基於合成孔徑雷達數據建構卷積長短期記憶模型之洪水測繪技術

、線上人數：48

、訪客IP：18.226.163.123

姓名	武樂(Noel Ivan Ulloa) 查詢紙本館藏	畢業系所	土木工程學系
論文名稱	基於合成孔徑雷達數據建構卷積長短期記憶模型之洪水測繪技術 (SAR-based change detection for flood mapping using a Convolutional LSTM-driven framework)
檔案	[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] [檢視] [下載] 本電子論文使用權限為同意立即開放。已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	利用合成孔徑雷達 (Synthetic Aperture Radar, SAR) 影像進行地表變遷偵測已廣泛應用於洪泛監測。然而利用變遷監測界定洪泛區域時，由於並非所有地表變化都由洪水引起，使得其應用有諸多限制，例如農田上的作物生長或收成、林地或地表植生的移除，以及都市內建物的變遷等，加之SAR訊號對這些變化十分敏感，往往導致監測結果產生錯誤。據此，本研究提出以深度學習為基礎之時空模擬框架—卷積長短期記憶模型 (Convolutional Long Short-Term Memory, ConvLSTM)來處理多時序影像地表變分析，並應用於Sentinel-1之多時序SAR影像，針對於洪水事件發生前進行影像模擬，並備妥合成影像（以下稱模擬影像），配合洪水事件後影像（以下稱觀測影像）進行變遷偵測來界定洪泛區域。具體之方法為透過設定閾值於差異Delta影像（模擬減去觀察影像），進而界定出模擬影像和觀察影像間有顯著差異之洪泛區域。針對極端天氣事件之洪災問題，能有效增進衛星影像偵測之效能。本研究為基於ConvLSTM框架，利用多時序SAR影像進行洪泛區域變遷偵測，並實際應用於三個地區之不同淹水事件：澳大利亞昆士蘭艾薩克地區的黛比颶風 (Cyclone Debbie)、莫三比克索法拉地區的伊代颶風(Cyclone Idai)、以及巴西貝洛奧里藏特都會區的布魯馬迪紐市潰壩事件 (Brumadinho Dam Collapse)。為量化結果之正確率，本研究將偵測之淹水潛勢圖與Sentinel 2和Planet Labs等光學影像進行比對；為進一步證實本模型之效能，本研究試驗了四種變遷偵測方案：(1)使用最接近事件的事後影像減事件前影像，(2)使用歷史平均值減事件後影像，(3)傳統LSTM以及(3)本研究使用之ConvLSTM。結果顯示，ConvLSTM的Delta影像閾值化結果在所有研究案例皆有最高的Cohen Kappa係數：艾薩克地區為0.92、索法拉省為0.78、以及貝洛奧里藏特地區為 0.68。其中於莫三比克以及貝洛奧里藏特都會區的試驗得到了較低的Kappa值，分析其主要因素可歸因於地表植生類型、土壤水含量以及地形對SAR影像造成之影響。綜合本研究成果可以發現，應用本研究提出之卷積運算變遷偵測框架，可針有效針對SAR影像進行空間相關性之時間序列分析，並提高變遷偵測在洪泛區域界定上之正確性。
摘要(英)	Synthetic Aperture Radar (SAR) imagery has been widely applied for flooding mapping based on change detection approaches. However, errors in the mapping result are expected since not all land-cover changes are flood-induced, and those changes are sensitive to SAR data, such as crop growth or harvest over agricultural lands, clearance of forested areas, and/or modifications to the urban landscape. This study, therefore, incorporated historical SAR images to boost the detection of flood-induced changes during extreme weather events, by applying a deep learning-driven spatiotemporal simulation framework, Convolutional Long Short-Term Memory (ConvLSTM), for simulating a synthetic image using Sentinel 1 intensity time series. This synthetic image can be prepared in advance of flood events, and then it can be used to detect flood areas using change detection when the post-image is available. Practically, a significant difference between the simulated and observed image is expected over inundated zones, which can be mapped by applying cut-off values to the Delta image (simulated minus observed image). The proposed ConvLSTM-driven SAR-based change detection framework was applied to three different events from Isaac Region, Queensland Australia (Cyclone Debbie), Sofala Region, Mozambique (Cyclone Idai), and Belo Horizonte Metropolitan Area, Brazil (Brumadinho Dam Collapse). The generated Flood Proxy Maps were compared against reference data derived from Sentinel 2 and Planet Labs optical data. To corroborate the effectiveness of the proposed methods, Delta products were also generated for two baseline models (closest post-image minus pre-image and historical mean minus post-image) and two LSTM architectures: traditional LSTM and ConvLSTM. Results show that thresholding of ConvLSTM Delta yielded the highest Cohen’s Kappa coefficients in all study cases: 0.92 for Isaac Region, 0.78 for Sofala Province, and 0.68 for Belo Horizonte Metropolitan Area. Lower Kappa values obtained in the Mozambique case can be subject to the topographic effect on SAR imagery. These results still confirm the benefits in terms of classification accuracy that convolutional operations provide in time series analysis of Earth Observation satellite data employing spatially correlated information in a deep learning framework.
關鍵字(中)	★ 淹水 ★ 合成孔徑雷達	關鍵字(英)	★ Flooding ★ Synthetic Aperture Radar ★ LSTM
論文目次	Table of contents Chinese Abstract i English Abstract iii Acknowledgments v Table of contents vi List of Figures ix List of Tables xiv Explanation of Symbols xv I. Introduction 1 II. Literature Review 13 2.1 Flood Mapping 13 2.2 Optical EOS and Flood Mapping 14 2.3 SAR Sensors and Flood Mapping 22 2.3.2 Radar Microwaves used in SAR 22 2.3.3 SAR Polarization 25 2.3.4 Geometric Distortion in SAR Images 26 2.3.5 Delineation of flooded areas 27 2.3.6 Change detection 32 2.3.7 Approaches with multi-temporal SAR 36 2.4 Deep Learning Approaches for Flood Mapping 41 2.5 Convolutional LSTM (ConvLSTM) 47 III. Theory and Model 52 3.1 Theory and Research Framework 52 3.1.1 SAR-based Change detection 52 3.1.2 Modeling framework 53 3.2 Applied Models 54 3.2.1 LSTM 54 3.2.2 ConvLSTM 57 IV. Study Cases 62 4.1 The Three Study Sites 62 4.2 Isaac Region - Cyclone Debbie 64 4.3 Sofala Province - Cyclone Idai 67 4.4 Belo Horizonte Metropolitan Area - Brumandinho Dam Collapse 69 V. Model Experiment and Data 71 5.1 Model Experiments – Flood Proxy Maps 71 5.2 Data 73 5.2.1 Sentinel 1 imagery 73 5.2.2 Reference data 78 5.3 Model Application and Assessment 80 5.3.1 Threshold grid search 80 5.3.2 Multi threshold approach 83 VI. Results 84 6.3 Flood Mapping Results (FPM) 84 6.3.1 FPM - Isaac Region flooding (Cyclone Debbie) 84 6.3.2 FPM - Sofala Province Flooding (Cyclone Idai) 91 6.3.3 FPM - Belo Horizonte Metropolitan Area (Brumadinho Dam Collapse) 93 VII. Discussion 97 7.3 Flood Proxy Maps 97 7.3.1 Isaac Region (Cyclone Debbie) 97 7.3.2 Sofala Province (Cyclone Idai) 103 7.3.3 Belo Horizonte Metropolitan Area (Brumadinho Dam Collapse) 103 7.4 ConvLSTM simulation applicability in dry conditions 104 7.4.1 ConvLSTM simulation applicability 104 7.4.2 Model training 108 7.4.3 Influence of model parameters 110 7.4.4 Influence of land cover 113 7.4.5 Influence of soil moisture 117 7.4.6 Influence of the spatial and temporal structure of data 121 7.5 Model Comparison 124 7.6 Application of Multi-threshold for Flood Mapping 127 7.7 Potential Effect of Land Cover on Model Application 130 VIII. Conclusions 133 Bibliography 137
