Evaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianos

En este trabajo se evalúa el uso de técnicas de Interferometría de Radar de Apertura Sintética (InSAR, por sus iniciales en inglés) para la detección de movimientos en masa en ambientes tropicales de montaña, específicamente en los Andes colombianos. Además, se propone una metodología para la integr...

Full description

Autores:
Ospina Urán, Alejandro
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/86910
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/86910
https://repositorio.unal.edu.co/
Palabra clave:
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
550 - Ciencias de la tierra
Riesgo ambiental
Interferometría
Desgaste de masa
Movimientos en masa
Teledetección
InSAR
Coherencia
Sistemas de Alerta Temprana
Colombia
Procesamiento InSAR
Landslides
Coherence
Remote Sensing Tecniques
InSAR
Early Warning System
Riesgo geológico
Rights
openAccess
License
Atribución-NoComercial-CompartirIgual 4.0 Internacional
id UNACIONAL2_7892941c5417e30560587c08d246868d
oai_identifier_str oai:repositorio.unal.edu.co:unal/86910
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Evaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianos
dc.title.translated.eng.fl_str_mv Evaluation of InSAR Techniques for Monitoring and Detection of Landslides in an Early Warning System in the Colombian Andes
title Evaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianos
spellingShingle Evaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianos
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
550 - Ciencias de la tierra
Riesgo ambiental
Interferometría
Desgaste de masa
Movimientos en masa
Teledetección
InSAR
Coherencia
Sistemas de Alerta Temprana
Colombia
Procesamiento InSAR
Landslides
Coherence
Remote Sensing Tecniques
InSAR
Early Warning System
Riesgo geológico
title_short Evaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianos
title_full Evaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianos
title_fullStr Evaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianos
title_full_unstemmed Evaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianos
title_sort Evaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianos
dc.creator.fl_str_mv Ospina Urán, Alejandro
dc.contributor.advisor.none.fl_str_mv Aristizábal Giraldo, Edier Vicente
dc.contributor.author.none.fl_str_mv Ospina Urán, Alejandro
dc.contributor.researchgroup.spa.fl_str_mv Investigación en Geología Ambiental Gea
dc.contributor.researchgate.spa.fl_str_mv Ospina Uran, Alejandro
dc.subject.ddc.spa.fl_str_mv 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
550 - Ciencias de la tierra
topic 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
550 - Ciencias de la tierra
Riesgo ambiental
Interferometría
Desgaste de masa
Movimientos en masa
Teledetección
InSAR
Coherencia
Sistemas de Alerta Temprana
Colombia
Procesamiento InSAR
Landslides
Coherence
Remote Sensing Tecniques
InSAR
Early Warning System
Riesgo geológico
dc.subject.lemb.none.fl_str_mv Riesgo ambiental
Interferometría
Desgaste de masa
dc.subject.proposal.spa.fl_str_mv Movimientos en masa
Teledetección
InSAR
Coherencia
Sistemas de Alerta Temprana
Colombia
Procesamiento InSAR
dc.subject.proposal.eng.fl_str_mv Landslides
Coherence
Remote Sensing Tecniques
InSAR
Early Warning System
dc.subject.wikidata.none.fl_str_mv Riesgo geológico
description En este trabajo se evalúa el uso de técnicas de Interferometría de Radar de Apertura Sintética (InSAR, por sus iniciales en inglés) para la detección de movimientos en masa en ambientes tropicales de montaña, específicamente en los Andes colombianos. Además, se propone una metodología para la integración de estas técnicas en un sistema de alertas tempranas en zona urbana-suburbana tomando como área de estudio el Valle de Aburrá, Colombia. El documento se estructura en cuatro artículos científicos independientes entre sí, los cuales serán potencialmente sometidos a publicación. El Artículo 1 presenta el marco teórico para la aplicación de técnicas InSAR en ambientes tropicales de montaña. Este primer artículo busca aportar al conocimiento de InSAR a la literatura en español. El Artículo 2 aborda la aplicación exitosa de InSAR a escala regional y la detección de múltiples zonas de deformación del terreno, asociadas a movimientos en masa en el área de estudio. El Artículo 3 se centra en un caso de estudio en el Valle de Aburrá, donde se aplica InSAR a un movimiento en masa que ha causado graves afectaciones desde 2018, encontrando que la zona de deformación supera en más de diez veces el perímetro definido inicialmente con recorridos de campo e instrumentación geotécnica tradicional. Este análisis permitió aproximar la extensión real de la zona de deformación, lo cual no había sido posible debido a las limitaciones del monitoreo geotécnico, además, encontrar relaciones entre los desplazamientos InSAR e información pluviométrica. Finalmente, el Artículo 4 presenta una propuesta metodológica conceptual para integrar InSAR en un sistema de alertas tempranas regional. Se concluye que InSAR es una herramienta eficaz para detectar movimientos en masa en los Andes colombianos y que su aplicación tendría positivos impactos en la gestión del riesgo de desastres.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-10-07T20:37:12Z
dc.date.available.none.fl_str_mv 2024-10-07T20:37:12Z
dc.date.issued.none.fl_str_mv 2024
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_71e4c1898caa6e32
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/86910
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/86910
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Agram, P., Jolivet, R., Riel, B., Lin, Y., Simons, M., Hetland, E., Doin, M.-P., & Lasserre, C. (2013). New radar interferometric time series analysis toolbox released. Eos, Transactions American Geophysical Union, 94(7), 69--70.
Agram, P. & Simons, M. (2015). A noise model for insar time series. Journal of Geophysical Research: Solid Earth, 120(4), 2752--2771.
Aristizábal, E. & Sánchez, O. (2020). Spatial and temporal patterns and the socioeconomic impacts of landslides in the tropical and mountainous colombian andes. Disasters, 44(3), 596--618.
Aslan, G., Foumelis, M., Raucoules, D., De Michele, M., Bernardie, S., & Cakir, Z. (2020). Landslide mapping and monitoring using persistent scatterer interferometry (psi) technique in the french alps. Remote Sensing, 12(8), 1305.
Acosta, J. H. C. (2011). Las avenidas torrenciales: una amenaza potencial en el Valle de Aburrá. Gestión y ambiente, 14(3), 45--50.
Aristizábal, E. & Gómez, J. (2007). Inventario de emergencias y desastres en el valle de aburrá originados por fenómenos naturales y antrópicos en el período 1880-2007. Gestión y ambiente, 10(2), 17--30.
Aristizábal, E. & Yokota, S. (2006). Geomorfología aplicada a la ocurrencia de deslizamientos en el valle de aburrá. Dyna, 73(149), 5--16.
Agapiou, A. & Lysandrou, V. (2020). Detecting displacements within archaeological sites in cyprus after a 5.6 magnitude scale earthquake event through the hybrid pluggable processing pipeline (hyp3) cloud-based system and sentinel-1 interferometric synthetic aperture radar (insar) analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6115--6123.
Agustan, A., Ito, T., Kriswati, E., Priyadi, H., Sadmono, H., & Hernawati, R. (2022). Time series insar analysis over jakarta metropolitan area. In 2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) (pp. 30--35).: IEEE.
Aristizábal, E., Gamboa, M. F., & Leoz, F. J. (2010). Sistema de alerta temprana por movimientos en masa inducidos por lluvia para el valle de aburrá, colombia. Revista EIA, (13), 155--169.
Barra, A., Reyes-Carmona, C., Herrera, G., Galve, J. P., Solari, L., Mateos, R. M., Azañón, J. M., Béjar-Pizarro, M., López-Vinielles, J., Palamà, R., et al. (2022). From satellite interferometry displacements to potential damage maps: A tool for risk reduction and urban planning. Remote Sensing of Environment, 282, 113294.
Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential sar interferograms. IEEE Transactions on geoscience and remote sensing, 40(11), 2375--2383.
Biggs, J., Wright, T., Lu, Z., & Parsons, B. (2007). Multi-interferogram method for measuring interseismic deformation: Denali fault, alaska. Geophysical Journal International, 170(3), 1165--1179.
