Name of FieldAliasType of DataDescriptionAPNAPNSTRINGAssessor's Parcel NumberHOUSE_NUMHOUSE NUMINTEGERHouse NumberPREFIX_DIRPREFIX DIRSTRINGPrefix DirectionST_NAMESTREET NAMESTRINGStreet NameST_TYPESTREET TYPESTRINGStreet TypeUNITUNITSTRINGUnitFULL_ADDFULL ADDRESSSTRINGFull AddressFULL_STNAFULL STREET NAMESTRINGFull Street NameCITYCITYSTRINGCityZIP_CODEZIP CODESTRINGZip CodeLAND_USELAND USESTRINGLand Use (County)ZONINGZONINGSTRINGZoningSCAG_LUCOSCAG LANDUSE CODESTRINGLand Use (SCAG Code)SCAG_LUSCAG LANDUSESTRINGLand Use (SCAG)FHSZ_CODEFIRE HZ ZONE CODESTRINGFire Hazard Zone CodeFHSZ_DESCFIRE HZ ZONE DESCSTRINGFire Hazard Zone DescriptionFLO_CODEFLOOD CODESTRINGFlood Risk CodeFLO_DESCFLOOD DESCSTRINGFlood Risk DescriptionFAULTZONEFAULT ZONESTRINGFault ZoneLANDSLIDELANDSLIDESTRINGLandslideLIQ_ZONELIQUIFACTION ZONESTRINGLiquifaction ZoneDROUGHTDROUGHTSTRINGDroughtDRO_DESCDROUGHT DESCSTRINGDrought DescriptionSOL_RADSOLAR RADIATIONDOUBLESolar RadiationWIND_SPWIND SPEEDDOUBLEWind SpeedLAND_VALLAND VALUEDOUBLELand ValueBUI_AREABUILT AREADOUBLEBuilt AreaTAXTAXSTRINGTaxOWNERSHIPOWNERSHIPSTRINGOwnershipAREA_ACAREA ACRESDOUBLEAcresAREA_SQFTAREA SQ FTDOUBLESquare Feet

Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

Data was enriched with Fire Hazard Zone, Flood Risk Zone, Fault Zones, Landslide Risk, Liquefaction Zones, Drought Conditions, and the potential for Renewable Energy (Solar Radiation and Wind Speed) layers, in addition to the layers provided by the county. The table schema is detailed below.Name of FieldAliasType of DataDescriptionAPNAPNSTRINGAssessor's Parcel NumberHOUSE_NUMHOUSE NUMINTEGERHouse NumberPREFIX_DIRPREFIX DIRSTRINGPrefix DirectionST_NAMESTREET NAMESTRINGStreet NameST_TYPESTREET TYPESTRINGStreet TypeUNITUNITSTRINGUnitFULL_ADDFULL ADDRESSSTRINGFull AddressFULL_STNAFULL STREET NAMESTRINGFull Street NameCITYCITYSTRINGCityZIP_CODEZIP CODESTRINGZip CodeLAND_USELAND USESTRINGLand Use (County)ZONINGZONINGSTRINGZoningSCAG_LUCOSCAG LANDUSE CODESTRINGLand Use (SCAG Code)SCAG_LUSCAG LANDUSESTRINGLand Use (SCAG)FHSZ_CODEFIRE HZ ZONE CODESTRINGFire Hazard Zone CodeFHSZ_DESCFIRE HZ ZONE DESCSTRINGFire Hazard Zone DescriptionFLO_CODEFLOOD CODESTRINGFlood Risk CodeFLO_DESCFLOOD DESCSTRINGFlood Risk DescriptionFAULTZONEFAULT ZONESTRINGFault ZoneLANDSLIDELANDSLIDESTRINGLandslideLIQ_ZONELIQUIFACTION ZONESTRINGLiquifaction ZoneDROUGHTDROUGHTSTRINGDroughtDRO_DESCDROUGHT DESCSTRINGDrought DescriptionSOL_RADSOLAR RADIATIONDOUBLESolar RadiationWIND_SPWIND SPEEDDOUBLEWind SpeedLAND_VALLAND VALUEDOUBLELand ValueBUI_AREABUILT AREADOUBLEBuilt AreaTAXTAXSTRINGTaxOWNERSHIPOWNERSHIPSTRINGOwnershipAREA_ACAREA ACRESDOUBLEAcresAREA_SQFTAREA SQ FTDOUBLESquare Feet

This shapefile contains tax rate area (TRA) boundaries in Madera County for the specified assessment roll year. Boundary alignment is based on the 2020 county parcel map. A tax rate area (TRA) is a geographic area within the jurisdiction of a unique combination of cities, schools, and revenue districts that utilize the regular city or county assessment roll, per Government Code 54900. Each TRA is assigned a six-digit numeric identifier, referred to as a TRA number. TRA = tax rate area number

Geospatial data about Madera County, California Master Address Point File. Export to CAD, GIS, PDF, CSV and access via API.

