scholarly journals InSAR observations of the 2009 Racha earthquake, Georgia

2016 ◽  
Vol 16 (9) ◽  
pp. 2137-2144 ◽  
Author(s):  
Elena Nikolaeva ◽  
Thomas R. Walter
Keyword(s):  
Dislocation Model ◽  
Earthquake Hazards ◽  
Fault Surface ◽  
Landslide Hazards ◽  
Line Of Sight Propagation ◽  
Future Earthquake ◽  
Multi Temporal ◽  
Seismic Records ◽  
Sar Data ◽  
Racha Earthquake

Abstract. Central Georgia is an area strongly affected by earthquake and landslide hazards. On 29 April 1991 a major earthquake (Mw  =  7.0) struck the Racha region in Georgia, followed by aftershocks and significant afterslip. The same region was hit by another major event (Mw  =  6.0) on 7 September 2009. The aim of the study reported here was to utilize interferometric synthetic aperture radar (InSAR) data to improve knowledge about the spatial pattern of deformation due to the 2009 earthquake. There were no actual earthquake observations by InSAR in Georgia. We considered all available SAR data images from different space agencies. However, due to the long wavelength and the frequent acquisitions, only the multi-temporal ALOS L-band SAR data allowed us to produce interferograms spanning the 2009 earthquake. We detected a local uplift around 10 cm (along the line-of-sight propagation) in the interferogram near the earthquake's epicenter, whereas evidence of surface ruptures could not be found in the field along the active thrust fault. We simulated a deformation signal which could be created by the 2009 Racha earthquake on the basis of local seismic records and by using an elastic dislocation model. We compared our modeled fault surface of the September 2009 with the April 1991 Racha earthquake fault surfaces and identify the same fault or a sub-parallel fault of the same system as the origin. The patch that was active in 2009 is just adjacent to the 1991 patch, indicating a possible mainly westward propagation direction, with important implications for future earthquake hazards.

scholarly journals InSAR observations of the 2009 Racha earthquake, the Republic Georgia

2015 ◽  
Vol 3 (8) ◽  
pp. 4695-4714
Author(s):  
E. Nikolaeva ◽  
T. R. Walter
Keyword(s):  
Dislocation Model ◽  
Earthquake Hazards ◽  
Fault Surface ◽  
Synthetic Aperture Radar Interferometry ◽  
Before And After ◽  
Future Earthquake ◽  
Multi Temporal ◽  
Seismic Records ◽  
The Republic ◽  
Racha Earthquake

Abstract. Central Georgia is an area strongly affected by earthquake and landslide hazards. On 29 April 1991 a major earthquake (Mw = 7.0) struck the Racha region in the republic Georgia, followed by aftershocks and significant afterslip. The same region was hit by another major event (Mw = 6.0) on 7 September 2009. The aim of the study reported here was to utilize geodetic data as synthetic aperture radar interferometry (InSAR) to improve a knowledge about the spatial pattern of deformation due to the earthquakes in the seismic active central Georgia. There were no actual earthquake observations by InSAR in Georgia. We used the multi-temporal ALOS L-band InSAR data to produce interferograms spanning times before and after the 2009 earthquake. We detected a local uplift around 10 cm in the interferogram near the earthquake's epicenter whereas evidence of surface ruptures could not be found in the field along the active thrust fault. We simulated a deformation signal which could be created by the 2009 Racha earthquake on the basis of local seismic records and by using an elastic dislocation model. The observed InSAR deformation is in good agreement with our model. We compared our modeled fault surface of the September 2009 with the April 1991 Racha earthquake fault surfaces, and identify the same fault or a sub-parallel fault of the same system as the origin. The patch that was active in 2009 is just adjacent to the 1991 patch, indicating a possible mainly westward propagation direction, with important implications for future earthquake hazards.

scholarly journals Discrimination of glacier facies using multi-temporal SAR data

1998 ◽  
Vol 44 (146) ◽  
pp. 42-53 ◽  
Author(s):  
K. C. Partington
Keyword(s):  
European Space Agency ◽  
Greenland Ice Sheet ◽  
Sensor Calibration ◽  
Lower Altitude ◽  
Mount Pinatubo ◽  
South Central ◽  
Space Agency ◽  
Wet Snow ◽  
Multi Temporal ◽  
Sar Data

AbstractGlacier facies from the Greenland ice sheet and the Wrangell-St Elias Mountains, Alaska, are analyzed using multi-temporal synthetic aperture radar (SAR) data from the European Space Agency ERS-1 satellite. Distinct zones and facies are visible in multi-temporal SAR data, including the dry-snow facies, the combined percolation and wet-snow facies, the ice facies, transient melt areas and moraine. In Greenland and south-central Alaska, very similar multi-temporal signatures are evident for the same facies, although these facies are found at lower altitude in West Greenland where the equilibrium line appears to be found at sea level at 71°30?N during the year analyzed (1992-93), probably because of the cooling effect of the eruption of Mount Pinatubo. In Greenland, both the percolation and dry-snow facies are excellent distributed targets for sensor calibration, with backscatter coefficients stable to within 0.2 dB. However, the percolation facies near the top of Mount Wrangell are more complex and less easily delineated than in Greenland, and at high altitude the glacier facies have a multi-temporal signature which depends sensitively on slope orientation.

scholarly journals DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1619 ◽  
Author(s):  
Zhou Ya’nan ◽  
Luo Jiancheng ◽  
Feng Li ◽  
Zhou Xiaocheng
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Hunan Province ◽  
Spatial Features ◽  
Optical Images ◽  
Multi Temporal ◽  
Crop Classification ◽  
Sar Data

Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.

