Centimeter-level High-precision Map Market Outlook

The global centimeter-level high-precision map market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 15.2 billion by 2032, growing at a CAGR of 17.2% from 2024 to 2032. This robust growth can be attributed to the increasing demand for highly accurate geospatial data driven by advancements in autonomous vehicle technology and the rising adoption of automation across various industries.

One primary growth factor for the centimeter-level high-precision map market is the accelerated development and deployment of autonomous vehicles. These vehicles require highly accurate mapping data to navigate safely and efficiently. As the automotive industry pushes towards greater levels of autonomy, the demand for precise maps that can deliver real-time data for navigation, obstacle detection, and route planning is expected to surge. Additionally, the involvement of major technology companies and automakers in autonomous vehicle research and development further propels the market forward.

Another significant driver is the expanding use of drones and UAVs (unmanned aerial vehicles) across diverse applications, including agriculture, construction, and surveying. Drones equipped with advanced mapping technologies like LiDAR and photogrammetry offer unparalleled accuracy in capturing geographic information. This capability is crucial for applications that require precise measurements, such as crop health monitoring, land surveying, and infrastructure inspection. The growing adoption of these technologies in various sectors underscores their critical role in delivering centimeter-level accuracy.

Moreover, the integration of IoT (Internet of Things) and smart city initiatives has amplified the need for high-precision mapping. As cities become smarter, the reliance on accurate geospatial data increases. Applications such as traffic management, urban planning, and emergency response benefit greatly from centimeter-level precision maps. The continuous development of IoT sensors and connectivity solutions enhances the capability to collect and utilize real-time geospatial data, driving further market growth.

Regionally, North America holds a significant share in the centimeter-level high-precision map market due to the presence of leading technology companies and substantial investments in autonomous vehicle research. The Asia Pacific region is also witnessing rapid growth, driven by large-scale infrastructure projects and increasing adoption of automation in industries like manufacturing and agriculture. Europe, with its strong automotive industry and focus on smart city initiatives, represents another critical market. Latin America and the Middle East & Africa are gradually adopting these technologies, spurred by development in sectors such as construction and mining.

Component Analysis

The centimeter-level high-precision map market is segmented by components into hardware, software, and services. The hardware segment includes various devices and instruments such as LiDAR sensors, GNSS receivers, and photogrammetry cameras that are essential for capturing high-precision geospatial data. Hardware forms the backbone of high-precision mapping solutions, offering the necessary tools to collect accurate spatial information. The increasing deployment of advanced hardware solutions in autonomous vehicles and drones significantly drives this segment's growth.

The software segment encompasses mapping software and platforms that process and analyze the geospatial data collected by hardware devices. These software solutions are crucial for converting raw data into usable maps and models. The growing demand for advanced data analytics and visualization tools in applications like urban planning and disaster management fuels this segment. Companies are increasingly investing in software development to enhance the functionality and user experience of their mapping solutions.

Services in the high-precision mapping market include installation, maintenance, and consulting services. Service providers play a vital role in ensuring the smooth deployment and operation of high-precision mapping systems. The increasing complexity of these systems necessitates expert guidance and ongoing support. Service offerings also include custom mapping solutions tailored to specific industry needs, which adds significant value and drives market growth. The integration of AI and machine learning into mapping services represents a key trend, enhancing the accuracy and efficiency

This layer of the map based index (GeoIndex) shows where aquifer vulnerability maps are available for England and Wales. These maps identify areas in which the groundwater resources require protection from potentially polluting activities. The maps are designed to be used by planners, developers, consultants and regulatory bodies to ensure that developments conform to the Policy and Practice of the Environment Agency for the protection of Groundwater. The Soil Survey, Land Research Centre and the British Geological Survey were commissioned by the Environment Agency to prepare 53 groundwater vulnerability maps at 1:100,000 scale. Currently we are unable to provide scanned copies of these maps due to Copyright restrictions. Please note that these maps are based on data from the late 1980's and early 1990's. More up-to-date digital data may now be available from the Environment Agency.

The purpose of Colorado Geological Survey’s (CGS) Geologic Map of the Rattlesnake Mesa Quadrangle,Rio Blanco County, Colorado is to describe the geology of this 7.5-minute quadrangle located in the vicinity of the town of Meeker in northwestern Colorado. CGS staff geologist Jonathan L. White and field assistants James Hodge and Michael J. Zawaski completed the field work on this project at the end of the summer of 2010. Jon White, the principal mapper and author, created this report using field maps, photographs, structural measurements, and field notes generated by all the investigators. Significant knowledge was also gained by a compilation of the available published geologic literature listed in the references. This map was improved from reviews by Larry Moyer (consulting petroleum geologist), David Noe (Colorado Geological Survey), as well as pertinent edits of the adjacent Meeker quadrangle by Rex Cole (Colorado Mesa University). This mapping project was funded jointly by the U.S. Geological Survey (USGS) and the CGS. USGS funding comes from the STATEMAP component of the National Cooperative Geologic Mapping Program, award number G10AC00410, authorized by the National Geologic Mapping Act of 1997, reauthorized in 2009. CGS matching funding comes from the Colorado Department of Natural Resources Severance Tax Operational Funds, from severance taxes paid on the production of natural gas, oil, coal, and metals in Colorado. Digital PDF and ESRI ArcGIS download. OF-13-06D

1:24,000 scale Geologic Map of the Nelson Quadrangle, Clark County, Nevada. Nevada Bureau of Mines Map 134. Detailed Geologic Mapping By Jame s E . Faulds, John W. Bell, and Eric L. Olson in 2002. Field work done 1999. Map includes two cross sections and 42 geologic units. The quadrangle includes part of the Highland range, Eldorado Valley, and Piute Valley. It contains excellent exposures of early to middle Miocene volcanic and sedimentary rocks, the upper part of the ~16.6 Ma Searchlight. Mining district. The Miocene section rests nonconformably on Early Proterozoic gneiss. As a result of the middle Miocene extension, Tertiary strata are moderately to steeply tilted and cut by complex arrays of normal faults. Flat-lying Quaternary alluvial-fan deposits dominate Eldorado and Piute Valleys and onlap tilted Miocene strata in the Highland Range. The GIS work was in support of the U.S. Geological Survey COGEOMAP program. Office Reviewers: Frank Hillemeyer, La Cuesta International, Inc., Kingman, AZ.; Jonathan Miller, Dept. of Geology, San Jose State University, San Jose, CA.; Alan Ramelli, NBMG; Eugene Smith, Dept. of Geoscience, UNLV. Field Reviewers: Frank Hillemeyer, La Cuesta International, Inc., Kingman, AZ.; Werner Hellmer, Dept. of Building, Clark County; Ryan Murphy, Dept. of Geological Sciences , University of Nevada; John Peck, Consulting Geologist, Las Vegas , NV.; Jonathan Price, NBMG; Alan Ramelli, NBMG. The geologic mapping was supported by the U.S. Geological Survey STATEMAP Program (Agreement No. HQ-AG-2036) and a grant from the National Science Foundation (E AR 98-96032). The 40Ar/39Ar dates were obtained through geochronology labs at the U.S. Geological Survey in Denver, for which we thank Steve Harlan, and the New Mexico Bureau of Mines, for which we thank Bill McIntosh and Matt Heizler. We greatly appreciated the hospitality of several landowners in the area, including Barney and Elaine Reagan, Gene Lambert, and John Kuyger. We also thank the Lake Mead National Recreation Area for providing housing during part of this study. Base map: U.S. Geological Survey Nelson SW 7.5' Quadrangle. To download and view this map resource, map text, and associated GIS zipped data-set, please see the links provided.

