NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

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This map is designed to be used as a general reference map for informational and educational purposes as well as a basemap by GIS professionals and other users for creating web maps and web mapping applications.To launch a web map containing this service, click here.The map was developed by National Geographic and Esri and reflects the distinctive National Geographic cartographic style in a multi-scale reference map of the world. The map was authored using data from a variety of leading data providers, including DeLorme, HERE, UNEP-WCMC, NASA, ESA, USGS, and others.This reference map includes administrative boundaries, cities, protected areas, highways, roads, railways, water features, buildings and landmarks, overlaid on shaded relief and land cover imagery for added context. The map includes global coverage down to ~1:144k scale and more detailed coverage for North America down to ~1:9k scale.Map Note: Although small-scale boundaries, place names and map notes were provided and edited by National Geographic, boundaries and names shown do not necessarily reflect the map policy of the National Geographic Society, particularly at larger scales where content has not been thoroughly reviewed or edited by National Geographic.Data Notes: The credits below include a list of data providers used to develop the map. Below are a few additional notes:Reference Data: National Geographic, Esri, DeLorme, HERE, iPC, NRCAN, METILand Cover Imagery: NASA Blue Marble, ESA GlobCover 2009 (Copyright notice: © ESA 2010 and UCLouvain)Protected Areas: IUCN and UNEP-WCMC (2011), The World Database on Protected Areas (WDPA) Annual Release. Cambridge, UK: UNEP-WCMC. Available at:www.protectedplanet.net.Ocean Data: GEBCO, NOAAExplore the Map: You can Explore the National Geographic Map using this live map presentation authored by Allen Carroll. Allen was formerly the Chief Cartographer at National Geographic and is currently part of the ArcGIS Online team at Esri.Web Map: Here's a ready-to-use web map that uses the National Geographic World Map as its basemap. Tip: Remember that you can open a web map, zoom in to a location of interest, then click the Share button to get a URL link or code you can embed in your own web page that launches the map at that location. This makes it really easy to share the web map with others showing the location of your choice. This doesn't even require that you sign-in to ArcGIS Online, so anyone can do it.Tip: To add this service into ArcGIS Desktop or ArcGIS Explorer Desktop directly by making a server connection, connect to this ArcGIS Serverhttp://services.arcgisonline.com/arcgis/services and add the service called NatGeo_World_Map into your map.

This dataset contains 63 shapefiles that represent the areas of relevance for each research project under the National Environmental Science Program Marine and Coastal Hub, northern and southern node projects for Rounds 1, 2 & 3.

Methods: Each project map is developed using the following steps: 1. The project map was drawn based on the information provided in the research project proposals. 2. The map was refined based on feedback during the first data discussions with the project leader. 3. Where projects are finished most maps were updated based on the extents of datasets generated by the project and followup checks with the project leader.

The area mapped includes on-ground activities of the project, but also where the outputs of the project are likely to be relevant. The maps were refined by project leads, by showing them the initial map developed from the proposal, then asking them "How would you change this map to better represent the area where your project is relevant?". In general, this would result in changes such as removing areas where they were no longer intending research to be, or trimming of the extents to better represent the habitats that are relevant.

The project extent maps are intentionally low resolution (low number of polygon vertices), limiting the number of vertices 100s of points. This is to allow their easy integration into project metadata records and for presenting via interactive web maps and spatial searching. The goal of the maps was to define the project extent in a manner that was significantly more accurate than a bounding box, reducing the number of false positives generated from a spatial search. The geometry was intended to be simple enough that projects leaders could describe the locations verbally and the rough nature of the mapping made it clear that the regions of relevance are approximate.

In some cases, boundaries were drawn manually using a low number of vertices, in the process adjusting them to be more relevant to the project. In others, high resolution GIS datasets (such as the EEZ, or the Australian coastline) were used, but simplified at a resolution of 5-10km to ensure an appopriate vertices count for the final polygon extent. Reference datasets were frequently used to make adjustments to the maps, for example maps of wetlands and rivers were used to better represent the inner boundary of projects that were relevant for wetlands.

