scholarly journals Ground Displacement Trends in an Urban Environment Using Multi-Temporal InSAR Analysis and two Decades of Multi-Sensor Satellite-Based SAR Imagery

2015 ◽  
Author(s):  
Iuliana Armas ◽  
Marius Necsoiu ◽  
Diana Aldea Mendes ◽  
Mihaela Gheorghe ◽  
Diana Alexandra Gheorghe
Keyword(s):  
Urban Environment ◽  
Ground Displacement ◽  
Multi Temporal ◽  
Sar Imagery

scholarly journals ESTIMATION OF DAMAGED AREAS DUE TO THE 2010 CHILE EARTHQUAKE AND TSUNAMI USING SAR IMAGERY OF ALOS/PALSAR

2016 ◽  
Vol III-8 ◽  
pp. 35-42
Author(s):  
Pertiwi Jaya Ni Made ◽  
Fusanori Miura ◽  
A. Besse Rimba
Keyword(s):  
Large Scale ◽  
Aerial Photographs ◽  
Alos Palsar ◽  
Aster Gdem ◽  
Tsunami Damage ◽  
Before And After ◽  
Chile Earthquake ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Inundation Map

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km<sup>2</sup>. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.

scholarly journals Geometric Accuracy Improvement Method for High-Resolution Optical Satellite Remote Sensing Imagery Combining Multi-Temporal SAR Imagery and GLAS Data

2020 ◽  
Vol 12 (3) ◽  
pp. 568
Author(s):  
Quansheng Zhu ◽  
Wanshou Jiang ◽  
Ying Zhu ◽  
Linze Li
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Geometric Accuracy ◽  
Laser Altimetry ◽  
Multi Temporal ◽  
Improvement Method ◽  
Optical Imagery ◽  
Sar Imagery

With the widespread availability of satellite data, a single region can be described using multi-source and multi-temporal remote sensing data, such as high-resolution (HR) optical imagery, synthetic aperture radar (SAR) imagery, and space-borne laser altimetry data. These have become the main source of data for geopositioning. However, due to the limitation of the direct geometric accuracy of HR optical imagery and the effect of the small intersection angle of HR optical imagery in stereo pair orientation, the geometric accuracy of HR optical imagery cannot meet the requirements for geopositioning without ground control points (GCPs), especially in uninhabited areas, such as forests, plateaus, or deserts. Without satellite attitude error, SAR usually provides higher geometric accuracy than optical satellites. Space-borne laser altimetry technology can collect global laser footprints with high altitude accuracy. Therefore, this paper presents a geometric accuracy improvement method for HR optical satellite remote sensing imagery combining multi-temporal SAR Imagery and GLAS data without GCPs. Based on the imaging mechanism, the differences in the weight matrix determination of the HR optical imagery and SAR imagery were analyzed. The laser altimetry data with high altitude accuracy were selected and applied as height control point in combined geopositioning. To validate the combined geopositioning approach, GaoFen2 (GF2) optical imagery, GaoFen6 (GF6) optical imagery, GaoFen3 (GF3) SAR imagery, and the Geoscience Laser Altimeter System (GLAS) footprint were tested. The experimental results show that the proposed model can be effectively applied to combined geopositioning to improve the geometric accuracy of HR optical imagery. Moreover, we found that the distribution and weight matrix determination of SAR images and the distribution of GLAS footprints are the crucial factors influencing geometric accuracy. Combined geopositioning using multi-source remote sensing data can achieve a plane accuracy of 1.587 m and an altitude accuracy of 1.985 m, which is similar to the geometric accuracy of geopositioning of GF2 with GCPs.

scholarly journals Multi-Temporal DInSAR to Characterise Landslide Ground Deformations in a Tropical Urban Environment: Focus on Bukavu (DR Congo)

2018 ◽  
Vol 10 (4) ◽  
pp. 626 ◽  
Author(s):  
Adriano Nobile ◽  
Antoine Dille ◽  
Elise Monsieurs ◽  
Joseph Basimike ◽  
Toussaint Bibentyo ◽  
...  
Keyword(s):  
Urban Environment ◽  
Dr Congo ◽  
Ground Deformations ◽  
Multi Temporal

scholarly journals ESTIMATION OF DAMAGED AREAS DUE TO THE 2010 CHILE EARTHQUAKE AND TSUNAMI USING SAR IMAGERY OF ALOS/PALSAR

2016 ◽  
Vol III-8 ◽  
pp. 35-42 ◽  
Author(s):  
Pertiwi Jaya Ni Made ◽  
Fusanori Miura ◽  
A. Besse Rimba
Keyword(s):  
Large Scale ◽  
Aerial Photographs ◽  
Alos Palsar ◽  
Aster Gdem ◽  
Tsunami Damage ◽  
Before And After ◽  
Chile Earthquake ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Inundation Map