1:24,000 scale Geologic Map of the Willow Creek Reservoir SE Quadrangle, Elko, Eureka, and Lander Counties, Nevada, Map 136. Detailed geologic mapping by Alan R. Wallace in 2003. Field work conducted from 1996-98 by A.R. Wallace. Office review by: Christopher D. Henry (NBMG), David Boden (Consulting Geologist). Field review by: Christopher D. Henry (NBMG), David Boden (Consulting Geologist). Funding provided by the U.S. Geological Survey Mineral Resources Program. Geologic Map includes two cross sections and description of 24 units. The GIS work was in support of the U.S. Geological Survey COGEOMAP program. Topographic base from U.S. Geological Survey, Willow Creek Reservoir SE Quadrangle, 1965.

This dataset (WETLAND_BROAD_POLY) is a composite of several individual wetland mapping projects, collectively referred to as “Wetlands - Broad Scale". The methodology used, map accuracy, and credits vary by project; these project details are outlined in Table 5 (see below). The spatial extent of individual projects are delineated in a separate feature class entitled, "WETLAND_BROAD_EXTENTS_POLY". The "Project Name" attribute field, common to both feature classes, denotes the specific project each feature is associated with. This dataset is intended to be used as a broad scale planning and management tool to identify potential distribution and abundance of wetlands. Wetlands were mapped to wetland class (shallow water, marsh, swamp, fen, and bog), following the Canadian Wetland Classification System using a predictive model. This dataset is intended to support land management and regional land use planning processes. Local scale (10k) manual wetland mapping, and additional physical assessments (i.e. ground inspections) may be required to undertake habitat enhancement, environmental assessment, reclamation planning, or environmental mitigation over small to moderate areas.Map development:The "Wetlands - Broad Scale" dataset was developed using a random forest machine learning model to predict wetland classes. Various satellite imagery sources and landscape variables derived from a digital elevation model (DEM) were used as primary inputs to predict wetlands. The source dataset has a resolution of 10 x 10 m. Training and validation data are a mix of ground plots (site visit and ecosystem plots), aerial survey plots, and interpreted polygons. Each predictive wetland map within the composite has met the minimum criteria of a map accuracy greater than or equal to 70 % and a Kappa coefficient greater than 0.60. The map (or producer's) accuracy measures the percentage of wetland features that are correctly classified to one of the five wetland classes. The Kappa coefficient statistic is used to measure the extent to which the model has correctly predicted, given the set of validation data. A value of 0 indicates predicted values are entirely random. A value of 1 indicates a perfect model. As a general rule, Kappa coefficients less than 0.60 indicate a poorly performing model, values of 0.61 to 0.80 indicate substantial agreement between predicted and validation data, and values of 0.81 to 1.00 indicate almost perfect agreement.The size of the smallest wetland that can be reliably mapped, the Target Mapping Unit (TMU), was not established for this dataset at the time of publication. Wetland classes smaller than a TMU of 2.0 hectares in this dataset should be used with caution. Wetlands below the TMU have a higher potential to be associated with classification error. The reported map accuracy is adequate for the intended purpose, and assumes that training data has adequately captured variation in landscape and vegetation structure between and within wetland classes. Feature attributes:Each polygon feature is associated with a combination of feature attributes grouped by value number, as shown in Table 1: ecological realm, wetland group, and wetland class. Table 1. Feature attributes grouped by value numberValueEcological RealmWetland GroupWetland Class0Other1FreshwaterWater (non-wetland)2WetlandMineralShallow Water3WetlandMineralMarsh4WetlandMineralSwamp5WetlandPeatlandFen6WetlandPeatlandBogThe ecological realm is a broadly defined ecosystem with common water source and character, described in Table 2.Table 2. Ecological realm breakdown into broad ecosystem categoriesEcological RealmDescriptionFreshwaterInland aquatic ecosystemsWetlandEcosystems dominated by plants adapted to saturated soils and periodically or permanently anaerobic soil conditionsThe wetland group is defined by the accumulation (or lack) of organic matter or peat, as described in Table 3. Table 3. Wetland groupsWetland GroupDescriptionPeatlandOrganic wetland classes that have more than 40 cm of organic matter (peat) accumulation (Warner and Rubec 1997) on which Organic soils or Organic Cryosols develop. Organic wetlands are characterized by poorly to moderately decomposed peat, mostly comprised of peat mosses, brown mosses, and/or sedges, but can also include woody remains of shrubs, or other plants. Mapped fen and bog wetland classes may have as little as 30 cm of peat accumulation, in the Yukon Wetland Classification System to be recognized as a peatland.MineralMineral wetlands occur in areas where an excess of water collects on the surface or within the rooting zone of plants for a significant portion of the growing season and which, for geomorphic, hydrologic, biotic, edaphic (factors related to soil), or climatic reasons, accumulate little to no organic matter or peat (typically less than 40 cm). Gleysol or Gleysolic Cryosol soils, or peaty phases of these soils, are characteristic of these wetlands (Warner and Rubec 1997). Swamps may have more than 30 cm of peat accumulation; however mineral swamps and peatland swamps are not distinguished from each other at the group level in this map.Table 4 describes five wetland classes and one non-wetland class that apply to this dataset. The five wetland classes that are recognized based on broadly similar site conditions along dominant environmental gradients as reflected in physiognomy (the life form, structure, and stature of vegetation) and species with similar adaptations. Non-wetland water systems are also mapped at this level.Table 4. Wetland class breakdown.Wetland ClassDescriptionShallow WaterShallow water wetlands have standing or flowing water above the surface and less than 2 m deep in mid-summer. Vegetation is dominated by submerged or floating aquatic plants, algae, and aquatic mosses.MarshMarshes are mineral wetlands characterized by shallow surface water, which fluctuates dynamically daily, seasonally, or annually. The water table may be below, at, or above the ground surface at a given time. They are dominated by aquatic macrophytes largely rushes, reeds, grasses, sedges, and sometimes herbs.SwampA swamp is a treed or tall or medium shrub dominated wetland that is influenced by minerotrophic groundwater. A swamp occurs on either mineral or organic soils.FenFens are nutrient medium peatlands where minerotrophic groundwater is within the rooting zone. Stands can be treed, shrubby, or sedge dominated. Brown mosses usually dominate the moss layer. BogBogs are nutrient poor peatlands where the rooting zone occurs above the mineral-enriched groundwater. Stands can be treed, shrubby, or moss dominated, where the moss layer is comprised mostly of peat moss.Water (non-wetland)This is a land cover class in the freshwater realm. It represents lacustrine (lake) and riverine (moving water) systems that are not wetlands. Table 5: Unique project details for each mapping area within the "Wetlands - Broad Scale" dataset.Project NameInformationDescriptionBeaver RiverLast UpdateOctober 2019Project AreaThe Beaver River Watershed wetland map is located in east central Yukon and has a total area of 6,146 km2. The wetland map consists of the Beaver River watershed, including the Rackla and East Rackla rivers, and a portion of the Keno Ladue watershed.MethodsSentinel-1, Sentinel-2 and landscape variables derived from the ArcticDEM, version 3, were used as primary inputs to predict wetlands. Sentinel imagery was from 2018. Training and validation data was comprised of 250 ground plots (site visit and ecosystem plots), 264 aerial survey plots, and 1,621 interpreted polygons. Polygons were interpreted from a combination of SPOT-6, Pleiades-1, ESRI World Imagery, and Sentinel-2 imagery. Training polygons reflected the extent and variability of nine land cover classes within the planning area and are in proportion to the aerial extent of land cover class (including wetland classes). Interpreted polygons were used to train the model. The model was validated using point location of aerial field calls and ground plots. The ratio of training to validation data was 3:1. In this dataset, the smallest mapped wetland, or minimum mapping unit (MMU), is 2 pixels or 200 square metres. The pixel resolution of the wetland map is 10 m, however all single pixels were merged into to their neighbouring pixel value.AccuracyThe final classification map accuracy was 81 % with a Kappa of 0.77 across all wetland and land cover classes. Isolating specifically the wetland classes, the map accuracy was 78 % (Kappa 0.69). The resulting map accuracy meets the project goal of greater than 70 % accuracy for a predictive map produced at a survey level intensity 4 to 5 as per the ELC guidelines for mapping. CreditsPreliminary wetland classification and final predictive map was completed by the Ecosystem and Landscape Classification (ELC) Program, Fish and Wildlife (F&W) Branch, Department of Environment, Government of Yukon, the Government of Yukon with input from Palmer Environmental Consulting Group, Drosera Ecological Consulting, and CryoGeographic Consulting. Training and assessment data was collected by Drosera Ecological Consulting, Lori Schroeder Consulting, CryoGeographic Consulting, and F&W staff. Classification of wetlands was completed by CryoGeographic Consulting with input from Drosera Ecological Consulting and ELC program staff. PeelLast UpdateMarch 2022Project AreaThe Peel Watershed wetland map is located in northern Yukon and has a total area of 67,366 km2. The watershed is drained by six major tributaries—the Snake, Wind, Bonnet Plume, Hart, Ogilvie, and Blackstone. MethodsSentinel-1, Sentinel-2, ALOS PALSAR (HH and HV polarizations), and landscape variables derived from the ArcticDEM, version 3, were used as primary inputs to predict wetlands. Sentinel imagery was from 2018. Segmented objects were used to assign a wetland class* and can be considered the minimum map unit (MMU) (as opposed to a single pixel). Segments were