參考文獻	[1] Commission of European Comminities, "Directive of the European Parliament and of the Council on the Assessment and Management of Floods," 2007: CEC Brussels. [2] Z. W. Kundzewicz, "Adapting flood preparedness tools to changing flood risk conditions: the situation in Poland," Oceanologia, Vol 56, 2014, no. 2, pp. 385-407. [3] G. J. P. Schumann and D. K. Moller, "Microwave remote sensing of flood inundation," Physics and Chemistry of the Earth, Parts A/B/C, Vol 83-84, 2015, pp. 84-95. [4] D. G. Proverbs, C. A. Brebbia, and E. C. Penning-Rowsell, Flood Recovery, Innovation and Response. WIT, 2008. [5] L. A. K. Mertes, "Inundation Hydrology," in Inland Flood Hazards: Human, Riparian, and Aquatic Communities, E. E. Wohl, Ed. Cambridge: Cambridge University Press, 2000, pp. 145-166. [6] E. E. Wohl, "Anthropogenic Impacts on Flood Hazards," in Inland Flood Hazards: Human, Riparian, and Aquatic Communities, E. E. Wohl, Ed. Cambridge: Cambridge University Press, 2000, pp. 104-142. [7] R. B. Mudashiru, N. Sabtu, I. Abustan, and W. Balogun, "Flood hazard mapping methods: A review," Journal of Hydrology, Vol 603, Dec 01 2021, p. 126846. [8] D. Szejba, "Importance of the Influence of Drained Clay Soil Retention Properties on Flood Risk Reduction," Water, Vol 12, 2020, no. 5. [9] B. Crouzy, K. Edmaier, N. Pasquale, and P. Perona, "Impact of floods on the statistical distribution of riverbed vegetation," Geomorphology, Vol 202, Nov 15 2013, pp. 51-58. [10] S. E. Bunn and A. H. Arthington, "Basic Principles and Ecological Consequences of Altered Flow Regimes for Aquatic Biodiversity," Environmental Management, Vol 30, Oct 01 2002, no. 4, pp. 492-507. [11] G. A. Parvin, A. C. Shimi, R. Shaw, and C. Biswas, "Flood in a Changing Climate: The Impact on Livelihood and How the Rural Poor Cope in Bangladesh," Climate, Vol 4, 2016, no. 4. [12] F. A. Armah, D. O. Yawson, G. T. Yengoh, J. O. Odoi, and E. K. A. Afrifa, "Impact of Floods on Livelihoods and Vulnerability of Natural Resource Dependent Communities in Northern Ghana," Water, Vol 2, 2010, no. 2. [13] A. Deshmukh, E. Ho Oh, and M. Hastak, "Impact of flood damaged critical infrastructure on communities and industries," Built Environment Project and Asset Management, Vol 1, 2011, no. 2, pp. 156-175. [14] X. H. Wu, S. Q. Zhang, X. J. Xu, Y. X. Huang, P. Steinmann, J. Utzinger, T. P. Wang, J. Xu, J. Zheng, and X. N. Zhou, "Effect of floods on the transmission of schistosomiasis in the Yangtze River valley, People′s Republic of China," Parasitol Int, Vol 57, Sep 2008, no. 3, pp. 271-6. [15] R. Boyce, R. Reyes, M. Matte, M. Ntaro, E. Mulogo, J. P. Metlay, L. Band, and M. J. Siedner, "Severe Flooding and Malaria Transmission in the Western Ugandan Highlands: Implications for Disease Control in an Era of Global Climate Change," J Infect Dis, Vol 214, Nov 1 2016, no. 9, pp. 1403-1410. [16] A. S. Akanda, A. S. Jutla, and S. Islam, "Dual peak cholera transmission in Bengal Delta: A hydroclimatological explanation," Geophysical Research Letters, https://doi.org/10.1029/2009GL039312 Vol 36, 2009/10/01 2009, no. 19. [17] Organization for Economic Cooperation and Development, Financial Management of Flood Risk. 2016. [18] U. Amarasinghe, G. Amarnath, N. Alahacoon, and S. Ghosh, "How Do Floods and Drought Impact Economic Growth and Human Development at the Sub-National Level in India?," Climate, Vol 8, 2020, no. 11. [19] M. I. Vousdoukas, L. Mentaschi, E. Voukouvalas, M. Verlaan, S. Jevrejeva, L. P. Jackson, and L. Feyen, "Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard," Nature Communications, Vol 9, 2018, no. 1, p. 2360. [20] G. Schumann. (2018, February 11). The Challenges of Global Flood Hazard Mapping and Prediction. Available: https://eos.org/editors-vox/the-challenges-of-global-flood-hazard-mapping-and-prediction [21] N. Del Estal, M. J. Mathew, D. Menier, E. Gensac, H. Delanoe, R. Le Gall, T. Joostens, A. Rouille, B. Guillement, and G. Lemaitre, "Chapter 11 - An Assessment of the Administrative-Legal, Physical-Natural, and Socio-Economic Subsystems of the Bay of Saint Brieuc, France: Implications for Effective Coastal Zone Management," in Coastal Zone Management, M. Ramkumar, R. A. James, D. Menier, and K. Kumaraswamy, Eds.: Elsevier, 2019, pp. 251-276. [22] P. J. Ward, B. Jongman, P. Salamon, A. Simpson, P. Bates, T. De Groeve, S. Muis, E. C. de Perez, R. Rudari, M. A. Trigg, and H. C. Winsemius, "Usefulness and limitations of global flood risk models," Nature Climate Change, Vol 5, Aug 01 2015, no. 8, pp. 712-715. [23] J. Schanze, "FLOOD RISK MANAGEMENT – A BASIC FRAMEWORK," in Flood Risk Management: Hazards, Vulnerability and Mitigation Measures, Dordrecht, 2006, pp. 1-20: Springer Netherlands. [24] M. A. A. Baky, M. Islam, and S. Paul, "Flood Hazard, Vulnerability and Risk Assessment for Different Land Use Classes Using a Flow Model," Earth Systems and Environment, Vol 4, Mar 01 2020, no. 1, pp. 225-244. [25] K. Phongsapan, F. Chishtie, A. Poortinga, B. Bhandari, C. Meechaiya, T. Kunlamai, K. S. Aung, D. Saah, E. Anderson, K. Markert, A. Markert, and P. Towashiraporn, "Operational Flood Risk Index Mapping for Disaster Risk Reduction Using Earth Observations and Cloud Computing Technologies: A Case Study on Myanmar," Original Research Vol 7, Dec 11 2019. [26] L. Devitt, J. Neal, T. Wagener, and G. Coxon, "Uncertainty in the extreme flood magnitude estimates of large-scale flood hazard models," Environmental Research Letters, Vol 16, 2021, no. 6. [27] J. P. Patra, R. Kumar, and P. Mani, "Combined Fluvial and Pluvial Flood Inundation Modelling for a Project Site," Procedia Technology, Vol 24, Jan 01 2016, pp. 93-100. [28] M. S. Tehrany, B. Pradhan, and M. N. Jebur, "Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS," Journal of Hydrology, Vol 512, May 06 2014, pp. 332-343. [29] A. Betsholtz and B. Nordlöf, "Potentials and limitations of 1D, 2D and coupled 1D-2D flood modelling in HEC-RAS," 2017. [30] B. Jamali, R. Löwe, P. M. Bach, C. Urich, K. Arnbjerg-Nielsen, and A. Deletic, "A rapid urban flood inundation and damage assessment model," Journal of Hydrology, Vol 564, Sep 01 2018, pp. 1085-1098. [31] Q. Zhou, J. Su, K. Arnbjerg-Nielsen, Y. Ren, J. Luo, Z. Ye, and J. Feng, "A GIS-Based Hydrological Modeling Approach for Rapid Urban Flood Hazard Assessment," Water, Vol 13, 2021, no. 11. [32] W. Palash, Y. Jiang, A. S. Akanda, D. L. Small, A. Nozari, and S. Islam, "A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra, and Meghna Rivers with Requisite Simplicity," Journal of Hydrometeorology, Vol 19, Jan 01 2018, no. 1, pp. 201-225. [33] K. Jaafar, N. Ismail, M. Tajjudin, R. Adnan, and M. H. F. Rahiman, "A review on flood modelling and rainfall-runoff relationships," in 2015 IEEE 6th Control and System Graduate Research Colloquium (ICSGRC), 2015, pp. 158-162. [34] G. J. P. Schumann, Earth Observation for Flood Applications: Progress and Perspectives. Elsevier Science, 2021. [35] B. Revilla-Romero, N. Wanders, P. Burek, P. Salamon, and A. de Roo, "Integrating remotely sensed surface water extent into continental scale hydrology," Journal of Hydrology, Vol 543, Dec 01 2016, pp. 659-670. [36] X. Lai, Q. Liang, H. Yesou, and S. Daillet, "Variational assimilation of remotely sensed flood extents using a 2-D flood model," Hydrol. Earth Syst. Sci., Vol 18, 2014, no. 11, pp. 4325-4339. [37] R. Hostache, M. Chini, L. Giustarini, J. Neal, D. Kavetski, M. Wood, G. Corato, R.