scholarly journals Investigation of Intensity- Duration- Area of Rainfall and its Impact on Floods Using Radar Images (Case Study of the Flood on May 2014)

2020 ◽  
Vol 12 (1) ◽  
pp. 73-86
Author(s):  
parviz panjehkoobi ◽  
mohammad jrayahni parvari ◽  
mehdi javardi ◽  
mohammad reza rahmannia
Keyword(s):  
Radar Images

scholarly journals Using RGISTools to Estimate Water Levels in Reservoirs and Lakes

2020 ◽  
Vol 12 (12) ◽  
pp. 1934 ◽  
Author(s):  
Ana F. Militino ◽  
Manuel Montesino-SanMartin ◽  
Unai Pérez-Goya ◽  
M. Dolores Ugarte
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Water Levels ◽  
Process Time ◽  
Landsat 8 ◽  
Northern Spain ◽  
Software Environment ◽  
Efficient Manner ◽  
Radar Images

The combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and data standards. Ris an open-source software environment with state-of-the-art statistical packages for the analysis of optical imagery. However, it lacks the tools for providing unified access to multi-program archives to customize and process the time series of images. This manuscript introduces RGISTools, a new software that solves these issues, and provides a working example on water mapping, which is a socially and environmentally relevant research field. The case study uses a digital elevation model and a rarely assessed combination of Landsat-8 and Sentinel-2 imagery to determine the water level of a reservoir in Northern Spain. The case study demonstrates how to acquire and process time series of surface reflectance data in an efficient manner. Our method achieves reasonably accurate results, with a root mean squared error of 0.90 m. Future improvements of the package involve the expansion of the workflow to cover the processing of radar images. This should counteract the limitation of the cloud coverage with multi-spectral images.

scholarly journals DETECTION OF LANDSLIDES WITH SAR POLARYMETRY: CASE STUDY OF SOUTH-EASTERN COLOMBIA

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 177-184
Author(s):  
N. A. Correa-Muñoz ◽  
C. A. Murillo-Feo
Keyword(s):  
Principal Component ◽  
Unsupervised Classification ◽  
Bare Soil ◽  
Surface Scattering ◽  
Total Power ◽  
Detection Model ◽  
Radar Images ◽  
Dispersion Mechanism ◽  
Forested Areas

<p><strong>Abstract.</strong> SAR polarimetry (PolSAR) is a method that can be used to investigate landslides. Polarimetric scattering power decomposition allows to separate the total power received by the SAR antenna, which is divided in surface scattering power, double bounce scattering and volume scattering power. Polarimetric indices are expected to serve for landslide recognition, because landslides’ scattering properties are different from those of the surrounding forested areas. The surface scattering mechanism is mainly caused by rough surfaces like bare soil and agricultural fields, so we hope that this will be the predominant dispersion mechanism in landslides. In a study area located in south-western Colombia, we used dual-Pol provided by ESA’s Sentinel-1 satellites and quad-pol from NASA’s UAVSAR aerial platform. Using C-band and L-band radar images, we analysed the interaction between radar signals and landslides. First, with dual-pol we found backscatter calibrate coefficients over four GRD radar images acquired between 2015 and 2017. The analysis gave an average backscatter value of &amp;minus;14.47&amp;thinsp;dB for VH polarisation and &amp;minus;8.40&amp;thinsp;dB for VV polarisation. Then, using H-a decomposition for quad-pol data, we validated the high relationship between entropy and alpha parameter, which has the highest contribution to the first axis in a principal component analysis. These results were used to obtain an unsupervised classification of landslides, that separated the Colombian Geological Service landslide inventory in three classes characterized by the mechanism of dispersion. These results will be combined with InSAR parameters, morphometric parameters and optical spectral indexes to obtain a local detection model of landslides.</p>

scholarly journals Investigation of Intensity- Duration- Area of Rainfall and its Impact on Floods Using Radar Images (Case Study of the Flood on May 2014)