1:24,000 scale Geologic Map of the Willow Creek Reservoir Quadrangle, Nevada, Map 135. Detailed geologic mapping by Alan R. Wallace in 2003. Geologic Map includes two cross sections and description of 21 units. The GIS work was in support of the U.S. Geological Survey COGEOMAP program. Topographic base from U.S. Geological Survey, Willow Creek Reservoir Quadrangle, 1965. Field work conducted from 1996-98 by A.R. Wallace. Office review by: Christopher D. Henry (NBMG), David Boden (Consulting Geologist). Field review by: Christopher D. Henry (NBMG), David Boden (Consulting Geologist). Funding provided by the U.S. Geological Survey Mineral Resources Program.

Statewide AEM Surveys Project Overview

The Department of Water Resources’ (DWR’s) Statewide Airborne Electromagnetic (AEM) Surveys Project is funded through California’s Proposition 68 and the General Fund. The goal of the project is to improve the understanding of groundwater aquifer structure to support the state and local goal of sustainable groundwater management and the implementation of the Sustainable Groundwater Management Act (SGMA).

During an AEM survey, a helicopter tows electronic equipment that sends signals into the ground which bounce back. The data collected are used to create continuous images showing the distribution of electrical resistivity values of the subsurface materials that can be interpreted for lithologic properties. The resulting information will provide a standardized, statewide dataset that improves the understanding of large-scale aquifer structures and supports the development or refinement of hydrogeologic conceptual models and can help identify areas for recharging groundwater.

DWR collected AEM data in all of California’s high- and medium-priority groundwater basins, where data collection is feasible. Data were collected in a coarsely spaced grid, with a line spacing of approximately 2-miles by 8-miles. AEM data collection started in 2021 and was completed in 2023. Additional information about the project can be found on the Statewide AEM Survey website. See the publication below for an overview of the project and a preliminary analysis of the AEM data.

California’s Statewide Airborne Electromagnetic Surveys and Preliminary Hydrogeologic Interpretations

Survey Areas

AEM data are being collected in groups of groundwater basins, defined as a Survey Area. See Survey Area Map for groundwater subbasins within a Survey Area:

• Survey Area 1: 180/400 Foot Aquifer (partial), East Side (partial), Upper Valley, Forebay Aquifer, Paso Robles, Atascadero (limited), Adelaida (limited), Cuyama Valley.
• Survey Area 2: Scott River Valley, Shasta Valley, Butte Valley, Tulelake, Fall River Valley (limited), Big Valley (Modoc/Lassen County).
• Survey Area 3: Big Valley (Lake County), Ukiah Valley, Santa Rosa Plain, Petaluma Valley, Sonoma Valley.
• Survey Area 4: White Wolf, Kern County, Tulare Lake, Tule, Kaweah.
• Survey Area 5: Pleasant Valley, Westside, Kings, Madera, Chowchilla, Merced, Turlock, Modesto, Delta-Mendota
• Survey Area 6: Cosumnes, Tracy, Eastern San Joaquin, East Contra Costa, Solano, Livermore, South American, North American, Yolo, Sutter, South Yuba, North Yuba
• Survey Area 7: Colusa, Butte, Wyandotte Creek, Vina, Los Molinos, Corning, Red Bluff, Antelope, Bowman, Bend, Millville, South Battle Creek, Anderson, Enterprise, Eel River, Sierra Valley
• Survey Area 8: Seaside, Monterey, 180/400 (partially surveyed Summer 2021), Eastside (partially surveyed Summer 2021), Langley, Pajaro, Santa Cruz Mid-County, Santa Margarita, San Benito, and Llagas (partial).
• Survey Area 9: Basin Characterization Pilot Study 1 - Madera and Kings.
• Survey Area 10: San Antonio Creek Valley, Arroyo Grande, Santa Maria, San Luis Obispo, Los Osos Area, Warden Creek, Chorro Valley (limited), Morro Valley (limited)
• Survey Area 11: Indian Wells Valley, Rose Valley, Owens Valley, Fish Slough, Indio, Mission Creek, West Salton Sea (limited), East Salton Sea (limited), Ocotillo-Clark Valley (limited), Imperial Valley (limited),Chocolate Valley (limited), Borrego Springs, and San Jacinto

