| dc.contributor.author | Schlagel, Nathan | |
| dc.contributor.author | Dere, Ashlee L. | |
| dc.contributor.author | Johnson, William C. | |
| dc.contributor.author | Shroder, John F. | |
| dc.date.accessioned | 2017-03-07T16:09:26Z | |
| dc.date.available | 2017-03-07T16:09:26Z | |
| dc.date.issued | 2016-11-16 | |
| dc.identifier.uri | http://hdl.handle.net/1808/23351 | |
| dc.description | This presentation was given as part of the GIS Day@KU symposium on November 16, 2016. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2016/. | en_US |
| dc.description.abstract | Landslides are among the most destructive forces of nature. Estimating susceptibility through modeling is an essential tool for
planning and mitigation efforts. Some regions, however, are too dangerous or lack the capacity to develop extensive inventories for rigorous analyses. Remote sensing and GIS allow for initial risk assessment and hazard planning. Data derived primarily from remote sensing, or developed before and during war efforts of the last few decades were used for this study of landslide susceptibility in Afghanistan. | en_US |
| dc.description.sponsorship | Platinum Sponsors: KU Department of Geography and Atmospheric Science. Gold Sponsors: Enertech, KU Environmental Studies Program, KU Libraries. Silver Sponsors: Douglas County, Kansas, KansasView, State of Kansas Data Access & Support Center (DASC) and the KU Center for Global and International Studies. | en_US |
| dc.publisher | GIS Day @ KU Planning Committee | en_US |
| dc.subject | GIS Day | en_US |
| dc.title | Multi-criteria analysis of landslide susceptibility, Afghanistan | en_US |
| dc.type | Poster | en_US |
| kusw.oastatus | na | |
| dc.rights.accessrights | openAccess | |