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km<sup>2</sup>. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.

scholarly journals Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2745 ◽  
Author(s):  
Alberto Refice ◽  
Marina Zingaro ◽  
Annarita D’Addabbo ◽  
Marco Chini
Keyword(s):  
Land Cover ◽  
Visual Analysis ◽  
Ground Cover ◽  
Ground Truth ◽  
Integrated Analysis ◽  
Corine Land Cover ◽  
Multi Temporal ◽  
L Band ◽  
Flood Detection ◽  
Sar Imagery

Flood detection and monitoring is increasingly important, especially on remote areas such as African tropical river basins, where ground investigations are difficult. We present an experiment aimed at integrating multi-temporal and multi-source data from the Sentinel-1 and ALOS 2 synthetic aperture radar (SAR) sensors, operating in C band, VV polarization, and L band, HH and HV polarizations, respectively. Information from the globally available CORINE land cover dataset, derived over Africa from the Proba V satellite, and available publicly at the resolution of 100 m, is also exploited. Integrated multi-frequency, multi-temporal, and multi-polarizations analysis allows highlighting different drying dynamics for floodwater over various land cover classes, such as herbaceous vegetation, wetlands, and forests. 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. The approach is validated through visual analysis from Google EarthTM imagery. This kind of integrated analysis, exploiting multi-source remote sensing to partially make up for the unavailability of reliable ground truth, is expected to assume increasing importance as constellations of satellites, observing the Earth in different electromagnetic radiation bands, will be available.

Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake

2017 ◽  
Vol 12 (sp) ◽  
pp. 646-655 ◽  
Author(s):  
Yanbing Bai ◽  
Bruno Adriano ◽  
Erick Mas ◽  
Shunichi Koshimura ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Building Damage ◽  
2016 Kumamoto Earthquake ◽  
Sar Images ◽  
Kumamoto Earthquake ◽  
Multi Temporal ◽  
Sar Data ◽  
Sar Imagery ◽  
Damage Mapping

Synthetic Aperture Radar (SAR) remote sensing is a useful tool for mapping earthquake-induced building damage. A series of operational methodologies based on SAR data using either multi-temporal or only post-event SAR images have been developed and used to serve disaster activities. This presents a critical problem: which method is more likely to obtain reliable results and should be adopted for disaster response when both pre- and post-event SAR data are available? To explore this question, this study takes the 2016 Kumamoto earthquake as a case study. ALOS-2/PALSAR-2 SAR images were employed with a machine learning framework to quantitatively compare the performance of building damage mapping using only post-event SAR images and mapping using multi-temporal SAR images. The results show that an overall accuracy of 64.5% was achieved when only post-event SAR images were used, which is 2.3% higher than the overall accuracy when multi-temporal SAR images were used. The estimated building damage ratio for the former and the latter are 29.7% and 31.1%, respectively, which are both close to the building damage ratio obtained from an optical image.

scholarly journals Problems with the retrieval of glacier net surface balance from SAR imagery

2005 ◽  
Vol 42 ◽  
pp. 209-216 ◽  
Author(s):  
Ian A. Brown ◽  
Per Klingbjer ◽  
Andy Dean
Keyword(s):  
Mass Balance ◽  
Glacier Mass Balance ◽  
In Situ Data ◽  
Amplitude Data ◽  
Multi Temporal ◽  
Geophysical Processes ◽  
Sar Imagery ◽  
Arctic Glacier ◽  
Surface Balance

AbstractThere are relatively few comparisons between synthetic aperture radar (SAR) observations and glacier mass-balance measurements. More typically, SAR has been deployed to identify changes in the end-of-summer snowline and other facies boundaries. In this paper, we analyze the geophysical processes affecting SAR amplitude data over two Arctic glacier systems in northern Scandinavia to assess the potential of SAR observations for the retrieval of surface balance parameters. Using a backscatter model and in situ data, we identify the controls on SAR imagery in terms of mass-balance measurement and discuss the glaciological parameters which can reasonably be derived from multi-temporal SAR data. Our results show that amplitude SAR imagery, in the absence of in situ measurements, is not capable of providing meaningful mass-balance data. We show that backscatter from temperate glaciers is affected primarily by snow grain-size and density, and therefore processes such as firnification or depth-hoar formation can complicate the analysis of imagery. We conclude that SAR imagery over temperate glaciers can provide valuable proxy information but not direct mass-balance terms.

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