Data Reports

Data reports detail the AEM data collection, processing, inversion, interpretation, and uncertainty analyses methods and procedures. Data reports also describe additional datasets used to support the AEM surveys, including digitized lithology and geophysical logs. Multiple data reports may be provided for a single Survey Area, depending on the Survey Area coverage.

Data Availability and Types

All data collected as a part of the Statewide AEM Surveys will be made publicly available, by survey area, approximately six to twelve months after individual surveys are complete (depending on survey area size). Datasets that will be publicly available include:

AEM Datasets

• Raw AEM Data
• Processed AEM Data
• Inverted AEM Data
• Inverted AEM Data Uncertainty Analysis
• Interpreted AEM Data (for coarse fraction)
• Interpreted AEM Data Uncertainty Analysis

Supporting Datasets

• Flown Survey Lines
• Digitized Lithology Logs
• Digitized Geophysical Logs

AEM Data Viewers

DWR has developed AEM Data Viewers to provides a quick and easy way to visualize the AEM electrical resistivity data and the AEM data interpretations (as texture) in a three-dimensional space. The most recent data available are shown, which my be the provisional data for some areas that are not yet finalized. The Data Viewers can be accessed by direct link, below, or from the Data Viewer Landing Page.

• AEM 3D Viewer (Beta) (computer only)

• AEM Profile Viewer

• SGMA Data Viewer (Basin Characterization tab)

AEM Depth Slice and Shallow Subsurface Average Maps

As a part of DWR’s upcoming Basin Characterization Program, DWR will be publishing a series of maps and tools to support advanced data analyses. The first of these maps have now been published and provide analyses of the Statewide AEM Survey data to support the identification of potential recharge areas. The maps are located on the SGMA Data Viewer (under the Hydrogeologic Conceptual Model tab) and show the AEM electrical resistivity and AEM-derived texture data as the following:

• Shallow Subsurface Average: Maps showing the average electrical resistivity and AEM-derived texture in the shallow subsurface (the top approximately 50 feet below ground surface). These maps support identification of potential recharge areas, where the top 50 feet is dominated by high resistivity or coarse-grained materials.

• Depth Slices: Depth slice automations showing changes in electrical resistivity and AEM-derived texture with depth. These maps aid in delineating the geometry of large-scale features (for example, incised valley fills).

Shapefiles for the formatted AEM electrical resistivity data and AEM derived texture data as depth slices and the shallow subsurface average can be downloaded here:

• Electrical Resistivity Depth Slices and Shallow Subsurface Average Maps

• Texture Interpretation (Coarse Fraction) Depth Slices and Shallow Subsurface Average Maps

Technical Memos

Technical memos are developed by DWR's consultant team (Ramboll Consulting) to describe research related to AEM survey planning or data collection. Research described in the technical memos may also be formally published in a journal publication.

• AEM Test Flights to Evaluate the Bias Signal Caused by Vineyard Trellises Containing Metal

• SkyTEM Instrument Comparison for Airborne EM

2018-2020 AEM Pilot Studies

Three pilot studies were conducted in California from 2018-2020 to support the development of the Statewide AEM Survey Project. The AEM Pilot Studies were conducted in the Sacramento Valley in Colusa and Butte county groundwater basins, the Salinas Valley in Paso Robles groundwater basin, and in the Indian Wells Valley groundwater basin.

Provisional Statement

Data Reports and datasets labeled as provisional may be incomplete and are subject to revision until they have been thoroughly reviewed and received final approval. Provisional data and reports may be inaccurate and subsequent review may result in revisions to the data and reports. Data users are cautioned to consider carefully the provisional nature of the information before using it for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences.

London Heat Map --------------- The London Heat Map is a tool designed to help you identify areas of high heat demand, explore opportunities for new and expanding district heat networks and to draw potential heat networks and assess their financial feasibility. The new version of the London Heat Map was created for the Greater London Authority by the Centre for Sustainable Energy (CSE) in July 2019. The London Heat Map is regularly updated with new network data and other datasets. Background datasets such as building heat demand was last updated on 26/06/2023. The London Heatmap is a map-based web application you can use to find and appraise opportunities for decentralised energy (DE) projects in London. The map covers the whole of Greater London, and provides very local information to help you identify and develop DE opportunities, including data such as: * Heat demand values for each building * Locations of potential heat supply sites * Locations of existing and proposed district heating networks * A spatial heat demand density map layer The map also includes a user-friendly visual tool for heat network design. This is intended to support preliminary techno-economic appraisal of potential district heat networks. The London Heat Map is used by a wide variety of people in numerous ways: * London Boroughs can use the new map to help develop their energy master plans. * Property developers can use the map to help them meet the decentralised energy policies in the London Plan. * Energy consultants can use the map to gather initial data to inform feasibility studies. More information is available here, and an interactive map is available here. Building-level estimated annual and peak heat demand data from the London Heat Map has been made available through the data extracts below. The data was last updated on 26/06/2023. The data contains Ordnance Survey mapping and the data is published under Ordnance Survey's 'presumption to publish'. © Crown copyright and database rights 2023. The Decentralised Energy Master planning programme (DEMaP) ---------------------------------------------------------- The Decentralised Energy Master planning programme (DEMaP), was completed in October 2010. It included a heat mapping support package for the London boroughs to enable them to carry out high resolution heat mapping for their area. To date, heat maps have been produced for 29 London boroughs with the remaining four boroughs carrying out their own data collection. All of the data collected through this process is provided below. ### Carbon Calculator Tool Arup have produced a Carbon Calculator Tool to assist projects in their early estimation of the carbon dioxide (CO2) savings which could be realised by a district heating scheme with different sources of heating. The calculator's estimates include the impact of a decarbonising the electrical grid over time, based on projections by the Department for Energy and Climate Change, as well as the Government's Standard Assessment Procedure (SAP). The Excel-based tool can be downloaded below. ### Borough Heat Maps Data and Reports (2012) In March 2012, all London boroughs did a heat mapping exercise. The data from this includes the following and can be downloaded below: * Heat Load for all boroughs * Heat Supplies for all boroughs * Heat Network * LDD 2010 database * Complete GIS London Heat Map Data The heat maps contain real heat consumption data for priority buildings such as hospitals, leisure centres and local authority buildings. As part of this work, each of the boroughs developed implementation plans to help them take the DE opportunities identified to the next stages. The implementation plans include barriers and opportunities, actions to be taken by the council, key dates, personnel responsible. These can be downloaded below. Other Useful Documents ---------------------- Other useful documents can be downloaded from the links below: Energy Masterplanning Manual Opportunities for Decentralised Energy in London - Vision Map London Heat Network Manual London Heat Network Manual II

