The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this 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. 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.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. 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 ArcGIS 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.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This digital data release contains spatial datasets of bedrock geology, volcanic ash bed locations, test hole locations, bedrock outcrops, and structure contours of the top of bedrock and the base of the Ogallala Group from a previously published map (Souders, 2000). The GeologicMap feature dataset contains separate feature classes for the Ogallala Group map unit (ContactsAndFaults and MapUnitPolys) and the underlying pre-Ogallala bedrock map units (ContactsAndFaults_Bedrock and MapUnitPolys_Bedrock). The VolcanicAshBedPoints feature class contains the locations of volcanic ash beds within the Ogallala Group. The contours depicting the elevation of the top of bedrock (top of Ogallala Group where present and top of pre-Ogallala bedrock where Ogallala is absent) are contained in the IsoValueLines_TopBedrock feature class. The contours depicting the elevation of the base of the Ogallala Group are contained in the IsoValueLines_BaseOgallala feature class. Contoured values are given in both feet and meters. Feature classes containing the location of test holes (TestHolePoints) and bedrock outcrops (OverlayPolys) that were used in generating the structure contour surfaces are included. Nonspatial tables define the data sources used, define terms used in the dataset, and describe the geologic units. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables. Surficial geologic units that are only represented as cross-sections on the original map publication, and the cross-sections themselves, are not included in this digital data release.

Retirement Notice: This item is in mature support as of February 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.This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map Viewer To show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021 By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this: 4. Click the styles button.5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off. Showing just one pair of years in ArcGIS Pro To show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well. How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022 What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This is a publicly available map image service with limited GIS attributes. A downloadable version of this data is now available through the MDOT GIS Open Data Portal: Download MDOT SHA Right-of-Way Polygons (Open Data Portal) The following related versions of this data are available here:MDOT SHA Right-of-Way (Secured)Line dataFull attribute tableAccessible to only MDOT employees and contractors upon requestMDOT SHA Right-of-Way (Map Image Service)Read-only map serviceLine dataLimited attributes (quality level)Accessible to publicMDOT SHA Right-of-Way data is a composite layer of PSD field-collected survey sources, PSD in-house computations, traced PSD hardcopy materials, and historical Maryland Department of Planning (MDP) parcel boundaries.This data product was intended to replace MDOT SHA Planning Level Right-of-Way (Tax Map Legacy), which is an increasingly obsolete legacy product for MDOT SHA Right-of-Way information that in some areas remains the most comprehensive. For continuity, many MDP parcel boundaries found in MDOT SHA Planning Level Right-of-Way (Tax Map Legacy) have been incorporated into MDOT SHA Right-of-Way data with an "Estimated" quality level. Please see below for a description of the primary attribute.-----------------------------------------------------The polygons in this layer are divided into 318 arbitrary grid zones across the State of Maryland. Updates to the parent ROW boundary line data set [MDOT SHA Right-of-Way (Secured)] are made by grid and reflected in this polygon layer.For more information or to report errors in this data, please contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this 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 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. 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.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.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 ArcGIS 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.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

