Efecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)

The generation of a Digital Elevation Model (DEM) using LiDAR technology is a key tool in hydraulic modelling for water resource management. The main objective of this research was to compare the effect of combinations of flight pathways and filtering techniques on the generation of a DEM and the re...

Full description

Autores:
Moreno Otero, Marina Alejandra
Ramírez Cordero, Juan Camilo
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
spa
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10169
Acceso en línea:
https://hdl.handle.net/11323/10169
https://repositorio.cuc.edu.co/
Palabra clave:
Lidar
Modelo digital de elevación
Modelación hidráulica
Técnicas de filtrado
Vegetación
Digital Elevation Model
hydraulic modeling
Filtering techniques
vegetation
Rights
openAccess
License
Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
id RCUC2_6b9d1307a08851fe9ab1eb7b7ce6b535
oai_identifier_str oai:repositorio.cuc.edu.co:11323/10169
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Efecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)
title Efecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)
spellingShingle Efecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)
Lidar
Modelo digital de elevación
Modelación hidráulica
Técnicas de filtrado
Vegetación
Digital Elevation Model
hydraulic modeling
Filtering techniques
vegetation
title_short Efecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)
title_full Efecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)
title_fullStr Efecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)
title_full_unstemmed Efecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)
title_sort Efecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)
dc.creator.fl_str_mv Moreno Otero, Marina Alejandra
Ramírez Cordero, Juan Camilo
dc.contributor.advisor.none.fl_str_mv Diaz Martínez, Karina Sofía
dc.contributor.author.none.fl_str_mv Moreno Otero, Marina Alejandra
Ramírez Cordero, Juan Camilo
dc.contributor.jury.none.fl_str_mv Daza González, Ricardo
Acuña Robledo, Guillermo Jesús
Villadiego Rojas, Leydis Lucía
dc.subject.proposal.spa.fl_str_mv Lidar
Modelo digital de elevación
Modelación hidráulica
Técnicas de filtrado
Vegetación
topic Lidar
Modelo digital de elevación
Modelación hidráulica
Técnicas de filtrado
Vegetación
Digital Elevation Model
hydraulic modeling
Filtering techniques
vegetation
dc.subject.proposal.eng.fl_str_mv Digital Elevation Model
hydraulic modeling
Filtering techniques
vegetation
description The generation of a Digital Elevation Model (DEM) using LiDAR technology is a key tool in hydraulic modelling for water resource management. The main objective of this research was to compare the effect of combinations of flight pathways and filtering techniques on the generation of a DEM and the response of a hydraulic model in a section of the stream Granada in the municipality of Galapa (Atlántico). For this purpose, a DEM was generated in an area located in the basin of the Granada stream with dense vegetation applying different filtering techniques to LiDAR surveys with different flight patterns. The DEM sections generated for each combination of flight form and filtering techniques using error metrics were then evaluated. Finally, we evaluated the response of hydraulic modelling in HEC-RAS for different precipitation events based on the information obtained from IDEAM. The results suggest that the unification of flight techniques, carried by nearby filtering techniques, produced more consistent DEM
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-05-23T16:49:06Z
dc.date.available.none.fl_str_mv 2023-05-23T16:49:06Z
dc.date.issued.none.fl_str_mv 2023
dc.type.spa.fl_str_mv Trabajo de grado - Pregrado
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TP
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_7a1f
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10169
dc.identifier.instname.spa.fl_str_mv Corporacion Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/10169
https://repositorio.cuc.edu.co/
identifier_str_mv Corporacion Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Abu-Aly, T. R., Pasternack, G. B., Wyrick, J. R., Barker, R., Massa, D., & Johnson, T. (2014). Effects of LiDAR-derived, spatially distributed vegetation roughness on two-dimensional hydraulics in a gravel-cobble river at flows of 0.2 to 20 times bankfull. Geomorphology, 206, 468–482. https://doi.org/10.1016/j.geomorph.2013.10.017
Addy, S., & Wilkinson, M. E. (2019). Representing natural and artificial in‐channel large wood in numerical hydraulic and hydrological models. WIREs Water, 6(6). https://doi.org/10.1002/wat2.1389
Alexander Vega, J. (2019). The Use of Lidar Data and VHR Imagery to Estimate the Effects of Tree Roots on Shallow Landslides Assessment. IOP Conference Series: Materials Science and Engineering, 603(2), 022010. https://doi.org/10.1088/1757-899X/603/2/022010
Alvarez Maya, J., & Ramírez Hernández, S. (2014). MODELACIÓN HIDROLÓGICA CON BASE EN SOFTWARE HEC ®. https://ri-ng.uaq.mx/bitstream/123456789/4064/1/TA-FING-IC-144058-2014.pdf
Álvarez-Villa, O. D., Vélez, J. I., & Poveda, G. (2011). Improved long-term mean annual rainfall fields for Colombia: MEAN ANNUAL RAINFALL FIELDS FOR COLOMBIA. International Journal of Climatology, 31(14), 2194–2212. https://doi.org/10.1002/joc.2232
APPLIED IMAGERY. (2022). Quick Terrain Modeler [Https://appliedimagery.com/author/appliedimagery/]. https://appliedimagery.com/quick-terrain-modeler-v8-4-0-is-available/
Aragón Hernández, J. L., Aguilar Martínez, G. A., Velázquez Ríos, U., Jiménez Magaña, M. R., & Maya Franco, A. (2019). Distribución espacial de variables hidrológicas. Implementación y evaluación de métodos de interpolación. Ingeniería Investigación y Tecnología, 20(2), 1–15. https://doi.org/10.22201/fi.25940732e.2019.20n2.023
