Generated by one of the python scripts in the T:\GIS\Scripts folder.This csv file lists the approximate area of each school district in units of square kilometers, US survey acres, and square (US statute) miles. Note that there are two possible values for square miles due to the fact that there are two definitions of the foot--the US survey foot and the "international foot" which was standardized by international agreement in 1959. The data stores the shape_area column in square meters, and the two conversion factors are 2,589,998 square meters to a (US statute) square mile based on the US survey foot, and 2,589,988.11036 square meters to a square mile based on the international foot. This can lead to a small, but possibly significant, difference (roughly .0004% or 4 parts per million) depending on which factor is chosen. The python script that generates this file currently uses the US survey foot conversion factor for both the acres and sq. miles calculation. This decision was arrived at after some study, and is based in part on the fact that the older survey foot is still commonly used for land surveying in the United States.As this was generated directly from the Districts shapefile, the values given should be treated as approximations. This data is not the result of an on-the-ground survey, and should not be used for any critical business, engineering, or legal purpose.

The Maryland Agricultural Land Preservation Foundation (MALPF), housed within the Maryland Department of Agriculture (MDA), protects agricultural lands through the use of perpetual easements. This program was created by the Maryland General Assembly in 1977, is governed by the Agricultural Article, Sections 2-501 through 2-519 of the Annotated Code of Maryland. MALPF’s primary purpose is to preserve productive agricultural land and woodland to provide for the continuing production of food and fiber for the citizens of Maryland. This is accomplished by landowners voluntarily applying to sell an easement on their property through a competitive State-wide application process. MALPF easement boundaries are estimated based on property descriptions provided by MDA, aligned to iMap parcel polygons and supplemented with MdProperty View data to facilitate use with vectorized parcel data. This GIS dataset is intended for general guidance and use only and is not designed as a substitute for legal survey. The coordinates displayed do not represent legal parcel corners and/or boundaries and they cannot be used for establishment of or in lieu of legal land survey boundaries. New fields have been added since the previous update to facilitate the integration of this dataset with the Protected Areas Database of the United States (PAD-US). Attributes for these fields, which are identified below as PAD-US fields, are not available for all data collected historically. The Maryland Department of Planning will continue to populate these new fields as updates become available. For a full description of the contents of the PAD-US fields, see https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual. Attributes within this dataset include: Source Protected Area ID (PAD-US field): MALPF easement numberSettled Acres: Settlement acreage Settled Date: Settlement date, including month date and year County: County in which protected land is located Weblink: Link to more information about the protection program Category (PAD-US field): PAD-US classification for the protection mechanism associated with the protected property Owner Type (PAD-US field): General land owner type of the fee property interest, standardized for the US Owner Name (PAD-US field): Owner of the fee interest of the property, standardized for the nation Local Owner (PAD-US field): The actual name of the owner of the fee interest Easement Holder Type (PAD-US field): Where the ‘Category’ of protection code (above) is listed as “Easement”, this field specifies the type of holder of the easement. Easement Holder (PAD-US field): Where the ‘Category’ of protection code (above) is listed as “Easement”, this field indicates the actual name of the holder of the conservation easement. Unit Name (PAD-US field): The name of the land management unit or protected area State (PAD-US field): Name of state in which the protected land is located Aggregator Source (PAD-US field): Organization credited with data aggregation. Attributed in the format 'organization name_filename YearPublished.filetype'GIS Source (PAD-US field): The original source of GIS spatial and attribute information the aggregator obtained where availableGIS Source Date (PAD-US field): The date (yyyy/mm/dd) GIS data was obtained by the data source for aggregation GIS Acres (PAD-US field): Acres calculated for each polygon in NAD83 Meters. The GIS Acres may differ from the Settled Acres, particularly in cases where easement boundaries are estimated based on parcel polygon boundaries. Date of Protection (PAD-US field): The year (yyyy) the property was legally protected via fee acquisition or enactment of a conservation easement Public Access (PAD-US field):Accessibility of the property to the public, standardized

The Maryland Agricultural Land Preservation Foundation (MALPF), housed within the Maryland Department of Agriculture (MDA), protects agricultural lands through the use of perpetual easements. This program was created by the Maryland General Assembly in 1977, is governed by the Agricultural Article, Sections 2-501 through 2-519 of the Annotated Code of Maryland. MALPF’s primary purpose is to preserve productive agricultural land and woodland to provide for the continuing production of food and fiber for the citizens of Maryland. This is accomplished by landowners voluntarily applying to sell an easement on their property through a competitive State-wide application process. MALPF easement boundaries are estimated based on property descriptions provided by MDA, aligned to MD iMap parcel polygons and supplemented with MdProperty View data to facilitate use with vectorized parcel data. This GIS dataset is intended for general guidance and use only and is not designed as a substitute for legal survey. The coordinates displayed do not represent legal parcel corners and/or boundaries and they cannot be used for establishment of or in lieu of legal land survey boundaries.