This teaching data subset contains1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). 2. Some other general spatial boundary layers from natural earth3. NEON lidar data and insitu measurements for SOAP and SJER sites. The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. We have recently added an additional vector layer so that cropping raster data can be taught using this data set as well.

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.Provisionaldata 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 2007 Tulare County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM), Water Use Efficiency Branch (WUE). Digitized land use boundaries and associated attributes were gathered by staff from DWR’s South Central Region (SCRO), using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Prior to the summer field survey by SCRO, WUE staff analyzed Landsat 5 imagery to identify fields likely to have winter crops. The combined land use data went through standard quality control procedures before final processing. Quality control procedures were performed jointly by staff at DWR’s WUE Land Use Unit and SCRO. 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 western Madera County conducted by DWR, South Central Regional Office staff, under the leadership of Steve Ewert, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2011. SCRO staff physically visited each delineated field, noting the crops grown at each location. Land use field boundaries were digitized using 2006 National Agriculture Imagery Program (NAIP) imagery as the base reference. Roads and waterways were delineated from a countywide shapefile using the U.S. Census Bureau's TIGER® (Topologically Integrated Geographic Encoding and Referencing) database and then clipped to match the USGS quadrangle boundaries. Digitized field boundaries were created on a quadrangle by quadrangle basis. Digitizing was completed at 1:4000 scale for the entire survey area. Field boundaries were delineated to depict observable areas of the same (homogeneous) land use type. Field boundaries do not represent legal parcel (ownership) boundaries, and are not meant to be used as formal parcel boundaries. Field work for DWR land use surveys typically occur during the summer and early fall agricultural seasons, so it can be difficult to identify fields where winter crops have been produced earlier during the survey year. To improve the mapping of winter crops, Landsat 5 imagery was analyzed to identify fields with high vegetative cover in late winter/early spring. Visual inspection of the Landsat scene displayed in false color infrared was used to select fields with both high and low vegetative cover as training data sets. These fields were used to develop spectral signatures using ERDAS Imagine and eCognition Developer software. The Landsat image was classified using a maximum likelihood supervised classification to label each pixel as vegetated or not vegetated. Then, the zonal attributes of polygons representing agricultural fields were summarized to identify fields vegetated during the winter. Polygons representing potential winter crops were used as an additional reference during field visits, and closely checked for winter crop residue. Site visits occurred from July through October 2007. Images and land use boundaries were loaded onto laptop computers that, in most cases, were used as the field data collection tools. GPS units connected to the laptops were used to confirm the surveyor's location with respect to each field. Some staff took printed copies of aerial photos into the field and wrote directly onto these photo field sheets. The data from the photo field sheets were digitized and entered back in the office. Land use codes associated with each polygon were entered in the field on laptop computers using ESRI ArcGIS software, version 9.3. Virtually all delineated fields were visited to positively observe and identify the land use type. 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. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR's South 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 6 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 dataset is the geocoded addresses of the LPA office in California. This dataset's source is DDW's SDWIS contacts database and this publicly available document: https://www.waterboards.ca.gov/drinking_water/programs/documents/web_contact_info_district_lpa.pdfThe dataset was last updated in December 2020 and will be updated as needed.Contact for this dataset/layer is DDW GIS workgroup, DDW-GIS-workgroup@waterboards.ca.gov.

Under contract to the California Department of Fish and Wildlife (CDFW), California Native Plant Society (CNPS) created a fine-scale vegetation map of portions of the Millerton Lake East quadrangle, including protected areas of Big Table Mountain and the McKenzie Preserve at Table Mountain. CNPS conducted field reconnaissance assistance for this project, as well as accuracy assessment (AA) field data collection. CDFW’s Vegetation Classification and Mapping Program (VegCAMP) provided in-kind service to allocate and score the AA. The mapping study area, consists of approximately 11,505 acres, of Fresno and Madera Counties. Work was performed on the project between 2008 and 2010. The primary purpose of the project was to generate an accurate and detailed basemap of a focus area within the southern Sierra Nevada Foothills, supported by field surveys, that would assist in long-term management of sensitive plant communities. Additionally, this map was created to further CDFW’s goal of developing fine-scale digital vegetation maps as part of the California Biodiversity Initiative Roadmap of 2018. CNPS under separate contract and in collaboration with CDFW VegCAMP developed the floristic vegetation classification used for the project. The floristic classification follows protocols compliant with the Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS). The vegetation map was produced applying heads-up digitizing techniques using a 2009 base of one-meter National Agricultural Imagery Program (NAIP) imagery (true-color and color infrared), in conjunction with ancillary data and imagery sources. Map polygons are assessed for Vegetation Type, Percent Cover, Exotics, Development Disturbance, and other attributes. The minimum mapping unit (MMU) is 1 acre. Field reconnaissance and accuracy assessment enhanced map quality. There was a total of 24 mapping classes.