Asgari, M., Yang, W., Lindsay, J., Tolson, B., & Dehnavi, M. M. (2022). A review of parallel computing applications in calibrating watershed hydrologic models. Environmental Modelling & Software, 151, 105370. https://doi.org/10.1016/j.envsoft.2022.105370
Avella Rodríguez, M. P. (2022). ANÁLISIS COMPARATIVO DE MODELOS DIGITALES DE TERRENO OBTENIDOS POR TECNOLOGIA LIDAR CON AERONAVE NO TRIPULADA Y POR FOTOGRAMETRIA CON UAV EN ZONA DE MONTAÑA. http://repositorio.uan.edu.co/bitstream/123456789/5967/1/2022_MiryamPaolaAvellaRodr%C3%ADguez.pdf
Baccini, A., & Asner, G. P. (2013). Improving pantropical forest carbon maps with airborne LiDAR sampling. Carbon Management, 4(6), 591–600. https://doi.org/10.4155/cmt.13.66
Brendel, C. E., Dymond, R. L., & Aguilar, M. F. (2020). Integration of quantitative precipitation forecasts with real-time hydrology and hydraulics modeling towards probabilistic forecasting of urban flooding. Environmental Modelling & Software, 134, 104864. https://doi.org/10.1016/j.envsoft.2020.104864
Brown, J. A., Bell, C. D., Hogue, T. S., Higgins, C. P., & Selbig, W. R. (2019). An integrated statistical and deterministic hydrologic model for analyzing trace organic contaminants in commercial and high-density residential stormwater runoff. Science of The Total Environment, 673, 656–667. https://doi.org/10.1016/j.scitotenv.2019.03.327
Bures, L., Roub, R., Sychova, P., Gdulova, K., & Doubalova, J. (2019). Comparison of bathymetric data sources used in hydraulic modelling of floods. Journal of Flood Risk Management, 12(S1). https://doi.org/10.1111/jfr3.12495
Casas, A., Lane, S. N., Yu, D., & Benito, G. (2010). A method for parameterising roughness and topographic sub-grid scale effects in hydraulic modelling from LiDAR data. Hydrology and Earth System Sciences, 14(8), 1567–1579. https://doi.org/10.5194/hess-14-1567-2010
Casas Planes, A., Benito, G., Thorndycraft, V. R., & Rico, M. (2005). Efectos de las fuentes cartográficas en los resultados de la modelación hidráulica de crecidas. Ingeniería Del Agua, 12(4), 309. https://doi.org/10.4995/ia.2005.2567
Chen, H., Liang, Q., Liu, Y., & Xie, S. (2018). Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling. Journal of Hydrology, 559, 56–70. https://doi.org/10.1016/j.jhydrol.2018.01.056
Cooper, H. M., Zhang, C., Davis, S. E., & Troxler, T. G. (2019). Object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades. Environmental Modelling & Software, 112, 179–191. https://doi.org/10.1016/j.envsoft.2018.11.003
Dehvari, A., & Heck, R. J. (2012). Removing non-ground points from automated photo-based DEM and evaluation of its accuracy with LiDAR DEM. Computers & Geosciences, 43, 108–117. https://doi.org/10.1016/j.cageo.2012.02.013
Deng, Q., Li, X., Ni, P., Li, H., & Zheng, Z. (2019). Enet-CRF-Lidar: Lidar and Camera Fusion for Multi-Scale Object Recognition. IEEE Access, 7, 174335–174344. https://doi.org/10.1109/ACCESS.2019.2956492
Dou, X. P., Zhang, Z. L., Gao, X. Y., Zhang, X. Z., Ding, L., & Jiao, J. (2020). Analysis on Changes of Current, Sediment and Riverbed Evolution in Yangtze Estuary for the Past 20 Years. In N. Trung Viet, D. Xiping, & T. Thanh Tung (Eds.), APAC 2019 (pp. 641–647). Springer Singapore. https://doi.org/10.1007/978-981-15-0291-0_88
Dwarakish, G. S., & Ganasri, B. P. (2015). Impact of land use change on hydrological systems: A review of current modeling approaches. Cogent Geoscience, 1(1), 1115691. https://doi.org/10.1080/23312041.2015.1115691
Escobar Villanueva, J. R., Iglesias Martínez, L., & Pérez Montiel, J. I. (2019a). DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors, 19(14), 3205. https://doi.org/10.3390/s19143205
Escobar Villanueva, J. R., Iglesias Martínez, L., & Pérez Montiel, J. I. (2019b). DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors, 19(14), 3205. https://doi.org/10.3390/s19143205
ESRI. (2022). ARCGIS (2.9). https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview
Flórez Gálvez, J. H., & Bolaños Mora, A. (2009). Manual de drenaje para carreteras. https://www.invias.gov.co/index.php/archivo-y-documentos/documentos-tecnicos/especificaciones-tecnicas/984-manual-de-drenaje-para-carreteras/file
Galindo Jiménez, Inés., Laín Huerta, L., & Llorenta Isidor, M. (2008). El estudio y la gestión de los riesgos geológicos. Instituto Geológico y Minero de España.
Gil, C., Villanueva, I., & Godiksen, P. (2006). Efectos de la cartografía sobre la modelización hidráulica bidimensional de crecidas. https://www.researchgate.net/profile/Ignacio-Villanueva-5/publication/323991827_Efectos_de_la_cartografia_sobre_la_modelizacion_hidraulica_bidimensional_de_crecidas/links/5ab6785b45851515f59d8bfb/Efectos-de-la-cartografia-sobre-la-modelizacion-hidraulica-bidimensional-de-crecidas.pdf
González-Díez, A., Barreda-Argüeso, J. A., Rodríguez-Rodríguez, L., & Fernández-Lozano, J. (2021). The use of filters based on the Fast Fourier Transform applied to DEMs for the objective mapping of karstic features. Geomorphology, 385, 107724. https://doi.org/10.1016/j.geomorph.2021.107724
Guo, Q., Li, W., Yu, H., & Alvarez, O. (2010). Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods. Photogrammetric Engineering & Remote Sensing, 76(6), 701–712. https://doi.org/10.14358/PERS.76.6.701
Haile, A. T., & Rientjes, T. H. M. (2005). EFFECTS OF LIDAR DEM RESOLUTION IN FLOOD MODELLING: A MODEL SENTITIVITY STUDY FOR THE CITY OF TEGUCIGALPA, HONDURAS. 6.
Hancock, S., Lewis, P., Foster, M., Disney, M., & Muller, J.-P. (2012). Measuring forests with dual wavelength lidar: A simulation study over topography. Agricultural and Forest Meteorology, 161, 123–133. https://doi.org/10.1016/j.agrformet.2012.03.014 HEC-RAS User’s Manual. (n.d.).