In June 2019 new fields were added to facilitate the integration of this dataset with the Protected Areas Database of the United States (PAD-US). Attributes for these fields, which are identified below as PAD-US fields, are not available for all data collected historically. The Maryland Department of Planning will continue to populate these new fields as updates become available. For a full description of the contents of the PAD-US fields, see https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-datamanual.Attributes within this dataset include: Source Protected Area ID (PAD-US field): MALPF easement number Settled Acres: Settlement acreage Settled Date: Settlement date, including month date and year County: County in which protected land is located Weblink: Link to more information about the protection program Category (PAD-US field): PAD-US classification for the protection mechanism associated with the protected property Owner Type (PAD-US field): General land owner type of the fee property interest, standardized for the US Owner Name (PAD-US field): Owner of the fee interest of the property, standardized for the nation Local Owner (PAD-US field): The actual name of the owner of the fee interest Easement Holder Type (PAD-US field): Where the ‘Category’ of protection code (above) is listed as “Easement”, this field specifies the type of holder of the easement. Easement Holder (PAD-US field): Where the ‘Category’ of protection code (above) is listed as “Easement”, this field indicates the actual name of the holder of the conservation easement. Unit Name (PAD-US field): The name of the land management unit or protected area State (PAD-US field): Name of state in which the protected land is located Aggregator Source (PAD-US field): Organization credited with data aggregation. Attributed in the format 'organization name_filenameYearPublished.filetype' GIS Source (PAD-US field): The original source of GIS spatial and attribute information the aggregator obtained where available GIS Source Date (PAD-US field): The date (yyyy/mm/dd) GIS data was obtained by the data source for aggregation GIS Acres (PAD-US field): Acres calculated for each polygon in NAD83 Meters. The GIS Acres may differ from the Settled Acres, particularly in cases where easement boundaries are estimated based on parcel polygon boundaries. Date of Protection (PAD-US field): The year (yyyy) the property was legally protected via fee acquisition or enactment of a conservation easement Public Access (PAD-US field):Accessibility of the property to the public, standardized

This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link: https://mdgeodata.md.gov/imap/rest/services/Environment/MD_ProtectedLands/FeatureServer/4

Historical FiresLast updated on 06/17/2022OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimeters in 2021.https://nifc.maps.arcgis.com/home/item.html?id=098ebc8e561143389ca3d42be3707caaFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send email

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2005 Shasta County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). DPLA was later reorganized into the Division of Statewide Integrated Water Management and the Division of Integrated Regional Water Management. The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters and Northern Region, under the supervision of Tito Cervantes. The finalized countywide land use vector data is in a single, polygon, shapefile format. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Shasta County conducted by DWR, Northern District Office staff(ND), currently known as Northern Region Office, under the leadership of Tito Cervantes, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2005. ND staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary date was developed using: 1. Linework developed for DWR’s 1995 survey of Shasta County was used as the starting point for the digital field boundaries developed for this survey. Where needed, Northern Region staff made corrections to the field boundaries using the 1993 Digital Orthophoto Quarter Quadrangle (DOQQ) images. After field visits had been completed, 2005 National Agricultural Imagery Program (NAIP), one-meter resolution imagery from the U.S. Department of Agriculture’s Farm Services Agency was used to locate boundary changes that had occurred since the 1993 imagery was taken. Field boundaries for this survey follow the actual borders of fields, not road center lines. Line work for the Redding area was downloaded from the City of Redding website and modified to be compatible with DWR land use categories and linework. 2. For field data collection, digital images and land use boundaries were copied onto laptop computers. The staff took these laptops into the field and virtually all agricultural fields were visited to positively identify agricultural land uses. Site visits occurred from July through September 2005. Using a standardized process, land use codes were digitized directly into the laptop computers using ArcMap. For most areas of urban land use, attributes were based upon aerial photo interpretation rather than fieldwork. 3. The digital land use map was reviewed using the 2005 NAIP four-band imagery and 2005 Landsat 5 images to identify fields that may have been misidentified. The survey data was also reviewed by summarizing land use categories and checking the results for unusual attributes or acreages. 4. After quality control procedures were completed, the data was finalized by staff in both ND and Sacramento's DPLA. Important Points about Using this Data Set: 1. The land use boundaries were drawn on-screen using orthorectified imagery. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and whatever additional information the aerial photography might provide. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. Double cropping and mixed land use must be taken into account when calculating the acreage of each crop or other land use mapped in this survey. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. For double cropped fields, a “D” will be entered in the “MULTIUSE” field of the DBF file of the shapefile. To calculate the crop acreage for that field, 40 acres should be allocated to the grain category and then 40 acres should also be allocated to corn. For polygons mapped as “mixed land use”, an “M” will be entered in the “MULTIUSE” field. To calculate the appropriate acreages for each land use within this polygon, multiply the percent (as a decimal fraction) associated with each land use by the acres represented by the polygon. 4. All Land Use Codes are respresentative of the current 2016 Legend unless otherwise noted. Not all land use codes will be represented in the survey. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Before final processing, standard quality control procedures were performed jointly by staff at DWR's Northern District, and at DPLA headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the 9' x 9' color photos, is approximately 23 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This is a copy of another layer - see original source: https://www.arcgis.com/home/item.html?id=e02b85c0ea784ce7bd8add7ae3d293d0OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimetersFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send emailLayers

