20240813
08492800
1.0
FALSE
IrrigatedLands_2019
002
file://\\dwrboifsvp03\GISData\Spatial\LandCover_Vegetation\Camas\IrrigatedLands.gdb
Local Area Network
2382929.602800
2470374.066600
1323655.888000
1381549.319000
1
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>
20240814
10153100
20240814
10155600
150000000
5000
FGDC
FGDC CSDGM Metadata
Version 6.2 (Build 9200) ; Esri ArcGIS 10.9.1.28388
2019 Irrigated Lands for the Camas Prairie: Hand-Digitized Generated
2024-08-12T00:00:00
2024-08-12T00:00:00
First Edition
20240812
Irrigated Lands for the Camas Prairie
2019
external
b522197d5b5a574d2d7819b7ba7121e1
Geospatial Technology Section
Idaho Department of Water Resources
GIS
322 East Front Street
Boise
Idaho
83720
US
gisinfo@idwr.idaho.gov
(208) 287-4800
(208) 287-6700
8:00 AM - 5:00 PM
True
Geospatial Technology Section
1
-115.451103
-114.364139
43.634330
43.104573
Irrigation status for the 2019 growing season.
2019-04-01T00:00:00
2019-10-31T00:00:00
IDWR is partnering with the U.S. Geological Survey (USGS) to complete a four-year water resources investigation of the Camas Prairie. The study began in March 2023 and will end in 2027.
The objective of this project is to provide an updated characterization of groundwater and surface water resources in the Camas Prairie and Magic Reservoir. Data and analysis obtained from the research activities will provide IDWR with information to help address current water resource challenges, including assessing whether groundwater pumping in the Camas Prairie affects the surface water availability in Magic Reservoir. The study also will provide baseline information for a potential future groundwater flow model.
<div style='text-align:Left;'><div><div><p><span>This layer depicts polygons representing land within the Camas Prarie in Southern Idaho classified as either "irrigated", "non-irrigated" or "semi-irrigated", where the semi-irrigated classification typically depicts residential land. The creation of this dataset will be a multi-year effort. </span></p><p><span>Field boundaries are hand-digitized using National Agriculture Imagery Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, or other high resolution imagery for determining field boundaries. </span></p><p><span>Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Sentinel2, Landsat7, and Landsat8 satellite imagery for background reference. The Sentinel2 constellation is made up of two satellites with a 5-day revisit schedule. IDWR processes Sentinel2 imagery into 3-band, false color, 10-meter resolution scenes for the growing season. The Landsat constellation used for this project is made up of Landsat7 and Landsat8 with a 8-day revisit rate. IDWR processes Landsat imagery into 3-band, false-color, 30-meter resolution scenes for the growing season. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery may be used for determining irrigation status and refining the polygon geometry.</span></p><p><span>If you are interested in the evolution of IDWR's various “Irrigated Lands” projects, please contact IDWR’s GISInfo to obtain the Editing Methodology and Guidelines documentation where the interpretation and classification process is described in detail.</span></p></div></div></div>
Idaho Department of Water Resources (IDWR)
Camas Prairie
Groundwater
Surface Water
Hydrology
IDWR GIS Department
Idaho
ID
Irrigation
Monitoring
Regulatory
Irrigated
Non-Irrigated
Semi-Irrigated
Water
Water Use
<div style='text-align:Left;'><div><div><p><span>Access and Use Constraint: 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.</span></p><p><span>Please check sources, scale, accuracy, currentness and other available information. Please confirm that you are using the most recent copy of both data and metadata. Acknowledgement of IDWR in any products derived from this dataset is appreciated.</span></p><p><span>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.</span></p><p><span>Field boundaries depicted within this dataset are not to be used for legal purposes.</span></p></div></div></div>
IDWR GIS Department
Groundwater
Surface Water
Hydrology
Irrigation
Monitoring
Regulatory
Irrigated
Non-Irrigated
Semi-Irrigated
Water
Water Use
Camas Prairie
Idaho
ID
10000
Geospatial Technology Section
Idaho Department of Water Resources
GIS
This layer depicts polygons representing land within the Camas Prarie in Southern Idaho classified as either "irrigated", "non-irrigated" or "semi-irrigated", where the semi-irrigated classification typically depicts residential land. The creation of this dataset will be a multi-year effort.