-M. Pelich, and P. Matgen, "Near-Real-Time Assimilation of SAR-Derived Flood Maps for Improving Flood Forecasts," Water Resources Research, https://doi.org/10.1029/2017WR022205 Vol 54, 2018/08/01 2018, no. 8, pp. 5516-5535. [38] R. B. Morrison and M. Cooley, "Assessment of flood damage in Arizona by means of ERTS-1 imagery," in NASA. Goddard Space Flight Center Symp. on Significant Results obtained from the ERTS-1, Vol. 1, Sect. A and B, 1973. [39] A. Rango and V. V. Salomonson, "Regional flood mapping from space," Water Resources Research, https://doi.org/10.1029/WR010i003p00473 Vol 10, Jun 01 1974, no. 3, pp. 473-484. [40] M. Deutsch and F. Ruggles, "OPTICAL DATA PROCESSING AND PROJECTED APPLICATIONS OF THE ERTS-1 IMAGERY COVERING THE 1973 MISSISSIPPI RIVER VALLEY FLOODS1," JAWRA Journal of the American Water Resources Association, https://doi.org/10.1111/j.1752-1688.1974.tb00622.x Vol 10, Oct 01 1974, no. 5, pp. 1023-1039. [41] P. Bohorquez and D. J. Del Moral-Erencia, "100 Years of Competition between Reduction in Channel Capacity and Streamflow during Floods in the Guadalquivir River (Southern Spain)," Remote Sensing, Vol 9, 2017, no. 7. [42] X. Tong, X. Luo, S. Liu, H. Xie, W. Chao, S. Liu, S. Liu, A. N. Makhinov, A. F. Makhinova, and Y. Jiang, "An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery," ISPRS Journal of Photogrammetry and Remote Sensing, Vol 136, Feb 01 2018, pp. 144-153. [43] N. Mueller, A. Lewis, D. Roberts, S. Ring, R. Melrose, J. Sixsmith, L. Lymburner, A. McIntyre, P. Tan, S. Curnow, and A. Ip, "Water observations from space: Mapping surface water from 25years of Landsat imagery across Australia," Remote Sensing of Environment, Vol 174, Mar 01 2016, pp. 341-352. [44] M. Volpi, G. P. Petropoulos, and M. Kanevski, "Flooding extent cartography with Landsat TM imagery and regularized kernel Fisher′s discriminant analysis," Computers & Geosciences, Vol 57, Aug 01 2013, pp. 24-31. [45] A. Ahamed and J. D. Bolten, "A MODIS-based automated flood monitoring system for southeast asia," International Journal of Applied Earth Observation and Geoinformation, Vol 61, Sep 01 2017, pp. 104-117. [46] C. S. Arvind, A. Vanjare, S. N. Omkar, J. Senthilnath, V. Mani, and P. G. Diwakar, "Flood Assessment using Multi-temporal Modis Satellite Images," Procedia Computer Science, Vol 89, Jan 01 2016, pp. 575-586. [47] Y.-E. Yan, Z.-T. Ouyang, H.-Q. Guo, S.-S. Jin, and B. Zhao, "Detecting the spatiotemporal changes of tidal flood in the estuarine wetland by using MODIS time series data," Journal of Hydrology, Vol 384, Apr 15 2010, no. 1, pp. 156-163. [48] B. M. Sukojo and F. Alfiansyah, "Flood Disaster Analysis Using Landsat-8 and SPOT-6 Imagery for Determination of Flooded Areas in Sampang, Madura," IOP Conference Series: Earth and Environmental Science, Vol 98, 2017, p. 012021. [49] I. Elkhrachy, "Flash Flood Hazard Mapping Using Satellite Images and GIS Tools: A case study of Najran City, Kingdom of Saudi Arabia (KSA)," The Egyptian Journal of Remote Sensing and Space Science, Vol 18, Dec 01 2015, no. 2, pp. 261-278. [50] C. M. M. Kittel, L. Jiang, C. Tøttrup, and P. Bauer-Gottwein, "Sentinel-3 radar altimetry for river monitoring – a catchment-scale evaluation of satellite water surface elevation from Sentinel-3A and Sentinel-3B," Hydrol. Earth Syst. Sci., Vol 25, 2021, no. 1, pp. 333-357. [51] I. Caballero, J. Ruiz, and G. Navarro, "Sentinel-2 Satellites Provide Near-Real Time Evaluation of Catastrophic Floods in the West Mediterranean," Water, Vol 11, 2019, no. 12. [52] G. Konapala, S. V. Kumar, and S. Khalique Ahmad, "Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning," ISPRS Journal of Photogrammetry and Remote Sensing, Vol 180, Oct 01 2021, pp. 163-173. [53] S. W. Cooley, L. C. Smith, L. Stepan, and J. Mascaro, "Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery," Remote Sensing, Vol 9, 2017, no. 12. [54] A. Refice, A. D′Addabbo, and D. Capolongo, Flood Monitoring through Remote Sensing. Springer, 2018, p. 209. [55] F. Zhang, X. Zhu, and D. Liu, "Blending MODIS and Landsat images for urban flood mapping," International Journal of Remote Sensing, Vol 35, May 03 2014, no. 9, pp. 3237-3253. [56] M. Wieland and S. Martinis, "A Modular Processing Chain for Automated Flood Monitoring from Multi-Spectral Satellite Data," Remote Sensing, Vol 11, 2019, no. 19. [57] G. Mateo-Garcia, J. Veitch-Michaelis, L. Smith, S. V. Oprea, G. Schumann, Y. Gal, A. G. Baydin, and D. Backes, "Towards global flood mapping onboard low cost satellites with machine learning," Scientific Reports, Vol 11, Mar 31 2021, no. 1, p. 7249. [58] G. Schumann, P. D. Bates, H. Apel, and G. T. Aronica, "Global Flood Hazard Mapping, Modeling, and Forecasting," Global Flood Hazard, Jul 03 2018, pp. 239-244. [59] L. White, B. Brisco, M. Dabboor, A. Schmitt, and A. Pratt, "A Collection of SAR Methodologies for Monitoring Wetlands," Remote Sensing, Vol 7, 2015, no. 6. [60] E. Ndikumana, D. Ho Tong Minh, N. Baghdadi, D. Courault, and L. Hossard, "Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France," Remote Sensing, Vol 10, 2018, no. 8. [61] P. Townsend, "Mapping Seasonal Flooding in Forested Wetlands Using Multi-Temporal Radarsat SAR," Photogrammetric Engineering & Remote Sensing, Vol 67, 2001, no. 7, pp. 857-864. [62] N. Y. Lin, S.-H. Yun, A. Bhardwaj, and M. E. Hill, "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, Vol 11, 2019, no. 15. [63] C. W. J. Tay, S.