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