Land Use - 2014 - Land IQ [ds2677]

SDE Feature Class

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Tags
irrigated land, fallow, boundaries, boundaries, agriculture, Central Valley, State of California, environment, 2014, California agriculture, 2014, crop, farming, land use


Summary

This dataset represents a statewide, comprehensive, field-scale assessment of agricultural land use, as well as urban and managed wetland boundaries for the 2014 year. This data is prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. This dataset is meant to provide information for resource planning and assessments across multiple agencies and serves as a consistent base layer for a broad array of potential users and multiple end 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 Standard version 2.1, dated May 11, 2016. This data set was not produced by DWR. Data were originally developed and supplied by LandIQ, LLC, under contract to California Department of Water Resources. 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. The official DWR GIS Steward for this data set is John Lance, who may be contacted at 530-528-7442, or at john.lance@water.ca.gov. Comments, problems, improvements, updates, or suggestions should be forwarded to the official GIS Steward as available and appropriate.

Description

This dataset presents the 2014 agricultural land use, managed wetlands, and urban boundaries for all 58 counties in California. This data is prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. Delineated from 2014 NAIP Imagery. The data are derived from a combination of remote sensing and agronomic analysis and ground verification.

Credits

Land IQ, www.LandIQ.com

Use limitations

Extent

West -124.469095 East -113.499687
North 42.069558 South 32.325102

Scale Range
Maximum (zoomed in) 1:5,000
Minimum (zoomed out) 1:150,000,000

ArcGIS Metadata

Topics and Keywords

Themes or categories of the resource environment, boundaries, farming


* Content type Downloadable Data
Export to FGDC CSDGM XML format as Resource Description No

Place keywords Central Valley, State of California

Temporal keywords 2014

Theme keywords irrigated land, fallow, boundaries, agriculture, 2014, California agriculture, crop, land use

Theme keywords boundaries, environment, farming

Thesaurus
Title ISO 19115 Topic Categories


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Citation

Title Land Use - 2014 - Land IQ [ds2677]
Publication date 2017-05-0800:00:00


Edition 2017.05.08


Presentation formats digital map
FGDC geospatial presentation format vector digital data


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Citation Contacts

Responsible party
Organization's name Land IQ, LLC
Contact's role originator


Responsible party
Individual's name Joel Kimmelshue
Organization's name Land IQ, LLC
Contact's position Owner
Contact's role point of contact


Contact information
Phone
Voice 916-265-6358

Address
Type both
Delivery point 2020 L St. Suite 110
City Sacramento
Administrative area CA
Postal code 95811
e-mail address jkimmelshue@landiq.com

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Resource Details

Dataset languages English(UNITED STATES)
Dataset character set utf8 - 8 bit UCS Transfer Format


Status completed
Spatial representation type vector


Graphic overview
File name N/A
File description N/A
File type N/A

Supplemental information
A full report is available as submitted from LandIQ. Contact John Lance for a file copy.
* Processing environment Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.5.1.7333


Credits
Land IQ, www.LandIQ.com
ArcGIS item properties
* Name Land Use - 2014 - Land IQ [ds2677]
* Location Server=; :; Database=; User=; Version=
* Access protocol ArcSDE Connection

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Extents

Extent
Description
Unknown
Temporal extent
Beginning date 2014-01-0100:00:00
Ending date 2014-12-3100:00:00

Vertical extent
* Minimum value 0.000000
* Maximum value 0.000000


Extent
Geographic extent
Bounding rectangle
West longitude -124.469095
East longitude -113.499687
South latitude 32.325102
North latitude 42.069558

Vertical extent
* Minimum value 0.000000
* Maximum value 0.000000


Extent
Geographic extent
Bounding rectangle
Extent type Extent used for searching
* West longitude -124.469095
* East longitude -113.499687
* North latitude 42.069558
* South latitude 32.325102
* Extent contains the resource Yes

Extent in the item's coordinate system
* West longitude -370900.969400
* East longitude 539923.240300
* South latitude -615444.682800
* North latitude 450139.582400
* Extent contains the resource Yes

