The Technological Development Model for Environmental Monitoring Aimed at the Conservation of the Santa María del Lago Wetland Using IoT Sensors, Satellite Imagery, and Drones
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i1.1189Keywords:
Environmental monitoring, IoT sensors, satellite remote sensing, urban wetlands.Abstract
In Bogotá, the absence of integrated data for wetland monitoring persists; therefore, a technological application structured in three phases was developed. In the first phase, information acquisition was conducted through flights using DJI Mini 2 and Phantom 4 Pro drones, with orthomosaics processed in Pix4D and WebODM, together with Landsat and Sentinel satellite imagery obtained from Google Earth Engine. In the second phase, the SIBIA platform was designed. This platform integrates Google Earth Engine panels, machine learning models implemented in Teachable Machine (a low-code environment), and their scaling in Python with OpenCV to classify vegetation and bodies of water. In the third phase, technical and community validation was carried out using IoT sensors, accuracy metrics (RMSE, MAE), and surveys with high reliability (α = 0.894). The results indicate high model accuracy (~0.98–1.00) and the generation of quality orthomosaics. In conclusion, the tool is shown to enhance environmental management and support sustainable decision-making for urban wetlands.
Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1189
Dimensions.
Open Alex.
References
Abdelkader, M., Bravo Mendez, J. H., Temimi, M., Brown, D. R. N., Spellman, K. V., Arp, C. D., Bondurant, A., & Kohl, H. (2024). A Google Earth Engine platform to integrate multi-satellite and citizen science data for the monitoring of river ice dynamics. Remote Sensing, 16(8), 1368. https://doi.org/10.3390/rs16081368
Avtar, R., Saito, O., Singh, G., Kobayashi, H., Ali, Y., Herath, S., & Takeuchi, K. (2014). Monitoring responses of terrestrial ecosystem to climate variations using multi-temporal remote sensing data in Ghana. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2014). IEEE. https://doi.org/10.1109/IGARSS.2014.6946535
Avtar, R., Yunus, A. P., Saito, O., Kharrazi, A., Kumar, P., & Takeuchi, K. (2022). Multi-temporal remote sensing data to monitor terrestrial ecosystem responses to climate variations in Ghana. Geocarto International, 37(2), 498–514. https://doi.org/10.1080/10106049.2020.1723716
Barros, J. (2020). Especial: El preocupante ranking de los humedales de Bogotá. Revista Semana. https://www.semana.com/impacto/informe-especial/articulo/especial-el-preocupante-ranking-de-los-humedales-de-bogota/56731/
Barros, J. (2020, April 12). Cuatro humedales bogotanos sucumben ante las basuras, cemento, ruido e incendios. Revista Semana. https://www.semana.com/medio-ambiente/articulo/cuatro-humedales-bogotanos-sucumben-ante-las-basuras-cemento-ruido-e-incendios/49524/
Bhatnagar, S., Gill, L., Regan, S., Waldren, S., & Ghosh, B. (2021). A nested drone-satellite approach to monitoring the ecological conditions of wetlands. ISPRS Journal of Photogrammetry and Remote Sensing, 174, 151–165. https://doi.org/10.1016/j.isprsjprs.2021.01.012
Bonilla, C., Brentan, B., Montalvo, I., Ayala-Cabrera, D., & Izquierdo, J. (2023). Digitalization of water distribution systems in small cities, a tool for verification and hydraulic analysis: A case study of Pamplona, Colombia. Water, 15(21), 3824. https://doi.org/10.3390/w15213824
Cuellar, Y., & Perez, L. (2023). Multitemporal modeling and simulation of the complex dynamics in urban wetlands: The case of Bogotá, Colombia. Scientific Reports, 13(1), 36600. https://doi.org/10.1038/s41598-023-36600-8
Das, N., & Mehrotra, S. (2021). Wetlands in urban contexts: A case of Bhoj Wetland. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2021). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9554693
Hao, Z., Cai, X., Ge, Y., Foody, G. M., Li, X., Yin, Z., Du, Y., & Ling, F. (2024). Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy. Journal of Hydrology, 640, 131673. https://doi.org/10.1016/j.jhydrol.2024.131673
Hellweger, F. L., Schlosser, P., Lall, U., & Weissel, J. K. (2004). Use of satellite imagery for water quality studies in New York Harbor. Estuarine, Coastal and Shelf Science, 61(3), 437–448. https://doi.org/10.1016/j.ecss.2004.06.019
IDEAM. (2023). IDEAM Informe 2023.
Imdad, K., Sahana, M., Ravetz, J., Areendran, G., Gautam, O., Dwivedi, S., Chaudhary, A., & Sajjad, H. (2023). A sustainable solution to manage ecosystem health of wetlands in urban and peri-urban areas of Lucknow district, India using geospatial techniques and community-based pragmatic approach. Journal of Cleaner Production, 414, 137646. https://doi.org/10.1016/j.jclepro.2023.137646
iNaturalist Colombia. (n.d.). Humedal Santa María del Lago [Proyecto de ciencia ciudadana]. Retrieved March 15, 2025, from https://colombia.inaturalist.org/projects/humedal-santa-maria-del-lago?tab=stats
Navarro Rau, M. F., Calamari, N. C., Navarro, C. S., Enriquez, A., Mosciaro, M. J., Saucedo, G., Barrios, R., Curcio, M., Dieta, V., Martínez, G. G., Iturralde Elortegui, M. del R., Michard, N. J., Paredes, P., Umaña, F., Alday, S., Pezzola, A., Vidal, C., Winschel, C., Franco, S. A., … Kurtz, D. B. (2025). Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring. Watershed Ecology and the Environment, 7, 144–158. https://doi.org/10.1016/j.wsee.2025.04.001
Pan, M., Hu, T., Zhan, J., Hao, Y., Li, X., & Zhang, L. (2023). Unveiling spatiotemporal dynamics and factors influencing the provision of urban wetland ecosystem services using high-resolution images. Ecological Indicators, 151, 110305. https://doi.org/10.1016/j.ecolind.2023.110305
Quy, V. K., Hau, N. Van, Anh, D. Van, Quy, N. M., Ban, N. T., Lanza, S., Randazzo, G., & Muzirafuti, A. (2022). IoT-enabled smart agriculture: Architecture, applications, and challenges. Applied Sciences, 12(7), 3396. https://doi.org/10.3390/app12073396
Secretaria de Ambiente de Bogotá. (2020). Humedales de Bogotá.
Shuai, X., & Qian, H. (2011). Design of wetland monitoring system based on the Internet of Things. Procedia Environmental Sciences, 10(Part B), 1046–1051. https://doi.org/10.1016/j.proenv.2011.09.167
Venturini, V., Marchetti, Z. Y., Walker, E., & Fagioli, G. (2023). Performance analysis of machine learning techniques to identify aquatic vegetation with Sentinel-2 bands. Ecología Austral, 33(3), 743–756. https://doi.org/10.25260/EA.23.33.3.0.1960
Zhao, Y., He, X., Pan, S., Bai, Y., Wang, D., Li, T., Gong, F., & Zhang, X. (2024). Satellite retrievals of water quality for diverse inland waters from Sentinel-2 images: An example from Zhejiang Province, China. International Journal of Applied Earth Observation and Geoinformation, 132, 104048. https://doi.org/10.1016/j.jag.2024.104048
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Combinatorial Optimization Problems and Informatics

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.