Abstract
Abstract. In the past few years, the frequent geological disasters have caused enormous casualties and economic losses. Therefore, D-InSAR (differential interferometry synthetic aperture radar) has been widely used in early-warning and post disaster assessment. However, large area of decorrelation often occurs in the areas covered with abundant vegetation, which seriously affects the accuracy of surface deformation monitoring. In this paper, we analysed the effect of sensor parameters and external environment parameters on special decorrelation. Then Synthetic Aperture Radar (SAR) datasets acquired by X-band TerraSAR-X, Phased Array type L-band Synthetic Aperture Satellite-2 (ALOS-2), and C-band Sentinel-1 in Guizhou province were collected and analysed to generate the maps of coherence, which were used to evaluating the applicability of datasets of different wavelengths for D-InSAR in forest area. Finally, we found that datasets acquired by ALOS-2 had the best monitoring effect.
Highlights
Over the past couple of years, the geologic hazard occurred frequently in China
The monitoring precision of the land surface deformation is enable to achieve centimeter level, even millimeter level, along the line of sight (LOS), it still has a lot of restrictions (Ge, 2013) : 1) The loss of coherence, or decorrelation, will result in the inability of the technology to correctly invert the changes in geophysical properties and surface deformation monitoring (Massonnet, 1998); 2) The change in phase gradient due to changes in surface deformation gradient ; 3) The phase delay due to atmospheric fluctuations
Spatial decorrelation is affected by two observation positions, which is closely related to the parameters of the selected data
Summary
Over the past couple of years, the geologic hazard occurred frequently in China. In 2017, more than 7000 geological disasters occurred, which caused 327 deaths and direct economic losses of 3 billion 540 million yuan. More than 85% of the geological hazards occurred in the mountainous areas covered by many vegetation in South and South China, where the natural conditions were changeable that the bench marks and GPS points in traditional survey technology were destroyed. The factors that influence the decorrelation include temporal decorrelation, spatial decorrelation, imaging area geomorphology, geophysical activity, and data processing. Spatial decorrelation is affected by two observation positions, which is closely related to the parameters of the selected data. This article quantitatively analyze the influence of several components (incident angle, wavelength, frequency bandwidth of LFM signals, etc.) related to sensor parameters on spatial decorrelation, and compares and analyzes the monitoring applicability of each sensor. The TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS-2 data in Guizhou were selected for verification
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
