Introduction to a Thematic Set of Papers on Remote Sensing for Natural Hazards Assessment and Control
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Published:2023-02-15
Issue:4
Volume:15
Page:1048
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Mazzanti Paolo12ORCID, Romeo Saverio3ORCID
Affiliation:
1. Department of Earth Sciences & CERI Research Centre, Sapienza University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy 2. NHAZCA s.r.l., Via Vittorio Bachelet, 12, 00185 Rome, Italy 3. Italian Institute for Environmental Protection and Research (ISPRA), Geological Survey of Italy, 00144 Rome, Italy
Abstract
Remote sensing is currently showing high potential to provide valuable information at various spatial and temporal scales concerning natural hazards and their associated risks. Recent advances in technology and processing methods have strongly contributed to the development of disaster risk reduction research. In this Special Issue titled “Remote Sensing for Natural Hazards Assessment and Control”, we propose state-of-the-art research that specifically addresses multiple aspects of the use of remote sensing for natural hazards. The aim was to collect innovative methodologies, expertise, and capabilities to detect, assess monitor, and model natural hazards. In this regard, 18 open-access papers showcase scientific studies based on the exploitation of a broad range of remote sensing data and techniques, as well as focusing on a well-assorted sample of natural hazard types.
Subject
General Earth and Planetary Sciences
Reference18 articles.
1. Chen, J., Zhang, J., Wu, T., Hao, J., Wu, X., Ma, X., Zhu, X., Lou, P., and Zhang, L. (2022). Activity and Kinematics of Two Adjacent Freeze–Thaw-Related Landslides Revealed by Multisource Remote Sensing of Qilian Mountain. Remote Sens., 14. 2. Wang, Y., Feng, G., Feng, Z., Wang, Y., Wang, X., Luo, S., Zhao, Y., and Lu, H. (2022). An MT-InSAR Data Partition Strategy for Sentinel-1A/B TOPS Data. Remote Sens., 14. 3. Ma, S., Shao, X., and Xu, C. (2022). Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China. Remote Sens., 14. 4. Wang, Y., Feng, G., Li, Z., Luo, S., Wang, H., Xiong, Z., Zhu, J., and Hu, J. (2022). A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China. Remote Sens., 14. 5. Xiong, Z., Deng, K., Feng, G., Miao, L., Li, K., He, C., and He, Y. (2022). Settlement Prediction of Reclaimed Coastal Airports with InSAR Observation: A Case Study of the Xiamen Xiang’an International Airport, China. Remote Sens., 14.
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