We present a preliminary point inventory of landslides triggered by Hurricane Helene, which impacted southern Appalachia between September 25–27, 2024. This inventory is a result of a rapid response mapping effort led by the U.S. Geological Survey’s Landslide Assessments, Situational Awareness, and Event Response Research (LASER) project. LASER collaborated with state surveys and landslide researchers to identify landslides and their impacts for situational awareness and emergency response. The area of interest (AOI) for this effort was informed by a preliminary landslide hazard map created for the event (Martinez et al., 2024), and encompasses western North Carolina as well as parts of Tennessee, Virginia, Georgia, and South Carolina. This point inventory contains the following attributes: ‘Source’ and ‘Impact’. The ‘Source’ attribute identifies the data source(s) used to map each landslide. Note that the data sources listed in this attribute refer only to those used for mapping a given landslide; this does not imply that the landslide is absent or undocumented in other unlisted sources. We do not provide any specific information or metadata (e.g., footprint ID, imagery date, hyperlinks, etc.) for the listed source(s) used to map a landslide. The sources used for mapping landslides in this inventory are listed in Table 1. We relied heavily on Sentinel-2 satellite data during the mapping phase and exclusively during the review phase. While Sentinel-2 has a lower spatial resolution (10m) compared to other satellite and aerial sources (ranging from 0.15 to 3m), it is the only dataset with complete mapping AOI coverage and pre- and post-event multi-spectral imagery. The primary Sentinel-2 images used were acquired on August 26, 2024, and September 22, 2024 (pre-event), as well as October 2, 5, 7, 10, and 12, 2024 (post-event). To assist in rapid landslide detection, we derived Normalized Difference Vegetation Index (NDVI) change products using various combinations of the pre- and post-event Sentinel-2 data. NDVI change analysis was instrumental in identifying areas where vegetation loss or damage occurred, thus helping to pinpoint potential landslide activity in this heavily vegetated region. Additionally, red-green-blue (RGB) composite imagery from both pre- and post-event acquisitions was used to validate that NDVI changes were indeed indicative of landslides. Details on these data sources and analysis methods area can be found in Burgi et al. (2024). The data sources listed in the ‘Source’ attribute listed in alphabetical order. The ‘Impact’ attribute indicates the primary impact of a landslide. The options for the impact attribute are listed in Table 2. A landslide is deemed to have an impact if it appears to intersect with river(s) (including streams and creeks), road(s), building(s), or other human-modified land or infrastructure (e.g., bridges, railroads, powerlines, trails, agricultural fields, lawns, etc.) Impact was determined to the best of a mapper’s ability with the available data and at the time that the imagery was acquired. Many landslides had multiple impacts; however, in most cases, a primary impact could be identified. For example, many landslides appeared to severely impact a road and continue to fail into a nearby river, with no visible impact on the river. In this case, the primary impact would be “road”. If a landslide appeared to have multiple and equally significant impacts, it was classified as “various”. We do not report the number of impacts; for example, a landslide with a “building” Impact may have impacted more than one building. Emergency response landslide mapping efforts took place between September 28 to October 23, 2024. All landslides were mapped with a single point, irrespective of size or impact. Given the urgency of providing situational awareness for emergency response, landslide points were placed at the location of greatest visible impact, such as buildings, roads, and rivers, rather than at the headscarp. In cases where there was no visible impact, the landslide point was placed at the headscarp. Following the emergency mapping phase, all points underwent a basic review process to refine attributes, remove duplicate/low confidence points, add points for multi-source failures that coalesced into a single failure, and, where possible, adjust point locations from impact zones to the landslide headscarp(s). Reviewers utilized only Sentinel-2 NDVI and RGB imagery (pre- and post-event) for reference during the review process, relying most heavily on the 9/22 pre-event and 10/12 post-event products. Impactful landslides that are not clearly visible in the Sentinel-2 data (likely mapped using higher resolution data) were not repositioned to a headscarp and may remain at the impact location. Due to the rapid and extensive nature of this mapping effort, a formal and systematic assessment of the positional accuracy of the mapped points has not yet been conducted. As a result, there may be some degree of uncertainty in the location and classification of landslides within this inventory. We estimate our accuracy of most landslide headscarp points to be within tens of meters of their correct location. However, in some cases, dense vegetation and imaging geometry may obscure the true headscarp location, further decreasing the accuracy of some mapped landslide points. Furthermore, field or high-resolution validation was not possible for every landslide, therefore some mapped points may not correspond to actual landslide events. In particular, distinguishing landslides from severe tree blowdowns or areas of recently human-modified land cover (e.g., clearcutting or construction activities) sometimes proved challenging. It is possible that a small number of points mistakenly represent these features instead of genuine landslides. Finally, it is important to note that this inventory is preliminary and does not capture the full extent of landslides triggered by Hurricane Helene. Factors such as the rapid response nature of the mapping effort, limitations in imagery resolution, and dense forest canopy that obstructed the overhead (i.e., aerial and satellite) view of smaller or non-catastrophic landslides may contribute to underrepresentation of the total landslide count. References Burgi, P.M., Collins, E.A., Allstadt, K.E., Einbund, M.M., 2024, Normalized Difference Vegetation Index (NDVI) Change Map between 9/22/2024 and 10/12/2024, Southern Appalachian Mountains: 2024 USGS provisional data release. https://doi.org/10.5066/P14KDUKK Martinez, S.N., Stanley, T., Allstadt, K.E., Baxstrom, K.W., Mirus, B.B., Einbund, M.M., Bedinger, E.C., 2024, Preliminary Landslide Hazard Models for the 2024 Hurricane Helene Landslide Emergency Response: 2024 USGS Provisional Data Release. https://doi.org/10.5066/P134ERB9

This map contains continuously updated U.S. tornado, wind, hail, and 12 other storm reports filtered to present the past 24-hours of available incidents reported. You can click on each to receive information about the specific location and read a short description about the issue.The layer content is updated 4 times hourly from data provided by NOAA’s National Weather Service Storm Prediction Center.A full archive of storm events can be accessed from the NOAA National Centers for Environmental Information.SourceNOAA Storm Prediction Center https://www.spc.noaa.gov/climo/reportsSample DataSee Sample Layer Item for sample data during inactive periods!Update FrequencyThe service is updated every 15 minutes using the Aggregated Live Feeds MethodologyArea CoveredCONUS (Contiguous United States)Host Feature Service Item: USA Storm ReportsWhat can you do with this layer?This map service is suitable for data discovery and visualization.Change the symbology of each layer using single or bi-variate smart mapping. For instance, use size or color to indicate the intensity of a tornado.You can click on each to receive information about the specific location and read a short description about the issue.Query the attributes to show only specific event types or locations.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Connecticut Hydrography Set:

Connecticut Hydrography Line includes the line features of a layer named Hydrography. Hydrography is a 1:24,000-scale, polygon and line feature-based layer that includes all hydrography features depicted on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. These hydrography features include waterbodies, inundation areas, marshes, dams, aqueducts, canals, ditches, shorelines, tidal flats, shoals, rocks, channels, and islands. Hydrography is comprised of polygon and line features. Polygon features represent areas of water for rivers, streams, brooks, reservoirs, lakes, ponds, bays, coves, and harbors. Polygon features also depict inundation areas, marshes, dams, aqueducts, canals, tidal flats, shoals, rocks, channels, and islands shown on the USGS 7.5 minute topographic quadrangle maps. Line features represent single-line rivers and streams, aqueducts, canals, and ditches. Line features also enclose all polygon features in the form of natural shorelines, manmade shorelines, dams, closure lines separating adjacent waterbodies, and the apparent limits for tidal flats, rocks, and areas of marsh. The layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 so it does not depict conditions at any one particular point in time. Also, the layer does not reflect recent changes with the course of streams or location of shorelines impacted by natural events or changes in development since the time the USGS 7.5 minute topographic quadrangle maps were published. Attribute information is comprised of codes to identify hydrography features by type, cartographically represent (symbolize) hydrography features on a map, select waterbodies appropriate to display at different map scales, identify individual waterbodies on a map by name, and describe feature area and length. The names assigned to individual waterbodies are based on information published on the USGS 7.5 minute topographic quadrangle maps or other state and local maps. The layer does not include bathymetric, stream gradient, water flow, water quality, or biological habitat information. This layer was originally published in 1994. The 2005 edition includes the same water features published in 1994, however some attribute information has been slightly modified and made easier to use. Also, the 2005 edition corrects previously undetected attribute coding errors.

