Monitoreo de la variación de los espejos de agua de los humedales Ramsar de Bogotá D.C. utilizando inteligencia artificial a partir de imágenes satelitales

Vista sencilla de ítem
dc.contributor.advisorLadino Moreno, Edgar Orlando
dc.contributor.authorViasus Wilches, Jennyfer Damaris
dc.contributor.orcidLadino Moreno Edgar Orlando [0000-0002-7770-452X]
dc.date.accessioned2025-09-01T13:11:22Z
dc.date.available2025-09-01T13:11:22Z
dc.date.created2025-08-13
dc.descriptionSe plantea una metodología que utiliza conjuntamente imágenes satelitales y métodos de inteligencia artificial para detectar y monitorear cambios en las zonas hídricas dentro de los humedales de Bogotá. Dichas variaciones suelen estar relacionadas con la pérdida o reducción de las superficies acuáticas debido a la presencia de especies invasoras, especialmente la Eichhornia crassipes, conocida comúnmente como Buchón. El crecimiento de estas especies representa una amenaza para los cuerpos de agua, ya que genera diversos impactos en las especies nativas, incluida la pérdida de hábitat debido a la eutrofización. La detección oportuna de estas alteraciones en las áreas hídricas permite a las autoridades intervenir de manera oportuna e iniciar procesos de restauración ecológica para conservar el ecosistema.
dc.description.abstractA methodology is proposed that combines satellite imagery and artificial intelligence methods to detect and monitor changes in water areas within the wetlands of Bogotá. Such variations are often related to the loss or reduction of water surfaces due to the presence of invasive species, particularly Eichhornia crassipes, commonly known as water hyacinth. The growth of these species poses a threat to water bodies, as it generates various impacts on native species, including habitat loss caused by eutrophication. Timely detection of these alterations in water areas enables authorities to intervene promptly and initiate ecological restoration processes to preserve the ecosystem.
dc.format.mimetypepdf
dc.identifier.urihttp://hdl.handle.net/11349/98761
dc.language.isospa
dc.publisherUniversidad Distrital Francisco José de Caldas
dc.relation.referencesAal, H. A. A. El, Taie, S. A., & El-Bendary, N. (2021). An optimized rnn-lstm approach for parkinson’s disease early detection using speech features. Bulletin of Electrical Engineering and Informatics, 10(5), 2503–2512. https://doi.org/10.11591/eei.v10i5.3128
dc.relation.referencesAcevedo, E., Serna, A., & Serna, E. (2017). CAPÍTULO 10 Principios y características de las redes neuronales artificiales. In DESARROLLO E INNOVACIÓN EN INGENIERÍA (Vol. 1, pp. 173–182).
dc.relation.referencesAdelabu, S., Mutanga, O., & Adam, E. (2015). Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods. Geocarto International, 30(7), 810–821. https://doi.org/10.1080/10106049.2014.997303
dc.relation.referencesAhmed, B., & Ahmed, R. (2012). Modeling urban land cover growth dynamics using multioral satellite images: A case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information, 1(1), 3–31. https://doi.org/10.3390/ijgi1010003
dc.relation.referencesAldiansyah, S., & Saputra, R. A. (2023). Comparison of Machine Learning Algorithms for Land Use and Land Cover Analysis Using Google Earth Engine (Case Study: Wanggu Watershed). International Journal of Remote Sensing and Earth Sciences (IJReSES), 19(2), 197. https://doi.org/10.30536/j.ijreses.2022.v19.a3803
dc.relation.referencesAlem, A., & Kumar, S. (2020). Deep Learning Methods for Land Cover and Land Use Classification in Remote Sensing: A Review. ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 903–908. https://doi.org/10.1109/ICRITO48877.2020.9197824
dc.relation.referencesAli, Y. M., & El-Din Khedr, I. S. (2018). Estimation of water losses through evapotranspiration of aquatic weeds in the Nile River (Case study: Rosetta Branch). Water Science, 32(2), 259–275. https://doi.org/10.1016/j.wsj.2018.08.002
dc.relation.referencesAlikhani, S., Nummi, P., & Ojala, A. (2021). Urban wetlands: A review on ecological and cultural values. Water (Switzerland), 13(22), 1–47. https://doi.org/10.3390/w13223301
dc.relation.referencesAlqadhi, S., Mallick, J., Balha, A., Bindajam, A., Singh, C. K., & Hoa, P. V. (2021). Spatial and decadal prediction of land use/land cover using multi-layer perceptron-neural network (MLP-NN) algorithm for a semi-arid region of Asir, Saudi Arabia. Earth Science Informatics, 14(3), 1547–1562. https://doi.org/10.1007/s12145-021-00633-2
dc.relation.referencesAlqurashi, A. F., & Kumar, L. (2013). Investigating the Use of Remote Sensing and GIS Techniques to Detect Land Use and Land Cover Change: A Review. Advances in Remote Sensing, 02(02), 193–204. https://doi.org/10.4236/ars.2013.22022
dc.relation.referencesÁlvarez Vega, M., Quirós Mora, L. M., & Cortés Badilla, M. V. (2020). Inteligencia artificial y aprendizaje automático en medicina. Revista Medica Sinergia, 5(8), e557. https://doi.org/10.31434/rms.v5i8.557
dc.relation.referencesApín, Y., & Torres, B. (2016). Introducción De Especies Invasoras a Partir Del Agua De Lastre Proveniente Del Transporte Marítimo Comercial: Estado Del Arte. Red de Revistas Científicas de América Latina, El Caribe, España y Portugal.
dc.relation.referencesAraneda C, E. (2002). Uso de Sistemas de Información Geográficos y análisis espacial en arqueología: Proyecciones y limitaciones. Estudios Atacameños, 22, 59–75. https://doi.org/10.4067/S0718-10432002002200004
dc.relation.referencesAriza, A., Roa Melgarejo, O. J., Serrato, P. K., & León Rincón, H. A. (2018). Uso de índices espectrales derivados de sensores remotos para la caracterización geomorfológica en zonas insulares del Caribe colombiano. Perspectiva Geográfica, 23(1), 105–122. https://doi.org/10.19053/01233769.5863
dc.relation.referencesArpitha, M., Ahmed, S. A., & Harishnaika, N. (2023). Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, 16(4), 3057–3073. https://doi.org/10.1007/s12145-023-01073-w
dc.relation.referencesAryal, J., Sitaula, C., & Frery, A. C. (2023). Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia. Scientific Reports, 13(1), 1–11. https://doi.org/10.1038/s41598-023-40564-0
dc.relation.referencesAtehortua, E., & Gartner, C. (2013). Estudios Preliminares De La Biomasa Seca De Eichhornia Crassipes Como Adsorbente De Plomo Y Cromo En Aguas Preliminary Studies of Eichhornia Crassipes Dry Biomass for Lead and Chromium Removal From Waters. Revista Colombiana de Materiales N.4., 81–92.
