Pluvials can have dramatic impacts on the shoreline bluffs of Lake Michigan due to increases in both shallow subsurface moisture conditions related to the prolonged wet weather pattern and wave erosion as the lake level rises. These changes can result in an increased frequency and magnitude of slope failures. During the most recent pluvial, the monthly average level of Lake Michigan rose 1.9 m from a record low in January 2013 to a near record high in June-July 2020. To assess the impacts on coastal bluffs from slope failures during the recent pluvial, an inventory of landslides was completed, including slope failures active during the early part of the pluvial, on the coastal bluffs of South Manitou Island, part of the Sleeping Bear Dunes National Lakeshore in Michigan. Landslides were mapped using high-resolution orthoimagery, collected in April 2012, and high-resolution topography derived from two LiDAR data sets, the first collected in December 2014 and the second collected between November 2015 and March 2016. This data release presents geographic information system (GIS) data, provided as line and polygon shapefiles (.shp), depicting landslides and related landforms and features. Polygon map data delineates the areas of deposits, source areas, and related landforms (such as alluvial fans and colluvial aprons). Scarps (such as headscarps and minor scarps) are presented as hachured line data. An attribute file is included providing a definition of the mapped units and a brief description of the approach used in the mapping.

Pluvials can have dramatic impacts on the shoreline bluffs of Lake Michigan due to increases in both shallow subsurface moisture conditions related to the prolonged wet weather pattern and wave erosion as the lake level rises. These changes can result in an increased frequency and magnitude of slope failures. During the most recent pluvial, the monthly average level of Lake Michigan rose 1.9 m from a record low in January 2013 to a near record high in June-July 2020. To assess the impacts on coastal bluffs from slope failures during the recent pluvial, an inventory of landslides was completed, including slope failures active during the early part of the pluvial, on the coastal bluffs of North Manitou Island, part of the Sleeping Bear Dunes National Lakeshore in Michigan. Landslides were mapped using high-resolution orthoimagery, collected in April 2012, and high-resolution topography derived from a LiDAR data set, collected in December 2014. This data release presents geographic information system (GIS) data, provided as line and polygon shapefiles (.shp), depicting landslides and related landforms and features. Polygon map data delineates the areas of deposits, source areas, and related landforms (such as alluvial fans and colluvial aprons). Scarps (such as headscarps and minor scarps) are presented as hachured line data. An attribute file is included providing a definition of the mapped units and a brief description of the approach used in the mapping.

Pluvials can have dramatic impacts on the shoreline bluffs of Lake Michigan due to increases in both shallow subsurface moisture conditions related to the prolonged wet weather pattern and wave erosion as the lake level rises. These changes can result in an increased frequency and magnitude of slope failures. During the most recent pluvial, the monthly average level of Lake Michigan rose 1.9 m from a record low in January 2013 to a near record high in June-July 2020. To assess the impacts on coastal bluffs from slope failures during the recent pluvial, an inventory of landslides was completed, including slope failures active during the early part of the pluvial, on the coastal bluffs of South Manitou Island, part of the Sleeping Bear Dunes National Lakeshore in Michigan. Landslides were mapped using high-resolution orthoimagery, collected in April 2012, and high-resolution topography derived from two LiDAR data sets, the first collected in December 2014 and the second collected between November 2015 and March 2016. This data release presents geographic information system (GIS) data, provided as line and polygon shapefiles (.shp), depicting landslides and related landforms and features. Polygon map data delineates the areas of deposits, source areas, and related landforms (such as alluvial fans and colluvial aprons). Scarps (such as headscarps and minor scarps) are presented as hachured line data. An attribute file is included providing a definition of the mapped units and a brief description of the approach used in the mapping.

Pluvials can have dramatic impacts on the shoreline bluffs of Lake Michigan due to increases in both shallow subsurface moisture conditions related to the prolonged wet weather pattern and wave erosion as the lake level rises. These changes can result in an increased frequency and magnitude of slope failures. During the most recent pluvial, the monthly average level of Lake Michigan rose 1.9 m from a record low in January 2013 to a near record high in June-July 2020. To assess the impacts on coastal bluffs from slope failures during the recent pluvial, an inventory of landslides was completed, including slope failures active during the early part of the pluvial, on the coastal bluffs of North Manitou Island, part of the Sleeping Bear Dunes National Lakeshore in Michigan. Landslides were mapped using high-resolution orthoimagery, collected in April 2012, and high-resolution topography derived from a LiDAR data set, collected in December 2014. This data release presents geographic information system (GIS) data, provided as line and polygon shapefiles (.shp), depicting landslides and related landforms and features. Polygon map data delineates the areas of deposits, source areas, and related landforms (such as alluvial fans and colluvial aprons). Scarps (such as headscarps and minor scarps) are presented as hachured line data. An attribute file is included providing a definition of the mapped units and a brief description of the approach used in the mapping.