Bayer, B., Simoni, A., Mulas, M., Corsini, A., & Schmidt, D. (2018). Deformation responses of slow moving landslides to seasonal rainfall in the northern apennines, measured by insar. Geomorphology, 308, 293--306.
Bekaert, D. P., Handwerger, A. L., Agram, P., & Kirschbaum, D. B. (2020). Insar-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to nepal. Remote Sensing of Environment, 249, 111983.
Béjar-Pizarro, M., Notti, D., Mateos, R. M., Ezquerro, P., Centolanza, G., Herrera, G., Bru, G., Sanabria, M., Solari, L., Duro, J., et al. (2017). Mapping vulnerable urban areas affected by slow-moving landslides using sentinel-1 insar data. Remote Sensing, 9(9), 876.
Biggs, J. & Wright, T. J. (2020). How satellite insar has grown from opportunistic science to routine monitoring over the last decade. Nature Communications, 11(1), 3863.
Campbell, J. B. & Wynne, R. H. (2011). Introduction to remote sensing. Guilford press.
Casagli, N., Intrieri, E., Tofani, V., Gigli, G., & Raspini, F. (2023). Landslide detection, monitoring and prediction with remote-sensing techniques. Nature Reviews Earth & Environment, 4(1), 51--64.
Cascini, L., Fornaro, G., & Peduto, D. (2009). Analysis at medium scale of low-resolution dinsar data in slow-moving landslide-affected areas. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 598--611.
Chen, X., Tessari, G., Fabris, M., Achilli, V., & Floris, M. (2021). Comparison between ps and sbas insar techniques in monitoring shallow landslides. Understanding and Reducing Landslide Disaster Risk: Volume 3 Monitoring and Early Warning 5th, (pp. 155--161).
Cigna, F., Bateson, L. B., Jordan, C. J., & Dashwood, C. (2014). Simulating sar geometric distortions and predicting persistent scatterer densities for ers-1/2 and envisat c-band sar and insar applications: Nationwide feasibility assessment to monitor the landmass of great britain with sar imagery. Remote Sensing of Environment, 152, 441--466.
Cigna, F., Esquivel Ramírez, R., & Tapete, D. (2021). Accuracy of sentinel-1 psi and sbas insar displacement velocities against gnss and geodetic leveling monitoring data. Remote Sensing, 13(23), 4800.
Closson, D. & Milisavljevic, N. (2017). Insar coherence and intensity changes detection. Mine Action-The Research Experience of the Royal Military Academy of Belgium.
Cloude, S. R. & Papathanassiou, K. P. (1998). Polarimetric sar interferometry. IEEE Transactions on geoscience and remote sensing, 36(5), 1551--1565.
Colesanti, C. & Wasowski, J. (2006). Investigating landslides with space-borne synthetic aperture radar (sar) interferometry. Engineering geology, 88(3-4), 173--199.
Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., & Crippa, B. (2016). Persistent scatterer interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 78--89.
Crosetto, M., Solari, L., Mróz, M., Balasis-Levinsen, J., Casagli, N., Frei, M., Oyen, A., Moldestad, D. A., Bateson, L., Guerrieri, L., et al. (2020). The evolution of wide-area dinsar: From regional and national services to the european ground motion service. Remote Sensing, 12(12), 2043.
Cutrona, L. (1990). Synthetic aperture radar, volume 2. McGraw-Hill New York.
Correa, A. M., Martens, U., Restrepo, J. J., Ordóñez-Carmona, O., & Pimentel, M. M. (2005). Subdivisión de las metamorfitas básicas de los alrededores de medellín--cordillera central de colombia. Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 29(112), 325--343.
Cruden, D. M. (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(1), 27--29.
Cao, Z. & Wang, T. (2022). Water-temperature controlled deformation patterns in heifangtai loess terraces revealed by wavelet analysis of insar time series and hydrological parameters. Frontiers in Environmental Science, 10, 957339.
Cai, J., Liu, G., Jia, H., Zhang, B., Wu, R., Fu, Y., Xiang, W., Mao, W., Wang, X., & Zhang, R. (2022). A new algorithm for landslide dynamic monitoring with high temporal resolution by kalman filter integration of multiplatform time-series insar processing. International Journal of Applied Earth Observation and Geoinformation, 110, 102812.
Ding, X.-l., Li, Z.-w., Zhu, J.-j., Feng, G.-c., & Long, J.-p. (2008). Atmospheric effects on insar measurements and their mitigation. Sensors, 8(9), 5426--5448.
Dai, K., Deng, J., Xu, Q., Li, Z., Shi, X., Hancock, C., Wen, N., Zhang, L., & Zhuo, G. (2022). Interpretation and sensitivity analysis of the insar line of sight displacements in landslide measurements. GIScience & Remote Sensing, 59(1), 1226--1242.
Dilley, M. (2005). Natural disaster hotspots: a global risk analysis, volume 5. World Bank Publications.
Doerry, A. W. (2006). Performance limits for Synthetic Aperture Radar. Technical report, Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA, USA
Doin, M.-P., Lasserre, C., Peltzer, G., Cavalié, O., & Doubre, C. (2009). Corrections of stratified tropospheric delays in sar interferometry: Validation with global atmospheric models. Journal of Applied Geophysics, 69(1), 35--50.
Du, Y., Zhang, L., Feng, G., Lu, Z., & Sun, Q. (2016). On the accuracy of topographic residuals retrieved by mtinsar. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 1053--1065.
Duan, H., Li, Y., Li, B., & Li, H. (2022). Fast insar time-series analysis method in a full-resolution sar coordinate system: A case study of the yellow river delta. Sustainability, 14(17), 10597.
El-Darymli, K., McGuire, P., Gill, E., Power, D., & Moloney, C. (2014). Understanding the significance of radiometric calibration for synthetic aperture radar imagery. In 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1--6).: IEEE.
Eriksen, H. Ø., Lauknes, T. R., Larsen, Y., Corner, G. D., Bergh, S. G., Dehls, J., & Kierulf, H. P. (2017). Visualizing and interpreting surface displacement patterns on unstable slopes using multi-geometry satellite sar interferometry (2d insar). Remote Sensing of Environment, 191, 297--312.
Fattahi, H. & Amelung, F. (2013). Dem error correction in insar time series. IEEE Transactions on Geoscience and Remote Sensing, 51(7), 4249--4259.
Ferretti, A., Monti-Guarnieri, A., Prati, C., Rocca, F., & Massonet, D. (2007). InSAR principles-guidelines for SAR interferometry processing and interpretation, volume 19.
Ferretti, A., Prati, C., & Rocca, F. (1999). Permanent scatterers in sar interferometry. In IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS’99 (Cat. No. 99CH36293), volume 3 (pp. 1528--1530).: IEEE.
Fobert, M.-A., Singhroy, V., & Spray, J. G. (2021). Insar monitoring of landslide activity in dominica. Remote Sensing, 13(4).
Froude, M. J. & Petley, D. N. (2018). Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 18(8), 2161--2181.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., et al. (2007). The shuttle radar topography mission. Reviews of geophysics, 45(2).
García, C. (2005). 5. el deslizamiento de villatina. DESASTRES, (pp.5̃5).
Fikri, S., Anjasmara, I. M., & Taufik, M. (2021). Application of different coherence threshold on ps-insar technique for monitoring deformation on the lusi affected area during 2017 and 2019. In IOP Conference Series: Earth and Environmental Science, volume 731 (pp. 012036).: IOP Publishing.
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I., Rossi, M., & Melillo, M. (2020). Geographical landslide early warning systems. Earth-Science Reviews, 200, 102973.
Hogenson, K., Arko, S. A., Buechler, B., Hogenson, R., Herrmann, J., & Geiger, A. (2016). Hybrid pluggable processing pipeline (hyp3): A cloud-based infrastructure for generic processing of sar data. In Agu fall meeting abstracts, volume 2016 (pp. IN21B--1740).
Hrysiewicz, A., Wang, X., & Holohan, E. P. (2023). Ez-insar: An easy-to-use open-source toolbox for mapping ground surface deformation using satellite interferometric synthetic aperture radar. Earth Science Informatics, 16(2), 1929--1945.