Under contract to the California Department of Fish and Wildlife (CDFW), Aerial Information Systems (AIS) created a fine-scale vegetation map of portions of the Southern Sierra Nevada Foothills in central California. AIS subcontracted the California Native Plant Society (CNPS) to conduct field reconnaissance assistance for this project, as well as accuracy assessment (AA) field data collection; and Soar Environmental Consulting to assist in the AA field data collection. CDFW''s Vegetation Classification and Mapping Program (VegCAMP) provided in-kind service to allocate and score the AA. The mapping study area, consists of approximately 1,824,939 acres, of Mariposa, Madera, Tulare, Kern, and Los Angeles counties. Work was performed on the project between 2019 and 2022. The primary purpose of the project was to further CDFW''s goal of developing fine-scale digital vegetation maps as part of the California Biodiversity Initiative Roadmap of 2018.CNPS under separate contract and in collaboration with CDFW VegCAMP developed the floristic vegetation classification used for the project. The floristic classification follows protocols compliant with the Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS).The vegetation map was produced applying heads-up digitizing techniques using a 2018 base of one-meter National Agricultural Imagery Program (NAIP) imagery (true-color and color infrared), in conjunction with ancillary data and imagery sources. Map polygons are assessed for Vegetation Type, Percent Cover, Exotics, Development Disturbance, and other attributes. The minimum mapping unit (MMU) is 2 acres; exceptions are made for wetlands and riparian types, which were mapped to a 1-acre MMU.Field reconnaissance and accuracy assessment enhanced map quality. There was a total of 111 mapping classes. The overall Fuzzy Accuracy Assessment rating for the final vegetation map,at the Alliance and Group levels, is 89.5 percent.

The 2011 Madera County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s South Central Region using extensive field visits and aerial photography. The land uses that were mapped were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. Landsat 5 imagery was analyzed prior to the field survey by DSIWM staff to map fields likely to have winter crops. The land use data went through standard quality control procedures before final processing. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters and South Central Region.The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.5, dated April 12, 2023. 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.

The 1995 Madera County land use survey data set was developed by DWRthrough its Division of Planning and Local Assistance (DPLA). Thedata 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. Not all land use codes will be represented in the survey.

CDFW BIOS GIS Dataset, Contact: Bob Kingman, Description: According to GreenInfo Network and the California Department of Fish and Game, the blue oak woodland used to define a portion of the Sierra Nevada Conservancy's western boundary was delineated using referenced vegetation and imagery data. To display the Sierra Nevada Conservancy boundary.

This shapefile contains polygons representing areas that had a change to the tax rate area number or boundary according to Statement of Boundary Changes filed with the California State Board of Equalization, per Government Code 54900. The change number refers to the Statement of Boundary Change documents on file with the California State Board of Equalization-Tax Area Services Section. CHG_NO = Board of Equalization (BOE) file number

CDFW BIOS GIS Dataset, Contact: Chris Stermer, Description: This database was established to record furbearer and raptor presence through photographs taken at camera stations. The general study area where camera station were placed include mountainous areas of Madera, Fresno, and Tulare counties.