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Resource Points of Contact

Point of contact
Individual's name Joel Kimmelshue
Organization's name Land IQ, LLC
Contact's position Owner
Contact's role point of contact


Contact information
Phone
Voice 916-265-6358

Address
Type both
Delivery point 2020 L St. Suite 110
City Sacramento
Administrative area CA
Postal code 95811
e-mail address jkimmelshue@landiq.com

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Resource Maintenance

Resource maintenance
Update frequency biannually


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Resource Constraints

Legal constraints
Limitations of use
None
Security constraints
Classification unclassified
Classification system Public domain


Additional restrictions
Available upon request


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Spatial Reference

ArcGIS coordinate system
* Type Projected
* Geographic coordinate reference GCS_WGS_1984
* Projection WGS_1984_Web_Mercator_Auxiliary_Sphere
* Coordinate reference details
Projected coordinate system
Well-known identifier 102100
X origin -20037700
Y origin -30241100
XY scale 10000
Z origin -100000
Z scale 10000
M origin -100000
M scale 10000
XY tolerance 0.001
Z tolerance 0.001
M tolerance 0.001
High precision true
Latest well-known identifier 3857
Well-known text PROJCS["WGS_1984_Web_Mercator_Auxiliary_Sphere",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Mercator_Auxiliary_Sphere"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",0.0],PARAMETER["Standard_Parallel_1",0.0],PARAMETER["Auxiliary_Sphere_Type",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",3857]]

Reference system identifier
* Value 3857
* Codespace EPSG
* Version 8.8(9.3.1.2)


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Spatial Data Properties

Vector
* Level of topology for this dataset geometry only


Geometric objects
Feature class name DS2677_20171011
* Object type composite
* Object count 0

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ArcGIS Feature Class Properties
Feature class name DS2677_20171011
* Feature type Simple
* Geometry type Polygon
* Has topology FALSE
* Feature count 0
* Spatial index TRUE
* Linear referencing TRUE

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Data Quality

Scope of quality information
Resource level dataset


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Data quality report - Conceptual consistency
Measure description
Data are considered logically consistent.


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Data quality report - Completeness omission
Measure description
Data are complete as of final delivery 5/11/2017.


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Data quality report - Quantitative attribute accuracy


Data quality report - Absolute external positional accuracy
Dimension horizontal


Measure description
Positional accuracy for this mapping dataset was determined to be +/- 2.0 meters horizontal accuracy at a 95% confidence level when registered against NAIP reference imagery. The NAIP reference image has a reported positional accuracy of 6.0 meters. Therefore, the combined horizontal accuracy is 8.0 meters. Positional accuracy was evaluated by review of all polygon boundaries in the subject dataset against the NAIP reference dataset. Positional offset was measured in a randomly selected subset of approximately 10% of all fields.