Huggel, C., Khabarov, N., Obersteiner, M., & Ramírez, J. M. (2010). Implementation and integrated numerical modeling of a landslide early warning system: a pilot study in colombia. Natural Hazards, 52, 501--518.
Hungr, O., Leroueil, S., & Picarelli, L. (2014). The varnes classification of landslide types, an update. Landslides, 11, 167--194.
Hermelin, M. (2007). Valle de aburrá:?‘ quo vadis? Gestión y ambiente, 10(2), 07--16.
Hrysiewicz, A., Wang, X., & Holohan, E. P. (2023). Ez-insar: An easy-to-use open-source toolbox for mapping ground surface deformation using satellite interferometric synthetic aperture radar. Earth Science Informatics, 16(2), 1929--1945.
He, K., Zhang, X., Li, Z., Jiang, W., Zhou, J., & Han, B. (2024). A mask r-cnn network for wide-area mining subsidence automatic detection with insar observations. IEEE Transactions on Geoscience and Remote Sensing.
Handwerger, A. L., Fielding, E. J., Huang, M.-H., Bennett, G. L., Liang, C., & Schulz, W. H. (2019). Widespread initiation, reactivation, and acceleration of landslides in the northern california coast ranges due to extreme rainfall. Journal of Geophysical Research: Earth Surface, 124(7), 1782--1797.
Hoeser, T. (2018). Analysing the Capabilities and Limitations of InSAR using Sentinel-1 Data for Landslide Detection and Monitoring. PhD thesis.
Jacquemart, M. & Tiampo, K. (2021). Leveraging time series analysis of radar coherence and normalized difference vegetation index ratios to characterize pre-failure activity of the mud creek landslide, california. Natural Hazards and Earth System Sciences, 21(2), 629--642.
Jiang, M., Li, Z., Ding, X., Zhu, J., & Feng, G. (2011). Modeling minimum and maximum detectable deformation gradients of interferometric sar measurements. International journal of applied earth observation and geoinformation, 13(5), 766--777.
Jolivet, R., Grandin, R., Lasserre, C., Doin, M.-P., & Peltzer, G. (2011). Systematic insar tropospheric phase delay corrections from global meteorological reanalysis data. Geophysical Research Letters, 38(17).
Intrieri, E., Gigli, G., Mugnai, F., Fanti, R., & Casagli, N. (2012). Design and implementation of a landslide early warning system. Engineering Geology, 147, 124--136.
Kerle, N., Janssen, L. L., Huurneman, G. C., Bakker, W., Grabmaier, K., van der Meer, F., Prakash, A., Tempfli, K., Gieske, A., Hecker, C., et al. (2004). Principles of remote sensing: an introductory textbook.
Khalil, R. Z. et al. (2018). Insar coherence-based land cover classification of okara, pakistan. The Egyptian Journal of Remote Sensing and Space Science, 21, S23--S28.
Khorram, S., Koch, F. H., Van der Wiele, C. F., & Nelson, S. A. (2012). Remote sensing. Springer Science & Business Media.
Kropatsch, W. G. & Strobl, D. (1990). The generation of sar layover and shadow maps from digital elevation models. IEEE Transactions on Geoscience and Remote Sensing, 28(1), 98--107.
Kelman, I. & Glantz, M. H. (2014). Early warning systems defined. Reducing disaster: Early warning systems for climate change, (pp. 89--108).
Li, S., Xu, W., & Li, Z. (2022). Review of the sbas insar time-series algorithms, applications, and challenges. Geodesy and Geodynamics, 13(2), 114--126.
Liang, J., Dong, J., Zhang, S., Zhao, C., Liu, B., Yang, L., Yan, S., & Ma, X. (2022). Discussion on insar identification effectivity of potential landslides and factors that influence the effectivity. Remote Sensing, 14(8), 1952.
Lacasse, S., Nadim, F., & Kalsnes, B. (2005). Living with landslide risk. Geotechnical Engineering Journal of the SEAGS & AGSSEA, 41(4)
Liu, Y., Qiu, H., Yang, D., Liu, Z., Ma, S., Pei, Y., Zhang, J., & Tang, B. (2022). Deformation responses of landslides to seasonal rainfall based on insar and wavelet analysis. Landslides, (pp. 1--12).
Lu, Z. & Kim, J. (2021). A framework for studying hydrology-driven landslide hazards in northwestern us using satellite insar, precipitation and soil moisture observations: early results and future directions. GeoHazards, 2(2), 17--40.
Lanari, R., Casu, F., Manzo, M., Zeni, G., Berardino, P., Manunta, M., & Pepe, A. (2007). An overview of the small baseline subset algorithm: A dinsar technique for surface deformation analysis. Deformation and Gravity Change: Indicators of Isostasy, Tectonics, Volcanism, and Climate Change, (pp. 637--661).
Leroueil, S. (2001). Natural slopes and cuts: movement and failure mechanisms. Géotechnique, 51(3), 197--243.
Manavalan, R. (2017). Sar image analysis techniques for flood area mapping-literature survey. Earth Science Informatics, 10(1), 1--14.
Marr, W. A. (2007). Why monitor performance? In FMGM 2007: Seventh International Symposium Field Measurements in Geomechanics (pp. 1--27).
Massonnet, D. & Feigl, K. L. (1998). Radar interferometry and its application to changes in the earth’s surface. Reviews of geophysics, 36(4), 441--500.
Meyer, F. (2019). Spaceborne synthetic aperture radar: Principles, data access, and basic processing techniques. Synthetic Aperture Radar (SAR) Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation, (pp. 21--64).
Mondini, A. C., Chang, K.-T., & Yin, H.-Y. (2011). Combining multiple change detection indices for mapping landslides triggered by typhoons. Geomorphology, 134(3-4), 440--451.
Mondini, A. C., Guzzetti, F., Chang, K.-T., Monserrat, O., Martha, T. R., & Manconi, A. (2021). Landslide failures detection and mapping using synthetic aperture radar: Past, present and future. Earth-Science Reviews, 216, 103574.
Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., & Papathanassiou, K. P. (2013). A tutorial on synthetic aperture radar. IEEE Geoscience and remote sensing magazine, 1(1), 6--43.
Moretto, S., Bozzano, F., & Mazzanti, P. (2021). The role of satellite insar for landslide forecasting: Limitations and openings. Remote sensing, 13(18), 3735.
Morishita, Y., Lazecky, M., Wright, T. J., Weiss, J. R., Elliott, J. R., & Hooper, A. (2020). Licsbas: An open-source insar time series analysis package integrated with the licsar automated sentinel-1 insar processor. Remote Sensing, 12(3), 424.
Maya, M. & González, H. (1995). Unidades litodémicas en la cordillera central de colombia. Boletín geológico, 35(2-3), 44--57.
Mejía, N. (1984). Geología y geoquímica de las planchas 130 (santafé de antioquia) y 146 (medellín occidental), escala 1: 100.000, memoria explicativa. Instituto Colombiano de Geología y Minería (INGEOMINAS).
Mirmazloumi, S. M., Gambin, A. F., Palamà, R., Crosetto, M., Wassie, Y., Navarro, J. A., Barra, A., & Monserrat, O. (2022). Supervised machine learning algorithms for ground motion time series classification from insar data. Remote Sensing, 14(15), 3821.
Notti, D., Meisina, C., Zucca, F., Colombo, A., et al. (2011). Models to predict persistent scatterers data distribution and their capacity to register movement along the slope. In Fringe 2011 Workshop (pp. 19--23).
Medina-Cetina, Z. & Nadim, F. (2008). Stochastic design of an early warning system. Georisk, 2(4), 223--236.
Plank, S., Singer, J., Minet, C., & Thuro, K. (2012). Pre-survey suitability evaluation of the differential synthetic aperture radar interferometry method for landslide monitoring. International journal of remote sensing, 33(20), 6623--6637.
Petley, D. (2012). Global patterns of loss of life from landslides. Geology, 40(10), 927--930.
