Name
Abstract | ||
|---|---|---|
Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere-both in remote and/or in highly populated areas-and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology-especially satellite-based-can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13214269 | REMOTE SENSING |
Keywords | DocType | Volume |
geohazards, landslide detection, landslide inventory, remote sensing, InSAR, MT-InSAR, offset tracking, change vector analysis, land cover change | Journal | 13 |
Issue | Citations | PageRank |
21 | 0 | 0.34 |
References | Authors | |
0 | 22 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joaquim João Sousa | 1 | 0 | 1.01 |
| Guang Liu | 2 | 0 | 0.34 |
| Jinghui Fan | 3 | 0 | 0.34 |
| Zbigniew Perski | 4 | 0 | 0.34 |
| Stefan Steger | 5 | 0 | 0.34 |
| Shibiao Bai | 6 | 0 | 0.34 |
| Lianhuan Wei | 7 | 0 | 1.35 |
| Stefano Salvi | 8 | 0 | 0.34 |
| Qun Wang | 9 | 0 | 0.34 |
| Jienan Tu | 10 | 0 | 0.34 |
| Liqiang Tong | 11 | 0 | 0.68 |
| Peter Mayrhofer | 12 | 0 | 0.34 |
| Ruth Sonnenschein | 13 | 0 | 0.34 |
| Shanjun Liu | 14 | 0 | 0.34 |
| Yachun Mao | 15 | 0 | 1.69 |
| Cristiano Tolomei | 16 | 0 | 0.34 |
| Christian Bignami | 17 | 0 | 0.34 |
| Simone Atzori | 18 | 0 | 0.34 |
| Giuseppe Pezzo | 19 | 0 | 0.34 |
| Lixin Wu | 20 | 94 | 35.60 |
| Shiyong Yan | 21 | 0 | 0.34 |
| Emanuel Peres | 22 | 0 | 0.34 |