-H. Yun, S. T. Chin, A. Bhardwaj, J. Jung, and E. M. Hill, "Rapid flood and damage mapping using synthetic aperture radar in response to Typhoon Hagibis, Japan," Scientific Data, Vol 7, Mar 25 2020, no. 1, p. 100. [64] M. Chini, R. Hostache, L. Giustarini, and P. Matgen, "A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case," IEEE Transactions on Geoscience and Remote Sensing, Vol 55, 2017, no. 12, pp. 6975-6988. [65] S. Long, T. E. Fatoyinbo, and F. Policelli, "Flood extent mapping for Namibia using change detection and thresholding with SAR," Environmental Research Letters, Vol 9, 2014, no. 3. [66] Y. You, J. Cao, and W. Zhou, "A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios," Remote Sensing, Vol 12, 2020, no. 15. [67] D. R. Panuju, D. J. Paull, and A. L. Griffin, "Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics," Remote Sensing, Vol 12, 2020, no. 11. [68] J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor, "Detecting trend and seasonal changes in satellite image time series," Remote Sensing of Environment, Vol 114, Jan 15 2010, no. 1, pp. 106-115. [69] Z. Zhu, "Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications," ISPRS Journal of Photogrammetry and Remote Sensing, Vol 130, Aug 01 2017, pp. 370-384. [70] P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys, and E. Lambin, "Review ArticleDigital change detection methods in ecosystem monitoring: a review," International Journal of Remote Sensing, Vol 25, May 01 2004, no. 9, pp. 1565-1596. [71] T. Taillade, L. Thirion-Lefevre, and R. Guinvarc’h, "Detecting Ephemeral Objects in SAR Time-Series Using Frozen Background-Based Change Detection," Remote Sensing, Vol 12, 2020, no. 11. [72] S. Saha, F. Bovolo, and L. Bruzzone, "Change Detection in Image Time-Series Using Unsupervised LSTM," IEEE Geoscience and Remote Sensing Letters, Vol 19, 2022, pp. 1-5. [73] J. Wang, S. Wang, F. Wang, Y. Zhou, Z. Wang, J. Ji, Y. Xiong, and Q. Zhao, "FWENet: a deep convolutional neural network for flood water body extraction based on SAR images," International Journal of Digital Earth, Vol 15, 2022, no. 1, pp. 345-361. [74] B. Liu, X. Li, and G. Zheng, "Coastal Inundation Mapping From Bitemporal and Dual-Polarization SAR Imagery Based on Deep Convolutional Neural Networks," Journal of Geophysical Research: Oceans, https://doi.org/10.1029/2019JC015577 Vol 124, Dec 01 2019, no. 12, pp. 9101-9113. [75] T. Mayer, A. Poortinga, B. Bhandari, A. P. Nicolau, K. Markert, N. S. Thwal, A. Markert, A. Haag, J. Kilbride, F. Chishtie, A. Wadhwa, N. Clinton, and D. Saah, "Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine," ISPRS Open Journal of Photogrammetry and Remote Sensing, Vol 2, Dec 01 2021, p. 100005. [76] M. Rogger, B. Kohl, H. Pirkl, A. Viglione, J. Komma, R. Kirnbauer, R. Merz, and G. Blöschl, "Runoff models and flood frequency statistics for design flood estimation in Austria – Do they tell a consistent story?," Journal of Hydrology, Vol 456-457, Aug 16 2012, pp. 30-43. [77] B. Winter, K. Schneeberger, N. V. Dung, M. Huttenlau, S. Achleitner, J. Stötter, B. Merz, and S. Vorogushyn, "A continuous modelling approach for design flood estimation on sub-daily time scale," Hydrological Sciences Journal, Vol 64, Apr 04 2019, no. 5, pp. 539-554. [78] J. Yang, R. Townsend, and B. Daneshfar, "Applying the HEC-RAS model and GIS techniques in river network floodplain delineation," Canadian Journal of Civil Engineering, Vol 33, 2006, no. 1, pp. 19-28. [79] E. Huţanu, A. Mihu-Pintilie, A. Urzica, L. E. Paveluc, C. C. Stoleriu, and A. Grozavu, "Using 1D HEC-RAS Modeling and LiDAR Data to Improve Flood Hazard Maps Accuracy: A Case Study from Jijia Floodplain (NE Romania)," Water, Vol 12, 2020, no. 6. [80] G. Papaioannou, A. Loukas, L. Vasiliades, and G. T. Aronica, "Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach," Natural Hazards, Vol 83, Oct 01 2016, no. 1, pp. 117-132. [81] T. A. Nigussie and A. Altunkaynak, "Modeling the effect of urbanization on flood risk in Ayamama Watershed, Istanbul, Turkey, using the MIKE 21 FM model," Natural Hazards, Vol 99, Nov 01 2019, no. 2, pp. 1031-1047. [82] P. Kadam and D. Sen, "Flood inundation simulation in Ajoy River using MIKE-FLOOD," ISH Journal of Hydraulic Engineering, Vol 18, Jun 01 2012, no. 2, pp. 129-141. [83] H. Tansar, M. Babur, and S. L. Karnchanapaiboon, "Flood inundation modeling and hazard assessment in Lower Ping River Basin using MIKE FLOOD," Arabian Journal of Geosciences, Vol 13, Sep 10 2020, no. 18, p. 934. [84] V. R. Baker, "Geomorphological understanding of floods," Geomorphology, Vol 10, Aug 01 1994, no. 1, pp. 139-156. [85] R. A. Schowengerdt, "The Nature of Remote Sensing," in Remote Sensing (Third Edition), R. A. Schowengerdt, Ed. Burlington: Academic Press, 2007. [86] S. Liang and J. Wang, "Chapter 1 - A systematic view of remote sensing," in Advanced Remote Sensing (Second Edition): Academic Press, 2020, pp. 1-57. [87] W. Emery and A. Camps, "Chapter 2 - Basic Electromagnetic Concepts and Applications to Optical Sensors," in Introduction to Satellite Remote Sensing, W. Emery and A. Camps, Eds.: Elsevier, 2017, pp. 43-83. [88] J. Sanyal and X. X. Lu, "Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review," Natural Hazards, Vol 33, Oct 01 2004, no. 2, pp. 283-301. [89] P. A. Brivio, R. Colombo, M. Maggi, and R. Tomasoni, "Integration of remote sensing data and GIS for accurate mapping of flooded areas," International Journal of Remote Sensing, Vol 23, 2002, no. 3, pp. 429-441. [90] C. Huang, Y. Chen, and J. Wu, "Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery," International Journal of Applied Earth Observation and Geoinformation, Vol 26, Feb 01 2014, pp. 350-362. [91] C. Ordoyne and M. A. Friedl, "Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades," Remote Sensing of Environment, Vol 112, Nov 15 2008, no. 11, pp. 4107-4119. [92] K. Ose, T. Corpetti, and L. Demagistri, "2 - Multispectral Satellite Image Processing," in Optical Remote Sensing of Land Surface, N. Baghdadi and M. Zribi, Eds.: Elsevier, 2016, pp. 57-124. [93] H. Arst, K. I. U. Arst, and K. I. U. Arst, Optical Properties and Remote Sensing of Multicomponental Water Bodies. Springer, 2003. [94] C. Verpoorter, T. Kutser, and L. Tranvik, "Automated mapping of water bodies using Landsat multispectral data," Limnology and Oceanography: Methods, Vol 10, 2012, no. 12, pp. 1037-1050. [95] D. Leroux and T. Pellarin, "11 - Remote Sensing Data Assimilation: Applications to Catchment Hydrology," in Land Surface Remote Sensing in Continental Hydrology, N. Baghdadi and M. Zribi, Eds.: Elsevier, 2016, pp. 363-399. [96] R. C. Lins, J.