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Ramesh Gautam, Jean Woods, Simon Eching, Mohammad Mostafavi, Scott Hayes, tom Hawkins, Jeff milliken Division of Statewide Integrated Water Management.

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Presentation on theme: "Ramesh Gautam, Jean Woods, Simon Eching, Mohammad Mostafavi, Scott Hayes, tom Hawkins, Jeff milliken Division of Statewide Integrated Water Management."— Presentation transcript:

1 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

2 Overview of Presentation Challenges as well as Solution Approach Algorithm Development & Testing Results & Discussion Summary & Conclusion 11/19/14DWR-DSIWM-Land & Water Use

3 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 11/19/14DWR-DSIWM-Land & Water Use

4 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. 11/19/14DWR-DSIWM-Land & Water Use

5 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 11/19/14DWR-DSIWM-Land & Water Use

6 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)

7 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

8 Grain/Corn Indices: EVI and LSWI

9 Orchards Indices: EVI and LSWI 11/19/14DWR-DSIWM-Land & Water Use

10 Alfalfa Indices: EVI and LSWI 11/19/14DWR-DSIWM-Land & Water Use

11 Rice Indices: EVI and LSWI 11/19/14DWR-DSIWM-Land & Water Use

12 Reservoir or Wetland Indices: EVI and LSWI 11/19/14DWR-DSIWM-Land & Water Use

13 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? 11/19/14DWR-DSIWM-Land & Water Use

14 Algorithm Development Cont’d… 11/19/14DWR-DSIWM-Land & Water Use Field A: Initially Flooded in April Flooded in May? Yes No Flooded in June? No Yes Calculate EVI after 40 days from May Image date Calculate 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

15 Algorithm Development Cont’d… 11/19/14DWR-DSIWM-Land & Water Use What if the fields are flooded in April, May, June, July, August, September? Reservoir, Pond, River or Lake Remove all of them

16 Algorithm development cont’d… 11/19/14DWR-DSIWM-Land & Water Use 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

17 Classified map: Stanislaus county 11/19/14DWR-DSIWM-Land & Water Use

18 Results & Discussions Accurately ClassifiedError of OmmissionError of commission Field ID(Acres) Field ID (Acres) Field ID(Acres) 11943013.341203400.3113630019.35 11960015.341203701.1213490014.46 137930169.68 13171038.25 13796016.23 13274014.23 13900036.02 13084029.36 14100011.56 15513038.47 15739024.46 15764022.23 15803041.81 Total389.14 1.43 115.64 11/19/14DWR-DSIWM-Land & Water Use

19 11/19/14DWR-DSIWM-Land & Water Use ERROR ANALYSIS, LANDSAT 5(April 17, 2010)

20 11/19/14DWR-DSIWM-Land & Water Use ERROR ANALYSIS CONT’D…(May 19, 2010)

21 11/19/14DWR-DSIWM-Land & Water Use ERROR ANALYSIS CONT’D…(June 20, 2010)

22 11/19/14DWR-DSIWM-Land & Water Use ERROR ANALYSIS CONT’D…(July 6, 2010)

23 11/19/14DWR-DSIWM-Land & Water Use ERROR ANALYSIS CONT’D…(August 7, 2010)

24 11/19/14DWR-DSIWM-Land & Water Use ERROR ANALYSIS CONT’D…(September 24, 2010)

25 11/19/14DWR-DSIWM-Land & Water Use RICE FIELD AND CONFUSED FIELD: EVI PLOT

26 11/19/14DWR-DSIWM-Land & Water Use AREA MAPPED AS PASTURE IN THE LAND USE SURVEY, BUT FLOODED (June 13, 2010)

27 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. 11/19/14DWR-DSIWM-Land & Water Use

28 Error Matrix-Rice Classification RiceOthersTotal%Accuracy Rice 157,394.00 2,259.63 159,653.6399% Others 5,226.00 353,899.50 359,125.5099% Total 162,620.00 356,159.13 518,779.13 %Accuracy97%99% 11/19/14DWR-DSIWM-Land & Water Use

29 Glenn and Colusa Counties Surveyed and Classified Rice Fields in 2003 11/19/14DWR-DSIWM-Land & Water Use

30 Rice Classification-Error Analysis 11/19/14DWR-DSIWM-Land & Water Use

31 Rice Classification-Error Analysis 11/19/14DWR-DSIWM-Land & Water Use

32 Rice Classification-Error Analysis Cont’d… 11/19/14DWR-DSIWM-Land & Water Use

33 Rice Classification-Error Analysis Cont’d… 11/19/14DWR-DSIWM-Land & Water Use

34 Rice Classification-Error Analysis Cont’d… 11/19/14DWR-DSIWM-Land & Water Use

35 Rice Classification-Error Analysis Cont’d… 11/19/14DWR-DSIWM-Land & Water Use

36 Rice Classification-Error Analysis Cont’d… 11/19/14DWR-DSIWM-Land & Water Use

37 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. 11/19/14DWR-DSIWM-Land & Water Use

38 Questions ? 11/19/14DWR-DSIWM-Land & Water Use

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