Potential loess landslide deformation monitoring using L-band SAR interferometry-Reference-Cited by-同舟云学术

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Potential loess landslide deformation monitoring using L-band SAR interferometry

Published:2016-10 Issue:4 Volume:19 Page:273-277
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Liu Yuzhou¹,Liao Mingsheng¹²,Shi Xuguo¹,Zhang Lu¹²,Cunningham Cory³

Affiliation:

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China

2. Collaborative Innovation Center for Geospatial Technology, Wuhan, China

3. Southern Alberta Institute of Technology, Calgary, Canada

Funder

National Key Basic Research Program of China

Publisher

Informa UK Limited

Subject

Computers in Earth Sciences,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2016.1258202

Reference13 articles.

1. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms

2. Sar monitoring of progressive and seasonal ground deformation using the permanent scatterers technique

3. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry

4. Permanent scatterers in SAR interferometry

5. A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms

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2. New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading;Geo-spatial Information Science;2024-04-15

3. Surface Deformation Modelling Using C-Band SAR Data—A Case Study on Shimla Town, Himachal Pradesh, India;Making India Disaster Resilient;2024

4. Monitoring tilting angle of the slope surface to predict loess fall landslide: an on-site evidence from Heifangtai loess fall landslide in Gansu Province, China;Landslides;2022-01-07

5. A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods;Remote Sensing;2022-01-04

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