1. Ramesh Gautam, Jean Woods, Simon Eching, Mohammad Mostafavi Land Use Section, Division of Statewide Integrated Water Management California Department of Water Resources

2.  Quantify crop acreage based on crop types  Estimate evapotranspiration  Determine urban landscape acreage  Input for groundwater and surface water models  Verify fields fallowed for water transfers  Map urban growth patterns  Estimate economic impacts of flooding

3.  Reduce the extent of required field mapping by identifying permanent crops  Accurately assess crop acreage  Estimate annual crop water use for the California Water Plan  Accurately estimate evapotranspiration on a field level  Determine the annual extent of fallowing  Verify fields fallowed for water transfers

4. Stanislaus County Area: 1,515 sq mile Population: 515,000

5. Decision Tree Based Classification Orchards Non-Orchards LCRAS Based Classification Time series based Vegetation Index Analysis Corn, Mixed Pasture, Fallow, Dry Beans, Tomato, Melons Alfalfa Autocorrelation & LIDAR Vineyards

6.  Classify orchards from non-orchard crops  Gray Level Co-occurrence Matrix Algorithm was used to classify orchards  Textural patterns distinguish orchards from other crops  eCognition Developer software was used to develop the algorithm

7. Textural parameters are analyzed to evaluate the fields having coarse texture versus fine texture

8. Recently planted orchards were classified in next level as shown in next slide Recently planted orchards were classified in next level as shown in next slide Non-orchards Orchards Bare land and new orchards Farmsteads Urban area Poultry farms Highways/RoadsLEGEND

9. How recently planted orchards have been captured in second level of classification Non-orchards Orchards Bare land and new orchards Farmsteads Urban area Poultry farms Highways/RoadsLEGEND

10. Non-orchards Orchards Bare land and new orchards Farmsteads Urban area Poultry farms Highways/RoadsLEGEND

12. Ground Truth Survey Collect Crop Attributes (12% of Total Fields) QC Ground Truth Data Update Field Border Database Develop Personal Geo-database of Ground-truth data in ArcGIS Randomly Select Training Data (60%) Perform Image Segmentation in eCognition Developer eCognition Developer for Training Data for Training Data Create Signatures in Erdas Imagine in Erdas Imagine

13. Using eCognition Developer software, crop fields are segmented into polygons of similar spectral characteristics.

14. LANDSAT-5 Image Bands 1-5 and 7 Perform Supervised Classification of Spectral Characteristics Identify Crops at the Field Level Based on Classification Based on Classification Perform Accuracy Assessment Re-evaluate signature sets signature sets Identify Mislabeled Fields Based on Ground Truth Overall Classification ≥ 90%? Yes End No