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Journal articles on the topic 'Flood Assesment - Satellite Images'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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1

Mahmud, Husniyah Binti, Vaibhav Katiyar, and Masahiko Nagai. "Improved Consistency of an Automated Multisatellite Method for Extracting Temporal Changes in Flood Extent." Mathematical Problems in Engineering 2021 (November 1, 2021): 1–17. http://dx.doi.org/10.1155/2021/6164161.

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Malaysia is affected by floods almost every year. In this situation, high-frequency flood monitoring is crucial so that timely measures can be taken. However, the low revisit time of the satellites, as well as occlusion cast by clouds in optical images, limits the frequency of flood observation of the focused area. Therefore, this study proposes utilising multisatellite data from optical satellites such as Landsat 7, Landsat 8, and Moderate Resolution Imaging Spectroradiometer (MODIS), as well as Synthetic Aperture Radar (SAR) images from Advanced Land Observation Satellite (ALOS-2) and Sentin
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Arvind, C. S., Ashoka Vanjare, S. N. Omkar, J. Senthilnath, V. Mani, and P. G. Diwakar. "Flood Assessment using Multi-temporal Modis Satellite Images." Procedia Computer Science 89 (2016): 575–86. http://dx.doi.org/10.1016/j.procs.2016.06.017.

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Huang, Minmin, and Shuanggen Jin. "Backscatter Characteristics Analysis for Flood Mapping Using Multi-Temporal Sentinel-1 Images." Remote Sensing 14, no. 15 (2022): 3838. http://dx.doi.org/10.3390/rs14153838.

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Change detection between images of pre-flood and flooding periods is a critical process for flood mapping using satellite images. Flood mapping from SAR images is based on backscattering coefficient differences. The change rules of the backscattering coefficient with different flooding depths of ground objects are essential prior knowledge for flood mapping, while their absence greatly limits the precision. Therefore, minimizing the backscattering coefficient differences caused by non-flood factors is of great significance for improving the accuracy of flood mapping. In this paper, non-flood f
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Sadek, Mohammed, and Xuxiang Li. "Low-Cost Solution for Assessment of Urban Flash Flood Impacts Using Sentinel-2 Satellite Images and Fuzzy Analytic Hierarchy Process: A Case Study of Ras Ghareb City, Egypt." Advances in Civil Engineering 2019 (May 2, 2019): 1–15. http://dx.doi.org/10.1155/2019/2561215.

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Natural hazards are indeed counted as the most critical challenges facing our world, represented in floods, earthquakes, volcanoes, hurricanes, and forest fires. Among these natural hazards, the flash flood is regarded the most frequent. In this work, we utilized two Sentinel-2 satellite images, before and after the flash flood, SRTM and photos captured by using a helicopter. This paper aims at three prime objectives. Firstly, the flood influence is determined on the city of Ras Ghareb, Egypt, based on analyzing free satellite data (Sentinel-2 images). Secondly, fuzzy the analytical hierarchy
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Harichandana, M., V. Sowmya, V. V. Sajithvariyar, and R. Sivanpillai. "COMPARISON OF IMAGE ENHANCEMENT TECHNIQUES FOR RAPID PROCESSING OF POST FLOOD IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-2-2020 (November 17, 2020): 45–50. http://dx.doi.org/10.5194/isprs-archives-xliv-m-2-2020-45-2020.

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Abstract. Satellite images are widely used for assessing the areal extent of flooded areas. However, presence of clouds and shadow limit the utility of these images. Numerous digital algorithms are available for enhancing such images and highlighting areas of interest. These algorithms range from simple to complex, and the time required to process these images also varies considerably. For disaster response, it is important to select an algorithm that can enhance the quality of the images in relatively short time. This study compared the relative performance of five traditional (Histogram Equa
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Kapilaratne, R. G. C. J., and S. Kaneta. "TOWARDS AN AUTOMATED FLOOD AREA EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 97–104. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-97-2020.