Plank, S., Singer, J., Thuro, K., & Minet, C. (2010). The suitability of the differential radar interferometry method for deformation monitoring of landslides—a new gis based evaluation tool. In Proceedings of the 11th IAEG Congress Geologically Active, Auckland, New Zealand (pp. 5--10).
Ren, K., Yao, X., Li, R., Zhou, Z., Yao, C., & Jiang, S. (2022). 3d displacement and deformation mechanism of deep-seated gravitational slope deformation revealed by insar: a case study in wudongde reservoir, jinsha river. Landslides, 19(9), 2159--2175.
Richards, J., Woodgate, P., & Skidmore, A. (1987). An explanation of enhanced radar backscattering from flooded forests. International Journal of Remote Sensing, 8(7), 1093--1100.
Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., & Goldstein, R. M. (2000). Synthetic aperture radar interferometry. Proceedings of the IEEE, 88(3), 333--382.
Rotaru, A., Oajdea, D., & Răileanu, P. (2007). Analysis of the landslide movements. International journal of geology, 1(3), 70--79.
Scaioni, M., Longoni, L., Melillo, V., & Papini, M. (2014). Remote sensing for landslide investigations: An overview of recent achievements and perspectives. Remote Sensing, 6(10), 9600--9652.
Sepúlveda, S. A. & Petley, D. N. (2015). Regional trends and controlling factors of fatal landslides in latin america and the caribbean. Natural Hazards and Earth System Sciences, 15(8), 1821--1833.
Solari, L., Del Soldato, M., Raspini, F., Barra, A., Bianchini, S., Confuorto, P., Casagli, N., & Crosetto, M. (2020). Review of satellite interferometry for landslide detection in italy. Remote Sensing, 12(8), 1351.
Segalini, A., Carri, A., & Savi, R. (2017). Role of geotechnical monitoring: state of the art and new perspectives. Geotechnical Society of Bosnia and Herzegovina GEO-EXPO.
Steinberg, L. J., Sengul, H., & Cruz, A. M. (2008). Natech risk and management: an assessment of the state of the art. Natural Hazards, 46, 143--152.
Serna Quintana, C. A. (2011). La naturaleza social de los desastres asociados a inundaciones y deslizamientos en medellín (1930-1990). Historia crítica, (43), 198--223.
Smith, L. C. (1997). Satellite remote sensing of river inundation area, stage, and discharge: A review. Hydrological processes, 11(10), 1427--1439.
Tomás, R., Pagán, J. I., Navarro, J. A., Cano, M., Pastor, J. L., Riquelme, A., Cuevas-González, M., Crosetto, M., Barra, A., Monserrat, O., et al. (2019). Semi-automatic identification and pre-screening of geological--geotechnical deformational processes using persistent scatterer interferometry datasets. Remote Sensing, 11(14), 1675.
Thirugnanam, H., Uhlemann, S., Reghunadh, R., Ramesh, M. V., & Rangan, V. P. (2022). Review of landslide monitoring techniques with iot integration opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5317--5338.
Urgilez Vinueza, A., Handwerger, A. L., Bakker, M., & Bogaard, T. (2022). A new method to detect changes in displacement rates of slow-moving landslides using insar time series. Landslides, 19(9), 2233--2247.
Universidad de los Andes y Área Metropolitana del Valle de Aburrá (2016). Estudio de microzonificación sismica del valle de aburrá. Informe técnico.
van Natijne, A. L., Bogaard, T., van Leijen, F. J., Hanssen, R. F., & Lindenbergh, R. C. (2022). World-wide insar sensitivity index for landslide deformation tracking. International Journal of Applied Earth Observation and Geoinformation, 111, 102829.
Vicari, A., Famiglietti, N. A., Colangelo, G., & Cecere, G. (2019). A comparison of multi temporal interferometry techniques for landslide susceptibility assessment in urban area: an example on stigliano (mt), a town of southern of italy. Geomatics, Natural Hazards and Risk, 10(1), 836--852.
Wang, T., Liao, M., & Perissin, D. (2009). Insar coherence-decomposition analysis. IEEE Geoscience and Remote Sensing Letters, 7(1), 156--160.
Wasowski, J. & Bovenga, F. (2014). Investigating landslides and unstable slopes with satellite multi temporal interferometry: Current issues and future perspectives. Engineering Geology, 174, 103--138.
Wasowski, J. & Pisano, L. (2020). Long-term insar, borehole inclinometer, and rainfall records provide insight into the mechanism and activity patterns of an extremely slow urbanized landslide. Landslides, 17, 445--457.
Wegnüller, U., Werner, C., Strozzi, T., Wiesmann, A., Frey, O., & Santoro, M. (2016). Sentinel-1 support in the gamma software. Procedia Computer Science, 100, 1305--1312.
Werthmann, C., Sapena, M., Kühnl, M., Singer, J., Garcia, C., Menschik, B., Schäfer, H., Schröck, S., Seiler, L., Thuro, K., et al. (2023). Inform@ risk. the development of a prototype for an integrated landslide early warning system in an informal settlement: the case of bello oriente in medellín, colombia. Natural Hazards and Earth System Sciences Discussions, 2023, 1--42.
White, L., Brisco, B., Dabboor, M., Schmitt, A., & Pratt, A. (2015). A collection of sar methodologies for monitoring wetlands. Remote sensing, 7(6), 7615--7645.
Xie, M., Zhao, W., Ju, N., He, C., Huang, H., & Cui, Q. (2020). Landslide evolution assessment based on insar and real-time monitoring of a large reactivated landslide, wenchuan, china. Engineering Geology, 277, 105781.
Yao, J., Yao, X., & Liu, X. (2022). Landslide detection and mapping based on sbas-insar and ps-insar: A case study in gongjue county, tibet, china. Remote Sensing, 14(19), 4728.
Yi, Y., Xu, X., Xu, G., & Gao, H. (2023). Rapid mapping of slow-moving landslides using an automated sar processing platform (hyp3) and stacking-insar method. Remote Sensing, 15(6), 1611.
Yu, H., Lan, Y., Yuan, Z., Xu, J., & Lee, H. (2019). Phase unwrapping in insar: A review. IEEE Geoscience and Remote Sensing Magazine, 7(1), 40--58.
Yunjun, Z., Fattahi, H., & Amelung, F. (2019). Small baseline insar time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences, 133, 104331.
Yamaguchi, Y. (2020). Polarimetric SAR imaging: theory and applications. CRC Press.
Yagüe-Martínez, N., Prats-Iraola, P., Gonzalez, F. R., Brcic, R., Shau, R., Geudtner, D., Eineder, M., & Bamler, R. (2016). Interferometric processing of sentinel-1 tops data. IEEE transactions on geoscience and remote sensing, 54(4), 2220--2234.
Zebker, H. A., Villasenor, J., et al. (1992). Decorrelation in interferometric radar echoes. IEEE Transactions on geoscience and remote sensing, 30(5), 950--959.
Zhang, L., Dai, K., Deng, J., Ge, D., Liang, R., Li, W., & Xu, Q. (2021). Identifying potential landslides by stacking-insar in southwestern china and its performance comparison with sbas-insar. Remote Sensing, 13(18), 3662.
Zhang, Y., Meng, X., Dijkstra, T., Jordan, C., Chen, G., Zeng, R., & Novellino, A. (2020). Forecasting the magnitude of potential landslides based on insar techniques. Remote Sensing of Environment, 241, 111738.
Zhang, Z., Duan, P., Li, J., Chen, D., Peng, K., & Fan, C. (2023). A time-series insar processing chain for wide-area geohazard identification. Natural Hazards, 118(1), 691--707.
Zheng, Y. & Zebker, H. A. (2017). Phase correction of single-look complex radar images for user-friendly efficient interferogram formation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6), 2694--2701.
Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., et al. (2022). Esa worldcover 10 m 2021 v200.