In general, the areas represented in the maps tend to show an area larger then the actual project activities, for example a project focusing on coastal restoration might include marine areas up to 50 km offshore and 50 km inshore. This buffering allows the coastline to be represented with a low number of verticies without leading to false negatives, where a project doesn't come up in a search because the area being searched is just outside the core area of a project.

Limitations of the data: The areas represented in this data are intentionally low resolution. The polygon features from the various projects overlap significantly and thus many boundaries are hidden with default styling. This dataset is not a complete representation of the work being done by the NESP MaC projects as it was collected only 3 years into a 7 year program.

Format of the data: The maps were drawn in QGIS using relevant reference layers and saved as shapefiles. These are then converted to GeoJSON or WKT (Well-known Text) and incorporated into the ISO19115-3 project metadata records in GeoNetwork. Updates to the map are made to the original shapefiles, and the metadata record subsequently updated.

All projects are represented as a single multi-polygon. The multiple polygons was developed by merging of separate areas into a single multi-polygon. This was done to improve compatibility with web platforms, allowing easy conversion to GeoJSON and WKT.

This dataset will be updated periodically as new NESP MaC projects are developed and as project progress and the map layers are improved. These updates will typically be annual.

Data dictionary: NAME - Title of the layer PROJ - Project code of the project relating to the layer NODE - Whether the project is part of the Northern or Southern Nodes TITLE - Title of the project P_LEADER - Name of the Project leader and institution managing the project PROJ_LINK - Link to the project metadata MAP_DESC - Brief text description of the map area MAP_TYPE - Describes whether the map extent is a 'general' area of relevance for the project work, or 'specific' where there is on ground survey or sampling activities MOD_DATE - Last modification date to the individual map layer (prior to merging)

Updates & Processing: These maps were created by eAtlas and IMAS Data Wranglers as part of the NESP MaC Data Management activities. As new project information is made available, the maps may be updated and republished. The update log will appear below with notes to indicate when individual project maps are updated: 20220626 - Dataset published (All shapefiles have MOD_DATE 20230626)

Location of the data: This dataset is filed in the eAtlas enduring data repository at: data\custodian esp-mac-3\AU_AIMS-UTAS_NESP-MaC_Project-extents-maps

These are products of the Soil and Landscape Grid of Australia Facility generated through disaggregation of the Western Australian soil mapping. There are 9 soil attribute products available from the Soil Facility: Available Water Holding Capacity - Volumetric (AWC); Bulk Density - Whole Earth (BDw); Bulk Density - Fine Earth (BDf); Clay (CLY); Course Fragments (CFG); Electrical Conductivity (ECD); pH Water (pHw); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.

The DSMART tool (Odgers et al. 2014) tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes (Holmes et al. Submitted). The soil class maps were then used to produce corresponding soil property surfaces using the PROPR tool (Odgers et al. 2015; Odgers et al. Submitted). Legacy mapping was compiled for the state of WA from surveys ranging in map scale from 1:20,000 to 1:2,000,000 (Schoknecht et al., 2004). The polygons are attributed with the soils and proportions of soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) and kriging based on site data by Viscarra Rossel et al. (Submitted). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. In Prep).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for Western Australia’s agricultural zone was developed via WA’s Department of Agriculture and Food (Schoknecht et al., 2004). Seventy-three soil classes (termed ‘WA soil groups’ Schoknecht and Pathan, 2013) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

Western Australia’s expert defined typical range of soil properties by soil class was used to provide reference soil properties to PROPR. These estimates were made separately for each physiographic zone across WA, and are based on available profile data and surveyor experience. Uncertainty bounds were determined by the minimum and maximum soil properties at the ‘qualified soil group’ level, and the property value of the most common soil in the map unit was used to define the typical soil property. This methodology was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties. Depth averaging was applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (Submitted).

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These products are derived from disaggregation of legacy soil mapping in the agricultural zone of South Australia using the DSMART tool (Odgers et al. 2014a); produced for the Soil and Landscape Grid of Australia Facility. There are 10 soil attribute products available from the Soil Facility: Available Water Capacity (AWC); Bulk Density - Whole Earth (BDw); Cation Exchange Capacity (CEC); Clay (CLY); Coarse Fragments (CFG); Electrical Conductivity (ECD); Organic Carbon (SOC); pH - CaCl2( pHc); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices (except for effective depth and total depth). Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the specifications of the GlobalSoilMap.