Quantitative test results
Value 8.0


Evaluation method
meters


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Lineage

Process step
When the process occurred 2017-05-1100:00:00
Description
Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. The mapping approach employs advanced spatial statistical analysis approaches to determine prediction probabilities and inform QA/QC efforts. A rigorous QA/QC and analysis refinement process is employed to improve predictions on all lower probability fields. Individual fields (boundaries of homogeneous crop types representing true irrigated area, rather than legal parcel boundaries) are used so that each independent field could be analyzed independently and assigned to a crop class. The result represents the true irrigated area and not legal or other less detailed boundaries that may be available elsewhere. The classification legend was developed in coordination with DWR with consideration of the known crop variation, existing DWR legends used in current models, and Land IQ mapping classes. Two legend levels were selected in order to retain the detail in Land IQs base mapping while providing a rolled-up legend consistent with DWRs classification that groups some crops into categories. The legends and crop classes can be related and cross-referenced and are summarized in Table 1. TABLE 1. 2014 STATEWIDE MAPPING LEGEND CROSS-REFERENCED Land IQ Class DWR Class Avocados Citrus/Subtropical Citrus Citrus/Subtropical Dates Citrus/Subtropical Kiwis Citrus/Subtropical Miscellaneous Subtropical Fruits Citrus/Subtropical Olives Citrus/Subtropical Almonds Deciduous Fruits and Nuts Apples Deciduous Fruits and Nuts Cherries Deciduous Fruits and Nuts Miscellaneous Deciduous Deciduous Fruits and Nuts Peaches/Nectarines Deciduous Fruits and Nuts Pears Deciduous Fruits and Nuts Pistachios Deciduous Fruits and Nuts Plums, Prunes and Apricots Deciduous Fruits and Nuts Pomegranates Deciduous Fruits and Nuts Walnuts Deciduous Fruits and Nuts Beans (Dry) Field Crops Corn, Sorghum and Sudan Field Crops Cotton Field Crops Miscellaneous Field Crops Field Crops Safflower Field Crops Sunflowers Field Crops Miscellaneous Grain and Hay Grain and Hay Wheat Grain and Hay Idle Idle Alfalfa and Alfalfa Mixtures Pasture Miscellaneous Grasses Pasture Mixed Pasture Pasture Rice Rice Wild Rice Rice Bush Berries Truck Crops Carrots Truck Crops Cole Crops Truck Crops Flowers, Nursery and Christmas Tree Farms Truck Crops Greenhouse Truck Crops Lettuce/Leafy Greens Truck Crops Melons, Squash and Cucumbers Truck Crops Miscellaneous Truck Crops Truck Crops Onions and Garlic Truck Crops Peppers Truck Crops Potatoes and Sweet Potatoes Truck Crops Strawberries Truck Crops Tomatoes Truck Crops Urban Urban Grapes Vineyards Young Perennials Young Perennial Managed Wetland Wetland


Process contact
Organization's name Land IQ, LLC
Contact's position Owner
Contact's role processor


Contact information
Phone
Voice 9162656330

Address
Type postal
Delivery point 2020 L Street, Suite 110
City Sacramento
Administrative area CA
Postal code 95811
e-mail address jkimmelshue@landiq.com

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Process step
When the process occurred 2017-05-1100:00:00
Description
2.1 DATA COLLECTION Both aerial and satellite data resources were used for the classification. Aerial imagery provided by the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) was collected throughout the summer of 2014 by the USDA and used for field delineation, classification and QA/QC of the final product. Multiple Landsat 8 images were used for the initial crop classification. Imagery from the Landsat 8 satellite is free and available every 16 days and provided for temporal analysis throughout the growing season. Ground truth data were collected during the 2014 growing season prior to the initiation of this project. These data were used for training and validation of the mapping analysis (Table 2). Field data from over 15% of all irrigated land in the Central Valley was collected. This represented 32,698 data points and 56 crop classes. This dataset was split to provide for data training and to maintain a separate, independent validation dataset. 25% of the ground truth data were set aside for independent validation. Ground truth data was not collected outside of the Central Valley for this year. However, ground truth data collected in 2014 by the Bureau of Reclamation from Imperial County was used for the analysis of that specific county. Analysis in areas that lacked ground truth data was performed using imagery and classification approaches established in areas that were informed by training data. TABLE 2. TOP 25 CENTRAL VALLEY GROUND TRUTH DATA POINTS DISTRIBUTED AMONG CROP TYPE Crop Name Ground Truth Count Percent of Total Almonds 5121 15.7% Corn 3637 11.1% Grapes 2622 8.0% Alfalfa 2548 7.8% Walnuts 2238 6.8% Rice 2188 6.7% Fallow 2046 6.3% Citrus 1545 4.7% Irrigated Pasture 1380 4.2% Cotton 1229 3.8% Native 892 2.7% Tomatoes 775 2.4% Rangeland 771 2.4% Pistachios 701 2.1% Plums 677 2.1% Peaches/Nectarines 588 1.8% Wheat 497 1.5% Olives 471 1.4% Fallow Prep 351 1.1% Forage Grass 242 0.7% Beans 239 0.7% Cherries 219 0.7% Pomegranates 138 0.4% Melons 129 0.4% Others 1454 4.5%


Process contact
Organization's name Land IQ, LLC
Contact's position Owner
Contact's role processor


