Interpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionales
ilustraciones, fotografías, gráficas, mapas, planos
- Autores:
-
Pérez Moreno, Michael Alejandro
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81273
- Palabra clave:
- 550 - Ciencias de la tierra
550 - Ciencias de la tierra
Deslizamientos
Movimientos en masa
Clasificación basado en objetos geográficos
Rasgos pictórico - morfológicos
Landslide
Mass movements
GEOBIA
GIS
Sistema de información geográfica
Análisis de datos
Geographical information systems
Data analysis
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Interpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionales |
| dc.title.translated.eng.fl_str_mv |
Visual and digital interpretation of remote sensing data for the identification of rotational and translational landslides |
| title |
Interpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionales |
| spellingShingle |
Interpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionales 550 - Ciencias de la tierra 550 - Ciencias de la tierra Deslizamientos Movimientos en masa Clasificación basado en objetos geográficos Rasgos pictórico - morfológicos Landslide Mass movements GEOBIA GIS Sistema de información geográfica Análisis de datos Geographical information systems Data analysis |
| title_short |
Interpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionales |
| title_full |
Interpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionales |
| title_fullStr |
Interpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionales |
| title_full_unstemmed |
Interpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionales |
| title_sort |
Interpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionales |
| dc.creator.fl_str_mv |
Pérez Moreno, Michael Alejandro |
| dc.contributor.advisor.none.fl_str_mv |
Lizarazo Salcedo, Iván Alberto Ochoa Gutierrez, Luis Hernan |
| dc.contributor.author.none.fl_str_mv |
Pérez Moreno, Michael Alejandro |
| dc.subject.ddc.spa.fl_str_mv |
550 - Ciencias de la tierra 550 - Ciencias de la tierra |
| topic |
550 - Ciencias de la tierra 550 - Ciencias de la tierra Deslizamientos Movimientos en masa Clasificación basado en objetos geográficos Rasgos pictórico - morfológicos Landslide Mass movements GEOBIA GIS Sistema de información geográfica Análisis de datos Geographical information systems Data analysis |
| dc.subject.proposal.spa.fl_str_mv |
Deslizamientos Movimientos en masa Clasificación basado en objetos geográficos Rasgos pictórico - morfológicos |
| dc.subject.proposal.eng.fl_str_mv |
Landslide Mass movements GEOBIA GIS |
| dc.subject.unesco.spa.fl_str_mv |
Sistema de información geográfica Análisis de datos |
| dc.subject.unesco.eng.fl_str_mv |
Geographical information systems Data analysis |
| description |
ilustraciones, fotografías, gráficas, mapas, planos |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-12-10 |
| dc.date.accessioned.none.fl_str_mv |
2022-03-17T17:40:11Z |
| dc.date.available.none.fl_str_mv |
2022-03-17T17:40:11Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/81273 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/81273 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
[lia, 2012a] (2012a). Chapter 1 - a systematic view of remote sensing. In S. Liang, X. Li, & J. Wang (Eds.), Advanced Remote Sensing (pp. 1 – 31). Boston: Academic Press. [lia, 2012b] (2012b). Chapter 2 - geometric processing and positioning techniques. In S. Liang, X. Li, & J. Wang (Eds.), Advanced Remote Sensing (pp. 33 – 74). Boston: Academic Press. [Acosta et al., 2004] Acosta, J. P., Vanegas, D. J., Ballén, F. R., Olarte, J. M., & Calderón, Y. (2004). Proyecto Compilación y Levantamiento de la Información Geomecánica: Propuesta Metodológica para el Desarrollo de la Cartografía Geológica para Ingeniería Volumen II. Bogotá: Instituto Colombiano de Geológica y Minería (INGEOMINAS). [Addink et al., 2012] Addink, E. A., Coillie, F. M. V., & Jong, S. M. D. (2012). Introduction to the geobia 2010 special issue: From pixels to geographic objects in remote sensing image analysis. International Journal of Applied Earth Observation and Geoinformation, 15, 1– 6. Special Issue on Geographic Object-based Image Analysis: GEOBIA. [Antonic, 2001] Antonic, O. (2001). DEM-based depth in sink as an environmental estimator. 