Topic Editors
Spatial Patterns of Disaster Risk Assessment via Remote Sensing
Topic Information
Dear Colleagues,
Remote sensing has become an important tool for assessing disaster risk and identifying areas vulnerable to natural hazards such as floods, earthquakes, and landslides. By using satellite imagery and other remote sensing data, scientists can analyze the spatial patterns of risk factors such as land cover, topography, and infrastructure and create maps on this basis to inform disaster management strategies. One of the ways that remote sensing is used to assess disaster risk is by mapping the land cover of an area. Land cover maps can identify areas prone to flooding or landslides, as well as areas at high risk of wildfire. These maps can be used to create early warning systems that alert communities to potential hazards and identify areas where mitigation efforts, such as reforestation or flood control measures, may be needed. Another way in which remote sensing is used to assess disaster risk is by mapping the topography of an area. By creating digital elevation models (DEMs) from satellite data, scientists can identify areas prone to landslides or flash floods. DEMs can also be used to identify areas that are at risk from sea level rise or storm surge. Infrastructure is another important factor in assessing disaster risk, and remote sensing can map roads, buildings, and other infrastructure that may be vulnerable to natural hazards. This information can identify areas where evacuation routes may be needed, or infrastructure upgrades may be necessary to improve resilience. One recent advance in the field of spatial patterns of disaster risk assessment via remote sensing is the use of machine learning algorithms to analyze large volumes of satellite imagery and identify patterns of risk factors. These algorithms can be used to detect changes in land cover or infrastructure that may indicate increased risk of natural hazards. Another advance is in the use of high-resolution satellite imagery to create detailed maps of infrastructure and land cover that can then be used to identify areas that are vulnerable to specific types of natural hazards. Additionally, the use of unmanned aerial vehicles (UAVs) and other airborne sensors is allowing for more precise and targeted assessments of disaster risk at the local level. Overall, these advances in remote sensing technology and analysis techniques are helping to improve our understanding of disaster risk and inform more effective disaster management strategies. Overall, remote sensing is a powerful tool for assessing disaster risk and identifying areas vulnerable to natural hazards. By analyzing the spatial patterns of risk factors, scientists can create maps that can be used to inform disaster management strategies and improve resilience in vulnerable communities. This topic is aimed at providing selected contributions on advances in the synthesis, characterization, and applications of most recent advancements in the field of spatial patterns of disaster risk assessment via remote sensing. Potential topics include, but are not limited to, the following topics:
- Remote sensing with natural hazard assessment
- Natural disaster risk analysis
- Most advance applications in natural hazards
- Natural disaster risk early warning
- Urban, community, and infrastructure disaster resilience assessment
- Disaster prevention and mitigation capability assessment
- Geomatics and natural hazards risk management
- Natural disaster risk survey
Dr. Aqil Tariq
Dr. Leila Hashemi Beni
Topic Editors
Keywords
- GIS and remote sensing
- vulnerability assessment
- decision making for natural disaster risk
- spatial data
- big data
- multi-hazards
- hazard assessment
- exposure evaluation
- risk assessment
- risk management
- integrated natural disaster risk
- natural disaster risk early warning
- multisource remote sensing data
- natural disaster insurance
- disaster resilience
Participating Journals
| Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC | |
|---|---|---|---|---|---|---|
Forests
|
2.9 | 4.5 | 2010 | 16.9 Days | CHF 2600 | Submit |
Land
|
3.9 | 3.7 | 2012 | 14.8 Days | CHF 2600 | Submit |
Remote Sensing
|
5.0 | 7.9 | 2009 | 23 Days | CHF 2700 | Submit |
Sustainability
|
3.9 | 5.8 | 2009 | 18.8 Days | CHF 2400 | Submit |
Water
|
3.4 | 5.5 | 2009 | 16.5 Days | CHF 2600 | Submit |
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