Hou, J., Van Dijk, A. I. J. M., & Renzullo, L. J. (2022). Merging Landsat and airborne LiDAR observations for continuous monitoring of floodplain water extent, depth and volume. Journal of Hydrology, 609, 127684. https://doi.org/10.1016/j.jhydrol.2022.127684
Hui, Z., Jin, S., Cheng, P., Ziggah, Y. Y., Wang, L., Wang, Y., Hu, H., & Hu, Y. (2019). An Active Learning Method for DEM Extraction From Airborne LiDAR Point Clouds. IEEE Access, 7, 89366–89378. https://doi.org/10.1109/ACCESS.2019.2926497
Ii, T. V. H., & Rao, P. (2019). Examination of Computational Precision versus Modeling Complexity for Open Channel Flow with Hydraulic Jump. Journal of Water Resource and Protection, 11(10), 1233–1244. https://doi.org/10.4236/jwarp.2019.1110071
Jaramillo Baltra, R., & Padró García, J. C. (2020). Generación de cartografía a partir de imágenes captadas con dron de ala fija, asociada a proyectos hidráulicos fluviales. GeoFocus Revista Internacional de Ciencia y Tecnología de La Información Geográfica, 26, 93–117. https://doi.org/10.21138/GF.680
Jiménez Sánchez, M. A. (2003). Las inundaciones en la Comunidad de Madrid. https://repositorio.aemet.es/bitstream/20.500.11765/12118/1/InundacionesMadrid_Jimenez_RAM2003.pdf
Klápště, P., Fogl, M., Barták, V., Gdulová, K., Urban, R., & Moudrý, V. (2020). Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds. International Journal of Digital Earth, 13(12), 1672–1694. https://doi.org/10.1080/17538947.2020.1791267
Kuang, Z., Liu, D., Wu, D., Wang, Z., Li, C., & Deng, Q. (2023). Parameter Optimization and Development of Mini Infrared Lidar for Atmospheric Three-Dimensional Detection. Sensors, 23(2), 892. https://doi.org/10.3390/s23020892
Loicq, P., Moatar, F., Jullian, Y., Dugdale, S. J., & Hannah, D. M. (2018). Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data. Science of The Total Environment, 624, 480–490. https://doi.org/10.1016/j.scitotenv.2017.12.129
Maculotti, G., Goti, E., Genta, G., Mazza, L., & Galetto, M. (2022). Uncertainty-based comparison of conventional and surface topography-based methods for wear volume evaluation in pin-on-disc tribological test. Tribology International, 165, 107260. https://doi.org/10.1016/j.triboint.2021.107260
Mason, D. C., Horritt, M. S., Dall’Amico, J. T., Scott, T. R., & Bates, P. D. (2007). Improving River Flood Extent Delineation From Synthetic Aperture Radar Using Airborne Laser Altimetry. IEEE Transactions on Geoscience and Remote Sensing, 45(12), 3932–3943. https://doi.org/10.1109/TGRS.2007.901032
Mayta Rojas, C. A., & Mamani Maquera, E. R. (2018). MODELACIÓN HIDRÁULICA DE LA DEFENSA DE CALANA CON EL FIN DE DETERMINAR LA VULNERABILIDAD ANTE MÁXIMAS AVENIDAS. https://repositorio.upt.edu.pe/bitstream/handle/20.500.12969/549/Mayta_Rojas-Mamani_Maquera.pdf?sequence=1&isAllowed=y
Mazzoleni, M., Paron, P., Reali, A., Juizo, D., Manane, J., & Brandimarte, L. (2020a). Testing UAV-derived topography for hydraulic modelling in a tropical environment. Natural Hazards, 103(1), 139–163. https://doi.org/10.1007/s11069-020-03963-4
Mihu-Pintilie, A., Cîmpianu, C. I., Stoleriu, C. C., Pérez, M. N., & Paveluc, L. E. (2019). Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach. Water, 11(9), 1832. https://doi.org/10.3390/w11091832
Mohtashami, S., Eliasson, L., Hansson, L., Willén, E., Thierfelder, T., & Nordfjell, T. (2022). Evaluating the effect of DEM resolution on performance of cartographic depth-to-water maps, for planning logging operations. International Journal of Applied Earth Observation and Geoinformation, 108, 102728. https://doi.org/10.1016/j.jag.2022.102728
Moudrý, V., Klápště, P., Fogl, M., Gdulová, K., Barták, V., & Urban, R. (2020). Assessment of LiDAR ground filtering algorithms for determining ground surface of non-natural terrain overgrown with forest and steppe vegetation. Measurement, 150, 107047. https://doi.org/10.1016/j.measurement.2019.107047
Muhadi, N. A., Abdullah, A. F., Bejo, S. K., Mahadi, M. R., & Mijic, A. (2020). The Use of LiDAR-Derived DEM in Flood Applications: A Review. Remote Sensing, 12(14), 2308. https://doi.org/10.3390/rs12142308
Munoth, P., & Goyal, R. (2019). Effects of DEM Source, Spatial Resolution and Drainage Area Threshold Values on Hydrological Modeling. Water Resources Management, 33(9), 3303–3319. https://doi.org/10.1007/s11269-019-02303-x
Ogden, F. L. (2021). Geohydrology: Hydrological Modeling. In Encyclopedia of Geology (pp. 457–476). Elsevier. https://doi.org/10.1016/B978-0-08-102908-4.00115-6
O’Neil, G. L., Saby, L., Band, L. E., & Goodall, J. L. (2019). Effects of LiDAR DEM Smoothing and Conditioning Techniques on a Topography‐Based Wetland Identification Model. Water Resources Research, 55(5), 4343–4363. https://doi.org/10.1029/2019WR024784
Pérez Brugal, A., Weber, J. F., & Castellanos, Y. R. (2011). Importancia de los modelos digitales del terreno en la simulación hidráulica de inundaciones. Revista Cubana de Ingeniería, 1(3), 51–60.
Rangel-Buitrago, N. G., Anfuso, G., & Williams, A. T. (2015). Coastal erosion along the Caribbean coast of Colombia: Magnitudes, causes and management. Ocean & Coastal Management, 114, 129–144. https://doi.org/10.1016/j.ocecoaman.2015.06.024
SANCHEZ FORERO, N. (2017). CÁLCULO DE LA PRECIPITACIÓN MEDIA SOBRE LA PENÍNSULA DE LA GUAJIRA USANDO EL MÉTODO THIESSEN.
Sánchez San Román, F. J. (2017). Hidrología Superficial y Subterránea. F. Javier Sánchez San Román.
