Santa Rosa Plain Programmatic Biological Opinion Parcels - Data "CDR_PARCELS" obtained from County of Sonoma GIS Central.

A distinction should be made with respect to this layer which includes GIS parcels and the official Assessor Parcels residing in the Assessor Map books at the Sonoma County Assessor Office. For official parcel records please contact the Sonoma County Assessor (707)565-1888. These parcels should NOT be represented as survey data, and the official record of survey takes precedence where there are discrepancies. It is the end user's responsibility to check the accuracy of the GIS data by comparing it with the published data from the Sonoma County Assessor / Recorder office. The Sonoma County parcel base was originally compiled from Assessor Parcel maps at a scale of 1:6000. The individual Assessor Parcel maps were enlarged or reduced in size using an electrostatic process to produce the maps at the 1:6000 scale, the maps were then fit together by hand and transcribed on to mylar. The mylar base consisted of 1:6000 USGS base map information typically found on the 7.5 USGS quad series. This base information consisted of Topography, Roads, Section, and Rancho lines to name some. Using this information, the Assessor Parcel maps were fit to the individual 1:6000 scale maps. Each 1:6000 scale map represents 1/6 (quad sixths) of a 7.5 minute USGS Quadrangle series map. In 1998 the State Board of Equalization provided the impetus to produce the Russian River Project for all of Planning Area 4. One aspect required for this project was a digital parcel base for Planning Area 4. This involved the conversion of the 1:6000 mylars with the transcribed parcels on them into a digital version of the parcels. The mylars where scanned and geo-referenced using the base map information originally included with the 1:6000 mylar base. The maps were geo-referenced to a digital version of the USGS 7.5 minute Quadrangle series available from the Teale Data Center. The original projection was California State Plane Zone 2 NAD 1927. County Staff then used AutoCAD software to heads up digitize each 1:6000 scale map in Planning Area 4. A custom application was created and used by GIS staff involving the use of Avenue and ArcView 3.2 to create a point for all the parcels in Planning Area 4, attributes included Assessor Parcel Number. The DWGs were then converted to shapefiles and then converted to ArcINFO coverages, the parcel tags were converted from shapefiles to ArcINFO coverages and the point coverage was merged with the polygon coverage with the IDENTITY command. An exhaustive process was involved to eliminate errors once the DWGs were converted to ArcINFO coverages so polygons could be generated. The coverages were then aggregated using the MAPJOIN command, the original boundary of the 1:6000 scale maps was removed using the REGIONDISSOLVE command to merge adjacent polygons with the same AP number. In 1999 the remainder of the planning areas were converted to digital form following the Russian River Project and the seamless base was completed in 2001. The seamless parcel base was maintained in ArcINFO until the release of ArcGIS 8.3, which included topology tools necessary for its maintenance. The seamless base prior to late 2002 was suitable for 1:100000 scale while the control points (the corners for the 1:6000 scale maps) were suitable for 1:24000 scale. Prior to rectification to the Merrick 2000 orthophotography, the parcel data were derived from 1:6000 scale maps (enlarged from USGS 7.5 minute quadrangle 1:24,000 series) and digitized in California State Plane, Zone II, NAD 27 coordinates (survey feet), but were converted to California State Plane, Zone II, NAD 83 coordinates (survey feet) as part of a rectification process now underway. The parcels used to use the USGS 7.5 minute quadrangle (1:24,000) series for coordinate control, but no guarantee is made for their spatial accuracy. The data were re-projected to NAD 83 coordinates to overlay the orthophotography, but the parcel boundaries will not correspond precisely with features in the images. The parcels were rectified to orthophotography flown in April - May 2000 using geo-referencing tools available in ArcGIS 8.3. This project was completed in July 2005. In general, the parcels meet National Accuracy Standards for 1:24,000 scale maps, and likely exceed that accuracy in urban areas. A complete description of the process is detailed in a series of documents located on a local file server: S:\COMMON\GIS\Documentation\Parcel Rectification & Update Process\Procedure - *. doc. A brief summary is as follows. Individual Assessor Parcel pages or CAD drawings are rectified to the orthophoto. COGO & survey data are used when available and in sufficient quantities to enable the bulk of an Assessor Parcel page to be digitized using said information. Polygons are generated directly from the COGO data, CAD dwg are exported to feature classes, where polygons are then generated, rectified Assessor Parcel pages are vectorized using ArcScan and subsequently polygons are generated. A spatial join is used to assign attributes to the newly generated polygons. Polygons are then assigned an accuracy rank based on source, quality of the fit to the orthophoto, and RMS error encountered during rectification (only the scanned Assessor maps will have and RMS error associated with them). See the fields RANK and DESCRIPTION for information on fit assessment. Areas that have been successfully updated as such have a reasonable expectation of accuracy of +/- 10 and possibly better, areas that have not been updated or are flagged in SCAMP under the GIS group Projects as Needs Survey Data, the original accuracy assessment of 1:100000 applies.

