Flood extent delineation using Sentinel-1 data as information source: systematic review of processing methods

Autores

DOI:

https://doi.org/10.26848/rbgf.v15.6.p3047-3076

Palavras-chave:

Flood mapping, Thresholding, Synthetic Aperture Radar, Remote Sensing.

Resumo

Delineating the flood extent using SAR data has been an unexceptional activity since the launch of the first sensors. The methodologies applied to image classification vary in complexity and accuracy and, for Sentinel-1, the technical procedures must be adapted considering their polarization, band, temporal resolution, and other factors. Considering these aspects, this article aimed to realize a systematic literature review discussing the most frequent methodologies applied to flood mapping classification employing Sentinel-1 data; describe the preferred methods by areas of expertise; and, inform about their accuracy level.  Results indicate that thresholding is the most common methodologies used to flood delineation whatsoever is the researcher area of work. Excellent accuracy levels (reaching 99%) can be obtained using thresholding as the major method, with refinement provided by additional procedures to correct limitations and misclassification. The Sentinel-1data is a good source of information to realize classification in flooded areas but has its limitations in small areas. The choice of methodology for processing SAR data is subjective and requires specific knowledge in the remote sensing field from who executes a hydrological study.

Keywords: Flood mapping, thresholding, synthetic aperture radar, remote sensing.

 

Delineamento de extensão de inundação usando dados Sentinel-1 como fonte de informação: revisão sistemática dos métodos de processamento

 

RESUMO

Delinear a extensão da inundação usando dados SAR tem sido uma atividade comum desde o lançamento dos primeiros sensores. As metodologias aplicadas à classificação de imagens variam em complexidade e precisão e, para o satélite Sentinel-1, os procedimentos técnicos devem ser adaptados considerando sua polarização, banda, resolução temporal, entre outros fatores. Considerando esses aspectos, este artigo teve como objetivo realizar uma revisão sistemática de literatura discutindo as metodologias frequentemente aplicadas à classificação do mapeamento de inundações empregando dados do Sentinel-1; descrever os métodos preferidos por áreas de especialização do analista; e, informar sobre seu nível de precisão. Os resultados indicam que a limiarização é a metodologia mais comum utilizada para o delineamento de inundação, seja qual for a área de trabalho do pesquisador. Excelentes níveis de precisão (cerca de 99%) podem ser obtidos usando a limiarização como o método principal, com refinamento fornecido por procedimentos adicionais para corrigir limitações e erros de classificação. Os dados do Sentinel-1 são uma boa fonte de informação para realizar a classificação em áreas inundadas, mas têm suas limitações em áreas pequenas. A escolha da metodologia para o processamento de dados SAR revela-se subjetiva, inerente ao campo de estudo do pesquisador, e requer conhecimento específico na área de sensoriamento remoto de quem executa um estudo hidrológico.

Palavras-chave: Mapeamento de inundação, limiarização, radar de abertura sintética, sensoriamento remoto.

Downloads

Não há dados estatísticos.

Biografia do Autor

Mariane Souza Melo de Liz, Universidade do Estado de Santa Catarina - UDESC

Doutoranda no Programa de Pós-graduação em Planejamento Territorial de Desenvolvimento Socioambiental (PPGPLAN) da Universidade do Estado de Santa Catarina (UDESC)

Rodrigo Pinheiro Ribas, Universidade do Estado de Santa Catarina (UDESC)

Centro de Ciências Humanas e da Educação (FAED)
Departamento de Geografia
Programa de Pós-graduação em Planejamento Territorial e Desenvolvimento Socioambiental (PPGPLAN)

Referências

Agnihotri, A. K., Ohri, A., Gaur, S., Shivam, Das, N., Mishra, S., 2019. Flood inundation mapping and monitoring using SAR data and its impact on Ramganga River in Ganga basin. Environmental Monitoring and Assessment, 191(12). DOI 10.1007/s10661-019-7903-4.

Alves, M. E. P., Fan, F. M., Siqueira, V. A., Laipelt, L., 2020. Flood mapping employing local, regional and global scale modeling methods for the Uruguay river. Revista Eletrônica em Gestão, Educação e Tecnologia Ambiental, 24(22). DOI 10.5902/2236117062697.

Amitrano, D., Di Martino, G., Iodice, A., Riccio, D., Ruello, G., 2018. Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 56(6), 3290-3299. DOI 10.1109/tgrs.2018.2797536.

Antzoulatos, G., Kouloglou, I.-O., Bakratsas, M., Moumtzidou, A., Gialampoukidis, I., Karakostas, A., Lombardo, F., Fiorin, R., Norbiato, D., Ferri, M., Symeonidis, A., Vrochidis, S., Kompatsiaris, I., 2022. Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data. Sustainability, 14(6). DOI 10.3390/su14063251.