2020 ◽  
Vol 12 (1) ◽  
pp. 73-86
Author(s):  
parviz panjehkoobi ◽  
mohammad jrayahni parvari ◽  
mehdi javardi ◽  
mohammad reza rahmannia
Keyword(s):  
Radar Images

scholarly journals Automatically delineating the calving front of Jakobshavn Isbræ from multi-temporal TerraSAR-X images: a deep learning approach

2019 ◽  
Author(s):  
Enze Zhang ◽  
Lin Liu ◽  
Lingcao Huang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Phase 1 ◽  
Greenland Ice Sheet ◽  
Tidewater Glaciers ◽  
Radar Images ◽  
Successful Case ◽  
Multi Temporal ◽  
Bed Slope

Abstract. The calving fronts of many tidewater glaciers in Greenland have been undergoing strong seasonal and inter-annual fluctuations. Conventionally, calving front positions have been manually delineated from remote sensing images. But manual practices can be labor-intensive and time-consuming, particularly when processing a large number of images taken over decades and covering large areas with many glaciers, such as Greenland. Applying U-Net, a deep learning architecture, to multi-temporal Synthetic Aperture Radar images taken by the TerraSAR-X satellite, we here automatically delineate the calving front positions of Jakobshavn Isbræ from 2009 to 2015. Our results are consistent with the manually delineated products generated by the Greenland Ice Sheet Climate Change Initiative project. We show that the calving fronts of Jakobshavn's two main branches retreated at mean rates of −117 ± 1 m yr−1 and −157 ± 1 m yr−1, respectively, during the years 2009 to 2015. The inter-annual calving front variations can be roughly divided into three phases for both branches. The retreat rates of the two branches tripled and doubled, respectively, from phase 1 (April 2009–January 2011) to phase 2 (January 2011–January 2013), then stabilized nearly zero in phase 3 (January 2013–December 2015). We suggest that the retreat of the calving front into an overdeepened basin whose bed is retrograde may have accelerated the retreat after 2011, while the inland-uphill bed slope behind the bottom of the overdeepened basin has prevented the glacier from retreating further after 2012. Demonstrating through this successful case study on Jakobshavn Isbræ and due to the transferable nature of deep learning, our methodology can be applied to many other tidewater glaciers both in Greenland and elsewhere in the world, using multi-temporal and multi-sensor remote sensing imagery.

scholarly journals Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan

2021 ◽  
Vol 7 (3) ◽  
pp. 267
Author(s):  
Pollen Chakma ◽  
Aysha Akter
Keyword(s):  
Synthetic Aperture Radar ◽  
Coastal Region ◽  
Flood Damage ◽  
Synthetic Aperture ◽  
Flood Mapping ◽  
Sar Images ◽  
Radar Images ◽  
Super Cyclone ◽  
Aperture Radar

Floods are triggered by water overflow into drylands from several sources, including rivers, lakes, oceans, or heavy rainfall. Near real-time (NRT) flood mapping plays an important role in taking strategic measures to reduce flood damage after a flood event. There are many satellite imagery based remote sensing techniques that are widely used to generate flood maps. Synthetic aperture radar (SAR) images have proven to be more effective in flood mapping due to its high spatial resolution and cloud penetration capacity. This case study is focused on the super cyclone, commonly known as Amphan, stemming from the west Bengal-Bangladesh coast across the Sundarbans on 20 May 2020, with a wind speed between 155 -165  gusting up to 185 . The flooding extent is determined by analyzing the pre and post-event synthetic aperture radar images, using the change detection and thresholding (CDAT) method. The results showed an inundated landmass of 2146 on 22 May 2020, excluding Sundarban. However, the area became 1425 about a week after the event, precisely on 28 May 2020 . This persistency generated a more severe and intense flood, due to the broken embankments. Furthermore, 13 out of 19 coastal districts were affected by the flooding, while 8 were highly inundated, including Bagerhat, Pirojpur, Satkhira, Khulna, Barisal, Jhalokati, Patuakhali and Barguna. These findings were subsequently compared with an inundation map created with a validation survey immediately after the event and also with the disposed location using a machine learning-based image classification technique. Consequently, the comparison showed a close similarity between the inundation scenario and the flood reports from the secondary sources. This circumstance envisages the significant role of CDAT application in providing relevant information for an effective decision support system.

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