Land cover describes the surface of the earth. This time-enabled service of the National Land Cover Database groups land cover into 20 classes based on a modified Anderson Level II classification system. Classes include vegetation type, development density, and agricultural use. Areas of water, ice and snow and barren lands are also identified. This layer displays land cover for the years 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019 for the conterminous United States. The layer displays land cover for Alaska for the years 2001, 2011, and 2016. For Puerto Rico there is only data for 2001. For Hawaii, Esri reclassed land cover data from NOAA Office for Coastal Management, C-CAP into NLCD codes. These reclassed C-CAP data were available for Hawaii for the years 2001, 2005, and 2011. Hawaii C-CAP land cover in its original form can be used in your maps by adding the Hawaii CCAP Land Cover layer directly from the Living Atlas.The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.Time SeriesThis layer is served as a time series. To display a particular year of land cover data, select the year of interest with the time slider in your map client. You may also use the time slider to play the service as an animation. We recommend a one year time interval when displaying the series. If you would like a particular year of data to use in analysis, be sure to use the analysis renderer along with the time slider to choose a valid year.North America Albers ProjectionThis layer is served in North America albers projection. Albers is an equal area projection, and this allows users of this service to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web mercator, if that is the destination projection of the service.Processing TemplatesCartographic Renderer - The default. Land cover drawn with Esri symbols. Each year's land cover data is displayed in the time series until there is a newer year of data available.Cartographic Renderer (saturated) - This renderer has the same symbols as the cartographic renderer, but the colors are extra saturated so a transparency may be applied to the layer. This renderer is useful for land cover over a basemap or relief. MRLC Cartographic Renderer - Cartographic renderer using the land cover symbols as issued by NLCD (the same symbols as is on the dataset when you download them from MRLC).Analytic Renderer - Use this in analysis. The time series is restricted by the analytic template to display a raster in only the year the land cover raster is valid. In a cartographic renderer, land cover data is displayed until a new year of data is available so that it plays well in a time series. In the analytic renderer, data is displayed for only the year it is valid. The analytic renderer won't look good in a time series animation, but in analysis this renderer will make sure you only use data for its appropriate year.Simplified Renderer - NLCD reclassified into 10 broad classes. These broad classes may be easier to use in some applications or maps.Forest Renderer - Cartographic renderer which only displays the three forest classes, deciduous, coniferous, and mixed forest.Developed Renderer - Cartographic renderer which only displays the four developed classes, developed open space plus low, medium, and high intensity development classes.Hawaii data has a different sourceMRLC redirects users interested in land cover data for Hawaii to a NOAA product called C-CAP or Coastal Change Analysis Program Regional Land Cover. This C-CAP land cover data was available for Hawaii for the years 2001, 2005, and 2011 at the time of the latest update of this layer. The USA NLCD Land Cover layer reclasses C-CAP land cover codes into NLCD land cover codes for display and analysis, although it may be beneficial for analytical purposes to use the original C-CAP data, which has finer resolution and untranslated land cover codes. The C-CAP land cover data for Hawaii is served as its own 2.4m resolution land cover layer in the Living Atlas.Because it's a different original data source than the rest of NLCD, different years for Hawaii may not be able to be compared in the same way different years for the other states can. But the same method was used to produce each year of this C-CAP derived land cover to make this layer. Note: Because there was no C-CAP data for Kaho'olawe Island in 2011, 2005 data were used for that island.The land cover is projected into the same projection and cellsize as the rest of the layer, using nearest neighbor method, then it is reclassed to approximate the NLCD codes. The following is the reclass table used to make Hawaii C-CAP data closely match the NLCD classification scheme:C-CAP code,NLCD code0,01,02,243,234,225,216,827,818,719,4110,4211,4312,5213,9014,9015,9516,9017,9018,9519,3120,3121,1122,1123,1124,025,12USA NLCD Land Cover service classes with corresponding index number (raster value):11. Open Water - areas of open water, generally with less than 25% cover of vegetation or soil.12. Perennial Ice/Snow - areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover.21. Developed, Open Space - areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes.22. Developed, Low Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20% to 49% percent of total cover. These areas most commonly include single-family housing units.23. Developed, Medium Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50% to 79% of the total cover. These areas most commonly include single-family housing units.24. Developed High Intensity - highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80% to 100% of the total cover.31. Barren Land (Rock/Sand/Clay) - areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover.41. Deciduous Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species shed foliage simultaneously in response to seasonal change.42. Evergreen Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.43. Mixed Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. 51. Dwarf Scrub - Alaska only areas dominated by shrubs less than 20 centimeters tall with shrub canopy typically greater than 20% of total vegetation. This type is often co-associated with grasses, sedges, herbs, and non-vascular vegetation.52. Shrub/Scrub - areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions.71. Grassland/Herbaceous - areas dominated by gramanoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling, but can be utilized for grazing.72. Sedge/Herbaceous - Alaska only areas dominated by sedges and forbs, generally greater than 80% of total vegetation. This type can occur with significant other grasses or other grass like plants, and includes sedge tundra, and sedge tussock tundra.73. Lichens - Alaska only areas dominated by fruticose or foliose lichens generally greater than 80% of total vegetation.74. Moss - Alaska only areas dominated by mosses, generally greater than 80% of total vegetation.Planted/Cultivated 81. Pasture/Hay - areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20% of total vegetation.82. Cultivated Crops - areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20% of total vegetation. This class also includes all land being actively tilled.90. Woody Wetlands - areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate is periodically saturated with or covered with water.95. Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for greater than 80% of vegetative cover and the soil or substrate is periodically saturated with or covered with water.