Sanz-Ramos, M., Bladé, E., González-Escalona, F., Olivares, G., & Aragón-Hernández, J. L. (2021). Interpreting the Manning Roughness Coefficient in Overland Flow Simulations with Coupled Hydrological-Hydraulic Distributed Models. Water, 13(23), 3433. https://doi.org/10.3390/w13233433
Shen, Y., Wang, S., Zhang, B., & Zhu, J. (2022). Development of a stochastic hydrological modeling system for improving ensemble streamflow prediction. Journal of Hydrology, 608, 127683. https://doi.org/10.1016/j.jhydrol.2022.127683
Simoniello, T., Coluzzi, R., Guariglia, A., Imbrenda, V., Lanfredi, M., & Samela, C. (2022). Automatic Filtering and Classification of Low-Density Airborne Laser Scanner Clouds in Shrubland Environments. Remote Sensing, 14(20), 5127. https://doi.org/10.3390/rs14205127
Sokolewicz, M., Wijma, E., Nomden, H., Driessen, T., van Agten, Q., & Carvajal, F. (2016). Flood protection as a key-component of the environmental restoration of Canal del Dique, Colombia. E3S Web of Conferences, 7, 12005. https://doi.org/10.1051/e3sconf/20160712005
Speak, A., Escobedo, F. J., Russo, A., & Zerbe, S. (2020). Total urban tree carbon storage and waste management emissions estimated using a combination of LiDAR, field measurements and an end-of-life wood approach. Journal of Cleaner Production, 256, 120420. https://doi.org/10.1016/j.jclepro.2020.120420
Su, Y., & Guo, Q. (2014a). A practical method for SRTM DEM correction over vegetated mountain areas. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 216–228. https://doi.org/10.1016/j.isprsjprs.2013.11.009
Su, Y., & Guo, Q. (2014b). A practical method for SRTM DEM correction over vegetated mountain areas. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 216–228. https://doi.org/10.1016/j.isprsjprs.2013.11.009
Tkáč, M., & Mésároš, P. (2019). Utilizing drone technology in the civil engineering. Selected Scientific Papers - Journal of Civil Engineering, 14(1), 27–37. https://doi.org/10.1515/sspjce-2019-0003
US Army Corps of Engineers,Hydrologic Engineering Center. (2022). HEC HMS (4.10.0). https://www.hec.usace.army.mil/software/hec-hms/
Valencia Ortiz, J. A., & Martínez-Graña, A. M. (2023). Morphometric Evaluation and Its Incidence in the Mass Movements Present in the Chicamocha Canyon, Colombia. Sustainability, 15(2), 1140. https://doi.org/10.3390/su15021140
Veeck, S., da Costa, F. F., Correia Lima, D. L., da Paz, A. R., & Allasia Piccilli, D. G. (2020). Scale dynamics of the HIDROPIXEL high-resolution DEM-based distributed hydrologic modeling approach. Environmental Modelling & Software, 127, 104695. https://doi.org/10.1016/j.envsoft.2020.104695
Wan, Y., Chen, J., Xu, C.-Y., Xie, P., Qi, W., Li, D., & Zhang, S. (2021). Performance dependence of multi-model combination methods on hydrological model calibration strategy and ensemble size. Journal of Hydrology, 603, 127065. https://doi.org/10.1016/j.jhydrol.2021.127065
Wawrzyniak, V., Allemand, P., Bailly, S., Lejot, J., & Piégay, H. (2017). Coupling LiDAR and thermal imagery to model the effects of riparian vegetation shade and groundwater inputs on summer river temperature. Science of The Total Environment, 592, 616–626. https://doi.org/10.1016/j.scitotenv.2017.03.019
Zhao, H., Zhang, B., Shang, J., Liu, J., Li, D., Chen, Y., Zuo, Z., & Chen, Z. (2018a). Aerial photography flight quality assessment with GPS/INS and DEM data. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 60–73. https://doi.org/10.1016/j.isprsjprs.2017.10.015
Zhao, H., Zhang, B., Shang, J., Liu, J., Li, D., Chen, Y., Zuo, Z., & Chen, Z. (2018b). Aerial photography flight quality assessment with GPS/INS and DEM data. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 60–73. https://doi.org/10.1016/j.isprsjprs.2017.10.015
Zhu, X., Nie, S., Wang, C., Xi, X., Wang, J., Li, D., & Zhou, H. (2021). A Noise Removal Algorithm Based on OPTICS for Photon-Counting LiDAR Data. IEEE Geoscience and Remote Sensing Letters, 18(8), 1471–1475. https://doi.org/10.1109/LGRS.2020.3003191
dc.rights.license.spa.fl_str_mv Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
https://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 107 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.spatial.none.fl_str_mv Arroyo Granada
dc.coverage.region.none.fl_str_mv Galapa
Atlántico
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.publisher.department.spa.fl_str_mv Civil y Ambiental
dc.publisher.place.spa.fl_str_mv Barranquilla, Colombia
dc.publisher.program.spa.fl_str_mv Ingeniería Civil
institution Corporación Universidad de la Costa
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstream/11323/10169/1/Efecto%20de%20t%c3%a9cnicas%20de%20filtrado%20y%20tipos%20de%20vuelo%20en%20modelo%20digital%20de%20elevaciones%20LiDAR%20y%20la%20modelaci%c3%b3n%20hidr%c3%a1ulica%20del%20arroyo%20Granada%20%28Galapa%2c%20Atl%c3%a1ntico%29.pdf
https://repositorio.cuc.edu.co/bitstream/11323/10169/2/license.txt
https://repositorio.cuc.edu.co/bitstream/11323/10169/3/Efecto%20de%20t%c3%a9cnicas%20de%20filtrado%20y%20tipos%20de%20vuelo%20en%20modelo%20digital%20de%20elevaciones%20LiDAR%20y%20la%20modelaci%c3%b3n%20hidr%c3%a1ulica%20del%20arroyo%20Granada%20%28Galapa%2c%20Atl%c3%a1ntico%29.pdf.txt
https://repositorio.cuc.edu.co/bitstream/11323/10169/4/Efecto%20de%20t%c3%a9cnicas%20de%20filtrado%20y%20tipos%20de%20vuelo%20en%20modelo%20digital%20de%20elevaciones%20LiDAR%20y%20la%20modelaci%c3%b3n%20hidr%c3%a1ulica%20del%20arroyo%20Granada%20%28Galapa%2c%20Atl%c3%a1ntico%29.pdf.jpg
bitstream.checksum.fl_str_mv d0e49fe594ee4c8220f608cb08522422
2f9959eaf5b71fae44bbf9ec84150c7a
085920907aed2b792b07d09fcae2f691
a05a97f2bf46f35eaa022c00ae709100
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Universidad de La Costa