The seamless, county-wide parcel layer was digitized from official Assessor Parcel (AP) Maps which were originally maintained on mylar sheets and/or maintained as individual Computer Aided Design (CAD) drawing files (e.g., DWG). The CRA office continues to maintain the official AP Maps in CAD drawings and Information Systems Department/Geographic Information Systems (ISD/GIS) staff apply updates from these maps to the seamless parcel base in the County’s Enterprise GIS. This layer is a partial view of the Information Sales System (ISS) extract, a report of property characteristics taken from the County’s Megabyte Property Tax System (MPTS). This layer may be missing some attributes (e.g., Owner Name) which may not be published to the Internet due to privacy conditions under the California Public Records Act (CPRA). Please contact the Clerk-Recorder-Assessor (CRA) office at (707) 565-1888 for information on availability, associated fees, and access to other versions of Sonoma County parcels containing additional property characteristics.The seamless parcel layer is updated and published to the Internet on a monthly basis.The seamless parcel layer was developed from the source data using the general methodology outlined below. The mylar sheets were scanned and saved to standard image file format (e.g., TIFF). The individual scanned maps or CAD drawing files were imported into GIS software and geo-referenced to their corresponding real-world locations using high resolution orthophotography as control. The standard approach was to rescale and rotate the scanned drawing (or CAD file) to match the general location on the orthophotograph. Then, appropriate control points were selected to register and rectify features on the scanned map (or CAD drawing file) to the orthophotography. In the process, features in the scanned map (or CAD drawing file) were transformed to real-world coordinates, and line features were created using “heads-up digitizing” and stored in new GIS feature classes. Recommended industry best practices were followed to minimize root mean square (RMS) error in the transformation of the data, and to ensure the integrity of the overall pattern of each AP map relative to neighboring pages. Where available Coordinate Geometry (COGO) & survey data, tied to global positioning systems (GPS) coordinates, were also referenced and input to improve the fit and absolute location of each page. The vector lines were then assembled into a polygon features, with each polygon being assigned a unique identifier, the Assessor Parcel Number (APN). The APN field in the parcel table was joined to the corresponding APN field in the assessor property characteristics table extracted from the MPTS database to create the final parcel layer. The result is a seamless parcel land base, each parcel polygon coded with a unique APN, assembled from approximately 6,000 individual map page of varying scale and accuracy, but ensuring the correct topology of each feature within the whole (i.e., no gaps or overlaps). The accuracy and quality of the parcels varies depending on the source. See the fields RANK and DESCRIPTION fields below for information on the fit assessment for each source page. These data should be used only for general reference and planning purposes. It is important to note that while these data were generated from authoritative public records, and checked for quality assurance, they do not provide survey-quality spatial accuracy and should NOT be used to interpret the true location of individual property boundary lines. Please contact the Sonoma County CRA and/or a licensed land surveyor before making a business decision that involves official boundary descriptions.

This shapefile contains tax rate area (TRA) boundaries in Sonoma County for the specified assessment roll year. Boundary alignment is based on the 2018 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

This dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology. Data are also provided on the research grids for georeferencing. The map data are from a variety of sources and encompass the period 1970-06-01 to 2015-08-31.

The BOREAS RSS-08 team utilized Landsat TM images to perform mapping of snow extent over the SSA. This data set consists of two Landsat TM images which were used to determine the snow-covered pixels over the BOREAS SSA on 18-Jan-1993 and on 06-Feb-1994. Companion files include example thumbnail images that may be viewed using a convenient viewer utility.

These DEMs were produced from digitized contours at a cell resolution of 100 meters. Vector contours of the area were used as input to a software package that interpolates between contours to create a DEM representing the terrain surface. The vector contours had a contour interval of 25 feet. The data cover the BOREAS MSAs of the SSA and NSA and are given in a UTM map projection.

This data set is a condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU). This map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile). The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base. This data set was prepared by SERM-FBIU. The data is a condensed forest cover type map of Saskatchewan at a scale of 1:1,000,000.

Parcels that have changed ownership/title since the October 2017 Tubbs Fire. Based upon .csv data received from Sonoma County Recorder's office on a monthly basis.

This dataset provides a land cover map focused on peatland ecosystems in the upper peninsula of Michigan. The map was produced at 12.5-m resolution using a multi-sensor fusion (optical and L-band SAR) approach with imagery from Landsat-5 TM and ALOS PALSAR collected between 2007 and 2011. A random forest classifier trained with polygons delineated from field data and aerial photography was used to determine pixel classes. Accuracy assessment based on field-sampled sites show high overall map accuracy (92%).

