Earth Observations for Geohazards: Volume 2 by Zhenhong LiEarth Observations for Geohazards: Volume 2 by Zhenhong Li

Earth Observations for Geohazards: Volume 2

Guest editorZhenhong Li, Roberto Tomás

Paperback | April 28, 2017

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Earth Observations (EO) encompasses different types of sensors (e.g., SAR, LiDAR, Optical and multispectral) and platforms (e.g., satellites, aircraft, and Unmanned Aerial Vehicles) and enables us to monitor and model geohazards over regions at different scales in which ground observations may not be possible due to physical and/or political constraints. EO can provide high spatial, temporal and spectral resolution, stereo-mapping and all-weather-imaging capabilities, but not by a single satellite at a time. Improved satellite and sensor technologies, increased frequency of satellite measurements, and easier access and interpretation of EO information have all contributed to the increased demand for satellite EO data. EO, combined with complementary terrestrial observations and with physical models, have been widely used to monitor geohazards, revolutionizing our understanding of how the Earth system works. 

Title:Earth Observations for Geohazards: Volume 2Format:PaperbackProduct dimensions:500 pages, 9.61 × 6.69 × 1.36 inShipping dimensions:9.61 × 6.69 × 1.36 inPublished:April 28, 2017Publisher:MDPI AGLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3038424005

ISBN - 13:9783038424000


Table of Contents

1) Earth Observations for Geohazards: Present and Future Challenges
2) Imaging Land Subsidence Induced by Groundwater Extraction in Beijing (China) Using Satellite Radar Interferometry
3) Anatomy of Subsidence in Tianjin from Time Series InSAR
4) Ground Subsidence in the Beijing-Tianjin-Hebei Region from 1992 to 2014 Revealed by Multiple SAR Stacks
5) Spatiotemporal Characterization of Land Subsidence and Uplift (2009–2010) over Wuhan in Central China Revealed by TerraSAR-X InSAR Analysis
6) Application of InSAR and Gravimetry for Land Subsidence Hazard Zoning in Aguascalientes, Mexico
7) Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis
8) Complex Deformation Monitoring over the Linfen–Yuncheng Basin (China) with Time Series InSAR Technology
9) InSAR Time Series Analysis of Natural and Anthropogenic Coastal Plain Subsidence: The Case of Sibari (Southern Italy)
10) DInSAR-Based Detection of Land Subsidence and Correlation with Groundwater Depletion in Konya Plain, Turkey
11) Coastal Subsidence Monitoring Associated with Land Reclamation Using the Point Target Based SBAS-InSAR Method: A Case Study of Shenzhen, China
12) Investigation on Mining Subsidence Based on Multi-Temporal InSAR and Time-Series Analysis of the Small Baseline Subset—Case Study of Working Faces 22201-1/2 in Bu’ertai Mine, Shendong Coalfield, China
13) Investigating the Ground Deformation and Source Model of the Yangbajing Geothermal Field in Tibet, China with the WLS InSAR Technique
14) InSAR Observation and Numerical Modeling of the Earth-Dam Displacement of Shuibuya Dam (China)
15) Precise Positioning of BDS, BDS/GPS: Implications for Tsunami Early Warning in South China Sea
16) Spatio-Temporal Error Sources Analysis and Accuracy Improvement in Landsat 8 Image Ground
Displacement Measurements
17) Second-Order Polynomial Equation-Based Block Adjustment for Orthorectification of DISP Imagery
18) Taking Advantage of the ESA G-POD Service to Study Ground Deformation Processes in High Mountain Areas: A Valle d’Aosta Case Study, Northern Italy
19) A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds
20) Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-
Words Approach
21) An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing
22) Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake
23) Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake
24) Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery
25) Building Earthquake Damage Information Extraction from a Single Post-Earthquake PolSAR Image