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Abstract. Flooding is considered as one of the most devastated natural disasters due to its adverse effect on human lives as well as economy. Since more population concentrate towards flood prone areas and frequent occurrence of flood events due to global climate change, there is an urgent need in remote sensing community for faster and reliable inundation mapping technologies to increase the preparedness of population and reduce the catastrophic impact. With the recent advancement in remote sensing technologies and integration capability of deep learning algorithms with remote sensing data ma
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Hirabayashi, Yukiko, Haireti Alifu, Dai Yamazaki, ‪Gennadii Donchyts, and Yuki Kimura. "Detectability of variation in river flood from satellite images." Hydrological Research Letters 15, no. 2 (2021): 37–43. http://dx.doi.org/10.3178/hrl.15.37.

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Kussul, Nataliia, Andrii Shelestov, and Serhiy Skakun. "Grid system for flood extent extraction from satellite images." Earth Science Informatics 1, no. 3-4 (2008): 105–17. http://dx.doi.org/10.1007/s12145-008-0014-3.

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Kimati, Andrew, Subira Munishi, and Victor Kongo. "Development of an Operational Satellite-Based Flood Monitoring Model for Tanzania." Tanzania Journal of Engineering and Technology 41, no. 4 (2022): 58–70. http://dx.doi.org/10.52339/tjet.v41i4.874.

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Timely information during water related disasters is of utmost importance for flood preparedness and risk reduction. Real time observation and monitoring of flooded areas is an expensive and time-consuming exercise. Satellite remote sensing is a quick and affordable approach that can be used for concurrent floods detection at different scales. This is important as it facilitates timely information for emergency response to disaster management departments, even in scarcely instrumented catchments. This study presents a novel approach for flood tracking using satellite technology to map flood af
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Notti, Davide, Daniele Giordan, Fabiana Caló, Antonio Pepe, Francesco Zucca, and Jorge Galve. "Potential and Limitations of Open Satellite Data for Flood Mapping." Remote Sensing 10, no. 11 (2018): 1673. http://dx.doi.org/10.3390/rs10111673.

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Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of flood maps at low cost around the world. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occur
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kalyani, Nadagouda. "REVIEW-EFFECT OF FLASH FLOOD." International Journal of Engineering Applied Sciences and Technology 7, no. 4 (2022): 127–30. http://dx.doi.org/10.33564/ijeast.2022.v07i04.017.

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Flash flood assessment by using physiographic features, flooded areas effect can be seen quickly by satellite imagery data. Radar and microwave data is improved to an extent that it can process images even on cloudy days. Aster digital elevation model data is preferred by most of authors to extract DEM from satellite data. Individual data used in evaluation of flood flow for future safety planning and to take precautions to reduce risk damage. These criteria used for identifying areas effected by flash floods.
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Tariq, Aqil, Hong Shu, Alban Kuriqi, et al. "Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images." Remote Sensing 13, no. 11 (2021): 2053. http://dx.doi.org/10.3390/rs13112053.

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Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a geographic information system (GIS) and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different land-use/land-cover (LU
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Hidayat, H., D. H. Hoekman, M. A. M. Vissers, and A. J. F. Hoitink. "Flood occurrence mapping of the middle Mahakam lowland area using satellite radar." Hydrology and Earth System Sciences 16, no. 7 (2012): 1805–16. http://dx.doi.org/10.5194/hess-16-1805-2012.

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Abstract. Floodplain lakes and peatlands in the middle Mahakam lowland area are considered as ecologically important wetland in East Kalimantan, Indonesia. However, due to a lack of data, the hydrological functioning of the region is still poorly understood. Among remote sensing techniques that can increase data availability, radar is well-suitable for the identification, mapping, and measurement of tropical wetlands, for its cloud unimpeded sensing and night and day operation. Here we aim to extract flood extent and flood occurrence information from a series of radar images of the middle Maha
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Hidayat, H., D. H. Hoekman, M. A. M. Vissers, and A. J. F. Hoitink. "Flood frequency mapping of the middle Mahakam lowland area using satellite radar." Hydrology and Earth System Sciences Discussions 8, no. 6 (2011): 11519–52. http://dx.doi.org/10.5194/hessd-8-11519-2011.