Public Water Supplies (PWSs) are managed by Irish Water, Ireland's national water utility, since 2013. Before this, public water supplies were managed by Local Authorities. More than 70% of public supplies take groundwater from boreholes, springs and infiltration galleries. This accounts for about 23% by volume (Irish Water, 2018).Source Protection Areas (SPAs) are areas outlined around groundwater abstraction points (e.g. borehole or spring) which provide drinking water. The aim of the SPAs is to protect groundwater by placing tighter controls on activities within all or part of the zone of contribution (ZOC) of the source. The Zone of Contribution (ZOC) is the land area that contributes water to the well or spring.Two Source Protection Areas (SPAs) are outlined. The Inner Protection Area (SI) aims to protect against the effects of human activities that might have an immediate effect on the source and, in particular, against microbial pollution. The Outer Protection Area (SO) covers the rest of the zone of contribution (ZOC) to the groundwater abstraction point.Not all groundwater-fed public supply sources have SPAs outlined around them. Most studies (more than 125) have been carried out by the Geological Survey Ireland as part of County Groundwater Protection Schemes. The Environmental Protection Agency carried out more than 40 studies as part of the national groundwater monitoring network characterisation. Further studies have been carried out by consultancies for Local Authorities and Irish Water.Different methods are used to map the entire Zone of Contribution to a spring, borehole or well, resulting in different degrees of confidence associated with the boundaries of the delineated area. To be able to specify the Inner Protection Zone within the entire Zone of Contribution, knowledge or estimates of groundwater travel time within the aquifer are needed (e.g. from site-specific hydrogeological parameters or tracer tests).Source Protection Areas have been mapped by the GSI and EPA following the ‘GSI method’ (e.g., GSI/EPA/IGI Source Protection Zonation course, 2009; Kelly, 2010; DELG/EPA/GSI, 1999). These SPAs were mapped as part of County Groundwater Protection Schemes or as part of the WFD Groundwater Monitoring network characterisation. Other SPAs have been mapped by consultants for Local Authorities/Irish Water. They have not been peer-reviewed by the GSI. The Zone of Contribution and the Source Protection Area account for the ‘horizontal’ movement of groundwater. Source Protection Zones are obtained by integrating the Source Protection Areas with the groundwater vulnerability categories. The Source Protection Zone includes the complete pathway, both vertical and horizontal, for re-charge and any entrained contaminants to the abstraction point.Whereas the aim of delineating ZOCs is to define approximate areas that contribute water to an abstraction point, the aim of SPZs is to geo-scientifically characterise the pathway and receptor elements of risk to groundwater within the ZOC of a given source (Kelly, 2010). EPA prepared an advice note on “Source Protection and Catchment Management to protect Groundwater Supplies” that outlines the key measures and policies in place in Ireland (EPA, 2011).This map shows the location of SPA's which have been mapped around public supplies of groundwater in Ireland. This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The data is shown as polygons. Each polygon holds information on Source Protection Area such as name, code, id, data source, county, reviewed by GSI and links to online reportsGroup Water Schemes (GWSs) are community-run water supply schemes. About 70% of GWSs take their water from a privately-sourced supply. The rest take their water from an Irish Water connection (DHPLG, 2017). 81% of the privately-sourced supplies affiliated to the National Federation of Group Water Schemes (NFGWS) take groundwater from boreholes, springs and dug wells. This is around 54% by volume (NFGWS, 2018).The NFGWS is the representative for community-owned rural water services in Ireland. The NFGWS assists schemes in meeting the challenges of water quality legislation and promotes a ‘multi-barrier approach’ to source protection. The ‘multi-barrier approach’ includes delineation of the Zone of Contribution to a supply source. A Zone of Contribution (ZOC) is the land area that contributes water to a well or spring (Misstear et al., 2006). It can be considered as the ‘catchment’ to the supply source. Like surface water bodies, springs have natural catchment areas, whereas catchment areas to boreholes depend on a number of hydrogeological and meteorological factors plus the abstraction rate. A ZOC accounts for the ‘horizontal’ movement of groundwater and any entrained contamination once it has reached the water table and is moving towards the abstraction point. The aim of delineating ZOCs is to define the area that contributes water to an abstraction point. Knowledge of where the water is coming from is critical when trying to interpret water quality data at the groundwater source. The ZOC also provides an area in which to focus further investigation and is an area where protective measures can be introduced to maintain or improve the quality of groundwater.Different methods can be used to map the ZOC to a spring, borehole or dug well, resulting in different degrees of confidence associated with the boundaries of the de-lineated area. The ZOCs and accompanying reports should be considered as preliminary source protection studies. The work was undertaken by consultants under supervision and review by GSI, and represents a partnership between the GWSs, the NFGWS and GSI. The work was funded through the Rural Water Programme funding initiative of grants towards specific source protection works on GWSs (DECLG Circular L5/13 and Explanatory Memorandum).The ZOCs were delineated in the period 2011 to 2019. The maps produced are based largely on the readily available information in the area, a field walkover survey, and on mapping techniques which use inferences and judgements based on experience at other sites. As such, the maps cannot claim to be definitively accurate across the whole area covered and should not be used as the sole basis for site-specific decisions, which will usually require the collection of additional site-specific data.This map shows the location of ZOCs which have been mapped around GWS supplies of groundwater in Ireland. This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The data is shown as polygons. Each polygon holds information on name, year and consultant.

The purpose of the Colorado Geological Survey’s Geologic Map of the Marmot Peak Quadrangle, Park and Chaffee Counties, Colorado is to describe the geologic setting, mineral and water resources, and geologic hazards of this 7.5-minute quadrangle located south of Fairplay in central Colorado. Consulting geologists Karen Houck, Jonathan Funk, and Bob Kirkham, staff geologist Chris Carroll, and field assistant Alyssa Heberton-Morimoto completed the field work on this project during the summer of 2007. Downloadable Adobe PDF and ESRI ArcGIS files. OF-12-07

