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This dataset represents the 2016 main season agricultural land use, wetlands, and urban boundaries for all 58 counties in California. This data was originally 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. The Land IQ base data was reviewed, and in some cases revised, by DWR Regional Office Land Use staff using additional analyses based on a combination of aerial photography, remote sensing multi-spectral imagery, agronomic analysis and ground verification. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accomodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor (see 'Contacts').
Revisions were made if:
- DWR corrected the original crop classification based on local knowledge and analysis,
- young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes),
- DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year,
- the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column),
- DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated),
- DWR determined that the crop was not irrigated.- DWR identified a distinct early crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop).
Land use boundaries were delineated by Land IQ from 2016 NAIP Imagery and were not revised by DWR.
Addendum:
Water Source Mapping.
Compiled DWR water source surveys into 2016 LandIQ analysis. Created 2020.
Accumulation of surveyed Land & Water Use fields with the utilization of multiple water source years data.
In past surveys, the land’s potential for idle fields water source was shown. Meaning, the field could be placed back in production after the field was visited. Some idle fields are non-irrigated while others are showing a water source. A water source was applied to Young Perennials because during the field visit the code may have been “idle”. The field was in transition or flux condition but currently is an irrigated young perennial crop.
Detailed Analysis Unit-DAUConvergence via County Boundary cnty24k09, (*See metadata for cnty24k09, Cal-Atlas), State of California.
The existing DAU boundaries were clipped via Cal-Atlas cnty24k09 by Michael L. Serna, NRO via ArcMap 10.1. DAU’s were sent out to: Northern Region Office (NRO), North Central Region Office (NCRO), South Central Region Office (SCRO) and Southern Region Office (SRO) respectively.
Collaboration was by Department of Water Resources, Region Office personnel, Michael L. Serna, NRO, Jason Harbaugh - NCRO, Cynthia Moffett - SCRO and Robert Fastenau - SRO with the final merge of all data into a cohesive workable dataset completed by Michael L. Serna - NRO, Land and Water Use Section.
Existing DAU feature class boundaries contained a multitude of variant sliver acreages along all DAU and county boundary lines through-out the State of California.
Each region office was sent their respective ‘clipped’ features where they ‘exploded’ each county to expose the DAU’s sliver anomalies. They subsequently merged the variant slivers into the proper DAUs. Upon completion, the DAU feature classes were sent back to Michael L. Serna, NRO where they were merged back together, ‘Unioned’ to the cnty24k09 State Boundary and ‘exploded’ to expose DAU/State of California boundary slivers. These features are reachable by County\DAU. This allows the county boundaries, the DAU boundaries and the State of California Boundary to match Cal-Atlas cnty24k09.
NAME_PCASE | = | County Name Upper\Lower Case |
NAME_UCASE | = | County Name Upper Case |
FMNAME_PC | = | Name Upper\Lower Case - County |
FMNAME_UC | = | Name Upper Case - County |
ABBREV | = | County Abbreviation |
ABCODE | = | Code |
ISLAND | = | Yes\No |
PERIMETER | = | Perimeter |
ACRES | = | Acres |
DAU_CODE | = | Detailed Analysis Unit Code |
DAU_NAME | = | Detailed Analysis Unit Name |
PSA_CODE | = | Historic Planning Area code |
PSA_NAME | = | Historic Planning Area Name |
HR_CODE | = | Hydrologic Regions |
HR_NAME | = | Hydrologic Names |
DAUID_CODE | = | Identification Code |
PA_NO | = | Planning Area |
PA_LABEL | = | Planning Area Label |
IslandName | = | Island Name |
VxCount | = | Vertex Count |
X_COORD | = | Coordinate |
Y_COORD | = | Coordinate |
Region | = | DWR Region |
FIPS | = | Code DAU |
Region_Abrev | = | DWR Region Abbreviation |
Shape_Length | = | Length |
Shape_Area | = | Area |
.............................................................................................................................................
cnty24k09_1_poly metadata Summary:
Ideally, state and federal agencies should be using the same framework data for common themes such as county boundaries. This layer provides an initial offering as "best available" at 1:24,000 scale. Additional improvements, including a review of data sources for the coastline, can be added over time based on interagency review and agreement on proposed changes.
