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dc.contributor.advisorMurillo Feo, Carol Andrea
dc.creatorCorrea Muñoz, Nixon Alexander
dc.date.accessioned2020-08-06T23:23:32Z
dc.date.available2020-08-06T23:23:32Z
dc.date.created2020-01-30
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/77975
dc.descriptionLos deslizamientos son una amenaza natural que causa pérdidas humanas, daños a la infraestructura y degradación del suelo. Una evaluación cuantitativa de su presencia se requiere mediante la detección y el reconocimiento de potenciales áreas inestables. Esta investigación tuvo como alcance desarrollar un método soportado en métodos semi-automáticos para detectar potenciales movimientos en masa a escala regional. Cinco técnicas fueron estudiadas: Morfometría, Interferometría radar, Interferometría con Persistent Scatterers, Polarimetría radar y composiciones del NDVI con los satélites Landsat 5, Landsat 7 y Landsat 8. El caso de estudio se seleccionó dentro de la región intermedia al este del departamento del Cauca, la cual se caracteriza por terreno montañoso y la presencia de inestabilidades de la pendiente oficialmente registrados en el servicio SIMMA del Servicio Geológico Colombiano. Este inventario reveló que el tipo de movimiento deslizamiento ocurrió con una frecuencia relativa de 77.4%, caidos con el 16.5% de los casos y reptaciones con 3%, flujos con 2.6% y propagación lateral con 0.43%. Como resultado, se obtuvo las variables morfométricas: pendiente, convergencia, índice topográfico de humedad y forma del terreno altamente asociados con los deslizamientos. El efecto de un DEM en el procesamiento del método InSAR fue similar para la variable coherencia usando los DEMs: ASTER, PAlSAR RTC, Topo-map y SRTM. Un análisis Multi-InSAR estimó velocidades de desplazamiento en dirección de vista del radar entre -10 y 10 mm/año. El análisis de polarimetría dual del Sentinel-1 arrojó valores de retrodispersión promedio de -14.5 dB en la banda VH y -8.5dB en la banda VV. Las cuatro polarimetrías del sensor aéreo UAVSAR permitió caracterizar el mecanismo de dispersión del Inventario de Deslizamiento así: 39% en el mecanismo de superficie, 46.4% en el mecanismo de volumen y 14.6% en el mecanismo de doble rebote. La información generada en el rango óptico permitió obtener composiciones de NDVI derivados de la plataforma Landsat entre los años 2012 y 2016, mostrando que el rango entre 0.4 y 0.7 tuvieron una alta asociación con los deslizamientos. En esta investigación se determinaron las categorías de las variables de Teledetección más altamente relacionadas con los movimientos en masa mediante el método de Pesos de Evidencias (WofE). Finalmente, estos resultados se fusionaron para generar el modelo de detección de deslizamientos usando el método supervisado de aprendizaje de máquina Random Forest. Tomando muestras aleatorias para entrenar y validar el modelo en una proporción 70:30, el modelo de detección, especialmente los movimientos de tipo rotacional y traslacional fueron clasificados con una tasa general de éxito del 70%.
dc.description.abstractLandslides are a common natural hazard that causes human casualties, but also infrastructure damage and land-use degradation. Therefore, a quantitative assessment of their presence is required by means of detecting and recognizing the potentially unstable areas. This research aims to develop a method supported on semiautomatic methods to detect potential mass movements at a regional scale. Five techniques were studied: Morphometry, SAR interferometry (InSAR), Persistent Scatterer InSAR (PS-InSAR), SAR polarimetry (PolSAR) and NDVI composites of Landsat 5, Landsat 7, and Landsat 8. The case study was chosen within the mid-eastern area of the Cauca state, which is characterised by its mountainous terrain and the presence of slope instabilities, officially registered in the CGS-SIMMA landslide inventory. This inventory revealed that the type `slide' occurred with 77.4% from the entire registries, `fall' with 16.5%, followed by `creeps' with 3%, flows with 2.6%, and `lateral spread' with 0.43%. As a result, we obtained the morphometric variables: slope, CONVI, TWI, landform, which were highly associated with landslides. The effect of a DEM in the processing flow of the InSAR method was similar for the InSAR coherence variable using the DEMs ASTER, PALSAR RTC, Topo-map, and SRTM. Then, a multiInSAR analysis gave displacement velocities in the LOS direction between -10 and 10 mm/year. With the dual-PolSAR analysis (Sentinel-1), VH and VV C-band polarised radar energy emitted median values of backscatters, for landslides, about of -14.5 dB for VH polarisation and -8.5 dB for VV polarisation. Also, L-band fully polarimetric NASA-UAVSAR data allowed to nd the mechanism of dispersion of CGS landslide inventory: 39% for surface scattering, 46.4% for volume dispersion, and 14.6% for double-bounce scattering. The optical remote sensing provided NDVI composites derived from Landsat series between 2012 and 2016, showing that NDVI values between 0.40 and 0.70 had a high correlation to landslides. In summary, we found the highest categories related to landslides by Weight of Evidence method (WofE) for each spaceborne technique applied. Finally, these results were merged to generate the landslide detection model by using the supervised machine learning method of Random Forest. By taking training and test samples, the precision of the detection model was of about 70% for the rotational and translational types.
dc.description.sponsorshipMinisterio de Ciencias
dc.format.extent286
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.relationN. A. Correa-Muñoz, C. A. Murillo-Feo & L. J. Martínez-Martínez (2018): The potential of PALSAR RTC elevation data for landform semi-automatic detection and landslide susceptibility modeling, European Journal of Remote Sensing, 52, sup1, 148-159. DOI: 10.1080/22797254.2018.1552087
dc.rights.urihttp://creativecommons.org/licenses/by-nd/2.5/co/
dc.subjectmovimientos en masa
dc.subjectinventario de deslizamientos
dc.subjectdetección semi-automática de deslizamientos
dc.subjectteledetección
dc.subjectrandom forest
dc.subject.ddc550 - Ciencias de la tierra
dc.subject.ddc600 - Tecnología (Ciencias aplicadas)
dc.subject.ddc620 - Ingeniería y operaciones afines
dc.titleMethod for landslides detection with semi-automatic procedures: The case in the zone center-east of Cauca department, Colombia
dc.typeOther
dc.rights.spaAcceso abierto
dc.contributor.institutionUniversidad Nacional de Colombia - Sede Bogotá
dc.subject.keywordlandslide
dc.subject.keywordmass movement
dc.subject.keywordlandslide inventory,
dc.subject.keywordsemi-automatic detection of landslides
dc.subject.keywordremote sensing
dc.subject.keywordrandom forest
dc.type.spaOtro
dc.type.hasversionAccepted Version
dc.contributor.gruplacGeotechnical Engineering Knowledge and Innovation - GENKI
dc.description.projectConvocatoria 647 de 2014
dc.description.additionalResearch line: Geotechnics and Geoenvironmental Hazard
dc.coverage.modalityDoctorado
dc.rights.accessRightsOpen Access
dc.rights.ccAtribución-SinDerivadas 2.5 Colombia
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dc.contributor.generoMasculino
dc.publisher.programBogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería Civil


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