The California Department of Parks and Recreation contracted Geographical Information Center (GIC) to conduct vegetation sampling across multiple California State Vehicle Recreation Areas (SVRA). The purpose of this map is to characterize the vegetation in various SVRAs, which includes Alameda Tesla, Carnegie, Claypit, Heber Dunes, Hollister Hills, Hungry Valley, Oceano Dunes, Ocotillo Wells and Prairie City. The development of this vegetation map was prompted by the passage of Senate Bill 249, in which California Department of Parks and Recreation’s Off-Highway Motor Vehicle Recreation Division (OHMVRD) was charged with meeting new legislative mandates to ensure resources compliance within all SVRAs. These mandates require (among other things) that OHMVRD compile an inventory of native plant communities within each SVRA [PRC 5090.35 (c)(1)]. To meet this requirement, OHMVRD has consulted the California Department of Fish and Wildlife’s Vegetation Classification and Mapping Program (VegCAMP) to source finescale vegetation maps that cover the SVRA footprint, or, if not available, used the VegCAMP methods to develop new finescale vegetation maps. This finescale vegetation map and associated data is intended to provide an inventory of native plant communities, inform the park’s natural resource management planning including the Wildlife Habitat Protection Plan (WHPP), and establish a baseline for measuring future vegetation change. About the individual SVRAs: Alameda Tesla: The finescale vegetation map for the Alameda Tesla area was created in 2021-2022 using CDFW's VegCAMP standard methods. At the time of surveying, this parcel was part of Carnegie SVRA and was sampled and analyzed together with that project, as part of informing the Carnegie SVRA Wildlife Habitat Protection Plan. However, after the legal separation of these two units in 2021, the mapping projects have also been separated. Carnegie: The finescale vegetation map for Carnegie SVRA was created in 2021-2022 for the park's Wildlife Habitat Protection Plan, using CDFW's VegCAMP standard methods. Field surveys were conducted in 2021. This mapping effort was part of a larger project within the Off Highway Motor Vehicle Division of State Parks to create updated vegetation maps and an inventory of native plant communities for each SVRA. When the project began in 2021, Carnegie SVRA and the adjacent Alameda-Tesla area were sampled and analyzed together. However, because of the legal the separation of these two units in 2021, the mapping projects were separated Clay Pit: Clay Pit SVRA is a small, 220-acre park in unincorporated Butte County, three miles southwest of Oroville. It consists of a narrow terrace surrounding a large bowl-shaped depression that was excavated for clay substrate to use in the construction of the Oroville Dam. It was a popular unofficial off-highway vehicle (OHV) riding area, and became an SVRA in 1981. The entire park is designated as open riding, except for an exclusion zone where a drainage canal flows through the park and into the Feather River oxbow. The park frequently floods from rainfall in wet months, and dries out in the summer. Because of the clay substrate, the shallow depressions formed from OHV use create vernal pools in the spring, providing habitat for native vernal pool plant species and branchiopod species. However, due to the history of disturbance and lack of original topography, many species at the park are ruderal non-natives. Heber Dunes: Heber Dunes SVRA is a small, 364-acre park in unincorporated Imperial County, seven miles northeast of Calexico, and is surrounded by agricultural fields, irrigation canals, and an undeveloped parcel owned by California Department of Transportation (CalTrans). It consists of open sand dunes, planted athel tamarisk (Tamarix aphylla) trees, and native and exotic desert scrub vegetation. The entire park is designated as open riding for off-highway vehicles. Hollister Hills: Hollister Hills SVRA is a 6,750 acre park located in northwest San Benito County, eight miles south of the city of Hollister. It is situated within the Gabilan Range of the California Coast ranges, in an area surrounded by primarily by rangelands. Hungry Valley: Hungry Valley SVRA is a 19,800 acre park within the Transverse Mountain Ranges, just south of Tejon Pass and the town of Gorman. The park is surrounded by National Forest land and by Tejon Ranch. Before becoming a SVRA in 1980, the park had a history of homesteading, mining, and unofficial OHV use. Oceano Dunes: This finescale vegetation map for Oceano Dunes SVRA was created to inform the park's Wildlife Habitat Protection Plan, using CDFW's VegCAMP standard methods. Field surveys were conducted in May 2022 by Chico State Geographic Information Center. Linework was conducted by Chico State Geographic Information Center. State Park staff provided edits to the draft map before it was finalized in 2023. An existing finescale map of the park was completed in 2013 (field surveys done in 2012) by MIG, report available here: https://nrm.dfg.ca.gov/documents/ContextDocs.aspx?cat=VegCAMP. Since vegetation in this park shifts frequently, and since large restoration projects have been conducted since the previous mapping effort, it was determined that an update to the map was needed. Chico State's Geographic Information Center (GIC) sampled the park in 2022 and conducted the linework to create this updated finescale vegetation map, with input from State Park staff. Vegetation was classified using a draft classification for the Santa Cruz-Santa Clara counties project, and by consulting with CDFW staff. Since GIC was also sampling and mapping other central coast State Parks in the region at the same time, the data for Pismo Beach is included here. Ocotillo Wells: This vegetation map was created in 2022-2023 to meet the above requirements and inform the Ocotillo Wells Wildlife Habitat Protection Plan. It was created by combining the existing maps from the DRECP mapping project 2016-2017 additions (Reyes et al.2021), and the Anza Borrego (1998) mapping project (See the VegCAMP website). State park staff including Melissa Patten, Leah Gardner, and Casey Paredes, conducted 25 recon surveys and additional map checks in March 2022 to groundtruth some areas, with a focus on the footprint of the older Anza Borrego project. Linework to edit the Anza Borrego project footprint area was done in 2023 using information from field surveys, and heads-up digitizing of NAIP 2020 imagery. Surveys conducted by State Parks staff in March 2022 focused on the Anza Borrego project footprint within the park, and then linework was done to update the vegetation polygons based on field surveys and 2020 NAIP aerial imagery. Prairie City: Prairie City SVRA is a 1,344 acre park located 20 miles east of Sacramento, in an ecological transition zone between the Central Valley and the Sierra foothills. Parts of the park have a history of dredge mining, and mine tailings form mounds and undulating topography in places. Other portions of the current park were formerly owned by Aerojet and used for a rocket engine program, contaminating groundwater and resulting in modern remediation and groundwater treatment efforts in the park, including monitoring and extraction wells. The imagery interpreted was NAIP 2020No accuracy assessment was done because almost all polygons were visited in the field. Minimum Mapping Units: Alameda Tesla, Carnegie, Heber Dunes, Hollister Hills, Hungry Valley, Prairie City.: The minimum mapping unit was 1 acre for upland vegetation types and ¼ acre for wetland vegetation types. Polygons were divided based on a change in cover class according to Braun-Blanquet categories (<1%, 1-5%, >5-15%, >15-25%, >25-50%, >50-75%, >75%). Breaks for the dominant overstory vegetation cover class required a 3-acre minimum mapping unit, and breaks for understory vegetation cover class required a 5-acre minimum mapping unit. Claypit: The minimum mapping unit was 1 acre, and ¼ acre for wetland or special types, which at the park includes only two small riparian stands and one patch of perennial grassland. The herbaceous stands that compose most of the park were split according to cover, but there was no maximum mapping unit size. Ocotillo Wells, Oceano Dunes: No minimum mapping unit was reported. Imagery: NAIP 2020 imagery was used for all SVRAs.