138, 247–254. [Bedoya, 2009] Bedoya, G. (2009). Inventario de los desastres de origen natural en Colombia, 19702006- Limitantes, tendencias y necesidades futuras. Gestión y Ambiente, 11(1), 109– 120. [Bialas et al., 2019] Bialas, J., Oommen, T., & Havens, T. C. (2019). Optimal segmentation of high spatial resolution images for the classification of buildings using random forests. International Journal of Applied Earth Observation and Geoinformation, 82(June), 101895. [Braun, 2020] Braun, A. (2020). Sentinel-1 Toolbox DEM generation with Sentinel-1 Workflow and challenges. (January), 1–27. [Burrough & A, 1998] Burrough, P. & A, M. (1998). Principles Of Geographical Information Systems. Spatial Information Systems and Geostatistics. Oxford University Press. [Chang & Liu, 2004] Chang, K.-T. & Liu, J.-K. (2004). Landslide features interpreted by neural network method using a high-resolution satellite image and digital topographic data. International Archives of Photogrammetry Remote Sensing and Spatial Information Science, 35. [Chuvieco, 1990] Chuvieco, E. (1990). Fundamentos de teledetection espacial. [Chuvieco, 2002] Chuvieco, E. (2002). Chuvieco Teledetección Ambiental. [Chuvieco, 2016] Chuvieco, E. (2016). Fundamentals of Satellite Remote Sensing: An Environmental Approach. Environmental science. CRC Press, Taylor & Francis Group. [Comaniciu & Meer, 2002] Comaniciu, D. & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619. [Comert et al., 2019] Comert, R., Avdan, U., Gorum, T., & Nefeslioglu, H. A. (2019). Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data. Engineering Geology, 260(August), 105264. [Cruden, 1991] Cruden, D. (1991). A simple definition of a landslide. Bulletin of the International Association of Engineering Geology - Bulletin de l’Association Internationale de Géologie de l’Ingénieur, 43. [Cruden, 1996] Cruden, V. (1996). Cruden,d.m., varnes, d.j., 1996, landslide types and processes, special report , transportation research board, national academy of sciences, 247:36-75. Special Report - National Research Council, Transportation Research Board, 247, 76. [Duque, 2008] Duque, G. (2008). Gestión del riesgo natural y el caso de Colombia Amenazas en la región. 2008, 54. Duque, 2017] Duque, G. (2017). El siniestro de Mocoa, designio de la imprevisión. (pp. 1–2). [Esa, 2020] Esa (2020). Resolutions. url:https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial. Accedido 28-03-2020. [Espejo, 2016] Espejo, O. (2016). Desarrollo de una metodología para estimación de la deforestación mediante el análisis multitemporal de imágenes multiespectrales en un entorno de análisis basado en objetos geográficos ( GEOBIA ). (pp. 177). [Espindola et al., 2006] Espindola, G. M., Camara, G., Reis, I. A., Bins, L. S., & Monteiro, A. M. (2006). Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing, 27(14), 3035–3040. [Fiorucci et al., 2019] Fiorucci, F., Ardizzone, F., Mondini, A. C., Viero, A., & Guzzetti, F. (2019). Visual interpretation of stereoscopic ndvi satellite images to map rainfall-induced landslides. Landslides, 16(1), 165–174. [Fiorucci et al., 2018] Fiorucci, F., Giordan, D., Santangelo, M., Dutto, F., Rossi, M., & Guzzetti, F. (2018). Criteria for the optimal selection of remote sensing optical images to map event landslides. Natural Hazards and Earth System Sciences, 18(1), 405–417. [Georgescu et al., 2003] Georgescu, B., Shimshoni, I., & Meer, P. (2003). Mean shift based clustering in high dimensions: A texture classifi cation example. Proceedings of the IEEE International Conference on Computer Vision, 1(Iccv), 456–463. [Gonçalves, 2018] Gonçalves, J. (2018). Advanced techniques with raster data: Part 1– unsupervised classification. url:https://www.r-exercises.com/2018/02/28/advanced-techniques-with-raster-data-part-1-unsupervised-classifi