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial-CompartirIgual 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-CompartirIgual 4.0 Internacional
http://creativecommons.org/licenses/by-nc-sa/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv 1 recursos en línea (83 páginas)
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.country.none.fl_str_mv Valle de Aburrá (Colombia)
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Medellín - Minas - Maestría en Medio Ambiente y Desarrollo
dc.publisher.faculty.spa.fl_str_mv Facultad de Minas
dc.publisher.place.spa.fl_str_mv Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Medellín
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/86910/1/license.txt
https://repositorio.unal.edu.co/bitstream/unal/86910/2/1037664280.2024.pdf
https://repositorio.unal.edu.co/bitstream/unal/86910/3/1037664280.2024.pdf.jpg
bitstream.checksum.fl_str_mv eb34b1cf90b7e1103fc9dfd26be24b4a
09ceb94204a467a95d6456d9049554d2
a8113e0b7aa7eac640cb5a624d3557e4
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1814090214238322688
spelling Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Aristizábal Giraldo, Edier Vicentefc0f511b018ee39d8c368b91780e0fa7Ospina Urán, Alejandrof59e0d2845b023e5b66f81dcfbe96366Investigación en Geología Ambiental GeaOspina Uran, Alejandro2024-10-07T20:37:12Z2024-10-07T20:37:12Z2024https://repositorio.unal.edu.co/handle/unal/86910Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/En este trabajo se evalúa el uso de técnicas de Interferometría de Radar de Apertura Sintética (InSAR, por sus iniciales en inglés) para la detección de movimientos en masa en ambientes tropicales de montaña, específicamente en los Andes colombianos. Además, se propone una metodología para la integración de estas técnicas en un sistema de alertas tempranas en zona urbana-suburbana tomando como área de estudio el Valle de Aburrá, Colombia. El documento se estructura en cuatro artículos científicos independientes entre sí, los cuales serán potencialmente sometidos a publicación. El Artículo 1 presenta el marco teórico para la aplicación de técnicas InSAR en ambientes tropicales de montaña. Este primer artículo busca aportar al conocimiento de InSAR a la literatura en español. El Artículo 2 aborda la aplicación exitosa de InSAR a escala regional y la detección de múltiples zonas de deformación del terreno, asociadas a movimientos en masa en el área de estudio. El Artículo 3 se centra en un caso de estudio en el Valle de Aburrá, donde se aplica InSAR a un movimiento en masa que ha causado graves afectaciones desde 2018, encontrando que la zona de deformación supera en más de diez veces el perímetro definido inicialmente con recorridos de campo e instrumentación geotécnica tradicional. Este análisis permitió aproximar la extensión real de la zona de deformación, lo cual no había sido posible debido a las limitaciones del monitoreo geotécnico, además, encontrar relaciones entre los desplazamientos InSAR e información pluviométrica. Finalmente, el Artículo 4 presenta una propuesta metodológica conceptual para integrar InSAR en un sistema de alertas tempranas regional. Se concluye que InSAR es una herramienta eficaz para detectar movimientos en masa en los Andes colombianos y que su aplicación tendría positivos impactos en la gestión del riesgo de desastres.This work evaluates the use of Interferometric Synthetic Aperture Radar (InSAR) techniques for the detection of landslides in tropical mountain environments, specifically in the Colombian Andes. Additionally, a methodology is proposed for integrating these techniques into an early warning system in urban-suburban areas, with the Aburrá Valley, Colombia, as the study area. The document is structured into four independent scientific articles. Article 1 presents the theoretical framework for the application of InSAR techniques in tropical mountain environments. This first article aims to contribute to the knowledge of InSAR in the Spanish literature. Article 2 addresses the successful application of InSAR on a regional scale and the detection of multiple areas of ground deformation associated with landslides in the study area. Article 3 focuses on a case study in the Aburrá Valley, where InSAR is applied to a landslide that has caused significant impacts since 2018, revealing that the deformation area exceeds the initially defined perimeter from field surveys and traditional geotechnical instrumentation by more than ten times. This analysis allowed for an estimation of the actual extent of the deformation area, which had not been possible due to the limitations of geotechnical monitoring. Additionally, it identified relationships between InSAR displacements and rainfall data. Finally, Article 4 presents a conceptual methodological proposal for integrating InSAR into a regional early warning system. It is concluded that InSAR is an effective tool for detecting mass movements in the Colombian Andes and that its application would have positive impacts on disaster risk management.MaestríaMagíster en Medio Ambiente y DesarrolloGestión del riesgo de desastresÁrea Curricular de Medio Ambiente1 recursos en línea (83 páginas)application/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Medio Ambiente y DesarrolloFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería550 - Ciencias de la tierraRiesgo ambientalInterferometríaDesgaste de masaMovimientos en masaTeledetecciónInSARCoherenciaSistemas de Alerta TempranaColombiaProcesamiento InSARLandslidesCoherenceRemote Sensing TecniquesInSAREarly Warning SystemRiesgo geológicoEvaluación del uso de técnicas InSAR para el monitoreo y detección de movimientos en masa en un sistema de alertas tempranas en los Andes colombianosEvaluation of InSAR Techniques for Monitoring and Detection of Landslides in an Early Warning System in the Colombian AndesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesishttp://purl.org/coar/version/c_71e4c1898caa6e32Texthttp://purl.org/redcol/resource_type/TMValle de Aburrá (Colombia)Agram, P., Jolivet, R., Riel, B., Lin, Y., Simons, M., Hetland, E., Doin, M.-P., & Lasserre, C. (2013). New radar interferometric time series analysis toolbox released. Eos, Transactions American Geophysical Union, 94(7), 69--70.Agram, P. & Simons, M. (2015). A noise model for insar time series. Journal of Geophysical Research: Solid Earth, 120(4), 2752--2771.Aristizábal, E. & Sánchez, O. (2020). Spatial and temporal patterns and the socioeconomic impacts of landslides in the tropical and mountainous colombian andes. Disasters, 44(3), 596--618.Aslan, G., Foumelis, M., Raucoules, D., De Michele, M., Bernardie, S., & Cakir, Z. (2020). Landslide mapping and monitoring using persistent scatterer interferometry (psi) technique in the french alps. Remote Sensing, 12(8), 1305.Acosta, J. H. C. (2011). Las avenidas torrenciales: una amenaza potencial en el Valle de Aburrá. Gestión y ambiente, 14(3), 45--50.Aristizábal, E. & Gómez, J. (2007). Inventario de emergencias y desastres en el valle de aburrá originados por fenómenos naturales y antrópicos en el período 1880-2007. Gestión y ambiente, 10(2), 17--30.Aristizábal, E. & Yokota, S. (2006). Geomorfología aplicada a la ocurrencia de deslizamientos en el valle de aburrá. Dyna, 73(149), 5--16.Agapiou, A. & Lysandrou, V. (2020). Detecting displacements within archaeological sites in cyprus after a 5.6 magnitude scale earthquake event through the hybrid pluggable processing pipeline (hyp3) cloud-based system and sentinel-1 interferometric synthetic aperture radar (insar) analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6115--6123.Agustan, A., Ito, T., Kriswati, E., Priyadi, H., Sadmono, H., & Hernawati, R. (2022). Time series insar analysis over jakarta metropolitan area. In 2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) (pp. 30--35).: IEEE.Aristizábal, E., Gamboa, M. F., & Leoz, F. J. (2010). Sistema de alerta temprana por movimientos en masa inducidos por lluvia para el valle de aburrá, colombia. Revista EIA, (13), 155--169.Barra, A., Reyes-Carmona, C., Herrera, G., Galve, J. P., Solari, L., Mateos, R. M., Azañón, J. M., Béjar-Pizarro, M., López-Vinielles, J., Palamà, R., et al. (2022). From satellite interferometry displacements to potential damage maps: A tool for risk reduction and urban planning. Remote Sensing of Environment, 282, 113294.Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential sar interferograms. IEEE Transactions on geoscience and remote sensing, 40(11), 2375--2383.Biggs, J., Wright, T., Lu, Z., & Parsons, B. (2007). Multi-interferogram method for measuring interseismic deformation: Denali fault, alaska. Geophysical Journal International, 170(3), 1165--1179.Bayer, B., Simoni, A., Mulas, M., Corsini, A., & Schmidt, D. (2018). Deformation responses of slow moving landslides to seasonal rainfall in the northern apennines, measured by insar. Geomorphology, 308, 293--306.Bekaert, D. P., Handwerger, A. L., Agram, P., & Kirschbaum, D. B. (2020). Insar-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to nepal. Remote Sensing of Environment, 249, 111983.Béjar-Pizarro, M., Notti, D., Mateos, R. M., Ezquerro, P., Centolanza, G., Herrera, G., Bru, G., Sanabria, M., Solari, L., Duro, J., et al. (2017). Mapping vulnerable urban areas affected by slow-moving landslides using sentinel-1 insar data. Remote Sensing, 9(9), 876.Biggs, J. & Wright, T. J. (2020). How satellite insar has grown from opportunistic science to routine monitoring over the last decade. Nature Communications, 11(1), 3863.Campbell, J. B. & Wynne, R. H. (2011). Introduction to remote sensing. Guilford press.Casagli, N., Intrieri, E., Tofani, V., Gigli, G., & Raspini, F. (2023). Landslide detection, monitoring and prediction with remote-sensing techniques. Nature Reviews Earth & Environment, 4(1), 51--64.Cascini, L., Fornaro, G., & Peduto, D. (2009). Analysis at medium scale of low-resolution dinsar data in slow-moving landslide-affected areas. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 598--611.Chen, X., Tessari, G., Fabris, M., Achilli, V., & Floris, M. (2021). Comparison between ps and sbas insar techniques in monitoring shallow landslides. Understanding and Reducing Landslide Disaster Risk: Volume 3 Monitoring and Early Warning 5th, (pp. 155--161).Cigna, F., Bateson, L. B., Jordan, C. J., & Dashwood, C. (2014). Simulating sar geometric distortions and predicting persistent scatterer densities for ers-1/2 and envisat c-band sar and insar applications: Nationwide feasibility assessment to monitor the landmass of great britain with sar imagery. Remote Sensing of Environment, 152, 441--466.Cigna, F., Esquivel Ramírez, R., & Tapete, D. (2021). Accuracy of sentinel-1 psi and sbas insar displacement velocities against gnss and geodetic leveling monitoring data. Remote Sensing, 13(23), 4800.Closson, D. & Milisavljevic, N. (2017). Insar coherence and intensity changes detection. Mine Action-The Research Experience of the Royal Military Academy of Belgium.Cloude, S. R. & Papathanassiou, K. P. (1998). Polarimetric sar interferometry. IEEE Transactions on geoscience and remote sensing, 36(5), 1551--1565.Colesanti, C. & Wasowski, J. (2006). Investigating landslides with space-borne synthetic aperture radar (sar) interferometry. Engineering geology, 88(3-4), 173--199.Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., & Crippa, B. (2016). Persistent scatterer interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 78--89.Crosetto, M., Solari, L., Mróz, M., Balasis-Levinsen, J., Casagli, N., Frei, M., Oyen, A., Moldestad, D. A., Bateson, L., Guerrieri, L., et al. (2020). The evolution of wide-area dinsar: From regional and national services to the european ground motion service. Remote Sensing, 12(12), 2043.Cutrona, L. (1990). Synthetic aperture radar, volume 2. McGraw-Hill New York.Correa, A. M., Martens, U., Restrepo, J. J., Ordóñez-Carmona, O., & Pimentel, M. M. (2005). Subdivisión de las metamorfitas básicas de los alrededores de medellín--cordillera central de colombia. Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 29(112), 325--343.Cruden, D. M. (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(1), 27--29.Cao, Z. & Wang, T. (2022). Water-temperature controlled deformation patterns in heifangtai loess terraces revealed by wavelet analysis of insar time series and hydrological parameters. Frontiers in Environmental Science, 10, 957339.Cai, J., Liu, G., Jia, H., Zhang, B., Wu, R., Fu, Y., Xiang, W., Mao, W., Wang, X., & Zhang, R. (2022). A new algorithm for landslide dynamic monitoring with high temporal resolution by kalman filter integration of multiplatform time-series insar processing. International Journal of Applied Earth Observation and Geoinformation, 110, 102812.Ding, X.-l., Li, Z.-w., Zhu, J.-j., Feng, G.-c., & Long, J.-p. (2008). Atmospheric effects on insar measurements and their mitigation. Sensors, 8(9), 5426--5448.Dai, K., Deng, J., Xu, Q., Li, Z., Shi, X., Hancock, C., Wen, N., Zhang, L., & Zhuo, G. (2022). Interpretation and sensitivity analysis of the insar line of sight displacements in landslide measurements. GIScience & Remote Sensing, 59(1), 1226--1242.Dilley, M. (2005). Natural disaster hotspots: a global risk analysis, volume 5. World Bank Publications.Doerry, A. W. (2006). Performance limits for Synthetic Aperture Radar. Technical report, Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA, USADoin, M.-P., Lasserre, C., Peltzer, G., Cavalié, O., & Doubre, C. (2009). Corrections of stratified tropospheric delays in sar interferometry: Validation with global atmospheric models. Journal of Applied Geophysics, 69(1), 35--50.Du, Y., Zhang, L., Feng, G., Lu, Z., & Sun, Q. (2016). On the accuracy of topographic residuals retrieved by mtinsar. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 1053--1065.Duan, H., Li, Y., Li, B., & Li, H. (2022). Fast insar time-series analysis method in a full-resolution sar coordinate system: A case study of the yellow river delta. Sustainability, 14(17), 10597.El-Darymli, K., McGuire, P., Gill, E., Power, D., & Moloney, C. (2014). Understanding the significance of radiometric calibration for synthetic aperture radar imagery. In 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1--6).: IEEE.Eriksen, H. Ø., Lauknes, T. R., Larsen, Y., Corner, G. D., Bergh, S. G., Dehls, J., & Kierulf, H. P. (2017). Visualizing and interpreting surface displacement patterns on unstable slopes using multi-geometry satellite sar interferometry (2d insar). Remote Sensing of Environment, 191, 297--312.Fattahi, H. & Amelung, F. (2013). Dem error correction in insar time series. IEEE Transactions on Geoscience and Remote Sensing, 51(7), 4249--4259.Ferretti, A., Monti-Guarnieri, A., Prati, C., Rocca, F., & Massonet, D. (2007). InSAR principles-guidelines for SAR interferometry processing and interpretation, volume 19.Ferretti, A., Prati, C., & Rocca, F. (1999). Permanent scatterers in sar interferometry. In IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS’99 (Cat. No. 99CH36293), volume 3 (pp. 1528--1530).: IEEE.Fobert, M.-A., Singhroy, V., & Spray, J. G. (2021). Insar monitoring of landslide activity in dominica. Remote Sensing, 13(4).Froude, M. J. & Petley, D. N. (2018). Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 18(8), 2161--2181.Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., et al. (2007). The shuttle radar topography mission. Reviews of geophysics, 45(2).García, C. (2005). 5. el deslizamiento de villatina. DESASTRES, (pp.5̃5).Fikri, S., Anjasmara, I. M., & Taufik, M. (2021). Application of different coherence threshold on ps-insar technique for monitoring deformation on the lusi affected area during 2017 and 2019. In IOP Conference Series: Earth and Environmental Science, volume 731 (pp. 012036).: IOP Publishing.Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I., Rossi, M., & Melillo, M. (2020). Geographical landslide early warning systems. Earth-Science Reviews, 200, 102973.Hogenson, K., Arko, S. A., Buechler, B., Hogenson, R., Herrmann, J., & Geiger, A. (2016). Hybrid pluggable processing pipeline (hyp3): A cloud-based infrastructure for generic processing of sar data. In Agu fall meeting abstracts, volume 2016 (pp. IN21B--1740).Hrysiewicz, A., Wang, X., & Holohan, E. P. (2023). Ez-insar: An easy-to-use open-source toolbox for mapping ground surface deformation using satellite interferometric synthetic aperture radar. Earth Science Informatics, 16(2), 1929--1945.Huggel, C., Khabarov, N., Obersteiner, M., & Ramírez, J. M. (2010). Implementation and integrated numerical modeling of a landslide early warning system: a pilot study in colombia. Natural Hazards, 52, 501--518.Hungr, O., Leroueil, S., & Picarelli, L. (2014). The varnes classification of landslide types, an update. Landslides, 11, 167--194.Hermelin, M. (2007). Valle de aburrá:?‘ quo vadis? Gestión y ambiente, 10(2), 07--16.Hrysiewicz, A., Wang, X., & Holohan, E. P. (2023). Ez-insar: An easy-to-use open-source toolbox for mapping ground surface deformation using satellite interferometric synthetic aperture radar. Earth Science Informatics, 16(2), 1929--1945.He, K., Zhang, X., Li, Z., Jiang, W., Zhou, J., & Han, B. (2024). A mask r-cnn network for wide-area mining subsidence automatic detection with insar observations. IEEE Transactions on Geoscience and Remote Sensing.Handwerger, A. L., Fielding, E. J., Huang, M.-H., Bennett, G. L., Liang, C., & Schulz, W. H. (2019). Widespread initiation, reactivation, and acceleration of landslides in the northern california coast ranges due to extreme rainfall. Journal of Geophysical Research: Earth Surface, 124(7), 1782--1797.Hoeser, T. (2018). Analysing the Capabilities and Limitations of InSAR using Sentinel-1 Data for Landslide Detection and Monitoring. PhD thesis.Jacquemart, M. & Tiampo, K. (2021). Leveraging time series analysis of radar coherence and normalized difference vegetation index ratios to characterize pre-failure activity of the mud creek landslide, california. Natural Hazards and Earth System Sciences, 21(2), 629--642.Jiang, M., Li, Z., Ding, X., Zhu, J., & Feng, G. (2011). Modeling minimum and maximum detectable deformation gradients of interferometric sar measurements. International journal of applied earth observation and geoinformation, 13(5), 766--777.Jolivet, R., Grandin, R., Lasserre, C., Doin, M.-P., & Peltzer, G. (2011). Systematic insar tropospheric phase delay corrections from global meteorological reanalysis data. Geophysical Research Letters, 38(17).Intrieri, E., Gigli, G., Mugnai, F., Fanti, R., & Casagli, N. (2012). Design and implementation of a landslide early warning system. Engineering Geology, 147, 124--136.Kerle, N., Janssen, L. L., Huurneman, G. C., Bakker, W., Grabmaier, K., van der Meer, F., Prakash, A., Tempfli, K., Gieske, A., Hecker, C., et al. (2004). Principles of remote sensing: an introductory textbook.Khalil, R. Z. et al. (2018). Insar coherence-based land cover classification of okara, pakistan. The Egyptian Journal of Remote Sensing and Space Science, 21, S23--S28.Khorram, S., Koch, F. H., Van der Wiele, C. F., & Nelson, S. A. (2012). Remote sensing. Springer Science & Business Media.Kropatsch, W. G. & Strobl, D. (1990). The generation of sar layover and shadow maps from digital elevation models. IEEE Transactions on Geoscience and Remote Sensing, 28(1), 98--107.Kelman, I. & Glantz, M. H. (2014). Early warning systems defined. Reducing disaster: Early warning systems for climate change, (pp. 89--108).Li, S., Xu, W., & Li, Z. (2022). Review of the sbas insar time-series algorithms, applications, and challenges. Geodesy and Geodynamics, 13(2), 114--126.Liang, J., Dong, J., Zhang, S., Zhao, C., Liu, B., Yang, L., Yan, S., & Ma, X. (2022). Discussion on insar identification effectivity of potential landslides and factors that influence the effectivity. Remote Sensing, 14(8), 1952.Lacasse, S., Nadim, F., & Kalsnes, B. (2005). Living with landslide risk. Geotechnical Engineering Journal of the SEAGS & AGSSEA, 41(4)Liu, Y., Qiu, H., Yang, D., Liu, Z., Ma, S., Pei, Y., Zhang, J., & Tang, B. (2022). Deformation responses of landslides to seasonal rainfall based on insar and wavelet analysis. Landslides, (pp. 1--12).Lu, Z. & Kim, J. (2021). A framework for studying hydrology-driven landslide hazards in northwestern us using satellite insar, precipitation and soil moisture observations: early results and future directions. GeoHazards, 2(2), 17--40.Lanari, R., Casu, F., Manzo, M., Zeni, G., Berardino, P., Manunta, M., & Pepe, A. (2007). An overview of the small baseline subset algorithm: A dinsar technique for surface deformation analysis. Deformation and Gravity Change: Indicators of Isostasy, Tectonics, Volcanism, and Climate Change, (pp. 637--661).Leroueil, S. (2001). Natural slopes and cuts: movement and failure mechanisms. Géotechnique, 51(3), 197--243.Manavalan, R. (2017). Sar image analysis techniques for flood area mapping-literature survey. Earth Science Informatics, 10(1), 1--14.Marr, W. A. (2007). Why monitor performance? In FMGM 2007: Seventh International Symposium Field Measurements in Geomechanics (pp. 1--27).Massonnet, D. & Feigl, K. L. (1998). Radar interferometry and its application to changes in the earth’s surface. Reviews of geophysics, 36(4), 441--500.Meyer, F. (2019). Spaceborne synthetic aperture radar: Principles, data access, and basic processing techniques. Synthetic Aperture Radar (SAR) Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation, (pp. 21--64).Mondini, A. C., Chang, K.-T., & Yin, H.-Y. (2011). Combining multiple change detection indices for mapping landslides triggered by typhoons. Geomorphology, 134(3-4), 440--451.Mondini, A. C., Guzzetti, F., Chang, K.-T., Monserrat, O., Martha, T. R., & Manconi, A. (2021). Landslide failures detection and mapping using synthetic aperture radar: Past, present and future. Earth-Science Reviews, 216, 103574.Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., & Papathanassiou, K. P. (2013). A tutorial on synthetic aperture radar. IEEE Geoscience and remote sensing magazine, 1(1), 6--43.Moretto, S., Bozzano, F., & Mazzanti, P. (2021). The role of satellite insar for landslide forecasting: Limitations and openings. Remote sensing, 13(18), 3735.Morishita, Y., Lazecky, M., Wright, T. J., Weiss, J. R., Elliott, J. R., & Hooper, A. (2020). Licsbas: An open-source insar time series analysis package integrated with the licsar automated sentinel-1 insar processor. Remote Sensing, 12(3), 424.Maya, M. & González, H. (1995). Unidades litodémicas en la cordillera central de colombia. Boletín geológico, 35(2-3), 44--57.Mejía, N. (1984). Geología y geoquímica de las planchas 130 (santafé de antioquia) y 146 (medellín occidental), escala 1: 100.000, memoria explicativa. Instituto Colombiano de Geología y Minería (INGEOMINAS).Mirmazloumi, S. M., Gambin, A. F., Palamà, R., Crosetto, M., Wassie, Y., Navarro, J. A., Barra, A., & Monserrat, O. (2022). Supervised machine learning algorithms for ground motion time series classification from insar data. Remote Sensing, 14(15), 3821.Notti, D., Meisina, C., Zucca, F., Colombo, A., et al. (2011). Models to predict persistent scatterers data distribution and their capacity to register movement along the slope. In Fringe 2011 Workshop (pp. 19--23).Medina-Cetina, Z. & Nadim, F. (2008). Stochastic design of an early warning system. Georisk, 2(4), 223--236.Plank, S., Singer, J., Minet, C., & Thuro, K. (2012). Pre-survey suitability evaluation of the differential synthetic aperture radar interferometry method for landslide monitoring. International journal of remote sensing, 33(20), 6623--6637.Petley, D. (2012). Global patterns of loss of life from landslides. Geology, 40(10), 927--930.Plank, S., Singer, J., Thuro, K., & Minet, C. (2010). The suitability of the differential radar interferometry method for deformation monitoring of landslides—a new gis based evaluation tool. In Proceedings of the 11th IAEG Congress Geologically Active, Auckland, New Zealand (pp. 5--10).Ren, K., Yao, X., Li, R., Zhou, Z., Yao, C., & Jiang, S. (2022). 