This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

Landslide susceptibility maps are essential tools in infrastructure planning, hazard mitigation, and risk reduction. Susceptibility maps trained in one area have been found to be unreliable when applied to different areas (Woodard et al., 2023). This limitation leads to the need for a national map that is higher resolution and rigorous, but simple enough to be applied to diverse terrains and landslide types. The susceptibility maps presented here cover the conterminous United States (CONUS), Alaska (AK), Hawaii (HI), and Puerto Rico (PR) with a resolution of 90-m. Other United States (U.S.) territories were not considered due to insufficient landslide and digital elevation data. We also provide information on the proportion of susceptible terrain as well as the density (landslides per square kilometer) of documented landslides within susceptible terrain for each U.S. county. To generate the susceptibility maps we used 1/3 arc-second digital elevation models (DEMs) (U.S. Geological Survey, 2019) to calculate slope and 100-m relief, 613,724 unique landslides from our national landslide inventory compilation (Belair et al., 2022) to train the models and compute U.S. county aggregated susceptibility information, and high-performance computing resources to train the models (Falgout and Gordon, 2023). We present two slope-relief threshold models: (1) a linear regression model weighted by landslide density of each ecoregion (Wiken et al., 2011), and (2) a quantile nonlinear regression model fitted to the 10th quantile of the data. We (1) removed extraneous landslide data, (2) averaged 50 model runs, and then (3) down-sampled the maps from 10-m to 90-m resolution to account for uncertainty in the DEM and landslide position. The nonlinear model (n10) performs better under most topographic conditions and optimally balances our priorities of capturing observed landslides (98.9%) while minimizing area covered by susceptible terrain (44.6%). The weighted linear model (lw) captures slightly fewer landslides (98.8%) and has slightly less susceptible terrain (43.1%). The values of both maps represent the number of susceptible 10-m cells within each 90-m cell after down-sampling and can range from 0 to 81. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration (or cell value) capture the majority of landslides, thus representing higher susceptibility areas. The susceptibility maps were then used to determine the total area of landslide susceptible terrain (square kilometers) for each U.S. county. The national landslide inventory compilation was used to determine the number of documented landslides within susceptible terrain for each county. This information was then used to calculate the proportion of susceptible terrain and the density of documented landslides within susceptible terrain for each county in the United States. This information is provided in tabular format, with columns corresponding to the information discussed above, and each row corresponding to a U.S. county. Further information about this analysis can be found in an interpretive publication (Mirus et al., 2024). This data release includes: (1) weighted linear susceptibility maps (lw_susc.zip), (2) quantile nonlinear susceptibility maps (n10_susc.zip), (3) landslide data used to develop the models (landslides.csv), (4) county aggregated susceptibility information (county_analysis.csv), (5) readme and analysis files, and (6) metadata. References Cited Belair, G. M., Jones, E. S., Slaughter, S. L., and Mirus, B. B., 2022, Landslide Inventories across the United States version 2: U.S. Geological Survey data release, https://doi.org/10.5066/P9FZUX6N Falgout, J. T., and Gordon, J., 2023, USGS Advanced Research Computing, USGS Yeti Supercomputer: U.S. Geological Survey, https://doi.org/10.5066/F7D798MJ Mirus, B. B., Belair, G. M., Wood, N. J., Jones, J. M., and Martinez, S. M., 2024, Parsimonious high-resolution landslide susceptibility modeling at continental scales, AGU Advances, https://doi.org/10.1029/2024AV001214 U.S. Geological Survey, 2019, 3D Elevation Program (3DEP) USGS 1/3 arc-second DEM [Data set], Retrieved from https://www.usgs.gov/3d-elevation-program/about-3dep-products-services Wiken, E., Nava, F. J., and Griffith, G., 2011, North American Terrestrial Ecoregions - Level III [Data set], Montreal, Canada: Commission for Environmental Cooperation, Retrieved from https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states Woodard, J. B., Mirus, B. B., Crawford, M. M., Or, D., Leshchinsky, B. A., Allstadt, K. E., and Wood, N. J., 2023, Mapping Landslide Susceptibility Over Large Regions With Limited Data, Journal of Geophysical Research: Earth Surface, 128(5), e2022JF006810, https://doi.org/10.1029/2022JF006810

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 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.

