scholarly journals Use of Sentinel-1 Data in Flood Mapping in the Buna River Area

2020 ◽  
pp. 147-156
Author(s):  
Freskida Abazaj ◽  
Gëzim Hasko
Keyword(s):  
European Space Agency ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Flood Mapping ◽  
Sar Images ◽  
Space Agency ◽  
Optical Images ◽  
River Region ◽  
Speckle Filtering ◽  
Flooded Areas

Floods are one of the disasters that cause many human lives and property. In Albania, most floods are associated with periods of heavy rainfall. In recent years, Synthetic Aperture Radar (SAR) sensors, which provide reliable data in all weather conditions and day and night, have been favored because they eliminate the limitations of optical images. In this study, a flood occurred in the Buna River region in March 2018, was mapped using SAR Sentinel-1 data. The aim of this study is to investigate the potential of flood mapping using SAR images using different methodologies. Sentinel-1A / B SAR images of the study area were obtained from the European Space Agency (ESA). Preprocessing steps, which include trajectory correction, calibration, speckle filtering, and terrain correction, have been applied to the images. RGB composition and the calibrated threshold technique have been applied to SAR images to detect flooded areas and the results are discussed here.

scholarly journals URBAN’S RIVER FLOOD ANALYSING USING SENTINEL-1 DATA CASE STUDY: (GORGANROOD, AQ’QALA)

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 415-419
Author(s):  
K. Ganji ◽  
S. Gharachelou ◽  
A. Ahmadi
Keyword(s):  
Agricultural Products ◽  
Synthetic Aperture ◽  
Flood Mapping ◽  
Sar Images ◽  
River Flood ◽  
Northern Regions ◽  
Almost All ◽  
The City ◽  
Flooded Areas

Abstract. Flood is one of the greatest disasters in the world, and the cause of a lot of damages to buildings and Agricultural products every year. Gorganrood river crossing the city of Aq’qala and it is always under flood risk. In the spring, due to the high intensity rainfall and melting of the snow, upstream areas bring much water into the Gorganrood river. On 23rd March, 2019 occurred a terrible flood in Aq’qala passing discharge 650 (m^3/s), it would occur every 100 years in this river. This river in normal time is passing discharge approximately 120 (m^3/s). A large of an urban and non-urban area was affected by this flood and mapping and analyzing of this flood have a key role for river and disaster management. Remote sensing is one of the best ways to flood mapping, especially in flood time weather is cloudy, Therefore, Synthetic Aperture Radar (SAR) images had high potentiality for flood analysis. In this study the Sentinel-1 data used for flood studying due to free available and shorter revisit time. After the processing has done, by selecting the VV band the flooded areas detected. After that overlapped the images and combination of RGB bands and the change the value of pixels, at last, we will be able to obtain the flood mapping images for Gorganrood river. In the primary days of the flooding, almost all the northern regions of the city were flooded, and during a week about 96.8 (km^2) city flooded.

scholarly journals Brief communication: Hurricane Dorian: automated near-real-time mapping of the “unprecedented” flooding in the Bahamas using synthetic aperture radar

2020 ◽  
Vol 20 (5) ◽  
pp. 1463-1468
Author(s):  
Diego Cerrai ◽  
Qing Yang ◽  
Xinyi Shen ◽  
Marika Koukoula ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Synthetic Aperture Radar ◽  
Real Time ◽  
European Space Agency ◽  
Synthetic Aperture ◽  
Management Service ◽  
Sar Images ◽  
The Bahamas ◽  
Space Agency ◽  
Continental Area ◽  
Aperture Radar

Abstract. In this communication, we present application of the automated near-real-time (NRT) system called RAdar-Produced Inundation Diary (RAPID) to European Space Agency Sentinel-1 synthetic aperture radar (SAR) images to produce flooding maps for Hurricane Dorian in the northern Bahamas. RAPID maps, released 2 d after the event, show that coastal flooding in the Bahamas reached areas located more than 10 km inland, covering more than 3000 km2 of continental area. RAPID flood estimates from subsequent SAR images show the recession of the flood across the islands and present high agreement scores when compared to Copernicus Emergency Management Service (Copernicus EMS) estimates.

scholarly journals Aplicación de imágenes Sentinel-1 y Sentinel-2 en la detección y delineación de información de crisis de desastres naturales en el marco de los servicios Copernicus EMS

2017 ◽  
pp. 49 ◽  
Author(s):  
U. Donezar-Hoyos ◽  
A. Larrañaga Urien ◽  
A. Tamés-Noriega ◽  
C. Sánchez-Gil ◽  
L. Albizua-Huarte ◽  
...  
Keyword(s):  
European Space Agency ◽  
Earth Observation ◽  
High Spectral Resolution ◽  
Radar Images ◽  
Space Agency ◽  
Optical Images ◽  
Different Types ◽  
Burnt Areas ◽  
Flooded Areas ◽  
Sentinel 2