Connecticut Hydrography Polygon includes the polygon features of a layer named Hydrography. Hydrography is a 1:24,000-scale, polygon and line feature-based layer that includes all hydrography features depicted on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. These hydrography features include waterbodies, inundation areas, marshes, dams, aqueducts, canals, ditches, shorelines, tidal flats, shoals, rocks, channels, and islands. Hydrography is comprised of polygon and line features. Polygon features represent areas of water for rivers, streams, brooks, reservoirs, lakes, ponds, bays, coves, and harbors. Polygon features also depict inundation areas, marshes, dams, aqueducts, canals, tidal flats, shoals, rocks, channels, and islands shown on the USGS 7.5 minute topographic quadrangle maps. Line features represent single-line rivers and streams, aqueducts, canals, and ditches. Line features also enclose all polygon features in the form of natural shorelines, manmade shorelines, dams, closure lines separating adjacent waterbodies, and the apparent limits for tidal flats, rocks, and areas of marsh. The layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 so it does not depict conditions at any one particular point in time. Also, the layer does not reflect recent changes with the course of streams or location of shorelines impacted by natural events or changes in development since the time the USGS 7.5 minute topographic quadrangle maps were published. Attribute information is comprised of codes to identify hydrography features by type, cartographically represent (symbolize) hydrography features on a map, select waterbodies appropriate to display at different map scales, identify individual waterbodies on a map by name, and describe feature area and length. The names assigned to individual waterbodies are based on information published on the USGS 7.5 minute topographic quadrangle maps or other state and local maps. The layer does not include bathymetric, stream gradient, water flow, water quality, or biological habitat information. This layer was originally published in 1994. The 2005 edition includes the same water features published in 1994, however some attribute information has been slightly modified and made easier to use. Also, the 2005 edition corrects previously undetected attribute coding errors.

This is a collection of Digital Surface Models and Highest Hit rasters covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. DSM and Highest Hit rasters represent elevation of Earth's surface, including its natural and human-made features, such as vegetation and buildings.The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.

See full Data Guide here. Connecticut Parcels for Protected Open Space Mapping is a polygon feature-based layer that includes basic parcel-level information for some towns in Connecticut. This 2009 parcel layer includes information provided by individual municipalities. These parcel data are incomplete and out of date. The accuracy, currency and completeness of the data reflect the content of the data at the time DEEP acquired the data from the individual municipalities. Attribute information is comprised of values such as town name and map lot block number. These data are not updated by CT DEEP and should only be used as a general reference. Critical decisions involving parcel-level information should be based on more recently acquired information from the respective municipalities. These parcels are not to be considered legal boundaries such as boundaries determined from certain classified survey maps or deed descriptions. Parcel boundaries shown in this layer are based on information from municipalities used for property tax purposes. Largely due to differences in horizontal accuracy among various data layers, do not expect these parcel boundaries to line up exactly with or be properly postioned relative to features shown on other layers available from CT DEEP such as scanned USGS topography quadrangle maps, roads, hydrography, town boundaries, and even orthophotograpy. The data in the parcel layer was obtained from individual Connecticut municipalities. An effort was made to collect data once from each municipality. The data acquisition date for each set of municipally-supplied parcel data was not recorded and CT DEEP does not keep this information up-to-date. Consequently, these data are out-of-date, incomplete and do not reflect the current state of property ownership in these municipalities. These parcels are not to be considered legal boundaries such as boundaries determined from certain classified survey maps or deed descriptions. Parcel boundaries shown in this layer are based on information from municipalities used for property tax purposes. Parcel boundaries and attribute information have not been updated in this layer since the time the information was originally acquired by CT DEEP. For example, property boundaries are incorrect where subdivisions have occurred. Also, field attribute values are populated only if the information was supplied to CT DEEP. For example, parcels in some towns lack location (street name) information or possibly map lot block values. Therefore, field attributes are inconsistent, may include gaps, and do not represent complete sets of values among all towns. They should not be compared and analyzed across towns. It is emphasized that critical decisions involving parcel-level information be based on more recently obtained information from the respective municipalities. These data are only suitable for general reference purposes. Be cautious when using these data. Many Connecticut municipalities provide access to more up-to-date and more detailed property ownership information on the Internet. This dataset includes parcel information for the following towns: Andover, Ansonia, Ashford, Avon, Beacon Falls, Berlin, Bethany, Bethel, Bethlehem, Bloomfield, Bolton, Branford, Bridgewater, Brookfield, Brooklyn, Canaan, Canterbury, Canton, Chaplin, Cheshire, Chester, Clinton, Colchester, Colebrook, Columbia, Cornwall, Coventry, Cromwell, Danbury, Darien, Deep River, Derby, East Granby, East Haddam, East Hampton, East Hartford, East Lyme, East Windsor, Eastford, Ellington, Enfield, Essex, Farmington, Franklin, Glastonbury, Granby, Greenwich, Griswold, Groton, Guilford, Haddam, Hamden, Hartford, Hebron, Kent, Killingly, Killingworth, Lebanon, Ledyard, Lisbon, Litchfield, Lyme, Madison, Manchester, Mansfield, Marlborough, Meriden, Middlebury, Middlefield, Middletown, Milford,

Tags

   soil survey, soils, Soil Survey Geographic, SSURGO




   Summary


   SSURGO depicts information about the kinds and distribution of

   soils on the landscape. The soil map and data used in the SSURGO

   product were prepared by soil scientists as part of the National

   Cooperative Soil Survey.


