This map provides a colorized representation of slope, generated dynamically using server-side slope function on the Terrain layer. The degree of slope steepness is depicted by light to dark colors - flat surfaces as gray, shallow slopes as light yellow, moderate slopes as light orange and steep slopes as red-brown. A scaling is applied to slope values to generate appropriate visualization at each map scale. This service should only be used for visualization, such as a base layer in applications or maps. Note: If access to non-scaled slope values is required, use the Slope Degrees or Slope Percent functions, which return values from 0 to 90 degrees, or 0 to 1000%, respectively.Units: DegreesUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: Yes. This colorized slope is appropriate for visualizing the steepness of the terrain at all map scales. This layer can be added to applications or maps to enhance contextual understanding. Use for Analysis: No. 8 bit color values returned by this service represent scaled slope values. For analysis with non-scaled values, use the Slope Degrees or Slope Percent functions.For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single export image request.

This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

This layer provides slope values calculated from elevation data. The values are integer and represent the angle of the downward sloping terrain from 0 (flat) to 90 degrees (vertical). The layer is designed for use in landscape-scale analysis.Dataset SummaryThis layer provides access to a 250m cell-sized raster of slope in degrees. The layer was created with the ArcGIS Slope Tool using the GMTED elevation layer as an input. The layer was created in 2014 by Esri.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

This layer provides slope percent rise values calculated dynamically from the elevation data (within the current extents) using the server-side slope function applied on the Terrain layer. Percent of slope is determined by dividing the amount of elevation change by the amount of horizontal distance covered (sometimes referred to as "the rise divided by the run"), and then multiplying the result by 100. The values range from 0 to essentially infinity. When the slope angle equals 45 degrees, the rise is equal to the run. Expressed as a percentage, the slope of this angle is 100 percent. As the slope approaches vertical (90 degrees), the percentage slope approaches infinity.Units: Percent (%)Update Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.

WARNING: Slope is computed in the projection specified by the client software. The server resamples the elevation data to the requested projection and pixel size and then computes slope. Slope should be requested in a projection that maintains correct scale in x and y directions for the area of interest. Using geographic coordinates will give incorrect results. For the WGS84 Mercator and WGS Web Mercator (auxiliary sphere) projections used by many web applications, a correction factor has been included to correct for latitude-dependent scale changes.What can you do with this layer?Use for Visualization: No. This image service provides numeric values indicating terrain characteristics. Due to the limited range of values, this service is not generally appropriate for visual interpretation, unless the client application applies an additional color map. Use for Analysis: Yes. This layer provides numeric values indicating slope percent, calculated based on the defined cell size. Cell size has an effect on the slope values. There is a limit of 5000 rows x 5000 columns. For Slope values in degrees, use Terrain - Slope Degrees layer. For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single export image request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

The Susceptibility to Deep-Seated Landslides map covers the entire state of California and was originally published in May of 2011 as CGS Map Sheet 58. It made use of several data layers of varying scales and formats, such as Landslide Inventory, Geology, Rock Strength, and Slope. For the statewide analysis of landslide susceptibility, the methodology of Wilson and Keefer (1985) was used in combining the rock strength and slope data layers as implemented by Ponti, el al. (2008) to create classes of landslide susceptibility (0 to 10, low to high). These classes express the generalization that on very low slopes, landslide susceptibility is low even in weak materials, and that landslide susceptibility increases with slope and in weak rocks.For downloads of the raster data, please visit: MS58 Downloads.

The elevation map incorporates multiple resolutions representing the best available elevation data. It also includes a collection of map layers derived from elevation, such as slope in degrees, slope in percent, aspect, and hillshade. Elevation data can also be used to create profiles, perform viewshed analysis, and define watersheds by using an available collection of analysis tools.The map layers and tools in this group are available to organizational accounts to use in ArcGIS Desktop, the ArcGIS.com map viewer as well as in Esri Maps for Office.

This map provides a colorized representation of aspect, generated dynamically using the server-side aspect function on the Terrain layer. The orientation of the downward sloping terrain (0° – 360°) is indicated by different colors, rotating from green (North) to blue (East), to magenta (South) to orange (West). Flat areas having no down slope direction are given a value of 361° and rendered as gray. This service can be used for visualization or analysis. Note: If you require access to numeric (float) aspect values, use the Terrain - Aspect layer, which returns orientation values from 0 to 360 degrees. Units: DegreesUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: Yes. This colorized aspect map is appropriate for visualizing the downslope direction of the terrain. This layer can be added to applications or maps to enhance contextual understanding.Use for Analysis: Yes. 8 bit color values returned by this service represent integer aspect values. For float values, use the Terrain - Aspect layer.For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single export image request.