The effects of climate change have the potential to impact slope stability. Negative impacts are expected to be greatest at high northerly latitudes where degradation of permafrost in rock and soil, debuttressing of slopes as a result of glacial retreat, and changes in ocean ice-cover are likely to increase the susceptibility of slopes to landslides. In the United States, the greatest increases in air temperature and precipitation are expected to occur in Alaska. In order to assess the impact that these environmental changes will have on landslide size (magnitude), mobility, and frequency, inventories of historical landslides are needed. These inventories provide baseline data that can be used to identify changes between historical and future landslide magnitude, mobility, and frequency. This data release presents GIS and attribute data for an inventory of rock avalanches in a 5000 sq. km area of western Glacier Bay National Park and Preserve, Alaska. We created the inventory from 30 m resolution Landsat imagery acquired from June 1984 to September 2016. For each calendar year, we visually examined a minimum of one Landsat image obtained between the months of May and October. We examined a total of 104 Landsat images. The contrast between the spectral signatures of freshly exposed rock avalanche source areas and deposits and surrounding, undisturbed snow and ice is typically significant enough to detect surficial changes. We identified and mapped rock avalanches by locating areas with 1) high contrast compared to surrounding snow and ice, 2) different spectral signatures between successive Landsat images, and 3) lobate forms typical of rock-avalanche deposits. Using these criteria, we mapped a total of 24 rock avalanches ranging in size from 0.1 to 22 km2. Attribute data for each rock avalanche includes: a date, or range in possible dates, of occurrence; the name of the Landsat image(s) used to identify and map the avalanche; the total area covered by the rock avalanche (including the source area and deposit); the maximum travel distance measured along a curvilinear centerline (L); and the change in elevation between the start and end points of the centerline (H). We also include a table containing a list of all the Landsat images examined. We acknowledge that our mapped polygons will be different, and less accurate, than polygons that could be mapped from higher-resolution satellite, aerial, and hand-held imagery. We specifically chose not to use high resolution imagery because we desired a long-term historical inventory that was unbiased by changes in image resolution. Eventually, new mapping should be done to create an inventory that fully utilizes recently available high-resolution imagery. Data included in this release form the basis of an interpretive paper available in the conference proceedings of the 3rd North American Symposium on Landslides held in Roanoke, Virginia in June, 2017.

The effects of climate change have the potential to impact slope stability. Negative impacts are expected to be greatest at high northerly latitudes where degradation of permafrost in rock and soil, debuttressing of slopes as a result of glacial retreat, and changes in ocean ice-cover are likely to increase the susceptibility of slopes to landslides. In the United States, the greatest increases in air temperature and precipitation are expected to occur in Alaska. In order to assess the impact that these environmental changes will have on landslide size (magnitude), mobility, and frequency, inventories of historical landslides are needed. These inventories provide baseline data that can be used to identify changes between historical and future landslide magnitude, mobility, and frequency. This data release presents GIS and attribute data for an inventory of rock avalanches in a 5000 sq. km area of western Glacier Bay National Park and Preserve, Alaska. We created the inventory from 30 m resolution Landsat imagery acquired from June 1984 to September 2016. For each calendar year, we visually examined a minimum of one Landsat image obtained between the months of May and October. We examined a total of 104 Landsat images. The contrast between the spectral signatures of freshly exposed rock avalanche source areas and deposits and surrounding, undisturbed snow and ice is typically significant enough to detect surficial changes. We identified and mapped rock avalanches by locating areas with 1) high contrast compared to surrounding snow and ice, 2) different spectral signatures between successive Landsat images, and 3) lobate forms typical of rock-avalanche deposits. Using these criteria, we mapped a total of 24 rock avalanches ranging in size from 0.1 to 22 km2. Attribute data for each rock avalanche includes: a date, or range in possible dates, of occurrence; the name of the Landsat image(s) used to identify and map the avalanche; the total area covered by the rock avalanche (including the source area and deposit); the maximum travel distance measured along a curvilinear centerline (L); and the change in elevation between the start and end points of the centerline (H). We also include a table containing a list of all the Landsat images examined. We acknowledge that our mapped polygons will be different, and less accurate, than polygons that could be mapped from higher-resolution satellite, aerial, and hand-held imagery. We specifically chose not to use high resolution imagery because we desired a long-term historical inventory that was unbiased by changes in image resolution. Eventually, new mapping should be done to create an inventory that fully utilizes recently available high-resolution imagery. Data included in this release form the basis of an interpretive paper available in the conference proceedings of the 3rd North American Symposium on Landslides held in Roanoke, Virginia in June, 2017.