   Description


   This data set is a digital soil survey and generally is the most

   detailed level of soil geographic data developed by the National

   Cooperative Soil Survey. The information was prepared by digitizing

   maps, by compiling information onto a planimetric correct base

   and digitizing, or by revising digitized maps using remotely

   sensed and other information.


   This data set consists of georeferenced digital map data and

   computerized attribute data. The map data are in a 3.75 minute

   quadrangle format and include a detailed, field verified inventory

   of soils and nonsoil areas that normally occur in a repeatable

   pattern on the landscape and that can be cartographically shown at

   the scale mapped. A special soil features layer (point and line

   features) is optional. This layer displays the location of features

   too small to delineate at the mapping scale, but they are large

   enough and contrasting enough to significantly influence use and

   management. The soil map units are linked to attributes in the

   National Soil Information System relational database, which gives

   the proportionate extent of the component soils and their properties.


   Credits

   There are no credits for this item.


   Use limitations


   The U.S. Department of Agriculture, Natural Resources Conservation

   Service, should be acknowledged as the data source in products

   derived from these data.


   This data set is not designed for use as a primary regulatory tool

   in permitting or citing decisions, but may be used as a reference

   source. This is public information and may be interpreted by

   organizations, agencies, units of government, or others based on

   needs; however, they are responsible for the appropriate

   application. Federal, State, or local regulatory bodies are not to

   reassign to the Natural Resources Conservation Service any

   authority for the decisions that they make. The Natural Resources

   Conservation Service will not perform any evaluations of these maps

   for purposes related solely to State or local regulatory programs.


   Photographic or digital enlargement of these maps to scales greater

   than at which they were originally mapped can cause misinterpretation

   of the data. If enlarged, maps do not show the small areas of

   contrasting soils that could have been shown at a larger scale. The

   depicted soil boundaries, interpretations, and analysis derived from

   them do not eliminate the need for onsite sampling, testing, and

   detailed study of specific sites for intensive uses. Thus, these data

   and their interpretations are intended for planning purposes only.

   Digital data files are periodically updated. Files are dated, and

   users are responsible for obtaining the latest version of the data.


   Extent



   West -76.713689  East -76.526117 

   North 39.374398  South 39.194856 




   Scale Range

   There is no scale range for this item.

This layer contains information for locating past and present legal city boundaries within Los Angeles County. The Los Angeles County Department of Public Works provides the most current shapefiles representing city annexations and city boundaries on the Los Angeles County GIS Data Portal. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California. Numerous records are freely available at the Land Records Information website, hosted by the Department of Public Works.Principal Attributes:NO: The row number in the attribute table of the PDF Annexation Maps. (See Below)

ANNEX_No: These values are only used for the City of Los Angeles and Long Beach.

NAME: The official annexation name.

TYPE: Indicates the legal action.

A - represents an Annexation to that city. D - represents a Detachment from that city. V - is used to indicate the annexation was void or withdrawn before an effective date could be declared. 33 - Some older city annexation maps indicate a city boundary declared 'as of February 8, 1933'.

ANNEX_AREA: is the land area annexed or detached, in square miles, per the recorded legal description.

TOTAL_AREA: is the cumulative total land area for each city, arranged chronologically.

SHADE: is used by some of our cartographers to store the color used on printed maps.

INDEXNO: is a matching field used for retrieving documents from our department's document management system.

STATE (Secretary of State): Date filed with the Secretary of State. These are not available for earlier annexations and are Null.

COUNTY (County Recorder): Date filed with the County Recorder. These are not available for earlier annexations and are Null.

EFFECTIVE (Effective Date): The effective date of the annexation or detachment.

CITY: The city to which the annexation or detachment took place.

URL: This text field contains hyperlinks for viewing city annexation documents. See the ArcGIS Help for using the Hyperlink Tool.

FEAT_TYPE: contains the type of feature each polygon represents:

Land - Use this value for your definition query if you want to see only land features on your map. Pier - This value is used for polygons representing piers along the coastline. One example is the Santa Monica Pier. Breakwater - This value is used for polygons representing man-made barriers that protect the harbors. Water - This value is used for polygons representing navigable waters inside the harbors and marinas. 3NM Buffer - Per the Submerged Lands Act, the seaward boundaries of coastal cities and unincorporated county areas are three nautical miles from the coastline. (A nautical mile is 1,852 meters, or about 6,076 feet.) Annexation Maps by City (PDF)Large format, high quality wall maps are available for each of the 88 cities in Los Angeles County in PDF format.Agoura HillsHermosa BeachNorwalkAlhambraHidden HillsPalmdaleArcadiaHuntington ParkPalos Verdes EstatesArtesiaIndustryParamountAvalonInglewoodPasadenaAzusaIrwindalePico RiveraBaldwin ParkLa Canada FlintridgePomonaBellLa Habra HeightsRancho Palos VerdesBell GardensLa MiradaRedondo BeachBellflowerLa PuenteRolling HillsBeverly HillsLa VerneRolling Hills EstatesBradburyLakewoodRosemeadBurbankLancasterSan DimasCalabasasLawndaleSan FernandoCarsonLomitaSan GabrielCerritosLong BeachSan MarinoClaremontLos Angeles IndexSanta ClaritaCommerceLos Angeles Map 1Santa Fe SpringsComptonLos Angeles Map 2Santa MonicaCovinaLos Angeles Map 3Sierra MadreCudahyLos Angeles Map 4Signal HillCulver CityLos Angeles Map 5South El MonteDiamond BarLos Angeles Map 6South GateDowneyLos Angeles Map 7South PasadenaDuarteLos Angeles Map 8Temple CityEl MonteLynwoodTorranceEl SegundoMalibuVernonGardenaManhattan BeachWalnutGlendaleMaywoodWest CovinaGlendoraMonroviaWest HollywoodHawaiian GardensMontebelloWestlake VillageHawthorneMonterey ParkWhittier

