Land subsidence characteristics of Bandung Basin as revealed by ENVISAT ASAR and ALOS PALSAR interferometry

2014 ◽  
Vol 154 ◽  
pp. 46-60 ◽  
Author(s):  
Linlin Ge ◽  
Alex Hay-Man Ng ◽  
Xiaojing Li ◽  
Hasanuddin Z. Abidin ◽  
Irwan Gumilar
Keyword(s):  
Land Subsidence ◽  
Alos Palsar ◽  
Envisat Asar

DInSAR Time Series of ALOS PALSAR and ENVISAT ASAR Data for Monitoring Hashtgerd Land Subsidence due to Overexploitation of Groundwater

2014 ◽  
Vol 2014 (6) ◽  
pp. 497-510
Author(s):  
Nazemeh Ashrafianfar ◽  
Wolfgang Busch ◽  
Maryam Dehghani ◽  
Steffen Knospe ◽  
Mahmud Mohammad Rezapour Tabari
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Alos Palsar ◽  
Envisat Asar

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.

scholarly journals Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas

2020 ◽  
Vol 12 (3) ◽  
pp. 369 ◽  
Author(s):  
Alessandro Lapini ◽  
Simone Pettinato ◽  
Emanuele Santi ◽  
Simonetta Paloscia ◽  
Giacomo Fontanelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Central Italy ◽  
Alos Palsar ◽  
Sar Images ◽  
Random Forest Classification ◽  
Envisat Asar ◽  
Learning Framework ◽  
Forest Classification ◽  
Average Accuracy ◽  
Reference Map

In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo-SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in-situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L-, C- and X-bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non-forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient ( σ ¯ °) was computed for each sensor-polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L- and X-bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors.

scholarly journals Understanding Land Subsidence Along the Coastal Areas of Guangdong, China, by Analyzing Multi-Track MTInSAR Data

2020 ◽  
Vol 12 (2) ◽  
pp. 299 ◽  
Author(s):  
Yanan Du ◽  
Guangcai Feng ◽  
Lin Liu ◽  
Haiqiang Fu ◽  
Xing Peng ◽  
...  
Keyword(s):  
Land Subsidence ◽  
Coastal Areas ◽  
Alos Palsar ◽  
Groundwater Exploitation ◽  
Average Deformation ◽  
Deformation Velocity ◽  
Multi Temporal ◽  
Small Baseline Subset ◽  
Small Baseline ◽  
The Stability

Coastal areas are usually densely populated, economically developed, ecologically dense, and subject to a phenomenon that is becoming increasingly serious, land subsidence. Land subsidence can accelerate the increase in relative sea level, lead to a series of potential hazards, and threaten the stability of the ecological environment and human lives. In this paper, we adopted two commonly used multi-temporal interferometric synthetic aperture radar (MTInSAR) techniques, Small baseline subset (SBAS) and Temporarily coherent point (TCP) InSAR, to monitor the land subsidence along the entire coastline of Guangdong Province. The long-wavelength L-band ALOS/PALSAR-1 dataset collected from 2007 to 2011 is used to generate the average deformation velocity and deformation time series. Linear subsidence rates over 150 mm/yr are observed in the Chaoshan Plain. The spatiotemporal characteristics are analyzed and then compared with land use and geology to infer potential causes of the land subsidence. The results show that (1) subsidence with notable rates (>20 mm/yr) mainly occurs in areas of aquaculture, followed by urban, agricultural, and forest areas, with percentages of 40.8%, 37.1%, 21.5%, and 0.6%, respectively; (2) subsidence is mainly concentrated in the compressible Holocene deposits, and clearly associated with the thickness of the deposits; and (3) groundwater exploitation for aquaculture and agricultural use outside city areas is probably the main cause of subsidence along these coastal areas.

Sign in / Sign up

Export Citation Format

Share Document