This data set provides map images of hydrographic, morphologic, and edaphic features for the northern Amazon Basin in eastern Ecuador. The hydrographic data are available at two scales based on the 1:50,000 and 1:250,000-scale topographic source maps that were generated in 1990 and 1993, respectively. Morphological and edaphological data were digitized from a 1:500,000 map published in 1983. There are 3 compressed (*.zip) data files with this data set.

As part of BOREAS, the RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with SAR and Landsat TM data and development of simple mapping techniques using combinations of SAR channels. For the SSA, the SIR-C data used were acquired on 06-Oct-1994, and the Landsat TM data used were acquired on September 2, 1995. The maps of the NSA were developed from SIR-C data acquired on 13-Apr-1994.

This data set was prepared by BORIS staff by reformatting the original data into the ARC/INFO Generate format. The original data were received in SIF at a scale of 1:50,000. BORIS staff could not find a format document or commercial software for reading SIF; the BOREAS HYD-08 team provided some C source code that could read some of the SIF files. The data cover the BOREAS NSA and SSA. The original data were compiled from information available in the 1970s and 1980s.

This data set provides the spatial distributions of vegetation types, soil carbon, and physiographic features in the Toolik Lake area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology.

This dataset provides maps of tidal marsh green vegetation, non-vegetation, and open water for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from current National Agriculture Imagery Program data (2013-2015) using object-based classification for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program (C-CAP) map. These 1m resolution maps were used to calculate the fraction of green vegetation within 30m Landsat pixels for the same tidal marsh regions and these data are provided in a related dataset.

This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the Arctic tundra region of Alaska for the period 1993-2005. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, and substrate chemistry. This data set generally includes areas North and West of the forest boundary and excludes areas that have a boreal flora such as the Aleutian Islands and alpine tundra regions south of treeline.

This data set includes classified land cover transition maps at 30-m resolution derived from Landsat TM, MSS, ETM+ imagery and aerial photos of Altamira, Santarem, and Ponta de Pedras, in the state of Para, Brazil. The Landsat images were classified into several types of land use (e.g., forest, secondary succession, pasture, annual crops, perennial crops, and water) and subjected to change detection analysis to create transition matrices of land cover change. Dates of acquired images represent the most cloud-free image retrievals from 1970-2001 for each site and are therefore not continuous. There are 3 GeoTIFF files (.tif) with this data set.

This data set provides classified land cover transition images (maps) derived from Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) imagery for Ariquemes, Luiza, and Ji-Parana¡ areas in Rondonia, Brazil, at 30-m resolution. Images depict the age relative to the year 2000, of cleared land from the date the land was cut, to the date when primary forests transitioned into nonforest class (for example, 25 = cut by 1975, or 25 years before the year 2000).

Temporal changes in three regions are represented by 31 TM scenes acquired between 1984 and 1999, and a pair of MSS scenes from 1975 and 1978.

Data are provided as three GeoTiff (*.tif) images, one for each of the three areas.

This dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019. An additional file for 2015 was created for accuracy assessment. A high-quality and low-cloud coverage image from Landsat 5 or Landsat 8 over each 5-year period was selected or composited for the January-August timeframe. Investigators used their knowledge to manually identify training polygons in these images for five LUC classes: peat swamp forest, tall shrubs/ secondary forest, low shrubs/ferns/grass, urban/bare land/open flooded areas, and river. Pixel values of Landsat Tier 1 surface reflectance products and selected indices were extracted for each LUC and used to predict LUC classes across the Mawas study area using the Classification and Regression Trees (CART) method. These data can be used to evaluate the relationship between fire occurrence and land cover type in the study site.

This dataset includes aboveground biomass (AGB) and vegetation of herbaceous and forest wetland at 5.4 m resolution across the Wax Lake Delta (WLD) in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Vegetation classes were derived from Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquired over the Atchafalaya Basin and the Terrebonne Basin in October 2016 in combination with a digital elevation model. The AVIRIS-NG surface reflectance data were also combined with L-band Uninhabited Airborne Vehicle Synthetic Aperture Radar (UAVSAR) HV backscatter and scattering component values from coincident vegetation sample sites to develop and test AGB models for emergent herbaceous and forested wetland vegetation. This study used the integrated airborne data from AVIRIS-NG and UAVSAR to assess the instruments' unique capabilities in combination for estimating AGB in coastal deltaic wetlands. The 5.4 m resolution vegetation classification map for the WLD study area was then used to apply the best models to estimate AGB across the WLD.