This dataset supports the manuscript "Forest attribute maps: a support for small area estimation of forest disturbances" published in Annals of Forest Sciences (DOI: 10.1186/s13595-025-01293-8). It contains two files saved in parquet format, which can be read using library arrow in R.

Due of restrictions associated with the use of NFI data, the datasets do not contain any spatial information and map data are restricted to the pixels describing the areas impacted by bark-beetle outbreaks such as detected using the FORDEAD approach. Additional data are available from the authors upon reasonable request and with permission of IGN.

File plot.parquet contains growing stock volume and basal area from NFI surveys, as well as the auxiliary data used to compute the forest attribute maps. The auxiliary data were extracted at a 30 m resolution.

The file includes the following attributes:

• "nfi_year" : year of the NFI field survey
• "forest_type" : dominant forest type i.e. conifer or harwood
• "specific_model" : binary field indicating if the plot have been used (1) or not (0) to model forest attributes with the specific model (spruce and fir stands only)
• "growing_stock_volume_in_m3_per_ha" : plot level nfi growing stock volume in cubic meter per hectare
• "basal_area_in_m2_per_ha" : plot level nfi basal area in squared meter per hectare
• "total_inner_canopy_volume" : volume of canopy dervied from a lidar canopy height model (see table 7 of the manuscript for computational details)
• "gap_area" : surface of the lidar canopy height model below 5 m (see table 7 of the manuscript for computational details)
• "second_cummulative_percentage_of_returns" : cumulative percentage of return in the 2^th layer according to Woods et al. (2008); https://doi.org/10.5558/tfc84827-6)
• "near_infrared_band8" : mean value of the mean infrared band of Sentinel.

The file map_bark_beetle.parquet contains auxiliary data over FORDEAD polygons in the area of interest, computed at 30 m resolution.

The file included the following attributes:

• "rowid" : line index of the map
• "columnid" : column index of the map
• "total_inner_canopy_volume" : volume of canopy derived from a lidar canopy height model (see table 7 of the manuscript for computational details)
• "gap_area" : surface of the lidar canopy height model below 5 m (see table 7 of the manuscript for computational details)
• "second_cummulative_percentage_of_returns" : cumulative percentage of return in the 2^th layer according to Woods et al. (2008); https://doi.org/10.5558/tfc84827-6)
• "near_infrared_band8" : mean value of the mean infrared band of Sentinel.
• "proportion_of_bark_beetle_area_in_m2_in_xxx_in_class_y" : proportion of the pixel detected by FORDEAD method as an anomalie in Sentinel data for the year "xxxx" and a given intensity class (y). The attribute is dennoted prop_outbreak in the manuscript.

This is a collection of bare-Earth digital elevation models covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. Bare-Earth DEMs, also commonly called Digital Terrain Models (DTM), represent the ground topography after removal of persistent objects such as vegetation and buildings, and therefore show the natural terrain.The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. userdata and unzip the LayerFiles.zip folder.Data from the four SSURGO tables were assembled into the single table included in each map package. Data from the component table were aggregated using a dominant component model (listed below under Component Table - Dominant Component) or a weighted average model (listed below under Component Table - Weighted Average) using custom Python scripts. The the Mapunit table - the MUAGATTAT table and the processed Component table data were joined to the Mapunit Feature Class. Field aliases were added and indexes calculated. A field named Map Symbol was created and populated with random integers from 1-10 for symbolizing the soil units in the map package.For documentation of the SSURGO dataset see:http://soildatamart.nrcs.usda.gov/SSURGOMetadata.aspxFor documentation of the Watershed Boundary Dataset see: http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/datasetThe map packages contain the following attributes in the Map Units layer:Mapunit Feature Class:Survey AreaSpatial VersionMapunit SymbolMapunit KeyNational Mapunit SymbolMapunit Table:Mapunit NameMapunit KindFarmland ClassHighly Erodible Lands Classification - Wind and WaterHighly Erodible Lands Classification - WaterHighly Erodible Lands Classification - WindInterpretive FocusIntensity of MappingLegend KeyMapunit SequenceIowa Corn Suitability RatingLegend Table:Project ScaleTabular VersionMUAGGATT Table:Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table - Weighted Average:Mean Annual Air Temperature - High Value Mean Annual Air Temperature - Low Value Mean Annual Air Temperature - Representative Value Albedo - High Value Albedo - Low Value Albedo - Representative Value Slope - High Value Slope - Low Value Slope - Representative Value Slope Length - High Value Slope Length - Low Value Slope Length - Representative Value Elevation - High Value Elevation - Low Value Elevation - Representative Value Mean Annual Precipitation - High Value Mean Annual Precipitation - Low Value Mean Annual Precipitation - Representative Value Days between Last and First Frost - High Value Days between Last and First Frost - Low Value Days between Last and First Frost - Representative Value Crop Production Index Range Forage Annual Potential Production - High Value Range Forage Annual Potential Production - Low Value Range Forage Annual Potential Production - Representative Value Initial Subsidence - High Value Initial Subsidence - Low Value Initial Subsidence - Representative Value Total Subsidence - High ValueTotal Subsidence - Low Value Total Subsidence - Representative Value Component Table - Dominant Component:Component KeyComponent Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoffSoil Loss Tolerance FactorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupForage Suitability GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic Class NameOrderSuborderGreat GroupSubgroupParticle SizeParticle Size ModifierCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoisture SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilThe U.S. Department of Agriculture - Natural Resources Conservation Service - should be acknowledged as the data source in products derived from these data. This data set is not designed for use as a primary regulatory tool in permitting or citing decisions - but may be used as a reference source. This is public information and may be interpreted by organizations - agencies - units of government - or others based on needs; however - they are responsible for the appropriate application. Federal - State - or local regulatory bodies are not to reassign to the Natural Resources Conservation Service any authority for the decisions that they make. The Natural Resources Conservation Service will not perform any evaluations of these maps for purposes related solely to State or local regulatory programs. Photographic or digital enlargement of these maps to scales greater than at which they were originally mapped can cause misinterpretation of the data. If enlarged - maps do not show the small areas of contrasting soils that could have been shown at a larger scale. The depicted soil boundaries - interpretations - and analysis derived from them do not eliminate the need for onsite sampling - testing - and detailed study of specific sites for intensive uses. Thus - these data and their interpretations are intended for planning purposes only. Digital data files are periodically updated. Files are dated - and users are responsible for obtaining the latest version of the data.The attribute accuracy is tested by manual comparison of the source with hard copy plots and/or symbolized display of the map data on an interactive computer graphic system. Selected attributes that cannot be visually verified on plots or on screen are interactively queried and verified on screen. In addition - the attributes are tested against a master set of valid attributes. All attribute data conform to the attribute codes in the signed classification and correlation document and amendment(s). Last Updated: Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Geoscientific/MD_SSURGOSoils/MapServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Tags

