Flood Monitoring from SAR Data

2010 ◽  
pp. 19-29 ◽  
Author(s):  
Nataliia Kussul ◽  
Andrii Shelestov ◽  
Sergii Skakun
Keyword(s):  
Flood Monitoring ◽  
Sar Data

Automatic Flood Monitoring based on SAR Intensity and Interferometric Coherence using Machine Learning

2020 ◽  
Author(s):  
Binayak Ghosh ◽  
Mahdi Motagh ◽  
Mahmud Haghshenas Haghighi ◽  
Setareh Maghsudi
Keyword(s):  
Machine Learning ◽  
Open Water ◽  
Semantic Segmentation ◽  
Flood Mapping ◽  
Assistance Programs ◽  
Amplitude Data ◽  
Flood Monitoring ◽  
Sar Data ◽  
Flood Detection ◽  
Flood Maps

<p><span xml:lang="EN-US" data-contrast="auto"><span>Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. 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. Flood mapping algorithms are usually based on automatic thresholding algorithms for the initialization of the classification process in SAR amplitude data. These thresholding processes like Otsu thresholding, histogram leveling etc., are followed by clustering techniques like K-means, ISODATA for segmentation of water and non-water areas. These methods are capable of extracting the flood extent if there is a significant contrast between water and non-water areas in the SAR data. However, the classification result may be related to overestimations if non-water areas have a similar low backscatter as open water surfaces and also, these backscatter values differentiate from VV and VH polarizations. Our method aims at improving existing satellite-based emergency mapping methods by incorporating systematically acquired Sentinel-1A/B SAR data at high spatial (20m) and temporal (3-5 days) resolution. Our method involves a supervised learning method for flood detection by leveraging SAR intensity and interferometric coherence as well as polarimetry information. </span></span><span xml:lang="EN-US" data-contrast="auto"><span>It uses multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. By incorporating multitemporal satellite imagery, our method allows for rapid and accurate post-disaster damage assessment and can be used for better coordination of medium- and long-term financial assistance programs for affected areas. In this paper, we present a strategy using machine learning for semantic segmentation of the flood map, which extracts the </span></span><span xml:lang="EN-US" data-contrast="auto"><span>spatio</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-temporal information from the SAR images having both </span></span><span xml:lang="EN-US" data-contrast="auto"><span>intensity</span></span><span xml:lang="EN-US" data-contrast="auto"><span> as well coherence bands. The flood maps produced by the fusion of intensity and coherence are validated against state-of-the art methods for producing flood maps.</span></span><span> </span></p>

Multi-temporal segmentation of Cosmo-SkyMed SAR data for flood monitoring

2011 ◽  
Author(s):  
Marco Chini ◽  
Luca Pulvirenti ◽  
Nazzareno Pierdicca ◽  
Leila Guerriero
Keyword(s):  
Temporal Segmentation ◽  
Flood Monitoring ◽  
Multi Temporal ◽  
Sar Data

Systematic and Automatic Large-Scale Flood Monitoring System Using Sentinel-1 SAR Data

2020 ◽  
Author(s):  
Marco Chini ◽  
Ramona Pelich ◽  
Renaud Hostache ◽  
Patrick Matgen ◽  
Christian Bossung ◽  
...  
Keyword(s):  
Monitoring System ◽  
Large Scale ◽  
Flood Monitoring ◽  
Sar Data

Flood monitoring in remote areas: integration of multi-frequency SAR data

2021 ◽  
Author(s):  
Alberto Refice ◽  
Annarita D'Addabbo ◽  
Marco Chini ◽  
Marina Zingaro
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Microwave Remote Sensing ◽  
Integrated Analysis ◽  
Satellite Mission ◽  
Flood Monitoring ◽  
L Band ◽  
Sar Data ◽  
Sar Imagery ◽  
Flooded Areas

<p>The monitoring of inundation phenomena through synthetic aperture radar (SAR) data on vegetated areas can be improved through an integrated analysis of different spectral bands. The combination of data with different penetration depths beneath the vegetated canopy can help determine the response of flooded areas with distinct types of vegetation cover to the microwave signal. This is useful especially in cases, which actually constitute the majority, where ground data are scarce or not available.</p><p>The present study concerns the application of multi-temporal, multi-frequency, and multi-polarization SAR images, specifically data from the Sentinel-1 and PALSAR 2 SAR sensors, operating in C band, VV polarization, and L band, HH and HV polarizations, respectively, in synergy with globally-available land cover data, for improving flood mapping in densely vegetated areas, such as the Zambezi-Shire basin, Mozambique [1], characterized by wetlands, open and closed forest, cropland, grassland (herbaceous and shrubs), and a few urban areas.</p><p>We show how the combination of various data processing techniques and the simultaneous availability of data with different frequencies and polarizations can help to monitor floodwater evolution over various land cover classes. They also enable detection of different scattering mechanisms, such as double bounce interaction of vegetation stems and trunks with underlying floodwater, giving precious information about the distribution of flooded areas among the different ground cover types present on the site.</p><p>This kind of studies are expected to assume increasing importance as the availability of multi-frequency data from SAR satellite constellations will increase in the future, thanks to initiatives such as the EU Copernicus program L-band satellite mission ROSE-L [2], and their tight integration with Sentinel-1 as well as with other national constellations such as ALOS 2, or SAOCOM.</p><p><strong>References</strong></p><p>[1] Refice, A.; Zingaro, M.; D’Addabbo, A.; Chini, M. Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas. Water <strong>2020</strong>, 12, 2745, doi:10.3390/w12102745.</p><p>[2] Pierdicca, N.; Davidson, M.; Chini, M.; Dierking, W.; Djavidnia, S.; Haarpaintner, J.; Hajduch, G.; Laurin, G.V.; Lavalle, M.; López-Martínez, C.; et al. The Copernicus L-band SAR mission ROSE-L (Radar Observing System for Europe). In Active and Passive Microwave Remote Sensing for Environmental Monitoring III; SPIE: Washington, DC, USA, 2019; Volume 11154, p. 13.</p>

Sliding Spotlight SAR Data Processing Using the Azimuth Frequency De-ramping Algorithm

2010 ◽  
Vol 32 (11) ◽  
pp. 2655-2660
Author(s):  
Yun-kai Deng ◽  
Xiao-xue Jia ◽  
Jin Feng ◽  
Wei Xu
Keyword(s):  
Data Processing ◽  
Sar Data

A Modified Wave-number Domain Algorithm for Missile-borne Squinted SAR Data Processing

2011 ◽  
Vol 33 (6) ◽  
pp. 1453-1458 ◽  
Author(s):  
Zhong-yuan Xiao ◽  
Hua-ping Xu ◽  
Chun-sheng Li
Keyword(s):  
Data Processing ◽  
Wave Number ◽  
Sar Data

Bistatic SAR Data Focusing Using a Frequency Scaling Algorithm Based on an Analytical Spectrum in Tandem Configuration

2011 ◽  
Vol 33 (6) ◽  
pp. 1447-1452
Author(s):  
Shi-chao Chen ◽  
Qi-song Wu ◽  
Ming Liu ◽  
Meng-dao Xing ◽  
Zheng Bao
Keyword(s):  
Frequency Scaling ◽  
Tandem Configuration ◽  
Bistatic Sar ◽  
Sar Data

SAXON-FPN NAWC/ERIM P-3 SAR Data Distribution and Analysis

1992 ◽  
Author(s):  
Christopher C. Wackerman
Keyword(s):  
Data Distribution ◽  
Sar Data
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