repository.mail.fl_str_mv bdigital@metabiblioteca.com
_version_ 1808400117808496640
spelling Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Diaz Martínez, Karina Sofíae232a5021e65b0290007b46ec8b92f33Moreno Otero, Marina Alejandra3447164252d40c9949e035fa7a4ad1a1Ramírez Cordero, Juan Camilo9544abcb03f979c728c6e5c32b07a96cDaza González, RicardoAcuña Robledo, Guillermo JesúsVilladiego Rojas, Leydis LucíaArroyo GranadaGalapaAtlántico2023-05-23T16:49:06Z2023-05-23T16:49:06Z2023https://hdl.handle.net/11323/10169Corporacion Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The generation of a Digital Elevation Model (DEM) using LiDAR technology is a key tool in hydraulic modelling for water resource management. The main objective of this research was to compare the effect of combinations of flight pathways and filtering techniques on the generation of a DEM and the response of a hydraulic model in a section of the stream Granada in the municipality of Galapa (Atlántico). For this purpose, a DEM was generated in an area located in the basin of the Granada stream with dense vegetation applying different filtering techniques to LiDAR surveys with different flight patterns. The DEM sections generated for each combination of flight form and filtering techniques using error metrics were then evaluated. Finally, we evaluated the response of hydraulic modelling in HEC-RAS for different precipitation events based on the information obtained from IDEAM. The results suggest that the unification of flight techniques, carried by nearby filtering techniques, produced more consistent DEMLa generación de un Modelo Digital de Elevación (DEM) mediante tecnología LiDAR es una herramienta clave en la modelación hidráulica para la gestión de recursos hídricos. El objetivo principal de esta investigación fue comparar el efecto de las combinaciones de formas de vuelo y técnicas de filtrado en la generación de un DEM y en la respuesta de un modelo hidráulico en un tramo del arroyo Granada en el municipio de Galapa (Atlántico). Para ello, se generaron DEM en un área ubicada en la cuenca del arroyo Granada con vegetación densa aplicando diferentes técnicas de filtrado a levantamientos LiDAR con diferentes patrones de vuelo. Luego, se evaluaron las secciones del DEM generadas para cada combinación de forma de vuelo y técnicas de filtrado mediante métricas de error. Finalmente, se evaluó la respuesta de la modelación hidráulica en HEC-RAS para diferentes eventos de precipitación basados en la información obtenida desde IDEAM. Los resultados sugieren que la unificación de las técnicas de vuelo, llevado de la mano de técnicas de filtrado de puntos cercanos, produjeron DEM más consistentes.Introducción 12--Planteamiento del problema 14--Hipótesis 15--Objetivos 16--Objetivo --general 16--Objetivos específicos 16--Marco teórico y estado del arte 18--Levantamiento de terreno 18--Tecnología LiDAR18--Técnicas de filtrado 20--Variables relacionadas al diseño de técnicas de filtrado 21--Antecedentes asociados a las técnicas de filtrado 23 Procesamiento de la información25--Modelación hidrológica e hidráulica 26 Estado del Arte 27--Materiales y equipo 32--Método 32--Levantamiento LiDAR 33 Métodos de filtrado 35--Área de estudio e hidrología 40--Modelación hidráulica 51 Almacenamiento y gestión de datos, gráficos, cartografía e informes 52--Análisis de Resultados 57--Comportamiento de inundación simulada 57--Comparación --morfológica de la vaguada 65--Comparación de la velocidad del canal en la vaguada 73--Conclusiones 83--Recomendaciones 86--Referencias88Ingeniero(a) CivilPregrado107 páginasapplication/pdfspaCorporación Universidad de la CostaCivil y AmbientalBarranquilla, ColombiaIngeniería CivilEfecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico)Trabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttp://purl.org/redcol/resource_type/TPinfo:eu-repo/semantics/acceptedVersionAbu-Aly, T. R., Pasternack, G. B., Wyrick, J. R., Barker, R., Massa, D., & Johnson, T. (2014). Effects of LiDAR-derived, spatially distributed vegetation roughness on two-dimensional hydraulics in a gravel-cobble river at flows of 0.2 to 20 times bankfull. Geomorphology, 206, 468–482. https://doi.org/10.1016/j.geomorph.2013.10.017Addy, S., & Wilkinson, M. E. (2019). Representing natural and artificial in‐channel large wood in numerical hydraulic and hydrological models. WIREs Water, 6(6). https://doi.org/10.1002/wat2.1389Alexander Vega, J. (2019). The Use of Lidar Data and VHR Imagery to Estimate the Effects of Tree Roots on Shallow Landslides Assessment. IOP Conference Series: Materials Science and Engineering, 603(2), 022010. https://doi.org/10.1088/1757-899X/603/2/022010Alvarez Maya, J., & Ramírez Hernández, S. (2014). MODELACIÓN HIDROLÓGICA CON BASE EN SOFTWARE HEC ®. https://ri-ng.uaq.mx/bitstream/123456789/4064/1/TA-FING-IC-144058-2014.pdfÁlvarez-Villa, O. D., Vélez, J. I., & Poveda, G. (2011). Improved long-term mean annual rainfall fields for Colombia: MEAN ANNUAL RAINFALL FIELDS FOR COLOMBIA. International Journal of Climatology, 31(14), 2194–2212. https://doi.org/10.1002/joc.2232APPLIED IMAGERY. (2022). Quick Terrain Modeler [Https://appliedimagery.com/author/appliedimagery/]. https://appliedimagery.com/quick-terrain-modeler-v8-4-0-is-available/Aragón Hernández, J. L., Aguilar Martínez, G. A., Velázquez Ríos, U., Jiménez Magaña, M. R., & Maya Franco, A. (2019). Distribución espacial de variables hidrológicas. Implementación y evaluación de métodos de interpolación. Ingeniería Investigación y Tecnología, 20(2), 1–15. https://doi.org/10.22201/fi.25940732e.2019.20n2.023Asgari, M., Yang, W., Lindsay, J., Tolson, B., & Dehnavi, M. M. (2022). A