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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/59516

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spelling 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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