Resumen (es):
Este estudio se centra en el análisis multitemporal de la lámina de agua superficial en dos humedales: uno ubicado en un área protegida y el otro altamente antropizado por las presiones de su entorno. La metodología se basa en el uso de datos obtenidos mediante sensores de radar de apertura sintética (Synthetic Aperture Radar - SAR) de la misión Sentinel-1. El procesamiento de datos SAR se realizó principalmente en Google Earth Engine (GEE). Los resultados revelan comportamientos similares a lo largo del tiempo en ambos sistemas de humedales, con mayor variabilidad estacional y estocástica en el humedal mejor conservado. Además, se observan considerables variaciones en el tamaño de la lámina de agua superficial en momentos de mínima y máxima retrodispersión, llegando a cambiar hasta cinco veces su tamaño. En conclusión, los datos SAR proporcionan información suficiente para comprender las dinámicas de estos paisajes, siendo particularmente valiosos en la región tropical, donde la constante presencia de nubes limita el uso de sensores ópticos.
Resumen (en):
This study focuses on the multitemporal analysis of surface water in two nearby wetlands: One is situated within a protected area, while the other is in a highly anthropized zone subject to significant environmental pressures. The methodology is based on the use of data obtained through Synthetic Aperture Radar (SAR) sensors from the Sentinel-1 mission. Most of the SAR data processing was conducted in Google Earth Engine (GEE). The results reveal similar behaviors over time in both wetland systems, with greater seasonal and stochastic variability in the better-conserved wetland. Additionally, significant variations in the size of the water surface were observed during periods of minimum and maximum backscatter, with changes of up to five times its size. In conclusion, SAR data provides sufficient information to understand the dynamics of these landscapes, being particularly valuable in regions like Colombia, where the constant presence of clouds limits the use of optical sensors.
Palabras clave:
wetland, surface water extent, backscatter, remote sensing, threshold (en)
Referencias
Autoridad Nacional de Licencias Ambientales. (2019). PIS - Proyecto Hidroeléctrico Pescadero Ituango. https://www.anla.gov.co/proyectos-de-interes-en-seguimiento/pis-proyecto-hidroelectrico-pescadero-ituango
Barbier, E. B. (2011). Wetlands as natural assets. Hydrological Sciences Journal, 56(8), 1360-1373. https://doi.org/10.1080/02626667.2011.629787
Bauer-Marschallinger, B., Cao, S., Navacchi, C., Freeman, V., Reuß, F., Geudtner, Rommen, B., Ceba Vega, F., Snoeji, P., Attema, E., Reimer, C., & Wagner, W. (2021). The normalised Sentinel-1 global backscatter model, mapping Earth’s land surface with C-band microwaves. Scientific Data, 8(1), 1-18. https://doi.org/10.1038/s41597-021-01059-7
Betancur-Vargas, T., García-Giraldo, D. A., Vélez-Duque, A. J., Gómez, A. M., Flórez-Ayala, C., Patiño, J., & Ortiz-Tamayo, J. Á. (2017). Groundwater, wetlands and ecosystem services in Colombia. Biota Colombiana, 18(1), 1-28. https://doi.org/10.21068/c2017.v18n01a1
Betancur, T., Mejía, O., & Palacio, C. (2009). Modelo hidrogeológico conceptual del Bajo Cauca antioqueño: un sistema acuífero tropical. Revista Facultad de Ingeniería, 48, 107-118.