dc.relation.referencesAwad, M. (2021). Google Earth Engine (GEE) cloud computing based crop classification using radar, optical images and Support Vector Machine Algorithm (SVM). 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology, IMCET 2021, 71–76. https://doi.org/10.1109/IMCET53404.2021.9665519
dc.relation.referencesAyala-Niño, D., & González-Camacho, J. M. (2024). ALGORITMOS DE APRENDIZAJE AUTOMÁTICO PARA IDENTIFICAR VARIEDADES DE DURAZNO CON BASE EN DESCRIPTORES CROMÁTICOS Y MORFOLÓGICOS MACHINE LEARNING ALGORITHMS TO IDENTIFY PEACH VARIETIES BASED ON CHROMATIC AND MORPHOLOGICAL DESCRIPTORS Daniel Ayala-Niño y Jua. 47(1), 62–69.
dc.relation.referencesBai, B., Tan, Y., Donchyts, G., Haag, A., Xu, B., Chen, G., & Weerts, A. H. (2023). Naive Bayes classification-based surface water gap-filling from partially contaminated optical remote sensing image. 616(November 2022). https://doi.org/10.1016/j.jhydrol.2022.128791
dc.relation.referencesBaptiste, M. P., Castaño, N., Cárdenas López, D., Gutiérrez, F. de P., Gil, D. L., Lasso, C. A., & Baptiste, M. P. (2010). Análisis de riesgo y propuesta de categorización de especies introducidas para Colombia.
dc.relation.referencesBengio, Y. (2012). Practical Recommendations for Gradient-Based Training of Deep Architectures. Researchgate.Net, 437–478. http://deeplearning.net/software/pylearn2
dc.relation.referencesBerhane, T. M., Lane, C. R., Wu, Q., Autrey, B. C., Anenkhonov, O. A., Chepinoga, V. V., & Liu, H. (2018). Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory. Remote Sensing, 10(4). https://doi.org/10.3390/rs10040580
dc.relation.referencesBhosle, K., & Musande, V. (2019). Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images. Journal of the Indian Society of Remote Sensing, 47(11), 1949–1958. https://doi.org/10.1007/s12524-019-01041-2
dc.relation.referencesBlackburn, T. M., Pyšek, P., Bacher, S., Carlton, J. T., Duncan, R. P., Jarošík, V., Wilson, J. R. U., & Richardson, D. M. (2011). A proposed unified framework for biological invasions. Trends in Ecology and Evolution, 26(7), 333–339. https://doi.org/10.1016/j.tree.2011.03.023
dc.relation.referencesBlanco, D. E. (1999). Los humedales como hábitat de aves acuáticas. Tópicos Sobre Humedales Subtropicales y Templados de Sudamérica, 2142(1428), 219–228.
dc.relation.referencesBorràs, J., Delegido, J., Pezzola, A., Pereira, M., Morassi, G., & Camps-Valls, G. (2017). Clasificación de usos del suelo a partir de imágenes sentinel-2. Revista de Teledeteccion, 2017(48), 55–66. https://doi.org/10.4995/raet.2017.7133
dc.relation.referencesBrown De Colstoun, E. C., Story, M. H., Thompson, C., Commisso, K., Smith, T. G., & Irons, J. R. (2003). National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier. Remote Sensing of Environment, 85(3), 316–327. https://doi.org/10.1016/S0034-4257(03)00010-5
dc.relation.referencesCao, C., Dragićević, S., & Li, S. (2019). Short-term forecasting of land use change using recurrent neural network models. Sustainability (Switzerland), 11(19), 1–18. https://doi.org/10.3390/su11195376
dc.relation.referencesCAR. (2020). Informe del registro e impactos de heladas en el territorio car Durante Enero de 2020. 37. https://www.car.gov.co/uploads/files/5e543336c829c.pdf
dc.relation.referencesCárdenas, J., Baptiste, P., Ramirez, W., & Aguilar-garavito, M. (2015). Herramienta para la gestión de áreas afectadas por invasiones biológicas en colombia. In Instituto de Investigación de Recursos Biológicos Alexander von HumboldtHumboldt.
dc.relation.referencesCarreño, U. (2020). “Buchón de agua” (Eichhornia Crassipes): impulsor de la fitorremediación (L. Libertadores (ed.)).
dc.relation.referencesCastillo, S. P. (2016). Introducción intencional de fauna exótica y futuros invasores: ¿seguimos tropezando con la misma piedra una y otra vez? Bosque, 37(2), 237–241. https://doi.org/10.4067/S0717-92002016000200002
dc.relation.referencesCerda Lorca, J., & Villarroel Del P., L. (2008). Evaluación de la concordancia inter-observador en investigación pediátrica: Coeficiente de Kappa. Revista Chilena de Pediatria, 79(1), 54–58. https://doi.org/10.4067/s0370-41062008000100008
dc.relation.referencesCheng, W., Sun, Y., Li, G., Jiang, G., & Liu, H. (2019). Jointly network: a network based on CNN and RBM for gesture recognition. Neural Computing and Applications, 31(s1), 309–323. https://doi.org/10.1007/s00521-018-3775-8
dc.relation.referencesCigliano, M., & Torrusio, S. (2003). Sistemas de información geográfica y teledetección en entomología: aplicación en tucuras y langostas (Orthoptera: Acridoidea). Revista de La Sociedad Entomológica Argentina, 62(1–2), 1–14.