review of parallel computing applications in calibrating watershed hydrologic models. Environmental Modelling & Software, 151, 105370. https://doi.org/10.1016/j.envsoft.2022.105370Avella Rodríguez, M. P. (2022). ANÁLISIS COMPARATIVO DE MODELOS DIGITALES DE TERRENO OBTENIDOS POR TECNOLOGIA LIDAR CON AERONAVE NO TRIPULADA Y POR FOTOGRAMETRIA CON UAV EN ZONA DE MONTAÑA. http://repositorio.uan.edu.co/bitstream/123456789/5967/1/2022_MiryamPaolaAvellaRodr%C3%ADguez.pdfBaccini, A., & Asner, G. P. (2013). Improving pantropical forest carbon maps with airborne LiDAR sampling. Carbon Management, 4(6), 591–600. https://doi.org/10.4155/cmt.13.66Brendel, C. E., Dymond, R. L., & Aguilar, M. F. (2020). Integration of quantitative precipitation forecasts with real-time hydrology and hydraulics modeling towards probabilistic forecasting of urban flooding. Environmental Modelling & Software, 134, 104864. https://doi.org/10.1016/j.envsoft.2020.104864Brown, J. A., Bell, C. D., Hogue, T. S., Higgins, C. P., & Selbig, W. R. (2019). An integrated statistical and deterministic hydrologic model for analyzing trace organic contaminants in commercial and high-density residential stormwater runoff. Science of The Total Environment, 673, 656–667. https://doi.org/10.1016/j.scitotenv.2019.03.327Bures, L., Roub, R., Sychova, P., Gdulova, K., & Doubalova, J. (2019). Comparison of bathymetric data sources used in hydraulic modelling of floods. Journal of Flood Risk Management, 12(S1). https://doi.org/10.1111/jfr3.12495Casas, A., Lane, S. N., Yu, D., & Benito, G. (2010). A method for parameterising roughness and topographic sub-grid scale effects in hydraulic modelling from LiDAR data. Hydrology and Earth System Sciences, 14(8), 1567–1579. https://doi.org/10.5194/hess-14-1567-2010Casas Planes, A., Benito, G., Thorndycraft, V. R., & Rico, M. (2005). Efectos de las fuentes cartográficas en los resultados de la modelación hidráulica de crecidas. Ingeniería Del Agua, 12(4), 309. https://doi.org/10.4995/ia.2005.2567Chen, H., Liang, Q., Liu, Y., & Xie, S. (2018). Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling. Journal of Hydrology, 559, 56–70. https://doi.org/10.1016/j.jhydrol.2018.01.056Cooper, H. M., Zhang, C., Davis, S. E., & Troxler, T. G. (2019). Object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades. Environmental Modelling & Software, 112, 179–191. https://doi.org/10.1016/j.envsoft.2018.11.003Dehvari, A., & Heck, R. J. (2012). Removing non-ground points from automated photo-based DEM and evaluation of its accuracy with LiDAR DEM. Computers & Geosciences, 43, 108–117. https://doi.org/10.1016/j.cageo.2012.02.013Deng, Q., Li, X., Ni, P., Li, H., & Zheng, Z. (2019). Enet-CRF-Lidar: Lidar and Camera Fusion for Multi-Scale Object Recognition. IEEE Access, 7, 174335–174344. https://doi.org/10.1109/ACCESS.2019.2956492Dou, X. P., Zhang, Z. L., Gao, X. Y., Zhang, X. Z., Ding, L., & Jiao, J. (2020). Analysis on Changes of Current, Sediment and Riverbed Evolution in Yangtze Estuary for the Past 20 Years. In N. Trung Viet, D. Xiping, & T. Thanh Tung (Eds.), APAC 2019 (pp. 641–647). Springer Singapore. https://doi.org/10.1007/978-981-15-0291-0_88Dwarakish, G. S., & Ganasri, B. P. (2015). Impact of land use change on hydrological systems: A review of current modeling approaches. Cogent Geoscience, 1(1), 1115691. https://doi.org/10.1080/23312041.2015.1115691Escobar Villanueva, J. R., Iglesias Martínez, L., & Pérez Montiel, J. I. (2019a). DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors, 19(14), 3205. https://doi.org/10.3390/s19143205Escobar Villanueva, J. R., Iglesias Martínez, L., & Pérez Montiel, J. I. (2019b). DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors, 19(14), 3205. https://doi.org/10.3390/s19143205ESRI. (2022). ARCGIS (2.9). https://www.esri.com/en-us/arcgis/products/arcgis-pro/overviewFlórez Gálvez, J. H., & Bolaños Mora, A. (2009). Manual de drenaje para carreteras. https://www.invias.gov.co/index.php/archivo-y-documentos/documentos-tecnicos/especificaciones-tecnicas/984-manual-de-drenaje-para-carreteras/fileGalindo Jiménez, Inés., Laín Huerta, L., & Llorenta Isidor, M. (2008). El estudio y la gestión de los riesgos geológicos. Instituto Geológico y Minero de España.Gil, C., Villanueva, I., & Godiksen, P. (2006). Efectos de la cartografía sobre la modelización hidráulica bidimensional de crecidas. https://www.researchgate.net/profile/Ignacio-Villanueva-5/publication/323991827_Efectos_de_la_cartografia_sobre_la_modelizacion_hidraulica_bidimensional_de_crecidas/links/5ab6785b45851515f59d8bfb/Efectos-de-la-cartografia-sobre-la-modelizacion-hidraulica-bidimensional-de-crecidas.pdfGonzález-Díez, A., Barreda-Argüeso, J. A., Rodríguez-Rodríguez, L., & Fernández-Lozano, J. (2021). The use of filters based on the Fast Fourier Transform applied to DEMs for the objective mapping of karstic features. Geomorphology, 385, 107724. https://doi.org/10.1016/j.geomorph.2021.107724Guo, Q., Li, W., Yu, H., & Alvarez, O. (2010). Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods. Photogrammetric Engineering & Remote Sensing, 76(6), 701–712. https://doi.org/10.14358/PERS.76.6.701Haile, A. T., & Rientjes, T. H. M. (2005). EFFECTS OF LIDAR DEM RESOLUTION IN FLOOD MODELLING: A MODEL SENTITIVITY STUDY FOR THE CITY OF TEGUCIGALPA, HONDURAS. 6.Hancock, S., Lewis, P., Foster, M., Disney, M., & Muller, J.