This dataset is a polygon shapefile representing the most recent update of the coal fields of the conterminous United States. Scale of data is 1:5,000,000. This publication is based on a USGS paper map that was a representation of the coal fields and major regions of the time (Trumbull, 1960). Trumbull's 1960 map was digitized and coal fields from the Gulf Coast were added to create USGS OFR 96-92, Coal Fields of the Conterminous United States (Tully, 1996). Tully's (1996) publication consisted of a map in pdf format that could be printed, and an ArcInfo coverage of the coal fields, attributed with rank and potential economic use (minability) of the coal. This new dataset includes a pdf showing updated coal fields and a shapefile that contains attributes on coal rank (without regard to outdated economic standards), province, name, and age. The data used to update Tully's (1996) digital map was collected from the National Coal Resource Assessment (NCRA) regional Professional Papers produced by the USGS and from AAPG Discovery Series 14/Studies in Geology 62, all of which were conducted by USGS geologists and professional staff. A small number of field names were added and or updated in the western states of Washington, Oregon, California, Utah, Colorado and New Mexico using additional coal resource literature.The full study is available from USGS: https://doi.org/10.3133/ofr20121205

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This shapefile shows the extent of the Grafton-Rapville bedrock in the CLM bioregion. Dataset History The shapefile is derived from a preliminary geological map provided by the NSW Geological Survey that shows the extent of this formation in NSW (see lineage). This polygon shapefile has been created from a preliminary datasets provided by the NSW Geological Survey. The original shapefile consisted of multiple polygons that have been separated for example where alluvia overlie this bedrock unit. This polygons have been dissolved to create a single shapefile that shows the extent of the unit at the surface and in the subsurface. Dataset Citation Bioregional Assessment Programme (2014) CLM - Grafton-Rapville bedrock. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/1c217d86-ba4e-480b-8786-2ab083c2495f. Dataset Ancestors Derived From Qld 100K mapsheets - Allora Derived From Qld 100K mapsheets - Jandowae Derived From Qld 100K mapsheets - Mount Lindsay Derived From Qld 100k mapsheets - Warwick Derived From Qld 100k mapsheets - Beenleigh Derived From Qld 100K mapsheets - Caboolture Derived From Qld 100K mapsheets - Ipswich Derived From NSW Geological Survey - geological units DRAFT line work. Derived From CLM - Qld Surface Geology Mapsheets Derived From Qld 100K mapsheets - Toowoomba Derived From Qld 100K mapsheets - Esk Derived From Qld 100k mapsheets - Murwillumbah Derived From Qld 100K mapsheets - Helidon Derived From CLM - NSW Surface Geology Mapsheets in the Clarence-Moreton bioregion Derived From CLM - Geology NSW & Qld combined v02 Derived From Qld 100K mapsheets - Inglewood Derived From Qld 100K mapsheets - Oakey Derived From Clarence-Moreton SEEBASE & Structural GIS Project data. Derived From Qld 100k mapsheets - Kingaroy

This dataset contains a classification of the stream reaches in the Murray Darling Basin by flow continuity assessed as perennial / intermittent / ephemeral within the network of the AWRA-R river systems model. The classification is applied on a rolling 10-year basis from 1/1/1873 to 31/12/2020. The dataset was created for the purpose of identifying which reaches are perennial / intermittent / ephemeral and how these conditions have changed through time.

The definitions used in the flow continuity classification are: •\tPerennial stream has continuous flow for minimum of three out of five years (i.e. flow may cease in 2 out of 5 years) •\tEphemeral stream has flow events that do not exceed continuous 30 days duration in three out of five years (can have multiple short events per year) •\tIntermittent streams are everything else not classified as perennial or ephemeral.

The dataset contains: •\ta figure displaying a summary of the data as a long term average (flow_continuity_LTA.png) and another figure with the classification applied over the most recent period of 2011-2020 (flow_continuity_2011-2020.png). •\tA shapefile containing the location of all 519 applicable gauges with the classification results for the long term average and the most recent period (Flow_Continuity.shp). •\tA csv file containing the results for each of the 139 10-year periods for each gauge (Flow_Continuity_10yr_summary.csv). These results can be joined to the shapefile to display spatially suing the GaugeId field. •\tA polyline shapefile of the stream reaches used in the AWRA-R model of the Murray Darling Basin classified using the long term average data and the most recent 10 years. This shapefile is an interpretation of the spatial interpolation between gauges (generally the gauge is assumed representative of what the conditions are upstream). This shapefile was created to aid the visual interpretation of the differences in flow continuity between the most recent 10 years compared to the long term average, the point data at the gauge is the point of truth not this polyline shapefile.

The point shapefile contains fields for identifying the stream gauge used such as the gauge ID (GaugeId), gauge name (GaugeName) and the latitude and longitude (GDA94). The start date (StartDate) and end date (EndDate) refer the period of observation and the days of valid observations (DaysOfVali) refer to the number of days between these dates that have valid observations. There are two fields for the results of the classification for the long term average (Cont_LTA) and the most recent 10 years (Cont_11_20).

The polyline shapefile contains the river network from the AWRA-R river model, the reaches within this model are identified by ID_updated and Reach_ID. The downstream gauge for each reach is identified by the fields for the gauge ID (GaugeID) and gauge name (GaugeName). There are two fields for the results of the classification for the long term average (Cont_LTA) and the most recent 10 years (Cont_11_20).

Lineage: The stream flow data was downloaded from the Bureau of Meteorology (2015) for all available gauges. In some cases where a gauge had moved (with the same gauge number with a letter suffix) the records were combined into a single time series. For each year the presence of no flow days was recorded along the with the maximum number of continuous days of flow needed for the classification. Each gauge was classified according to the definitions above for the entire record and a rolling 10 year period.

The stream reaches included in the classification are from the AWRA-R model of the Murray Darling Basin (Dutta et al, 2015) with the polylines selected from GA’s 250K topographic maps.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This polygon shapefile shows the extent of the Bungawalbin member (Orara Formation). It has been created from a preliminary datasets provided by the NSW Geological Survey (not released as of August 2014). The original shapefile consisted of multiple polygons that have been separated for example where alluvia overlie this bedrock unit. The boundaries have been dissolved using GoCAD™ (Paradigm Geophysical Pty Ltd) 3D geological modelling software to also include the extent of this unit where it is not present at the surface. The final shapefile represents the extent of the unit at the surface and in the subsurface. This dataset was created by the Bioregional Assessment Program in the understanding that the preliminary work done by NSW Geological Survey may change but was the best available source data at the time. NSW Geological Survey bear no responsibility for any errors in this dataset. Dataset History This shapefile shows the extent of the Bungawalbin Member (part of the Orara Formation) in the CLM bioregion. It is modified from a preliminary datasets provided by the NSW Geological Survey (not released as of August 2014). In some areas, the exact extent of the Bungawalbin Member is not well constrained, as this bedrock unit is covered by younger rocks (e.g. volcanic rocks) or alluvium. Where this is the case, the original maps which only show the presence of the Bungawalbin Member at the surface have been combined with geological expert knowledge and manual interpolation (digitizing) using GoCAD™ (Paradigm Geophysical Pty Ltd) 3D geological modelling software to also include the extent of this unit where it is not present at the surface. The final shapefile has been created from all sources and expert knowledge in GoCAD. Dataset Citation Bioregional Assessment Programme (2014) CLM - Orara-Bungawalbin bedrock. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/c5f52b4d-5a3e-4772-97f9-171b875e32dc. Dataset Ancestors Derived From NSW Geological Survey - geological units DRAFT line work.

This data represents a seamless bedrock geological dataset encompassing Republic of Ireland and parts of Northern Ireland. The seamless geological dataset was created from the GSI Bedrock 1:100,000 scale digital geological map series.

The dataset comprises 6 key shape files.

1) Bed100k.shp - A polygon shapefile that contains bedrock geological information on Stratigraphy, Igneous, Lithology and Diagentic codes, their unitnames and brief descriptions.

Fields: AREA: Area of the polygon in square metres. Field type: Double. PERIMETER: Perimeter of polygons in metres: Field type: Double. NEWCODE: unique identifier for every formation or member. Field type: String. SHEETNO: 100k sheets from which they originated before creating the seamless version. Field type: Double. STRATCODE: Stratigraphic code for unit, as labeled on printed maps and their legends. Field type: String. LITHCODE: Lithological code. Field type: String. DESCRIPT: Brief description of the dominant rock types. Field type: String. C,M,Y,K: cyan, magenta, yellow and black percentage. Field types: Double. UNITNAME: Name of the geological formation or member. Field Type: String.

2) Index100k.shp - An overlay polygon index of 1:100,000 scale printed map sheets.

Fields: SHEETNO: 1:100,000 printed sheet number. Field type: Double.

3) Struc100k.shp (Structural Linework) - A linework shapefile that contains structural geological linework codes and descriptions

Fields: LENGTH: Length of the feature in metres: Field type: Float. CODE: Unique identifier for structure type. Field type: String. SHEET: The 1:100,000 printed map sheet number on which the structure was originally located. Field type: Double. FOLDNAME: Name field for regionally important folds. Field type: String. FAULTNAME: Name field for regionally important faults. Field type: String. ADDITION: Additional information field for structure. Field type: String. DESCRIPT: Description of structure type. Field type: String. SLIDE: Name field for regionally important slides. Field type: String.

4) Strat100k.shp (Stratigraphic Linework) - A linework shapefile that contains stratigraphic geological line codes and descriptions.

Fields: LENGTH: Length of the feature in metres: Field type: Float. CODE: Unique identifier for stratigraphic line type. Field type: String. SHEET: The 1:100,000 printed map sheet number on which the stratigraphic line was originally located. Field type: Double. DESCRIPT: Description of stratigraphic line type. Field type: String. ADDITION: Additional information field for stratigraphic line. Field type: String. DYKELABEL: Igneous dyke code identifier. Field type: String. STRATCODE: Stratigraphic code for narrow formations or members which are represented by a line rather than a polygon in Bed100k. Field type: String. LITHCODE: Lithological code for narrow formations or members which are represented by a line rather than a polygon in Bed100k. Field type: String.

5) Sect100k.shp (Crosss section) - A linework shapefile indicating the locations of map sheet cross sections as per paper printed maps.

Fields: LENGTH: Length of the feature in metres. Field type: Double. XSECTNAME: The name of the cross section, as determined by the letters indicating the starting point, intermediate turning points and end point on the printed map sheets and as on the marginalia diagrams. Field type: String. SHEETNO: The 1:100,000 map sheet on whose marginalia the cross-section diagram is published. Field type: Double.

6) Mins100k.shp - A point shapefile contains mineral and quarry descriptions from Bedrock 1:100,000 map series. This is a subset of the MINLOCS database held by Minerals Section in the GSI.

Fields: CODE: GSI Minerals Section code for the type of deposit. Field type: String. MINTEXT: Short code indicating dominant mineral type(s). Field type: String. SHEET: 1:100,000 printed map sheet on which the deposit occurs. Field type: Long. LOCNUM: MINLOCS database unique identifier for deposit. Field type: Double. DESCRIPTION: Descriptive comment on the type of mine or quarry. Field type: String. MINLEGEND: Descriptive text, based on the MINTEXT field, lisitng the dominant mineral type(s). Field type: String.