dc.relation.referencesCopernicus. (n.d.). S2 Products. Retrieved January 2, 2025, from https://sentiwiki.copernicus.eu/web/s2-products
dc.relation.referencesCorporación AutónomaRegional de Cundinamarca, & Secretaría Distrital de Ambiente De Bogotá. (2023). Plan de Manejo Ambiental del Sitio Ramsar Complejo de Humedales Urbanos del Distrito Capital de Bogotá. https://www.ambientebogota.gov.co/plan-de-manejo-ambiental-pma-sitio-ramsar-complejo-de-humedales-urbanos-del-distrito-capital-de-bogota
dc.relation.referencesCortés Ballén, L. A. (2018). Aproximación al paisaje de los humedales urbanos de Bogotá dentro de la estructura ecológica principal de la ciudad. Cuadernos de Geografía: Revista Colombiana de Geografía, 27(1), 118–130. https://doi.org/10.15446/rcdg.v27n1.60584
dc.relation.referencesCuellar, Y., & Perez, L. (2023). Multitemporal modeling and simulation of the complex dynamics in urban wetlands: the case of Bogota, Colombia. Scientific Reports, 13(1), 1–18. https://doi.org/10.1038/s41598-023-36600-8
dc.relation.referencesDabija, A., Kluczek, M., Zagajewski, B., Raczko, E., Kycko, M., Al-Sulttani, A. H., Tardà, A., Pineda, L., & Corbera, J. (2021). Comparison of support vector machines and random forests for corine land cover mapping. Remote Sensing, 13(4), 1–35. https://doi.org/10.3390/rs13040777
dc.relation.referencesDamtie, Y. A., Mengistu, D. A., & Meshesha, D. T. (2021). Spatial coverage of water hyacinth (Eichhornia crassipes (Mart.) Solms) on Lake Tana and associated water loss. Heliyon, 7(10), e08196. https://doi.org/10.1016/j.heliyon.2021.e08196
dc.relation.referencesDANE. (2022). Cuentas Departamentales 2022 - Producto Interno Bruto por Departamento. 1–19. https://www.dane.gov.co/files/operaciones/PIB/bol-PIBDep-2022p.pdf
dc.relation.referencesDavidson, N. C. (2014). How much wetland has the world lost? Long-term and recent trends in global wetland area. Marine and Freshwater Research, 65(10), 934–941. https://doi.org/10.1071/MF14173
dc.relation.referencesDeLancey, E. R., Simms, J. F., Mahdianpari, M., Brisco, B., Mahoney, C., & Kariyeva, J. (2020). Comparing deep learning and shallow learning for large-scalewetland classification in Alberta, Canada. Remote Sensing, 12(1). https://doi.org/10.3390/RS12010002
dc.relation.referencesDelpino, M., Portillo, V., & Mora, C. (2018). Evaluación de índices espectrales derivados de sensores remotos para la caracterización de ambientes de humedales. Anais 7o Simpósio de Geotecnologias No Pantanal, Jardim, MS, 20 a 24 de Outubro 2018, 111–121.
dc.relation.referencesDíaz-Ramírez, J. (2021). Aprendizaje Automático y Aprendizaje Profundo. Ingeniare. Revista Chilena de Ingeniería, 29(2), 182–183. https://www.mckinsey.com/business-functions/sustainability/our-insights/artificial-intelligence-and-the-
dc.relation.referencesDiaz, A., Diaz, J., & Vargas, O. (2012). Catalogo de Plantas Invasoras de los humedales de Bogot[a.
dc.relation.referencesDuan, H., Deng, Z., Deng, F., & Wang, D. (2016). Assessment of groundwater potential based on multicriteria decision making model and decision tree algorithms. Mathematical Problems in Engineering, 2016. https://doi.org/10.1155/2016/2064575
dc.relation.referencesEhrenfeld, J. G. (2008). Exotic invasive species in urban wetlands: Environmental correlates and implications for wetland management. Journal of Applied Ecology, 45(4), 1160–1169. https://doi.org/10.1111/j.1365-2664.2008.01476.x
dc.relation.referencesEtter, A., Andrade, A., Saavedra, K., & Cortés, J. (2022). Actualización de la Lista Roja de los Ecosistemas Terrestres de Colombia | Biodiversidad 2022. Ficha: 208. http://reporte.humboldt.org.co/biodiversidad/2022/cap2/208/#seccion1
dc.relation.referencesFarhadi, H., & Najafzadeh, M. (2021). Flood Risk Mapping by Remote Sensing Data and Random. MDPI.
dc.relation.referencesFeng, J., & Lu, S. (2019). Performance Analysis of Various Activation Functions in Artificial Neural Networks. Journal of Physics: Conference Series, 1237(2). https://doi.org/10.1088/1742-6596/1237/2/022030
dc.relation.referencesFialho Cordeiro, P., Figueiredo Goulart, F., Rodrigues Macedo, D., Souza Campos, M. de C., & Rodrigues Castro, S. (2019). Modeling of the potential distribution of Eichhornia crassipes on a global scale: risks and threats to water ecosystems. Revista Ambiente e Agua, 9(3), 445–458. https://doi.org/10.4136/1980-993X
dc.relation.referencesGarcía, F., & Miranda, V. (2018). Eutrofización, una amenaza para el recurso hídrico. Volumen II de La Colección: Agenda Pública Para El Desarrollo Regional, La Metropolización y La Sostenibilidad, 35–367. http://ru.iiec.unam.mx/4269/1/2-Vol2_Parte1_Eje3_Cap5-177-García-Miranda.pdf
dc.relation.referencesGhatkar, J. G., Singh, R. K., & Shanmugam, P. (2019). Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model. International Journal of Remote Sensing, 40(24), 9412–9438. https://doi.org/10.1080/01431161.2019.1633696
dc.relation.referencesGhayour, L., Neshat, A., Paryani, S., Shahabi, H., Shirzadi, A., Chen, W., Al-Ansari, N., Geertsema, M., Amiri, M. P., Gholamnia, M., Dou, J., & Ahmad, A. (2021). Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms. Remote Sensing, 13(7). https://doi.org/10.3390/rs13071349
dc.relation.referencesGibson, R., Danaher, T., Hehir, W., & Collins, L. (2020). A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest. Remote Sensing of Environment, 240(December 2019), 111702. https://doi.org/10.1016/j.rse.2020.111702
dc.relation.referencesGil-Vera, V. D., & Seguro-Gallego, C. (2022). Machine learning aplicado al análisis del rendimiento de desarrollos de software. Revista Politécnica, 18(35), 128–139. https://doi.org/10.33571/rpolitec.v18n35a9
dc.relation.referencesGómez, I., & Martín, I. (2008). Estudio Comparativo de Índices Espectrales para la Cartografía de Áreas Quemadas con Imágenes MODIS. Revista de Teledetección: Revista de La Asociación Española de Teledetección, 29, 15–24.