cnty24k09_1_poly metadata Description:
In late 1996, the Dept of Conservation (DOC) surveyed state and federal agencies about the county boundary coverage they used. As a result, DOC adopted the 1:24,000 (24K) scale U.S. Bureau of Reclamation (USBR) dataset (USGS source) for their Farmland Mapping and Monitoring Program (FMMP) but with several modifications. Detailed documentation of these changes is provided by FMMP and included in the lineage section of the metadata. A dataset named cnty24k97_1 was made available (approximately 2004) through the California Department of Forestry and Fire Protection - Fire and Resource Assessment Program (CDF - FRAP) and the California Spatial Information Library (CaSIL). In late 2006, the Department of Fish and Game (DFG) reviewed cnty24k97_1. Comparisons were made to a high-quality 100K dataset (co100a/county100k from the former Teale Data Center GIS Solutions Group) and legal boundary descriptions from ( http://www.leginfo.ca.gov ). The cnty24k97_1 dataset was missing Anacapa and Santa Barbara islands. DFG added the missing islands using previously-digitized coastline data (coastn27 of State Lands Commission origin), corrected a few county boundaries, built region topology, added additional attributes, and renamed the dataset to county24k. In 2007, the California Mapping Coordinating Committee (CMCC) requested that the California Department of Forestry and Fire Protection (CAL FIRE) resume stewardship of the statewide county boundaries data. CAL FIRE adopted the changes made by DFG and collected additional suggestions for the county data from DFG, DOC, and local government agencies. CAL FIRE incorporated these suggestions into the latest revision, which has been renamed cnty24k09_1. Detailed documentation of changes is included in the Process Step section of the metadata.
cnty24k09_1_poly metadata Credits:
U.S. Bureau of Reclamation, California Department of Conservation, California Department of Fish and Game, California Department of Forestry and Fire protection
cnty24k09_1_poly metadata Use Limitations:
See Citation section for FRAP disclaimer
Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2020 water year (WY 2020). The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014, 2016, 2018, and 2019 land use mapping, which classified over 14 million acres of land into irrigated agriculture and urban area. Unlike the 2014 and 2016 datasets, the WY 2018, 2019, and 2020 datasets include multi-cropping and incorporates DWR ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. 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. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 97.8% using the Land IQ legend and 99% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, -PARTIALLY IRRIGATED CROPS Crops irrigated for only part of their normal irrigation season were given the special condition of ‘X’, -In certain areas, DWR changed the irrigation status to irrigated or non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column, - The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found, - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the water year, the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the water year, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column. The column 'MAIN_CROP' was added in 2019 and has been continued in 2020 dataset. This column indicates which field Land IQ identified as the main season crop for the water year representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 dataset, indicates the NDVI peak date for this main season crop. Asterisks (* or **) in attribute table indicates no data have been collected for that specific attribute.
Detailed Analysis Unit-DAUConvergence via County Boundary cnty24k09, (*See metadata for cnty24k09, Cal-Atlas), State of California.
The existing DAU boundaries were clipped via Cal-Atlas cnty24k09 by Michael L. Serna, NRO via ArcMap 10.1. DAU’s were sent out to: Northern Region Office (NRO), North Central Region Office (NCRO), South Central Region Office (SCRO) and Southern Region Office (SRO) respectively.
Collaboration was by Department of Water Resources, Region Office personnel, Michael L. Serna, NRO, Jason Harbaugh - NCRO, Cynthia Moffett - SCRO and Robert Fastenau - SRO with the final merge of all data into a cohesive workable dataset completed by Michael L. Serna - NRO, Land and Water Use Section.
Existing DAU feature class boundaries contained a multitude of variant sliver acreages along all DAU and county boundary lines through-out the State of California.
Each region office was sent their respective ‘clipped’ features where they ‘exploded’ each county to expose the DAU’s sliver anomalies. They subsequently merged the variant slivers into the proper DAUs. Upon completion, the DAU feature classes were sent back to Michael L. Serna, NRO where they were merged back together, ‘Unioned’ to the cnty24k09 State Boundary and ‘exploded’ to expose DAU/State of California boundary slivers. These features are reachable by County\DAU. This allows the county boundaries, the DAU boundaries and the State of California Boundary to match Cal-Atlas cnty24k09.