cation/. Accedido 07-06-2020. [Guzzetti et al., 2012] Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112(1-2), 42–66. [Highland & Bobrowsky, 2008] Highland, L. M. & Bobrowsky, P. (2008). The landslide Handbook - A guide to understanding landslides. US Geological Survey Circular, (1325), 1–147. [Horning, 2013] Horning, N. (2013). Training guide to classify satellite images using segmentation and random forests. [Hölbling et al., 2017] Hölbling, D., Eisank, C., Albrecht, F., Vecchiotti, F., Friedl, B., Weinke, E., & Kociu, A. (2017). Comparing manual and semi-automated landslide mapping based on optical satellite images from different sensors. Geosciences (Switzerland), 7, 37. [Iw, 2020] Iw (2020). Sentinel 1. url:https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes/interferometric-wide-swath. Accedido 28-03-2020. [Jensen, 2015] Jensen, J. R. (2015). Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River, NJ, USA: Prentice Hall Press, 4th edition. [Kanungo et al., 2009] Kanungo, D., Arora, M., Sarkar, S., & Gupta, R. (2009). Landslide Susceptibility Zonation (LSZ) Mapping–A Review. Journal of South Asia Disaster Studies, 2(1), 81–105. [Keyport et al., 2018] Keyport, R. N., Oommen, T., Martha, T. R., Sajinkumar, K. S., & Gierke, J. S. (2018). Int J Appl Earth Obs Geoinformation A comparative analysis of pixel-and object-based detection of landslides from very high-resolution images. 64(September 2017), 1–11. [Lewis, 2007] Lewis, Y. W. (2007). Deslizamientos de tierra: los básicos. [Lillesand et al., 2015] Lillesand, T., Kiefer, R., & Chipman, J. (2015). Remote Sensing and Image Interpretation, 7th Edition. Wiley. [Martha et al., 2010] Martha, T. R., Kerle, N., Jetten, V., van Westen, C. J., & Kumar, K. V. (2010). Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology, 116(1-2), 24–36. [Martha et al., 2011] Martha, T. R., Kerle, N., Van Westen, C. J., Jetten, V., & Kumar, K. V. (2011). Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. In IEEE Transactions on Geoscience and Remote Sensing. [Martha et al., 2012] Martha, T. R., Kerle, N., van Westen, C. J., Jetten, V., & Vinod Kumar, K. (2012). Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 67(1), 105–119. [Miller & LaFlamme, 1958] Miller, C. L. & LaFlamme, R. A. (1958). The digital terrain model - Theory & Application. The American Society of photogrammetry, XXIV(3), 11. [Moine et al., 2009] Moine, M., Puissant, A., & Malet, J.-P. (2009). Detection of landslides from aerial and satellite images with a semi-automatic method. Application to the Barcelonette basin (Alpes-de-Haute-Provence, France). International Conference 'Landslide Processes: From Geomorphological Mapping to Dynamic Modelling', (pp. 63–68). [Montero, 2017] Montero, J. (2017). Clasificación de movimientos en masa y su distribución en terrenos geológicos de Colombia. Bogotá: Servicio Geológico Colombiano. [Olson, C.E., 1960] Olson, C.E. (1960). Elements of photographic interpretation common to several sensors. Photogrammetric Engineering, 26(4), 651–656. [Parker, 2010] Parker, M. (2010). Chapter 16 - radar basics. In M. Parker (Ed.), Digital Signal Processing 101 (pp. 191 – 200). Boston: Newnes. [Pérez Cerón et al., 2017] Pérez Cerón, R., Trejos González, G. A., Camargo Holguín, B. L., Chaparro Cordón, J. L., Navarro Alarcón, S. d. R., Ruiz Peña, G. L., Gamboa Rodríguez, C. A., & Ramírez Hernández, K. C. (2017). Las Amenazas por Movimientos en Masa de Colombia Una Visión a escala 1:100.000. Bogotá: Servicio Geológico Colombiano. [PMA-GMA, 2007] PMA-GMA (2007). Movimientos en Masa en la Región Andina: Una guía para la evaluación de amenazas. Publicación Geológica Multinacional, 4(0717-3733), 432. [Pulido & Gómez, 2001] Pulido, O. & Gómez, L. (2001). Geología de la Plancha 266 Villavicencio. [Rajbhandari et al., 2017] Rajbhandari, S., Aryal, J., Osborn, J., Musk, R., & Lucieer, A. (2017). Benchmarking the applicability of ontology in geographic object-based image analysis. ISPRS International Journal of Geo-Information, 6(12). [Rib & Liang, 