dc.relation.referencesGonzález-Vélez, J. C., Martinez-Vargas, J. D., & Torres-Madronero, M. C. (2022). Land Cover Classification Using CNN and Semantic Segmentation: A Case of Study in Antioquia, Colombia. Communications in Computer and Information Science, 1532 CCIS, 306–317. https://doi.org/10.1007/978-3-030-99170-8_22
dc.relation.referencesGonzález Angarita, G., Henríquez, C., Peña Angulo, D., Castro Álvarez, D., & Forero Buitrago, G. (2022). Técnicas de análisis geomático en la pérdida de humedales urbanos de Bogotá. ¿Qué rol juegan los asentamientos ilegales? Revista de Geografia Norte Grande, 2022(81), 207–233. https://doi.org/10.4067/S0718-34022022000100207
dc.relation.referencesGonzález, R. M., González, M. A. B., Cruz, A. M., González, A. R., & Pérez, A. L. (2022). Classification of land use and vegetation with convolutional neural networks. Revista Mexicana de Ciencias Forestales, 13(74), 97–119. https://doi.org/10.29298/rmcf.v13i74.1269
dc.relation.referencesGranadas, S. (2024). ¿Por qué se acaba el agua en Bogotá? Te explicamos las razones. ALCALDÍA DE BOGOTÁ. https://bogota.gov.co/mi-ciudad/habitat/por-que-se-acaba-el-agua-en-bogota-te-explicamos-las-razones#:~:text=Del 100 %25 de abastecimiento de,Sistema Sur%2C el 5 %25.
dc.relation.referencesGuan, H. L., Wu, L. Q., & Luo, Y. P. (2011). A GIS-based approach for information management in ecotourism region. In Procedia Engineering (Vol. 15, pp. 1988–1992). https://doi.org/10.1016/j.proeng.2011.08.371
dc.relation.referencesGuerrero, J., & Tendilla, E. (2022). Aprendizaje profundo: Redes neuronales. Revistas.Uvp.Mx, 15(8), 10–22. https://revistas.uvp.mx/index.php/nextia/article/view/193
dc.relation.referencesGuevara, M. F., & Ramirez-Cando, L. J. (2015). Eichhornia crassipes, su invasividad y potencial fitorremediador. La Granja, 22(1), 18–25. https://doi.org/10.17163/lgr.n22.2015.01
dc.relation.referencesGunawan, T. S., Ashraf, A., Riza, B. S., Haryanto, E. V., Rosnelly, R., Kartiwi, M., & Janin, Z. (2020). Development of video-based emotion recognition using deep learning with Google Colab. Telkomnika (Telecommunication Computing Electronics and Control), 18(5), 2463–2471. https://doi.org/10.12928/TELKOMNIKA.v18i5.16717
dc.relation.referencesHailu, A. (2018). Water Hyacinth (Eichhornia crassipes) Biology and its Impacts on Ecosystem, Biodiversity, Economy and Human Well-being. Journal of Life Science and Biomedicine, 8(6), 343–372. https://doi.org/10.1093/acprof:oso/9780199646142.003.0012
dc.relation.referencesHandoko, J., Herwindiati, D. E., & Hendryli, J. (2020). Gradient Boosting Tree for Land Use Change Detection Using Landsat 7 and 8 Imageries: A Case Study of Bogor Area as Water Buffer Zone of Jakarta. IOP Conference Series: Earth and Environmental Science, 581(1). https://doi.org/10.1088/1755-1315/581/1/012045
dc.relation.referencesHoward, G. W., & Harley, K. L. S. (1997). How do floating aquatic weeds affect wetland conservation and development? How can these effects be minimised? Wetlands Ecology and Management, 5(3), 215–225. https://doi.org/10.1023/A:1008209207736
dc.relation.referencesHu, Q., Woldt, W., Neale, C., Zhou, Y., Drahota, J., Varner, D., Bishop, A., LaGrange, T., Zhang, L., & Tang, Z. (2021). Utilizing unsupervised learning, multi-view imaging, and CNN-based attention facilitates cost-effective wetland mapping. Remote Sensing of Environment, 267, 112757. https://doi.org/10.1016/J.RSE.2021.112757
dc.relation.referencesHumboldt, I. de I. de R. B. A. von. (n.d.). FAUNA EXÓTICA E INVASORA EN COLOMBIA.
dc.relation.referencesIGAC. (n.d.). Datos Geodésicos | Instituto Geográfico Agustín Codazzi. Retrieved March 25, 2025, from https://antiguo.igac.gov.co/es/contenido/areas-estrategicas/informacion-geodesica
dc.relation.referencesJagannathan, J., & Divya, C. (2021). Deep learning for the prediction and classification of land use and land cover changes using deep convolutional neural network. Ecological Informatics, 65(August), 101412. https://doi.org/10.1016/j.ecoinf.2021.101412
dc.relation.referencesJiménez Prado, P. J., Vásquez, F., Rodríguez-Olarte, D., & Taphorn, D. (2020). Efectos de la especie invasora (Cyprinodontiformes: Poeciliidae) sobre Pseudopoecilia fria en ríos costeros de la región del Chocó, Ecuador. Revista de Biología Tropical, 68(1), 122–138. https://doi.org/10.15517/rbt.v68i1.36000
dc.relation.referencesKandel, I., & Castelli, M. (2020). The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express, 6(4), 312–315. https://doi.org/10.1016/j.icte.2020.04.010
dc.relation.referencesKaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
dc.relation.referencesKaur, R., & Pandey, P. (2022). A review on spectral indices for built-up area extraction using remote sensing technology. Arabian Journal of Geosciences, 15(5). https://doi.org/10.1007/s12517-022-09688-x
dc.relation.referencesKaur, S. (2020). CONTROL DE CALIDAD OPERACIONAL DE PROCESADOR DE INDICADORES AGRONÓMICOS DE ALTA RESOLUCIÓN ESTIMADOS CON DATOS DE SATÉLITES SENTINEL 2 Y LANDSAT 8. Universidad Politecnica de Valencia.
dc.relation.referencesKavran, D., Mongus, D., Žalik, B., & Lukač, N. (2023). Graph Neural Network-Based Method of Spatiotemporal Land Cover Mapping Using Satellite Imagery. Sensors, 23(14). https://doi.org/10.3390/s23146648
dc.relation.referencesKeskes, M. I., Mohamed, A. H., & Borz, S. A. (2025). Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery.