Interagency Wildland Fire Perimeter History (IFPH) Overview This national fire history perimeter data layer of conglomerated agency perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2024 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer, links are provided where possible below. In addition, many agencies are now using WFIGS as their authoritative source, beginning in mid-2020.Alaska fire history (WFIGS pull for updates began 2022)USDA FS Regional Fire History Data (WFIGS pull for updates began 2024)BLM Fire Planning and Fuels (WFIGS pull for updates began 2020)National Park Service - Includes Prescribed Burns (WFIGS pull for updates began 2020)Fish and Wildlife Service (WFIGS pull for updates began 2024)Bureau of Indian Affairs (Incomplete, 2017-2018 from BIA, WFIGS pull for updates began 2020)CalFire FRAS - Includes Prescribed Burns (CALFIRE only source, non-fed fires)WFIGS - updates included since mid-2020, unless otherwise noted Data LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoritative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.Attributes This dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer maintained by IrWIN. (This unique identifier may NOT replace the GeometryID core attribute) FORID - Unique identifier assigned to each incident record in the Fire Occurence Data Records system. (This unique identifier may NOT replace the GeometryID core attribute) INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name. FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT). AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin. SOURCE - System/agency source of record from which the perimeter came. DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy. MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Other GIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9 UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001 LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456. UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMP COMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or Unknown GEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID). Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4,781 Records thru 2021), other federal sources for AK data removed. No RX data included.CA: GEOID = OBJECT ID of downloaded file geodatabase (8,480 Records, federal fires removed, includes RX. Significant cleanup occurred between 2023 and 2024 data pulls resulting in fewer perimeters).FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2,959 Records), includes RX.BIA: GEOID = "FireID" 2017/2018 data (382 records). No RX data included.NPS: GEOID = EVENT ID 15,237 records, includes RX. In 2024/2023 dataset was reduced by combining singlepart to multpart based on valid Irwin, FORID or Unique Fire IDs. RX data included.BLM: GEOID = GUID from BLM FPER (23,730 features). No RX data included.USFS: GEOID=GLOBALID from EDW records (48,569 features), includes RXWFIGS: GEOID=polySourceGlobalID (9724 records added or replaced agency record since mid-2020)Attempts to repair Unique Fire ID not made. Attempts to repair dates not made. Verified all IrWIN IDs and FODRIDs present via joins and cross checks to the respective dataset. Stripped leading and trailing spaces, fixed empty values to

Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process.Digitizing is done as Geodatabase feature classes using ArcMap 10.X with NAIP or Google imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process.Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.LUID - Unique ID number for each polygon in the final dataset, not consistent between yearly datasets.Landuse - A general land cover classification differentiating how the land is used.Agriculture: Land managed for crop or livestock purposes.Other: A broad classification of wildland.Riparian/Wetland: Wildland influenced by a high water table, often close to surface water.Urban: Developed areas, includes urban greenspace such as parks.Water: Surface water such as wet flats, streams, and lakes.CropGroup - Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc.Description - Attribute that describes/indicates the various crop types and land use types determined by the GIS process.IRR_Method - Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the crop.Dry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plot.None: Associated with non-agricultural landSprinkler: Water is applied above the crop via sprinklers that generally move across the field.Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian area.Acres - Calculated acreage of the polygon.State - State where the polygons are found.Basin - The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes.SubArea - The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe.Label_Class - Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop.LABEL - A shorthand descriptive label for each crop description and irrigation type.Class_Name - The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description).OldLanduse - Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse.LU_Group - These codes represent some in-house groupings that are useful for symbology and other summarizing.Field_Check - Indicates the year the polygon was last field checked. *New for 2019SURV_YEAR - Indicates which year/growing season the data represents.

