Sweltering temperatures in North America have led to wildfires while unexpectedly wet weather in Europe, China and Australia led to massive flooding, jolting even climate change naysayers. Accounting for only the first six months of 2021, economic losses have been estimated to be to the tune of $93 billion according to financial risk-mitigation provider Aon. While this appears to be a marked decrease over previous years, where the 10-year median was $101 million, the sheer magnitude of the losses are still a cause of immense concern. Moreover, at the time of writing this piece, 383,279 acres were burnt in California alone due to wildfires. In China, 33 people have lost their lives even as over a million people have felt its impact. Armin Laschet, governor of North Rhine-Westphalia, one of the worst-affected German regions was quoted saying that the country was experiencing a “flooding catastrophe of historic proportions.”
Also read: Satellite imagery shows flood-induced damage in Germany
Where remote sensing comes in
In the course of these weather-induced crises, remote sensing has had a lot to contribute by way of responding to and managing the disaster. Aerial imagery shot from drones in China have provided detailed, higher resolution images and data from synthetic aperture radar at quicker response times compared to traditional methods of aerial survey. Not just surveying, drones came to the rescue in China by establishing a mobile public network that can provide up to 50 square kilometers of audio and video communication. The drone in question was Wing Loong-2H, better known as an armed reconnaissance drone for military purposes, powered by AVIC for the Ministry of Emergency Management.
Satellite imagery acquired for Germany, conducted by Maxar, have helped survey the extent of the devastating floods to not just better respond to the calamitous events, but also covered larger swathes than is otherwise possible, providing a broader perspective on the unfolding events.
In Australia, NASA Earth Observatory images using modified Copernicus Sentinel data (2021), processed by ESA and analyzed by the National Central University of Taiwan in collaboration with NASA-JPL and Caltech estimated flood severity for the Australia flooding event in March 2021.
Similarly, a large part of the data on wildfires and smoke, and their possible spread, in the US and have been provided by remote sensing. Because of their size and maneuverability, drones were widely deployed to access places that fixed-wing aircraft and helicopters couldn’t, making them a great choice for combatting wildfires. They were not only able to ‘see’ beyond the smoke, they were also able to pinpoint hotspots. Another innovation using drones was by dropping incendiary devices that would start small fires, robbing an approaching fire of the fuel it needs to spread.
In Canada, Advanced Very High Resolution Radiometer (AVHRR) imagery, Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and Visible Infrared Imaging Radiometer Suite (VIIRS) imagery were used for the fire monitoring, mapping and modeling (M3) system.
Remote sensing data, such as satellite images and aerial photos, allow us to map the variabilities of terrain properties. This detailed knowledge in turn can help manage disasters through engaging the ‘disaster cycle’. The first step is to mitigate natural disasters by providing insights into the expected frequency, character, and magnitude of hazardous events in an area.
For any successful rescue operation, the critical component is time. A precise knowledge of locations and ongoing phenomena helps save time and reduces the risk to property and human lives. Remote sensing has helped assess situations in the recently occurred and currently ongoing catastrophes and have outlined the measures that needed to be taken. Past occurrences have been mapped and lessons have been drawn from them.
Once remotely sensed data is gathered and processed, structural measures of mitigation, such as the construction of embankments in flood prone areas, can take place. Non-structural measures like risk assessment and land-use planning are also employed as practices towards mitigating disasters.
This in turn, provides better preparedness to tackle the situation on the ground. Communication strategies can be developed, early warning systems can be successfully deployed — whose response to the floods in Central China and Germany, respectively have been criticized — while necessary supplies can be stockpiled. Further, emergency services can be better mobilized, more dexterous search and rescue operations can take place, and the extent of the damage can be mapped through remotely sensed data. Recovery through rehabilitation and rebuilding are also an integral part of the so-called disaster cycle.
Remote sensing coupled with GIS — which helps to store, analyze, and retrieve data — shines as a real boon in disaster management. A mix of active and passive sensors now serve the purposes of remote sensing. The former provide their own source of energy to illuminate the objects they observe while the latter detect natural energy (radiation) that is emitted or reflected by the object being observed. Radar and LiDAR are excellent examples of active sensors while accelerometers are typical examples of passive sensors.
Data has long been collected in situ but remote sensing is changing the game. In fact, data collected in situ is being used more and more to only verify that which has been remotely gathered. As technology continues to evolve, the reliance on remote sensing to provide detailed, instantaneous data that looks at the larger picture (historically, currently and in the future) has grown by leaps and bounds.
Also read: How the use of ArcGIS is helping us plan around disasters
The way forward
Like everything, remote sensing has a few chinks in its armor too. Foremost among them is the exorbitant cost of building and operating the instruments. They are designed to be highly specialized and only the most technologically advanced countries/agencies can operate them. Data interpretation can be difficult as well. It takes a high level of expertise to read and analyze the collected data. How an instrument is making the measurements needs to be theoretically understood. Measurement uncertainty can be large and those operating the instrument need to have some understanding of the phenomenon being observed.
With such a large-scale approach in handling spatial data, it becomes imperative to collaborate across disciplines and organizations. Thankfully this data is becoming more accessible as private players and open source initiatives enter the field. Recent innovations have included small, relatively inexpensive satellites, such as cube satellites, working in constellations to gather data. The integration of LiDAR into satellite, aerial and UAV platforms has also been a recent development that is already reaping a lot of benefits. Increased computing capabilities have also made the road less hard and are only bound to improve in the years to come.
While natural disasters may not be entirely preventable, spatial data collected through remote sensing has unquestionably made them more manageable, foreseeable and has led to quicker recovery.
Also read: Accurate, open data is crucial to cross-sector grid planning and disaster prevention