dc.relation.referencesKriticos, D. J., & Brunel, S. (2016). Assessing and managing the current and future pest risk from water hyacinth, (Eichhornia crassipes), an invasive aquatic plant threatening the environment and water security. PLoS ONE, 11(8), 1–18. https://doi.org/10.1371/journal.pone.0120054
dc.relation.referencesLary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2016). Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1), 3–10. https://doi.org/10.1016/j.gsf.2015.07.003
dc.relation.referencesLedesma, C., Bonansea, M., Rodriguez, C. M., & Delgado, A. R. S. (2013). Determinación de indicadores de eutrofización en el embalse Río Tercero, Córdoba (Argentina). Revista Ciencia Agronomica, 44(3), 419–425. https://doi.org/10.1590/S1806-66902013000300002
dc.relation.referencesLi, M., Dong, J., Zhang, Y., Yang, H., Van Zwieten, L., Lu, H., Alshameri, A., Zhan, Z., Chen, X., Jiang, X., Xu, W., Bao, Y., & Wang, H. (2021). A critical review of methods for analyzing freshwater eutrophication. Water (Switzerland), 13(2), 1–20. https://doi.org/10.3390/w13020225
dc.relation.referencesLiao, C., Wang, J., Xie, Q., Baz, A. Al, Huang, X., Shang, J., & He, Y. (2020). Synergistic use of multi-temporal RADARSAT-2 and VENμS data for crop classification based on 1D convolutional neural network. Remote Sensing, 12(5), 1–17. https://doi.org/10.3390/rs12050832
dc.relation.referencesMahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plümer, L., Steiner, U., & Oerke, E. C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30. https://doi.org/10.1016/j.rse.2012.09.019
dc.relation.referencesMain-Knorn, Pflug, Bebaecker, & Louis. (2015). CALIBRATION AND VALIDATION PLAN FOR THE L2A PROCESSOR AND PRODUCTS OF THE SENTINEL-2 MISSION M. XL(May), 11–15. https://doi.org/10.5194/isprsarchives-XL-7-W3-1249-2015
dc.relation.referencesMartínez, A. A. A., Rodríguez, J. M., & Hernández, A. C. (2014). LOS PAISAJES DE HUMEDALES, MARCO CONCEPTUAL Y ASPECTOS METODOLÓGICOS PARA SU ESTUDIO Y ORDENAMIENTO. Mercator (Fortaleza), 13(2), 169–191. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1984-22012014000200169&lang=pt%0Ahttp://www.scielo.br/pdf/mercator/v13n2/1676-8329-mercator-13-02-0169.pdf
dc.relation.referencesMazón, B., Rivas, W., & Mejia, E. (2018). Capítulo 1: Generalidades de las redes neuronales artificiales. Redes Neuronales Artificiales Aplicadas Al Reconocimiento de Patrones, September, 11–35. https://www.researchgate.net/publication/327703478_Capitulo_1_Generalidades_de_las_redes_neuronales_artificiales
dc.relation.referencesMcCarty, D. A., Kim, H. W., & Lee, H. K. (2020). Evaluation of light gradient boosted machine learning technique in large scale land use and land cover classification. Environments - MDPI, 7(10), 1–22. https://doi.org/10.3390/environments7100084
dc.relation.referencesMcFeeters, S. K. (2013). Using the normalized difference water index (ndwi) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7), 3544–3561. https://doi.org/10.3390/rs5073544
dc.relation.referencesMehmood, K., Anees, S. A., Rehman, A., Pan, S., Tariq, A., Zubair, M., Liu, Q., Rabbi, F., Khan, K. A., & Luo, M. (2024). Exploring spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: Insights for sustainable management and climate resilience. Ecological Informatics, 80(February), 102532. https://doi.org/10.1016/j.ecoinf.2024.102532
dc.relation.referencesMengistu, B. B., Unbushe, D., & Abebe, E. (2017). Invasion of Water Hyacinth (Eichhornia crassipes) Is Associated with Decline in Macrophyte Biodiversity in an Ethiopian Rift-Valley Lake—Abaya. Open Journal of Ecology, 07(13), 667–681. https://doi.org/10.4236/oje.2017.713046
dc.relation.referencesMigas-Mazur, R., Kycko, M., Zwijacz-Kozica, T., & Zagajewski, B. (2021). Assessment of sentinel-2 images, support vector machines and change detection algorithms for bark beetle outbreaks mapping in the tatra mountains. Remote Sensing, 13(16). https://doi.org/10.3390/rs13163314
dc.relation.referencesMintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy and Allied Technologies, 28(2), 73–81. https://doi.org/10.1080/13645706.2019.1575882
dc.relation.referencesMontalvo, J. F., García Ramil, I. de los A., Almeida Rodríguez, M., Betanzos Vega, A., & García García, N. (2014). Modelación de la eutroficación e índice de calidad del agua en algunas bahías del archipiélago Sabana Camagüey. Tecnologí­a Química, 34(3), 184–196. https://www.redalyc.org/pdf/4455/445543783002.pdf
dc.relation.referencesMora-Goyes, M. F., Rubio, J., Ocampo, R., & Barrera-Cataño, J. I. (2015). Catálogo de especies invasoras del territorio CAR. Pontificia Universidad Javeriana, Corporación Autónoma Regional de Cundinamarca – CAR, 238. https://www.car.gov.co/uploads/files/5b451c903677d.pdf
dc.relation.referencesMousa, A., Shahin, I., Nassif, A. B., & Elnagar, A. (2020). Cascaded RBF-CBiLSTM for Arabic Named Entity Recognition. Proceedings of the 2020 IEEE International Conference on Communications, Computing, Cybersecurity, and Informatics, CCCI 2020, 1–5. https://doi.org/10.1109/CCCI49893.2020.9256638
dc.relation.referencesMu, M., Li, Y., Bi, S., Lyu, H., Xu, J., Lei, S., Miao, S., Zeng, S., Zheng, Z., & Du, C. (2021). Prediction of algal bloom occurrence based on the naive Bayesian model considering satellite image pixel differences. Ecological Indicators, 124, 107416. https://doi.org/10.1016/j.ecolind.2021.107416
dc.relation.referencesMunizaga, J., García, M., Ureta, F., Novoa, V., Rojas, O., & Rojas, C. (2022). Mapping Coastal Wetlands Using Satellite Imagery and Machine Learning in a Highly Urbanized Landscape. Sustainability (Switzerland), 14(9), 1–19. https://doi.org/10.3390/su14095700
dc.relation.referencesNaidu, G., Zuva, T., & Sibanda, E. M. (2023). A Review of Evaluation Metrics in Machine Learning Algorithms. Lecture Notes in Networks and Systems, 724 LNNS, 15–25. https://doi.org/10.1007/978-3-031-35314-7_2
dc.relation.referencesNyenje, P. M., Foppen, J. W., Uhlenbrook, S., Kulabako, R., & Muwanga, A. (2010). Eutrophication and nutrient release in urban areas of sub-Saharan Africa - A review. Science of the Total Environment, 408(3), 447–455. https://doi.org/10.1016/j.scitotenv.2009.10.020
dc.relation.referencesOlaniyi, E., Oyedotun, O., & Khashman, A. (2017). A simple and practical review of over-fitting in neural network learning. International Journal of Applied Pattern Recognition, 4(4), 307. https://doi.org/10.1504/ijapr.2017.10010243
dc.relation.referencesPadilla-Mendoza, C., Torres-Bejarano, F., Campo-Daza, G., & González-Márquez, L. C. (2023). Potential of Sentinel Images to Evaluate Physicochemical Parameters Concentrations in Water Bodies—Application in a Wetlands System in Northern Colombia. Water (Switzerland), 15(4). https://doi.org/10.3390/w15040789
dc.relation.referencesPark, Y. S., & Lek, S. (2016). Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling. In Developments in Environmental Modelling (Vol. 28). Elsevier. https://doi.org/10.1016/B978-0-444-63623-2.00007-4
dc.relation.referencesPatel, S. (2012). Threats, management and envisaged utilizations of aquatic weed Eichhornia crassipes: An overview. Reviews in Environmental Science and Biotechnology, 11(3), 249–259. https://doi.org/10.1007/s11157-012-9289-4
dc.relation.referencesPatil, S. D., Gu, Y., Dias, F. S. A., Stieglitz, M., & Turk, G. (2017). Predicting the spectral information of future land cover using machine learning. International Journal of Remote Sensing, 38(20), 5592–5607. https://doi.org/10.1080/01431161.2017.1343512
dc.relation.referencesPeña-Guzmán, C. A., Melgarejo, J., & Prats, D. (2016). El ciclo urbano del agua en Bogotá, Colombia: Estado actual y desafíos para la sostenibilidad. Tecnologia y Ciencias Del Agua, 7(6), 57–71.