<p>This study shows the inclusion of Sentinel-1 and Sentinel-2 images in the workflows to obtain of crisis information of different types of events and their applicability in the detection and monitoring of those events. Sentinel is an Earth Observation (EO) program that is currently being developed by the European Space Agency (ESA) in the scope of the Copernicus program operative since April 2012, formerly known as Global Monitoring for Environment and Security (GMES). This program comprises six missions, out of which three are active, Sentinel-1 that provides radar images, Sentinel-.2 providing High Resolution optical images and Sentinel-3 developed to support GMES ocean, land, atmospheric, emergency, security and cryospheric applications. The present paper describes the use of Sentinel-1 radar to detect and delineate flooded areas, and the MultiTemporal Coherence (MTC) analysis applied with pre and post-event images to delimit and monitor burnt areas and lava flows. With respect to Sentinel-2, its high spectral resolution bands allowed the delineation of burnt areas by calculating differences of vegetation and burnt indices using pre and postevent images. Results using Sentinel-1 and Sentinel-2 data were compared with results using higher spatial resolution images, both optical and radar. In all cases, the usability of Sentinel images was proven.</p>

scholarly journals Brief communication: Hurricane Dorian: automated near-real-time mapping of the unprecedented flooding on the Bahamas using SAR

2019 ◽  
Author(s):  
Diego Cerrai ◽  
Qing Yang ◽  
Xinyi Shen ◽  
Marika Koukoula ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
European Space Agency ◽  
Sar Images ◽  
The Bahamas ◽  
In Situ Data ◽  
Space Agency ◽  
Continental Area ◽  
Flooded Areas

Abstract. Lack of real-time, in situ data on the extent of flooding in many parts of the world can hinder efficient disaster response. With the advent of satellite-based synthetic aperture radar (SAR) sensors, we can deploy techniques to identify flooded areas worldwide while storms are occurring. In this communication, we present an automated near-real-time (NRT) system called RAdar-Produced Inundation Diary (RAPID), applying it to European Space Agency Sentinel-1 SAR images to produce flooding maps for Hurricane Dorian in the northern Bahamas. Images from RAPID released two days after the event show coastal flooding in the Bahamas reached areas located more than 10 km inland, covering more than 3,000 km2 of continental area. RAPID flood estimates from subsequent SAR images show the recession of the flood across the islands.

scholarly journals LAND-COVER CLASSIFICATION USING FREELY AVAILABLE MULTITEMPORAL SAR DATA (WORK IN PROGRESS)

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 133-138
Author(s):  
M. Rajngewerc ◽  
R. Grimson ◽  
J. L. Bali ◽  
P. Minotti ◽  
P. Kandus
Keyword(s):  
Land Cover ◽  
European Space Agency ◽  
Weather Conditions ◽  
Land Cover Classification ◽  
Free Access ◽  
Kappa Index ◽  
Sar Images ◽  
Space Agency ◽  
Different Seasons ◽  
Sar Data

Abstract. Synthetic Aperture Radar (SAR) images are a valuable tool for wetlands monitoring since they are able to detect water below the vegetation. Furthermore, SAR images can be acquired regardless of the weather conditions. The monitoring and study of wetlands have become increasingly important due to the social and ecological benefits they provide and the constant pressures they are subject to. The Sentinel-1 mission from the European Space Agency enables the possibility of having free access to multitemporal SAR data. This study aims to investigate the use of multitemporal Sentinel-1 data for wetlands land-cover classification. To perform this assessment, we acquired 76 Sentinel-1 images from a portion of the Lower Delta of the Paraná River, and considering different seasons, texture measurements, and polarization, 30 datasets were created. For each dataset, a Random Forest classifier was trained. Our experiments show that datasets that included the winter dates achieved kappa index values (κ) higher than 0.8. Including textures measurements showed improvements in the classifications: for the summer datasets, the κ increased more than 14%, whereas, for Winter datasets in the VH and Dual polarization, the improvements were lower than 4%. Our results suggest that for the analyzed land-cover classes, winter is the most informative season. Moreover, for Summer datasets, the textures measurements provide complementary information.

Synergetic use of L- and C-band SAR data in Earth Sciences – The JAXA-ESA mutual cooperation

2021 ◽  
Author(s):  
Julia Kubanek ◽  
Malcolm Davidson ◽  
Maurice Borgeaud ◽  
Shin-ichi Sobue ◽  
Takeo Tadono
Keyword(s):  
Snow Water Equivalent ◽  
Surface Deformation ◽  
Earth Science ◽  
European Space Agency ◽  
Synthetic Aperture ◽  
Mutual Cooperation ◽  
Space Agency ◽  
First Results ◽  
International Projects ◽  
Snow Water