   soil survey, soils, Soil Survey Geographic, SSURGO




   Summary


   SSURGO depicts information about the kinds and distribution of

   soils on the landscape. The soil map and data used in the SSURGO

   product were prepared by soil scientists as part of the National

   Cooperative Soil Survey.


   Description


   This data set is a digital soil survey and generally is the most

   detailed level of soil geographic data developed by the National

   Cooperative Soil Survey. The information was prepared by digitizing

   maps, by compiling information onto a planimetric correct base

   and digitizing, or by revising digitized maps using remotely

   sensed and other information.


   This data set consists of georeferenced digital map data and

   computerized attribute data. The map data are in a 3.75 minute

   quadrangle format and include a detailed, field verified inventory

   of soils and nonsoil areas that normally occur in a repeatable

   pattern on the landscape and that can be cartographically shown at

   the scale mapped. A special soil features layer (point and line

   features) is optional. This layer displays the location of features

   too small to delineate at the mapping scale, but they are large

   enough and contrasting enough to significantly influence use and

   management. The soil map units are linked to attributes in the

   National Soil Information System relational database, which gives

   the proportionate extent of the component soils and their properties.


   Credits

   There are no credits for this item.


   Use limitations


   The U.S. Department of Agriculture, Natural Resources Conservation

   Service, should be acknowledged as the data source in products

   derived from these data.


   This data set is not designed for use as a primary regulatory tool

   in permitting or citing decisions, but may be used as a reference

   source. This is public information and may be interpreted by

   organizations, agencies, units of government, or others based on

   needs; however, they are responsible for the appropriate

   application. Federal, State, or local regulatory bodies are not to

   reassign to the Natural Resources Conservation Service any

   authority for the decisions that they make. The Natural Resources

   Conservation Service will not perform any evaluations of these maps

   for purposes related solely to State or local regulatory programs.


   Photographic or digital enlargement of these maps to scales greater

   than at which they were originally mapped can cause misinterpretation

   of the data. If enlarged, maps do not show the small areas of

   contrasting soils that could have been shown at a larger scale. The

   depicted soil boundaries, interpretations, and analysis derived from

   them do not eliminate the need for onsite sampling, testing, and

   detailed study of specific sites for intensive uses. Thus, these data

   and their interpretations are intended for planning purposes only.

   Digital data files are periodically updated. Files are dated, and

   users are responsible for obtaining the latest version of the data.


   Extent



   West -76.713689  East -76.526117 

   North 39.374398  South 39.194856 




   Scale Range

   There is no scale range for this item.

This hosted feature layer has been published in RI State Plane Feet NAD 83.This hosted view layer has been filtered to include only Prime and Important Farmland Soils. This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the Rhode Island Soil Survey Program in partnership with the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped.

The data represent web-scraping of hyperlinks from a selection of environmental stewardship organizations that were identified in the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017). There are two data sets: 1) the original scrape containing all hyperlinks within the websites and associated attribute values (see "README" file); 2) a cleaned and reduced dataset formatted for network analysis. For dataset 1: Organizations were selected from from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a publicly available, spatial data set about environmental stewardship organizations working in New York City, USA (N = 719). To create a smaller and more manageable sample to analyze, all organizations that intersected (i.e., worked entirely within or overlapped) the NYC borough of Staten Island were selected for a geographically bounded sample. Only organizations with working websites and that the web scraper could access were retained for the study (n = 78). The websites were scraped between 09 and 17 June 2020 to a maximum search depth of ten using the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020). For dataset 2: The complete scrape results were cleaned, reduced, and formatted as a standard edge-array (node1, node2, edge attribute) for network analysis. See "READ ME" file for further details. References: R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Version 4.0.3. Stockton, T. (2020). snaWeb Package: An R package for finding and building social networks for a website, version 1.0.1. USDA Forest Service. (2017). Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. Available online at https://www.nrs.fs.fed.us/STEW-MAP/data/. This dataset is associated with the following publication: Sayles, J., R. Furey, and M. Ten Brink. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Applied Network Science. Springer Nature, New York, NY, 7: 36, (2022).