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Abstract. Floodplain lakes and peatlands in the middle Mahakam lowland area are considered as an ecologically important wetland in East Kalimantan, Indonesia. However, due to a lack of data, the hydrological functioning of the region is still poorly understood. Among remote sensing techniques that can increase data availability, radar is well-suitable for the identification, mapping, and measurement of tropical wetlands, for its cloud unimpeded sensing and night and day operation. Here we aim to extract flood extent and flood frequency information from a series of radar images of the middle Ma
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Jun, Jung Nam, Doo Chun Seo, and Hyo Suk Lim. "A Flood Disaster Area Extraction Using KOMPSAT-1 EOC Satellite Image Data." Key Engineering Materials 277-279 (January 2005): 809–15. http://dx.doi.org/10.4028/www.scientific.net/kem.277-279.809.

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The main objective of this study is to observe and extract relevant geographical information of areas afflicted with floods using KOMPSAT-1 EOC image data. The satellite images taken on September 2, 2002 of the downstream of the river of Nakdong in the province of South Gyeongsang afflicted with floods at that time are used for the purpose of demonstration. To extract information which is the boundary lines and area in flood disaster area should be made to the ortho-image with characteristic of map. The generation of ortho-images involve sensor modeling using control points and DEM to restore
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de la Cruz, R. M., N. T. Olfindo Jr., M. M. Felicen, N. J. B. Borlongan, J. K. L. Difuntorum, and J. J. S. Marciano Jr. "NEAR-REALTIME FLOOD DETECTION FROM MULTI-TEMPORAL SENTINEL RADAR IMAGES USING ARTIFICIAL INTELLIGENCE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1663–70. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1663-2020.

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Abstract. Flood extent delineation from RADAR images usually entails manual thresholding per scene, which is not feasible when tackling large-scale floods that often covers multiple RADAR scenes. It is also computationally intensive when processed through traditional remote sensing techniques that limit its use during emergency situations. To hasten the production of flood maps from RADAR images during flooding incidents, a deep learning model using Fully connected Convolutional Neural Network (FCNN) has been developed to delineate flooded areas with minimal human intervention. The model was f
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Tarpanelli, Angelica, Bianca Bonaccorsi, Marco Sinagra, Alessio Domeneghetti, Luca Brocca, and Silvia Barbetta. "Flooding in the Digital Twin Earth: The Case Study of the Enza River Levee Breach in December 2017." Water 15, no. 9 (2023): 1644. http://dx.doi.org/10.3390/w15091644.

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The accurate delineation of flood hazard maps is a key element of flood risk management policy. Flood inundation models are fundamental for reproducing the boundaries of flood-prone areas, but their calibration is limited to the information available on the areas affected by inundation during observed flood events (typically fragmentary photo, video or partial surveys). In recent years, Earth Observation data have supported flood monitoring and emergency response (e.g., the Copernicus Emergency Service) thanks to the proliferation of available satellite sensors, also at high spatial resolution
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18

Singh, Abujam Manglem. "Flood Mapping in Valley Districts of Manipur Using Satellite-based Synthetic Aperture Radar (SAR) Images." Ecology, Environment and Conservation 28 (2022): 481–88. http://dx.doi.org/10.53550/eec.2022.v28i07s.079.

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Impacts from flood pose high risk to lives and properties of river plain inhabitants across the globe. Plains of Northeast India, including central valley in Manipur, often experience floods of variable intensities in different seasons of the year. Current flood mitigation measures of the region scarcely consider the dissimilarity in inundation patterns produced by floods of different seasons. Considering this, the current study examined the inundation pattern of pre-monsoon and monsoon floods in five valley districts of Manipur using Synthetic Aperture Radar (SAR) data of Sentinel-1 satellite
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Liu, Du, and Liu. "Flood Distance Algorithms and Fault Hidden Danger Recognition for Transmission Line Towers Based on SAR Images." Remote Sensing 11, no. 14 (2019): 1642. http://dx.doi.org/10.3390/rs11141642.

