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Application of Satellite Radar Remote Sensing to Landslide Detection and Monitoring: Challenges and Solutions卫星雷达遥感在滑坡灾害探测和监测中的应用: 挑战与对策

Lookup NU author(s): Professor Zhenhong Li, Chen Yu, Dr Ruya Xiao, Dr Lifu Chen, Dr Keren Dai, Leila Zhang

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Abstract

© 2019, Research and Development Office of Wuhan University. All right reserved.Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously, which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements, and the geometric distortion introduced by the side-looking orbit. In this paper, from landslide detection and monitoring perspective, the four major challenges of satellite radar technologies are discussed: ①The phase decorrelation caused by heavy vegetation can be weakened by use of synthetic aperture radar (SAR) imagery with a long radar wavelength (e.g. S-band or L-band), a short temporal resolution, and/or a high spatial resolution (e.g. 1 m or even higher), and/or advanced interferometric SAR (InSAR) time series, and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range split-spectrum interferometry techniques.②Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The generic atmospheric correction online service (GACOS) which is developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis.③The geometric distortions such as shadows and layovers can be pre-analyzed using an external digital elevation model (DEM) for medium-spatial-resolution SAR data; in contrast, for high-resolution SAR data, a machine learning approach can be used to identify water bodies, shadow and layover areas without a requirement of a high-spatial-resolution DEM.④Residual topographic phase exhibits in areas with high buildings or steep slopes, which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e.g. ground-based radar, LiDAR and GNSS) to develop an automated landslide detection and monitoring system. It is expected that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development.


Publication metadata

Author(s): Li Z, Song C, Yu C, Xiao R, Chen L, Luo H, Dai K, Ge D, Ding Y, Zhang Y, Zhang Q

Publication type: Article

Publication status: Published

Journal: Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University

Year: 2019

Volume: 44

Issue: 7

Pages: 967-979

Online publication date: 05/07/2019

Acceptance date: 14/05/2019

ISSN (electronic): 1671-8860

Publisher: Wuhan University

URL: https://doi.org/10.13203/j.whugis20190098

DOI: 10.13203/j.whugis20190098


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