Abstract
Extreme precipitation events may cause flooding, slope failure, erosion, deposition, and damage to infrastructure over a regional scale, but the impacts of these events are often difficult to fully characterize. Regional-scale landscape change occurred during an extreme rain event in June 2012 in northeastern Minnesota. Landscape change was documented by 8,000 km2 of airborne lidar data collected before and after the event. Following improved alignment of the lidar point data and reducing error using insight from analysis of extensive stable areas, elevation differences were classified into map objects representing geomorphic change in relation to process and landscape position using object-based image analysis. This remote mapping compares favorably to field and imagery-based mapping and provides the basis for volumetric sediment budgeting. Elevation differences in these objects indicate that 4.5 × 106 ± 1.0 × 106 m3 of sediment was eroded in the study area. Of this, 2.5 × 106 ± 3.3 × 105 m3 was deposited in deposits on hillslopes and valley floors, and 2.0 × 106 ± 4.6 × 105 m3 were removed from watersheds and exported to the Saint Louis River Estuary and Lake Superior. Multivariate logistic regression analysis emphasized that topographic slope and presence of glaciolacustrine clay lithology are the primary control on landslide occurrence, and landslides occur most frequently on slopes within tens of meters of stream channels. These results provide the basis to anticipate the impacts of similar future storm events. Because precipitation events are forecast to continue to increase in frequency and intensity owing to climate change, characterizing and anticipating their effects may support hazard planning.
| Original language | English (US) |
|---|---|
| Article number | e2022EA002420 |
| Journal | Earth and Space Science |
| Volume | 9 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2022 |
Bibliographical note
Funding Information:We are grateful to the late Martin Isenberg who provided much to the community of lidar data users. We thank the Minnesota Department of Natural Resources for collecting the lidar data and making them publicly available. We thank Woolpert for providing lidar mission trajectory data. We thank W. DeLong for introducing us to object based image analysis and helped us get started with the method, and J. Knight and the Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota for shared resources. We thank L. Hempel and K. Barnhart for comments on an earlier version of this manuscript. Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR), and by the U.S. Geological Survey Landslide Hazards Program. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Funding Information:
We are grateful to the late Martin Isenberg who provided much to the community of lidar data users. We thank the Minnesota Department of Natural Resources for collecting the lidar data and making them publicly available. We thank Woolpert for providing lidar mission trajectory data. We thank W. DeLong for introducing us to object based image analysis and helped us get started with the method, and J. Knight and the Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota for shared resources. We thank L. Hempel and K. Barnhart for comments on an earlier version of this manuscript. Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative‐Citizen Commission on Minnesota Resources (LCCMR), and by the U.S. Geological Survey Landslide Hazards Program. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Publisher Copyright:
© 2022 The Authors. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Keywords
- debris flow and landslides
- erosion
- extreme events
- remote sensing
- sedimentation