The original printed map series and seamless dataset is based on the © Ordnance Survey of Ireland topological maps at 1/2 inch to one mile which were converted photographically to the metric 1:100,000 scale by the Geological Survey of Ireland Cartographic Unit. The topological base maps are not provided in the data set.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This shapefile shows the extent of the Grafton-Piora bedrock in the CLM bioregion. Dataset History The shapefile is based on the digital 1:250000 surface geological map (see lineage) obtained from the Clarence-Moreton SEEBASE project in NSW. This polygon shapefile has been created from a preliminary dataset provided by the NSW Geological Survey. The original shapefile consisted of multiple polygons that have been separated for example where alluvia overlie this bedrock unit. The polygons have been dissolved to create a single shapefile that shows the extent of the unit at the surface and in the subsurface. Dataset Citation Bioregional Assessment Programme (2014) CLM - Grafton-Piora bedrock. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/59addbea-b8a5-4409-9c36-da260efe81f1. Dataset Ancestors Derived From Qld 100K mapsheets - Allora Derived From CLM - Geology NSW & Qld combined v02 Derived From Qld 100k mapsheets - Warwick Derived From CLM - Qld Surface Geology Mapsheets Derived From Qld 100k mapsheets - Beenleigh Derived From Qld 100K mapsheets - Caboolture Derived From Qld 100K mapsheets - Ipswich Derived From Qld 100K mapsheets - Mount Lindsay Derived From Qld 100K mapsheets - Esk Derived From Qld 100K mapsheets - Toowoomba Derived From Clarence-Moreton SEEBASE & Structural GIS Project data. Derived From Qld 100K mapsheets - Helidon Derived From CLM - NSW Surface Geology Mapsheets in the Clarence-Moreton bioregion Derived From Qld 100K mapsheets - Jandowae Derived From NSW Geological Survey - geological units DRAFT line work. Derived From Qld 100K mapsheets - Inglewood Derived From Qld 100K mapsheets - Oakey Derived From Qld 100k mapsheets - Murwillumbah Derived From Qld 100k mapsheets - Kingaroy

The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA) and the Massachusetts Office of Coastal Zone Management (MA CZM), is producing detailed geologic maps of the coastal sea floor. Imagery, originally collected by NOAA for charting purposes, provide a fundamental framework for research and management activities along this part of the Massachusetts coastline, show the composition and terrain of the seabed, and provide information on sediment transport and benthic habitat. Interpretive data layers were derived from multibeam echo-sounder and sidescan sonar data collected in the vicinity of Quicks Hole, a passage through the Elizabeth Islands that extend in a chain southwestward off Cape Cod, Massachusetts. In June 2005, bottom photographs and surficial sediment data were acquired as part of a ground-truth reconaissance survey.