Hurricane Maria brought intense rainfall and caused widespread landsliding throughout Puerto Rico during September 2017. Previous detailed landslide inventories following the hurricane include Bessette-Kirton et al. (2017, 2019). Here we continue that work with an in-depth look at two areas in San Lorenzo, which is a municipality in the east-central part of the main island. To study a characteristic sample of landslides in San Lorenzo, we mapped all visible landslides in two physiographically diverse areas, but all within the San Lorenzo Formation. We used aerial imagery collected between 9-15 October 2017 (Quantum Spatial, Inc., 2017) to map landslide source and runout areas, and 1-m-resolution pre-event and post-event lidar (U.S. Geological Survey, 2018, 2020) as a digital base map for mapping. Difficulties with using these tools arose when aerial imagery was not correctly georeferenced to the lidar, when cloud cover was present in all images of an area, and in interpreting failure modes using only two-dimensional aerial photos. These difficulties with aerial imagery were partially resolved using the lidar. The map data comprises headscarp points, travel distance lines, source area polygons, and affected area polygons that are provided as point, line, and polygon shapefiles that may be viewed using common geographic information systems. Various characteristics of the landslides and their geomorphic settings are included in attribute tables of the mapped features, and this information is described in the "Attribute Summary" document in the accompanying files. Quantitative attributes (e.g., failure travel distance, failure fall height, watershed contributing area, etc.) were determined using tools available with the ESRI ArcGIS Pro v. 3.0.36056 geographic information system. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Bessette-Kirton, E.K., Cerovski-Darrian, C., Schulz, W.H., Coe, J.A., Kean, J.W., Godt, J.W., Thomas, M.A. and Hughes, K.S., 2019, Landslides triggered by Hurricane Maria: Assessment of an extreme event in Puerto Rico: GSA Today, v. 29, no. 6. Bessette-Kirton, E.K., Coe, J.A., Godt, J.W., Kean, J.W., Rengers, F.K., Schulz, W.H., Baum, R.L., Jones, E.S., and Staley, D.M., 2017, Map data showing concentration of landslides caused by Hurricane Maria in Puerto Rico: U.S. Geological Survey data release, https://doi.org/10.5066/F7JD4VRF. Quantum Spatial, Inc., 2017 FEMA PR Imagery: https://s3amazonaws.com/fema-cap-imagery/Others/Maria (accessed October 2017). U.S. Geological Survey, 2018, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018). U.S. Geological Survey, 2020, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018).

Surficial Aquifer Texture Map was prepared from the Surficial Materials Map of Connecticut (Stone, J.R., Schafer, J.P., London, E.H. and Thompson, W.B., 1992, U.S. Geological Survey special map, 2 sheets, scale 1:125,000) to describe unconsolidated areas of the subsurface with similar properties relative to ground water flow. Surficial aquifers are unconsolidated geologic deposits capable of yielding a sufficient quantity of groundwater to wells. Surficial aquifer textures were identified from original surficial materials mapping for use in ground water applications. These are qualitative interpretations of material properties relative to ground water flow. Surficial aquifer texture groups were identified to represent aquifer textures with similar hydraulic conductivities. Some interpretations were made beneath postglacial alluvium and swamp deposits. Alluvium without a subsurface interpretation was classified as having similar hydrologic properties as till. Alluvium areas with subsurface interpretations of fines or coarse grained deposits were classified as having the hydrologic characteristics of the underlying deposits. The aquifer textures include areas of till, fine grained, fine overlying coarse grained, coarse grained, coarse overlying fine grained deposits, artificial fill, beach, salt marsh, swamp, and water. Aquifer texture groups include areas of fine grained , fine overlying coarse grained, coarse grained, and coarse overlying fine grained deposits. Surficial materials not included in the surficial aquifer texture groups include till, artificial fill, beach, salt marsh, swamp, and water. All textural terms follow the grain size classification of Stone et al 1992, modified from Wentworth, 1922. The surficial aquifer texture classifications are suitable for use at 1:24,000 scale. Original mapping of the Surficial Materials Map of Connecticut is preserved as polygon attribute values in this data layer, and is herein described. The Surficial Materials Map of Connecticut portrays the glacial and postglacial deposits of Connecticut in terms of their aerial extent and subsurface textural relationships. Glacial Ice-Laid Deposits (thin till, thick till, end moraine deposits) and Postglacial Deposits (alluvium, swamp deposits, marsh deposits, beach deposits, talus, and artificial fill) are differentiated from Glacial Meltwater Deposits. The meltwater deposits are further characterized using four texturally-based map units (g = gravel, sg = sand and gravel, s = sand, and f = fines). In many places a single map unit (e.g. sand) is sufficient to describe the entire meltwater section. Where more complex stratigraphic relationships exist, "stacked" map units are used to characterize the subsurface (e.g. sg/s/f - sand and gravel overlying sand overlying fines). Where postglacial deposits overlie meltwater deposits, this relationship is also described (e.g. alluvium overlying sand). Map unit definitions (Surficial Materials Polygon Code definitions, found in the metadata) provide a short description of the inferred depositional environment for each of the glacial meltwater map units. This map was compiled at 1:24,000 scale, and published at 1:125,000 scale. Connecticut Surficial Materials is a 1:24,000-scale, polygon and line feature-based layer describing the unconsolidated glacial and postglacial deposits of Connecticut in terms of their grain-size distribution (texture) as compiled at 1:24,000 scale for the Surficial Materials Map of Connecticut. Glacial meltwater deposits (stratified deposits) are particularly emphasized because these sediments are the major groundwater aquifers in the State and are also the major source of construction aggregate. These deposits are described in terms of their subsurface distribution of textures as well as their extent. The texture of meltwater deposits through their total vertical thickness in the subsurface is shown to the extent that it is known or can be inferred. In some places only one textural unit (such as SG - Sand and Gravel) describes the whole vertical extent of the meltwater deposits; in other places 'stacked units' (such as SG/S/F - Sand and Gravel overlying Sand overlying Fines) indicate changes of textural units in the subsurface. Polygon features represent individual textural (surficial material) units with attributes that describe textural unit type and size. Examples of polygon features that are postglacial deposits include floodplain alluvium, swamp deposits, salt-marsh and estuarine deposits, talus, coastal beach and dune deposits, and artificial fill. Examples of glacial ice-laid deposits include till, thin till, thick till and end moraine deposits. Examples of glacial melt-water deposits include gravel, sand and gravel, sand, and very fine sand, silt and clay. Additional polygon features are incorporated to define surface water areas for streams, lakes, ponds, bays, and estuaries greater than 5 acres in size. Line features describe the type of boundary between individual textural units such as a geologic contact line between two different textural units or a linear shoreline feature between a textural unit and an adjacent waterbody. Data is compiled at 1:24,000 scale and is not updated.