Contact information
Phone
Voice 9162656330

Address
Type postal
Delivery point 2020 L Street, Suite 110
City Sacramento
Administrative area California
Postal code 95811
e-mail address jkimmelshue@landiq.com

HideContact information


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Process step
When the process occurred 2017-05-1100:00:00
Description
2.3 ACCURACY ASSESSMENT After completion of the final classification dataset, a comprehensive accuracy assessment is completed. Independent ground truthing samples set aside for this purpose (25% of the ground truth data) are used in this process. A stratified random sampling method is used for accuracy assessment sample selection. The datasets are stratified by land cover type and county boundary. In the 2014 analysis, more than 6300 samples were selected for accuracy assessment. These sites are not used to train the classification algorithm and therefore represent unbiased reference information.


Process contact
Organization's name Land IQ, LLC
Contact's position Owner
Contact's role processor


Contact information
Phone
Voice 9162656330

Address
Type postal
Delivery point 2020 L Street, Suite 110
City Sacramento
Administrative area California
Postal code 95811
e-mail address jkimmelshue@landiq.com

HideContact information


HideProcess step


Process step
When the process occurred 2017-05-1100:00:00
Description
2.2 ANALYSIS The Land IQ land use mapping unit is a field-scale layer focused on agricultural production areas greater than 2 acres across the state. More than 300,000 delineated fields are classified utilizing ground training examples and multiple image sources and dates. These images and ground truth data are used to develop classification algorithms for crop identification. Multiple selected image sources and timeframes serve as input data for the remote sensing classification process, along with comprehensive ground truth training samples. Ground truth data are reviewed and evaluated statistically to identify any samples considered unrepresentative (crops that are very stressed or sparse, for example). These data points are flagged and not used for training samples. The ground truthing data is then stratified based on Land IQs classification schema, with 75% of the data selected for model building and calibration, and the remaining 25% dedicated to independent accuracy assessment, These independent data are set aside and are not used during any stage of modeling process. A supervised classification algorithm was applied to classify delineated fields. The supervised classification used a random forest approach and is carried out county by county where training samples are available. Random Forest approaches are currently some of the highest performing for data classification and regression. They are advantageous because of their ability to classify large amounts of data with high accuracy. Random Forest approaches have other advantages over some more traditional classification methods like maximum likelihood algorithms and Classification and Regression Tree (CART). Random Forest algorithms are non-parametric and require no assumption of input data being normally distributed, and that they are flexible and can incorporate categorical and continuous input data and complex relationships within the dataset. Multiple geoprocessing tools were employed to assess the model dataset, including ArcGIS, ERDAS Imagine, and other open source statistical tools. These tools are used to generate spectral characteristics, textural characteristics, and temporal representations that are related to the specific attributes of each crop or land use. The input features are produced using satellite imagery from Landsat 8 OLI/TIRS sensors and NAIP collected during the growing season. Additional satellite imagery and ancillary inputs were used in some counties to supplement and improve the classification. These additional sources include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the USDA Crop Data Layer (CDL 2012-2014). Selected ground truth data and feature data are fed into the Random Forest algorithm for model building and calibration. A portion of these data are used for model calibration and the remainder is used for training Random Forest models. Multiple Random Forest models are assessed and compared to determine which is the highest performing for classification. The preferred Land IQ model is applied to all delineated fields to predict land cover type, as well as prediction confidence, which is used to inform QA/QC efforts. Classified fields with a lower confidence level are carefully reviewed by reviewing image resources using photo interpretation methods. Results are also cross-validated with ancillary data sources such as the coinciding USDA Crop Data Layer and county agricultural surveys and county crop reports, to assess and evaluate significant differences. Differences do not always indicate incorrect classification but are used both to evaluate the classification result and explain deviation from other data sources if any exists. The geospatial database is attributed with field size in acres, relevant county, and the appropriate crop classification category per the Land IQ and DWR legends (Table 1). Table 3 summarizes the database attributes (columns) associated with the final mapping product and their definitions. TABLE 3. DEFINITION OF DATABASE FIELDS Field Data Type Description Object ID Object ID Auto generated by ArcMAP Crop2014 String Crop classification type based on 2014 DWR/Land IQ standard Land Use Legend dated August 2016. County String Indicated the county the centroid of each field resides in. Acres Double Area of the agricultural field. Comments String Any user-provided information. Source String Original source of the boundary and attribute information Last_Modified_Date Date Date record was last modified. Modified_By String Name of person who last modified the record. Date_Data_Refers_To String Date the data refers to.