The Automatic Detection of Fire Scar in Alaska using Multi-Temporal PALSAR Polarimetric SAR Data

2018 ◽  
Vol 44 (5) ◽  
pp. 447-461 ◽  
Author(s):  
Jujie Wei ◽  
Yonghong Zhang ◽  
Hong’an Wu ◽  
Bin Cui
Keyword(s):  
Automatic Detection ◽  
Polarimetric Sar ◽  
Fire Scar ◽  
Multi Temporal ◽  
Sar Data

scholarly journals Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests

2019 ◽  
Vol 11 (6) ◽  
pp. 670 ◽  
Author(s):  
Sarah Banks ◽  
Lori White ◽  
Amir Behnamian ◽  
Zhaohua Chen ◽  
Benoit Montpetit ◽  
...  
Keyword(s):  
Land Cover ◽  
Random Forests ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Wetland Classification ◽  
Model Based ◽  
Angle Data ◽  
Significant Difference ◽  
Multi Temporal ◽  
Sar Data

To better understand and mitigate threats to the long-term health and functioning of wetlands, there is need to establish comprehensive inventorying and monitoring programs. Here, remote sensing data and machine learning techniques that could support or substitute traditional field-based data collection are evaluated. For the Bay of Quinte on Lake Ontario, Canada, different combinations of multi-angle/temporal quad pol RADARSAT-2, simulated compact pol RADARSAT Constellation Mission (RCM), and high and low spatial resolution Digital Elevation and Surface Models (DEM and DSM, respectively) were used to classify six land cover classes with Random Forests: shallow water, marsh, swamp, water, forest, and agriculture/non-forested. Results demonstrate that high accuracies can be achieved with multi-temporal SAR data alone (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image and a summer image), or via fusion of SAR and DEM and DSM data for single dates/incidence angles (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image, DEM, and DSM data). For all models based on single SAR images, simulated compact pol data generally achieved lower accuracies than quad pol RADARSAT-2 data. However, it was possible to compensate for observed differences through either multi-temporal/angle data fusion or the inclusion of DEM and DSM data (i.e., as a result, there was not a statistically significant difference between multiple models). With a higher repeat-pass cycle than RADARSAT-2, RCM is expected to be a reliable source of C-band SAR data that will contribute positively to ongoing efforts to inventory wetlands and monitor change in areas containing the same land cover classes evaluated here.

scholarly journals Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)

2019 ◽  
Vol 11 (13) ◽  
pp. 1582 ◽  
Author(s):  
Mahdianpari ◽  
Mohammadimanesh ◽  
McNairn ◽  
Davidson ◽  
Rezaee ◽  
...  
Keyword(s):  
Variable Importance ◽  
Sar Images ◽  
Object Based ◽  
Multi Temporal ◽  
Importance Analysis ◽  
Crop Mapping ◽  
Crop Classification ◽  
Sar Data ◽  
Highly Correlated ◽  
Marginal Improvement

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.

scholarly journals A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy

2002 ◽  
Vol 2 (1/2) ◽  
pp. 57-72 ◽  
Author(s):  
M. Cardinali ◽  
P. Reichenbach ◽  
F. Guzzetti ◽  
F. Ardizzone ◽  
G. Antonini ◽  
...  
Keyword(s):  
Central Italy ◽  
Landslide Hazard ◽  
Aerial Photographs ◽  
Site Specific ◽  
Landslide Hazards ◽  
Umbria Region ◽  
Multi Temporal ◽  
Landslide Inventory Map ◽  
Hazard And Risk ◽  
Inventory Map

Abstract. We present a geomorphological method to evaluate landslide hazard and risk. The method is based on the recognition of existing and past landslides, on the scrutiny of the local geological and morphological setting, and on the study of site-specific and historical information on past landslide events. For each study area a multi-temporal landslide inventory map has been prepared through the interpretation of various sets of stereoscopic aerial photographs taken over the period 1941–1999, field mapping carried out in the years 2000 and 2001, and the critical review of site-specific investigations completed to solve local instability problems. The multi-temporal landslide map portrays the distribution of the existing and past landslides and their observed changes over a period of about 60 years. Changes in the distribution and pattern of landslides allow one to infer the possible evolution of slopes, the most probable type of failures, and their expected frequency of occurrence and intensity. This information is used to evaluate landslide hazard, and to estimate the associated risk. The methodology is not straightforward and requires experienced geomorphologists, trained in the recognition and analysis of slope processes. Levels of landslide hazard and risk are expressed using an index that conveys, in a simple and compact format, information on the landslide frequency, the landslide intensity, and the likely damage caused by the expected failure. The methodology was tested in 79 towns, villages, and individual dwellings in the Umbria Region of central Italy.

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