🇺🇸 미국 English This dataset was created to depict approximate tree canopy cover for all land within the City of Austin's "full watershed regulation area." Intended for planning purposes and measuring citywide percent canopy. Definition: Tree canopy is defined as the layer of leaves, branches, and stems of trees that cover the ground when viewed from above. Methods: The 2022 tree canopy layer was derived from satellite imagery (Maxar) and aerial imagery (NAIP). Images were used to extract tree canopy into GIS vector features. First, a “visual recognition engine” generated the vector features. The engine used machine learning algorithms to detect and label image pixels as tree canopy. Then using prior knowledge of feature geometries, more modeling algorithms were used to predict and transform probability maps of labeled pixels into finished vector polygons depicting tree canopy. The resulting features were reviewed and edited through manual interpretation by GIS professionals. When appropriate, NAIP 2022 aerial imagery supplemented satellite images that had cloud cover, and a manual editing process made sure tree canopy represented 2022 conditions. Finally, an independent accuracy assessment was performed by the City of Austin and the Texas A&M Forest Service for quality assurance. GIS professionals assessed agreement between the tree canopy data and its source satellite imagery. An overall accuracy of 98% was found. Only 23 errors were found out of a total 1,000 locations reviewed. These were mostly omission errors (e.g. not including canopy in this dataset when canopy is shown in the satellite or aerial image). Best efforts were made to ensure ground-truth locations contained a tree on the ground. To ensure this, location data were used from City of Austin and Texas A&M Forest Service databases. Analysis: The City of Austin measures tree canopy using the calculation: acres of tree canopy divided by acres of land. The area of interest for the land acres is evaluated at the City of Austin's jurisdiction including Full Purpose, Limited Purpose, and Extraterritorial jurisdictions as of May 2023. New data show, in 2022, tree canopy covered 41% of the total land area within Austin's city limits (using city limit boundaries May 2023 and included in the download as layer name "city_of_austin_2023"). 160,046.50 canopy acres (2022) / 395,037.53 land acres = 40.51% ~41%. This compares to 36% last measured in 2018, and a historical average that’s also hovered around 36%. The time period between 2018 and 2022 saw a 5 percentage point change resulting in over 19K acres of canopy gained (estimated). Data Disclaimer: It's possible changes in percent canopy over the years is due to annexation and improved data methods (e.g. higher resolution imagery, AI, software used, etc.) in addition to actual in changes in tree canopy cover on the ground. For planning purposes only. Dataset does not account for individual trees, tree species nor any metric for tree canopy height. Tree canopy data is provided in vector GIS format housed in a Geodatabase. Download and unzip the folder to get started. Please note, errors may exist in this dataset due to the variation in species composition and land use found across the study area. This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries. This product has been produced by the City of Austin for the sole purpose of geographic reference. No warranty is made by the City of Austin regarding specific accuracy or completeness. Data Provider: Ecopia AI Tech Corporation and PlanIT Geo, Inc. Data derived from Maxar Technologies, Inc. and USDA NAIP imagery

This TNC Lands spatial dataset represents the lands and waters in which The Nature Conservancy (TNC) currently has, or historically had, an interest, legal or otherwise in New Mexico. The system of record for TNC Lands is the Legal Records Management (LRM) system, which is TNC’s database for all TNC land transactions.TNC properties should not be considered open to the public unless specifically designated as being so. TNC may change the access status at any time at its sole discretion. It's recommended to visit preserve-specific websites or contact the organization operating the preserve before any planned visit for the latest conditions, notices, and closures. TNC prohibits redistribution or display of the data in maps or online in any way that misleadingly implies such lands are universally open to the public.The types of current land interests represented in the TNC Lands data include: Fields and Attributes included in the public dataset:Field NameField DefinitionAttributesAttribute Definitions Public NameThe name of the tract that The Nature Conservancy (TNC) Business Unit (BU) uses for public audiences.Public name of tract if applicableN/A TNC Primary InterestThe primary interest held by The Nature Conservancy (TNC) on the tractFee OwnershipProperties where TNC currently holds fee-title or exclusive rights and control over real estate. Fee Ownership can include TNC Nature Preserves, managed areas, and properties that are held for future transfer. Conservation EasementProperties on which TNC holds a conservation easement, which is a legally binding agreement restricting the use of real property for conservation purposes (e.g., no development). The easement may additionally provide the holder (TNC) with affirmative rights, such as the rights to monitor species or to manage the land. It may run forever or for an expressed term of years. Deed RestrictionProperties where TNC holds a deed restriction, which is a provision placed in a deed restricting or limiting the use of the property in some manner (e.g., if a property goes up for sale, TNC gets the first option). TransferProperties where TNC historically had a legal interest (fee or easement), then subsequently transferred the interest to a conservation partner. AssistProperties where TNC assisted another agency/entity in protecting. Management Lease or AgreementAn agreement between two parties whereby one party allows the other to use their property for a certain period of time in exchange for a periodic fee. Grazing Lease or PermitA grazing lease or permit held by The Nature Conservancy Right of WayAn access easement or agreement held by The Nature Conservancy. OtherAnother real estate interest or legal agreement held by The Nature Conservancy Fee OwnerThe name of the organization serving as fee owner of the tract, or "Private Land Owner" if the owner is a private party. If The Nature Conservancy (TNC) primary interest is a "Transfer" or "Assist", then this is the fee owner at the time of the transaction.Fee Owner NameN/A Fee Org TypeThe type of organization(s) that hold(s) fee ownership. Chosen from a list of accepted values.Organization Types for Fee OwnershipFED:Federal, TRIB:American Indian Lands, STAT:State,DIST:Regional Agency Special District, LOC:Local Government, NGO:Non-Governmental Organization, PVT:Private, JNT:Joint, UNK:Unknown, TERR:Territorial, DESG:Designation Other Interest HolderThe name of the organization(s) that hold(s) a different interest in the tract, besides fee ownership or TNC Primary Interest. This may include TNC if the Other Interest is held or co-held by TNC. Multiple interest holders should be separated by a semicolon (;).Other Interest Holder NameN/A Other Interest Org TypeThe type of organization(s) that hold(s) a different interest in the tract, besides fee ownership. This may include TNC if the Other Interest is held or co-held by TNC. Chosen from a list of accepted values.Organization Types for interest holders:FED:Federal, TRIB:American Indian Lands, STAT:State,DIST:Regional Agency Special District, LOC:Local Government, NGO:Non-Governmental Organization, PVT:Private, JNT:Joint, UNK:Unknown, TERR:Territorial, DESG:Designation Other Interest TypeThe other interest type held on the tract. Chosen from a list of accepted values.​Access Right of Way; Conservation Easement; Co-held Conservation Easement; Deed Restriction; Co-held Deed Restriction; Fee Ownership; Co-held Fee Ownership; Grazing Lease or Permit; Life Estate; Management Lease or Agreement; Timber Lease or Agreement; OtherN/A Preserve NameThe name of The Nature Conservancy (TNC) preserve that the tract is a part of, this may be the same name as the as the "Public Name" for the tract.Preserve Name if applicableN/APublic AccessThe level of public access allowed on the tract.Open AccessAccess is encouraged on the tract, trails are maintained, signage is abundant, and parking is available. The tract may include regular hours of availability.Open with Limited AccessThere are no special requirements for public access to the tract, the tract may include regular hours of availability with limited amenities.Restricted AccessThe tract requires a special permit from the owner for access, a registration permit on public land, or has highly variable times or conditions to use.Closed AccessNo public access is allowed on the tract.UnknownAccess information for the tract is not currently available.Gap CategoryThe Gap Analysis Project (GAP) code for the tract. Gap Analysis is the science of determining how well we are protecting common plants and animals. Developing the data and tools to support that science is the mission of the Gap Analysis Project (GAP) at the US Geological Survey. See their website for more information, linked in the field name.1 - Permanent Protection for BiodiversityPermanent Protection for Biodiversity2 - Permanent Protection to Maintain a Primarily Natural StatePermanent Protection to Maintain a Primarily Natural State3 - Permanently Secured for Multiple Uses and in natural coverPermanently Secured for Multiple Uses and in natural cover39 - Permanently Secured and in agriculture or maintained grass coverPermanently Secured and in agriculture or maintained grass cover4 - UnsecuredUnsecured (temporary easements lands and/or municipal lands that are already developed (schools, golf course, soccer fields, ball fields)9 - UnknownUnknownProtected AcresThe planar area of the tract polygon in acres, calculated by the TNC Lands geographic information system (GIS).Total geodesic area of polygon in acresProjection: WGS 1984 Web Mercator Auxiliary SphereOriginal Protection DateThe original protection date for the tract, from the Land Resource Management (LRM) system record.Original protection dateN/AStateThe state within the United States of America or the Canadian province where the tract is located.Chosen from a list of state names.N/ACountryThe name of the country where the tract is located.Chosen from a list of countries.N/ADivisionThe name of the TNC North America Region Division where the tract is located. Chosen from a list of TNC North America DivisionsN/A