3d displacement and deformation mechanism of deep-seated gravitational slope deformation revealed by insar: a case study in wudongde reservoir, jinsha river. Landslides, 19(9), 2159--2175.Richards, J., Woodgate, P., & Skidmore, A. (1987). An explanation of enhanced radar backscattering from flooded forests. International Journal of Remote Sensing, 8(7), 1093--1100.Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., & Goldstein, R. M. (2000). Synthetic aperture radar interferometry. Proceedings of the IEEE, 88(3), 333--382.Rotaru, A., Oajdea, D., & Răileanu, P. (2007). Analysis of the landslide movements. International journal of geology, 1(3), 70--79.Scaioni, M., Longoni, L., Melillo, V., & Papini, M. (2014). Remote sensing for landslide investigations: An overview of recent achievements and perspectives. Remote Sensing, 6(10), 9600--9652.Sepúlveda, S. A. & Petley, D. N. (2015). Regional trends and controlling factors of fatal landslides in latin america and the caribbean. Natural Hazards and Earth System Sciences, 15(8), 1821--1833.Solari, L., Del Soldato, M., Raspini, F., Barra, A., Bianchini, S., Confuorto, P., Casagli, N., & Crosetto, M. (2020). Review of satellite interferometry for landslide detection in italy. Remote Sensing, 12(8), 1351.Segalini, A., Carri, A., & Savi, R. (2017). Role of geotechnical monitoring: state of the art and new perspectives. Geotechnical Society of Bosnia and Herzegovina GEO-EXPO.Steinberg, L. J., Sengul, H., & Cruz, A. M. (2008). Natech risk and management: an assessment of the state of the art. Natural Hazards, 46, 143--152.Serna Quintana, C. A. (2011). La naturaleza social de los desastres asociados a inundaciones y deslizamientos en medellín (1930-1990). Historia crítica, (43), 198--223.Smith, L. C. (1997). Satellite remote sensing of river inundation area, stage, and discharge: A review. Hydrological processes, 11(10), 1427--1439.Tomás, R., Pagán, J. I., Navarro, J. A., Cano, M., Pastor, J. L., Riquelme, A., Cuevas-González, M., Crosetto, M., Barra, A., Monserrat, O., et al. (2019). Semi-automatic identification and pre-screening of geological--geotechnical deformational processes using persistent scatterer interferometry datasets. Remote Sensing, 11(14), 1675.Thirugnanam, H., Uhlemann, S., Reghunadh, R., Ramesh, M. V., & Rangan, V. P. (2022). Review of landslide monitoring techniques with iot integration opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5317--5338.Urgilez Vinueza, A., Handwerger, A. L., Bakker, M., & Bogaard, T. (2022). A new method to detect changes in displacement rates of slow-moving landslides using insar time series. Landslides, 19(9), 2233--2247.Universidad de los Andes y Área Metropolitana del Valle de Aburrá (2016). Estudio de microzonificación sismica del valle de aburrá. Informe técnico.van Natijne, A. L., Bogaard, T., van Leijen, F. J., Hanssen, R. F., & Lindenbergh, R. C. (2022). World-wide insar sensitivity index for landslide deformation tracking. International Journal of Applied Earth Observation and Geoinformation, 111, 102829.Vicari, A., Famiglietti, N. A., Colangelo, G., & Cecere, G. (2019). A comparison of multi temporal interferometry techniques for landslide susceptibility assessment in urban area: an example on stigliano (mt), a town of southern of italy. Geomatics, Natural Hazards and Risk, 10(1), 836--852.Wang, T., Liao, M., & Perissin, D. (2009). Insar coherence-decomposition analysis. IEEE Geoscience and Remote Sensing Letters, 7(1), 156--160.Wasowski, J. & Bovenga, F. (2014). Investigating landslides and unstable slopes with satellite multi temporal interferometry: Current issues and future perspectives. Engineering Geology, 174, 103--138.Wasowski, J. & Pisano, L. (2020). Long-term insar, borehole inclinometer, and rainfall records provide insight into the mechanism and activity patterns of an extremely slow urbanized landslide. Landslides, 17, 445--457.Wegnüller, U., Werner, C., Strozzi, T., Wiesmann, A., Frey, O., & Santoro, M. (2016). Sentinel-1 support in the gamma software. Procedia Computer Science, 100, 1305--1312.Werthmann, C., Sapena, M., Kühnl, M., Singer, J., Garcia, C., Menschik, B., Schäfer, H., Schröck, S., Seiler, L., Thuro, K., et al. (2023). Inform@ risk. the development of a prototype for an integrated landslide early warning system in an informal settlement: the case of bello oriente in medellín, colombia. Natural Hazards and Earth System Sciences Discussions, 2023, 1--42.White, L., Brisco, B., Dabboor, M., Schmitt, A., & Pratt, A. (2015). A collection of sar methodologies for monitoring wetlands. Remote sensing, 7(6), 7615--7645.Xie, M., Zhao, W., Ju, N., He, C., Huang, H., & Cui, Q. (2020). Landslide evolution assessment based on insar and real-time monitoring of a large reactivated landslide, wenchuan, china. Engineering Geology, 277, 105781.Yao, J., Yao, X., & Liu, X. (2022). Landslide detection and mapping based on sbas-insar and ps-insar: A case study in gongjue county, tibet, china. Remote Sensing, 14(19), 4728.Yi, Y., Xu, X., Xu, G., & Gao, H. (2023). Rapid mapping of slow-moving landslides using an automated sar processing platform (hyp3) and stacking-insar method. Remote Sensing, 15(6), 1611.Yu, H., Lan, Y., Yuan, Z., Xu, J., & Lee, H. (2019). Phase unwrapping in insar: A review. IEEE Geoscience and Remote Sensing Magazine, 7(1), 40--58.Yunjun, Z., Fattahi, H., & Amelung, F. (2019). Small baseline insar time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences, 133, 104331.Yamaguchi, Y. (2020). Polarimetric SAR imaging: theory and applications. CRC Press.Yagüe-Martínez, N., Prats-Iraola, P., Gonzalez, F. R., Brcic, R., Shau, R., Geudtner, D., Eineder, M., & Bamler, R. (2016). Interferometric processing of sentinel-1 tops data. IEEE transactions on geoscience and remote sensing, 54(4), 2220--2234.Zebker, H. A., Villasenor, J., et al. (1992). Decorrelation in interferometric radar echoes. IEEE Transactions on geoscience and remote sensing, 30(5), 950--959.Zhang, L., Dai, K., Deng, J., Ge, D., Liang, R., Li, W., & Xu, Q. (2021). Identifying potential landslides by stacking-insar in southwestern china and its performance comparison with sbas-insar. Remote Sensing, 13(18), 3662.Zhang, Y., Meng, X., Dijkstra, T., Jordan, C., Chen, G., Zeng, R., & Novellino, A. (2020). Forecasting the magnitude of potential landslides based on insar techniques. Remote Sensing of Environment, 241, 111738.Zhang, Z., Duan, P., Li, J., Chen, D., Peng, K., & Fan, C. (2023). A time-series insar processing chain for wide-area geohazard identification. Natural Hazards, 118(1), 691--707.Zheng, Y. & Zebker, H. A. (2017). Phase correction of single-look complex radar images for user-friendly efficient interferogram formation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6), 2694--2701.Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., et al. (2022). Esa worldcover 10 m 2021 v200.EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86910/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1037664280.2024.pdf1037664280.2024.pdfTesis de Maestría en Medio Ambiente y Desarrolloapplication/pdf2347765https://repositorio.unal.edu.co/bitstream/unal/86910/2/1037664280.2024.pdf09ceb94204a467a95d6456d9049554d2MD52THUMBNAIL1037664280.2024.pdf.jpg1037664280.2024.pdf.jpgGenerated Thumbnailimage/jpeg4973https://repositorio.unal.edu.co/bitstream/unal/86910/3/1037664280.2024.pdf.jpga8113e0b7aa7eac640cb5a624d3557e4MD53unal/86910oai:repositorio.unal.edu.co:unal/869102024-10-08 00:36:34.288Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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