This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

This dynamic World Elevation Terrain layer returns float values representing ground heights in meters and compiles multi-resolution data from many authoritative data providers from across the globe. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns.Note: This layer combine data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

This layer generally describes Geologically Hazardous Areas as defined in TMC 13.11.700, including erosion and landslide hazard areas. It is used to review changes to these areas including development proposals, proposals for vegetation modification, and potential violations for compliance with critical area and building codes.This layer was derived from 2018 bare earth lidar. The initial analysis steps include: slope tool to create a % rise surface then using the int tool and reclassify using the 0-15, 15-25, 25-40 and >40 percent slope. Those classifications were converted to polygons. Further refinement was done to reduce the number of polygons. All areas in the >15% classification were deleted, all polygons <200ft in length and all polygons < 100 sq. ft. in area were deleted. Additional simplifying was done to create smoother boundaries of areas and a series of positive and negative buffers was used to remove holes in areas. Additional refinement to this was done including: (Deleted polygons <= 200 sq. ft. for Slope Category 15 - 25%. Deleted polygons <= 100 sq. ft. for Slope Category 25 - 40% & Over 40%)Data Steward contact: Craig Kuntz, ckuntz@cityoftacoma.org or Lisa Spadoni, Natural Resources Program Manager, lspadoni@cityoftacoma.org.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines. Dataset SummaryPhenomenon Mapped: LandformsGeographic Extent: GlobalProjection: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereUnits: MetersCell Size: 231.91560581932 metersPixel Depth: 8-bit unsigned integerAnalysis: Restricted single source analysis. Maximum size of analysis is 30,000 x 30,000 pixels.Source: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/ In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS. The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plains Smooth plains with some local relief Irregular plains with moderate relief Irregular plains with low hills Scattered moderate hills Scattered high hills Scattered low mountains Scattered high mountains Moderate hills High hills Tablelands with moderate relief Tablelands with considerable relief Tablelands with high relief Tablelands with very high relief Low mountains High mountains To produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain"s texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class. The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them: What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This dynamic image service provides numeric values representing ground surface heights, based on a digital terrain model (DTM). Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as hillshade, slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis.NOTE: The image service uses North America Albers Equal Area Conic projection (WKID: 102008) and resamples the data dynamically to the requested projection, extent and pixel size. For analyses requiring the highest accuracy, when using ArcGIS Desktop, you will need to use native coordinates (GCS_North_American_1983, WKID: 4269) and specify the native resolutions (0.0002777777777779 degrees) as the cell size geoprocessing environment setting and ensure that the request is aligned with the source pixels.Server Functions: This layer has server functions defined for the following elevation derivatives:Slope DegreesSlope PercentageAspectHillshadePre-symbolized Slope Degrees Map Data Sources: The data for this layer comes from NED 1 arc-second dataset from the USGS's National Elevation Dataset program with original source data in its native coordinate system.Data Coverage: The dataset covers the conterminous United States, Hawaii, partial Alaska, Puerto Rico, Territorial Islands of the United States, Canada and Mexico.This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit. This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Note: This LCMS CONUS Cause of Change image service has been deprecated. It has been replaced by the LCMS CONUS Annual Change image service, which provides updated and consolidated change data.Please refer to the new service here: https://usfs.maps.arcgis.com/home/item.html?id=085626ec50324e5e9ad6323c050ac84dThis product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. https://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This layer is subset of World Ecological Facets Landform Classes Image Layer. Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This World Elevation TopoBathy service combines topography (land elevation) and bathymetry (water depths) from various authoritative sources from across the globe. Heights are orthometric (sea level = 0), and bathymetric values are negative downward from sea level. The source data of land elevation in this service is same as in the Terrain layer. When possible, the water areas are represented by the best available bathymetry. Height/Depth units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select additional functions, applied on the server, that return rendered data. For visualizations such as hillshade or elevation tinted hillshade, consider using the appropriate server-side function defined on this service. Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns. NOTE: This image services combine data from different sources and resample the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the max extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percentage Hillshade Multi-Directional Hillshade Elevation Tinted HillshadeSlope MapMosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 is included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request. This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks. Disclaimer: Bathymetry data sources are not to be used for navigation/safety at sea.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

ArcGIS is a platform, and the platform is extending to the web. ArcGIS Online offers shared content, and has become a living atlas of the world. Ready-to-use curated content is published by Esri, Partners, and Users, and Esri is getting the ball rolling by offering authoritative data layers and tools.Specifically for Natural Resources data, Esri is offering foundational data useful for biogeographic analysis, natural resource management, land use planning and conservation. Some of the layers available are Land Cover, Wilderness Areas, Soils Range Production, Soils Frost Free Days, Watershed Delineation, Slope. The layers are available as Image Services that are analysis-ready and Geoprocessing Services that extract data for download and perform analysis.We've made large strides with online analysis. The latest release of ArcGIS Online's map viewer allows you to perform analysis on ArcGIS Online. Some of the currently available analysis tools are Find Hot Spots, Create Buffers, Summarize Within, Summarize Nearby. In addition, we've created Ready-to-use Esri hosted analysis tools that run on Esri hosted data. These are in Beta, and they include Watershed Delineation, Viewshed, Profile, and Summarize Elevation.

