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Land-Use Data Quality Control
Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level. Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.
The 2004 Stanislaus County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use data was gathered and reviewed by DWR staff using extensive field visits, 2004 National Agriculture Imagery Program (NAIP) aerial photography and Landsat 5 imagery. NAIP imagery from 2004 was used for data review. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of: Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of central and eastern Stanislaus County. The northern, eastern and southern boundaries are defined by the Stanislaus County boundary. The western extent of the survey area extends to the western edges of the Solyo (U.S.G.S. No. 37121E3) and Howard Ranch (U.S.G.S. No. 37121B1) 7.5’ quadrangles and is also bounded by the western and southern borders of the Copper Mountain (U.S.G.S. No. 37121D3) and Orestimba Peak (U.S.G.S. No. 37121C2) quadrangles. Land use boundaries were developed by updating line work from DWR's 2004 land use survey of Stanislaus County. Boundaries were modified on a quadrangle by quadrangle basis. Roads were delineated using the U.S. Census Bureau's TIGER®(Topologically Integrated Geographic Encoding and Referencing) database as guidelines. Other land use boundaries were adjusted and new fields were added based upon 2009 NAIP imagery. Field boundaries were drawn to depict observable areas of the same crop or other land uses and are not intended to represent legal parcel (ownership) boundaries. In this survey, some areas of creeks and rivers were included within polygons of riparian areas and not delineated separately. The primary field data collection for this survey was conducted between July 2010 and February 2011 by DWR staff from the South Central Region Office who visited each field and noted what was grown at that time. Supplemental field visits took place from April 28 through June 14, 2010 and from July 12 through August 3, 2010 when randomly selected fields were visited by SIWM staff to collect data for mapping crops using Landsat imagery analysis. For field data collection, 2009 NAIP imagery and vector files of land use boundaries were loaded onto laptop computers that, in most cases, were used as the field data collection tools. Some surveyors also used Landsat 5 imagery for the field survey. GPS units connected to the laptops were used to confirm the surveyors’ locations with respect to the fields. Virtually all agricultural fields were visited to positively identify the land use. Land use codes were entered in the field on laptop computers using ESRI ArcMAP software, version 9.3. Some staff took printed aerial photos into the field and wrote directly onto these photo field sheets. Attribute data from photo field sheets were coded and entered back in the office. Any necessary field boundary changes were digitized at the same time. In addition to the identification of crops through the collection of data in the field, a supervised classification of Landsat 5 data was used to identify fields with winter crops. The Landsat images of a selection of fields mapped by surveyors as grain, spinach, lettuce or fallow were reviewed using a time series of Landsat 5 images to confirm that the pattern of vegetation over time was consistent with the expected pattern for these crops. The selected fields were then used to develop spectral signatures for the represented crop categories using ERDAS Imagine and eCognition Developer software. Two Landsat 5 images, March 16, 2010 and April 17, 2010, were selected for identifying winter crops using a maximum likelihood supervised classification. The classified images were used to calculate zonal attributes for fields mapped during the summer survey as field crops, truck crops or fallow. Fields mapped during the survey as winter truck crops or grains were also included. For the fields that were classified as winter crops, a time series of Landsat imagery was reviewed for consistency with the classification results. Fields for which the identified winter crops were confirmed by the review of time series data were added to the shapefile database using the special condition “U”, indicating that they were identified by a method other than having been mapped during the field survey. To identify fields with summer crops that were missed during the field survey, fields identified as fallow were reviewed using 2010 NAIP and Landsat 5 imagery. Where the imagery indicated that crops had been produced, the attributes of these fields were changed to identify them as cropped. They are also labeled with special condition "U". Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North 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.