1978] Rib, H. T. & Liang, T. (1978). Recognition and identification. [Richards, 1993] Richards, J. A. (1993). Remote Sensing Digital Image Analysis: An Introduction. Berlin, Heidelberg: Springer-Verlag, 2nd edition. [Roa, 2007] Roa, J. G. (2007). Estimación de áreas susceptibles a deslizamientos mediante datos e imágenes satelitales: Cuenca del río Mocotíes, estado Mérida-Venezuela. Revista Geográfica Venezolana , 48(2), 183–219. [Rodríguez et al., 2017] Rodríguez , E. A., Sandoval Ramírez, J. H., Chaparro Cordón, J. L., Trejos González, G. A., Enif, M. B., Ramírez Hernández, K. C., Castro Marín, E., Castro Guerra, J. A., & Ruiz Pe˜na, G. L. (2017). Guía Metodológica Para La Zonificación De Amenaza Por Movimientos En Masa. [Rodríguez et al., 2012] Rodríguez, E., Medina Bello, E., & Cárdenas, M. A. (2012). Hydrogeology applied to landslide hazard zoning. [Rousseeuw, 1987] Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(C), 53–65. [Ruiz Peña et al., 2018] Ruiz Peña, G. L., Navarro Alarcón, S., Pérez Cerón, R., Sandoval Martínez, A., Chaparro Cordón, J. L., Rangel Flórez, M. S., Trejos González, G. A., Medina Ávila, D. F., Galindo Sáchica, Á. M., Reyes Merchán, A. A., Murcia Cañón, C. A., Pérez Moreno, M. A., Gamboa Rodríguez, C. A., Ramírez Hernández, K. C., & Avellaneda Avellaneda, G. (2018). Actualización del Estudio de Zonificación de Amenaza por Movimientos en Masa a Escala 1:25.000 y la Zonificación de Amenaza por Movimientos en Masa Tipo Avenida Torrencial Escala 1:25.000 en el Municipio Villavicencio. Departamento del Meta. Versión año 2018. [Santangelo et al., 2015] Santangelo, M., Marchesini, I., Bucci, F., Cardinali, M., Fiorucci, F., & Guzzetti, F. (2015). An approach to reduce mapping errors in the production of landslide inventory maps. Natural Hazards and Earth System Sciences, 15(9), 2111–2126. [Sentinel 1, 2020] Sentinel 1 (2020). Sentinel 1. url:https://sentinel.esa.int/web/sentinel/missions/sentinel-1. Accedido 28-03-2020. [Sentinel2, 2020] Sentinel2 (2020). Sentinel 2. url:https://sentinel.esa.int/web/sentinel/missions/sentinel-2. Accedido 28-03-2020. [Serrato et al., 2015] Serrato, P. K., Muñoz, C. A., Alexander, L., & Garzón, V. (2015). Criterios pictórico-morfológicos para la identificación de movimientos en masa empleando imágenes de muy alta resolución espacial. (pp. 97–108). [Shivers, 2020] Shivers, S. P. (2020). Ndvi, the foundation for remote sensing phenology. url:https://www.usgs.gov/core-science-systems/eros/phenology/science/ndvi-foundation-remote-sensing-phenology? Accedido 31-10-2020. [Snap, 2020] Snap (2020). Sentinel 1. url:https://step.esa.int/main/download/snap-download. Accedido 28-03-2020. [Soeters & Westen, 1996] Soeters, R. & Westen, C. (1996). Slope instability recognition,analysis and zonation. In: Turner, A.K., Schuster, R.L. (Eds.), Landslide: Investigations and Mitigation. Special Report, vol. 247. Transportation Research Board, National Research Council, National Academy Press, Washington, D.C., (pp. 129–17). [Suarez Díaz, 1998] Suarez Díaz, J. (1998). 1- Caracterización de los movimientos. Deslizamientos y estabilidad de taludes en zonas tropicales. [Tarboton et al., 1991] Tarboton, D. G., Bras, R. L., & Rodríguez-Iturbe, I. (1991). On the extraction of channel networks from digital elevation data. Hydrological Processes, 5(1), 81–100. [TEP, 2020] TEP, F. S. (2020). Sentinel-2 Brightness Index derived with ESA’s SNAP Sentinel-2 Toolbox. url:https://foodsecurity-tep.net/node/210. Accedido 24-08-2020. [Weih & Riggan, 2010] Weih, R. C. & Riggan, N. D. (2010). Object-based classification vs. pixel-based classifi cation: Comparitive importance of multi-resolution imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII, 1–6. [Wieczorek, 1984] Wieczorek, G. F. (1984). Preparing a Detailed Landslide-Inventory Map for Hazard Evaluation and Reduction. Environmental and Engineering Geoscience, xxi(3), 337–342. [Yang et al., 2015] Yang, W., Wang, M., Shi, P., Shen, L., & Liu, L. (2015). Object-oriented rapid identification of landslides based on terrain factors segmentation and classification. Journal of Natural Disasters, 24(4), 1–6. [Zevenbergen & Thorne, 1987] Zevenbergen, L. W. & Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12(1), 47–56. [Zhou et al., 2008] Zhou, Y. M., Jiang, S. Y., & Yin, M. L. (2008). A region-based image segmentation method with mean-shift clustering algorithm. Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008, 2, 366–370. [Zou & Lin, 2013] Zou, Z. & Lin, X. (2013). Geoinformatics production for urban disasters risk reduction: A zero cost solution. volume 398. [Zuluaga & García, 2015] Zuluaga, J. & García, A. (2015). Plan de Ordenamiento Territorial Municipio de Villavicencio Componente General. |
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Derechos reservados al autor, 2021 |
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http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional Derechos reservados al autor, 2021 http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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xx, 189 páginas |
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application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
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Bogotá - Ciencias Agrarias - Maestría en Geomática |
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Escuela de posgrados |
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Facultad de Ciencias Agrarias |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Universidad Nacional de Colombia |
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Atribución-NoComercial-SinDerivadas 4.0 InternacionalDerechos reservados al autor, 2021http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lizarazo Salcedo, Iván Albertob7a911d83f30c19f50a8d2f7b4e94e02600Ochoa Gutierrez, Luis Hernan42361dea7c3cf56533207ccfecfb018d600Pérez Moreno, Michael Alejandro97de4c7e3632c8f94ad8fd3659618cf62022-03-17T17:40:11Z2022-03-17T17:40:11Z2021-12-10https://repositorio.unal.edu.co/handle/unal/81273Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, gráficas, mapas, planosEsta tesis presenta un método para la detección y ubicación de movimientos en masa al analizar su distribución, patrón y recurrencia. El método propuesto para la detección de movimientos en masa utiliza herramientas de la geomática buscando reducir tiempos y facilitar la creación de inventarios de movimientos en masa. En este estudio se realizó la identificación visual de rasgos pictórico - morfológicos en deslizamientos ya identificados, y su posterior uso para la definición de criterios de clasificación de deslizamientos en un método semiautomático de clasificación basado en objetos geográficos (GEOBIA). Se identificaron patrones cualitativos que identifican los rasgos pictórico – morfológicos de deslizamientos. Posteriormente se realizó la clasificación basada en objetos, generando la segmentación de la imagen seguido de la clasificación basada en objetos geográficos identificando las coberturas de la tierra y deslizamientos. A partir de los patrones cualitativos se refinaron los objetos clasificados como deslizamientos y se clasificaron mediante el uso de la curvatura del terreno en deslizamientos del subtipo traslacional o rotacional. El resultado se validó en términos de área entre los polígonos clasificados como deslizamientos y de un inventario de movimientos en masa precedente. Se obtuvo que el área correctamente clasificada se situó entre un 60% a 50% y el área erróneamente clasificada fue entre el 25% a 15%. (Texto tomado de la fuente)The detection and location of mass movements allows to analyze their distribution, pattern and recurrence. The creation of methods that use tools provided by Geomatics seeks to reduce times, facilitate the creation, production of inventories and mass movement maps; which are the basic input for the generation of susceptibility maps. Computer advances allow the use of tools for the semi-automatic location of objects in satellite images, although there are several studies on the use of geographic information