scholarly journals Random Forest Classification of Inundation Following Hurricane Florence (2018) via L-Band Synthetic Aperture Radar and Ancillary Datasets

2021 ◽  
Vol 13 (24) ◽  
pp. 5098
Author(s):  
Alexander M. Melancon ◽  
Andrew L. Molthan ◽  
Robert E. Griffin ◽  
John R. Mecikalski ◽  
Lori A. Schultz ◽  
...  
Keyword(s):  
South Carolina ◽  
Random Forest ◽  
Synthetic Aperture Radar ◽  
Open Water ◽  
Synthetic Aperture ◽  
Validation Data ◽  
Random Forest Classification ◽  
Forest Classification ◽  
L Band ◽  
Aperture Radar

In response to Hurricane Florence of 2018, NASA JPL collected quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument, observing record-setting river stages across North and South Carolina. Fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging, stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study used random forest classification to generate maps of inundation from L-band UAVSAR imagery processed using the Freeman–Durden decomposition method. An average overall classification accuracy of 87% is achieved with this methodology, with areas of both under- and overprediction for the focus classes of open water and inundated forest. Fuzzy logic operations using hydrologic variables are used to reduce the number of small noise-like features and false detections in areas unlikely to retain water. Following postclassification refinement, estimated flood extents were combined to an event maximum for societal impact assessments. Results from the Hurricane Florence case study are discussed in addition to the limitations of available validation data for accuracy assessments.

scholarly journals Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan

2018 ◽  
Vol 10 (8) ◽  
pp. 1304 ◽  
Author(s):  
Yusupujiang Aimaiti ◽  
Fumio Yamazaki ◽  
Wen Liu
Keyword(s):  
Synthetic Aperture Radar ◽  
Survey Data ◽  
Land Subsidence ◽  
Ground Deformation ◽  
Tohoku Earthquake ◽  
Synthetic Aperture ◽  
Alos Palsar ◽  
The 2011 Tohoku Earthquake ◽  
L Band ◽  
Aperture Radar

In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as a part of its land was built by a massive land-fill project. To investigate the long-term land deformation patterns in Urayasu City, three sets of synthetic aperture radar (SAR) data acquired during 1993–2006 from European Remote Sensing satellites (ERS-1/-2 (C-band)), during 2006–2010 from the Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS PALSAR (L-band)) and from 2014–2017 from the ALOS-2 PALSAR-2 (L-band) were processed by using multitemporal interferometric SAR (InSAR) techniques. Leveling survey data were also used to verify the accuracy of the InSAR-derived results. The results from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data processing showed continuing subsidence in several reclaimed areas of Urayasu City due to the integrated effects of numerous natural and anthropogenic processes. The maximum subsidence rate of the period from 1993 to 2006 was approximately 27 mm/year, while the periods from 2006 to 2010 and from 2014 to 2017 were approximately 30 and 18 mm/year, respectively. The quantitative validation results of the InSAR-derived deformation trend during the three observation periods are consistent with the leveling survey data measured from 1993 to 2017. Our results further demonstrate the advantages of InSAR measurements as an alternative to ground-based measurements for land subsidence monitoring in coastal reclaimed areas.

Monitoring Everglades freshwater marsh water level using L-band synthetic aperture radar backscatter

2014 ◽  
Vol 150 ◽  
pp. 66-81 ◽  
Author(s):  
Jin-Woo Kim ◽  
Zhong Lu ◽  
John W. Jones ◽  
C.K. Shum ◽  
Hyongki Lee ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Water Level ◽  
Freshwater Marsh ◽  
Synthetic Aperture ◽  
Radar Backscatter ◽  
L Band ◽  
Aperture Radar

scholarly journals Synthetic aperture radar (SAR)-based mapping of volcanic flows: Manam Island, Papua New Guinea

2004 ◽  
Vol 4 (2) ◽  
pp. 339-346 ◽  
Author(s):  
J. K. Weissel ◽  
K. R. Czuchlewski ◽  
Y. Kim
Keyword(s):  
Synthetic Aperture Radar ◽  
Papua New Guinea ◽  
Forest Cover ◽  
New Guinea ◽  
Pyroclastic Flows ◽  
Synthetic Aperture ◽  
Global Volcanism ◽  
L Band ◽  
Bare Surface ◽  
Aperture Radar

Abstract. We present new radar-based techniques for efficient identification of surface changes generated by lava and pyroclastic flows, and apply these to the 1996 eruption of Manam Volcano, Papua New Guinea. Polarimetric L- and P-band airborne synthetic aperture radar (SAR) data, along with a C-band DEM, were acquired over the volcano on 17 November 1996 during a major eruption sequence. The L-band data are analyzed for dominant scattering mechanisms on a per pixel basis using radar target decomposition techniques. A classification method is presented, and when applied to the L-band polarimetry, it readily distinguishes bare surfaces from forest cover over Manam volcano. In particular, the classification scheme identifies a post-1992 lava flow in NE Valley of Manam Island as a mainly bare surface and the underlying 1992 flow units as mainly vegetated surfaces. The Smithsonian's Global Volcanism Network reports allow us to speculate whether the bare surface is a flow dating from October or November in the early part of the late-1996 eruption sequence. This work shows that fully polarimetric SAR is sensitive to scattering mechanism changes caused by volcanic resurfacing processes such as lava and pyroclastic flows. By extension, this technique should also prove useful in mapping debris flows, ash deposits and volcanic landslides associated with major eruptions.

scholarly journals Consistent ice and open water classification combining historical synthetic aperture radar satellite images from ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1A/B

2020 ◽  
Vol 61 (82) ◽  
pp. 40-50 ◽  
Author(s):  
A. Malin Johansson ◽  
Eirik Malnes ◽  
Sebastian Gerland ◽  
Anca Cristea ◽  
Anthony P. Doulgeris ◽  
...  
Keyword(s):  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Satellite Images ◽  
Satellite Image ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Synthetic Aperture ◽  
Envisat Asar ◽  
Aperture Radar

AbstractSynthetic Aperture Radar (SAR) satellite images are used to monitor Arctic sea ice, with systematic data records dating back to 1991. We propose a semi-supervised classification method that separates open water from sea ice and can utilise ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1 SAR images. The classification combines automatic segmentation with a manual segment selection stage. The segmentation algorithm requires only the backscatter intensities and incidence angle values as input, therefore can be used to establish a consistent decadal sea ice record. In this study we investigate the sea ice conditions in two Svalbard fjords, Kongsfjorden and Rijpfjorden. Both fjords have a seasonal ice cover, though Rijpfjorden has a longer sea ice season. The satellite image dataset has weekly to daily records from 2002 until now, and less frequent records between 1991 and 2002. Time overlap between different sensors is investigated to ensure consistency in the reported sea ice cover. The classification results have been compared to high-resolution SAR data as well as in-situ measurements and sea ice maps from Ny-Ålesund. For both fjords the length of the sea ice season has shortened since 2002 and for Kongsfjorden the maximum sea ice coverage is significantly lower after 2006.

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