification-Error Analysis Cont’d…

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Rice Classification-Error Analysis Cont’d…

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Rice Classification-Error Analysis Cont’d…

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Conclusion

A classification algorithm was developed to classify rice crop in Stanislaus County and tested in Glenn as well as Colusa Counties

It was found that the rice crop can be classified with an overall accuracy of 99%.

This method will be applied to other counties in order to further evaluate the consistency of the developed algorithm.

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Questions ?

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