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 datasets 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 2009 Fresno County, east, 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, under the supervision of Steve Ewert. 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 eastern Fresno 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 2009. SCRO staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary data was developed using: 1. Eastern Fresno County was surveyed using the 2006 two-meter resolution National Agriculture Imagery Program (NAIP) digital aerial photos as a base for the preliminary line work. Line work for this survey was digitized using ArcMap software. When the 2009 one-meter resolution NAIP aerial photography became available, this was used to review the digital land use data. 2. The western boundary of the survey area is defined by the western boundaries of DWR’s Detailed Analysis Units 235 and 237. The northern boundary of the survey area is defined in part by the county boundary and also by the northern boundaries of the following U.S. Geological Survey’s (U.S.G.S) 7.5’ quadrangles: Friant (U.S.G.S. No. 36119H6), Academy (U.S.G.S. No. 36119H5), Piedra (U.S.G.S. No. 36119G4) and Pine Flat Dam (U.S.G.S. No. 36119G3). The eastern boundary of the survey area is defined in part by the county boundary and also by the eastern boundaries of the following quadrangles: Academy (U.S.G.S. No. 36119H5), Pine Flat Dam (U.S.G.S. No. 36119G3) and Orange Cove North (U.S.G.S. No. 36119F3). The southern boundary of the survey area is defined by the county boundary. 3. Digital aerial photographs and land use field boundaries were copied onto laptop computers for field data collection. The staff took these laptops into the field and virtually all areas were visited to positively identify the agricultural land uses. Land use codes were digitized directly into the laptop computers using ArcMap software using a standardized digitizing process. Some staff took printed aerial photos into the field instead of laptops and wrote land use codes directly onto these photo field sheets. Attributes for these areas were digitized later in the office. The field visits occurred between July 2009 and January 2010. Urban areas were primarily mapped by photo interpretation. Sources of irrigation water were not mapped in this survey. 4. Shapefiles of the field boundary lines and point attributes of the survey data were brought into ARCINFO. Both quadrangle and survey-wide polygon shapefiles were created, and underwent quality checks. 5. Winter grain fields were mapped using an analysis of Landsat 5 imagery. Two major assumptions in the analysis were that 1.) Winter grain was grown on some of the fields where corn, sudan or tomatoes were grown during the summer or where fields were fallow during the summer. 2.) For the fields listed above, we assumed that fields with high winter canopy cover were grain fields. To detect the winter grain fields of eastern Fresno County for the 2009 land use survey, corn fields were queried from the initial shapefile of the land use survey and classified using Landsat 5 imagery. The corn field polygons were buffered in 30 meters to reduce edge effects on the classification. The buffering eliminated some of the smaller fields leaving 798 fields to be classified. The Landsat 5 image acquired on 04/23/2009 was selected as the most appropriate for mapping grain for this survey. Approximately 10 percent of the corn fields were non-randomly selected to represent winter grain and fallow fields. Using a false color infrared display, bright red fields were selected to represent grain and light blue (non-red) fields were selected to represent fallow fields. Using the Hawth’s tools function, the selected fields were randomly divided into training (60%) and accuracy assessment (40%) categories. The polygons were then converted into raster format from vector format. Using ERDAS Imagine, the raster files were used to mask the Landsat 5 image and create two subset Landsat images representing training fields only and training plus accuracy assessment fields. eCognition Developer version 8.0 software was used with the Landsat image of training fields to segment each field into smaller signature areas. Polygons representing these signature areas were exported from eCognition Developer and the attributes of grain or fallow were added to these polygons. Spectral signatures based upon Landsat 5 bands 1,2,3,4,5, and 7 were created using ERDAS Imagine 2010. After associating the signatures with the image of training and accuracy assessment fields combined, a supervised classification was performed using the maximum likelihood parametric rule to classify each pixel. Zonal attributes of the fields were calculated using the recoded image. Based on the zonal attribute plurality, fields were classified as either winter grain or winter fallow. When there were no errors in the identification of “grain” and “fallow” fields in the fields reserved for accuracy assessment, a supervised classification was performed on the Landsat pixels representing all summer corn fields. Landsat images of each classified corn field were visually inspected in ArcMap to determine the reasonableness of the classification results. In addition to the Landsat 5 scene acquired on 04/23/2009, scenes acquired on 08/10/2008, 11/14/2008, 03/06/2009, 03/22/2009, 04/07/2009, 05/09/2009, 05/25/2009 and 06/26/2009 were used for the visual review of the results. Using the above methods, 660 fields were identified as winter grain. In a second process, polygons representing 315 fields that had initially been mapped as fallow, sudan or tomatoes during the 2009 summer field work were selected from the original land use survey shapefile. These were combined with the polygons representing the previously selected training fields. The polygons were converted from vector to raster format. The resulting raster file was used to mask the April 23, 2009 Landsat 5 image using ERDAS Imagine to produce a subset image. This new image was associated with the signatures previously developed to classify winter grain fields, and a supervised classification of each pixel was performed using the maximum likelihood parametric rule. After recoding, zonal attributes were calculated for each polygon. Based on the plurality calculated for each field, fields were identified as either grain or fallow for the winter season. Polygons identified as grain fields were individually inspected in ArcMap to assure the reasonableness of the classification results. Using the above methods, 67 fields were identified as winter grain. Polygons representing all winter grain crops identified by classifying the Landsat 5 images were merged together. The original land use shapefile was updated by adding grain as a first crop to the selected polygons and moving the summer crop into the set of cells that represent a second crop. All field boundary changes were incorporated into the original shapefile. The area, perimeter and acreages were updated at the end of the process. 6. After quality control/assurance procedures were completed on each file, the data was processed into a final polygon shapefile. The primary

The 1998 Mariposa 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 land use vector data is a single, polygon, shapefile format. Important points about using this dataset: 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 information was not collected for this survey. 5. Not all land use codes will be represented in the survey.

