Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data
- Autores
- Mora, Omar E; Liu, Juang Kuan; Lenzano, María Gabriela; Toth, Charles Karoly; Grejner Brzezinska, Dorota A.
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Landslides are natural disasters that cause environmental and infrastructure damage worldwide. To prevent future risk posed by such events, effective methods to detect and map their hazards are needed. Traditional landslide susceptibility mapping techniques, based on field inspection, aerial photograph interpretation, and contour map analysis are often subjective, tedious, difficult to implement and may not have the spatial resolution and temporal frequency necessary to map small slides, which is the focus of this investigation. We present a methodology that is based on a Support Vector Machine (SVM) that utilizes a LiDAR-derived Digital Elevation Model (DEM) to quantify and map the topographic signatures of landslides. The algorithm employs several geomorphological features to calibrate the model and delineate between landslide and stable terrain. To evaluate the performance of the proposed algorithm, a road corridor in Zanesville, OH, was used for testing. The resulting landslide susceptibility map was validated to correctly identify 67 of the 80 mapped landslides in the independently compiled landslide inventory map of the area. These results suggest that the proposed landslide surface feature extraction method and airborne LiDAR data can be used as efficient tools for small landslide susceptibility and hazard mapping
Fil: Mora, Omar E. Ohio State University; Estados Unidos
Fil: Liu, Juang Kuan. Ohio State University; Estados Unidos
Fil: Lenzano, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. International Center For Earth Sciences; Argentina
Fil: Toth, Charles Karoly. Ohio State University; Estados Unidos
Fil: Grejner Brzezinska, Dorota A.. Ohio State University; Estados Unidos - Materia
-
Lidar
Landslide
Feature Extraction
Dem - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/59516
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Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR DataMora, Omar ELiu, Juang KuanLenzano, María GabrielaToth, Charles KarolyGrejner Brzezinska, Dorota A.LidarLandslideFeature ExtractionDemhttps://purl.org/becyt/ford/4.5https://purl.org/becyt/ford/4Landslides are natural disasters that cause environmental and infrastructure damage worldwide. To prevent future risk posed by such events, effective methods to detect and map their hazards are needed. Traditional landslide susceptibility mapping techniques, based on field inspection, aerial photograph interpretation, and contour map analysis are often subjective, tedious, difficult to implement and may not have the spatial resolution and temporal frequency necessary to map small slides, which is the focus of this investigation. We present a methodology that is based on a Support Vector Machine (SVM) that utilizes a LiDAR-derived Digital Elevation Model (DEM) to quantify and map the topographic signatures of landslides. The algorithm employs several geomorphological features to calibrate the model and delineate between landslide and stable terrain. To evaluate the performance of the proposed algorithm, a road corridor in Zanesville, OH, was used for testing. The resulting landslide susceptibility map was validated to correctly identify 67 of the 80 mapped landslides in the independently compiled landslide inventory map of the area. These results suggest that the proposed landslide surface feature extraction method and airborne LiDAR data can be used as efficient tools for small landslide susceptibility and hazard mappingFil: Mora, Omar E. Ohio State University; Estados UnidosFil: Liu, Juang Kuan. Ohio State University; Estados UnidosFil: Lenzano, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. International Center For Earth Sciences; ArgentinaFil: Toth, Charles Karoly. Ohio State University; Estados UnidosFil: Grejner Brzezinska, Dorota A.. Ohio State University; Estados UnidosAmer Soc Photogrammetry2015-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59516Mora, Omar E; Liu, Juang Kuan; Lenzano, María Gabriela; Toth, Charles Karoly; Grejner Brzezinska, Dorota A.; Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data; Amer Soc Photogrammetry; Photogrammetric Engineering And Remote Sensing; 81; 3-2015; 11-190099-1112CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.14358/PERS.81.3.239info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0099111215303475info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:38:49Zoai:ri.conicet.gov.ar:11336/59516instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:38:49.464CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data |
title |
Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data |
spellingShingle |
Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data Mora, Omar E Lidar Landslide Feature Extraction Dem |
title_short |
Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data |
title_full |
Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data |