Process contact
Organization's name Land IQ, LLC
Contact's position Owner
Contact's role processor


Contact information
Phone
Voice 9162656330

Address
Type postal
Delivery point 2020 L Street, Suite 110
City Sacramento
Administrative area California
Postal code 95811
e-mail address jkimmelshue@landiq.com

HideContact information


HideProcess step


Source data
Description
contract, dataset steward


Source citation
Title LandIQ California 2014
Alternate titles DWR
Publication date 2017-05-11


Edition 2014


Responsible party
Organization's name John Lance
Contact's role originator


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Extent of the source data
Description
publication date
Temporal extent
Date and time 2017-05-11

HideSource data


Source data
Description
producer


Source medium name online link
Source citation
Title 2014 LandIQ California
Alternate titles LandIQ


Edition 2014


Responsible party
Organization's name Joel Kimmelshue, Land IQ, LLC, Owner
Contact's role originator


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Extent of the source data
Description
publication date
Temporal extent
Date and time 2017-05-11

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Geoprocessing history

Process
Date 2017-10-1111:18:57
Tool location c:\program files (x86)\arcgis\desktop10.5\ArcToolbox\Toolboxes\Data Management Tools.tbx\Project
Command issued
Include in lineage when exporting metadata No


Process
Date 2017-10-1111:32:06
Tool location c:\program files (x86)\arcgis\desktop10.5\ArcToolbox\Toolboxes\Conversion Tools.tbx\FeatureClassToFeatureClass
Command issued
Include in lineage when exporting metadata No


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Distribution

Distributor
Contact information
Individual's name John Lance
Organization's name California Department of Water Resources
Contact's position Data Steward
Contact's role distributor


Contact information
Phone
Voice 530-528-7442

Address
Type postal
Delivery point 2440 Main Street
City Red Bluff
Administrative area CA
Postal code 96080
e-mail address john.lance@water.ca.gov

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Distribution format
* Name SDE Feature Class


Transfer options
Online source
Location https://www.wildlife.ca.gov/Data/BIOS
Function performed information

Online source
Location ftp://ftp.wildlife.ca.gov/BDB/GIS/BIOS/Public_Datasets/2600_2699/ds2677.zip
Function performed download

Online source
Location https://gis.water.ca.gov/app/CADWRLandUseViewer/
Function performed download

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Fields

Details for object DS2677_20171011
* Type Feature Class
* Row count 0


Field Source
* Alias Source
* Data type String
* Width 50
* Precision 0
* Scale 0
Field description
Original source of the boundary and attribute information
Description source
DWR
List of values
Value LandIQ, LLC
Description Name of source
Enumerated domain value definition source LandIQ, LLC


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency None planned


HideFieldSource


Field County
* Alias County
* Data type String
* Width 50
* Precision 0
* Scale 0
Field description
Indicates the county that the centroid of each crop field resides in. Due to the size of many managed wetland and urban areas we did not attribute the county/counties for these features because some extended beyond a single county.
Description source
Land IQ
Description of values
California Counties


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency As needed


HideFieldCounty


Field Shape
* Alias Shape
* Data type Geometry
* Width 4
* Precision 0
* Scale 0
Field description
Feature geometry.
Description source
Esri
Description of values
Coordinates defining the features.


