A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment
Abstract
:1. Introduction
2. The Role of Geospatial Data in Earthquake Studies
2.1. GIS Data
- Evaluating short- and long-term reconstruction and recovery processes.
- Ranking the stages of search-and-rescue operations.
- Determining the post-disaster assembly areas, emergency management operations centers, and other incidental services aimed at minimizing the disastrous consequences.
- Analyzing service area of hospitals and fire stations, which play a key role in providing the quickest response.
- Preparing the strategic databases for pharmacies and medical supplies.
- Predicting the aftermath of earthquakes, such as tsunamis and fires, which helps to recognize the possible affected areas via buffer analysis.
- Utilizing ArcView 3D Analyst, which can be used to prepare a 3D view of the buildings. Earthquake-vulnerable buildings will be defined (based on a specific number of floors, materials, commercial or residential use, etc.).
2.2. Optical Data
2.3. Thermal Infrared (TIR) Data
2.4. Optical Data
2.4.1. Passive Microwave
2.4.2. Active Microwave
RADAR Data
LiDAR Data
2.5. GNSS
2.6. Data Fusion
2.7. Time-Series Data
3. The Role of Remote Sensing at Different Stages of an Earthquake
3.1. Pre-Earthquake Studies
3.1.1. Thermal Anomaly Studies
3.1.2. Electromagnetic Signal Anomaly Studies
3.1.3. Crustal Deformation Studies
3.1.4. Gravity Anomaly Studies
- Thermal remote sensing is one of the most frequently used techniques in pre-seismic monitoring;
- Remote sensing of electromagnetic pulse and variations in their patterns requires a complex mechanism with high-precision control performance;
- InSAR and GNSS enable the measurement of pre-seismic movements of deformation, producing meaningful results;
- Remote sensing of gravitational field anomalies remains a lesser-used tool due to the difficulties in detecting and isolating gravitational field anomalies.
3.2. Post-Earthquake Studies
3.2.1. Post-Earthquake Rescue and Relief Activities
3.2.2. Damage Assessment
- An interpretation technique applied to a dataset after an earthquake;
- Change detection using pre- and post-earthquake images with the same sensor type and measurement geometry;
- A change detection method using pre- and post-seismic data from different sensor types;
- Data fusion with already-existing pre-seismic GIS layers and new in situ information (e.g., from seismic sensors).
4. The Application of RS in Earthquake Analysis
- Experimental or numerical approaches, such as the Analytic Hierarchy Process (AHP) and the Analytical Network Process (ANP);
- Individual analytical techniques, such as Artificial Neural Networks (ANNs), Multiple Logistic Regression (LR), Support Vector Machine (SVM), Ordered Weight Averaging (OWA), and Random Forest (RF);
- Hybrid approaches, such as the Adaptive Neuro-fuzzy Inference System (ANFIS).
5. Earthquake Follow-on Disasters
6. Limitations and Challenges
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Natural Disasters | Brief Description and Consequences | RS Data Acquisition System and Corresponding Reference | |
---|---|---|---|
Ground shaking | Ground shaking is a disruptive upwards, downwards, and sideways vibration of the surface during an earthquake. Effects: structural damage or collapse; may consequently cause other hazards such as liquefaction or landslides. | InSAR | [196] |
GPS | [197] | ||
QuickBird | [198] | ||
IKONOS | [199] | ||
SPOT HRV | [200] | ||
PALSAR-2 | [201] | ||
Ground rupture | Ground rupture can be defined as permanent deformation which occurs when sudden movement along a fault breaks the earth’s surface. Effects: fracturing, cracking, and ground displacement due to movement of the fault. | ALOS-2 SAR | [202] |
ALOS-2 InSAR | [203] | ||
DInSAR | [204] | ||
Sentinel-1 | [205] | ||
LiDAR | [206] | ||
Liquefaction | Liquefaction is a phenomenon in which sediments at or near the ground surface lose their strength in response to ground shaking and behave like liquid. Effects: liquefaction usually occurs under buildings and other structures and can cause severe damage during earthquakes. | Landsat-7 | [207] |
sUAV-based optical sensor | [208] | ||
Airbone LiDAR | [209] | ||
GNSS | [210] | ||
Landslides | Earthquake-induced landslide is a down slope movement of rocks, soil, or other debris, usually caused by a strong shaking. Effects: soil erosion, blocking of roads and railways, destruction of buildings and other structures. | SPOT-5 | [211] |
ASTER | [212] | ||
QuickBird | [213] | ||
IKONOS | [214] | ||
PALSAR-2 | [215] | ||
Landsat | [216] | ||
Tsunamis | Earthquake-induced tsunami manifests itself in the form of a series of high waves. Effects: causes severe flooding coastal erosion, drowning, and property damage. | TerraSAR-X | [217] |
SAR | [218] | ||
Worldview-2 | [167] | ||
QuickBird | [219] | ||
IKONOS | [220] | ||
Flooding | An earthquake can severely damage or break dams. The water from the river or the reservoir would then flood the area, damaging buildings, and in the worst case, may wash away or drown people. | Sentinel-2 | [221] |
Landsat-2 | [222] | ||
SAR | [223] | ||
QuickBird | [224] |
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Shafapourtehrany, M.; Batur, M.; Shabani, F.; Pradhan, B.; Kalantar, B.; Özener, H. A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment. Remote Sens. 2023, 15, 1939. https://doi.org/10.3390/rs15071939
Shafapourtehrany M, Batur M, Shabani F, Pradhan B, Kalantar B, Özener H. A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment. Remote Sensing. 2023; 15(7):1939. https://doi.org/10.3390/rs15071939
Chicago/Turabian StyleShafapourtehrany, Mahyat, Maryna Batur, Farzin Shabani, Biswajeet Pradhan, Bahareh Kalantar, and Haluk Özener. 2023. "A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment" Remote Sensing 15, no. 7: 1939. https://doi.org/10.3390/rs15071939