dc.relation.referencesPiao, Y., Jeong, S., Park, S., & Lee, D. (2021). Analysis of land use and land cover change using time-series data and random forest in north korea. Remote Sensing, 13(17), 1–18. https://doi.org/10.3390/rs13173501
dc.relation.referencesPiedad, A., Hernández, D., Lárraga, H., & González, E. (2020). TELEDETECCIÓN EN LA AGRICULTURA DE PRECISIÓN: ESTADO DEL ARTE DE LOS ÍNDICES DE VEGETACIÓN. TECTZAPIC, 6(2), 46–58.
dc.relation.referencesPolykretis, C., Grillakis, M. G., & Alexakis, D. D. (2020). Exploring the impact of various spectral indices on land cover change detection using change vector analysis: A case study of Crete Island, Greece. Remote Sensing, 12(2). https://doi.org/10.3390/rs12020319
dc.relation.referencesPranckutė, R. (2021). Web of Science (WoS) and Scopus: the titans of bibliographic information in today’s academic world. Publications, 9(1). https://doi.org/10.3390/publications9010012
dc.relation.referencesQuispe, L., Arias Chavez, J. B., Martinez Suarez, C. F., & Cruz Huaranga, M. (2017). Eficiencia de la especie macrófita Eichhornia crassipes (Jacinto de agua) para la remoción de parámetros fisicoquímicos, metal pesado (Pb) y la evaluación de su crecimiento en función al tiempo y adopción al medio en una laguna experimental. Revista de Investigación Ciencia, Tecnología y Desarrollo, 3(1). https://doi.org/10.17162/rictd.v1i1.899
dc.relation.referencesRamsar. (1971a). Criterios para Sitios Ramsar. Ramsar.Org, 1–2. http://www.ramsar.org/sites/default/files/documents/library/ramsarsites_criteria_sp.pdf
dc.relation.referencesRamsar. (2024). La Lista de Humedales de Importancia Internacional | The Convention on Wetlands, La Convención sobre los Humedales. https://www.ramsar.org/es/document/la-lista-de-humedales-de-importancia-internacional
dc.relation.referencesRamsar, I. (1971b). Convención Relativa a los Humedales de Importancia Internacional Especialmente como Hábitat de Aves Acuáticas.
dc.relation.referencesRanganath, R., . Jayaraman, V., & Roy, P. (2007). Remote sensing applications: An overview. Current Science, 93(12), 1747–1766. https://www.jstor.org/stable/24102069
dc.relation.referencesRifai, M., & Harintaka. (2024). Integration of Cloud Score+ with Sentinel-2 Harmonized for land use and land cover classification using machine learning algorithms. IOP Conference Series: Earth and Environmental Science, 1418(1). https://doi.org/10.1088/1755-1315/1418/1/012039
dc.relation.referencesRodríguez-Lara, J. W., Cervantes-Ortiz, F., Arámbula-Villa, G., Mariscal-Amaro, L. A., Aguirre-Mancilla, C. L., & Andrio-Enríquez, E. (2022). Lirio acuático (Eichhornia crassipes): una revisión. Agronomia Mesoamericana, 33(1). https://doi.org/10.15517/am.v33i1.44201
dc.relation.referencesRodríguez-Puerta, F., Perroy, R. L., Barrera, C., Price, J. P., & García-Pascual, B. (2024). Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i. Remote Sensing, 16(24). https://doi.org/10.3390/rs16244791
dc.relation.referencesRosselli, L., & Stiles, F. G. (2012). Wetland habitats of the Sabana de Bogotá Andean Highland Plateau and their birds. Aquatic Conservation: Marine and Freshwater Ecosystems, 22(3), 303–317. https://doi.org/10.1002/aqc.2234
dc.relation.referencesRuiz, A. E., Stephens, C. R., & Flores, H. (2015). Una Generalización del Clasificador Naive Bayes para Usarse en Bases de Datos con Dependencia de Variables. Research in Computing Science, 94(1), 59–71. https://doi.org/10.13053/rcs-94-1-5
dc.relation.referencesRuiz, M. P., Huamán, E., & Mejía, F. (2019). Diagnóstico ecológico del humedal Chochoc. Revista de Investigacion Cientifica REBIOL, 39(2), 1–16.
dc.relation.referencesSandoval, D. (2012). Protected areas in the city, urban wetlands of Bogotá. Cuadernos de Arquitectura y Urbanismo, 6(11), 80–103.
dc.relation.referencesSecretaría Distrital de Ambiente. (2016, November 1). Humedal Juan Amarillo ahora tiene más oxígeno gracias a recuperación En siete meses, Acueducto ha retirado 35 toneladas de buchón en 10 hectáreas de este espejo de agua. » Observatorio Ambiental de Bogotá. Observatorio Ambiental de Bogotá. https://oab.ambientebogota.gov.co/humedal-juan-amarillo-ahora-tiene-mas-oxigeno-gracias-a-recuperacion-en-siete-meses-acueducto-ha-retirado-35-toneladas-de-buchon-en-10-hectareas-de-este-espejo-de-agua/
dc.relation.referencesSecretaría Distrital de Ambiente, & Corporación Autónoma Regional de Cundinamarca. (2023). Resolución Conjunta 37 de 2023 Por la cual se adopta el Plan de Manejo Ambiental del sitio Ramsar Complejo de Humedales Urbanos del Distrito Capital de Bogotá y se toman otras determinaciones.