systems for the detection of these natural events, in Colombia a methodology has not been implemented or studied efficient and economical, which is an opportunity to further develop the implementation of geomatics in the location of mass movements in large areas. This project proposes the visual identification of pictorial-morphological features in already identified landslides, and their subsequent use for the definition of landslide classification criteria in a semi-automatic classification method based on geographic objects (GEOBIA). This semi-automatic method identifies some types of landslides with the use of satellite imagery supported by an existing inventory of mass movements. The method was applied in (2) two areas located in the rural part of the municipality of Villavicencio in the department of Meta, using satellite multispectral images from the Sentinel 2 mission and a digital terrain model (DTM) obtained from radar images of the Sentinel mission 1. In a first step, qualitative patterns were identified that identify a pictorial-morphological feature in landslides of an existing inventory of mass movements. For this, spectral criteria, temporal criteria, spatial criteria and an area criterion were used. Subsequently, the classification based on objects was carried out, generating the segmentation of the image followed by the classification based on geographical objects, identifying the land covers and landslides located in the study area. Next, with the identified qualitative patterns, the use of parameters such as the normalized vegetation index (NDVI), the soil gloss index (S2 BI), contextual data and the slope of the terrain was defined, which allowed to refine the objects. classified as landslides obtained from GEOBIA. Once these polygons were refined with the curvature of the terrain, they were classified into landslides of the translational and rotational subtype. Finally, the result obtained was validated in terms of area (extension) between the polygons classified as landslides and the pre-existing mass movement inventory data. It was obtained that the correctly classified area was between 56.6 % and 51 % in the two study areas analyzed. Regarding the erroneously classified area, 17 % and 25 % were obtained. According to the results obtained from this methodology, these allow an approximation of delimitation of candidate areas for the presence of landslides in large areas.MaestríaMagíster en GeomáticaTecnologías Geoespacialesxx, 189 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaEscuela de posgradosFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá550 - Ciencias de la tierra550 - Ciencias de la tierraDeslizamientosMovimientos en masaClasificación basado en objetos geográficosRasgos pictórico - morfológicosLandslideMass movementsGEOBIAGISSistema de información geográficaAnálisis de datosGeographical information systemsData analysisInterpretación visual y digital de datos de sensores remotos para la identificación de deslizamientos rotacionales y traslacionalesVisual and digital interpretation of remote sensing data for the identification of rotational and translational landslidesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[lia, 2012a] (2012a). 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Plan de Ordenamiento Territorial Municipio de Villavicencio Componente General.InvestigadoresORIGINAL10324187102021.pdf10324187102021.pdfTesis de Maestría de Geomáticaapplication/pdf6739798https://repositorio.unal.edu.co/bitstream/unal/81273/3/10324187102021.pdf554eddd193df3efe5369fb3bd5534db0MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81273/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL10324187102021.pdf.jpg10324187102021.pdf.jpgGenerated Thumbnailimage/jpeg4898https://repositorio.unal.edu.co/bitstream/unal/81273/5/10324187102021.pdf.jpg661f37d03f0557a5fc113ae6328fed5aMD55unal/81273oai:repositorio.unal.edu.co:unal/812732024-08-03 23:10:23.558Repositorio Institucional Universidad Nacional de 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