The DSMART tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes and corresponding soil properties. Legacy mapping was performed at 1:50,000 and 1:100,000 scales to delineate associated soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) kriging based on site data by Viscarra Rossel et al. (2014). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. 2014).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for South Australia’s agricultural zone was developed via SA’s State Land and Soil Mapping Program (DEWNR 2014, Hall et al. 2009). Sixty one soil classes (termed ‘subgroup soils’) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015b) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

South Australia’s national- or ASRIS-format soil mapping was used to provide reference soil properties. This dataset was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties of approximately 1500 regional variants of the original sixty one ‘subgroup soil’ classes. Equal area depth smoothing splines were applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (2015a).

Important Note: This item is in mature support as of December 2024. See blog for more information.This web map presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. It presents the map in a cartographic style is like a blueprint technical drawing. Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.

Important Note: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) buildings data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

California State Lands Commission Offshore Oil Leases in the vicinity of Santa Barbara, Ventura, and Orange County.The polygons in this layer show the position of Offshore Oil Leases as documented by former State Lands Senior Boundary Determination Officer, Cris N. Perez and as reviewed and updated by GIS and Boundary staff.Background: This layer represents active offshore oil and gas agreements in California waters, which are what remain of the more than 60 originally issued. These leases were issued prior to the catastrophic 1969 oil spill from Platform A in federal waters off Santa Barbara County, and some predate the formation of the Commission. Between 2010 and 2014, the bulk of the approximately $300 million generated annually for the state's General Fund from oil and gas agreements was from these offshore leases.In 1921, the Legislature created the first tidelands oil and gas leasing program. Between 1921 and 1929, approximately 100 permits and leases were issued and over 850 wells were drilled in Santa Barbara and Ventura Counties. In 1929, the Legislature prohibited any new leases or permits. In 1933, however, the prohibition was partially lifted in response to an alleged theft of tidelands oil in Huntington Beach. It wasn't until 1938, and again in 1955, that the Legislature would allow new offshore oil and gas leasing. Except for limited circumstances, the Legislature has consistently placed limits on the areas that the Commission may offer for lease and in 1994, placed the entirety of California's coast off-limits to new oil and gas leases. Layer Creation Process:In 1997 Cris N. Perez, Senior Boundary Determination Officer of the Southern California Section of the State Lands Division, prepared a report on the Commission’s Offshore Oil Leases to:A. Show the position of Offshore Oil Leases. B. Produce a hard copy of 1927 NAD Coordinates for each lease. C. Discuss any problems evident after plotting the leases.Below are some of the details Cris included in the report:I have plotted the leases that were supplied to me by the Long Beach Office and computed 1927 NAD California Coordinates for each one. Where the Mean High Tide Line (MHTL) was called for and not described in the deed, I have plotted the California State Lands Commission CB Map Coordinates, from the actual field surveys of the Mean High Water Line and referenced them wherever used. Where the MHTL was called for and not described in the deed and no California State Lands Coordinates were available, I digitized the maps entitled, “Map of the Offshore Ownership Boundary of the State of California Drawn pursuant to the Supplemental Decree of the U.S. Supreme Court in the U.S. V. California, 382 U.S. 448 (1966), Scale 1:10000 Sheets 1-161.” The shore line depicted on these maps is the Mean Lower Low Water (MLLW) Line as shown on the Hydrographic or Topographic Sheets for the coastline. If a better fit is needed, a field survey to position this line will need to be done.The coordinates listed in Cris’ report were retrieved through Optical Character Recognition (OCR) and used to produce GIS polygons using Esri ArcGIS software. Coordinates were checked after the OCR process when producing the polygons in ArcMap to ensure accuracy. Original Coordinate systems (NAD 1927 California State Plane Zones 5 and 6) were used initially, with each zone being reprojected to NAD 83 Teale Albers Meters and merged after the review process.While Cris’ expertise and documentation were relied upon to produce this GIS Layer, certain polygons were reviewed further for any potential updates since Cris’ document and for any unusual geometry. Boundary Determination Officers addressed these issues and plotted leases currently listed as active, but not originally in Cris’ report. On December 