dc.relation.referencesSenhadji-Navarro, K., Ruiz-Ochoa, M. A., & Rodríguez Miranda, J. P. (2017). Estado ecológico de algunos humedales colombianos en los últimos 15 años: Una evaluación prospectiva. Colombia Forestal, 20(2), 181–191. https://doi.org/10.14483/udistrital.jour.colomb.for.2017.2.a07
dc.relation.referencesShang, C., Li, X., Foody, G. M., Du, Y., & Ling, F. (2022). Superresolution Land Cover Mapping Using a Generative Adversarial Network. IEEE Geoscience and Remote Sensing Letters, 19, 1–5. https://doi.org/10.1109/LGRS.2020.3020395
dc.relation.referencesShrestha, M., Mitra, C., Rahman, M., & Marzen, L. (2023). Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques. Remote Sensing, 15(1). https://doi.org/10.3390/rs15010106
dc.relation.referencesSilva, L. P. e., Xavier, A. P. C., da Silva, R. M., & Santos, C. A. G. (2020). Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil. Global Ecology and Conservation, 21. https://doi.org/10.1016/j.gecco.2019.e00811
dc.relation.referencesSingh, G., Reynolds, C., Byrne, M., & Rosman, B. (2020). A remote sensing method to monitor water, aquatic vegetation, and invasive water hyacinth at national extents. Remote Sensing, 12(24), 1–24. https://doi.org/10.3390/rs12244021
dc.relation.referencesSirous, A., Satari, M., Shahraki, M. M., & Pashayi, M. (2023). A Conditional Generative Adversarial Network for urban area classification using multi-source data. Earth Science Informatics, 16(3), 2529–2543. https://doi.org/10.1007/s12145-023-01050-3
dc.relation.referencesSmith Guerra, P., & Romero Aravena, H. (2009). Efectos del crecimiento urbano del Área Metropolitana de Concepción sobre los humedales de Rocuant-Andalién, Los Batros y Lenga. Revista de Geografía Norte Grande, 43. https://doi.org/10.4067/s0718-34022009000200005
dc.relation.referencesSoria, E., Sánchez, M., Gamero, R., Castillo, B., & Cano, P. (2023). Sistemas de Aprendizaje Automático (R. Editorial (ed.)). Ra - Ma.
dc.relation.referencesSoto-Durán, D. E., Marín-Morales, M. I., & Vargas-Agudelo, F. A. (2014). Caracterización de Formatos de Almacenamiento, Transporte y Visualización de Datos Geográficos. Lámpsakos, 12, 23. https://doi.org/10.21501/21454086.1309
dc.relation.referencesSrivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958.
dc.relation.referencesStiles, F. G., Rosselli, L., & De la Zerda, S. (2021). Una avifauna en cambio: 26 años de conteos navideños en la Sabana de Bogotá, Colombia. Ornitologia Colombiana, 1–65.
dc.relation.referencesSun, Z., Di, L., & Fang, H. (2019). Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series. International Journal of Remote Sensing, 40(2), 593–614. https://doi.org/10.1080/01431161.2018.1516313
dc.relation.referencesTarazona, M., Salamanca-Coy, J. L., Forero-Gutièrrez, K., Núñez, L. A., Pisco-Guabave, J., Escobar-Diaz, F., & Sierra-Porta, D. (2024). Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges. International Journal of Remote Sensing, 45(17), 5713–5736. https://doi.org/10.1080/01431161.2024.2373338
dc.relation.referencesTegegne, A. M. (2022). Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones. Journal of Engineering (United Kingdom), 2022. https://doi.org/10.1155/2022/6372089
dc.relation.referencesTorres, J., Zarzoso, J., Ten, M., Gaitán, R., & Lluch, J. (2009). Edición y visualización de información vectorial en aplicaciones SIG. 19th Spanish Computer Graphics Conference, CEIG 2009, 11, 115–124. https://doi.org/10.2312/LocalChapterEvents/CEIG/CEIG09/115-124
dc.relation.referencesTran, T. V., Reef, R., & Zhu, X. (2022). A Review of Spectral Indices for Mangrove Remote Sensing. Remote Sensing, 14(19). https://doi.org/10.3390/rs14194868
dc.relation.referencesTsutsumida, N., Nasahara, K., Tadono, T., Birch, T., & Erickson, T. (2023). 10-Meter Resolution Land Cover Classification Mapping Using Sentinel-1 & 2 and Dynamic World. International Geoscience and Remote Sensing Symposium (IGARSS), 2023-July, 2954–2957. https://doi.org/10.1109/IGARSS52108.2023.10282556
dc.relation.referencesUnited Nations. (n.d.). Water – at the center of the climate crisis. Retrieved May 13, 2024, from https://www.un.org/en/climatechange/science/climate-issues/water
dc.relation.referencesvan Duynhoven, A., & Dragićević, S. (2021). Exploring the sensitivity of recurrent neural network models for forecasting land cover change. Land, 10(3). https://doi.org/10.3390/land10030282
dc.relation.referencesVilardy, S., Jaramillo, U., Florez, C., Cortés, J., Estupiñan, L., Acevedo, O., Samacá, W., Santos, A., Peláez, S., & Aponte, C. (2014). Principios y criterios para la delimitación de humedales continentales principios y criterios para la delimitación de humedales continentales una herramienta para fortalecer la resiliencia y la adaptación al cambio climático en colombia. Instituto de Investigación de Recursos Biológicos Alexander von Humbold.