title_fullStr |
Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data |
title_full_unstemmed |
Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data |
title_sort |
Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data |
dc.creator.none.fl_str_mv |
Mora, Omar E Liu, Juang Kuan Lenzano, María Gabriela Toth, Charles Karoly Grejner Brzezinska, Dorota A. |
author |
Mora, Omar E |
author_facet |
Mora, Omar E Liu, Juang Kuan Lenzano, María Gabriela Toth, Charles Karoly Grejner Brzezinska, Dorota A. |
author_role |
author |
author2 |
Liu, Juang Kuan Lenzano, María Gabriela Toth, Charles Karoly Grejner Brzezinska, Dorota A. |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Lidar Landslide Feature Extraction Dem |
topic |
Lidar Landslide Feature Extraction Dem |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/4.5 https://purl.org/becyt/ford/4 |
dc.description.none.fl_txt_mv |
Landslides are natural disasters that cause environmental and infrastructure damage worldwide. To prevent future risk posed by such events, effective methods to detect and map their hazards are needed. Traditional landslide susceptibility mapping techniques, based on field inspection, aerial photograph interpretation, and contour map analysis are often subjective, tedious, difficult to implement and may not have the spatial resolution and temporal frequency necessary to map small slides, which is the focus of this investigation. We present a methodology that is based on a Support Vector Machine (SVM) that utilizes a LiDAR-derived Digital Elevation Model (DEM) to quantify and map the topographic signatures of landslides. The algorithm employs several geomorphological features to calibrate the model and delineate between landslide and stable terrain. To evaluate the performance of the proposed algorithm, a road corridor in Zanesville, OH, was used for testing. The resulting landslide susceptibility map was validated to correctly identify 67 of the 80 mapped landslides in the independently compiled landslide inventory map of the area. These results suggest that the proposed landslide surface feature extraction method and airborne LiDAR data can be used as efficient tools for small landslide susceptibility and hazard mapping Fil: Mora, Omar E. Ohio State University; Estados Unidos Fil: Liu, Juang Kuan. Ohio State University; Estados Unidos Fil: Lenzano, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. International Center For Earth Sciences; Argentina Fil: Toth, Charles Karoly. Ohio State University; Estados Unidos Fil: Grejner Brzezinska, Dorota A.. Ohio State University; Estados Unidos |
description |
Landslides are natural disasters that cause environmental and infrastructure damage worldwide. To prevent future risk posed by such events, effective methods to detect and map their hazards are needed. Traditional landslide susceptibility mapping techniques, based on field inspection, aerial photograph interpretation, and contour map analysis are often subjective, tedious, difficult to implement and may not have the spatial resolution and temporal frequency necessary to map small slides, which is the focus of this investigation. We present a methodology that is based on a Support Vector Machine (SVM) that utilizes a LiDAR-derived Digital Elevation Model (DEM) to quantify and map the topographic signatures of landslides. The algorithm employs several geomorphological features to calibrate the model and delineate between landslide and stable terrain. To evaluate the performance of the proposed algorithm, a road corridor in Zanesville, OH, was used for testing. The resulting landslide susceptibility map was validated to correctly identify 67 of the 80 mapped landslides in the independently compiled landslide inventory map of the area. These results suggest that the proposed landslide surface feature extraction method and airborne LiDAR data can be used as efficient tools for small landslide susceptibility and hazard mapping |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-03 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/59516 Mora, Omar E; Liu, Juang Kuan; Lenzano, María Gabriela; Toth, Charles Karoly; Grejner Brzezinska, Dorota A.; Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data; Amer Soc Photogrammetry; Photogrammetric Engineering And Remote Sensing; 81; 3-2015; 11-19 0099-1112 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/59516 |
identifier_str_mv |
Mora, Omar E; Liu, Juang Kuan; Lenzano, María Gabriela; Toth, Charles Karoly; Grejner Brzezinska, Dorota A.; Small Landslide Susceptibility and Hazard Assessment Based on Airborne LiDAR Data; Amer Soc Photogrammetry; Photogrammetric Engineering And Remote Sensing; 81; 3-2015; 11-19 0099-1112 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.14358/PERS.81.3.239 info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0099111215303475 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Amer Soc Photogrammetry |
publisher.none.fl_str_mv |
Amer Soc Photogrammetry |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844614412181176320 |
score |
13.070432 |