24, 2014, the SLA boundary offshore of California was fixed (permanently immobilized) by a decree issued by the U.S. Supreme Court United States v. California, 135 S. Ct. 563 (2014). Offshore leases were clipped so as not to exceed the limits of this fixed boundary. Lease Notes:PRC 1482The “lease area” for this lease is based on the Compensatory Royalty Agreement dated 1-21-1955 as found on the CSLC Insider. The document spells out the distinction between “leased lands” and “state lands”. The leased lands are between two private companies and the agreement only makes a claim to the State’s interest as those lands as identified and surveyed per the map Tract 893, Bk 27 Pg 24. The map shows the State’s interest as being confined to the meanders of three sloughs, one of which is severed from the bay (Anaheim) by a Tideland sale. It should be noted that the actual sovereign tide and or submerged lands for this area is all those historic tide and submerged lands minus and valid tide land sales patents. The three parcels identified were also compared to what the Orange County GIS land records system has for their parcels. Shapefiles were downloaded from that site as well as two centerline monuments for 2 roads covered by the Tract 893. It corresponded well, so their GIS linework was held and clipped or extended to make a parcel.MJF Boundary Determination Officer 12/19/16PRC 3455The “lease area” for this lease is based on the Tract No. 2 Agreement, Long Beach Unit, Wilmington Oil Field, CA dated 4/01/1965 and found on the CSLC insider (also recorded March 12, 1965 in Book M 1799, Page 801).Unit Operating Agreement, Long Beach Unit recorded March 12, 1965 in Book M 1799 page 599.“City’s Portion of the Offshore Area” shall mean the undeveloped portion of the Long Beach tidelands as defined in Section 1(f) of Chapter 138, and includes Tract No. 1”“State’s Portion of the Offshore Area” shall mean that portion of the Alamitos Beach Park Lands, as defined in Chapter 138, included within the Unit Area and includes Tract No. 2.”“Alamitos Beach Park Lands” means those tidelands and submerged lands, whether filled or unfilled, described in that certain Judgment After Remittitur in The People of the State of California v. City of Long Beach, Case No. 683824 in the Superior Court of the State of California for the County of Los Angeles, dated May 8, 1962, and entered on May 15, 1962 in Judgment Book 4481, at Page 76, of the Official Records of the above entitled court”*The description for Tract 2 has an EXCEPTING (statement) “therefrom that portion lying Southerly of the Southerly line of the Boundary of Subsidence Area, as shown on Long Beach Harbor Department {LBHD} Drawing No. D-98. This map could not be found in records nor via a PRA request to the LBHD directly. Some maps were located that show the extents of subsidence in this area being approximately 700 feet waterward of the MHTL as determined by SCC 683824. Although the “EXCEPTING” statement appears to exclude most of what would seem like the offshore area (out to 3 nautical miles from the MHTL which is different than the actual CA offshore boundary measured from MLLW) the 1964, ch 138 grant (pg25) seems to reference the lands lying seaward of that MHTL and ”westerly of the easterly boundary of the undeveloped portion of the Long Beach tidelands, the latter of which is the same boundary (NW) of tract 2. This appears to then indicate that the “EXCEPTING” area is not part of the Lands Granted to City of Long Beach and appears to indicate that this portion might be then the “State’s Portion of the Offshore Area” as referenced in the Grant and the Unit Operating Agreement. Section “f” in the CSLC insider document (pg 9) defines the Contract Lands: means Tract No. 2 as described in Exhibit “A” to the Unit Agreement, and as shown on Exhibit “B” to the Unit Agreement, together with all other lands within the State’s Portion of the Offshore Area.Linework has been plotted in accordance with the methods used to produce this layer, with record lines rotated to those as listed in the descriptions. The main boundaries being the MHTL(north/northeast) that appears to be fixed for most of the area (projected to the city boundary on the east/southeast); 3 nautical miles from said MHTL on the south/southwest; and the prolongation of the NWly line of Block 50 of Alamitos Bay Tract.MJF Boundary Determination Officer 12-27-16PRC 4736The “lease area” for this lease is based on the Oil and Gas Lease and Agreement as found on the CSLC insider and recorded August 17, 1973 in BK 10855 PG 432 Official Records, Orange County. The State’s Mineral Interests are confined to Parcels “B-1” and “B-2” and are referred to as “State Mineral Lands” comprising 70.00 Acres. The lessee each has a right to certain uses including but not limited to usage of utility corridors, 110 foot radius parcels surrounding well-sites and roads. The State also has access to those same roads per this agreement/lease. Those uses are allowed in what are termed “State Lands”-Parcel E and “Leased Lands” which are defined as the “South Bolsa Lease Area”-Parcel C (2 parcels) and “North Bolsa Lease Area”-Parcel D. The “State Lands”-Parcel E are actually 3 parcels, 2 of which are within road right-of-ways. MJF Boundary Determination Officer 12-28-16