dc.relation.referencesVizcaíno Mendoza, L., Fuentes Molina, N., & González Fragozo, H. (2017). Adsorción de plomo (II) en solución acuosa con tallos y hojas de Eichhornia crassipes. Revista U.D.C.A Actualidad & Divulgación Científica, 20(2), 435–444. https://doi.org/10.31910/rudca.v20.n2.2017.400
dc.relation.referencesWang, M., Lu, S., Zhu, D., Lin, J., & Wang, Z. (2018). A High-Speed and Low-Complexity Architecture for Softmax Function in Deep Learning. 2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018, 223–226. https://doi.org/10.1109/APCCAS.2018.8605654
dc.relation.referencesWaoo, A. A., & Soni, B. K. (2021). Performance Analysis of Sigmoid and Relu Activation Functions in Deep Neural Network. 39–52. https://doi.org/10.1007/978-981-16-2248-9_5
dc.relation.referencesWei, P., Ye, H., Qiao, S., Liu, R., Nie, C., Zhang, B., Song, L., & Huang, S. (2023). Early Crop Mapping Based on Sentinel-2 Time-Series Data and the Random Forest Algorithm. Remote Sensing, 15(13), 1–18. https://doi.org/10.3390/rs15133212
dc.relation.referencesWu, H., & Ding, J. (2020). Abiotic and Biotic Determinants of Plant Diversity in Aquatic Communities Invaded by Water Hyacinth [Eichhornia crassipes (Mart.) Solms]. Frontiers in Plant Science, 11(August), 1–11. https://doi.org/10.3389/fpls.2020.01306
dc.relation.referencesXiao, Z., Xu, P., Wang, X., Chen, L., & An, F. (2020). A Multi-Class Objects Detection Coprocessor with Dual Feature Space and Weighted Softmax. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(9), 1629–1633. https://doi.org/10.1109/TCSII.2020.3010517
dc.relation.referencesXu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
dc.relation.referencesXu, Y., Yang, Y., Chen, X., & Liu, Y. (2022). Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021. Remote Sensing, 14(16), 1–20. https://doi.org/10.3390/rs14163967
dc.relation.referencesYang, L., Hu, C., Hu, W., Wang, Z., Zhang, M., Cang, Y., & Yang, B. (2024). Artificial neural network assisted omnidirectional strain sensors for human motion perception. Chemical Engineering Journal, 502(September), 158115. https://doi.org/10.1016/j.cej.2024.158115
dc.relation.referencesYasin, M. Y., Abdullah, J., Noor, N. M., Yusoff, M. M., & Noor, N. M. (2022). Landsat observation of urban growth and land use change using NDVI and NDBI analysis. IOP Conference Series: Earth and Environmental Science, 1067(1). https://doi.org/10.1088/1755-1315/1067/1/012037
dc.relation.referencesZarkami, R., Ahmadi, M., & Abedini, A. (2021). Modelling habitat preferences of water hyacinth (Eichhornia crassipes) in some wetlands of Guilan province. Iranian Journal of Biology, 34(2), 449–466.
dc.relation.referencesZarkami, R., Esfandi, J., & Sadeghi, R. (2021). Modelling Occurrence of Invasive Water Hyacinth (Eichhornia crassipes) in Wetlands. Wetlands, 41(1). https://doi.org/10.1007/s13157-021-01405-w
dc.relation.referencesZhang, C., Liu, Y., & Tie, N. (2023). Forest Land Resource Information Acquisition with Sentinel-2 Image Utilizing Support Vector Machine , K-Nearest Neighbor , Random Forest , Decision Trees and Multi-Layer Perceptron.
dc.relation.referencesZhang, L., & Zhang, L. (2022). Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities. IEEE Geoscience and Remote Sensing Magazine, 10(2), 270–294. https://doi.org/10.1109/MGRS.2022.3145854
dc.relation.referencesZhang, R., Yu, L., Tian, S., & Lv, Y. (2019). Unsupervised remote sensing image segmentation based on a dual autoencoder. Https://Doi.Org/10.1117/1.JRS.13.038501, 13(3), 038501. https://doi.org/10.1117/1.JRS.13.038501
dc.relation.referencesZhang, X., Chen, G., Wang, W., Wang, Q., & Dai, F. (2017). Object-Based Land-Cover Supervised Classification for Very-High-Resolution UAV Images Using Stacked Denoising Autoencoders. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(7), 3373–3385. https://doi.org/10.1109/JSTARS.2017.2672736
dc.relation.referencesZhang, X., Zhou, Y., & Luo, J. (2022). Deep learning for processing and analysis of remote sensing big data: a technical review. Big Earth Data, 6(4), 527–560. https://doi.org/10.1080/20964471.2021.1964879
dc.relation.referencesZhang, Y., Yang, J., Wang, D., Wang, J., Yu, L., Yan, F., Chang, L., & Zhang, S. (2021). An integrated cnn model for reconstructing and predicting land use/cover change: A case study of the baicheng area, northeast china. Remote Sensing, 13(23). https://doi.org/10.3390/rs13234846
dc.relation.referencesZhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: a review. december. https://doi.org/10.1109/MGRS.2017.2762307
dc.rights.accesoAbierto (Texto Completo)
dc.rights.accessrightsOpenAccess
dc.subjecthumedales
dc.subjectimágenes satelitales
dc.subjectinteligencia artificial
dc.subjectsistemas de información geográfica
dc.subject.keywordwetlands
dc.subject.keywordsatellite imagery
dc.subject.keywordartificial intelligence
dc.subject.keywordGeographic Information Systems
dc.subject.lembMaestría en ingeniería Civil -- Tesis y disertaciones académicas
dc.titleMonitoreo de la variación de los espejos de agua de los humedales Ramsar de Bogotá D.C. utilizando inteligencia artificial a partir de imágenes satelitales
dc.title.titleenglishMonitoring the variation of water bodies in the Ramsar wetlands of Bogotá D.C. using artificial intelligence and satellite imagery
dc.typemasterThesis
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.degreeMonografía
dc.type.driverinfo:eu-repo/semantics/bachelorThesis

Archivos

Bloque original

Mostrando 1 - 2 de 2
No hay miniatura disponible
Nombre:
ViasusWilchesJennyferDamaris2025.pdf
Tamaño:
10.08 MB
Formato:
Adobe Portable Document Format
Descargar
No hay miniatura disponible
Nombre:
Licencia de uso y publicacion.pdf
Tamaño:
254.52 KB
Formato:
Adobe Portable Document Format
Descargar

Bloque de licencias

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
7 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Maestría en Ingeniería Civil
Universidad Distrital Francisco José de Caldas - Biblioteca UDFJC

Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional | Acuerdo de creación N° 10 de 1948 del Concejo de Bogotá | Acreditación Institucional de Alta Calidad - Resolución N° 23096 del 15 de diciembre del 2016.

Contáctenos
  • Calle 13 No. 31 - 75 Bogotá D.C. - República de Colombia
  • (+57 601) 3239300
  • Lunes a viernes de 8 a.m. a 5 p.m.
  • repositorio@udistrital.edu.co
Síguenos en redes sociales

Software DSpace copyright © 2002-2025 LYRASIS