Field boundaries are hand-digitized using National Agriculture Imagery Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, or other high resolution imagery for determining field boundaries.
Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Sentinel2, Landsat7, and Landsat8 satellite imagery for background reference. The Sentinel2 constellation is made up of two satellites with a 5-day revisit schedule. IDWR processes Sentinel2 imagery into 3-band, false color, 10-meter resolution scenes for the growing season. The Landsat constellation used for this project is made up of Landsat7 and Landsat8 with a 8-day revisit rate. IDWR processes Landsat imagery into 3-band, false-color, 30-meter resolution scenes for the growing season. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery may be used for determining irrigation status and refining the polygon geometry.
File Geodatabase Feature Class
ArcGIS Desktop 10.8.2
Geospatial Technology Section
Idaho Department of Water Resources
GIS
False
Geospatial Technology Section
322 East Front Street
Boise
Idaho
83720
gisinfo@idwr.idaho.gov
US
(208) 287-4800
(208) 287-6700
8:00 AM - 5:00 PM
dataset
EPSG
9.5.1(10.8.0)
7150
Simple
FALSE
7150
TRUE
FALSE
Irrigated Lands Attribute Information
Feature Class
7150
3-class classification
IDWR
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.
STATUS_2019
STATUS_2019
String
50
0
0
Irrigation status in 2019.
Possible values are: “irrigated”, “non-irrigated”, or “semi-irrigated”.
IDWR
20190401
20191031
irrigated
Using remote sensing data or aerial photography, area shows evidence of being irrigated.
IDWR
non-irrigated
Using remote sensing data or aerial photography, area shows no evidence of being irrigated.
IDWR
semi-irrigated
A mix of irrigated (lawns, pastures, etc) and non-irrigated land (impervious surfaces, structures, etc), typical of residential areas.
IDWR
ACRES
ACRES
Double
8
0
0
Calculated acres of polygon. These values are user calculated and not automatically calculated as polygon shapes are edited.
IDWR
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.
20240812
external
b522197d5b5a574d2d7819b7ba7121e1
Geospatial Technology Section
Idaho Department of Water Resources
GIS
322 East Front Street
Boise
Idaho
83720
US
gisinfo@idwr.idaho.gov
(208) 287-4800
(208) 287-6700
8:00 AM - 5:00 PM
True
Geospatial Technology Section
external
b522197d5b5a574d2d7819b7ba7121e1
Geospatial Technology Section
Idaho Department of Water Resources
GIS
322 East Front Street
Boise
Idaho
83720
US
gisinfo@idwr.idaho.gov
(208) 287-4800
(208) 287-6700
8:00 AM - 5:00 PM
True
Geospatial Technology Section
Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Landsat and Sentinel2 satellite imagery as background reference. Landsat imagery is typically 30-meter and Sentinel2 imagery is 10-meter 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.
Visual inspection against imagery and IDWR's water rights database. A topology check was performed to repair overlapping polygons and data gaps of consequential size. Overlapping polygons and data gaps of consequential size were minimized to the best of the analyst’s ability under existing time constraints.
The determination of Irrigation Status relied extensively on a field's MEAN METRIC value as well as the presence or absence of a water right.
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.
Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Landsat and Sentinel2 satellite imagery as background reference. Landsat imagery is typically 30-meter resolution; Sentinel2 imagery is 10-meter 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.
Concentrated animal feeding operations are classified as semi-irrigated in Irrigated Lands Data layers.
Visual inspection against imagery and IDWR's water rights database.
While data accuracy and completeness is highest inside the Camas Prairie Study Boundary, the layer does extend beyond that boundary.
Visual inspection against imagery.
Positional Accuracy requirements are determined by its use in future modelling efforts which accepts "less-than-perfectly accurate" horizontal accuracy. However, given any time constraints, efforts are made to refine positional accuracy based on NAIP and DOQQ imagery.
Visual inspection against imagery and IDWR's water rights database.