The Atlas of Canada's Relief Map shows Canada's relief with a colour ramp of elevation ranges. Colour tints of depth ranges show ocean bathymetry. This map shows the relief of Canada using three different resolutions. Starting with low resolution, as you zoom in, it transitions to medium and then high resolution. The low resolution is derived from the merging of Natural Resources Canada (NRCan) High Resolution Digital Elevation Model (HRDEM) from 2021 and the legacy Canadian Digital Elevation Model (CDEM), 1945-2011, resampled to 804 metres. The medium resolution is the NRCan, Medium Resolution Digital Elevation Model (MRDEM) from 2024, with modifications by the Atlas of Canada for cartographic purposes. The high resolution is NRCan's 2021 HRDEM product with 1 metre pixels. Also included, is a bathymetric layer from GEBCO 2021 (https://download.gebco.net/) resampled to 804 metre pixels. Other sources: Danish Ministry of Climate, Energy and Utilities; Geological Survey of Denmark and Greenland (GEUS) 2021; Government of Iceland; United States. National Geodetic Survey’s Integrated Database, 2021 (https://geodesy.noaa.gov/) All layers have been symbolized to match the Atlas of Canada Relief wall map. Copies of this map may be obtained from authorized map dealers in Canada, USA and abroad. For further information on purchasing the paper map MCR 0101 or downloading the digital version free of charge, go to atlas.gc.ca. Produced by the Canada Centre for Mapping and Earth Observation, Natural Resources Canada. Printed in 2025. ISBN 978-0-660-37948-7 Catalogue No. m57-1/46-2021e Permanent link: https://doi.org/10.4095/pe5mnk08hr Further information on all these maps can be found on the Atlas of Canada web site atlas.gc.ca.

The maps in this data release show active landslide structures in three areas along the north flank of the Slumgullion landslide. After the entire active part of the landslide was mapped in 1992 and 1993 (Fleming and others, 1999), we remapped these three smaller areas at roughly decadal intervals. Our goal was to learn what structures might persist and how they might change as heterogeneous landslide material of variable thickness passed through the areas. Together with the original 1999 map, these maps provide snapshots of the deformational features at converging and diverging margins of the landslide at four periods in about a 30-year time span (1992-2023). During summer months in 2002, 2013, and 2023, we conducted 1:1000-scale mapping using a traditional technique of manually drawing lines on topographic base maps to represent the structures we observed in the field. There was generally a lapse of two or more years between acquisition of the topographic base data and the field mapping. Meters of landslide displacement during the lapse resulted in a mismatch between the topographic map and topography on the active landslide at the time of our fieldwork. When drawing features on the topographic base, we referenced fixed topographic features directly north of the active landslide’s strike-slip boundary to compensate for the mismatch. The data are recorded in Geographic Information System (GIS) files that contain the line styles used to portray and distinguish the different landslide structures. The files record the shapes and positions of the mapped landslide structures. An index of line styles used to portray mapped structures is shown in Figure 1. Topographic base maps used for the 2002, 2013, and 2023 structural maps were from 2000, 2011, and 2018, respectively. One-meter Digital Elevation Models (DEMs), contours, and shaded-relief maps from these three years are included in this data release. The 2000 DEM was created from 2 m contours of the landslide on July 31, 2000, as originally published in Messerich and Coe (2003). The 2011 DEM was created by the authors using a structure-from-motion photogrammetric method and 1:6000 scale aerial photos acquired on September 23, 2011. The 2018 DEM is lidar data collected between October 5, 2018 and September 24, 2019, with the original data available from the U.S. Geological Survey 3DEP Lidar Explorer (U.S. Geological Survey, 2024). The contour interval used for the 2000 DEM is 2 m. The contour interval used for the 2011 and 2018 DEM is 1 m. All GIS data are projected in the Universal Transverse Mercator (UTM) zone 13N cartesian coordinate system. Portable Document Format (PDF) files of the landslide structure maps of each area in 2002, 2013, and 2023, are also provided. Figure 1. Line and polygon types used for landslide structures and features mapped at the Slumgullion landslide. References Fleming, R.W., Baum, R.L., and Giardino, Marco, 1999, Map and description of the active part of the Slumgullion Landslide, Hinsdale County, Colorado: U.S. Geological Survey Geologic Investigations Series Map I-2672 , scale 1:1,000, https://doi.org/10.3133/i2672 Messerich, J.A. and Coe, J.A., 2003, Topographic map of the active part of the Slumgullion landslide on July 31, 2000, Hinsdale County, Colorado: U.S. Geological Survey Open-File Report 03-144, 7 p., 1:1,000 scale map. http://pubs.usgs.gov/of/2003/ofr-03-144/ U.S. Geological Survey, 2024, 3DEP Lidar Explorer, data available at: http://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/1m/Projects/CO_Southwest_NRCS_2018_D18