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Synthetic Aperture Radar (SAR) has been extensively used in the monitoring of natural hazards such as floods and landslides. Predicting whether natural hazards will cause serious harm to important facilities on the ground is an important subject of study. In this study, the distance between the water body and the tower and the flood ratio in the search area and the elevation are defined as the evaluation indicators of the flood hazard of the tower, indicating whether flooding will threaten the safety of the transmission line tower. Herein, transmission tower flood identification algorithms bas
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Doan, To-Uyen Thi, Ariyo Kanno, Koichi Yamamoto, Tsuyoshi Imai, Takaya Higuchi, and Masahiko Sekine. "A METHOD FOR QUALITATIVELY MAPPING THE FLOOD PHYSICAL VULNERABILITY OF RESIDENTIAL AREAS." Vietnam Journal of Science and Technology 58, no. 5 (2020): 578. http://dx.doi.org/10.15625/2525-2518/58/5/14670.

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Mapping flood physical vulnerability is spatially limited because it requires input data such as building structures and materials, which are unavailable on large spatial scales. In this study, we propose a new method for qualitatively evaluating the flood vulnerability of residential areas in the context of the exposure and resilience to flood hazard on large spatial scales. This method utilizes the possible correlations between the structural physical vulnerability and residential types obtained from the statistical classifications of multispectral satellite images. Because multispectral cla
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Bauer-Marschallinger, Bernhard, Senmao Cao, Mark Edwin Tupas, et al. "Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube." Remote Sensing 14, no. 15 (2022): 3673. http://dx.doi.org/10.3390/rs14153673.

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Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust
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Srinivasa Rao, G., C. M. Bhatt, and P. G. Diwaker. "International Charter Support during Major Flood Disasters in India." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (December 23, 2014): 1501–5. http://dx.doi.org/10.5194/isprsarchives-xl-8-1501-2014.

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Earth observation (EO) satellites provide near real time, comprehensive, synoptic and multi-temporal coverage of inaccessible areas at frequent intervals, which is required support for a quick response and planning of emergency operations. Owing to their merits, satellite images have become an integral part of disaster management and are being extensively used globally for mapping, monitoring and damage assessment of extreme disaster events. During major disaster, information derived from satellite observation is not only highly useful, it may at times be indispensable because of the unfavoura
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Senthilnath, J., Shreyas P.B, Ritwik Rajendra, S. Suresh, Sushant Kulkarni, and J. A. Benediktsson. "Hierarchical clustering approaches for flood assessment using multi-sensor satellite images." International Journal of Image and Data Fusion 10, no. 1 (2018): 28–44. http://dx.doi.org/10.1080/19479832.2018.1513956.

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Chen, G., W. Ruan, and J. Zhao. "FLOOD FORECASTING METHOD BASIS AS FLOOD MONITORING PROGRESS OF MEKONG RIVER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W5 (October 29, 2018): 1–7. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w5-1-2018.

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<p><strong>Abstract.</strong> In this paper, used MODIS satellite image (MOD13) for monitoring the progress of flood in Mekong River Basin and testing the flood forecasting method for the Mekong Delta in flooding stage of 2015. The results showed that (80% reliability): MODIS image can be used to monitor the progress of flood in large areas of the Mekong River Basin. There was a close relationship between enhanced vegetation index EVI, land surface water index LSWI with growth status of plants and surface water of the flood. Risk flood maps during the flood season of the stud
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Tuyen, Do Ngoc, Tran Manh Tuan, Le Hoang Son, et al. "A Novel Approach Combining Particle Swarm Optimization and Deep Learning for Flash Flood Detection from Satellite Images." Mathematics 9, no. 22 (2021): 2846. http://dx.doi.org/10.3390/math9222846.

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Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellit
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Nafia, El-Alaouy, Badreddine Sebbar, El Houssaine Bouras, Aicha Moumni, Nour-Eddine Laftouhi, and Abderrahman Lahrouni. "Hydraulic Modeling and Remote Sensing Monitoring of Floodhazard in Arid Environments—A Case Study of Laayoune City in Saquia El Hamra Watershed Southern Morocco." Water 14, no. 21 (2022): 3582. http://dx.doi.org/10.3390/w14213582.