-P. (2012). Measuring forests with dual wavelength lidar: A simulation study over topography. Agricultural and Forest Meteorology, 161, 123–133. https://doi.org/10.1016/j.agrformet.2012.03.014 HEC-RAS User’s Manual. (n.d.).Hou, J., Van Dijk, A. I. J. M., & Renzullo, L. J. (2022). Merging Landsat and airborne LiDAR observations for continuous monitoring of floodplain water extent, depth and volume. Journal of Hydrology, 609, 127684. https://doi.org/10.1016/j.jhydrol.2022.127684Hui, Z., Jin, S., Cheng, P., Ziggah, Y. Y., Wang, L., Wang, Y., Hu, H., & Hu, Y. (2019). An Active Learning Method for DEM Extraction From Airborne LiDAR Point Clouds. IEEE Access, 7, 89366–89378. https://doi.org/10.1109/ACCESS.2019.2926497Ii, T. V. H., & Rao, P. (2019). Examination of Computational Precision versus Modeling Complexity for Open Channel Flow with Hydraulic Jump. Journal of Water Resource and Protection, 11(10), 1233–1244. https://doi.org/10.4236/jwarp.2019.1110071Jaramillo Baltra, R., & Padró García, J. C. (2020). Generación de cartografía a partir de imágenes captadas con dron de ala fija, asociada a proyectos hidráulicos fluviales. GeoFocus Revista Internacional de Ciencia y Tecnología de La Información Geográfica, 26, 93–117. https://doi.org/10.21138/GF.680Jiménez Sánchez, M. A. (2003). Las inundaciones en la Comunidad de Madrid. https://repositorio.aemet.es/bitstream/20.500.11765/12118/1/InundacionesMadrid_Jimenez_RAM2003.pdfKlápště, P., Fogl, M., Barták, V., Gdulová, K., Urban, R., & Moudrý, V. (2020). Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds. International Journal of Digital Earth, 13(12), 1672–1694. https://doi.org/10.1080/17538947.2020.1791267Kuang, Z., Liu, D., Wu, D., Wang, Z., Li, C., & Deng, Q. (2023). Parameter Optimization and Development of Mini Infrared Lidar for Atmospheric Three-Dimensional Detection. Sensors, 23(2), 892. https://doi.org/10.3390/s23020892Loicq, P., Moatar, F., Jullian, Y., Dugdale, S. J., & Hannah, D. M. (2018). Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data. Science of The Total Environment, 624, 480–490. https://doi.org/10.1016/j.scitotenv.2017.12.129Maculotti, G., Goti, E., Genta, G., Mazza, L., & Galetto, M. (2022). Uncertainty-based comparison of conventional and surface topography-based methods for wear volume evaluation in pin-on-disc tribological test. Tribology International, 165, 107260. https://doi.org/10.1016/j.triboint.2021.107260Mason, D. C., Horritt, M. S., Dall’Amico, J. T., Scott, T. R., & Bates, P. D. (2007). Improving River Flood Extent Delineation From Synthetic Aperture Radar Using Airborne Laser Altimetry. IEEE Transactions on Geoscience and Remote Sensing, 45(12), 3932–3943. https://doi.org/10.1109/TGRS.2007.901032Mayta Rojas, C. A., & Mamani Maquera, E. R. (2018). MODELACIÓN HIDRÁULICA DE LA DEFENSA DE CALANA CON EL FIN DE DETERMINAR LA VULNERABILIDAD ANTE MÁXIMAS AVENIDAS. https://repositorio.upt.edu.pe/bitstream/handle/20.500.12969/549/Mayta_Rojas-Mamani_Maquera.pdf?sequence=1&isAllowed=yMazzoleni, M., Paron, P., Reali, A., Juizo, D., Manane, J., & Brandimarte, L. (2020a). Testing UAV-derived topography for hydraulic modelling in a tropical environment. Natural Hazards, 103(1), 139–163. https://doi.org/10.1007/s11069-020-03963-4Mihu-Pintilie, A., Cîmpianu, C. I., Stoleriu, C. C., Pérez, M. N., & Paveluc, L. E. (2019). Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach. Water, 11(9), 1832. https://doi.org/10.3390/w11091832Mohtashami, S., Eliasson, L., Hansson, L., Willén, E., Thierfelder, T., & Nordfjell, T. (2022). Evaluating the effect of DEM resolution on performance of cartographic depth-to-water maps, for planning logging operations. International Journal of Applied Earth Observation and Geoinformation, 108, 102728. https://doi.org/10.1016/j.jag.2022.102728Moudrý, V., Klápště, P., Fogl, M., Gdulová, K., Barták, V., & Urban, R. (2020). Assessment of LiDAR ground filtering algorithms for determining ground surface of non-natural terrain overgrown with forest and steppe vegetation. Measurement, 150, 107047. https://doi.org/10.1016/j.measurement.2019.107047Muhadi, N. A., Abdullah, A. F., Bejo, S. K., Mahadi, M. R., & Mijic, A. (2020). The Use of LiDAR-Derived DEM in Flood Applications: A Review. Remote Sensing, 12(14), 2308. https://doi.org/10.3390/rs12142308Munoth, P., & Goyal, R. (2019). Effects of DEM Source, Spatial Resolution and Drainage Area Threshold Values on Hydrological Modeling. Water Resources Management, 33(9), 3303–3319. https://doi.org/10.1007/s11269-019-02303-xOgden, F. L. (2021). Geohydrology: Hydrological Modeling. In Encyclopedia of Geology (pp. 457–476). Elsevier. https://doi.org/10.1016/B978-0-08-102908-4.00115-6O’Neil, G. L., Saby, L., Band, L. E., & Goodall, J. L. (2019). Effects of LiDAR DEM Smoothing and Conditioning Techniques on a Topography‐Based Wetland Identification Model. Water Resources Research, 55(5), 4343–4363. https://doi.org/10.1029/2019WR024784Pérez Brugal, A., Weber, J. F., & Castellanos, Y. R. (2011). Importancia de los modelos digitales del terreno en la simulación hidráulica de inundaciones. Revista Cubana de Ingeniería, 1(3), 51–60.Rangel-Buitrago, N. G., Anfuso, G., & Williams, A. T. (2015). Coastal erosion along the Caribbean coast of Colombia: Magnitudes, causes and management. Ocean & Coastal Management, 114, 129–144. https://doi.org/10.1016/j.ocecoaman.2015.06.024SANCHEZ FORERO, N. (2017). CÁLCULO DE LA PRECIPITACIÓN MEDIA SOBRE LA PENÍNSULA DE LA GUAJIRA USANDO EL MÉTODO THIESSEN.Sánchez San Román, F. J. (2017). Hidrología Superficial y Subterránea. F. Javier Sánchez San Román.Sanz-Ramos, M., Bladé, E., González-Escalona, F., Olivares, G., & Aragón-Hernández, J. L. (2021). Interpreting the Manning Roughness Coefficient in Overland Flow Simulations with Coupled Hydrological-Hydraulic Distributed Models. Water, 13(23), 3433. https://doi.org/10.3390/w13233433Shen, Y., Wang, S., Zhang, B., & Zhu, J. (2022). Development of a stochastic hydrological modeling system for improving ensemble streamflow