-M. Martinez, D. D. Motta Marques, J. A. Cirilo, and C. R. Fragoso, "Assessment of Chlorophyll-a Remote Sensing Algorithms in a Productive Tropical Estuarine-Lagoon System," Remote Sensing, Vol 9, 2017, no. 6. [97] K. E. Sawaya, L. G. Olmanson, N. J. Heinert, P. L. Brezonik, and M. E. Bauer, "Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery," Remote Sensing of Environment, Vol 88, Nov 30 2003, no. 1, pp. 144-156. [98] E. P. Crist, "A TM Tasseled Cap equivalent transformation for reflectance factor data," Remote Sensing of Environment, Vol 17, Jun 01 1985, no. 3, pp. 301-306. [99] H. W. Khalid, R. M. Z. Khalil, and M. A. Qureshi, "Evaluating spectral indices for water bodies extraction in western Tibetan Plateau," The Egyptian Journal of Remote Sensing and Space Science, Vol 24, Dec 01 2021, no. 3, Part 2, pp. 619-634. [100] S. K. Jain and V. P. Singh, "Chapter 3 - Emerging Techniques for Data Acquisition and Systems Modeling," in Developments in Water Science, vol. 51, S. K. Jain and V. P. Singh, Eds.: Elsevier, 2003, pp. 123-205. [101] P. P. Mujumdar and D. N. Kumar, Floods in a Changing Climate: Hydrologic Modeling. Cambridge University Press, 2012. [102] V. M. Chowdary, R. V. Chandran, N. Neeti, R. V. Bothale, Y. K. Srivastava, P. Ingle, D. Ramakrishnan, D. Dutta, A. Jeyaram, J. R. Sharma, and R. Singh, "Assessment of surface and sub-surface waterlogged areas in irrigation command areas of Bihar state using remote sensing and GIS," Agricultural Water Management, Vol 95, Jul 01 2008, no. 7, pp. 754-766. [103] F. Hui, B. Xu, H. Huang, Q. Yu, and P. Gong, "Modelling spatial‐temporal change of Poyang Lake using multitemporal Landsat imagery," International Journal of Remote Sensing, Vol 29, Oct 20 2008, no. 20, pp. 5767-5784. [104] S. K. McFeeters, "The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features," International Journal of Remote Sensing, Vol 17, 1996, no. 7, pp. 1425-1432. [105] Suwarsono, Jalu Tejo Nugroho, and Wiweka, "Identification of inundated area using normalized difference water index (NDWI) on lowland region of java island," International Journal of Remote Sensing and Earth Sciences Vol 10, 2013, pp. 114-121. [106] S. K. Jain, R. D. Singh, M. K. Jain, and A. K. Lohani, "Delineation of Flood-Prone Areas Using Remote Sensing Techniques," Water Resources Management, Vol 19, 2005, no. 4, pp. 333-347. [107] A. Bannari, G. Kadhem, A. El-Battay, N. A. Hameid, and M. Rouai, "Assessment of Land Erosion and Sediment Accumulation Caused by Runoff after a Flash-Flooding Storm Using Topographic Profiles and Spectral Indices," Advances in Remote Sensing, Vol 05, 2016, no. 04, pp. 315-354. [108] M. Boschetti, F. Nutini, G. Manfron, P. A. Brivio, and A. Nelson, "Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems," PLOS ONE, Vol 9, 2014, no. 2. [109] A. A. Memon, S. Muhammad, S. Rahman, and M. Haq, "Flood monitoring and damage assessment using water indices: A case study of Pakistan flood-2012," The Egyptian Journal of Remote Sensing and Space Science, Vol 18, 2015, no. 1, pp. 99-106. [110] H. Xu, "Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery," International Journal of Remote Sensing, Vol 27, 2006, no. 14, pp. 3025-3033. [111] C. Huang, Y. Chen, S. Zhang, and J. Wu, "Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review," Reviews of Geophysics, https://doi.org/10.1029/2018RG000598 Vol 56, Jun 01 2018, no. 2, pp. 333-360. [112] J. Ledien, S. Sorn, S. Hem, R. Huy, P. Buchy, A. Tarantola, and J. Cappelle, "Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia," PLOS ONE, Vol 12, 2017, no. 7. [113] M. H. A. Baig, L. Zhang, S. Wang, G. Jiang, S. Lu, and Q. Tong, "COmparison of MNDWI and DFI for water mapping in flooding season," in 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013, pp. 2876-2879. [114] L. Ning, H. Liu, and A. Bao, "Identification of Inundation Hazard Zones in Manas Basin, China, Using Hydrodynamic Modeling and Remote Sensing," Journal of Water Resource and Protection, Vol 05, 2013, no. 04, pp. 469-473. [115] J. F. Rosser, D. G. Leibovici, and M. J. Jackson, "Rapid flood inundation mapping using social media, remote sensing and topographic data," Natural Hazards, Vol 87, 2017, no. 1, pp. 103-120. [116] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol 9, 1979, no. 1, pp. 62-66. [117] W. Li, Z. Du, F. Ling, D. Zhou, H. Wang, Y. Gui, B. Sun, and X. Zhang, "A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI," Vol 5, 2013, no. 11, pp. 5530-5549. [118] M. D. King, S. Platnick, W. P. Menzel, S. A. Ackerman, and P. A. Hubanks, "Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites," IEEE Transactions on Geoscience and Remote Sensing, Vol 51, 2013, no. 7, pp. 3826-3852. [119] NASA Earth Observatory. (2015, April 27). Data collected by a sensor on the Aqua satellite reveals the global distribution of clouds. Available: https://earthobservatory.nasa.gov/images/85843/cloudy-earth [120] The Earth Observatory. (2019, 12-11). Devastation in Mozambique. Available: https://earthobservatory.nasa.gov/images/144712/devastation-in-mozambique [121] R. Booysen, R. Gloaguen, S. Lorenz, R. Zimmermann, and P. A. M. Nex, "Geological Remote Sensing," in Encyclopedia of Geology (Second Edition), D. Alderton and S. A. Elias, Eds. Oxford: Academic Press, 2021, pp. 301-314. [122] Z. S. Haddad, "Remote-Sensing Missions and Sensors—Radar Sensors," in Comprehensive Remote Sensing, S. Liang, Ed. Oxford: Elsevier, 2018, pp. 519-545. [123] L. Ferro-Famil and E. Pottier, "Synthetic Aperture Radar Imaging," in Microwave Remote Sensing of Land Surface, N. Baghdadi and M. Zribi, Eds.: Elsevier, 2016, pp. 1-65. [124] W. G. Rees, Physical Principles of Remote Sensing. Cambridge University Press, 2013. [125] R. K. Raney, "Synthetic Aperture Radar Systems," in Encyclopedia of Coastal Science, C. W. Finkl and C. Makowski, Eds. Cham: Springer International Publishing, 2019, pp. 1674-1677. [126] N. Horning, J. A. Robinson, E. J. Sterling, S. Spector, and W. Turner, Remote Sensing for Ecology and Conservation: A Handbook of Techniques. OUP Oxford, 2010. [127] N. A. S. Administration;, NASA Conference Publication (no. 3070-3072). Scientific and Technical Information Office, National Aeronautics and Space Administration., 1988. [128] W. Lowrie, Fundamentals of