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Morocco often faces significant intense rainfall periods that can generate flash floods and raging torrents, causing serious damage in a very short period of time. This study aims to monitor wetland areas after a flash-flood event in an arid region, Saquia El hamra Saharan of Morocco, using a technique that combines hydraulic modeling and remote sensing technology, namely satellite images. The hydrological parameters of the watershed were determined by the WMS software. Flood flow was modeled and simulated using HEC HMS and HEC-RAS software. To map the flooded areas, two satellite images (Sent
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Reksten, J. H., A. B. Salberg, and R. Solberg. "FLOOD DETECTION IN NORWAY BASED ON SENTINEL-1 SAR IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 22, 2019): 349–55. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-349-2019.

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<p><strong>Abstract.</strong> After large flood incidents in Norway, The Norwegian Water Resources and Energy Directorate (NVE), has the responsibility for documenting the flooded areas. This has so far mainly been performed by utilising aerial images and visual interpretation. Satellite images are a valuable source of additional information as they are able to cover vast areas in each satellite pass. In this paper a fully automated system for detecting and delineating floods with the use of Synthetic Aperture Radar (SAR) images from the Sentinel-1 satellites is presented. In
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Tanim, Ahad Hasan, Callum Blake McRae, Hassan Tavakol-Davani, and Erfan Goharian. "Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning." Water 14, no. 7 (2022): 1140. http://dx.doi.org/10.3390/w14071140.

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Urban flooding poses risks to the safety of drivers and pedestrians, and damages infrastructures and lifelines. It is important to accommodate cities and local agencies with enhanced rapid flood detection skills and tools to better understand how much flooding a region may experience at a certain period of time. This results in flood management orders being announced in a timely manner, allowing residents and drivers to preemptively avoid flooded areas. This research combines information received from ground observed data derived from road closure reports from the police department, with remot
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Lee, SeungJae, and Han Oh. "Dataset for Water Body Detection Using Satellite SAR Images." GEO DATA 3, no. 2 (2021): 12–19. http://dx.doi.org/10.22761/dj2021.3.2.002.

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Satellite synthetic aperture radar (SAR) generates valid image information in all-weather. Thus, it can be effectively used for near real-time monitoring and damage analysis of flood areas which always involve overcast skies. Water body detection (WBD) using SAR images can be implemented by various techniques which discriminate electromagnetic characteristics between water and non-water areas. Especially, semantic segmentation exploiting artificial intelligence techniques can be used to develop a high-performance WBD model. To this end, Korea Aerospace Research Institute has built an WBD datas
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Al-Akad, S., Y. Akensous, and M. Hakdaoui. "MAPPING INFECTED AREA AFTER A FLASH-FLOODING STORM USING MULTI CRITERIA ANALYSIS AND SPECTRAL INDICES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W6 (November 13, 2017): 3–7. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w6-3-2017.

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This research article is summarize the applications of remote sensing and GIS to study the urban floods risk in Al Mukalla. Satellite acquisition of a flood event on October 2015 in Al Mukalla (Yemen) by using flood risk mapping techniques illustrate the potential risk present in this city. Satellite images (The Landsat and DEM images data were atmospherically corrected, radiometric corrected, and geometric and topographic distortions rectified.) are used for flood risk mapping to afford a hazard (vulnerability) map. This map is provided by applying image-processing techniques and using geogra
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Narimani, Roya, Changhyun Jun, Saqib Shahzad, Jeill Oh, and Kyoohong Park. "Application of a Novel Hybrid Method for Flood Susceptibility Mapping with Satellite Images: A Case Study of Seoul, Korea." Remote Sensing 13, no. 14 (2021): 2786. http://dx.doi.org/10.3390/rs13142786.

