Relevant bibliographies by topics / Flood Assesment - Satellite Images

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Flood Assesment - Satellite Images'

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

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

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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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Dissertations / Theses on the topic "Flood Assesment - Satellite Images"

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Bernales, Fredy Arturo. "A 17-YEAR ASSESMENT OF CHANGES IN BIOMASS IN THE SOUTH OF CHILE, USING LANDSAT SATELLITE IMAGES." MSSTATE, 2008. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04022008-091002/.

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Southern Chile is recognized by different international organizations such as UNESCO (United Nations Educational, Scientific and Cultural Organization), and Conservational International as an ecologically sensitive area. The country of Chile is home to one third of the earths remaining temperate forests. Monitoring and assessing land cover changes in these forests is important not only to international organizations but to the people of Chile. The lack of multi-temporal studies that evaluate changes in land cover biomass make this study an important one for increasing awareness of how the evol
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Senthilnath, J. "Nature Inspired Optimization Techniques For Flood Assesment And Land Cover Mapping Using Satellite Images." Thesis, 2014. https://etd.iisc.ac.in/handle/2005/2606.

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With the advancement of technology and the development of more sophisticated remote sensing sensor systems, the use of satellite imagery has opened up various fields of exploration and application. There has been an increased interest in analysis of multi-temporal satellite image in the past few years because of the wide variety of possible applications of in both short-term and long-term image analysis. The type of changes that might be of interest can range from short-term phenomena such as flood assessment and crop growth stage, to long-term phenomena such as urban fringe development. This
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Senthilnath, J. "Nature Inspired Optimization Techniques For Flood Assesment And Land Cover Mapping Using Satellite Images." Thesis, 2014. http://etd.iisc.ernet.in/handle/2005/2606.

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With the advancement of technology and the development of more sophisticated remote sensing sensor systems, the use of satellite imagery has opened up various fields of exploration and application. There has been an increased interest in analysis of multi-temporal satellite image in the past few years because of the wide variety of possible applications of in both short-term and long-term image analysis. The type of changes that might be of interest can range from short-term phenomena such as flood assessment and crop growth stage, to long-term phenomena such as urban fringe development. This
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Pillay, Maldean. "Gabor filter parameter optimization for multi-textured images : a case study on water body extraction from satellite imagery." Thesis, 2012. http://hdl.handle.net/10413/11070.

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The analysis and identification of texture is a key area in image processing and computer vision. One of the most prominent texture analysis algorithms is the Gabor Filter. These filters are used by convolving an image with a family of self similar filters or wavelets through the selection of a suitable number of scales and orientations, which are responsible for aiding in the identification of textures of differing coarseness and directions respectively. While extensively used in a variety of applications, including, biometrics such as iris and facial recognition, their effectiveness depend l
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Books on the topic "Flood Assesment - Satellite Images"

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Centre, Assam Remote Sensing Application. Satellite mapping of drainage congested/flood prone areas, Pagladiya river basin, Assam. Assam Remote Sensing Application Centre, 1993.

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Book chapters on the topic "Flood Assesment - Satellite Images"

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Dhongade, Amey, Akanksha Thorat, Divya Alone, Suraj Sawant, and Amit Joshi. "Flood Damage Detection Using Satellite Images." In Advancements in Interdisciplinary Research. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23724-9_34.

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Zhou, Guoqing. "On-Board Flood Change Detection Using SAR Images." In On-Board Processing for Satellite Remote Sensing Images. CRC Press, 2023. http://dx.doi.org/10.1201/9781003319634-10.

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Rambhad, Anushree, Dhirendra Pratap Singh, and Jaytrilok Choudhary. "Detection of Flood Events from Satellite Images Using Deep Learning." In Intelligent Data Engineering and Analytics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7524-0_23.

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Jeberson Retna Raj, R., and Senduru Srinivasulu. "Analyzing Heterogeneous Satellite Images for Detecting Flood Affected Area of Kerala." In Proceedings of the Third International Conference on Computational Intelligence and Informatics. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1480-7_78.

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Vanama, Venkata Sai Krishna, Ch Praveen Kumar, and Y. S. Rao. "Rapid Detection of Regional Level Flood Events Using AMSR-E Satellite Images." In Springer Series in Geomechanics and Geoengineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77276-9_2.

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Ahmad Mohtar, Intan Shafeenar, Wardah Tahir, Sahol Hamid Abu Bakar, and Ahmad Zikry Mohd Zuhari. "Use of Numerical Weather Prediction Model and Visible Weather Satellite Images for Flood Forecasting at Kelantan River Basin." In ISFRAM 2014. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-365-1_23.

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Tügel, Franziska, Ahmed Hadidi, Ilhan Özgen-Xian, Jingming Hou, and Reinhard Hinkelmann. "Validation of Flash Flood Simulations Using Satellite Images and Community-Based Observations—Impact of Infiltration and Small-Scale Topographical Features." In Natural Disaster Science and Mitigation Engineering: DPRI reports. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2904-4_6.