Irrigation status is determined using 2019 Landsat7 and Landsat8 imagery for the following dates:
Path40Row30:
05-06, 05-14, 05-30,
06-15, 06-23,
07-01, 07-09, 07-17, 07-25,
08-02, 08-10, 08-18, 08-26,
09-03, 09-11, 09-27,
10-13, and
Path41Row30:
05-07, 05-31,
06-08, 06-16, 06-24,
07-02, 07-18,
08-03, 08-11, 08-19, 08-27,
09-04, 09-12,
10-06, 10-14 and
AND
Irrigation status is determined using 2019 Sentinel2 imagery for the following tiles and dates:
T11TPJ / T11TPH / T11TQJ:
05-04, 05-24,
06-08, 06-23,
07-13, 07-18, 07-28,
08-07, 08-17, 08-22, 08-27
09-01, 09-26,
10-06
"Irrigated Lands" data is created from the best available data for each year.
"Irrigated Lands" data as provided by IDWR does not represent an on-the-ground survey nor is it based on a thorough water right review.
Irrigation status in the "Irrigated Lands" data is not determined using spectral signatures but is based on a GIS analyst's manual interpretation of remotely sensed imagery.
Field boundaries depicted within this dataset are not to be used for legal purposes.
The constantly expanding scope, completeness, and complexity of GIS technologies, including image processing and interpretation, allows IDWR to constantly improve upon the methodology used to create "Irrigated Lands" data. If you are interested in the evolution of the “Irrigated Lands” project, please contact IDWR’s GISInfo to obtain the Editing Methodology and Guidelines documentation.
Field boundaries are hand-digitized using National Agriculture Imagery Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, or other high resolution imagery for determining field boundaries.
This layer depicts polygons representing land within the Camas Prarie in Southern Idaho classified as either "irrigated", "non-irrigated" or "semi-irrigated", where the semi-irrigated classification typically depicts residential land. The creation of this dataset will be a multi-year effort.
Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Sentinel2, Landsat7, and Landsat8 satellite imagery for background reference. The Sentinel2 constellation is made up of two satellites with a 5-day revisit schedule. IDWR processes Sentinel2 imagery into 3-band, false color, 10-meter resolution scenes for the growing season. The Landsat constellation used for this project is made up of Landsat7 and Landsat8 with a 8-day revisit rate. IDWR processes Landsat imagery into 3-band, false-color, 30-meter resolution scenes for the growing season. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery may be used for determining irrigation status and refining the polygon geometry.
2024-08-12T00:00:00
Check Geometry Tool
Repair Geometry Tool
Check Topology: Correct overlapping polygon topology errors
*note: slightly overlapping polygons and gaps may still exist, typically along pivot edges and/or roads
1) No Overlaps
2) No Gaps (*of significant size)
"Irrigated Lands" data was created from the best available data for each year.
"Irrigated Lands" data as provided by IDWR does not represent an on-the-ground survey nor is it based on a thorough water right review.
Irrigation status in the "Irrigated Lands" data is not determined using spectral signatures but is based on a GIS analyst's manual interpretation of remotely sensed imagery.
Field boundaries depicted within this dataset are not to be used for legal purposes.
The constantly expanding scope, completeness, and complexity of GIS technologies, including image processing and interpretation, allows IDWR to constantly improve upon the methodology used to create "Irrigated Lands" data. If you are interested in the evolution of the “Irrigated Lands” project, please contact IDWR’s GISInfo to obtain the Editing Methodology and Guidelines documentation.
2024-08-12T00:00:00
Because this geographical area's land classification was conducted without the benefit of real-time ground truthing, a review of the land classifications are conducted as new information becomes available. Comparisons between an average precipitation year vs a dry precipitation year vs a wet precipitation year may be warranted.
The following three factors play a role in the accuracy of the manual image interpretation and land classification process and consequently the necessity for ongoing review as new details and lessons are learned:
1. three years of irrigation status were analyzed for this project - an average precipitation year (2016), a dry year (2021), and a wet year (2019)
2. the area of study is a low-lying geographical area with a high-water table
3. no real-time ground truthing was conducted during the land classification process.
2024-08-12T00:00:00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