Aria, M., Cuccurullo, C., 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. DOI 10.1016/j.joi.2017.08.007.

Barasa, B., Wanyama, J., 2020. Freshwater lake inundation monitoring using Sentinel-1 SAR imagery in Eastern Uganda. Annals of GIS, 26(2), 191-200. DOI 10.1080/19475683.2020.1743754

Benzougagh, B., Frison, P.-L., Meshram, S. G., Boudad, L., Dridri, A., Sadkaoui, D., Mimich, K., Khedher, K. M., 2021. Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study - Inaouene Watershed from Northeast of Morocco. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46, 1481 – 1490. DOI 10.1007/s40996-021-00683-y.

Berezowski, T., Bielinski, T., Osowicki, J., 2018. Flooding Extent Mapping for Synthetic Aperture Radar Time Series Using River Gauge Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2626-2638. DOI 10.1109/jstars.2020.2995888.

Bioresita, F., Puissant, A., Stumpf, A., Malet, J.-P., 2018. A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery. Remote Sensing, 10(2), 217. DOI 10.3390/rs10020217.

Budiman, J., Bahrawi, J., Hidayatulloh, A., Almazroui, M., Elhag, M., 2021. Volumetric Quantification of Flash Flood Using Microwave Data on a Watershed Scale on Arid Environments, Saudi Arabia. Sustainability, 13(8), 4115. DOI 10.3390/su13084115

Cao, W., Twele, A., Plank, S., Martinis, S., 2017. A three-class change detection methodology for SAR-data based on hypothesis testing and Markov Random field modelling. International Journal of Remote Sensing, 39(2), 488-504. DOI 10.1080/01431161.2017.1384590.

Cao, H., Zhang, H., Wang, C., Zhang, B., 2019. Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas. Water, 11(4), 786. DOI 10.3390/w11040786.

Cazals, C., Rapinel, S., Frison, P.-L., Bonis, A., Mercier, G., Mallet, C., Corgne, S., Rudant, J.-P., 2016. Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images. Remote Sensing, 8(7), 570. DOI 10.3390/rs8070570.

Chini, M., Pelich, R., Pulvirenti, L., Pierdicca, N., Hostache, R., Matgen, P., 2019. Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case. Remote Sensing, 11(2), 107. DOI 10.3390/rs11020107.

Chithra, K., Binoy, B. V., Bimal, P., 2022. Spatial Mapping of the Flood-Affected Regions of Northern Kerala: A Case Study of 2018 Kerala Floods. Journal of the Indian Society of Remote Sensing, 50(4). DOI 10.1007/s12524-021-01485-5.

Cian, F., Marconcini, M., Ceccato, P., 2018. Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data. Remote Sensing of Environment, 209, 712-730. DOI 10.1016/j.rse.2018.03.006

Carreño Conde, F., De Mata Muñoz, M., 2019. Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study. Water, 11(12), 2454. DOI 10.3390/w11122454.

Debusscher, B., Van Coillie, F., 2019. Object-Based Flood Analysis Using a Graph-Based Representation. Remote Sensing, 11(16), 1883. DOI 10.3390/rs11161883.

Debusscher, B., Landuyt, L., Van Coillie, F., 2020. A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach. Remote Sensing, 12(13), 2118. DOI 10.3390/rs12132118.

Di Baldassarre, G., Schumann, G., Bates, P. D., 2009. A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent. Journal of Hydrology, 367(3–4), 276–282. http://dx.doi.org/10.1016/j.jhydrol.2009.01.020

Elhag, M., Abdurahman, S. G., 2020. Advanced remote sensing techniques in flash flood delineation in Tabuk City, Saudi Arabia. Natural Hazards, 103(3), 3401-3413. DOI 10.1007/s11069-020-04135-0.

Elkhrachy, I., 2017. Assessment and Management Flash Flood in Najran Wady Using GIS and Remote Sensing. Journal of the Indian Society of Remote Sensing, 46(2), 297-308. DOI 10.1007/s12524-017-0670-1.

Elkhrachy, I., Pham, Q. B., Costache, R., Mohajane, M., Rahman, K. U., Shahabi, H., Linh, N. T. T., Anh, D. T., 2021. Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt. Journal of Flood Risk Management,14(2). DOI 10.1111/jfr3.12692

ESA. European Space Agency. 2022. Sentinel-1 Online: Level-1 SLC Products. Technical guides. Available in: https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/products-algorithms/level-1-algorithms/single-look-complex.

Ezzine, A., Darragi, F., Rajhi, H., Ghatassi, A., 2018. Evaluation of Sentinel-1 data for flood mapping in the upstream of Sidi Salem dam (Northern Tunisia). Arabian Journal of Geosciences, 11(8). DOI 10.1007/s12517-018-3505-7.

Filipponi, F., 2019. Sentinel-1 GRD Preprocessing Workflow. In: 3rd International Electronic Conference on Remote Sensing, 18(11). DOI 10.3390/ecrs-3-06201

Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep Learning. MIT Press.

Goumehei, E., Tolpekin, V., Stein, A., Yan, W., 2019. Surface Water Body Detection in Polarimetric SAR Data Using Contextual Complex Wishart Classification. Water Resources Research, 55(8), 7047-7059. DOI 10.1029/2019wr025192.

Guha-Sapir, D., Hoyois, P., Wallemacq, P., Below, R., 2016. Annual Disaster Statistical Review 2016: The numbers and trends. Centre for Research on the Epidemiology of Disasters (CRED). Brussels, Belgium.

Haralick, R. M., Shanmugam, K., Dinstein, I., 1973. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, smc-3(6), 610-621. DOI 10.1109/tsmc.1973.4309314

Hassan, M. M., Ash, K., Abedin, J., Paul, B. K., Southworth, J., 2020. A Quantitative Framework for Analyzing Spatial Dynamics of Flood Events: A Case Study of Super Cyclone Amphan. Remote Sensing, 12(20), 3454. DOI 10.3390/rs12203454.

Keerthana, N., Salma, S., Dodamani, B. M., 2022. Identifying Rice Crop Flooding Patterns Using Sentinel-1 SAR Data. Journal of the Indian Society of Remote Sensing, 50. DOI 10.1007/s12524-022-01553-4.

Kuntla, S.K., Manjusree, P., 2020. Development of an Automated Tool for Delineation of Flood Footprints from SAR Imagery for Rapid Disaster Response: A Case Study. Journal of Indian Society of Remote Sensing, 48(6), 935–944. DOI 10.1007/s12524-020-01125-4

Li, Y., Martinis, S., Plank, S., Ludwig, R., 2018. An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data. International Journal of Applied Earth Observation and Geoinformation, 73, 123-135. DOI 10.1016/j.jag.2018.05.023.

Li, Y., Martinis, S., Wieland, M., Schlaffer, S., Natsuaki, R., 2019. Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion. Remote Sensing, 11(19), 2231. DOI 10.3390/rs11192231.

Liang, J., Liu, D., 2020. A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 53-62. DOI 10.1016/j.isprsjprs.2019.10.017

Lin, Yun, Bhardwaj, Hill., 2019. Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew. Remote Sensing, 11(15), 1778. DOI 10.3390/rs11151778

Martinis, S., Plank, S., Ćwik, K., 2018. The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas. Remote Sensing, 10(4), 583. DOI 10.3390/rs10040583.

Mehrabi, A., 2020. Monitoring the Iran Pol-e-Dokhtar flood extent and detecting its induced ground displacement using sentinel 1 imagery techniques. Natural Hazards, 105(3), 2603-2617. DOI 10.1007/s11069-020-04414-w.

Melkamu, T., Bagyaraj, M., Adimaw, M., Ngusie, A., Karuppannan, S., 2022. Detecting and mapping flood inundation areas in Fogera-Dera Floodplain, Ethiopia during an extreme wet season using Sentinel-1 data. Physics and Chemistry of the Earth, Parts A/B/C, 127. DOI 10.1016/j.pce.2022.103189.

Moya, L., Mas, E., Koshimura, S., 2020. Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon. Remote Sensing, 12(14), 2244. DOI 10.3390/rs12142244.

Otsu, N., 1979. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66. DOI 10.1109/tsmc.1979.4310076.

Ouled Sghaier, M., Hammami, I., Foucher, S., Lepage, R., 2018. Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion. Remote Sensing, 10(2), 237. DOI 10.3390/rs10020237.

Page, M. J., Mckenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., Mcdonald, S., … Moher, D., 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Reviews, 10(1). DOI 10.1186/s13643-021-01626-4.

Paixão, M. A., Kobiyama, M., Zambrano, F. C., Michel, G. P., Fan, F. M., 2018. Lições sobre o gerenciamento de desastres hidrológicos obtidas a partir da ocorrência em Rolante/RS. Revista Gestão & Sustentabilidade Ambiental, 7, 251. DOI 10.19177/rgsa.v7e02018251-267.

Panahi, M., Rahmati, O., Kalantari, Z., Darabi, H., Rezaie, F., Moghaddam, D. D., Ferreira, C. S. S., Foody, G., Aliramaee, R., Bateni, S. M., Lee, C.-W., Lee, S., 2022. Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models. Journal of Hydrology, 611. DOI 10.1016/j.jhydrol.2022.128001.

Paradella, W. R., Mura, J. C., Gama, F. F., 2021. Monitoramento DInSAR para Mineração e Geotecnia: A tecnologia DInSAR orbital na mineração e geotecnia: monitoramento do espaço deformações na superfície. 1. Ed. São Paulo: Oficina de Textos.

Perrou, T., Garioud, A., Parcharidis, I., 2018. Use of Sentinel-1 imagery for flood management in a reservoir-regulated river basin. Frontiers of Earth Science, 12(3), 506-520. DOI 10.1007/s11707-018-0711-2.

Phan, A., N. Ha, D., D. Man, C., T. Nguyen, T., Q. Bui, H., T. N. Nguyen, T., 2019. Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery. Remote Sensing, 11(17), 2034. DOI 10.3390/rs11172034.

Pulvirenti, L., Chini, M., Pierdicca, N., 2020. InSAR Multitemporal Data over Persistent Scatterers to Detect Floodwater in Urban Areas: A Case Study in Beletweyne, Somalia. Remote Sensing,13(1), 37. DOI 10.3390/rs13010037

Qiu, J., Cao, B., Park, E., Yang, X., Zhang, W., Tarolli, P., 2021. Flood Monitoring in Rural Areas of the Pearl River Basin (China) Using Sentinel-1 SAR. Remote Sensing, 13(7), 1384. DOI 10.3390/rs13071384.

Quang, N. H., Tuan, V. A., Thi Thu Hang, L., Manh Hung, N., Thi The, D., Thi Dieu, D., Duc Anh, N., Hackney, C. R., 2019. Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin. Water, 12(1), 71. DOI 10.3390/w12010071.

Refice, A., Capolongo, D., Pasquariello, G., Daaddabbo, A., Bovenga, F., Nutricato, R., Lovergine, F. P., Pietranera, L., 2014. SAR and InSAR for Flood Monitoring: Examples With COSMO-SkyMED Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(7), 2711-2722. DOI 10.1109/jstars.2014.2305165.

Ruzza, Guerriero, Grelle, Guadagno, Revellino., 2019. Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery. Water, 11(11), 2289. DOI 10.3390/w11112289.

Sharifi, A., 2020. Flood Mapping Using Relevance Vector Machine and SAR Data: A Case Study from Aqqala, Iran. Journal of the Indian Society of Remote Sensing, 48(9), 1289-1296. DOI 10.1007/s12524-020-01155-y.

Tanim, A. H., McRae, C. B., Tavakol-Davani, H., Goharian, E, 2022. Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning. Water, 14(7). DOI 10.3390/w14071140.

Tsyganskaya, V., Martinis, S., Marzahn, P., Ludwig, R., 2018. Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data. Remote Sensing, 10(8), 1286. DOI 10.3390/rs10081286.

Tsyganskaya, V., Martinis, S., Marzahn, P., 2019. Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features. Water, 11(9), 1938. DOI 10.3390/w11091938.

Tripathi, G., Pandey, A. C., Parida, B. R., Kumar, A., 2020. Flood Inundation Mapping and Impact Assessment Using Multi-Temporal Optical and SAR Satellite Data: A Case Study of 2017 Flood in Darbhanga District, Bihar, India. Water Resources Management, 34(6), 1871-1892. DOI 10.1007/s11269-020-02534-3.

Twele, A., Cao, W., Plank, S., Martinis, S., 2016. Sentinel-1-based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990-3004. DOI 10.1080/01431161.2016.1192304.

Uddin, Matin, Meyer., 2019. Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh. Remote Sensing, 11(13), 1581. DOI 10.3390/rs11131581.

Wan, L., Liu, M., Wang, F., Zhang, T., You, H. J., 2019. Automatic extraction of flood inundation areas from SAR images: a case study of Jilin, China during the 2017 flood disaster. International Journal of Remote Sensing, 40(13), 5052-5077. DOI 10.1080/01431161.2019.1577999.

Zhang, M., Chen, F., Liang, D., Tian, B., Yang, A., 2020. Use of Sentinel-1 GRD SAR Images to Delineate Flood Extent in Pakistan. Sustainability, 12(14), 5784. DOI 10.3390/su12145784.

Zotou, I., Bellos, V., Gkouma, A., Karathanassi, V., Tsihrintzis, V. A., 2020. Using Sentinel-1 Imagery to Assess Predictive Performance of a Hydraulic Model. Water Resources Management, 34(14), 4415-4430. DOI 10.1007/s11269-020-02592-7.

Downloads

Publicado

2022-12-09

Como Citar

Souza Melo de Liz, M., & Pinheiro Ribas, R. (2022). Flood extent delineation using Sentinel-1 data as information source: systematic review of processing methods. Revista Brasileira De Geografia Física, 15(6), 3047–3076. https://doi.org/10.26848/rbgf.v15.6.p3047-3076

Edição

Seção

Geoprocessamento e Sensoriamento Remoto

Artigos Semelhantes

<< < 1 2 3 4 5 6 7 8 9 10 > >> 

Você também pode iniciar uma pesquisa avançada por similaridade para este artigo.