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This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulner
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Ahmad, T., A. C. Pandey, and A. Kumar. "IMPACT OF FLOODING ON LAND USE/ LAND COVER TRANSFORMATION IN WULAR LAKE AND ITS ENVIRONS, KASHMIR VALLEY, INDIA USING GEOINFORMATICS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 13–18. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-13-2017.

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Wular lake, located at an elevation of 1520 m above sea level in Kashmir valley, India. In the present study, the immediate and long term impact of flood (2014) over the Wular lake environs was analyzed by using satellite images and employing supervised classification technique in GIS environment. The LULC classification was performed on the images of 25th August 2014 (pre flood) and 13th September 2015 (post flood) and was compared, which indicated marked decrease in terrestrial vegetation (23.7 %), agriculture (43.7 %) and water bodies (39.9&a
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Ma, Y., F. Zhou, G. Wen, et al. "ASSESSMENT OF BUILDINGS AND ELECTRICAL FACILITIES DAMAGED BY FLOOD AND EARTHQUAKE FROM SATELLITE IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-3/W1-2022 (April 22, 2022): 133–40. http://dx.doi.org/10.5194/isprs-archives-xlvi-3-w1-2022-133-2022.

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Abstract. Natural disasters cause considerable losses to people’s lives and property. Satellite images can provide crucial information of the affected areas for the first time, conducive to relieving the people in disaster and reducing the economic loss. However, the traditional satellite image analysis method based on manual processing drains workforce and material resources, which slowed the government’s response to the disaster. Aiming at the natural disasters like floods and earthquakes that often happen in the south of China, we propose a dual-stage damage assessment method based on LEDNe
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Khero, ZarifIqbal, Farhan Hussain Wagan, and Naila Qadir Hisbani. "Using Satellite Images to Analyze Technically Sukkur Barrage During Flood in Pakistan." Software Engineering (Science Publishing Group) 7, no. 3 (2019): 53. http://dx.doi.org/10.11648/j.se.20190703.12.

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Jung, Younghun, Dongkyun Kim, Dongwook Kim, Munmo Kim, and Seung Lee. "Simplified Flood Inundation Mapping Based On Flood Elevation-Discharge Rating Curves Using Satellite Images in Gauged Watersheds." Water 6, no. 5 (2014): 1280–99. http://dx.doi.org/10.3390/w6051280.

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Rudner, Tim G. J., Marc Rußwurm, Jakub Fil, et al. "Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 702–9. http://dx.doi.org/10.1609/aaai.v33i01.3301702.

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We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite imagery-based flood maps, crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our model allows for rapid and accurate post-disaster damage assessment and can be used by governments to better coordinate medium- and long-term financial assistance programs for affected ar
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37

Debusscher, Bos, and Frieke Van Coillie. "Object-Based Flood Analysis Using a Graph-Based Representation." Remote Sensing 11, no. 16 (2019): 1883. http://dx.doi.org/10.3390/rs11161883.

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The amount of freely available satellite data is growing rapidly as a result of Earth observation programmes, such as Copernicus, an initiative of the European Space Agency. Analysing these huge amounts of geospatial data and extracting useful information is an ongoing pursuit. This paper presents an alternative method for flood detection based on the description of spatio-temporal dynamics in satellite image time series (SITS). Since synthetic aperture radar (SAR) satellite data has the capability of capturing images day and night, irrespective of weather conditions, it is the preferred tool
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O’Hara, R., S. Green, and T. McCarthy. "The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery." Irish Journal of Agricultural and Food Research 58, no. 1 (2019): 44–65. http://dx.doi.org/10.2478/ijafr-2019-0006.

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Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estim
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Sadek, Mohammed, Xuxiang Li, Eman Mostafa, et al. "Low-Cost Solutions for Assessment of Flash Flood Impacts Using Sentinel-1/2 Data Fusion and Hydrologic/Hydraulic Modeling: Wadi El-Natrun Region, Egypt." Advances in Civil Engineering 2020 (August 20, 2020): 1–21. http://dx.doi.org/10.1155/2020/1039309.