For much of the Geological Survey's existence, the County Series of maps were the standard large-scale maps on which geological mapping was undertaken. These maps are based on the Ordnance Survey County (or six-inch to the mile) series of maps. These maps were cut up to be used in the field to record geological observations, and on return to the office, the geology was transferred to a complete County Series map, which after approval was known as a 'standard' (England / Wales) or 'clean copy' (Scotland). This dataset contains the 'standard' or 'clean copy' County Series maps held by BGS. Geological maps represent a geologist's compiled interpretation of the geology of an area. A geologist will consider the data available at the time, including measurements and observations collected during field campaigns, as well as their knowledge of geological processes and the geological context to create a model of the geology of an area. This model is then fitted to a topographic basemap and drawn up at the appropriate scale, with generalization if necessary, to create a geological map, which is a representation of the geological model. Explanatory notes and vertical and horizontal cross sections may be published with the map. Geological maps may be created to show various aspects of the geology, or themes. The most common map themes held by BGS are solid (later referred to as bedrock) and drift (later referred to as superficial). These maps are hard-copy paper records stored in the National Geoscience Data Centre (NGDC) and are delivered as digital scans through the BGS website.

Important Note: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) trees data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

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. 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.

One of a collection of maps with hand-drawn bedrock geologic data used to create the Bedrock Geologic Map of New Hampshire (Lyons and others, 1997). Map is the northern portion of the Suncook Quadrangle, New Hampshire USGS topographic map. Scale: 1:62500

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.

Method: The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (not yet published) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.

Single merged composite GeoTiff: The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.

Change Log: 2023-03-02: Eric Lawrey Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

22 Nov 2023: Eric Lawrey Added the data and maps for close up of Mer. - 01-data/TS_DNRM_Mer-aerial-imagery/ - preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg - exports/Torres-Strait-Mer-Map-Landscape-A0.pdf Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

Source datasets: Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302 Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose. CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland This is the high resolution imagery used to create the map of Mer.

Marine satellite imagery (Sentinel 2 and Landsat 8) (AIMS), https://eatlas.org.au/data/uuid/5d67aa4d-a983-45d0-8cc1-187596fa9c0c - World_AIMS_Marine-satellite-imagery

Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.

Have you ever wanted to create a quick thematic map of London but lacked the GIS skills or software to do it yourself?

These free mapping tools from the GLA Intelligence Unit allows the user to input their own data to create an instant map that can be copied over into Word or another application of your choice. The user can also copy over the legend, and add labels.

The template allows the user to select either 4 or 5 ranges, and it even has a function to change the colours on the map (default colours are blue).

The tool now also allows users to input their own ranges, which will override the automatic ranges.

There is: Standard borough thematic map

Borough thematic map for categories (as opposed to numbers).

And ward maps for individual boroughs see list below.

Copyright notice: If you publish these maps, a copyright notice must be included within the report saying: "Contains Ordnance Survey data © Crown copyright and database rights."

Ward maps

Ward mapping tools for each borough have also been created. Select the borough you require from the list below:

All London Wards map with pre-2014 boundaries

Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney (pre 2014), Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea (pre 2014), Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets (pre 2014), Waltham Forest, Wandsworth, Westminster

New ward boundaries - came into effect from May 2014

All London wards map 2014 including the new ward boundaries for Hackney, Kensington and Chelsea, and Tower Hamlets following changes in May 2014.

Hackney, Kensington and Chelsea, Tower Hamlets

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NOTE: Excel 2003 users must 'ungroup' the map for it to work.

Full instructions are contained within the spreadsheet. If you have any questions about these tools please contact Gareth Piggott.