The Tribal grasslands feature layer is intended to display the approximate amount of grasslands represented in U.S. Survey Acres, that are contained within the boundaries of American Indian Land Area Representations (LAR). This Includes a feature layer generated from running the Summarize Within solution. The data does not include Alaska and may not include all BIA LAR areas. This map is only intended to highlight areas that that include grasslands within tribal boundaries. The geographic acreages contained in this dataset are not derived from legal documents associated with title documents or survey records. Rather, they are computed by area calculation in the mapping software. It is important to note, area and distance calculations in the mapping software require that the data be in a projected coordinate system and that different projected coordinate systems can result in different calculated values for area or length. It is recommended that the Universal Transverse Mercator (UTM) Projection for the UTM zone where the data is contained be used to calculate area or distance. National Grassland data is derived from the U.S. Forest Service "Original Proclaimed National Forests and National Grasslands", which can be found in the ESRI Living Atlas. This layer is filtered to only display National Grasslands as determined by the USFS. This viewer provides tools to assist with research, planning, and reporting. Land cover data is compiled from the National Land Cover Database (NLCD). The United States NLCD is as current as 2021 and can be found within ESRI’s living atlas. The original NLCD data can be found here. Land Area Representations of tribal boundaries and the BIA regions represented in this dashboard were created by the BIA. Please feel free to contact BOGS at 1-877-293-9494 geospatial@bia.gov