NAME_PCASE | = | County Name Upper\Lower Case |
NAME_UCASE | = | County Name Upper Case |
FMNAME_PC | = | Name Upper\Lower Case - County |
FMNAME_UC | = | Name Upper Case - County |
ABBREV | = | County Abbreviation |
ABCODE | = | Code |
ISLAND | = | Yes\No |
PERIMETER | = | Perimeter |
ACRES | = | Acres |
DAU_CODE | = | Detailed Analysis Unit Code |
DAU_NAME | = | Detailed Analysis Unit Name |
PSA_CODE | = | Historic Planning Area code |
PSA_NAME | = | Historic Planning Area Name |
HR_CODE | = | Hydrologic Regions |
HR_NAME | = | Hydrologic Names |
DAUID_CODE | = | Identification Code |
PA_NO | = | Planning Area |
PA_LABEL | = | Planning Area Label |
IslandName | = | Island Name |
VxCount | = | Vertex Count |
X_COORD | = | Coordinate |
Y_COORD | = | Coordinate |
Region | = | DWR Region |
FIPS | = | Code DAU |
Region_Abrev | = | DWR Region Abbreviation |
Shape_Length | = | Length |
Shape_Area | = | Area |
.............................................................................................................................................
cnty24k09_1_poly metadata Summary:
Ideally, state and federal agencies should be using the same framework data for common themes such as county boundaries. This layer provides an initial offering as "best available" at 1:24,000 scale. Additional improvements, including a review of data sources for the coastline, can be added over time based on interagency review and agreement on proposed changes.
cnty24k09_1_poly metadata Description:
In late 1996, the Dept of Conservation (DOC) surveyed state and federal agencies about the county boundary coverage they used. As a result, DOC adopted the 1:24,000 (24K) scale U.S. Bureau of Reclamation (USBR) dataset (USGS source) for their Farmland Mapping and Monitoring Program (FMMP) but with several modifications. Detailed documentation of these changes is provided by FMMP and included in the lineage section of the metadata. A dataset named cnty24k97_1 was made available (approximately 2004) through the California Department of Forestry and Fire Protection - Fire and Resource Assessment Program (CDF - FRAP) and the California Spatial Information Library (CaSIL). In late 2006, the Department of Fish and Game (DFG) reviewed cnty24k97_1. Comparisons were made to a high-quality 100K dataset (co100a/county100k from the former Teale Data Center GIS Solutions Group) and legal boundary descriptions from ( http://www.leginfo.ca.gov ). The cnty24k97_1 dataset was missing Anacapa and Santa Barbara islands. DFG added the missing islands using previously-digitized coastline data (coastn27 of State Lands Commission origin), corrected a few county boundaries, built region topology, added additional attributes, and renamed the dataset to county24k. In 2007, the California Mapping Coordinating Committee (CMCC) requested that the California Department of Forestry and Fire Protection (CAL FIRE) resume stewardship of the statewide county boundaries data. CAL FIRE adopted the changes made by DFG and collected additional suggestions for the county data from DFG, DOC, and local government agencies. CAL FIRE incorporated these suggestions into the latest revision, which has been renamed cnty24k09_1. Detailed documentation of changes is included in the Process Step section of the metadata.
cnty24k09_1_poly metadata Credits:
U.S. Bureau of Reclamation, California Department of Conservation, California Department of Fish and Game, California Department of Forestry and Fire protection
cnty24k09_1_poly metadata Use Limitations:
See Citation section for FRAP disclaimer
2021 STATEWIDE CROP MAPPING - PROVISIONAL
Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2021 water year (WY 2021). The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014, 2016, 2018, 2019, and 2020 land use mapping, which classified over 14 million acres of land into irrigated agriculture and urban area. Unlike the 2014 and 2016 datasets, the WY 2018, 2019, 2020, and 2021 datasets include multi-cropping and incorporates DWR ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. 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. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification method using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was XX.X% (UPDATE) using the Land IQ legend and XX% (UPDATE) using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, -PARTIALLY IRRIGATED CROPS Crops irrigated for only part of their normal irrigation season were given the special condition of ‘X’, -In certain areas, DWR changed the irrigation status to irrigated or non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column, - The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found, - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the water year, the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the water year, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column. The column 'MAIN_CROP' was added in 2019 and has been continued through the 2021 dataset. This column indicates which field Land IQ identified as the main season crop for the water year representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 dataset, indicates the NDVI peak date for this main season crop. Asterisks (* or **) in attribute table indicates no data have been collected for that specific attribute.
Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2020 water year (WY 2020). The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014, 2016, 2018, and 2019 land use mapping, which classified over 14 million acres of land into irrigated agriculture and urban area. Unlike the 2014 and 2016 datasets, the WY 2018, 2019, and 2020 datasets include multi-cropping and incorporates DWR ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. 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. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 97.8% using the Land IQ legend and 99% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, -PARTIALLY IRRIGATED CROPS Crops irrigated for only part of their normal irrigation season were given the special condition of ‘X’, -In certain areas, DWR changed the irrigation status to irrigated or non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column, - The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found, - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the water year, the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the water year, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column. The column 'MAIN_CROP' was added in 2019 and has been continued in 2020 dataset. This column indicates which field Land IQ identified as the main season crop for the water year representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 dataset, indicates the NDVI peak date for this main season crop. Asterisks (* or **) in attribute table indicates no data have been collected for that specific attribute.
Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2019 water year (WY 2019). The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014, 2016, and 2018 land use mapping, which classified over 14 million acres of land into irrigated agriculture and urban area. Unlike the 2014 and 2016 datasets, the WY 2018 and 2019 datasets include multi-cropping and incorporates DWR ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. 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. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.9% using the Land IQ legend and 98.1% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, -PARTIALLY IRRIGATED CROPS Crops irrigated for only part of their normal irrigation season were given the special condition of ‘X’, -In certain areas, DWR changed the irrigation status to irrigated or non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column, - The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found, - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the water year, the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the water year, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column. A new column for the 2019 dataset is called ‘MAIN_CROP’. This column indicates which field Land IQ identified as the main season crop for the water year representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 dataset, indicates the NDVI peak date for this main season crop. Asterisks (* or **) in attribute table indicates no data have been collected for that specific attribute.
The 2019 Crop Mapping dataset has been updated as of August 2022 and includes the following changes:
- Slightly shifted Urban polygons were relocated to their original correct positions.
- The following new rule has been included for ‘X’ Unclassified Fallow: “Unclassified Fallow is also used when indicating the planting of Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses. In these scenarios Unclassified fallow would be Crop1, and Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses would be Crop2.”
- Some UniqueID’s that were accidentally duplicated have been corrected back to their original UniqueID’s.
Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the Water Year 2018, covering over 9.4 million acres of Irrigable agriculture on a field scale and additional areas of urban extent. The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014 and 2016 land use mapping, which classified over 14 million acres of land into Irrigable agriculture and urban area. Unlike the 2014 and 2016 datasets, the Water Year 2018 dataset includes multi-cropping and incorporates ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. 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. Individual fields (boundaries of homogeneous crop types representing true Irrigable area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, young perennials and wetland. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.5% using the Land IQ legend and 98.3% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. The value ‘n’ in the ‘DWR_REVISE’ data field indicates a Regional Office added a boundary and attributes where none was included in the Land IQ data set. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated), - DWR determined that the crop was not irrigated. - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). DWR added Adjusted Day Of Year (ADOY) for peak NDVI date corresponding to CROPTYP category. The date received by Land IQ was delivered in a Julian date format (YYYYDDD) and was converted into the ADOY by DWR for statistical purposes. Land use boundaries delineated by Land IQ were not revised by DWR.
This dataset represents the 2016 main season agricultural land use, wetlands, and urban boundaries for all 58 counties in California. This data was originally 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. The Land IQ base data was reviewed, and in some cases revised, by DWR Regional Office Land Use staff using additional analyses based on a combination of aerial photography, remote sensing multi-spectral imagery, agronomic analysis and ground verification. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accomodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor (see 'Contacts').
Revisions were made if:
- DWR corrected the original crop classification based on local knowledge and analysis,
- young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes),
- DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year,
- the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column),
- DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated),
- DWR determined that the crop was not irrigated.- DWR identified a distinct early crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop).
Land use boundaries were delineated by Land IQ from 2016 NAIP Imagery and were not revised by DWR.