Geophysics. Cambridge University Press, 2007. [129] S. Elias and D. Alderton, Encyclopedia of Geology. Elsevier Science, 2020. [130] H. Fukatsu and V. Chandrasekar, "Global mapping of attenuation at X-band and higher frequencies," in IEEE International Geoscience and Remote Sensing Symposium, 2002, vol. 1, pp. 293-295 vol.1. [131] V. Chandrasekar, H. Fukatsu, and K. Mubarak, "Global mapping of attenuation at Ku- and Ka-band," IEEE Transactions on Geoscience and Remote Sensing, Vol 41, 2003, no. 10, pp. 2166-2176. [132] L. Jun, Y. Kuga, A. Ishimaru, P. Xiaoqing, and A. Freeman, "Ionospheric effects on SAR imaging: a numerical study," IEEE Transactions on Geoscience and Remote Sensing, Vol 41, 2003, no. 5, pp. 939-947. [133] Z. W. Xu, J. Wu, and Z. S. Wu, "Potential Effects of the Ionosphere on Space-Based SAR Imaging," IEEE Transactions on Antennas and Propagation, Vol 56, 2008, no. 7, pp. 1968-1975. [134] T. Lillesand, R. W. Kiefer, and J. Chipman, Remote Sensing and Image Interpretation, 7th Edition. Wiley, 2015. [135] M. Ramanuja, "Review of synthetic aperture radar frequency, polarization, and incidence angle data for mapping the inundated regions," Journal of Applied Remote Sensing, Vol 12, 5/1 2018, no. 2, pp. 1-15. [136] K. C. Sahu, Textbook of Remote Sensing and Geographical Information Systems. Atlantic Publishers & Distributors (P) Limited, 2007. [137] G. Fontanelli, S. Paloscia, M. Zribi, and A. Chahbi, "Sensitivity analysis of X-band SAR to wheat and barley leaf area index in the Merguellil Basin," Remote Sensing Letters, Vol 4, Nov 01 2013, no. 11, pp. 1107-1116. [138] M. Pourshamsi, J. Xia, N. Yokoya, M. Garcia, M. Lavalle, E. Pottier, and H. Balzter, "Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning," ISPRS Journal of Photogrammetry and Remote Sensing, Vol 172, Feb 01 2021, pp. 79-94. [139] J. M. Wempen and M. K. McCarter, "Comparison of L-band and X-band differential interferometric synthetic aperture radar for mine subsidence monitoring in central Utah," International Journal of Mining Science and Technology, Vol 27, Jan 01 2017, no. 1, pp. 159-163. [140] D. Ho Tong Minh, E. Ndikumana, G. Vieilledent, D. McKey, and N. Baghdadi, "Potential value of combining ALOS PALSAR and Landsat-derived tree cover data for forest biomass retrieval in Madagascar," Remote Sensing of Environment, Vol 213, 2018, pp. 206-214. [141] Y. Yu and S. Saatchi, "Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests," Remote Sensing, Vol 8, 2016, no. 6. [142] S. Saatchi, M. Marlier, R. L. Chazdon, D. B. Clark, and A. E. Russell, "Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass," Remote Sensing of Environment, Vol 115, Nov 15 2011, no. 11, pp. 2836-2849. [143] C. Pohl and J. van Genderen, Remote Sensing Image Fusion: A Practical Guide. CRC Press, 2016. [144] M. Marghany, "Chapter 8 - Principle theories of synthetic aperture radar," in Synthetic Aperture Radar Imaging Mechanism for Oil Spills, M. Marghany, Ed.: Gulf Professional Publishing, 2020, pp. 127-150. [145] B. Minchew, "Determining the mixing of oil and sea water using polarimetric synthetic aperture radar," Geophysical Research Letters, https://doi.org/10.1029/2012GL052304 Vol 39, Aug 28 2012, no. 16. [146] R. R. Mohan, B. Paul, S. Mridula, and P. Mohanan, "Measurement of Soil Moisture Content at Microwave Frequencies," Procedia Computer Science, Vol 46, Jan 01 2015, pp. 1238-1245. [147] A.-K. Ahlmer, M. Cavalli, K. Hansson, A. J. Koutsouris, S. Crema, and Z. Kalantari, "Soil moisture remote-sensing applications for identification of flood-prone areas along transport infrastructure," Environmental Earth Sciences, Vol 77, Jul 12 2018, no. 14, p. 533. [148] D. J. Daniels and I. o. E. Engineers, Ground Penetrating Radar (no. v. 1). Institution of Engineering and Technology, 2004. [149] D. Massonnet and J. C. Souyris, Imaging with Synthetic Aperture Radar. EFPL Press, 2008. [150] M. Dabboor and B. Brisco, "Wetland Monitoring and Mapping Using Synthetic Aperture Radar," in Wetlands Management - Assessing Risk and Sustainable Solutions, 2019. [151] S. Cloude, Polarisation: Applications in Remote Sensing. OUP Oxford, 2010. [152] D. P. Samuele, S. Filippo, T. Orusa, and B.-M. Enrico, "Mapping SAR geometric distortions and their stability along time: a new tool in Google Earth Engine based on Sentinel-1 image time series," International Journal of Remote Sensing, Vol 42, Dec 02 2021, no. 23, pp. 9135-9154. [153] W. C. Carrara, W. G. Carrara, R. S. Goodman, and R. M. Majewski, Spotlight Synthetic Aperture Radar: Signal Processing Algorithms. Artech House, 1995. [154] M. Simons and P. A. Rosen, "Interferometric Synthetic Aperture Radar Geodesy," in Treatise on Geophysics, G. Schubert, Ed. Amsterdam: Elsevier, 2007, pp. 391-446. [155] J. M. Kellndorfer and K. C. McDonald, "Active and Passive Microwave Systems," in The SAGE Handbook of Remote Sensing, T. Warner, M. D. Nellis, and G. M. Foody, Eds. 55 City Road, London: SAGE Publications, Inc., 2009, pp. 179-198. [156] "Synthetic aperture radar interferometry and related techniques," in Polar Remote Sensing: Volume II: Ice Sheets, R. Massom and D. Lubin, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 39-136. [157] S. Kingsley and S. Quegan, "Radar remote sensing," Understanding Radar Systems: Institution of Engineering and Technology, 1999, pp. 226-256. [Online]. Available: https://digital-library.theiet.org/content/books/10.1049/sbra034e_ch11. [158] G. J. P. Schumann, "Chapter 14 - The Full Potential of EO for Flood Applications: Managing Expectations," in Earth Observation for Flood Applications, G. J. P. Schumann, Ed.: Elsevier, 2021, pp. 305-320. [159] S. Martinis, C. Künzer, and A. Twele, "Flood studies using Synthetic Aperture Radar data," in Remote Sensing Handbook Volume III - Remote Sensing of Water Resources, Disasters, and Urban Studies, P. Thenkabail, Ed.: Taylor and Francis, 2015, pp. 145-173. [160] M. Pritchard and S. H. Yun, "Satellite Radar Imaging and Its Application to Natural Hazards," in Natural Hazards: Earthquakes, Volcanoes, and Landslides, R. Singh and D. Bartlett, Eds. New York: CRC Press, 2018. [161] A. Dasgupta, S. Grimaldi, R. A. A. J. Ramsankaran, V. R. N. Pauwels, and J. P. Walker, "Towards operational SAR-based flood mapping using neuro-fuzzy texture-based approaches," Remote Sensing of Environment, Vol 215, 