20190214
12061000
1.0
FGDC CSDGM Metadata
FALSE
20241115
13272400
150000000
5000
FGDC
IrrigatedLands_2011
002
0.000
2391980.200600
2740864.524400
1199411.123400
1519765.119100
1
file://\\dwrboifsvp03\GISData\Spatial\LandCover_Vegetation\SnakePlain\ESPA_IrrigatedLands\IrrigatedLands.gdb
Local Area Network
Projected
GCS_North_American_1983
Linear Unit: Meter (1.000000)
NAD_1983_Idaho_TM
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.8'><WKT>PROJCS["NAD_1983_Idaho_TM",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",2500000.0],PARAMETER["False_Northing",1200000.0],PARAMETER["Central_Meridian",-114.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",42.0],UNIT["Meter",1.0],AUTHORITY["EPSG",8826]]</WKT><XOrigin>-3120900</XOrigin><YOrigin>-13447900</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102605</WKID><LatestWKID>8826</LatestWKID></ProjectedCoordinateSystem>
RepairGeometry \\dwrboifs01\GISData\Spatial\LandCover_Vegetation\SnakePlain\ESPA_IrrigatedLands\IrrigatedLands.gdb\EasternSnakePlainAquifer\IrrigatedLands_2011 DELETE_NULL
20241115
13272400
Geospatial Technology Section
Idaho Department of Water Resources
GIS
Geospatial Technology Section
322 East Front Street
Boise
Idaho
83720
gisinfo@idwr.idaho.gov
(208) 287-4800
(208) 287-6700
8:00 AM - 5:00 PM
Environment
Groundwater
Human
Hydrology
IDWR GIS Department
Irrigated Areas
Irrigation
Monitoring
Non-Irrigated
Regulatory
Semi-Irrigated
Surface Water
Water
Water Use
>
Eastern Snake Plain Aquifer, ESPA
Idaho, ID
>
Eastern Snake Plain Aquifer
ESPA
Environment
Groundwater
Human
Hydrology
IDWR GIS Department
Idaho
ID
Irrigated Areas
Irrigation
Monitoring
Non-Irrigated
Regulatory
Semi-Irrigated
Surface Water
Water
Water Use
Geospatial Technology Section
Idaho Department of Water Resources
GIS
Geospatial Technology Section
Idaho Department of Water Resources (IDWR)
<div style='text-align:Left;'><p>The Idaho Department of Water Resources is making this data available as a public service. The Idaho Department of Water Resources strives to ensure that all technical data and other information made available to the public is accurate, complete and in conformance with the Idaho Public Records Act.</p><p>Neither the Department of Water Resources nor the State of Idaho, however, assumes any legal responsibility for the accuracy or completeness of the information contained on this site.</p><p>Persons using information from this site for official purposes, or other purposes, for which accuracy and completeness are required, are hereby notified that they should first verify the information with the public records or other primary sources from which the information was obtained.</p></div>
Distribution Liability: The Idaho Department of Water Resources is making this data available as a public service. The Idaho Department of Water Resources strives to ensure that all technical data and other information made available to the public is accurate, complete and in conformance with the Idaho Public Records Act. Neither the Department of Water Resources nor the State of Idaho, however, assumes any legal responsibility for the accuracy or completeness of the information contained on this site. Persons using information from this site for official purposes, or other purposes, for which accuracy and completeness are required, are hereby notified that they should first verify the information with the public records or other primary sources from which the information was obtained. Acknowledgment of IDWR would be appreciated in any work derived from this dataset.
2011 Irrigated Lands for the Eastern Snake Plain Aquifer: Hand-Digitized Generated
Geospatial Technology Section
Idaho Department of Water Resources
GIS
Geospatial Technology Section
322 East Front Street
Boise
Idaho
83720
gisinfo@idwr.idaho.gov
(208) 287-4800
(208) 287-6700
8:00 AM - 5:00 PM
Geospatial Technology Section
2016-05-27T00:00:00
2019-02-14T00:00:00
2019-02-14T00:00:00
First Edition
First Series
Version 6.2 (Build 9200) ; Esri ArcGIS 10.9.1.28388
This dataset was generated for use in the calculation of recharge and evapotranspiration in order to calibrate the Eastern Snake Plain Aquifer Hydrologic Model.
<div style='text-align:Left;'><div><div><p><span>This layer depicts polygons representing land within the ESPA boundary classified as either "irrigated", "non-irrigated" or "semi-irrigated", where the semi-irrigated classification typically depicts residential land. </span></p><p><span>The creation of this dataset is a multi-year effort. The original line work that was used as a basis to create this dataset, is the Farm Service Agency’s (FSA) 2006 Common Land Unit (CLU) dataset, which is the most recent year the FSA distributed this data to the public. Project time constraints during the development of this dataset required acceptance of "less-than-perfectly-accurate" line work. In essence, line work deemed "close enough" to ground truth is acceptable for Eastern Snake Plain Aquifer (ESPA) modeling purposes. Field boundaries were then further refined using National Agriculture Imagery Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, or other high resolution imagery. </span></p><p><span>Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Landsat imagery as background reference. Landsat imagery is typically 30-meter (Landsat5) or 15-meter (Landsat7) resolution. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery is used for determining irrigation status and refining the polygon geometry. The interpretation and classification process is described in detail in the report, "2006 Irrigated Land Classification for the Eastern Snake Plain Aquifer" archived on the IDWR website: Legal Actions > Delivery Call Actions > SWC > Archived Matters > Technical Working Group Documents (https://idwr.idaho.gov/legal-actions/delivery-call-actions/SWC/archived-matters.html#twg-documents)</span></p><p><span>While data accuracy is highest inside the Eastern Snake Plain Aquifer Model boundary (ESPAM version 2), the layer does extend beyond the ESPAM2 boundary.</span></p></div></div></div>
10000
Irrigation status for the 2011 growing season.
-115.311656
-110.952490
41.957893
44.879291
2011-05-01T00:00:00
2011-11-30T00:00:00
1
-115.367480
-110.952490
44.879291
41.957893
Geospatial Technology Section
Idaho Department of Water Resources
GIS
Geospatial Technology Section
322 East Front Street
Boise
Idaho
83720
gisinfo@idwr.idaho.gov
(208) 287-4800
(208) 287-6700
8:00 AM - 5:00 PM
2011-01-01T00:00:00
2011-12-31T00:00:00
https://data-idwr.opendata.arcgis.com/pages/gis-data#IrrigatedLands
0.000
File Geodatabase Feature Class
dataset
EPSG
9.5.1(10.8.0)
263515
Simple
FALSE
263515
TRUE
FALSE
Irrigated Lands Attribute Information
Feature Class
263515
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
ACRES
ACRES
Number of acres contained in the the polygon
IDWR
Double
8
0
0
STATUS_2011
STATUS 2011
Irrigation Status in 2011. Values are either "irrigated", "non-irrigated" or "semi-irrigated"
IDWR
irrigated
Area shows evidence of being irrigated
IDWR
non-irrigated
Area does not show evidence of being irrigated
IDWR
semi-irrigated
Residential area
IDWR
String
25
0
0
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
20241115
Irrigation status is determined using 2011 Landsat imagery for the following dates:
Path38Row30: 06-04, 07-06, 07-22, 08-07, 08-23, 09-08, 09-24 and
Path39Row29: 06-27, 07-13, 07-29, 08-14, 08-30, 09-15 and
Path39Row30: 06-11, 06-27, 07-13, 07-29, 08-14, 08-30, 09-15 and
Path40Row29: 07-04, 07-20, 07-28, 08-05, 08-21, 09-06, 09-22 and
Path40Row30: 05-01, 06-10, 06-26, 07-04, 07-20, 08-05, 08-21, 09-06, 09-22 and
Path40Row31: 06-02, 07-04, 07-20, 08-05, 08-21, 09-06, 09-22 and
Path41Row29: 06-25, 07-11, 07-27, 08-12, 08-28, 09-13, 09-29 and
Path41Row30: 06-09, 06-25, 07-11, 07-27, 08-12, 08-28, 09-13, 09-29
The creation of this dataset is a multi-year effort. Original linework is from the Farm Service Agency's (FSA) Common Land Unit (CLU) dataset of or around 2006. FSA no longer distributes this data to the public. Project time constraints require acceptance of "less-than-perfectly-accurate" linework. In essence, line work deemed "close enough" to ground truth is acceptable for Eastern Snake Plain Aquifer (ESPA) modeling purposes. Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Landsat imagery as background reference. Landsat imagery is typically 30-meter (l5) resolution. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery is used for determining irrigation status and refining linework geometry. This dataset extends beyond the Eastern Snake Plain Aquifer Model boundary (ESPAM2). Line and attribute correctness is more accurate within ESPAM2 boundary.
2016-05-30T00:00:00
Correct overlapping polygon topology errors *slightly overlapping polygons still exist, typically along pivot edges and/or roads
Check Geometry Tool
Repair Geometry Tool
Delete slivers < 1 m2
Eliminate slivers < 30 m2
2018-08-13T00:00:00
Positional Accuracy requirements are determined by its use in the ESPA modelling process which accepts "less-than-perfectly accurate" horizontal accuracy. However, given any time time constraints, efforts are made to refine positional accuracy based on NAIP and DOQQ imagery.
Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Landsat imagery as background reference. Landsat imagery is typically 30-meter (l5) or 15-meter (l7) resolution. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery is used for determining irrigation status and refining linework geometry. Hence, attribute accuracy is determined by the scale of those sources.
The data layer consists of polygon features attributed by GIS analysts based on visual interpretation of remote sensing and aerial imagery as well as supplemental information provided in the water rights database.
Concentrated animal feeding operations are identified using Landsat and NAIP imagery in the 2011 and later Irrigated Lands Data layers.
A topology check was performed to repair overlapping polygons and data gaps of consequential size.
The data layer consists of polygon features attributed by GIS analysts based on visual interpretation of remote sensing and aerial imagery as well as supplemental information provided in the water rights database.
Concentrated animal feeding operations are classified as semi-irrigated in the 2011 and later Irrigated Lands Data layers.
Overlapping polygons and data gaps of consequential size were minimized to the best of the analysts ability under existing time constraints.
While data accuracy and completeness is highest inside the Eastern Snake Plain Aquifer Model boundary (ESPAM version 2), the layer does extend beyond the ESPAM2 boundary and may or may not extend to the county line.
While data accuracy and completeness is highest inside the Eastern Snake Plain Aquifer Model boundary (ESPAM version 2), the layer does extend beyond the ESPAM2 boundary and may or may not extend to the county line.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==