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Layer: i15_LandUse_Fresno2009_east (0)

Name: i15_LandUse_Fresno2009_east

Display Field: WATERSOURC

Type: Feature Layer

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Description: This data represents a land use survey of eastern Fresno County conducted by DWR, South Central Regional Office staff, under the leadership of Steve Ewert, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2009. SCRO staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary data was developed using: 1. Eastern Fresno County was surveyed using the 2006 two-meter resolution National Agriculture Imagery Program (NAIP) digital aerial photos as a base for the preliminary line work. Line work for this survey was digitized using ArcMap software. When the 2009 one-meter resolution NAIP aerial photography became available, this was used to review the digital land use data. 2. The western boundary of the survey area is defined by the western boundaries of DWR’s Detailed Analysis Units 235 and 237. The northern boundary of the survey area is defined in part by the county boundary and also by the northern boundaries of the following U.S. Geological Survey’s (U.S.G.S) 7.5’ quadrangles: Friant (U.S.G.S. No. 36119H6), Academy (U.S.G.S. No. 36119H5), Piedra (U.S.G.S. No. 36119G4) and Pine Flat Dam (U.S.G.S. No. 36119G3). The eastern boundary of the survey area is defined in part by the county boundary and also by the eastern boundaries of the following quadrangles: Academy (U.S.G.S. No. 36119H5), Pine Flat Dam (U.S.G.S. No. 36119G3) and Orange Cove North (U.S.G.S. No. 36119F3). The southern boundary of the survey area is defined by the county boundary. 3. Digital aerial photographs and land use field boundaries were copied onto laptop computers for field data collection. The staff took these laptops into the field and virtually all areas were visited to positively identify the agricultural land uses. Land use codes were digitized directly into the laptop computers using ArcMap software using a standardized digitizing process. Some staff took printed aerial photos into the field instead of laptops and wrote land use codes directly onto these photo field sheets. Attributes for these areas were digitized later in the office. The field visits occurred between July 2009 and January 2010. Urban areas were primarily mapped by photo interpretation. Sources of irrigation water were not mapped in this survey. 4. Shapefiles of the field boundary lines and point attributes of the survey data were brought into ARCINFO. Both quadrangle and survey-wide polygon shapefiles were created, and underwent quality checks. 5. Winter grain fields were mapped using an analysis of Landsat 5 imagery. Two major assumptions in the analysis were that 1.) Winter grain was grown on some of the fields where corn, sudan or tomatoes were grown during the summer or where fields were fallow during the summer. 2.) For the fields listed above, we assumed that fields with high winter canopy cover were grain fields. To detect the winter grain fields of eastern Fresno County for the 2009 land use survey, corn fields were queried from the initial shapefile of the land use survey and classified using Landsat 5 imagery. The corn field polygons were buffered in 30 meters to reduce edge effects on the classification. The buffering eliminated some of the smaller fields leaving 798 fields to be classified. The Landsat 5 image acquired on 04/23/2009 was selected as the most appropriate for mapping grain for this survey. Approximately 10 percent of the corn fields were non-randomly selected to represent winter grain and fallow fields. Using a false color infrared display, bright red fields were selected to represent grain and light blue (non-red) fields were selected to represent fallow fields. Using the Hawth’s tools function, the selected fields were randomly divided into training (60%) and accuracy assessment (40%) categories. The polygons were then converted into raster format from vector format. Using ERDAS Imagine, the raster files were used to mask the Landsat 5 image and create two subset Landsat images representing training fields only and training plus accuracy assessment fields. eCognition Developer version 8.0 software was used with the Landsat image of training fields to segment each field into smaller signature areas. Polygons representing these signature areas were exported from eCognition Developer and the attributes of grain or fallow were added to these polygons. Spectral signatures based upon Landsat 5 bands 1,2,3,4,5, and 7 were created using ERDAS Imagine 2010. After associating the signatures with the image of training and accuracy assessment fields combined, a supervised classification was performed using the maximum likelihood parametric rule to classify each pixel. Zonal attributes of the fields were calculated using the recoded image. Based on the zonal attribute plurality, fields were classified as either winter grain or winter fallow. When there were no errors in the identification of “grain” and “fallow” fields in the fields reserved for accuracy assessment, a supervised classification was performed on the Landsat pixels representing all summer corn fields. Landsat images of each classified corn field were visually inspected in ArcMap to determine the reasonableness of the classification results. In addition to the Landsat 5 scene acquired on 04/23/2009, scenes acquired on 08/10/2008, 11/14/2008, 03/06/2009, 03/22/2009, 04/07/2009, 05/09/2009, 05/25/2009 and 06/26/2009 were used for the visual review of the results. Using the above methods, 660 fields were identified as winter grain. In a second process, polygons representing 315 fields that had initially been mapped as fallow, sudan or tomatoes during the 2009 summer field work were selected from the original land use survey shapefile. These were combined with the polygons representing the previously selected training fields. The polygons were converted from vector to raster format. The resulting raster file was used to mask the April 23, 2009 Landsat 5 image using ERDAS Imagine to produce a subset image. This new image was associated with the signatures previously developed to classify winter grain fields, and a supervised classification of each pixel was performed using the maximum likelihood parametric rule. After recoding, zonal attributes were calculated for each polygon. Based on the plurality calculated for each field, fields were identified as either grain or fallow for the winter season. Polygons identified as grain fields were individually inspected in ArcMap to assure the reasonableness of the classification results. Using the above methods, 67 fields were identified as winter grain. Polygons representing all winter grain crops identified by classifying the Landsat 5 images were merged together. The original land use shapefile was updated by adding grain as a first crop to the selected polygons and moving the summer crop into the set of cells that represent a second crop. All field boundary changes were incorporated into the original shapefile. The area, perimeter and acreages were updated at the end of the process. 6. After quality control/assurance procedures were completed on each file, the data was processed into a final polygon shapefile. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land.Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate.Before final processing, standard quality control procedures were performed jointly by staff at DWR's South Central Region, and at DSIWM headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

Copyright Text: California Department of Water Resources, DIRWM, South Central Region Office, Water Conservation and Land and Water Use Section.

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