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Flash floods are among the most common natural hazards in Egyptian and Arabian deserts. In this work, we utilized two Sentinel-1 and Sentinel-2 satellite images, before and after the flash flood, SRTM, and geolocated terrestrial photos captured by volunteers. This paper aims to three substantial objectives: (1) monitoring the flash flood impacts on Wadi El-Natrun region based on free satellite data and mapping the destroyed vegetation cover; (2) the integration of the free remote sensing data, geolocated terrestrial photos, and GIS techniques, along with hydrologic and hydraulic modeling, to e
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Islam, MD Samiul, Xinyao Sun, Zheng Wang, and Irene Cheng. "FAPNET: Feature Fusion with Adaptive Patch for Flood-Water Detection and Monitoring." Sensors 22, no. 21 (2022): 8245. http://dx.doi.org/10.3390/s22218245.

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In satellite remote sensing applications, waterbody segmentation plays an essential role in mapping and monitoring the dynamics of surface water. Satellite image segmentation—examining a relevant sensor data spectrum and identifying the regions of interests to obtain improved performance—is a fundamental step in satellite data analytics. Satellite image segmentation is challenging for a number of reasons, which include cloud interference, inadequate label data, low lighting and the presence of terrain. In recent years, Convolutional Neural Networks (CNNs), combined with (satellite captured) mu
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Wood, Melissa, Renaud Hostache, Jeffrey Neal, et al. "Calibration of channel depth and friction parameters in the LISFLOOD-FP hydraulic model using medium-resolution SAR data and identifiability techniques." Hydrology and Earth System Sciences 20, no. 12 (2016): 4983–97. http://dx.doi.org/10.5194/hess-20-4983-2016.

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Abstract. Single satellite synthetic aperture radar (SAR) data are now regularly used to estimate hydraulic model parameters such as channel roughness, depth and water slope. However, despite channel geometry being critical to the application of hydraulic models and poorly known a priori, it is not frequently the object of calibration. This paper presents a unique method to simultaneously calibrate the bankfull channel depth and channel roughness parameters within a 2-D LISFLOOD-FP hydraulic model using an archive of moderate-resolution (150 m) ENVISAT satellite SAR-derived flood extent maps a
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42

Wieland, Marc, and Sandro Martinis. "A Modular Processing Chain for Automated Flood Monitoring from Multi-Spectral Satellite Data." Remote Sensing 11, no. 19 (2019): 2330. http://dx.doi.org/10.3390/rs11192330.

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Emergency responders frequently request satellite-based crisis information for flood monitoring to target the often-limited resources and to prioritize response actions throughout a disaster situation. We present a generic processing chain that covers all modules required for operational flood monitoring from multi-spectral satellite data. This includes data search, ingestion and preparation, water segmentation and mapping of flooded areas. Segmentation of the water extent is done by a convolutional neural network that has been trained on a global dataset of Landsat TM, ETM+, OLI and Sentinel-
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Vanama, V. S. K., Y. S. Rao, and C. M. Bhatt. "Change detection based flood mapping using multi-temporal Earth Observation satellite images: 2018 flood event of Kerala, India." European Journal of Remote Sensing 54, no. 1 (2021): 42–58. http://dx.doi.org/10.1080/22797254.2020.1867901.

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44

Skakun, Sergii, Nataliia Kussul, Andrii Shelestov, and Olga Kussul. "Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia." Risk Analysis 34, no. 8 (2013): 1521–37. http://dx.doi.org/10.1111/risa.12156.

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45

Annis, Antonio, Fernando Nardi, and Fabio Castelli. "Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping." Hydrology and Earth System Sciences 26, no. 4 (2022): 1019–41. http://dx.doi.org/10.5194/hess-26-1019-2022.