Macros

The tool works in any version of Excel. But the user MUST ENABLE MACROS, for the features to work. There a some restrictions on functionality in the ward maps in Excel 2003 and earlier - full instructions are included in the spreadsheet.

To check whether the macros are enabled in Excel 2003 click Tools, Macro, Security and change the setting to Medium. Then you have to re-start Excel for the changes to take effect. When Excel starts up a prompt will ask if you want to enable macros - click yes.

In Excel 2007 and later, it should be set by default to the correct setting, but if it has been changed, click on the Windows Office button in the top corner, then Excel options (at the bottom), Trust Centre, Trust Centre Settings, and make sure it is set to 'Disable all macros with notification'. Then when you open the spreadsheet, a prompt labelled 'Options' will appear at the top for you to enable macros.

To create your own thematic borough maps in Excel using the ward map tool as a starting point, read these instructions. You will need to be a confident Excel user, and have access to your boundaries as a picture file from elsewhere. The mapping tools created here are all fully open access with no passwords.

The USGS National Hydrography Dataset (NHD) Downloadable Data Collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography.

DWR was the steward for NHD and Watershed Boundary Dataset (WBD) in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's NHD improvements were sent to USGS for incorporation into the national database. The most up-to-date products are accessible from the USGS website. Please note that the California portion of the National Hydrography Dataset is appropriate for use at the 1:24,000 scale.

For additional derivative products and resources, including the major features in geopackage format, please go to this page: https://data.cnra.ca.gov/dataset/nhd-major-features Archives of previous statewide extracts of the NHD going back to 2018 may be found at https://data.cnra.ca.gov/dataset/nhd-archive.

In September 2022, USGS officially notified DWR that the NHD would become static as USGS resources will be devoted to the transition to the new 3D Hydrography Program (3DHP). 3DHP will consist of LiDAR-derived hydrography at a higher resolution than NHD. Upon completion, 3DHP data will be easier to maintain, based on a modern data model and architecture, and better meet the requirements of users that were documented in the Hydrography Requirements and Benefits Study (2016). The initial releases of 3DHP will be the NHD data cross-walked into the 3DHP data model. It will take several years for the 3DHP to be built out for California. Please refer to the resources on this page for more information.

The FINAL,STATIC version of the National Hydrography Dataset for California was published for download by USGS on December 27, 2023. This dataset can no longer be edited by the state stewards.

The first public release of the 3D Hydrography Program map service may be accessed at https://hydro.nationalmap.gov/arcgis/rest/services/3DHP_all/MapServer.

Questions about the California stewardship of these datasets may be directed to nhd_stewardship@water.ca.gov.

A series of maps at the detailed scale of 1:25 000 have been produced for areas of outstanding geological interest in Great Britain. Some maps are accompanied by explanatory booklets. The maps were published between 1954 and 2007. About 60 maps have been published, some showing solid geology, some drift geology and some combined solid and drift. Most of the maps include geological cross sections and generalised vertical sections. Geological maps represent a geologist's compiled interpretation of the geology of an area. A geologist will consider the data available at the time, including measurements and observations collected during field campaigns, as well as their knowledge of geological processes and the geological context to create a model of the geology of an area. This model is then fitted to a topographic basemap and drawn up at the appropriate scale, with generalization if necessary, to create a geological map, which is a representation of the geological model. Explanatory notes and vertical and horizontal cross sections may be published with the map. Geological maps may be created to show various aspects of the geology, or themes. These maps are hard-copy paper records stored in the National Geoscience Data Centre (NGDC) and are delivered as digital scans through the BGS website.

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.

The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.Manager Type provides a coarse level land manager description from the PAD-US "Agency Type" Domain, "Manager Type" Field (for example, Federal, State, Local Government, Private).PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.Dataset SummaryPhenomenon Mapped: This layer displays protected areas symbolized by manager type.Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, 3 or 4GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Important Note: This item is in mature support as of December 2024. See blog for more information.This tile layer presents a new vector basemap of OpenStreetMap (OSM) data hosted by Esri. This version of the map is rendered using a creative cartographic style emulating the style of blueprint technical drawing. Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project. Precise Tile Registration: The tile layer uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.