2018, pp. 313-329. [162] L. Giustarini, R. Hostache, P. Matgen, G. J. Schumann, P. D. Bates, and D. C. Mason, "A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X," IEEE Transactions on Geoscience and Remote Sensing, Vol 51, 2013, no. 4, pp. 2417-2430. [163] G. Schumann, R. Hostache, C. Puech, L. Hoffmann, P. Matgen, F. Pappenberger, and L. Pfister, "High-Resolution 3-D Flood Information From Radar Imagery for Flood Hazard Management," IEEE Transactions on Geoscience and Remote Sensing, Vol 45, 2007, no. 6, pp. 1715-1725. [164] D. O’Grady, M. Leblanc, and D. Gillieson, "Relationship of local incidence angle with satellite radar backscatter for different surface conditions," International Journal of Applied Earth Observation and Geoinformation, Vol 24, Oct 01 2013, pp. 42-53. [165] X. Shen, D. Wang, K. Mao, E. Anagnostou, and Y. Hong, "Inundation Extent Mapping by Synthetic Aperture Radar: A Review," Remote Sensing, Vol 11, 2019, no. 7. [166] S. Martinis, A. Twele, and S. Voigt, "Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data," Natural Hazards and Earth System Sciences, Vol 9, 2009, pp. 303–314. [167] J. Kittler and J. Illingworth, "Minimum error thresholding," Pattern Recognition, Vol 19, Jan 01 1986, no. 1, pp. 41-47. [168] S. Martinis, J. Kersten, and A. Twele, "A fully automated TerraSAR-X based flood service," ISPRS Journal of Photogrammetry and Remote Sensing, Vol 104, 2015, pp. 203-212. [169] J. Liang and D. Liu, "A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery," ISPRS Journal of Photogrammetry and Remote Sensing, Vol 159, Jan 01 2020, pp. 53-62. [170] L. Pulvirenti, F. S. Marzano, N. Pierdicca, S. Mori, and M. Chini, "Discrimination of Water Surfaces, Heavy Rainfall, and Wet Snow Using COSMO-SkyMed Observations of Severe Weather Events," IEEE Transactions on Geoscience and Remote Sensing, Vol 52, 2014, no. 2, pp. 858-869. [171] S. Martinis, S. Plank, and K. Ćwik, "The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas," Remote Sensing, Vol 10, 2018, no. 4. [172] P. Matgen, R. Hostache, G. Schumann, L. Pfister, L. Hoffmann, and H. H. G. Savenije, "Towards an automated SAR-based flood monitoring system: Lessons learned from two case studies," Physics and Chemistry of the Earth, Parts A/B/C, Vol 36, Jan 01 2011, no. 7, pp. 241-252. [173] H. Cao, H. Zhang, C. Wang, and B. Zhang, "Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas," Water, Vol 11, 2019, no. 4. [174] B. Glaser, M. Antonelli, M. Chini, L. Pfister, and J. Klaus, "Technical note: Mapping surface-saturation dynamics with thermal infrared imagery," Hydrol. Earth Syst. Sci., Vol 22, 2018, no. 11, pp. 5987-6003. [175] N. I. Ulloa, S.-H. Chiang, and S.-H. Yun, "Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy," Remote Sensing, Vol 12, 2020, no. 9. [176] R. Furuta and N. Tomiyama, "A Study of Detection of Landslide Disasters due to the Pakistan Earthquake using ALOS data," presented at the 34th International Symposium on Remote Sensing of Environment, Sydney, Australia, April 10-15, 2011. [177] R. S. Westerhoff, M. P. H. Kleuskens, H. C. Winsemius, H. J. Huizinga, G. R. Brakenridge, and C. Bishop, "Automated global water mapping based on wide-swath orbital synthetic-aperture radar," Hydrol. Earth Syst. Sci., Vol 17, 2013, no. 2, pp. 651-663. [178] M. A. Clement, C. G. Kilsby, and P. Moore, "Multi-temporal synthetic aperture radar flood mapping using change detection," Journal of Flood Risk Management, Vol 11, 2018, no. 2, pp. 152-168. [179] F. Cian, M. Marconcini, and P. Ceccato, "Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data," Remote Sensing of Environment, Vol 209, 2018, pp. 712-730. [180] H. Ghanbari, M. Mahdianpari, S. Homayouni, and F. Mohammadimanesh, "A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, 2021, pp. 3602-3613. [181] W. Kang, Y. Xiang, F. Wang, L. Wan, and H. You, "Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks," Sensors, Vol 18, 2018, no. 9. [182] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," presented at the International Conference on Learning Representations, San Diego, CA, 2015. Available: http://arxiv.org/abs/1409.1556 [183] E. Nemni, J. Bullock, S. Belabbes, and L. Bromley, "Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery," Remote Sensing, Vol 12, 2020, no. 16. [184] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778. [185] A. G. Roy, N. Navab, and C. Wachinger, "Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Cham, 2018, pp. 421-429: Springer International Publishing. [186] L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 40, 2018, no. 4, pp. 834-848. [187] Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, "UNet++: A Nested U-Net Architecture for Medical Image Segmentation," in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Cham, 2018, pp. 3-11: Springer International Publishing. [188] R. Gupta, I. Sharma, and V. Kumar, "Chapter 4 - Skull stripping and tumor detection using 3D U-Net," in Machine Learning, Big Data, and IoT for Medical Informatics, P. Kumar, Y. Kumar, and M. A. Tawhid, Eds.: Academic Press, 2021, pp. 71-84. [189] N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, "Google Earth Engine: Planetary-scale geospatial analysis for everyone," Remote Sensing of Environment, Vol 202, 2017, pp. 18-27. [190] O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Cham, 2015, pp. 234-241: Springer International Publishing. [191] C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, "Understanding deep learning requires rethinking generalization," presented at the 5th International Conference on Learning Representations, Toulon, France, 2017. Available: https://openreview.net/forum?id=Sy8gdB9xx [192] D. Arpit, S. Jastrzębski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, and S. Lacoste-Julien, "A closer look at memorization in deep networks," presented at the Proceedings of the 34th International Conference on Machine Learning - Volume 70, Sydney, NSW, Australia, 2017. [193] X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. Woo, "Convolutional LSTM Network: a machine learning approach for precipitation nowcasting," presented at the Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, Montreal, Canada, 2015. [194] A. Kumar, T. Islam, Y. Sekimoto, C. Mattmann, and B. Wilson, "Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data," PLOS ONE, Vol 15, 2020, no. 3, p. e0230114. [195] G. Ayzel, M. Heistermann, and T. Winterrath, "Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)," Geosci. Model Dev., Vol 12, 2019, no. 4, pp. 1387-1402. [196] K.