This data provides the geographic location for parcel boundary lines within the jurisdiction of the Carteret County, NC and is based on recorded surveys and deeds. This dataset is maintained by the Carteret County GIS Division. Data is updated on an as needed basis. The description for each field name in the layer is included below.FieldAliasMeaningPIN15Parcel NumberTax Parcel ID Number (PIN4+ PIN5)ROLL_TYPERoll TypeRoll type of property (regularly taxed property or tax-exempt)GISMOTHERMother ParcelMother ParcelGISMAPNUMPIN1First 4 digits of PIN15MAPNAMPIN2PIN1 + 2 digitsGISBLOCKPIN3Digits 7 and 8 of PIN15GISPINPIN4PIN2 +PIN3GISPDOTPIN5Digit numbers 9 to 12 of PIN15CONDO_Condo unit #Condo numberGISPRIDOld Tax IDOld parcel number style based on prior mapping system before NC GRID system mapsOWNERTax Owner 1Tax parcel ownerOWNER2Tax Owner 2Secondary tax parcel ownerSITE_HOUSESite House #Site Address House NumberSITE_DIRSite DirSite Address Street directionalSITE_STSite StreetSite Address Street NameSITE_STTYPSite Street TypeSite Address Street Suffix (e.g., Dr., St., etc.)SITE_APTNOSite AptSite Address UnitSITE_CITYSite CitySite Address City/CommunityPropertyAddressPhysical AddressProperty AddressMAIL_ADDRESS1Mailing address and streetConcatenated mailing addressMAIL_ADDRESS2Mailing unitSecondary mailing addressMAIL_CITYMailing cityMailing address CityMAIL_STATEMailing stateMailing address StateMAIL_ZI4Mailing Zip4Mailing address Zip Code + 4MAIL_ZI5Mailing Zip5Mailing address Zip CodeFullMailingAddressFull Mailing AddressFull mailing address concatenatedDBOOKDeed BookDeed book containing the most recent deed to the propertyDPAGEDeed PageDeed page containing the most recent deed to the propertyDDATEDeed Dateunformatted most recent deed date for the propertyDeedDate_2Formatted Deed DateFormatted most recent deed date for the propertyMapBookPlat Map BookMap BookMapPagePlat Map PageMap PagePLATBOOKPlat BookRecorded Plat BookPLATPAGEPlat PageRecorded Plat PageLEGAL_DESCParcel Legal DescriptionLegal descriptionLegalAcresLegal Acres (new)Deeded or platted acresDeededAcresLegal AcresAcres from recorded deedsCalculatedLandUnitsTaxed AcresTaxed land acreageGISacresCalculated AcresCalculated GIS acresGISacres2Calculated Acres (new)Calculated GIS acresLandCodeTax Land Category CodeLand CodeLandCodeDescriptionTax Land Category DescriptionLand Code DescriptionMUNICIPALITYMunicipality/ETJIf parcel is within the corporate limits or ETJTOWNSHIPTownshipTownship codeRESCUE_DISTTax Rescue DistrictTax rescue district that the parcel is withinFIRE_DISTTax Fire DistrictTax fire district that the parcel is withinJurisdictionTax District CodeTax District CodeNBHDNeighborhood CodeNeighborhood codeNeighborhoodNameNeighborhood NameNeighborhood code descriptionBuildingCount# BuildingsNumber of buildings within the propertyY_BLT_HOUSEYear Built HouseYear house was builtBLT_CONDOYear Built CondoYear condo was builtTOT_SQ_FTTotal Sq FootTotal square footage of structure on propertyHtdSqFtHeated Sq FootHeated square footages of structure on the propertyBldgModelBuilding ModelBuilding ModelBEDROOMS# Bedrooms# BedroomsBATHROOMS# Bathrooms# BathroomsBldgUseBuilding UseBuilding UseConditionBuilding ConditionBuilding ConditionDwellingStyleDescriptionDwelling DescriptionDwelling style descriptionExWllDes1Exterior wall 1Exterior wall type 1 descriptionExWllDes2Exterior wall 2Exterior wall type 2 descriptionExWllTyp1Exterior wall 1 codeExterior wall type 1ExWllTyp2Exterior wall 2 codeExterior wall type 2FondDes1Foundation Description Foundation type 1 descriptionFondTyp1Foundation Description CodeFoundation type 1RCovDes1Roof # 1 Covering DescriptionRoof covering type 1 descriptionRCovDes2Roof # 2 CoveringDescriptionRoof covering type 2 descriptionRCovTyp1Roof # 1 CodeRoof covering type 1RCovTyp2Roof # 2 CodeRoof covering type 2RStrDes1Roof Structure DescriptionRoof structure type 1 descriptionRStrTyp1Roof Structure CodeRoof structure type 1GradeBuilding GradeTax gradeGradeAndCDUBuilding Grade & ConditionBuilding Grade & ConditionHeatDes1Heating/Cooling TypeHeating type 1 descriptionHeatTyp1Heating/Cooling CodeHeating type 1FireplaceCountFireplacesFireplacesSTRUC_VALStructure ValueValue of structure(s) on the propertyLAND_VALUETax Land ValueValue of the land on the propertyOTHER_VALOther Structures ValueOther value of the propertyTotal_EMVTotal Estimated Market ValueTotal estimated tax valueSaleImprovedorVacantSALE_PRICEVacant or Improved SaleRecorded Sale PriceVacant of Improved SaleRecorded sale priceSaleDateRecorded Sale DateRecorded sale dateSubdivision_NameSubdivision NameSubdivision NameSubdivision_Platbk_pagSubdivision Plat Book/PakeSubdivision Plat Book and PageCommissioner_DistrictCommissioner District #Commissioner"s DistrictCommissioner_Name1Commissioner NameCommissioner"s NameCommissioner_InfoCommissioner LinkLink to Commissioner"s contact info Elementary_SchoolElementary DistrictElementary school name/districtMiddle_SchoolMiddle School DistrictMiddle School name/districtHigh_SchoolHigh School DistrictHigh school name/districtIsImprovedProperty ImprovedBuilt on = 1, Vacant = 0IsQualifiedQualified SaleQualified = 1, Not Qualified = 0Use_codeTax Use CodeLand use codeUse_descTax Use Code DescriptionLand use descriptionPerm_De1Permit # 1 DescriptionPermit #1 descriptionPerm_De2Permit # 2 DescriptionPermit #2 descriptionPerm_Is1Permit # 1 Issue DatePermit #1 issue datePerm_Is2Permit # 2 Issue DatePermit #2 issue datePerm_N1Permit # 1 NumberPermit #1Perm_N2Permit #2 NumberPermit #2Perm_Ty1Permit # 1 TypePermit #1 typePerm_Ty2Permit # 2 TypePermit #2 typeACTL_DA1Permit #1 Completion DatePermit #1 - actual completion dateACTL_DA2Permit #2 Completion DatePermit #2 - actual completion dateReviewedDateTax Office Review DateDate ReviewedNoise_lvlBogue Air Field Noise LevelNoise level zones surrounding military air basesRisk_levelBogue Landing Risk LevelRisk level for military air plane accident potential within the AICUZ zonesaicuzBogue Air Field AICUZAir Installation Compatible Use Zone - planning zones pertaining to military air bases and the surrounding real estateOBJECTIDOBJECTIDESRI default unique IDSHAPEShapeESRI default fieldSHAPE.STArea()Shape AreaESRI default fieldSHAPE.STLength()Shape PerimeterESRI default field