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Description: This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisionaldata sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2004 Stanislaus County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use data was gathered and reviewed by DWR staff using extensive field visits, 2004 National Agriculture Imagery Program (NAIP) aerial photography and Landsat 5 imagery. NAIP imagery from 2004 was used for data review. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of: Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of central and eastern Stanislaus County. The northern, eastern and southern boundaries are defined by the Stanislaus County boundary. The western extent of the survey area extends to the western edges of the Solyo (U.S.G.S. No. 37121E3) and Howard Ranch (U.S.G.S. No. 37121B1) 7.5’ quadrangles and is also bounded by the western and southern borders of the Copper Mountain (U.S.G.S. No. 37121D3) and Orestimba Peak (U.S.G.S. No. 37121C2) quadrangles. Land use boundaries were developed by updating line work from DWR's 2004 land use survey of Stanislaus County. Boundaries were modified on a quadrangle by quadrangle basis. Roads were delineated using the U.S. Census Bureau's TIGER®(Topologically Integrated Geographic Encoding and Referencing) database as guidelines. Other land use boundaries were adjusted and new fields were added based upon 2009 NAIP imagery. Field boundaries were drawn to depict observable areas of the same crop or other land uses and are not intended to represent legal parcel (ownership) boundaries. In this survey, some areas of creeks and rivers were included within polygons of riparian areas and not delineated separately. The primary field data collection for this survey was conducted between July 2010 and February 2011 by DWR staff from the South Central Region Office who visited each field and noted what was grown at that time. Supplemental field visits took place from April 28 through June 14, 2010 and from July 12 through August 3, 2010 when randomly selected fields were visited by SIWM staff to collect data for mapping crops using Landsat imagery analysis. For field data collection, 2009 NAIP imagery and vector files of land use boundaries were loaded onto laptop computers that, in most cases, were used as the field data collection tools. Some surveyors also used Landsat 5 imagery for the field survey. GPS units connected to the laptops were used to confirm the surveyors’ locations with respect to the fields. Virtually all agricultural fields were visited to positively identify the land use. Land use codes were entered in the field on laptop computers using ESRI ArcMAP software, version 9.3. Some staff took printed aerial photos into the field and wrote directly onto these photo field sheets. Attribute data from photo field sheets were coded and entered back in the office. Any necessary field boundary changes were digitized at the same time. In addition to the identification of crops through the collection of data in the field, a supervised classification of Landsat 5 data was used to identify fields with winter crops. The Landsat images of a selection of fields mapped by surveyors as grain, spinach, lettuce or fallow were reviewed using a time series of Landsat 5 images to confirm that the pattern of vegetation over time was consistent with the expected pattern for these crops. The selected fields were then used to develop spectral signatures for the represented crop categories using ERDAS Imagine and eCognition Developer software. Two Landsat 5 images, March 16, 2010 and April 17, 2010, were selected for identifying winter crops using a maximum likelihood supervised classification. The classified images were used to calculate zonal attributes for fields mapped during the summer survey as field crops, truck crops or fallow. Fields mapped during the survey as winter truck crops or grains were also included. For the fields that were classified as winter crops, a time series of Landsat imagery was reviewed for consistency with the classification results. Fields for which the identified winter crops were confirmed by the review of time series data were added to the shapefile database using the special condition “U”, indicating that they were identified by a method other than having been mapped during the field survey. To identify fields with summer crops that were missed during the field survey, fields identified as fallow were reviewed using 2010 NAIP and Landsat 5 imagery. Where the imagery indicated that crops had been produced, the attributes of these fields were changed to identify them as cropped. They are also labeled with special condition "U". Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North 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: Department of Water Resources, Division of Integrated Regional Water Management, North Central Region Office, Land and Water Use and Conservation Section. Contact: gis@water.ca.gov.