The data include soil texture, soil-water, and estimated soil strength properties for soil map units and from geotechnical investigations of landslide areas affecting highways in the municipalities of Lares, Naranjito, and Utuado, Puerto Rico. The map units are derived from published soil mapping (https://websoilsurvey.sc.egov.usda.gov/app/) and have been grouped by municipality, Unified Soil Classification System symbol, and order-of-magnitude ranges of hydraulic conductivity to create generalized map units for assigning soil-water parameters and soil-strength parameters. Each group (generalized unit) includes one or more soil map units. The groups are numbered sequentially from one (1) for each municipality with low-permeability soils having lower numbers and permeable soils having higher numbers. However, each municipality has a unique number of groups resulting from its unique mix of soils, so groups having the same number may have different characteristics in each municipality. The tab-delimited data lists the following for each constituent soil map unit: map-unit symbol, hydraulic conductivity (micrometers per second), effective porosity (percent), plasticity index, Unified Soil Classification System symbol, total sand (percent), total silt (percent), total clay (percent), organic matter (percent), water content at 15 bars of suction (percent), saturated water content (percent). For each group that includes two or more soil map units, the data include the median of each of the numeric values enumerated previously. Each group (except those lacking sufficient soil texture data), includes estimates of the soil-water retention parameter (inverse meters), angle of internal friction (degrees), and cohesion (kilopascals). A separate file contains tab-delimited data summarizing geological and engineering characteristics of soil and rock layers intersected by boreholes in landslide areas that damaged highways in the three municipalities. We obtained these characteristics from unpublished engineering reports prepared by consultants and on file with the Puerto Rico Department of Transportation (Departamento de Transportación y Obras Públicas).

The purpose of the Colorado Geological Survey’s (CGS) Antero Reservoir Quadrangle Geologic Map, Park and Chaffee Counties, Colorado is to describe the geology, mineral and ground‐water resources, and geologic hazards of this 7.5‐minute quadrangle located in central Colorado. Consulting geologists Robert Kirkham and Karen Houck, CGS staff geologist Chris Carroll, and field assistant Alyssa Heberton‐Morimoto completed field work for the project during the 2007 field season. Mr. Kirkham, Dr. Houck, and Mr. Carroll, the principal mappers and authors, created this report using field maps, photographs, structural measurements, and field notes generated by all four investigators.

Under the Natural Capital and Ecosystem Assessment (NCEA) Pilot, Natural England and the Botanical Society of Britain and Ireland (BSBI) have been working in partnership to use BSBI's vast database of plant records to inform the evidence base for tree-planting activities. Poorly targeted tree planting risks damaging wildlife and carbon-rich habitats, therefore using these data we aim to ensure that areas of high conservation value are preserved in the landscape. The summarised botanical value map provides an easily interpretable output which categorises monads (1 x 1 km grid squares) as being of Low, Moderate or High botanical value according to the presence of Rare, Scarce and Threatened (RST) plant species and/or the proportion of Priority Habitat Positive Indicator (PHPI) species that were recorded within the 1 x 1 km grid square between 1970 and 2021. The PHPI species are a combination of BSBI axiophytes, positive indicators for common standards monitoring and ancient woodland indicators. The dataset includes an overall botanical value, as well as values based on only the presence of RST plant species, and a value for each broad habitat type based on the PHPI species records. By viewing the different attributes, you can gain insights into how valuable a monad is for different habitat types and for plant species of conservation concern, as well as an indication of how well a particular monad has been surveyed. The categories of 'No indicators, poor survey coverage' and 'No indicators, good survey coverage' indicate where no indicator species have been recorded and survey coverage either is above or below a threshold of 3 'recorder days'. A 'recorder day' is defined as being when 40 or more species have been recorded on a single visit and 3 recorder days is assumed sufficient to achieve good survey coverage within a 1 x 1 km grid square. This map is not intended to be used to carry out detailed assessments of individual site suitability for tree planting, for which the RST plant species heatmap at 100 x 100 m resolution and the PHPI heatmaps at 1 x 1 km resolution have been developed by BSBI and Natural England. However, the summarised botanical value map can provide useful insights at a strategic landscape scale, to highlight monads of high value for vascular plants and inform spatial planning and prioritisation, and other land management decision-making. These should be used alongside other environmental datasets and local knowledge to ensure decisions are supported by the appropriate evidence. Please get in contact if you have any queries about the data or appropriate uses at botanicalheatmaps@naturalengland.org.uk Further information can be found in the technical report here: http://nepubprod.appspot.com/publication/5063363230171136. Attribution statement: Contains data supplied by © Natural England © Botanical Society of Britain and Ireland. Reproduced by permission of Ordnance Survey on behalf of HMSO. © Crown copyright and database right 2020. Ordnance Survey Licence number 100022021. Source: Office for National Statistics licensed under the Open Government Licence v.3.0. Contains OS data © Crown copyright and database right [2020] © JNCC, licenced under Open Government Licence v.3.0. Walker, K.J. 2018. Vascular plant 'axiophyte' scores for Great Britain, derived from the assessments of the vice-county recorders of the Botanical Society of Britain and Ireland (May 2016). NERC Environmental Information Data Centre. (Dataset). Available under Open Government Licence v.3.0. Glaves, P., Rotherham, I.D., Wright, B., Handley, C. & Birkbeck, J. 2009. A survey of the coverage, use and application of ancient woodland indicator lists in the UK. Hallam Environmental Consultants Ltd., Biodiversity and Landscape History Research Institute and the Geography, Tourism and Environment Change Research Unit, Sheffield Hallam University. © NERC Copyright 2004. Hill, M. O., Preston C. D. & Roy D. B. 2004. PLANTATT. Attributes of British and Irish Plants: Status, Size, Life history, Geography and Habitats. NERC Centre for Ecology and Hydrology: Huntingdon.

Project: Recovery and Resilience of Oyster Reefs in the Big Bend of Florida

https://wec.ifas.ufl.edu/oysterproject/

Lone Cabbage Reef Restoration Spatial Data (2017-2023) Repository:

https://zenodo.org/communities/lonecabbagereef

Contact: Joe Aufmuth, University of Florida, George A. Smathers Libraries, Academic Research and Consulting Services Department, mapper@ufl.edu, (352) 273-0371.

Clarifying Publication: Aufmuth, Moore, Pine, and Ennis (2024 in progress), An Oyster’s Pearl: Restoring the Elevation of Lone Cabbage Reef, Florida.

The repository contains ArcGIS Map Packages (v3.2.0) that are listed in the repository file Descriptions_Lone_Cabbage_Reef_map_package_list_xls.

Purpose: Data collected varies in scale as well as positional and attribute accuracy. It is the responsibility of the user to verify that the data are appropriate for their project. No warranties or guarantees are made that the data are appropriate for uses other than the Recovery and Resilience of Oyster Reefs in the Big Bend of Florida project.

Data Collection: Elevation data was collected through professional certified surveyors (Lone Cabbage Reef 2017, 2018, and 2021) as well as through field data collection efforts using Trimble survey grade GPS equipment (University of Florida 2019). Oyster count data locations were collected through field efforts and mapped to field transects using Juniper GPS survey equipment (2018, 2019, 2020, 2021, 2022, 2023). Other spatial data layers included in this data set are credited in the layouts that produce the individual maps in the map packages.