This dataset contains an inventory of landslides in many of the most landslide-prone parts of Minnesota. This project was created to improve our understanding of the landslide hazard in Minnesota and to provide a nearly statewide base map of landslide data. The mapping was performed by geologists from the U.S. Geological Survey, the Freshwater Society, and several academic institutions where undergraduate students, graduate students and faculty performed mapping. Contributing academic institution include the University of Minnesota Duluth, the University of Minnesota Twin Cities, the University of Wisconsin-Superior, Gustavus Adolphus College, Winona State University, Minnesota State University, Mankato, St. Thomas University, and North Dakota State University. These landslides were identified using several methods. These include analysis of historical records, direct field observation, location using satellite or aerial imagery, and identification in topographic data products derived from the statewide lidar data coverage. Most of the mapped landslides were identified using lidar derivatives and have not been evaluated in the field by geologists or engineers. These data should be considered a preliminary survey and are not intended to represent a complete and accurate inventory of landslides for these areas. There may be a range in the accuracy, detail, and completeness with which landslides are mapped, and in the information associated with a given landslide; however, all mapped landslides were reviewed by USGS personnel and the senior project members. Mapping procedures including the assignment of numerical values for confidence follow guidelines found in DOGAMI Special Paper 42: https://www.oregongeology.org/pubs/sp/p-SP-42.htm. Site-specific investigations should be completed before using these data for land development or management decisions. This Data Release consists of: 1) Minnesota_Landslides_v1_1.gdb.zip which contains the landslide inventory mapping data and the areas that were mapped, to be used in a GIS, 2) Minnesota_Landslides_v1_3.sd which is an ESRI service layer definition file that enables use of the data in online and offline GIS, 3) MN_Landslide_Photos.zip that contains a collection of geotagged photos showing landslides; these can be imported into a GIS, and 4) metadata.xml which contains metadata for all included files. Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Landslide hazard susceptibility mapping in Homer, Alaska, Report of Investigation 2024-3, provides a map and database of historical and prehistoric slope failures, maps of shallow and deep-seated landslide susceptibility, and a map of simulated debris flow runouts for the City of Homer, Alaska and nearby populated areas including Kachemak City and Millers Landing. The landslide inventory map integrates existing maps of landslides caused by the 1964 Great Alaska Earthquake and newly mapped slope failures identified in sequences of aerial photos since 1950 and high-resolution light detection and ranging (lidar) data collected for this project. The Alaska Division of Geological & Geophysical Surveys (DGGS) staff created a shallow landslide susceptibility map following protocols like those developed by the Oregon Department of Geology and Mineral Industries, which includes incorporating landslide inventory data, geotechnical soil properties, and lidar-derived topographic slope to calculate the Factor of Safety (FOS), which serves as a proxy for landslide susceptibility. Debris flow runout extents were generated using the model Laharz, which simulates runout extents based on catchment-specific physical parameters (e.g., hypothetical sediment volumes). Data from these analyses are collectively intended to depict locations where landslides are relatively more likely to occur or are relatively more likely to travel. The results provide important hazard information that can help guide planning and future risk investigations. The maps are not intended to predict slope failures and are site-specific; detailed investigations should be conducted before development in vulnerable areas. Results are for informational purposes and are not intended for legal, engineering, or surveying uses. These data and the interpretive maps and report are available from the DGGS website: http://doi.org/10.14509/31155.

A hydrologic monitoring network was installed to investigate landslide hazards affecting the railway corridor along the eastern shore of Puget Sound between Seattle and Everett, near Mukilteo, Washington. During the summer of 2015, the U.S. Geological Survey installed instrumentation at four sites to measure rainfall and air temperature every 15 minutes. Two of the four sites are installed on contrasting coastal bluffs, one landslide scarred and one vegetated. At these two sites, in addition to rainfall and air temperature, volumetric water content, pore pressure, soil suction, soil temperature (via hydrologic instrumentation), and barometric pressure were measured every 15 minutes. The instrumentation was designed to supplement landslide-rainfall thresholds developed by the U.S. Geological Survey with a long-term goal of advancing the understanding of the relationship between landslide potential and hydrologic forcing along the coastal bluffs. Additionally, the system was designed to function as a prototype monitoring system to evaluate criteria for site selection, instrument selection, and placement of instruments. Two files are included with this release. A comma separated value (csv) file contains monitoring data for the time-periods described by its name, for example 20150711_20160809.csv contains data for the period between July 11, 2015 and August 9, 2016. A read-me file (readme.doc) describes the sensor naming convention used for column names in the data files. The following citation relates to a report that provides background information and is intended to accompany this data release. Smith, J.B.; Baum, R.L.; Mirus, Benjamin B.; Michel, Abigail R.; Stark, Ben, 2017, Results of Hydrologic Monitoring on Landslide-Prone Coastal Bluffs Near Mukilteo Washington: U.S. Geological Survey Open-File Report 2017-1095, 50 p., http://dx.doi.org/10.3133/ofr20171095

Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information on landslide occurrence across the entire U.S. The data release includes digital inventories created by both USGS and non-USGS authors. It provides an integrated database of all the landslides with a selection of uniform attributes, but also includes links to the original digital inventory files (whenever available). Given the wide range of landslide information sources in this data compilation, we also provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide. Further details about each landslide and more recent information (when it exists) can be accessed by clicking the “more information” attribute link to the original source information. This database will be updated intermittently and was most recently updated in March 2019. Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort.