HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya
Abstract
:1. Introduction
2. Flood Mapping Needs in the HKH
3. Co-Developing an Inundation Monitoring Service for the HKH
4. HydroSAR Product Portfolio and Product Algorithms
4.1. Input Data
4.2. Intermediary Data Products and Visual Aids
4.2.1. Height above Nearest Drainage
4.2.2. RTC30 Product
4.2.3. RTC30-Color
4.3. Quantitative HydroSAR Flood Information Products
4.3.1. HYDRO30: Surface Water Extent
- For each parent tile, we calculated the coefficient of variation () of the mean radar brightness values of its four child objects. This is a departure from [12], who used the standard deviation as a metric. We found to be more robust across regions with different average radar brightness. Parent tiles with high values are potential candidates for threshold calculation, as a high is expected for tiles which contain both the water and land semantic classes. Tiles which offer the highest (>95% percentile) were selected as candidates.
- We required the parent objects to have a mean radar brightness lower than the mean of all parent tiles. This ensured that tiles lying on the boundary between water and land areas were selected.
- To improve the robustness of the threshold calculation, we excluded parent tiles which were not in flood-prone regions. To accomplish this, we labeled the pixels with HAND elevations ≤15 m as unlikely to be flooded. This threshold was based on studies which found that average flood depths of up to 10 m are possible in the region [48]. We added an additional buffer of 5 m to ensure that most tiles which could include flood water were retained for further analysis. This is a second departure from [12], where the DEM instead of the HAND was used to identify tiles which could be discarded. As the HAND is a hydrologically conditioned dataset, we found it to be a more suitable and robust variable to identify tiles which were not in flood-prone regions. Tiles were only considered if less than 20% of their pixels were identified as not being flood-prone.
- RCS: with = RCS of initial flood candidate pixels;
- HAND: ;
- Surface slope : ;
- Area A: .
4.3.2. WD30: Water Depth
5. Product Visualization and Access Mechanisms
- Data products are exposed to the public via a web mapping service which allows visualizing water extent and depth information in a geographic context. This web map is supported by an ArcGIS image service on the backend, making it simple to distribute HydroSAR resources to desktop, mobile, and browser applications.
- A time slider is included to support the assessment of changes in water extent and depth over time.
- A layer selector provides the capability to switch between different HydroSAR data products for cross-comparison, cross-validation, and joint hazard assessment.
- A product download feature allows users to access and download HydroSAR products over their area of interest.
6. HydroSAR Cloud Computing Environment
- Integration with ASF’s cloud-based archives is achieved by co-locating the HydroSAR services with ASF’s archives in AWS region us-west-2. This design reduces data movement and enables rapid in-region data access without requiring data downloading.
- Cloud-based HydroSAR product generation is facilitated by ASF HyP3, a cloud-scaling service allowing science algorithms to run automatically from regional to global scales. Mature HydroSAR workflows are integrated into HyP3 using Docker containers [53] and are run automatically whenever new SAR data over an area of interest hits the ASF archive.
- HydroSAR cloud storage is provided in the form of an AWS S3 storage bucket. HydroSAR products are deposited in this bucket immediately after product generation and stored temporarily until pickup by ICIMOD.
- Product delivery to end users is facilitated by ICIMOD. Using a cron job scheduler utility, ICIMOD fetches new HydroSAR products on a daily basis from the project’s maintained S3 bucket for inclusion into their local database. As discussed in Section 5, ICIMOD serves out HydroSAR data to its end users via an image service-supported web interface.
7. Validation of Quantitative HydroSAR Information Layers
7.1. Validating HYDRO30
7.2. Validating WD30
8. Application Example: 2023 Bangladesh Flooding Season
8.1. Background and Data
8.2. 2023 Bangladesh Flood Progression
8.3. Total Annual Flood Duration Analysis
8.4. Affected Agriculture Areas
9. Summary and Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Vaidya, R.A.; Shrestha, M.S.; Nasab, N.; Gurung, D.R.; Kozo, N.; Pradhan, N.S.; Wasson, R.J. Disaster risk reduction and building resilience in the Hindu Kush Himalaya. In The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People; Springer: Cham, Switzerland, 2019; pp. 389–419. [Google Scholar]
- Bajracharya, B.; Thapa, R.B.; Matin, M.A. Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region: A Decade of Experience from SERVIR; Springer Nature: Cham, Switzerland, 2021. [Google Scholar]
- Shrestha, M.S.; Grabs, W.E.; Khadgi, V.R. Establishment of a regional flood information system in the Hindu Kush Himalayas: Challenges and opportunities. Int. J. Water Resour. Dev. 2015, 31, 238–252. [Google Scholar] [CrossRef]
- Uddin, K.; Matin, M.A.; Meyer, F.J. Operational flood mapping using multi-temporal Sentinel-1 SAR images: A case study from Bangladesh. Remote Sens. 2019, 11, 1581. [Google Scholar] [CrossRef]
- Potin, P.; Bargellini, P.; Laur, H.; Rosich, B.; Schmuck, S. Sentinel-1 mission operations concept. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012; pp. 1745–1748. [Google Scholar]
- Kellogg, K.; Hoffman, P.; Standley, S.; Shaffer, S.; Rosen, P.; Edelstein, W.; Dunn, C.; Baker, C.; Barela, P.; Shen, Y.; et al. NASA-ISRO synthetic aperture radar (NISAR) mission. In Proceedings of the 2020 IEEE Aerospace Conference, Big Sky, MT, USA, 7–14 March 2020; pp. 1–21. [Google Scholar]
- Martinis, S.; Twele, A.; Voigt, S. Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Nat. Hazards Earth Syst. Sci. 2009, 9, 303–314. [Google Scholar] [CrossRef]
- Long, S.; Fatoyinbo, T.E.; Policelli, F. Flood extent mapping for Namibia using change detection and thresholding with SAR. Environ. Res. Lett. 2014, 9, 035002. [Google Scholar] [CrossRef]
- Seitz, F.; Hedman, K.; Meyer, F.J.; Lee, H. Multi-sensor space observation of heavy flood and drought conditions in the Amazon region. In Earth on the Edge: Science for a Sustainable Planet: Proceedings of the IAG General Assembly, Melbourne, Australia, 28 June–2 July 2011; Springer: Berlin/Heidelberg, Germany, 2014; pp. 311–317. [Google Scholar]
- Meyer, F.; McAlpin, D.; Gong, W.; Ajadi, O.; Arko, S.; Webley, P.; Dehn, J. Integrating SAR and derived products into operational volcano monitoring and decision support systems. ISPRS J. Photogramm. Remote Sens. 2015, 100, 106–117. [Google Scholar] [CrossRef]
- Ajadi, O.A.; Meyer, F.J.; Webley, P.W. Change detection in synthetic aperture radar images using a multiscale-driven approach. Remote Sens. 2016, 8, 482. [Google Scholar] [CrossRef]
- Twele, A.; Cao, W.; Plank, S.; Martinis, S. Sentinel-1-based flood mapping: A fully automated processing chain. Int. J. Remote Sens. 2016, 37, 2990–3004. [Google Scholar] [CrossRef]
- Tran, K.H.; Menenti, M.; Jia, L. Surface water mapping and flood monitoring in the Mekong Delta using sentinel-1 SAR time series and Otsu threshold. Remote Sens. 2022, 14, 5721. [Google Scholar] [CrossRef]
- Qiu, J.; Cao, B.; Park, E.; Yang, X.; Zhang, W.; Tarolli, P. Flood monitoring in rural areas of the Pearl River Basin (China) using Sentinel-1 SAR. Remote Sens. 2021, 13, 1384. [Google Scholar] [CrossRef]
- Martinis, S.; Wieland, M.; Rättich, M. An Automatic System for Near-Real Time Flood Extent and Duration Mapping Based on Multi-Sensor Satellite Data. In Earth Observation for Flood Applications; Elsevier: Amsterdam, The Netherlands, 2021; pp. 7–37. [Google Scholar]
- Otsu, N. A threshold selection method from gray-level histograms. Automatica 1975, 11, 23–27. [Google Scholar] [CrossRef]
- Uddin, K.; Matin, M.A.; Thapa, R.B. Rapid flood mapping using multi-temporal sar images: An example from Bangladesh. In A Decade of Experience from SERVIR; Springer: Cham, Switzerland, 2021; p. 201. [Google Scholar]
- Aziz, M.A.; Moniruzzaman, M.; Tripathi, A.; Hossain, M.I.; Ahmed, S.; Rahaman, K.R.; Rahman, F.; Ahmed, R. Delineating flood zones upon employing synthetic aperture data for the 2020 flood in Bangladesh. Earth Syst. Environ. 2022, 6, 733–743. [Google Scholar] [CrossRef]
- Singha, M.; Dong, J.; Sarmah, S.; You, N.; Zhou, Y.; Zhang, G.; Doughty, R.; Xiao, X. Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 2020, 166, 278–293. [Google Scholar] [CrossRef]
- Pandey, A.C.; Kaushik, K.; Parida, B.R. Google Earth Engine for large-scale flood mapping using SAR data and impact assessment on agriculture and population of Ganga-Brahmaputra basin. Sustainability 2022, 14, 4210. [Google Scholar] [CrossRef]
- Soria-Ruiz, J.; Fernandez-Ordoñez, Y.M.; Ambrosio-Ambrosio, J.P.; Escalona-Maurice, M.J.; Medina-García, G.; Sotelo-Ruiz, E.D.; Ramirez-Guzman, M.E. Flooded extent and depth analysis using optical and SAR remote sensing with machine learning algorithms. Atmosphere 2022, 13, 1852. [Google Scholar] [CrossRef]
- Hao, C.; Yunus, A.P.; Subramanian, S.S.; Avtar, R. Basin-wide flood depth and exposure mapping from SAR images and machine learning models. J. Environ. Manag. 2021, 297, 113367. [Google Scholar] [CrossRef]
- Delaney, K.B.; Evans, S.G. The evolution (2010–2015) and engineering mitigation of a rockslide-dammed lake (Hunza River, Pakistan); characterisation by analytical remote sensing. Eng. Geol. 2017, 220, 65–75. [Google Scholar] [CrossRef]
- Surampudi, S.; Kumar, V. Flood depth estimation in agricultural lands from L and C-band synthetic aperture radar images and digital elevation model. IEEE Access 2023, 11, 3241–3256. [Google Scholar] [CrossRef]
- Ozaki, M. Disaster Risk Financing in Bangladesh; Working Paper Series; Asian Development Bank: Metro Manila, Philippines, 2016. [Google Scholar]
- Hussain, A.; Rasul, G.; Mahapatra, B.; Tuladhar, S. Household food security in the face of climate change in the Hindu-Kush Himalayan region. Food Secur. 2016, 8, 921–937. [Google Scholar] [CrossRef]
- Manjusree, P.; Prasanna Kumar, L.; Bhatt, C.M.; Rao, G.S.; Bhanumurthy, V. Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR images. Int. J. Disaster Risk Sci. 2012, 3, 113–122. [Google Scholar] [CrossRef]
- Thapa, R.B.; Bajracharya, B.; Matin, M.A.; Anderson, E.; Epanchin, P. Service planning approach and its application. In A Decade of Experience from SERVIR; Springer: Cham, Switzerland, 2021; p. 23. [Google Scholar]
- Molden, D.; Sharma, E. ICIMOD’s strategy for delivering high-quality research and achieving impact for sustainable mountain development. Mt. Res. Dev. 2013, 33, 179–183. [Google Scholar] [CrossRef]
- Cigna, F.; Xie, H. Imaging floods and glacier geohazards with remote sensing. Remote Sens. 2020, 12, 3874. [Google Scholar] [CrossRef]
- Bell, J.; Kubby, B.; Meyer, F.; Yadav, S. Identifying and Mapping Agricultural Areas Using Synthetic Aperture Radar Time Series. In Proceedings of the AGU Fall Meeting, New Orleans, LA, USA, 13–17 December 2021; Volume 2021, p. GC45I-0925. [Google Scholar]
- Copernicus DEM. 2022. Available online: https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM (accessed on 3 March 2024).
- Zink, M.; Moreira, A.; Hajnsek, I.; Rizzoli, P.; Bachmann, M.; Kahle, R.; Fritz, T.; Huber, M.; Krieger, G.; Lachaise, M.; et al. TanDEM-X: 10 years of formation flying bistatic SAR interferometry. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 3546–3565. [Google Scholar] [CrossRef]
- Wessel, B.; Huber, M.; Wohlfart, C.; Marschalk, U.; Kosmann, D.; Roth, A. Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data. ISPRS J. Photogramm. Remote Sens. 2018, 139, 171–182. [Google Scholar] [CrossRef]
- Lehner, B.; Verdin, K.; Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos Trans. Am. Geophys. Union 2008, 89, 93–94. [Google Scholar] [CrossRef]
- Buchhorn, M.; Lesiv, M.; Tsendbazar, N.E.; Herold, M.; Bertels, L.; Smets, B. Copernicus global land cover layers—Collection 2. Remote Sens. 2020, 12, 1044. [Google Scholar] [CrossRef]
- Nobre, A.D.; Cuartas, L.A.; Hodnett, M.; Rennó, C.D.; Rodrigues, G.; Silveira, A.; Saleska, S. Height Above the Nearest Drainage—A hydrologically relevant new terrain model. J. Hydrol. 2011, 404, 13–29. [Google Scholar] [CrossRef]
- Gharari, S.; Hrachowitz, M.; Fenicia, F.; Savenije, H.H.G. Hydrological landscape classification: Investigating the performance of HAND based landscape classifications in a central European meso-scale catchment. Hydrol. Earth Syst. Sci. 2011, 15, 3275–3291. [Google Scholar] [CrossRef]
- Hogenson, K.; Kristenson, H.; Kennedy, J.; Johnston, A.; Rine, J.; Logan, T.; Zhu, J.; Williams, F.; Herrmann, J.; Smale, J.; et al. Hybrid Pluggable Processing Pipeline (HyP3): A Cloud-Native Infrastructure for Generic Processing of SAR Data; Zenodo: Geneva, Switzerland, 2024. [Google Scholar] [CrossRef]
- Small, D.; Zuberbühler, L.; Schubert, A.; Meier, E. Terrain-flattened gamma nought Radarsat-2 backscatter. Can. J. Remote Sens. 2011, 37, 493–499. [Google Scholar] [CrossRef]
- Shiroma, G.H.; Lavalle, M.; Buckley, S.M. An area-based projection algorithm for SAR radiometric terrain correction and geocoding. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–23. [Google Scholar] [CrossRef]
- Small, D. Flattening gamma: Radiometric terrain correction for SAR imagery. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3081–3093. [Google Scholar] [CrossRef]
- Flores-Anderson, A.I.; Parache, H.B.; Martin-Arias, V.; Jiménez, S.A.; Herndon, K.; Mehlich, S.; Meyer, F.J.; Agarwal, S.; Ilyushchenko, S.; Agarwal, M.; et al. Evaluating SAR Radiometric Terrain Correction Products: Analysis-Ready Data for Users. Remote Sens. 2023, 15, 5110. [Google Scholar] [CrossRef]
- Truckenbrodt, J.; Freemantle, T.; Williams, C.; Jones, T.; Small, D.; Dubois, C.; Thiel, C.; Rossi, C.; Syriou, A.; Giuliani, G. Towards Sentinel-1 SAR analysis-ready data: A best practices assessment on preparing backscatter data for the cube. Data 2019, 4, 93. [Google Scholar] [CrossRef]
- Nicolau, A.P.; Flores-Anderson, A.; Griffin, R.; Herndon, K.; Meyer, F.J. Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest. Int. J. Appl. Earth Obs. Geoinf. 2021, 94, 102214. [Google Scholar] [CrossRef]
- Frey, O.; Santoro, M.; Werner, C.L.; Wegmuller, U. DEM-based SAR pixel-area estimation for enhanced geocoding refinement and radiometric normalization. IEEE Geosci. Remote Sens. Lett. 2012, 10, 48–52. [Google Scholar] [CrossRef]
- Martinis, S.; Kersten, J.; Twele, A. A fully automated TerraSAR-X based flood service. ISPRS J. Photogramm. Remote Sens. 2015, 104, 203–212. [Google Scholar] [CrossRef]
- Sciance, M.B.; Nooner, S.L. Decadal flood trends in Bangladesh from extensive hydrographic data. Nat. Hazards 2018, 90, 115–135. [Google Scholar] [CrossRef]
- Meyer, F.J.; Schultz, L.; Bell, J.; Molthan, A.L.; Osmanoglu, B.; Jo, M.; Lundell, E.; Chapman, B.D.; Kubby, B.; Meyer, T.; et al. Monitoring Weather-Related Hazards Using the HydroSAR Service: Application to the 2020 South Asia Monsoon Season. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021; pp. 893–896. [Google Scholar]
- Pekel, J.F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418–422. [Google Scholar] [CrossRef]
- Olson, B.; Shehu, A. Efficient basin hopping in the protein energy surface. In Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine, Philadelphia, PA, USA, 4–7 October 2012; pp. 1–6. [Google Scholar]
- Hogenson, K.; Arko, S.A.; Buechler, B.; Hogenson, R.; Herrmann, J.; Geiger, A. Hybrid Pluggable Processing Pipeline (HyP3): A cloud-based infrastructure for generic processing of SAR data. In Proceedings of the AGU Fall Meeting, San Francisco, CA, USA, 12–16 December 2016; Volume 2016, p. IN21B-1740. [Google Scholar]
- Rad, B.B.; Bhatti, H.J.; Ahmadi, M. An introduction to docker and analysis of its performance. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 2017, 17, 228. [Google Scholar]
- Nevo, S.; Morin, E.; Gerzi Rosenthal, A.; Metzger, A.; Barshai, C.; Weitzner, D.; Voloshin, D.; Kratzert, F.; Elidan, G.; Dror, G.; et al. Flood forecasting with machine learning models in an operational framework. Hydrol. Earth Syst. Sci. 2022, 26, 4013–4032. [Google Scholar] [CrossRef]
- Pal, S.; Lee, T.R.; Clark, N.E. The 2019 Mississippi and Missouri River flooding and its impact on atmospheric boundary layer dynamics. Geophys. Res. Lett. 2020, 47, e2019GL086933. [Google Scholar] [CrossRef]
- Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033. [Google Scholar] [CrossRef]
- Fahrland, E.; Jacob, P.; Schrader, H.; Kahabka, H. Copernicus Digital Elevation Model—Product Handbook; Airbus Defence and Space—Intelligence: Potsdam, Germany, 2020. [Google Scholar]
- Haque, M.N.; Siddika, S.; Sresto, M.A.; Saroar, M.M.; Shabab, K.R. Geo-spatial analysis for flash flood susceptibility mapping in the North-East Haor (Wetland) Region in Bangladesh. Earth Syst. Environ. 2021, 5, 365–384. [Google Scholar] [CrossRef]
- AM, D.; Nishigaki, M.; Komatsu, M. Floods in Bangladesh: A comparative hydrological investigation on two catastrophic events. J. Fac. Environ. Sci. Technol. 2003, 8, 53–62. [Google Scholar]
- Francois, M.; Santandrea, S.; Mellab, K.; Vrancken, D.; Versluys, J. The PROBA-V mission: The space segment. Int. J. Remote Sens. 2014, 35, 2548–2564. [Google Scholar] [CrossRef]
- Poudel, K.R.; Hamal, R. Assessment of Natural Hazard in the Himalayas: A Case Study of the Seti River Flash Flood 2012. Prithvi J. Res. Innov. 2021, 3, 12–25. [Google Scholar] [CrossRef]
Skill Score | S1: 04/04 | S1: 06/27 | S1: 08/02 | S1: 08/26 | S1: 11/13 |
S2: 03/13 | S2: 06/24 | S2: 07/19 | S2: 08/28 | S2: 11/12 | |
Accuracy | 0.98 | 0.99 | 0.99 | 0.99 | 0.99 |
Precision | 0.79 | 0.85 | 0.71 | 0.64 | 0.88 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Meyer, F.J.; Schultz, L.A.; Osmanoglu, B.; Kennedy, J.H.; Jo, M.; Thapa, R.B.; Bell, J.R.; Pradhan, S.; Shrestha, M.; Smale, J.; et al. HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya. Remote Sens. 2024, 16, 3244. https://doi.org/10.3390/rs16173244
Meyer FJ, Schultz LA, Osmanoglu B, Kennedy JH, Jo M, Thapa RB, Bell JR, Pradhan S, Shrestha M, Smale J, et al. HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya. Remote Sensing. 2024; 16(17):3244. https://doi.org/10.3390/rs16173244
Chicago/Turabian StyleMeyer, Franz J., Lori A. Schultz, Batuhan Osmanoglu, Joseph H. Kennedy, MinJeong Jo, Rajesh B. Thapa, Jordan R. Bell, Sudip Pradhan, Manish Shrestha, Jacquelyn Smale, and et al. 2024. "HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya" Remote Sensing 16, no. 17: 3244. https://doi.org/10.3390/rs16173244