Boundary Sierra Nevada Conservancy (SNC) boundary. The boundary was mapped to correspond with statute AB 2600 (2004) and as re-defined in SB 208 (2022). Work on the boundary was completed by CalFire, GreenInfo Network, and the California Department of Fish and Game. Meets and bounds description of the area as defined in statute: PRC Section 33302 (f) defines the Sierra Nevada Region as the area lying within the Counties of Alpine, Amador, Butte, Calaveras, El Dorado, Fresno, Inyo, Kern, Lassen, Madera, Mariposa, Modoc, Mono, Nevada, Placer, Plumas, Shasta, Sierra, Siskiyou, Tehama, Trinity, Tulare, Tuolumne, and Yuba, described as the area bounded as follows: On the east by the eastern boundary of the State of California; the crest of the White/Inyo ranges; and State Routes 395 and 14 south of Olancha; on the south by State Route 58, Tehachapi Creek, and Caliente Creek; on the west by the line of 1,250 feet above sea level from Caliente Creek to the Kern/Tulare County line; the lower level of the western slope’s blue oak woodland, from the Kern/Tulare County line to the Sacramento River near the mouth of Seven-Mile Creek north of Red Bluff; the Sacramento River from Seven-Mile Creek north to Cow Creek below Redding; Cow Creek, Little Cow Creek, Dry Creek, and up to the southern boundary of the Pit River watershed where Bear Creek Mountain Road and Dry Creek Road intersect; the southern boundary of the Pit River watershed; the western boundary of the upper Trinity watershed in the County of Trinity; on the north by the boundary of the upper Trinity watershed in the County of Trinity and the upper Sacramento, McCloud, and Pit River watersheds in the County of Siskiyou; and within the County of Modoc, the easterly boundary of the Klamath River watershed; and on the north in the County of Modoc by the northern boundary of the State of California; excluding both of the following: (1) The Lake Tahoe Region, as described in Section 6605.5 of the Government Code, where it is defined as "region" (2) The San Joaquin River Parkway, as described in Section 32510.According to GreenInfo Network and the California Department of Fish and Game, the blue oak woodland used to define a portion of the Sierra Nevada Conservancy's western boundary was delineated using referenced vegetation and imagery data.Subregions“Subregions” means the six subregions in which the Sierra Nevada Region is located, described as follows:(1) The northwest Sierra subregion, comprising the Counties of Shasta, Siskiyou, Tehama, and Trinity.(2) The northeast Sierra subregion, comprising the Counties of Lassen, Modoc, Plumas, and Sierra.(3) The north central Sierra subregion, comprising the Counties of Butte, Nevada, Placer, and Yuba.(4) The south central Sierra subregion, comprising the Counties of Amador, Calaveras, El Dorado, and Tuolumne.(5) The southeast Sierra subregion, comprising the Counties of Alpine, Inyo, Kern, and Mono.(6) The southwest Sierra subregion, comprising the Counties of Fresno, Madera, Mariposa, and Tulare.

This data represents a land use survey of western Madera County conducted by DWR, South Central Regional Office staff, under the leadership of Steve Ewert, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2011. SCRO staff physically visited each delineated field, noting the crops grown at each location. Land use field boundaries were digitized using 2006 National Agriculture Imagery Program (NAIP) imagery as the base reference. Roads and waterways were delineated from a countywide shapefile using the U.S. Census Bureau's TIGER® (Topologically Integrated Geographic Encoding and Referencing) database and then clipped to match the USGS quadrangle boundaries. Digitized field boundaries were created on a quadrangle by quadrangle basis. Digitizing was completed at 1:4000 scale for the entire survey area. Field boundaries were delineated to depict observable areas of the same (homogeneous) land use type. Field boundaries do not represent legal parcel (ownership) boundaries, and are not meant to be used as formal parcel boundaries. Field work for DWR land use surveys typically occur during the summer and early fall agricultural seasons, so it can be difficult to identify fields where winter crops have been produced earlier during the survey year. To improve the mapping of winter crops, Landsat 5 imagery was analyzed to identify fields with high vegetative cover in late winter/early spring. Visual inspection of the Landsat scene displayed in false color infrared was used to select fields with both high and low vegetative cover as training data sets. These fields were used to develop spectral signatures using ERDAS Imagine and eCognition Developer software. The Landsat image was classified using a maximum likelihood supervised classification to label each pixel as vegetated or not vegetated. Then, the zonal attributes of polygons representing agricultural fields were summarized to identify fields vegetated during the winter. Polygons representing potential winter crops were used as an additional reference during field visits, and closely checked for winter crop residue. Site visits occurred from July through October 2007. Images and land use boundaries were loaded onto laptop computers that, in most cases, were used as the field data collection tools. GPS units connected to the laptops were used to confirm the surveyor's location with respect to each field. Some staff took printed copies of aerial photos into the field and wrote directly onto these photo field sheets. The data from the photo field sheets were digitized and entered back in the office. Land use codes associated with each polygon were entered in the field on laptop computers using ESRI ArcGIS software, version 9.3. Virtually all delineated fields were visited to positively observe and identify the land use type.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. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land.Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate.Before final processing, standard quality control procedures were performed jointly by staff at DWR's South 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 6 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 database was established to record furbearer and raptor presence through photographs taken at camera stations. The general study area where camera stations were placed included parts of Madera, Fresno, and Tulare counties. Coordinates for the camera station were recorded with Garmin 12xl or Gramin Rhino GPS receivers. Coordinates written in the shapefile .dbf of this data layer are in NAD27. Output of data in viewer has been converted to NAD83.