prediction. Journal of Hydrology, 608, 127683. https://doi.org/10.1016/j.jhydrol.2022.127683Simoniello, T., Coluzzi, R., Guariglia, A., Imbrenda, V., Lanfredi, M., & Samela, C. (2022). Automatic Filtering and Classification of Low-Density Airborne Laser Scanner Clouds in Shrubland Environments. Remote Sensing, 14(20), 5127. https://doi.org/10.3390/rs14205127Sokolewicz, M., Wijma, E., Nomden, H., Driessen, T., van Agten, Q., & Carvajal, F. (2016). Flood protection as a key-component of the environmental restoration of Canal del Dique, Colombia. E3S Web of Conferences, 7, 12005. https://doi.org/10.1051/e3sconf/20160712005Speak, A., Escobedo, F. J., Russo, A., & Zerbe, S. (2020). Total urban tree carbon storage and waste management emissions estimated using a combination of LiDAR, field measurements and an end-of-life wood approach. Journal of Cleaner Production, 256, 120420. https://doi.org/10.1016/j.jclepro.2020.120420Su, Y., & Guo, Q. (2014a). A practical method for SRTM DEM correction over vegetated mountain areas. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 216–228. https://doi.org/10.1016/j.isprsjprs.2013.11.009Su, Y., & Guo, Q. (2014b). A practical method for SRTM DEM correction over vegetated mountain areas. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 216–228. https://doi.org/10.1016/j.isprsjprs.2013.11.009Tkáč, M., & Mésároš, P. (2019). Utilizing drone technology in the civil engineering. Selected Scientific Papers - Journal of Civil Engineering, 14(1), 27–37. https://doi.org/10.1515/sspjce-2019-0003US Army Corps of Engineers,Hydrologic Engineering Center. (2022). HEC HMS (4.10.0). https://www.hec.usace.army.mil/software/hec-hms/Valencia Ortiz, J. A., & Martínez-Graña, A. M. (2023). Morphometric Evaluation and Its Incidence in the Mass Movements Present in the Chicamocha Canyon, Colombia. Sustainability, 15(2), 1140. https://doi.org/10.3390/su15021140Veeck, S., da Costa, F. F., Correia Lima, D. L., da Paz, A. R., & Allasia Piccilli, D. G. (2020). Scale dynamics of the HIDROPIXEL high-resolution DEM-based distributed hydrologic modeling approach. Environmental Modelling & Software, 127, 104695. https://doi.org/10.1016/j.envsoft.2020.104695Wan, Y., Chen, J., Xu, C.-Y., Xie, P., Qi, W., Li, D., & Zhang, S. (2021). Performance dependence of multi-model combination methods on hydrological model calibration strategy and ensemble size. Journal of Hydrology, 603, 127065. https://doi.org/10.1016/j.jhydrol.2021.127065Wawrzyniak, V., Allemand, P., Bailly, S., Lejot, J., & Piégay, H. (2017). Coupling LiDAR and thermal imagery to model the effects of riparian vegetation shade and groundwater inputs on summer river temperature. Science of The Total Environment, 592, 616–626. https://doi.org/10.1016/j.scitotenv.2017.03.019Zhao, H., Zhang, B., Shang, J., Liu, J., Li, D., Chen, Y., Zuo, Z., & Chen, Z. (2018a). Aerial photography flight quality assessment with GPS/INS and DEM data. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 60–73. https://doi.org/10.1016/j.isprsjprs.2017.10.015Zhao, H., Zhang, B., Shang, J., Liu, J., Li, D., Chen, Y., Zuo, Z., & Chen, Z. (2018b). Aerial photography flight quality assessment with GPS/INS and DEM data. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 60–73. https://doi.org/10.1016/j.isprsjprs.2017.10.015Zhu, X., Nie, S., Wang, C., Xi, X., Wang, J., Li, D., & Zhou, H. (2021). A Noise Removal Algorithm Based on OPTICS for Photon-Counting LiDAR Data. IEEE Geoscience and Remote Sensing Letters, 18(8), 1471–1475. https://doi.org/10.1109/LGRS.2020.3003191LidarModelo digital de elevaciónModelación hidráulicaTécnicas de filtradoVegetaciónDigital Elevation Modelhydraulic modelingFiltering techniquesvegetationORIGINALEfecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico).pdfEfecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico).pdfTesisapplication/pdf2601238https://repositorio.cuc.edu.co/bitstream/11323/10169/1/Efecto%20de%20t%c3%a9cnicas%20de%20filtrado%20y%20tipos%20de%20vuelo%20en%20modelo%20digital%20de%20elevaciones%20LiDAR%20y%20la%20modelaci%c3%b3n%20hidr%c3%a1ulica%20del%20arroyo%20Granada%20%28Galapa%2c%20Atl%c3%a1ntico%29.pdfd0e49fe594ee4c8220f608cb08522422MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstream/11323/10169/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessTEXTEfecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico).pdf.txtEfecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico).pdf.txtExtracted texttext/plain146364https://repositorio.cuc.edu.co/bitstream/11323/10169/3/Efecto%20de%20t%c3%a9cnicas%20de%20filtrado%20y%20tipos%20de%20vuelo%20en%20modelo%20digital%20de%20elevaciones%20LiDAR%20y%20la%20modelaci%c3%b3n%20hidr%c3%a1ulica%20del%20arroyo%20Granada%20%28Galapa%2c%20Atl%c3%a1ntico%29.pdf.txt085920907aed2b792b07d09fcae2f691MD53open accessTHUMBNAILEfecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico).pdf.jpgEfecto de técnicas de filtrado y tipos de vuelo en modelo digital de elevaciones LiDAR y la modelación hidráulica del arroyo Granada (Galapa, Atlántico).pdf.jpgGenerated Thumbnailimage/jpeg7578https://repositorio.cuc.edu.co/bitstream/11323/10169/4/Efecto%20de%20t%c3%a9cnicas%20de%20filtrado%20y%20tipos%20de%20vuelo%20en%20modelo%20digital%20de%20elevaciones%20LiDAR%20y%20la%20modelaci%c3%b3n%20hidr%c3%a1ulica%20del%20arroyo%20Granada%20%28Galapa%2c%20Atl%c3%a1ntico%29.pdf.jpga05a97f2bf46f35eaa022c00ae709100MD54open access11323/10169oai:repositorio.cuc.edu.co:11323/101692023-05-24 03:02:21.038An error occurred on the license name.|||https://creativecommons.org/licenses/by-nc-sa/4.0/open accessRepositorio Universidad de La Costabdigital@metabiblioteca.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