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Abstract. Hydro-meteo hazard early warning systems (EWSs) are operating in many regions of the world to mitigate nuisance effects of floods. EWS performances are majorly impacted by the computational burden and complexity affecting flood prediction tools, especially for ungauged catchments that lack adequate river flow gauging stations. Earth observation (EO) systems may integrate the lack of fluvial monitoring systems supporting the setting up of affordable EWSs. But, EO data, constrained by spatial and temporal resolution limitations, are not sufficient alone, especially at medium–small scal
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Cuentas, Natalia I. Vargas, Sebastian J. Ramos Cosi, Gianmarco D. Velasquez Lopez, Paulo Becerra, and Avid Roman Gonzalez. "Experience in MAPATÓN 2021: "ApuMayu" a Tool for the Analysis of Flood Risk Zones in Piura." International Journal of Emerging Technology and Advanced Engineering 11, no. 9 (2021): 108–16. http://dx.doi.org/10.46338/ijetae0921_13.

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Abstract—MAPATÓN 2021 was organized by CONIDA (Peruvian Space Agency) as part of the celebrations for the bicentennial of Peru's independence. MAPATÓN was oriented to disaster management. This work aims to transmit the experience of participation in MAPATON and communicate the results achieved. The authors participated in developing flood simulation models and analyzing SENTINEL-1 satellite images to identify areas affected by floods that occurred in 2017, 2019 and 2020. The results obtained, shown in a story map, show the great utility of this type of analysis for risk management and disaster
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Li, Wenzhao, Dongfeng Li, and Zheng N. Fang. "Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood." Hydrology 10, no. 1 (2023): 17. http://dx.doi.org/10.3390/hydrology10010017.

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Numerous algorithms have been developed to automate the process of delineating water surface maps for flood monitoring and mitigation purposes by using multiple sources such as satellite sensors and digital elevation model (DEM) data. To better understand the causes of inaccurate mapping information, we aim to demonstrate the advantages and limitations of these algorithms through a case study of the 2022 Madagascar flooding event. The HYDRAFloods toolbox was used to perform preprocessing, image correction, and automated flood water detection based on the state-of-the-art Edge Otsu, Bmax Otsu,
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Abdulrazzaq, Zaidoon, Nadia Aziz, and Abdulkareem Mohammed. "Flood modelling using satellite-based precipitation estimates and digital elevation model in eastern Iraq." International Journal of Advanced Geosciences 6, no. 1 (2018): 72. http://dx.doi.org/10.14419/ijag.v6i1.8946.

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Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. The study conducted in Wasit province/Eastern Iraq when a flood occurs due to heavy rainfall in May 2013. In this study the capability of Tropical Rainfall Measuring Mission (TRMM) rainfall daily data have been used to estimate the relationship between measured precipitation and the Digital Elevation Model (DEM), also to study the relationship between rainfall intensity and flood waters are
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49

Wieland, M., S. Martinis, and Y. Li. "SEMANTIC SEGMENTATION OF WATER BODIES IN MULTI-SPECTRAL SATELLITE IMAGES FOR SITUATIONAL AWARENESS IN EMERGENCY RESPONSE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 273–77. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-273-2019.

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<p><strong>Abstract.</strong> Satellite-based crisis information is frequently requested in the context of flood disasters to gain rapidly situational awareness and to prioritize response actions under often limited resources during emergency response. To assure that information products have the highest possible spatial, temporal and thematic resolutions, it is critical to be able to simultaneously analyse data from a large variety of satellite sensors. In this contribution, we present a solution to rapidly extract water bodies from Landsat TM, ETM+, OLI and Sentinel-2 for u
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50

Wania, Annett, Inès Joubert-Boitat, Francesco Dottori, Milan Kalas, and Peter Salamon. "Increasing Timeliness of Satellite-Based Flood Mapping Using Early Warning Systems in the Copernicus Emergency Management Service." Remote Sensing 13, no. 11 (2021): 2114. http://dx.doi.org/10.3390/rs13112114.

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Remotely sensed images have become an important source of information for actors involved in disaster management and satellite-based emergency mapping (SEM) is increasingly used to support the response phase in the first hours and days after a disaster occurs. The delivery timeliness of the crisis information is key to the success of SEM. In the Copernicus Emergency Management Service (CEMS), a procedure was tested during the past 5 years which links the European Flood Awareness System (EFAS) with the on-demand Rapid Mapping module in order to anticipate satellite tasking in view of an upcomin
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