&lt;p&gt;Within the &amp;#8220;Cooperation for the Use of Synthetic Aperture Radar Satellites in Earth Science and Applications&amp;#8221;, the Japanese Aerospace Exploration Agency (JAXA) and the European Space Agency (ESA) agreed to mutually share C-band data from ESA&amp;#8217;s Sentinel-1 mission and L-band data from JAXA&amp;#8217;s ALOS-2 PALSAR-2 mission over selected test sites. Applications include wetland monitoring, hurricanes, sea ice, snow water equivalent and surface deformation.&lt;/p&gt;&lt;p&gt;The aim of the collaboration is to develop a better understanding of the benefits of combining L- and C-band data over various areas and for the different thematic applications. The findings of the different European, Japanese and international projects will help to develop future SAR satellite missions, such as JAXA&amp;#8217;s ALOS-4, and ESA&amp;#8217;s Copernicus mission ROSE-L and Sentinel-1 Next Generation.&lt;/p&gt;&lt;p&gt;This presentation will give an overview of the ongoing ESA-JAXA cooperation and will show highlights and first results of the different test sites and applications.&lt;/p&gt;

scholarly journals SAR Ship Detection Based on Improved Libra RetinaNet

2021 ◽  
Author(s):  
Haomiao Liu ◽  
Haizhou Xu ◽  
Lei Zhang ◽  
Weigang Lu ◽  
Fei Yang ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Weather Conditions ◽  
Maritime Transportation ◽  
Detection Methods ◽  
Sar Images ◽  
Style Transfer ◽  
Ship Detection ◽  
Multi Scale ◽  
Optical Images ◽  
Average Accuracy

Maritime ship monitoring plays an important role in maritime transportation. Fast and accurate detection of maritime ship is the key to maritime ship monitoring. The main sources of marine ship images are optical images and synthetic aperture radar (SAR) images. Different from natural images, SAR images are independent to daylight and weather conditions. Traditional ship detection methods of SAR images mainly depend on the statistical distribution of sea clutter, which leads to poor robustness. As a deep learning detector, RetinaNet can break this obstacle, and the problem of imbalance on feature level and objective level can be further solved by combining with Libra R-CNN algorithm. In this paper, we modify the feature fusion part of Libra RetinaNet by adding a bottom-up path augmentation structure to better preserve the low-level feature information, and we expand the dataset through style transfer. We evaluate our method on the publicly available SAR dataset of ship detection with complex backgrounds. The experimental results show that the improved Libra RetinaNet can effectively detect multi-scale ships through expansion of the dataset, with an average accuracy of 97.38%.

scholarly journals Ship Classification and Detection Based on CNN Using GF-3 SAR Images

2018 ◽  
Vol 10 (12) ◽  
pp. 2043 ◽  
Author(s):  
Mengyuan Ma ◽  
Jie Chen ◽  
Wei Liu ◽  
Wei Yang
Keyword(s):  
Large Scale ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Feature Analysis ◽  
Single Shot ◽  
Target Classification ◽  
Sar Images ◽  
Different Types ◽  
Ship Classification ◽  
Fully Connected

Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection.

scholarly journals The Use of Sentinel-1 Synthetic Aperture Radar (SAR) Images and Open-Source Software for Cultural Heritage: An Example from Paphos Area in Cyprus for Mapping Landscape Changes after a 5.6 Magnitude Earthquake

2019 ◽  
Vol 11 (15) ◽  
pp. 1766 ◽  
Author(s):  
Marios Tzouvaras ◽  
Dimitris Kouhartsiouk ◽  
Athos Agapiou ◽  
Chris Danezis ◽  
Diofantos G. Hadjimitsis
Keyword(s):  
Cultural Heritage ◽  
Synthetic Aperture Radar ◽  
Open Source ◽  
European Space Agency ◽  
Synthetic Aperture ◽  
Active Sensors ◽  
Heritage Sites ◽  
Space Agency ◽  
World Heritage List ◽  
Aperture Radar

Active satellite remote sensors have emerged in the last years in the field of archaeology, providing new tools for monitoring extensive cultural heritage landscapes and areas. These active sensors, namely synthetic aperture radar (SAR) satellites, provide systematic datasets for mapping land movements triggered from earthquakes, landslides, and so on. Copernicus, the European program for monitoring the environment, provides continuous radar datasets through the Sentinel-1 mission with an almost worldwide coverage. This paper aims to demonstrate how the use of open-access and freely distributed datasets such as those under the Copernicus umbrella, along with the exploitation of open-source radar processing software, namely the sentinel applications platform (SNAP) and SNAPHU tools, provided respectively by the European Space Agency (ESA) and the University of Stanford, can be used to extract an SAR interferogram in the wider area of Paphos, located in the western part of Cyprus. The city includes various heritage sites and monuments, some of them already included in the UNESCO World Heritage list. The interferogram was prepared to study the effects of an earthquake to the buildings and sites of the area. The earthquake of a 5.6 magnitude on the Richter scale was triggered on 15 April 2015 and was strongly felt throughout the whole island. The interferogram results were based on Differential Synthetic Aperture Radar Interferometry (D-InSAR) methodology, finding a maximum uplift of 74 mm and a maximum subsidence of 31 mm. The overall process and methodology are presented in this paper.

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5091
Author(s):  
Jinxiao Wang ◽  
Fang Chen ◽  
Meimei Zhang ◽  
Bo Yu
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Glacial Lake ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Sar Images ◽  
Radar Images ◽  
Optical Images ◽  
Sar Data ◽  
Aperture Radar

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

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