Multi-Window Identification of Landslide Hazards Based on InSAR Technology and Factors Predisposing to Disasters
出版年份 2023 全文链接
标题
Multi-Window Identification of Landslide Hazards Based on InSAR Technology and Factors Predisposing to Disasters
作者
关键词
-
出版物
Land
Volume 12, Issue 1, Pages 173
出版商
MDPI AG
发表日期
2023-01-05
DOI
10.3390/land12010173
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Detection and characterization of active landslides with multisource SAR data and remote sensing in western Guizhou, China
- (2022) Yifei Zhu et al. NATURAL HAZARDS
- Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area
- (2022) Chao Zhou et al. Landslides
- Landslide Susceptibility Assessment by Using Convolutional Neural Network
- (2022) Shahrzad Nikoobakht et al. Applied Sciences-Basel
- Evaluation of different machine learning models and novel deep learning-based algorithm for landslide susceptibility mapping
- (2022) Tingyu Zhang et al. Geoscience Letters
- Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data
- (2021) Xiangxiang Zheng et al. ISPRS International Journal of Geo-Information
- Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping
- (2021) Xin Yang et al. Remote Sensing
- Forecasting the magnitude of potential landslides based on InSAR techniques
- (2020) Y. Zhang et al. REMOTE SENSING OF ENVIRONMENT
- Automatic Mapping of Landslides by the ResU-Net
- (2020) Wenwen Qi et al. Remote Sensing
- Remote Sensing for Assessing Landslides and Associated Hazards
- (2020) Candide Lissak et al. SURVEYS IN GEOPHYSICS
- Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors
- (2020) Wenbin Li et al. Remote Sensing
- Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China
- (2019) Yi Wang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Characterization of the Kinematics of Three Bears Landslide in Northern California Using L-band InSAR Observations
- (2019) Yuanyuan Liu et al. Remote Sensing
- Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment
- (2019) Maher Ibrahim Sameen et al. CATENA
- Generative Modeling of InSAR Interferograms
- (2019) Guillaume Rongier et al. Earth and Space Science
- Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016)
- (2018) Hamid Reza Pourghasemi et al. Arabian Journal of Geosciences
- Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in Danba, China
- (2018) Jie Dong et al. REMOTE SENSING OF ENVIRONMENT
- Remote Sensing of Landslides—A Review
- (2018) Chaoying Zhao et al. Remote Sensing
- Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia
- (2018) Dieu Tien Bui et al. Remote Sensing
- The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution
- (2017) A. Rosi et al. Landslides
- Time series analysis of InSAR data: Methods and trends
- (2016) Batuhan Osmanoğlu et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Wide-Area Landslide Deformation Mapping with Multi-Path ALOS PALSAR Data Stacks: A Case Study of Three Gorges Area, China
- (2016) Xuguo Shi et al. Remote Sensing
- Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives
- (2014) Marco Scaioni et al. Remote Sensing
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started