يعرض 1 - 20 نتائج من 410 نتيجة بحث عن '"Riesgo geológico"', وقت الاستعلام: 0.55s تنقيح النتائج
  1. 1
    Academic Journal
  2. 2
    Academic Journal
  3. 3
    Academic Journal
  4. 4
    Book

    المصدر: Ediciones UO

    مصطلحات موضوعية: ingeniería sísmica, riesgo geológico, terremotos

    وصف الملف: Digital (DA)

  5. 5
    Academic Journal
  6. 6
    Book
  7. 7
    Book
  8. 8
    Conference

    المصدر: Repositorio Institucional INGEMMET ; Instituto Geológico, Minero y Metalúrgico – INGEMMET

    وصف الملف: application/pdf; 3 páginas

  9. 9
    Dissertation/ Thesis
  10. 10
    Academic Journal
  11. 11
    Dissertation/ Thesis
  12. 12
    Dissertation/ Thesis
  13. 13
    Dissertation/ Thesis

    المؤلفون: Ospina Urán, Alejandro

    المساهمون: Aristizábal Giraldo, Edier Vicente, Investigación en Geología Ambiental Gea, Ospina Uran, Alejandro

    جغرافية الموضوع: Valle de Aburrá (Colombia)

    وصف الملف: 1 recursos en línea (83 páginas); application/pdf

    Relation: 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; 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.; 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.; https://repositorio.unal.edu.co/handle/unal/86910; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

  14. 14
    Conference
  15. 15
    Academic Journal
  16. 16
    Conference

    المصدر: Repositorio Institucional INGEMMET ; Instituto Geológico, Minero y Metalúrgico – INGEMMET

    وصف الملف: application/pdf; 1 página

  17. 17
    Book
  18. 18
    Book

    جغرافية الموضوع: Alto Genil, Güéjar Sierra, España

    Time: Alto Genil, Güéjar Sierra, Sierra Nevada, provincia de Granada, España

    Relation: https://pcgr.congressus.es/cimas/acta-final/actas_cimas; Actas del I Congreso Internacional de las Montañas: Sierra Nevada 2018 : 8 al 11 de marzo de 2018 , Granada / Manuel Titos Martínez, Teodoro Luque Martínez, José Manuel Navarro Llena, editores, p.499-516; http://hdl.handle.net/10261/277641

  19. 19
    Academic Journal
  20. 20
    Dissertation/ Thesis

    المساهمون: Lagos Manrique, Alejandro Claudio

    المصدر: Universidad Nacional de Cajamarca ; Repositorio Institucional - UNC

    وصف الملف: application/pdf