Bhatt, C. M., Gupta, A., Roy, A., Dalal, P., & Chauhan, P. (2019). Geospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data. Gomatics, Natural Hazards and Risk, 12(1), 84-102. https://doi.org/10.1080/19475705.2020.1861113
Clement, M. A., Kilsby, C. G., & Moore, P. (2018). Multi-temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 11(2), 152-168. https://doi.org/10.1111/jfr3.12303
Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the swir band. Remote Sensing, 8(4). https://doi.org/10.3390/rs8040354
Empresas Públicas de Medellín. (2022). Hidroituango. Proyecto Hidroeléctrico Ituango. https://www.hidroituango.com.co/caracteristicas-del-proyecto/
Estupiñán-Suárez, L. M., Flórez-Ayala, C., Quiñones, M. J., Pacheco, A. M., & Santos, A. C. (2015). Detection and characterization of Colombian wetlands: Integrating geospatial data with remote sensing derived data. Using Alos Palsar and Modis imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Archives, 40(7W3), 375-382. https://doi.org/10.5194/isprsarchives-XL-7-W3-375-2015
European Space Agency (2023). Sentinel-1 Missions. https://sentinel.esa.int/web/sentinel/missions/sentinel-1
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., & Burbank, D., & Alsdorf, D. E. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2), 2004. https://doi.org/10.1029/2005RG000183
Flórez, C., Estupiñán-Suárez, L. M., Rojas, S., Aponte, C., Quiñones, M., Acevedo, Ó., Vilardy, S., & Jaramillo, Ú. (2016). Identificación espacial de los sistemas de humedales continentales de Colombia. Biota Colombiana, 17(1), 44-62. https://doi.org/10.21068/C2016s01a03
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/J.RSE.2017.06.031
Gulácsi, A., & Kovács, F. (2020). Sentinel-1-imagery-based high-resolutionwater cover detection on wetlands, aided by Google Earth Engine. Remote Sensing, 12(10). https://doi.org/10.3390/rs12101614
Hanjsek, I., & Desnos, Y.-L. (2021). Polarimetric Synthetic Aperture Radar. Principles and application. Remote Sensing and Digital Image Processing. Springer. https://doi.org/10.1007/978-3-030-56504-6
Henderson, F. M., & Lewis, A. J. (2008). Radar detection of wetland ecosystems: A review. International Journal of Remote Sensing, 29(20), 5809-5835. https://doi.org/10.1080/01431160801958405
Hu, S., Qin, J., Ren, J., Zhao, H., Ren, J., & Hong, H. (2020). Automatic extraction of water inundation areas using Sentinel-1 data for large plain areas. Remote Sensing, 12(2). https://doi.org/10.3390/rs12020243
Instituto de Hidrología, Meteorología y Estudios Ambientales. (2005). Atlas climatológico de Colombia. http://www.ideam.gov.co/AtlasWeb/index.html
Kasischke, E. S., & Bourgeau-Chávez, L. L. (1997). Monitoring south Florida wetlands using ERS-1 SAR imagery. Photogrammetric Engineering and Remote Sensing, 63(3), 281-291.
Kasischke, E. S., Melack, J. M., & Dobson, M. C. (1997). The use of imaging radars for ecological applications. A review. Remote Sensing of Environment, 59(2), 141-156. https://doi.org/10.1016/S0034-4257(96)00148-4
Kendall, M. G., Stuart, A., & Ord, J. K. (1983). Kendall’s advanced theory of statistics (vol. 3). Oxford University Press.
Manjusree, P., Prasanna Kumar, L., Bhatt, C. M., Rao, G. S., & Bhanumurthy, V. (2012). Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR Images. International Journal of Disaster Risk Science, 3(2), 113-122. https://doi.org/10.1007/s13753-012-0011-5
Markert, K. N., Markert, A. M., Mayer, T., Nauman, C., Haag, A., Poortinga, A., Bhandari, B., Soe Thwal, N. S., Kunlamai, T., Chishtie, F., Kwant, M, Phongsapan, K., Clinton, N., Towashiraporn, P., & Saah, D. (2020). Comparing Sentinel-1 surface water mapping algorithms and radiometric terrain correction processing in southeast Asia utilizing Google Earth Engine. Remote Sensing, 12(15), 1-20. https://doi.org/10.3390/RS12152469
Mestas-Núñez, A. M., Enfield, D. B., & Zhang, C. (2007). Water vapor fluxes over the Intra-Americas Sea: Seasonal and interannual variability and associations with rainfall. Journal of Climate, 20(9), 1910-1922. https://doi.org/10.1175/JCLI4096.1
Ministerio de Medio Ambiente. (2001). National wetland policies: Colombia. Política nacional para humedales interiores de Colombia. Estrategias para su conservación y uso racional. https://www.ramsar.org/sites/default/files/documents/library/national_wetland_policies_-_colombia.pdf
Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., & Papathanassiou, K. P. (2013). A tutorial on synthetic aperture radar. https://doi.org/10.1109/MGRS.2013.2248301
Organización Meteorológica Mundial. (2022). El Niño/La Niña hoy. Organización Meteorológica Mundial.
Organización de las Naciones Unidas (2022). Objetivos de desarrollo sostenible. https://www.un.org/sustainabledevelopment/es/objetivos-de-desarrollo-sostenible/
Otsu, N., Smith, P. L., Reid, D. B., Environment, C., Palo, L., Alto, P., & Smith, P. L. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, SMC, (1), 62-66.
Passaro, M., Müller, F. L., & Dettmering, D. (2018). Lead detection using Cryosat-2 delay-doppler processing and Sentinel-1 SAR images. Advances in Space Research, 62(6), 1610-1625. https://doi.org/10.1016/j.asr.2017.07.011
Planet. (2023). Norway's International Climate and Forests Initiative Satellite Data Program. https://www.planet.com/nicfi/
Parques Nacionales Naturales de Colombia. (2023). Runap. Ciénagas Corrales y El Ocho. https://runap.parquesnacionales.gov.co/area-protegida/1544
Poveda, G., Espinoza, J. C., Zuluaga, M. D., Solman, S. A., Garreaud, R., & Van Oevelen, P. J. (2020). High impact weather events in the Andes. Frontiers in Earth Science, 8, 1-32. https://doi.org/10.3389/feart.2020.00162
Poveda, G., & Mesa, O. J. (1996). Las fases extremas del fenomeno ENSO (El Nino y La Nina) y su influencia sobre la hidrología de Colombia. Ingeniería Hidráulica en México, 11(1), 21-37.