GEOLOGIC DISCUSSION - The following text is excerpted from the text on sheet 1 of the Surficial Materials Map of Connecticut, Stone and others, 1992. It has been modified as necessary for use with the 1:24,000 scale digital data, and is not considered a valid substitute for the information found on the published map. For a more complete understanding of the geologic principles behind the Surficial Materials data it is advisable to consult the published map, which contains cross sections, diagrams and text not available in digital form. DISCUSSION OF SURFICIAL MATERIALS - The unconsolidated deposits overlying bedrock in Connecticut range from a few feet to several hundred feet in thickness. These earth materials significantly affect human development of the land. Most of the unconsolidated materials are deposits of continental glaciers that covered all of New England at least twice during the Pleistocene ice age. These glacial deposits are divided into two broad categories, glacial till and glacial stratified deposits. Till, the most widespread glacial deposit, was laid down directly by glacier ice and is characterized by a nonsorted matrix of sand, silt, and clay with variable amounts of stones and large boulders. Glacial meltwater deposits are concentrated in both small and large valleys and were laid down by glacial meltwater in streams and lakes in front of the retreating ice margin during deglaciation. These deposits are characterized by layers of well-sorted to poorly sorted gravel, sand, silt, and clay. Postglacial sediments, primarily floodplain alluvium and swamp deposits, make up a lesser proportion of the unconsolidated materials of Connecticut. Alluvium is largely reworked from glacial materials and has similar physical characteristics. The distribution of surficial (unconsolidated) materials that lie between the land surface (below the pedogenic soil) and the bedrock surface is shown on this map to the extent that it is known or can be inferred. The cross sections and the block diagram shown on the published map (Stone and others, 1992) illustrate the characteristic vertical distribution of glacial till, glacial meltwater deposits, and postglacial deposits encountered in Connecticut. The areal distribution of till and stratified deposits is related to the physiographic regions of the State: the eastern and western highlands and the central lowland. In highland areas, till is the major unconsolidated material, present as a discontinuous mantle of variable thickness over the bedrock surface. Till is thickest in drumlins and on the northwest slopes of hills. Glacial meltwater deposits that average 10-40 feet in thickness overlie the till in small upland valleys and commonly in north-sloping pockets between bedrock hills. In the central lowland, especially in the north half, glacial stratified deposits are the predominant surficial materials. These deposits generally overlie till; however, well logs indicate that in some places till is not present and the stratified deposits lie directly on bedrock. The extensive stratified deposits of the central lowland average 50-100 feet in thickness, and in the northern part they almost completely mask the till-draped bedrock surface. Postglacial materials locally overlie the glacial deposits throughout the State. Alluvium occurs on the floodplains of most streams and rivers. Swamp deposits occur in poorly drained areas. Talus occurs along the bases of steep bedrock cliffs, principally along the traprock ridges within the central lowland. Salt-marsh and estuarine deposits occur mainly along the tidal portions of streams and rivers entering Long Island Sound. Beach deposits occur along the shoreline of Long Island Sound. The units on this map delineate textural changes in the subsurface as well as areally at the surface. An earlier map at 1:125,000 scale of central Connecticut (Stone and others, 1979) shows only surface textural units; a separate map in the same series (Langer, 1979) shows subsurface deposits of fine-grained materials. Several previous 1:24,000-scale quadrangle maps in Connecticut show three-dimensional textural units and refer to them as 'superposed deposits' (see Stone, 1976 and Radway and Schnabel, 1976, as examples). On this map, the term 'stack unit' (Kempton, 1981) is used in place of superposed deposits. DISTRIBUTION OF TEXTURES IN GLACIAL MELTWATER DEPOSITS - The distribution of textural units is extrapolated from both point data (well and test-hole logs, gravel pits, and shovel holes) and from interpretation of landforms based on the principles of morphosequence deposition and systematic northward ice retreat (Koteff, 1974; Koteff and Pessl, 1981). These concepts provide a model by which grain-size