-S. Kim, J.-B. Lee, M.-I. Roh, K.-M. Han, and G.-H. Lee, "Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM," Journal of Marine Science and Engineering, Vol 8, 2020, no. 10. [197] Z. Xu and Y. Lv, "Att-ConvLSTM: PM2.5 Prediction Model and Application," in Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, Cham, 2020, pp. 30-40: Springer International Publishing. [198] Q. Liu, R. Zhang, Y. Wang, H. Yan, and M. Hong, "Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network," Journal of Marine Science and Engineering, Vol 9, 2021, no. 3. [199] W. Hu, H. Li, L. Pan, W. Li, R. Tao, and Q. Du, "Spatial–Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, Vol 58, 2020, no. 6, pp. 4237-4250. [200] G. Xavier and B. Yoshua, "Understanding the difficulty of training deep feedforward neural networks," 2010/03/31, Available: https://proceedings.mlr.press/v9/glorot10a.html [201] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [202] D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," presented at the 3rd International Conference for Learning Representations, San Diego, 2015. Available: http://arxiv.org/abs/1412.6980 [203] A. Mustapha, L. Mohamed, and K. Ali, "Comparative study of optimization techniques in deep learning: Application in the ophthalmology field," Journal of Physics: Conference Series, Vol 1743, Jan 01 2021, no. 1, p. 012002. [204] R. Naushad, T. Kaur, and E. Ghaderpour, "Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study," Sensors, Vol 21, 2021, no. 23. [205] C. Cortes, M. Mohri, and A. Rostamizadeh, "L2 regularization for learning kernels," presented at the Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, Canada, 2009. [206] M. E. Paoletti and J. M. Haut, "Adaptable Convolutional Network for Hyperspectral Image Classification," Remote Sensing, Vol 13, 2021, no. 18. [207] C. L. Bruyère, J. M. Done, A. B. Jaye, G. J. Holland, B. Buckley, D. J. Henderson, M. Leplastrier, and P. Chan, "Physically-based landfalling tropical cyclone scenarios in support of risk assessment," Weather and Climate Extremes, Vol 26, 2019/12/01/ 2019, p. 100229. [208] J. Schmaltz. (2017, December 12). Cyclone Debbie. Available: https://visibleearth.nasa.gov/images/89920/cyclone-debbie/89922w [209] J. Pryce and G. Cotter, "Chapter 4 - Natural Disasters and Labor Markets: Impacts of Cyclones on Employment in Northeast Australia," in Economic Effects of Natural Disasters, T. Chaiechi, Ed.: Academic Press, 2021, pp. 35-54. [210] M. Lenzen, A. Malik, S. Kenway, P. Daniels, K. L. Lam, and A. Geschke, "Economic damage and spillovers from a tropical cyclone," Nat. Hazards Earth Syst. Sci., Vol 19, 2019, no. 1, pp. 137-151. [211] K. ONeill. (2021, October 5th, 2021). Immersive installation brings together first hand accounts of residents who experienced 2017 flood. Available: https://www.northernriversreview.com.au/story/7197533/lismore-flood-stories-highlights-the-importance-of-storytelling-in-disaster-recovery/ [212] Queensland Government. (2003, 12-08). Dominant soil orders of Queensland. Available: https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={A4EAC62F-A8F4-4E98-A52F-95A9F67AEA36} [213] Queensland Government. (2019, 12-05). Land use mapping - 1999 to Current - Queensland. Available: https://www.qld.gov.au/environment/land/management/mapping/statewide-monitoring/qlump/qlump-datasets [214] P. Probst and A. Annunziato, "Tropical Cyclone IDAI: analysis of the wind, rainfall and storm surge impact," 2019, Available: https://www.humanitarianresponse.info/en/operations/mozambique/document/tropical-cyclone-idai-analysis-wind-rainfall-and-storm-surge-impact-9. [215] Global Facility for Disaster Reduction and Recovery, "Mozambique Cyclone Idai Post-Disaster Needs Assessment," 2019, Available: https://www.gfdrr.org/en/publication/mozambique-cyclone-idai-post-disaster-needs-assessment-full-report-2019. [216] K. Hansen. (2019, June 17). Tropical Cyclone Idai. Available: https://earthobservatory.nasa.gov/images/144651/tropical-cyclone-idai [217] A. D. Gumbo, E. Kapangaziwiri, H. Chikoore, H. Pienaar, and F. Mathivha, "Assessing water resources availability in headwater sub-catchments of Pungwe River Basin in a changing climate," Journal of Hydrology: Regional Studies, Vol 35, Jun 01 2021, p. 100827. [218] European Space Agency. (2019, April 20). COPERNICUS Sentinel-1 maps floods in wake of idai. Available: https://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-1/Copernicus_Sentinel-1_maps_floods_in_wake_of_Idai [219] D. Lumbroso, M. Davison, R. Body, and G. Petkovšek, "Modelling the Brumadinho tailings dam failure, the subsequent loss of life and how it could have been reduced," Nat. Hazards Earth Syst. Sci., Vol 21, 2021, no. 1, pp. 21-37. [220] J. L. Porsani, F. A. Jesus, and M. C. Stangari, "GPR Survey on an Iron Mining Area after the Collapse of the Tailings Dam I at the Córrego do Feijão Mine in Brumadinho-MG, Brazil," Remote Sensing, Vol 11, 2019, no. 7. [221] L. H. Silva Rotta, E. Alcântara, E. Park, R. G. Negri, Y. N. Lin, N. Bernardo, T. S. G. Mendes, and C. R. Souza Filho, "The 2019 Brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil," International Journal of Applied Earth Observation and Geoinformation, Vol 90, Aug 01 2020, p. 102119. [222] F. F. Gama, J. C. Mura, W. R. Paradella, and C. G. de Oliveira, "Deformations Prior to the Brumadinho Dam Collapse Revealed by Sentinel-1 InSAR Data Using SBAS and PSI Techniques," Remote Sensing, Vol 12, 2020, no. 21. [223] Raimund Bleischwitz, P. Carvalho，, and L. Couto. (2019, February 2). Mining Giant Behind Deadly Dam Collapse Took Lax Approach to Corporate Responsibility. Available: https://www.newsecuritybeat.org/2019/03/mining-giant-deadly-dam-collapse-lax-approach-corporate-responsibility/ [224] D. Small, "Flattening Gamma: Radiometric Terrain Correction for SAR Imagery," IEEE Transactions on Geoscience and Remote Sensing, Vol 49, 2011, no. 8, pp. 3081-3093.
指導教授	姜壽浩 윤상호(Chiang Shou-Hao Yun Sang-Ho)	審核日期	2022-7-20
推文	facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu
網路書籤	Google bookmarks del.icio.us hemidemi myshare

博碩士論文 106382608 詳細資訊