scholarly journals VERTICAL LAND MOTION AND INUNDATION PROCESSES BASED ON THE INTEGRATION OF REMOTELY SENSED DATA AND IPCC AR5 SCENARIOS IN COASTAL SEMARANG, INDONESIA

2020 ◽  
Vol 16 (2) ◽  
pp. 121
Author(s):  
Muhammad Rizki Nandika ◽  
Setyo Budi Susilo ◽  
Vincentius Siregar
Keyword(s):  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Remotely Sensed Data ◽  
Sar Images ◽  
Intergovernmental Panel ◽  
Important Indicator ◽  
Central Java ◽  
Vertical Land Motion ◽  
Small Baseline Subset ◽  
Small Baseline

Vertical land motion (VLM) is an important indicator in obtaining information about relative sea-level rise (SLR) in the coastal environment, but this remains an area of study poorly investigated in Indonesia. The purpose of this study is to investigate the significance of the influence of VLM and SLR on inundation. We address this issue for Semarang, Central Java, by estimating VLM using the small baseline subset time series interferometry SAR method for 24 Sentinel-1 satellite data for the period March 2017 to May 2019. The interferometric synthetic aperture radar (InSAR) method was used to reveal the phase difference between two SAR images with two repetitions of satellite track at different times. The results of this study indicate that the average land subsidence that occurred in Semarang between March 2017 and May 2019 was from (-121) mm/year to + 24 mm/year. Through a combination of VLM and SLR scenario data obtained from the Intergovernmental Panel on Climate Change (IPCC), it was found that the Semarang coastal zone will continue to shrink due to inundation (forecast at 7% in 2065 and 10% in 2100).

scholarly journals Advanced analysis of satellite data reveals ground deformation precursors to the Brumadinho Tailings Dam collapse

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Stephen Grebby ◽  
Andrew Sowter ◽  
Jon Gluyas ◽  
David Toll ◽  
David Gee ◽  
...  
Keyword(s):  
Iron Ore ◽  
Ground Deformation ◽  
Synthetic Aperture ◽  
Environmental Damage ◽  
Interferometric Synthetic Aperture Radar ◽  
Tailings Dam ◽  
Consolidation Settlement ◽  
Small Baseline Subset ◽  
Advanced Analysis ◽  
Small Baseline

AbstractCatastrophic failure of a tailings dam at an iron ore mine complex in Brumadinho, Brazil, on 25th January 2019 released 11.7 million m3 of tailings downstream. Although reportedly monitored using an array of geotechnical techniques, the collapse occurred without any apparent warning. It claimed more than 200 lives and caused considerable environmental damage. Here we present the Intermittent Small Baseline Subset (ISBAS) technique on satellite-based interferometric synthetic aperture radar (InSAR) data to assess the course of events. We find that parts of the dam wall and tailings were experiencing deformation not consistent with consolidation settlement preceding the collapse. Furthermore, we show that the timing of the dam collapse would have been foreseeable based on this observed precursory deformation. We conclude that satellite-based monitoring techniques may help mitigate similar catastrophes in the future.

scholarly journals MONITORING OF LAND SUBSIDENCE IN RAVENNA MUNICIPALITY USING INTEGRATED SAR - GPS TECHNIQUES: DESCRIPTION AND FIRST RESULTS

2016 ◽  
Vol XLI-B7 ◽  
pp. 23-28 ◽  
Author(s):  
G. Artese ◽  
S. Fiaschi ◽  
D. Di Martire ◽  
S. Tessitore ◽  
M. Fabris ◽  
...  
Keyword(s):  
Land Subsidence ◽  
Interferometric Synthetic Aperture Radar ◽  
Persistent Scatterers ◽  
Gps Measurement ◽  
Measurement Results ◽  
Prone Area ◽  
First Results ◽  
Emilia Romagna Region ◽  
Small Baseline Subset ◽  
Small Baseline

The Emilia Romagna Region (N-E Italy) and in particular the Adriatic Sea coastline of Ravenna, is affected by a noticeable subsidence that started in the 1950s, when the exploitation of on and off-shore methane reservoirs began, along with the pumping of groundwater for industrial uses. In such area the current subsidence rate, even if lower than in the past, reaches the -2 cm/y. Over the years, local Authorities have monitored this phenomenon with different techniques: spirit levelling, GPS surveys and, more recently, Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, confirming the critical situation of land subsidence risk. In this work, we present the comparison between the results obtained with DInSAR and GPS techniques applied to the study of the land subsidence in the Ravenna territory. With regard to the DInSAR, the Small Baseline Subset (SBAS) and the Coherent Pixel Technique (CPT) techniques have been used. Different SAR datasets have been exploited: ERS-1/2, ENVISAT, TerraSAR-X and Sentinel-1. Some GPS campaigns have been also carried out in a subsidence prone area. 3D vertices have been selected very close to existing persistent scatterers in order to link the GPS measurement results to the SAR ones. GPS data were processed into the International reference system and the comparisons between the coordinates, for the first 6 months of the monitoring, provided results with the same trend of the DInSAR data, even if inside the precision of the method.

scholarly journals RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2919 ◽  
Author(s):  
Agnieszka Chojka ◽  
Piotr Artiemjew ◽  
Jacek Rapiński
Keyword(s):  
Image Processing ◽  
Synthetic Aperture Radar ◽  
Nearest Neighbor ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Sar Images ◽  
Permanent Scatterers ◽  
Level 1 ◽  
Processing Techniques ◽  
Aperture Radar

Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

Sign in / Sign up

Export Citation Format

Share Document