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AbstractThis work is aimed at investigating flash floods in the region of El Gouna, Egypt, by using a 2D robust shallow-water model that incorporates the Green-Ampt model to find the most realistic infiltration setting for this desert area. The results of different infiltration settings are compared to inundation areas observed from LANDSAT 8 images as well as to community-based information and photographs to validate the results despite scarce data availability. The model tends to overestimate infiltration in the study area if tabulated Green-Ampt parameters for the dominant soil texture class are considered. Specifically, bare soils with no vegetation tend to develop a surface crust, leading to significantly decreased infiltration rates during heavy rainfalls. Comparing the results of different infiltration settings with the observed data showed that the crust approach or the consideration of sandy clay loam instead of sand led to more plausible results for the considered study area than those obtained using the values for sand from two different sources in the literature. Furthermore, small-scale structures, which are not appropriately captured in the original digital surface model, but significantly affect the resulting flow field, have been included based on the available information leading to much more plausible results.
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Zare, Mohammad, and Guy J.-P. Schumann. "Emerging Techniques in Machine Learning for Processing Satellite Images of Floods." In Earth Observation for Flood Applications. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-819412-6.00015-8.

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Pukanská, Katarína, Karol Bartoš, Viera Hurčíková, Ľubomír Kseňak, Marcin Gil, and Agnieszka Bieda. "Integration of multispectral and radar satellite images for flood range determination of historical flood in Slovakia 2010." In Advances and Trends in Geodesy, Cartography and Geoinformatics II. CRC Press, 2020. http://dx.doi.org/10.1201/9780429327025-12.

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Dadhich, Gautam, Venkata Sai Krishna Vanama, Hiroyuki Miyazaki, and Indrajit Pal. "Flood damage assessment with multitemporal earth observation SAR satellite images: A case of coastal flooding in Southern Thailand." In Disaster Resilience and Sustainability. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-323-85195-4.00021-4.

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Conference papers on the topic "Flood Assesment - Satellite Images"

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Prinet, V., Zhou Zhixin, Ma Songde, O. Monga, and F. Perlant. "A framework for flood assessment using satellite images." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.699595.

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Abal, Gonzalo, Ricardo Siri, Pablo Musé, Rodrigo Alonso, and Pablo Toscano. "Global Solar Irradiation Assesment in Uruguay Using Tarpley's Model and Goes Satellite Images." In ISES Solar World Congress 2011. International Solar Energy Society, 2011. http://dx.doi.org/10.18086/swc.2011.24.01.

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Senthilnath, J., S. N. Omkar, V. Mani, Ram Prasad, Ritwik Rajendra, and P. B. Shreyas. "Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence." In 2015 International Conference on Cognitive Computing and Information Processing (CCIP). IEEE, 2015. http://dx.doi.org/10.1109/ccip.2015.7100706.

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Asokan, Anju, and J. Anitha. "Guided filtering based real time flood area identification on bitemporal satellite images." In 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2021. http://dx.doi.org/10.1109/icecct52121.2021.9616955.

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Kapilaratne, R. G. C. J., and S. Kaneta. "RAPID FLOOD MAPPING FROM HIGH RESOLUTION SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORKS." In 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021). WORLD SCIENTIFIC, 2022. http://dx.doi.org/10.1142/9789811260100_0053.

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"Identification of Flood Distribution Using Sentinel-1 Satellite Images (Case Study: Gresik Regency)." In International Seminar of Research Month 2021. Galaxy Science, 2022. http://dx.doi.org/10.11594/nstp.2022.2464.

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Caglayan, A., R. Saber, and V. Isik. "Assessment of August 11, 2021 Bozkurt Flood Disaster With Sentinel-2 Satellite Images, Turkey." In 11th Congress of the Balkan Geophysical Society. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202149bgs88.

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Misbari, S., J. I. A. Gisen, A. A. M. Fauzi, and N. A. F. M. Rosli. "Geo-analytical hierarchy process for flood risk mapping using moderate resolution of satellite images." In Engineering Technology International Conference (ETIC 2022). Institution of Engineering and Technology, 2022. http://dx.doi.org/10.1049/icp.2022.2606.

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Mishra, Nitin, Cyril Lucy Monica, Shanid Malayil, T. Ravichandran, Neerav Nishant, and N. Shilpa. "Application of WG-CNN Neural Network Approach Based on Early Flood Detection using Satellite Images." In 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2023. http://dx.doi.org/10.1109/icirca57980.2023.10220740.

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Hoffer, Robin L., Albert Ortiz, and Joel Gilbert. "GLOBE students in Sunland Park, New Mexico study satellite images to decipher August 2006 flood damage." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423275.

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