How can remote sensing data be used to reduce disaster risks?

It is interesting that this article sits under Engineering and the GIS threads. Why not a remote sensing to start with? Are the GIS community just getting started with Remote Sensing? I think not. I think the answer to this question is to get onto the satellite data hubs (such as those shipping Sentinel or Landsat data). Look at some before and after images. Think about temporal and spatial processes that result in landscape presenting as hazards. Work on e.g. Google Collab where loads of spatial data is ready for you to use. Contact Planet and MAXAR high res data providers for some of their data for evaluation. Process some data, build an algorithm, visualise it. You can empower yourself to generate information that will help society.

Remote sensing data helps in watershed monitoring thus enabling the monitoring of rainfall, snowmelt, and soil moisture in watersheds. This data can be used to model and predict the volume and timing of runoff that can lead to floods and therefore enable planning for mitigation measures additionally the data can be used for infrastructure planning to identify vulnerable areas and helps in designing and locating infrastructure like dams, levees, and flood control channels more effectively to enable implementation of mitigation intervention.

A few years ago I started wondering if it was possible to add "illegal wildlife trade and poaching" to the list of disaster risks. Today, my answer is yes, and remote sensing could give a valid contribution, for example supporting the mapping of trade/escape routes and poaching hot-spots. If anyone would be interested in better developing this proposal (mind the existence of the The International Charter Space and Major Disasters), I would be happy to get in touch.

Remote sensing is indeed a powerful tool in disaster risk reduction. It provides comprehensive, objective, and timely information about potential hazards. The ability to integrate different types of data, such as optical, radar, thermal, infrared, seismic, and gravity data, allows for a more accurate and detailed understanding of the risks. This information can be used to create hazard maps that are essential for planning and resource allocation. Furthermore, the use of GIS enhances the utility of remote sensing data by enabling spatial analysis and visualization. This combination of remote sensing and GIS is crucial in making informed decisions for disaster prevention and preparedness.

With the advancement of web GIS applications, the remote sensing products can now be easily visualized by decision makers, field staff, first responders who can make right and informed decisions just using their mobile or computer devices.

Assessing vulnerability is a crucial aspect of DRR, and remote sensing data is invaluable in this process. Vulnerability assessment involves understanding the susceptibility of an area, population, or infrastructure to potential disasters. Remote sensing data aids in assessing vulnerability by providing key information on factors such as land cover, land use, population density etc. For instance, remote sensing can help identify areas prone to flooding, or other natural hazards by analyzing topography and land cover. It can also provide insights into the proximity of infrastructure, such as roads, to hazard-prone zones. This data enables decision-makers to prioritize areas for disaster preparedness, mitigation, and adaptation measures.

Remote sensing is an ideal tool for prevention of disaster and, management once it happened. Remote sensing can be used to detect trends of land degradation, and other changes in land use and land cover. It is possible to detect the potential for flooding to happed from changes in vegetation cover and expansion of bare lands.

With regard to coastal erosion and marine submersion, the Coastal Vulnerability Index (CVI) is one of the most used tools to assess vulnerability degree of coastlines to these hazards, this tool is composed by multiple indicators of vulnerability (Sea level rise, Altitude, Coast type...etc.) linked by a mathematical formula giving a vulnerability score. The CVI was developed by V. Gornitz (1991) and has undergone improvements over time by introducing socio-economic indicators like population and landuse (McLaughlin S, McKenna J, Cooper JAG 2002 ; McLaughlin S and Cooper JAG 2010)

En effet, pour évaluer la vulnérabilité, il faut combiner les facteurs de sensibilité et la capacité d'adaptation. Pour ce faire : - Collecter les données de sensibilité biophysique et sensibilité socioéconomique - Collecter les données sur la capacité d'adaptation - Combiner ces données en utilisant les poids pour différents facteurs - Obtenez des cartes de vulnérabilité

Using the multiple temporal and spatial layers of different hazard and vulnerability components, estimation of risk has been simplified due to remote sensing. Risks from individual as well as multiple hazards can now be estimated easily from lab. Open access to global imageries, terrain datasets, social and economic data have facilitated the risk estimation. Such analysis plays a crucial role in conditions of quick decision making such as in case of natural disasters by providing a data-based reliable information. Multi-scenario risk assessments from different combination of available data layers provide wider spectrum of risks. This helps in evaluating maximum exposure towards a particular hazard and planning accordingly.

In the 2011 Tohoku earthquake and tsunami in Japan, coastal cities like Sendai had significant exposure due to their dense populations and infrastructure. The catastrophe resulted in significant damage to buildings, infrastructure, and loss of life, highlighting the importance of understanding which assets (and how many) are at risk. Insurance and reinsurance companies play a crucial role in the field of disaster risk management by incorporating advanced geospatial solutions. Their expertise in estimating exposure, combined with their financial stakes in these risks, means that they have both the motivation and the resources to develop cutting-edge solutions for risk assessment.

In fast-growing urban areas, remote sensing helps us monitor and assess issues like deforestation and land degradation over years, which very likely have impacts on flooding and landslides. It could help to create predictive models, providing valuable data for urban planners or policymakers to make informed decisions, and evaluate existing/future mitigation strategies for sustainable land-use planning.

GIS can be used to prevent or minimize disaster before it occurs. Through tools like Buffer, Erase, and more, we can create custom views of places of interest. Custom data can be created like watershed, tornado tracking, wind speeds and directions, historical seismic activity, soil types and susceptibilities. With this custom data, we can project and evaluate risk and plan mitigation strategies. This makes GIS a premier tool for stopping disaster before it occurs and minimizing the impact on people's lives.

One of the greatest challenges that remains in the wide scale implementation of remote sensing for community led environmental management is accessibility to the tools and ability to process imagery quickly. Although much of the raw satellite data and processing tools are available, technical capabilities, cost, and effort required are still significant barriers for broader application. I encourage communities and industry to collaborate on ways to make remote sensing more accessible.

Time series remote sensing data can be used in a predictive model/algorithm to predict the possibility for the occurrence of some disasters, e.g. flooding and drought.

Exposure is not static. Urban expansion, population growth, and other socio-economic processes can rapidly change the exposure landscape, requiring regular updates to exposure datasets.