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency N/A


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Field GlobalID
* Alias GlobalID
* Data type GlobalID
* Width 38
* Precision 0
* Scale 0
Field description
ESRI-defined
Description source
ESRI
Description of values
ESRI-assigned


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency None planned


HideFieldGlobalID


Field DWR_Standard_Legend
* Alias DWR_Standard_Legend
* Data type String
* Width 50
* Precision 0
* Scale 0
Field description
Used for symbolizing the data with DWR's standard legend.
Description source
DWR
Description of values
Contains DWR's land use code from previous survey years and the full crop type name


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency Unknown


HideFieldDWR_Standard_Legend


Field Date_Data_Refers_To
* Alias Date_Data_Refers_To
* Data type String
* Width 25
* Precision 0
* Scale 0
Field description
Date the data refers to
Description source
DWR
List of values
Value July, 2014
Description date of analysis
Enumerated domain value definition source LandIQ


Beginning date of values 2014-07-01
Ending date of values 2014-07-01


Measurement frequency None planned


HideFieldDate_Data_Refers_To


Field Comments
* Alias Comments
* Data type String
* Width 125
* Precision 0
* Scale 0
Field description
Any user-provided comments
Description source
DWR
Description of values
comments


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency None planned


HideFieldComments


Field Acres
* Alias Acres
* Data type Double
* Width 8
* Precision 38
* Scale 8
Field description
Area of the agricultural field, urban area, or managed wetland
Description source
Land IQ
Description of values
acreages


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency None planned


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Field OBJECTID
* Alias OBJECTID
* Data type OID
* Width 4
* Precision 10
* Scale 0
Field description
Internal feature number.
Description source
Esri
Description of values
Sequential unique whole numbers that are automatically generated.


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency As needed


HideFieldOBJECTID


Field Last_Modified_Date
* Alias Last_Modified_Date
* Data type Date
* Width 8
* Precision 0
* Scale 0
Field description
Date record was last modified
Description source
DWR
List of values
Value 5/7/2017
Description date of last pre-delivery modification
Enumerated domain value definition source LandIQ


Beginning date of values 2017-05-07
Ending date of values 2017-05-07


Measurement frequency None planned


HideFieldLast_Modified_Date


Field Crop2014
* Alias Crop2014
* Data type String
* Width 50
* Precision 0
* Scale 0
Field description
Crop classification type for the year 2014
Description source
Land IQ
List of values
Value explicit crop types
Description each of the crop types are explicitly named
Enumerated domain value definition source LandIQ


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency None planned


HideFieldCrop2014


Field Modified_By
* Alias Modified_By
* Data type String
* Width 25
* Precision 0
* Scale 0
Field description
Name of person who last modified the record
Description source
DWR
List of values
Value Zhongwu Wang
Description Name of last modifier
Enumerated domain value definition source LandIQ


Beginning date of values 2014-01-01
Ending date of values 2014-01-01


Measurement frequency None planned


HideFieldModified_By


Field Shape.STArea()
* Alias Shape.STArea()
* Data type Double
* Width 0
* Precision 0
* Scale 0


HideFieldShape.STArea()


Field Shape.STLength()
* Alias Shape.STLength()
* Data type Double
* Width 0
* Precision 0
* Scale 0


HideFieldShape.STLength()


HideDetails for objectDS2677_20171011


Overview Description
Entity and Attribute Overview
N/A


Entity and Attribute Detail Citation
DWR


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Metadata Details

Metadata language English(UNITED STATES)
Metadata character set utf8 - 8 bit UCS Transfer Format


Scope of the data described by the metadata dataset
Scope name * dataset


* Last update 2017-10-11


ArcGIS metadata properties
Metadata format ArcGIS1.0
Metadata style FGDC CSDGM Metadata
Standard or profile used to edit metadata FGDC


Created in ArcGIS for the item 2017-10-1111:17:23
Last modified in ArcGIS for the item 2017-10-1111:19:09


Automatic updates
Have been performed Yes
Last update 2017-10-1111:19:09


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Metadata Contacts

Metadata contact
Individual's name John Lance
Organization's name California Department of Water Resources
Contact's position Data Steward
Contact's role point of contact


Contact information
Phone
Voice 530-528-7442

Address
Type both
Delivery point 2440 Main Street
City Red Bluff
Administrative area CA
Postal code 96080
e-mail address john.lance@water.ca.gov

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Metadata Maintenance

Maintenance
Update frequency unknown


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