A comprehensive interferometric process for monitoring land deformation using ASAR and PALSAR satellite interferometric data

Abstract

This study was conducted to apply the enhanced differential interferometric process to interferometric data obtained from C-band Advanced Synthetic Aperture Radar (ASAR) and Phased Array type L-Band Synthetic Aperture Radar (PALSAR) systems, in the Marand plain, Iran. In advance, the capability of each sensor was examined with regard to the signal coherency for sensor-target interactions, which emphasized on the terrific excellency of PALSAR (L-band) to ASAR (C-band) measurements in study area. In the interferometric process, in addition to resolving the topography and baseline related errors (which is conventional in the standard D-InSAR process), subtle quantitative methods were outlined to minimize the secondary, but momentous errors (i.e., orbital and atmospheric) from differential phases. Subsequently, based on the outlined process, the mean deformation as well as three-dimensional displacement maps (using ascending and descending modes) were generated. The deformation maps significantly indicated the downward motion of the surface with the maximum rate of −0.5 millimetre per day for the period, i.e., from 2004 to 2010, with relative dominance in eastern and central to northern parts of the Marand plain. Finally, the results were validated and the source of deformation was inspected using field data such as seismic history; changes in piezometric levels and cross-checking the results from radar measurements itself.

Measuring Spatiotemporal Features of Land Subsidence, Groundwater Drawdown, and Compressible Layer Thickness in Beijing Plain, China

“…In this context, remote sensors bring useful information. The InSAR technique can be used to detect land subsidence with higher spatiotemporal resolutions [48][49][50][51][52]. The Gravity Recovery and Climate Experiment (GRACE) technique can reveal the large-scale time series water storage change and the groundwater storage loss [53,54]; most notably, it has a potential to detect heterogeneous groundwater storage variations at the subregional scale, smaller than the typical GRACE footprint (200,000 km 2 ) [55], and to detect anthropogenic signals over regions with high levels of groundwater consumption [56].…”

Characteristics of Surface Deformation in Lanzhou with Sentinel-1A TOPS

“…Currently, the technologies used for monitoring surface deformation mainly include leveling, the Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR). Among these, the precision of leveling and GPS technology for ground monitoring meets the requirements needed to monitor urban surface deformation, but the monitoring process is time-consuming, which is not conducive to the rapid emergency response required to address urban disasters [1,9]. GPS observes the shape variable in the three-dimensional direction, while InSAR technology can obtain the deformation on the oblique path.…”

“…needed to monitor urban surface deformation, but the monitoring process is time-consuming, which is not conducive to the rapid emergency response required to address urban disasters [1,9]. GPS observes the shape variable in the three-dimensional direction, while InSAR technology can obtain the deformation on the oblique path.…”

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

“…Lee et al (2012) assessed ground subsidence susceptibility (GSS) in South Korea using artificial neural networks (ANN). A GIS-MCDA method was also used by Ghorbanzadeh et al (2017) to identify areas that are prone to land subsidence in the Marand plain, while Dehghan-Soraki et al (2015) applied ASAR and PALSAR data to analyze and measure the land surface change rates caused by subsidence. Karimzadeh et al (2013) and Karimzadeh (2015) used the same type of data in order to distinguish the surface deformation in another basin in the Northwest of Iran.…”