Analysis of ‘East Fresno County Land Use Survey 2009’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7bcc9ccc-f011-4450-9f12-81a43f73a1dd on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Two marine geological surveys were conducted in Long Island Sound, Connecticut and New York, in fall 2017 and spring 2018 by the U.S. Geological Survey (USGS), University of Connecticut, and University of New Haven through the Long Island Sound Mapping and Research Collaborative. Sea-floor images and videos were collected at 210 sampling sites within the survey area, and surficial sediment samples were collected at 179 of the sites. The sediment data and the observations from the images and videos are used to identify sediment texture and sea-floor habitats.Attributes:ANALYSIS_ID: An identifier for the sample that is unique to the database. This identifier begins with the assigned multi-letter code GS-, which corresponds to the type of analysis performed on the sample (grain-size analysis), followed by a six-digit number assigned sequentially as samples are registered for analysis.SAMPLE_ID: The identification value assigned to the sample at the time of collection. This varies from field activity to field activity, and the ID can contain any combination of letters and numbers.FAN: The serial number assigned to the dataset field activity during which the sample was collected. This value is in the format YYYY-XXX-FA where YYYY is the year, XXX is the number assigned to the activity within the year, and FA indicates Field Activity.LATITUDE: Latitude coordinate, in decimal degrees (WGS 84), of sample location. South latitude isrecorded as negative values.LONGITUDE: Longitude coordinate, in decimal degrees (WGS 84), of sample location. West longitude is recorded as negative values.DEPTH_M: Approximate depth of water in meters at the sample location derived from an unpublished composite bathymetry dataset used by the Long Island Sound Mapping and Research Collaborative.T_DEPTH: Top depth of the sample below the sediment-water interface in centimeters.aborative project.B_DEPTH: Bottom depth of the sample below the sediment-water interface in centimeters.DEVICE: Sampling device used to collect the sample.DATE COLLECTED: Calendar date based on UTC time indicating when the sample was collected in the format MM/DD/YYYY where MM is the numeric month, DD is the day of the month, and YYYY is the year.ANALYSIS COMPLETION DATE: Calendar date indicating when analyses on the sample were completed in the format MM/DD/YYYY where MM is the numeric month, DD is the day of the month, and YYYY is the year.ANALYSIS METHOD: Method used to analyze the sample for grain-size distribution. Grain-size analysis using the HORIBA laser diffraction unit and sieving of the >= -2 phi fraction.WEIGHT WET SAMPLE (g): Weight of initial sample in gramsGRAVEL (wt%): Gravel content in percent dry weight of the sample. Gravel consists of particles with nominal diameters greater than 2 mm (-1 phi and larger).SAND (wt%):Sand content in percent dry weight of the sample. Sand consists of particles with nominal diameters less than 2 mm, but greater than or equal to 0.0625 mm (0 phi through 4 phi, inclusive).SILT (wt%): Silt content in percent dry weight of the sample. Silt consists of particles with nominal diameters less than 0.0625 mm, but greater than or equal to 0.004 mm (5 phi through 8 phi, inclusive).CLAY (wt%): Clay content in percent dry weight of the sample. Clay consists of particles with nominal diameters less than 0.004 mm (9 phi and smaller).CLASSIFICATION (Shepard): Sediment classification based on a rigorous definition (Shepard [1954] as modified by Schlee and Webster [1967], Schlee [1973], and Poppe and others [2005]). In the definitions below, gravel is defined as particles with nominal diameters greater than 2 mm; sand consists of particles with nominal diameters less than 2 mm, but greater than or equal to 0.0625 mm; silt consists of particles with nominal diameters less than 0.0625 mm, but greater than or equal to 0.004 mm; and clay consists of particles with nominal diameters less than 0.004 mm.The shapefile is a simplified version of the CSV file of the Multisizer analysis results (2017-056-FA_and_2018-018-FA_samples_GS-MS.csv) with fewer attribute fields. Specifically, STDEV, SKEWNESS, KURTOSIS, and the individual phi measurements (e.g., PHI_11) were removed. The shapefile also has two additional attributes, FID and Shape, which have the following descriptions:Attribute:Attribute Label: FIDAttribute Definition: Internal feature number.Attribute Definition Source: EsriAttribute Domain Values:Unrepresentable Domain: Sequential unique whole numbers that are automaticallygenerated.Attribute:Attribute Label: ShapeAttribute Definition: Feature geometry.Attribute Definition Source: EsriAttribute Domain Values:Unrepresentable Domain: Coordinates defining the features.All the other attributes in the shapefile have the same definitions as the CSV file attributes (see the detailed description section for the 2017-056-FA_and_2018-018-FA_samples_GS-MS entity for definitions of the CSV file attributes). Please note that some of the field names were truncated since a shapefile field name can only contain up to 10 characters. The following fields are included in the shapefile: FID, Shape, ANALYSIS_I (truncated field name for ANALYSIS_ID), FIELD_NO, PROJECT, FA_ID, CONTACT, AREA, LATITUDE, LONGITUDE, DEPTH_M, T_DEPTH, B_DEPTH, DEVICE, DATE_COLLE (truncated field name for DATE_COLLECTED), ANALYSIS_C (truncated field name for ANALYSIS_COMPLETION_DATE), WEIGHT, GRAVEL_PCT, SAND_PCT, SILT_PCT, CLAY_PCT, CLASSIFICA (truncated field name for CLASSIFICATION), MEDIAN, MEAN, ANALYST, and COMMENTS.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Following the development of the vegetation classification, the vegetation map was further edited and refined in 2005 to develop an association-level vegetation map. Using ArcGIS 9.0, polygon boundaries were revised onscreen based on the plot data and additional informal field observations collected while in the field during plot sampling. Field notes and limited field mapping supplemented the GIS mapping. Given the large amount of time used in gathering plot data, further ground-truthing was minimal. Each polygon was attributed with the name of a USNVC association or a land use/land cover map class based on plot data, field observations, aerial photography signatures, and topographic maps. The vegetation is mapped to the association level with one exception—because of their small size and interdigitization on the landscape, three of the herbaceous wetland communities, Bluejoint Wet Meadow (CEGL005174), Medium-depth Emergent Marsh (CEGL006519), and Cattail Marsh (CEGL006513) were mapped as a single map class: the Emergent Marsh - Shrub Swamp System. The Enriched Hardwood Forest Seeps, small occurrences within upland forests that are distinguished by their herb flora, are less than the minimum mapping unit (0.5 ha) and were not mapped. The shapefile was projected in Universal Transverse Mercator (UTM) Zone 18 North, North American Datum (NAD) 1983.