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Document Info:
Title: i15_LandUse_Stanislaus2004
Author:
Comments: This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisionaldata sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2004 Stanislaus County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use data was gathered and reviewed by DWR staff using extensive field visits, 2004 National Agriculture Imagery Program (NAIP) aerial photography and Landsat 5 imagery. NAIP imagery from 2004 was used for data review. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of: Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of central and eastern Stanislaus County. The northern, eastern and southern boundaries are defined by the Stanislaus County boundary. The western extent of the survey area extends to the western edges of the Solyo (U.S.G.S. No. 37121E3) and Howard Ranch (U.S.G.S. No. 37121B1) 7.5’ quadrangles and is also bounded by the western and southern borders of the Copper Mountain (U.S.G.S. No. 37121D3) and Orestimba Peak (U.S.G.S. No. 37121C2) quadrangles. Land use boundaries were developed by updating line work from DWR's 2004 land use survey of Stanislaus County. Boundaries were modified on a quadrangle by quadrangle basis. Roads were delineated using the U.S. Census Bureau's TIGER®(Topologically Integrated Geographic Encoding and Referencing) database as guidelines. Other land use boundaries were adjusted and new fields were added based upon 2009 NAIP imagery. Field boundaries were drawn to depict observable areas of the same crop or other land uses and are not intended to represent legal parcel (ownership) boundaries. In this survey, some areas of creeks and rivers were included within polygons of riparian areas and not delineated separately. The primary field data collection for this survey was conducted between July 2010 and February 2011 by DWR staff from the South Central Region Office who visited each field and noted what was grown at that time. Supplemental field visits took place from April 28 through June 14, 2010 and from July 12 through August 3, 2010 when randomly selected fields were visited by SIWM staff to collect data for mapping crops using Landsat imagery analysis. For field data collection, 2009 NAIP imagery and vector files of land use boundaries were loaded onto laptop computers that, in most cases, were used as the field data collection tools. Some surveyors also used Landsat 5 imagery for the field survey. GPS units connected to the laptops were used to confirm the surveyors’ locations with respect to the fields. Virtually all agricultural fields were visited to positively identify the land use. Land use codes were entered in the field on laptop computers using ESRI ArcMAP software, version 9.3. Some staff took printed aerial photos into the field and wrote directly onto these photo field sheets. Attribute data from photo field sheets were coded and entered back in the office. Any necessary field boundary changes were digitized at the same time. In addition to the identification of crops through the collection of data in the field, a supervised classification of Landsat 5 data was used to identify fields with winter crops. The Landsat images of a selection of fields mapped by surveyors as grain, spinach, lettuce or fallow were reviewed using a time series of Landsat 5 images to confirm that the pattern of vegetation over time was consistent with the expected pattern for these crops. The selected fields were then used to develop spectral signatures for the represented crop categories using ERDAS Imagine and eCognition Developer software. Two Landsat 5 images, March 16, 2010 and April 17, 2010, were selected for identifying winter crops using a maximum likelihood supervised classification. The classified images were used to calculate zonal attributes for fields mapped during the summer survey as field crops, truck crops or fallow. Fields mapped during the survey as winter truck crops or grains were also included. For the fields that were classified as winter crops, a time series of Landsat imagery was reviewed for consistency with the classification results. Fields for which the identified winter crops were confirmed by the review of time series data were added to the shapefile database using the special condition “U”, indicating that they were identified by a method other than having been mapped during the field survey. To identify fields with summer crops that were missed during the field survey, fields identified as fallow were reviewed using 2010 NAIP and Landsat 5 imagery. Where the imagery indicated that crops had been produced, the attributes of these fields were changed to identify them as cropped. They are also labeled with special condition "U". Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North 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.
Subject: The 2004 Stanislaus County land use survey data was developed by the State of California, Department of Water Resources (DWR).
Category:
Keywords: Agriculture,Native vegetation,Land use,Aerial photography,Landsat,Irrigation,Survey,Vineyard,Orthoimagery,Crop,Stanislaus County,boundaries,Digital imagery,Residential,Satellite imagery,California,Raster,Land cover,Cadastral,Survey year 2004,Global Positioning System (GPS),Multispectral analysis,Imagery,farming,Urban,Vector,Image classification,NDVI,Ground truth,Orthorectification
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