EUNIS ANNEX I habitat map for Bassurelle Sandbank SCI (CEND 03/13). An ArcGIS polygon shapefile of the potential extent of Annex I sandbank habitat identified at Bassurelle Sandbank SCI was created using the data collected and processed as described as follows. EUNIS class assignments from the particle size data were used in conjunction with the interpreted still images to inform the semi-automated process of map production using object-based image analysis (OBIA), implemented in the software package eCognition v8.8.1. The OBIA was used to map habitats. It was not possible to use this technique for the mapping of Annex I habitats (specifically the sandbank feature at Bassurelle Sandbank SCI). Instead, an expert driven process of slope analysis in ArcGIS 9.3.1 and 3D visual interpretation using the Fledermaus v7 software package was used to identify the potential extent of the bank feature.

The solar radiation layers are simulations of solar radiation based on the Digital Surface Model. The simulation considers the topographic situation (surrounding, slope, exposition) as well as time-based variation of the sun radiation for a specific geographic location. The result is a raster visualization of the sun duration per pixel (with 1 m ground resolution). The simulation is configured to return the sun hours per pixel for a given day. Currently 3 days were calculated: 15/02 (winter), 15/05 (spring) and 15/08 (summer).

The solar radiation analysis is based on the solar radiation toolset of the ESRI ArcMap toolbox. A detailed documentation can be found in the corresponding documentation by ESRI: http://desktop.arcgis.com/en/arcmap/10.6/tools/spatial-analyst-toolbox/area-solar-radiation.htm

The analysis used the following parameters:

- Input raster: Digital Surface model provided by the Administration de la navigation aérienne (ANA) based on a LiDAR flight from 2017. (DSM available here : https://data.public.lu/fr/datasets/digital-surface-model-high-dem-resolution/ )

- Latitude : 49.46 °

- Time configuration : Time Within a day (for 3 dates: 15/02 winter, 15/05 spring and 15/08 summer)

- Hour interval: 0.5 – The solar radiation was calculated in 30 min. intervals and summed up per day.

- Slope and aspect input : The slope and aspect rasters are calculated from the input digital surface model

- Calculation directions: 32, which is adequate for a complex topography.

- Diffuse proportion : 0.3 for a generally clear sky conditions.

- Transmittitivity : 0.5 for a generally clear sky.

- Output raster: The result is an output raster representing the duration of direct incoming solar radiation.

Retirement Notice: This item is in mature support as of August 2025 and will be retired in December 2026. Please use this source dataset and follow the steps in the From Vector to Raster blog as a replacement for this service. Esri recommends updating your maps and apps.Created as part of the USGS’s Africa Ecosystems Mapping project, the Africa Land Surface Forms layer classifies the landscape of Africa into seven classes: Smooth plains, irregular plains, escarpments, hills, breaks, low mountains, and high mountains/deep canyons.This layer provides access to a 100m cell size raster derived from SRTM and other data that divides the African landscape into seven classes based on land form. The data covers Africa, Madagascar, and other coastal islands near Africa. It was published in 2009 by the USGS Rocky Mountain Geographic Science Center.This layer was used as an input for the Africa Terrestrial Ecosystems mapping project. Link to source metadata Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 24,000 x 24,000 pixels. What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services, which represents an area roughly 2,400 kilometers on a side. The source data for this layer are available here.Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis.