The 1990 Kern County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s San Joaquin District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and San Joaquin District. Important Points about Using this Data Set: 1. The land use boundaries were hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Water source and irrigation method information was not collected for this survey. 5. During the transfer of data from the INTERGRAPH system to the AUTOCAD system, some attributes were lost. For those polygons that were attributed with either “D” (double cropped) or “I” (intercropped), the second crop has asterisks in the two fields “IRR_TYP2PA” (irrigated or non-irrigated) and “IRR_TYP2PB” (type of irrigation system). There should have been either and “i” or “n” in the “IRR_TYP2PA” field, and a “U” or “*” in the “IRR_TYP2PB” field. 6. Not all land use codes will be represented in the survey.

The 1997 Santa Cruz County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s San Joaquin District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and San Joaquin District. Important Points about Using this Data Set: 1. The land use boundaries were either drawn on-screen using developed photoquads, or hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Water source and irrigation method information were not collected for this survey. 5. Not all land use codes will be represented in the survey.

The 2001 Madera County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s San Joaquin District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and San Joaquin District. The finalized data include a shapefile of western Madera County (land use vector data), and JPEG files (raster data from aerial imagery). In May 2013, errors in acreage calculations were found in the original finalized data. The “Calculated Geometry” function of ArcGIS was used to correct the errors. The name of the original shapefile was 01ma.shp. The name of the revised shapefile is 01ma_v2.shp. Important Points about Using this Data Set: 1. The land use boundaries were drawn on-screen using developed photoquads. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. If the data is compared to the previous digital survey (i.e. the two coverages intersected for change detection determination), there will be land use changes that may be unexpected. The linework was created independently, so even if a field’s physical boundary hasn’t changed between surveys, the lines may differ due to difference in digitizing. Numerous thin polygons (with very little area) will result. A result could be UV1 (paved roads) to F1 (cotton). In reality, paved roads are not converted to cotton fields, but these small polygons would be created due to the differences in digitizing the linework for each survey. Additionally, this kind of comparison may yield polygons of significant size with unexpected changes. These changes will almost always involve non-cropped land, mainly U (urban), UR1 (single family homes on 1 – 5 acres), UV (urban vacant), NV (native vegetation), and I1 (land not cropped that year, but cropped within the past three years). The unexpected results (such as U to NV, or UR1 to NV) occur mainly because of interpretation of those non-cropped land uses with aerial imagery. Newer surveys or well funded surveys have had the advantage of using improved quality (higher resolution) imagery or additional labor, where more accurate identification of land use is possible, and more accurate linework is created. For example, an older survey may have a large polygon identified as UR, where the actual land use was a mixture of houses and vacant land. A newer survey may have, for that same area, delineated separately those land uses into smaller polygons. The result of an intersection would include changes from UR to UV (which is normally an unlikely change). It is important to understand that the main purpose of DWR performing land use surveys is to aid in development of agricultural water use data. Thus, given our goals and budget, our emphasis is on obtaining accurate agricultural land uses with less emphasis on obtaining accurate non-agricultural land uses (urban and native areas). 5. Water source information was not collected for this survey. 6. Not all land use codes will be represented in the survey.

The 1993 Sacramento County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Central District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and Central District. Important Points about Using this Data Set: 1. The land use boundaries were hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Not all land use codes will be represented the survey.