This data set shows the distribution of surficial sediments offshore of northern and eastern Cape Cod, Massachusetts. This interpretation is based on data collected with a multibeam sea floor mapping system during USGS survey 98015, conducted November 9 - 25, 1998 and on data collected with a bottom sampling and photographic system during USGS survey 04011, conducted during May and June, 2004.

This layer shows computer ownership and internet access by age and race. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of population age 18 to 64 in households with no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28005, B28003, B28009B, B28009C, B28009D, B28009E, B28009F, B28009G, B28009H, B28009I Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment. When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident Complex

Area Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.

Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.

Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.

Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created.

GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

An ArcGIS shapefile layer showing the extent of all extant and relic Adelie penguin (Pygoscelis adeliae) colonies at Whitney Point, Windmill Islands, February 2006. The field 'Status' describes each polygon as extant, relic or maximum. Extant refers to the area used by breeding birds in the summer 2005/06. Maximum refers to the historic maximal extent of the colony. Relic refers to any colony which was not occupied by any breeding pairs during 2005/06.

Positional accuracy is approx. 1-2 m, after accounting for dGPS errors and errors in identification of the boundaries of colonies. Mapping was conducted after the end of the breeding season, so boundaries were identified as the extent of nest pebbles/fresh faeces, and it was considered that they could be reliably identified to within 0.5m.

Data were acquired using a Trimble Pro XH differential GPS. This work was completed as part of ASAC project 1219 (ASAC_1219).

Also for this project, three aerial photographs of Whitney point showing the adelie penguin colonies and taken on 17 December 1990 were georeferenced. These aerial photographs are film ANTC1219 run 54 frames 21 to 23.