Quiñones, M., Vissers, M., Pacheco-Pascaza, A. M., Flórez, C., Estupiñán-Suárez, L. M., Aponte, C., Jaramillo, Ú., Huertas, C., & Hoekman, D. (2016). Un enfoque ecosistémico para el análisis de una serie densa de tiempo de imágenes de radar Alos Palsar, para el mapeo de zonas inundadas en el territorio continental colombiano. Biota Colombiana, 7(2), 304. https://doi.org/10.21068/C2016s01a04
R Core Team. (2021). The R Proyect for Statistical Computing. https://www.r-project.org/
Ramsar Convention Secretariat. (1971). Convention on wetlands of international importance especially as waterfowl habitat. https://www.ramsar.org/sites/default/files/documents/library/scan_certified_e.pdf
Sekertekin, A. (2021). A survey on global thresholding methods for mapping open water body using Sentinel-2 satellite imagery and normalized difference water index. Archives of Computational Methods in Engineering, 28(3), 1335-1347. https://doi.org/10.1007/s11831-020-09416-2
Tian, H., Li, W., Wu, M., Huang, N., Li, G., Li, X., & Niu, Z. (2017). Dynamic monitoring of the largest freshwater lake in China using a new water index derived from high spatiotemporal resolution Sentinel-1A data. Remote Sensing, 9(6), 6-9. https://doi.org/10.3390/rs9060521
Tian, H., Wang, J., Pei, J., Qin, Y., Zhang, L., & Wang, Y. (2020). High spatiotemporal resolution mapping of surface water in the southwest Poyang lake and its responses to climate oscillations. Sensors, 20(17), 1-17. https://doi.org/10.3390/s20174872
Tiwari, V., Kumar, V., Matin, M. A., Thapa, A., Ellenburg, W. L., Gupta, N., & Thapa, S. (2020). Flood inundation mapping-Kerala 2018; harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PLoS ONE, 15(8), 1-17. https://doi.org/10.1371/journal.pone.0237324
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B. Ö., Floury, N., Brown, M., Traver, I. N., Deghaye, P., Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L’Abbate, M., Croci, R., Pietropaolo, A., & Rostan, F. (2012). GMES Sentinel-1 mission. Remote Sensing of Environment, 120, 9-24. https://doi.org/10.1016/j.rse.2011.05.028
Velásquez-Franco, P. A., & Pérez-González, M. E. (2024). Análisis de la dinámica espaciotemporal de humedales tropicales a través de imágenes SAR Sentinel-1: Caso de estudio en Colombia. Cuadernos de Geografía: Revista Colombiana de Geografía, 33(1). https://doi.org/https://doi.org/10.15446/rcdg.v33n1.105225
White, L., Brisco, B., Dabboor, M., Schmitt, A., & Pratt, A. (2015). A collection of SAR methodologies for monitoring wetlands. Remote Sensing, 7(6), 7615-7645. https://doi.org/10.3390/rs70607615
Xing, L., Tang, X., Wang, H., Fan, W., & Wang, G. (2018). Monitoring monthly surface water dynamics of Dongting lake using Sentinel-1 data at 10 m. PeerJ, 2018(6), 1-22. https://doi.org/10.7717/peerj.4992
Yan, K., Di Baldassarre, G., Solomatine, D. P., & Schumann, G. J. P. (2015). A review of low-cost space-borne data for flood modelling: topography, flood extent and water level. Hydrological Processes, 29(15), 3368-3387. https://doi.org/10.1002/hyp.10449
Zapata, G., Bermúdez, J. G., Rodríguez, G., & Arango, M. I. (2013). Cartografía geológica de la plancha 83 Nechí (Departamento de Antioquia). Servicio Geológico Colombiano, 94. https://recordcenter.sgc.gov.co/B14/23008010024595/Documento/Pdf/2105245951101000.pdf
Zedler, J. B., & Kercher, S. (2005). Wetland resources: Status, trends, ecosystem services, and restorability. Annual Review of Environment and Resources, 30, 39-74. https://doi.org/10.1146/annurev.energy.30.050504.144248
Zhang, M., Chen, F., Tian, B., Liang, D., & Yang, A. (2020). High-frequency glacial lake mapping using time series of Sentinel-1A/1B Sar imagery: an assessment for the southeastern Tibetan plateau. International Journal of Environmental Research and Public Health, 17(3). https://doi.org/10.3390/ijerph17031072
Cómo citar
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Derechos de autor 2023 Instituto de Investigación de Recursos Biológicos Alexander Von Humboldt