The 1989 Yolo County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Central District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and Central District. Important Points about Using this Data Set: 1. The land use boundaries were hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 3. Irrigation method data was not collected for this survey. 4. The codes used for water source was different from the 1981 DWR Standard Land Use Legend. Following are the codes used and their meaning: 1. Surface water 3. Ground water A. Combination of surface and ground water 5. During the transfer of data from the INTERGRAPH system to the AUTOCAD system, some attributes were lost. For those polygons that were attributed with either “D” (double cropped) or “I” (intercropped), the second crop has asterisks in the two fields “IRR_TYP2PA” (irrigated or non-irrigated) and “IRR_TYP2PB” (type of irrigation system). There should have been either and “i” or “n” in the “IRR_TYP2PA” field, and a “U” or “*” in the “IRR_TYP2PB” field. 6. Not all land use codes will be represented in the survey.

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2005 Shasta County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). DPLA was later reorganized into the Division of Statewide Integrated Water Management and the Division of Integrated Regional Water Management. The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters and Northern Region, under the supervision of Tito Cervantes. The finalized countywide land use vector data is in a single, polygon, shapefile format. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Shasta County conducted by DWR, Northern District Office staff(ND), currently known as Northern Region Office, under the leadership of Tito Cervantes, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2005. ND staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary date was developed using: 1. Linework developed for DWR’s 1995 survey of Shasta County was used as the starting point for the digital field boundaries developed for this survey. Where needed, Northern Region staff made corrections to the field boundaries using the 1993 Digital Orthophoto Quarter Quadrangle (DOQQ) images. After field visits had been completed, 2005 National Agricultural Imagery Program (NAIP), one-meter resolution imagery from the U.S. Department of Agriculture’s Farm Services Agency was used to locate boundary changes that had occurred since the 1993 imagery was taken. Field boundaries for this survey follow the actual borders of fields, not road center lines. Line work for the Redding area was downloaded from the City of Redding website and modified to be compatible with DWR land use categories and linework. 2. For field data collection, digital images and land use boundaries were copied onto laptop computers. The staff took these laptops into the field and virtually all agricultural fields were visited to positively identify agricultural land uses. Site visits occurred from July through September 2005. Using a standardized process, land use codes were digitized directly into the laptop computers using ArcMap. For most areas of urban land use, attributes were based upon aerial photo interpretation rather than fieldwork. 3. The digital land use map was reviewed using the 2005 NAIP four-band imagery and 2005 Landsat 5 images to identify fields that may have been misidentified. The survey data was also reviewed by summarizing land use categories and checking the results for unusual attributes or acreages. 4. After quality control procedures were completed, the data was finalized by staff in both ND and Sacramento's DPLA. Important Points about Using this Data Set: 1. The land use boundaries were drawn on-screen using orthorectified imagery. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and whatever additional information the aerial photography might provide. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. Double cropping and mixed land use must be taken into account when calculating the acreage of each crop or other land use mapped in this survey. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. For double cropped fields, a “D” will be entered in the “MULTIUSE” field of the DBF file of the shapefile. To calculate the crop acreage for that field, 40 acres should be allocated to the grain category and then 40 acres should also be allocated to corn. For polygons mapped as “mixed land use”, an “M” will be entered in the “MULTIUSE” field. To calculate the appropriate acreages for each land use within this polygon, multiply the percent (as a decimal fraction) associated with each land use by the acres represented by the polygon. 4. All Land Use Codes are respresentative of the current 2016 Legend unless otherwise noted. Not all land use codes will be represented in the survey. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Before final processing, standard quality control procedures were performed jointly by staff at DWR's Northern District, and at DPLA headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the 9' x 9' color photos, is approximately 23 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This data represents a land use survey of Alameda County conducted by the California Department of Water Resources, Central District Office staff.Field survey boundaries data was developed using:1. The county was surveyed with a combination of 2005 one meter and 2006 two meter NAIP imagery. 2. The 2005 images were used in the spring of 2006 to develop the land use field boundary lines that would be used for the summer survey. The 2006 imagery was used for identification in the field and to edit any boundary line changes from the 2005 imagery. 3. These images and land use boundaries were copied onto laptop computers that were used as the field collection tools. The staff took these laptops in the field and virtually all areas were visited to positively identify the land use. The site visits occurred from June through September 2006. Land use codes were digitized directly into the laptop computers using AUTOCAD (and a standardized digitizing process) any land use boundaries changes were noted and corrected back in the office. 4. After quality control/assurance procedures were completed on each file (DWG), the data was finalized for the summer survey. Important points about using this data set:1. The land use boundaries were drawn on-screen using orthorectified imagery. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was created as a "snapshot" in the summer, and further improved by the addition of spring crops found through the use of satellite imagery. There still could be fields where there were crops grown before or after the field survey. The surveyor may not have been able to detect them from the field or the photographs, and the satellite imagery processing may not have identified the spring crop. Thus, although the data is very accurate for the summer, and probably the spring, it may not be an accurate determination of what was grown in the fields for the whole year. 3. If the data is to be brought into a GIS for analysis of copped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Not all land use codes will be represented in the survey. Sources of irrigation water were not identified. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 12.4 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.