Probabilistic Mapping of August 2018 Flood of Kerala, India, Using Space-Borne Synthetic Aperture Radar

被引:20
作者
Sherpa, Sonam Futi [1 ]
Shirzaei, Manoochehr [1 ]
Ojha, Chandrakanta [1 ]
Werth, Susanna [1 ,2 ]
Hostache, Renaud [3 ]
机构
[1] Arizona State Univ, Sch Earth & Space Explorat, Tempe, AZ 85287 USA
[2] Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ 85281 USA
[3] Luxembourg Inst Sci & Technol, Dept Environm Res & Innovat, L-4362 Esch Sur Alzette, Luxembourg
关键词
Kerala; 2018; flood; probabilistic flood map; synthetic aperture radar (SAR); SAR DATA; INUNDATION; CLIMATE; IMAGES; BASIN; MANAGEMENT; ALGORITHM; RESPONSES; SUPPORT; LOSSES;
D O I
10.1109/JSTARS.2020.2970337
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Synthetic aperture radar (SAR) imaging provides an all-weather sensing technique that is suitable for near-real-time mapping of disasters such as floods. In this article, we use SAR data acquired by Sentinel-1A/B satellites to investigate a flood event that affected the Indian state of Kerala in August 2018. We apply a Bayesian approach to generate probabilistic flood maps, which contain for each pixel its probability to be flooded rather than binary flood information. We find that the extent of the flooded area begins to increase throughout Kerala after August 8, with the highest values on August 9 and August 21. We observe no apparent correlation between the spatial distributions of the flooded areas and the rainfall amounts at the district level of the study area. Instead, larger flooded areas are in the districts of Alappuzha and Kottayam, located in the downstream floodplain of the Idduki dam, which released a significant volume of water on August 16. The lack of apparent correlation is likely due to two reasons: first, there is often some delay between the rainfall event and the flooding, especially for rather large catchments where flood waves need some time to reach floodplains from higher elevations. Second, rainfall is more abundant at overhead catchments (hills and mountains), whereas flood occurs further downstream in the floodplains. Further comparison of our SAR-based flood maps with optical data and flood maps produced by moderate resolution imaging spectroradiometer highlights the advantages of our data and approach for rapid response purposes and future flood forecasting.
引用
收藏
页码:896 / 913
页数:18
相关论文
共 81 条
[1]  
[Anonymous], 2000, P ESA SP 461 ERS ENV
[2]  
[Anonymous], [No title captured]
[3]  
[Anonymous], [No title captured]
[4]  
[Anonymous], [No title captured]
[5]  
Barros V, 2012, MANAGING THE RISKS OF EXTREME EVENTS AND DISASTERS TO ADVANCE CLIMATE CHANGE ADAPTATION, pIX
[6]   An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images [J].
Bazi, Y ;
Bruzzone, L ;
Melgani, F .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (04) :874-887
[7]   Characteristics of sediment deposits formed by intense rainfall events in small catchments in the Belgian Loam Belt [J].
Beuselinck, L ;
Steegen, A ;
Govers, G ;
Nachtergaele, J ;
Takken, I ;
Poesen, J .
GEOMORPHOLOGY, 2000, 32 (1-2) :69-82
[8]   A manifesto for the equifinality thesis [J].
Beven, K .
JOURNAL OF HYDROLOGY, 2006, 320 (1-2) :18-36
[9]   Surface water dynamics in the Amazon Basin: Application of satellite radar altimetry [J].
Birkett, CM ;
Mertes, LAK ;
Dunne, T ;
Costa, MH ;
Jasinski, MJ .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2002, 107 (D20) :LBA26-1
[10]   ESTIMATING THE EXTENT OF FLOODS IN BANGLADESH USING SPOT DATA [J].
BLASCO, F ;
BELLAN, MF ;
CHAUDHURY, MU .
REMOTE SENSING OF ENVIRONMENT, 1992, 39 (03) :167-178