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

Seafloor geomorphology is the study of physical features on the seafloor. This layer represents the characterizations of geomorphic features and the zones within the ocean where they occur. The data for this layer are from the first map of seafloor geomorphology ever published; this map was published 2014 by GRID Arendal.“The new seafloor features map provides a foundation on which to build an understanding of the living and non-living resources of the ocean and to improve decision making on a range of global issues like food security, resource use and conservation.” - Dr. Peter Harris, the project leader and Managing Director of GRID-Arendal.Dataset SummaryThe geomorphic features in this layer were created by automated and manual processes over the course of many months. The source data for the process is a modified 30-arc second (~1 km) resolution version of SRTM30_PLUS global bathymetry produced in 2009. The following features and zones are included as sub-layers:FeaturesCanyons - Submarine canyons are defined as steep-walled, sinuous valleys with V-shaped cross sections, axes sloping outward as continuously as river-cut land canyons and relief comparable to even the largest of land canyons.Seamounts - Seamounts are a single or group of peaks, greater than 1,000 meters in relief above the sea floor, characteristically of conical form.Guyots - Guyots are isolated or a group of seamount having a comparatively smooth flat top. Also called tablemounts.Troughs - Troughs are long depressions of the sea floor characteristically flat bottomed and steep sided and normally shallower than a trench. In this study we found that troughs are also commonly open at one end (i.e. not defined by closed bathymetric contours) and their broad, flat floors may exhibit a continuous gradient. Troughs may originate from glacial erosion processes or have form through tectonic processes.Glacial Troughs - Glacial troughs are the largest of the shelf valleys at high latitudes incised by glacial erosion during the Pleistocene ice ages to form elongate troughs, typically trending across the continental shelf and extending inland as fjord complexes. Glacial troughs are characterized by depths of over 100 m (often exceeding 1,000 m depth) and are distinguished from shelf valleys by an over-deepened longitudinal profile that reaches a maximum depth inboard of the shelf break, thus creating a perched basin on the shelf with an associated sill.Trenches - Trenches are long narrow very deep asymmetrical depressions of the sea floor, with relatively steep sides. Trenches are generally distinguished from troughs by their “V” shape in cross section (in contrast with flat-bottomed troughs). Bridges - Bridge features are blocks of material that partially infill Trenches forming a “bridge”across the trench.Sills - Sills are a sea floor barrier of relatively shallow depth restricting water movement between basins. Thus every basin has a sill, over which fluid would escape if the basin were filled to overflowing. Shelf Valleys - Shelf valleys are greater than 10 km in length and greater than 10 m in depth overall with an elongate shape more than 4 times greater in length than width.Rift Valleys - Rift valleys are confined to the central axis of mid-ocean spreading ridges; they are elongate, local depressions flanked generally on both sides by ridges.Ridges - Ridges are isolated or a group of elongated narrow elevations of varying complexity with steep sides, often separating basin features. Ridges have greater than 1,000 meters of relief.Spreading Ridges - Spreading ridges are mid-oceanic mountain systems of global extent.Terraces - Terraces an isolated or a group of relatively flat horizontal or gently inclined surface(s), sometimes long and narrow, which is (are) bounded by a steeper ascending slope on one side and by a steeper descending slope on the opposite side. Fans - Fans are relatively smooth, fan-like, depositional featured normally sloping away from the outer termination of a canyon or canyon system. Fans overlay and comprise part of the continental rise and are located offshore from the base of the continental slope. Fans are inter-related with submarine canyons and sediment drift deposits; in cases where canyon axes extend across the rise, the canyon-channels may be flanked by sediment drift deposits, which have been grouped with fans in this study. Fans are defined in the present study by 100 m isobaths that form a concentric series exhibiting an expanding spacing in a seaward direction away from the base of the slope, sometimes clearly associated with a canyon mouth, but also comprising low-relief ridges between canyon-channels on the abyssal plain.Rises - Continental rises are areas with sediment thickness greater than 300 meters and the occurrence of a smooth sloping seabed as indicated by evenly-spaced, slope-parallel contours. In this study, the term “Rise” was restricted to features that abut continental margins and does not include the mid-ocean ridge.Plateaus - Plateaus are flat or nearly flat elevations of considerable areal extent, dropping off abruptly on one or more sides. TerrainMountains - Greater than 1,000 meters of local relief within ~25 kilometers.Hills - Between 300 and 1,000 meters of local relief within ~25 kilometers.Plains - Less than 300 meters of local relief within ~25 kilometers.Basins - Basins are depressions in the sea floor that are more or less equi-dimensional in plan, of variable extent, and are restricted to seafloor depressions defined by closed bathymetric contours.Escarpments - Escarpments are “an elongated, characteristically linear, steep slope separating horizontal or gently sloping sectors of the sea floor in non-shelf areas. Also abbreviated to scarp” (IHO, 2008). Escarpments, like basins, overlay other features (i.e. other individual features may be partly or wholly covered by escarpments). Thus features like the continental slope, seamounts, guyots, ridges and submarine canyons (for example) may be sub-classified in terms of their area of overlain escarpment.ZonesShelf - The zone adjacent to the continents or islands. Slope - The deepening seafloor from the edge of the shelf to the top of the continental rise.Abyss - Areas below the foot of the continental rise and includes all depths up to 6,000 meters.Hadal - Depths greater than 6,000 metersNote that the above definitions are brief summarizations of the definitions contained in Geomorphology of the Oceans.Esri staff edited several of the layers: Zones, Terrain, Basins, and Glacial Troughs to improve drawing performance. All of these edits were split polygon operations; no vertexes were moved, only at cut points were vertexes introduced. If these layers are downloaded, these edits can be removed by using the Dissolve tool, with all fields, including shape, and producing no multi-part polygons in the output.For metadata info, please see Bluehabitats.org.What can you do with this layer?This layer is based on a dynamic map service, which means there are several sub-layers of vector features that can be used for visualization and analysis throughout the ArcGIS Platform. This layer is not editable.This layer is part of a larger collection of Oceans layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about oceans layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.