Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography |
| |
| Institution: | 1. Hospital Británico De Buenos Aires, Área de Enfermedades Desmielinizantes, Perdriel 74, Buenos Aires 1280, Argentina;2. Esclerosis Múltiple Argentina (EMA), Uriarte 1465, Buenos Aires 1414, Argentina;3. Hospital Ramos Mejía, Centro Universitario de Esclerosis Múltiple, General Urquiza 609, Buenos Aires 1221, Argentina;4. Renacer, Fundación Dominicana de EM, Spirit Coworking, Presidente González, Santo Domingo, Dominican Republic;5. Asociación Esclerosis Múltiple Perú, Calle Cayo Roca Zela 540 Urb. Hiquereta Surco, Lima, Perú;6. Asociación Hecho con Amor, Calle Galeano 645 Urb. Los Rosales Surco, Lima, Perú;7. Asociación Costarricense de Esclerosis Múltiple, Calle Socola, San Rafael, San José 10203, Costa Rica;8. Asociación de Pacientes con Esclerosis Múltiple y Enfermedades Desmielinizantes (APEMED), Presidente Franco 982, 3er Piso, Oficina 301, Asunción, Paraguay;9. Asociación Hondureña de Esclerosis Múltiple (ASOHEM), Edificio Centro Comercial Maya, Boulevard Morazán, Tegucigalpa, Honduras;10. Fundación esclerosis múltiple amor (FEMA). Barrio Las Acacias, San Pedro Sula, Honduras;11. Asociación de Lucha Contra la Esclerosis Múltiple (ALCEM), Barabino 690, San Antonio de Padua, Buenos Aires 1718, Argentina;12. Esclerosis Múltiple CUBA, EM CUBA Calle 223 23604, La Habana 19250, Cuba;13. Asociación Nicaragüense Esclerosis Múltiple (ANEM). Carretera Norte, Managua, Nicaragua;14. Asociación de Pacientes de Esclerosis Múltiple y Enfermedades Desmielinizantes de Ecuador (APEMEDE). Av. Atahualpa Oe1-198, Quito 170147, Ecuador;15. Fundación Ecuatoriana de Esclerosis Múltiple (FUNDEM), Jorge Drom 37-79 y Unión Nacional de Periodistas Sector Iñaquito, Quito, Ecuador;p. Asociación de Lucha contra la Esclerosis Múltiple de Colombia (ALEM), Carrera 50C #59-87, Barrio Prado Centro Medellín, Colombia;q. Corporación Esclerosis Múltiple Chile, Cristóbal Colón 5196, departamento 707, Santiago, Chile;r. Asociación Salvadoreña pro Enfermedades Neuromusculares, Colonia España, Avenida Buenos Aires #5, San Salvador, El Salvador;s. Federación Esclerosis Múltiple México (FEMMEX). Zacatecas 24, Roma, Ciudad de México 06700, México;t. Esclerosis Múltiple Uruguay (EMUR). Yaguarón 1407, Oficina 718, Montevideo 11100, Uruguay;u. MS Society, 372 Edgware Rd, NW2 6ND, London, United Kingdom;v. University Multiple Sclerosis Center, Biomedical Research Institute & Data Science Institute, Hasselt University, Belgium;w. MS International Federation, Skyline House 200 Union Street, London SE1 0LX, United Kingdom;1. Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan 610041,China;2. College of Urban and Environmental Science, Northwest University, Xi''an 710127, China;3. School of Geology Engineering and Geomatics, Chang''an University, Xi''an 710054, China |
| |
| Abstract: | Large deep-seated landslides can be reactivated during intense events, and they can evolve into destructive failures. They are generally difficult to recognize in the field, especially when they develop in densely forested areas. A detailed and constantly updated inventory map of such phenomena, and the recognition of their topographic signatures is absolutely a key tool for landslide risk mitigation.The aim of this work is to test in forested areas, the performance of the new automatic and objective methodology developed by Tarolli et al. (2012) for geomorphic features extraction (landslide crowns) from high resolution topography (LiDAR derived Digital Terrain Models – DTMs). The methodology is based on the detection of landslides through the use of thresholds obtained by the statistical analysis of variability of landform curvature. The study was conducted in a high-risk area located in the central-south Taiwan, where an accurate field survey on landsliding processes and a high-quality set of airborne laser scanner elevation data are available. The area has been chosen because some of the deep-seated landslides are located near human infrastructures and their reactivation is highly dangerous. Thanks to LiDAR’s capability to detect the bare ground elevation data in forested areas, it was possible to recognize in detail landslide features also in remote regions difficult to access. The results, if compared with the previous work of Tarolli et al. (2012), mainly focused on shallow landslides, and in a not forested area, indicate that for deep-seated landslides, where the crowns are more evident, and they are present at large scale, the tested methodology performs better (higher quality index). The method can be used to interactively assist the interpreter/user on the task of deep-seated landslide hazard mapping, and risk assessment planning of such regions. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