Work on this project also utilised a Digital Elevation Model (DEM) created for Shirley Island. See the metadata record, 'A digital elevation model (DEM) and orthophoto of the Whitney Point area of the Windmill Islands, Antarctica' for more information (linked below).

Since the 2005/06 summer was a low-ice year the opportunity was also taken to survey with differential GPS a section of coastline about 230 metres long east of Whitney Point on Clark Peninsula. This section of coastline was ice free and accessible. The data was collected with differential GPS on 10 February 2006.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets including a shapefile with CSG well locations from the QLD IRTM system, well completion reports from the QLD QDEX system and the NSW DIGS system and a publication by Wells and O'Brien (see reference below). You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This shapefile shows the locations of CSG and petroleum wells within the Clarence-Moreton bioregion derived from version 1 of this dataset (shown in lineage) with differentiation between CSG and Petroleum now included.

Reference:

Wells AT and O'Brien PE (compilers and editors). Geology and petroleum potential of the Clarence-Moreton Basin, New South Wales and Queensland. Australian Geological Survey Organisation, Bulletin 241.

Dataset History

The original files consisted of separate shapefiles for CSG and petroleum wells (see lineage). In here, an attribute was added that differentiates between CSG and petroleum wells. These were combined for maps in the Resource Assessment report.

The sources of the data are:

a shapefile with CSG well locations from the QLD IRTM system

well completion reports from the QLD QDEX system and the NSW DIGS system

a publication by Wells and O'Brien (see reference below).

Reference:

Wells AT and O'Brien PE (compilers and editors). Geology and petroleum potential of the Clarence-Moreton Basin, New South Wales and Queensland. Australian Geological Survey Organisation, Bulletin 241.

Dataset Citation

Bioregional Assessment Programme (2014) CLM - Coal seam gas and petroleum wells. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/598c923e-7eba-47ab-a1de-637dbe0d08fd.

Dataset Ancestors

• Derived From CLM - New South Wales well completion reports

• Derived From QLD Coal Seam Gas well locations - 14/08/2014

• Derived From CLM - Queensland well completion reports

Abstract

The dataset was derived by the Bioregional Assessment Programme from Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013. The source dataset ia identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Displays the original Hydstra measurement (HYDMEAS) tabular data records (as stored in the Hydstra software platform) in a GIS format for interpretation and analysis.

Analysis completed on this dataset includes extracts to determine location and status of current monitoring bores:

HYDMEAS - original tabular database file (dbf) showing groundwater levels

HYDMEAS_XY_all - displays all original tabular data in GIS shapefile format

HYDMEAS_unique_bores - shows one record for each unique bore station ID

HYDMEAS_2008 - All HYDMEAS data from 2008 or later

HYDMEAS_2008to2013_mulitple_reading - All HYDMEAS data from 2008 or later which has been monitored twice or more (in that period), produced to estimate groundwater level monitoring bores

National Groundwater Information System (NGIS) data supplied as a comparison of HYDMEAS monitoring estimates.

Hydstra is a water resources time series data management system developed by KISTERS Pty Ltd.

Purpose

Provide spatial distribution of groundwater level monitoring status and reading for New South Wales.

Dataset History

HYDMEAS - original tabular data

HYDMEAS_XY_all - displays all original tabular data in GIS format - Displayed as XY in ArcGIS based on Lat and Long attributes and exported as a point shapefile

HYDMEAS_unique_bores - shows one record for each unique bore ID - Dissolved HYDMEAS_XY_all based on STATION field

HYDMEAS_2008 - All HYDMEAS data from 2008 or later - Selected based on DATE field

HYDMEAS_2008to2013_mulitple_reading - All HYDMEAS data from 2008 or later which has been monitored twice or more (in that period), produced to estimate groundwater level monitoring bores - HYDMEAS_2008 dataset dissolved based on STATION and a count field added. Only bores with count of 2 or more were retained

Dataset Citation

Bioregional Assessment Programme (2014) GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/d414c703-aabd-43af-81e0-30aab4d9dfb1.

Dataset Ancestors

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013