Detecting disasters with satellite data
Researchers at Ohio State University are using satellite data to create 3D image models that could detect natural disasters in remote regions soon after they happen, giving first responders accurate information about the local needs.
Using imagery from the PlanetScope satellite constellation, researchers have been able to build 3D images to help them detect changes in the Earth’s surface.
PlanetScope operates over 130 satellites taking daily images at 3-5 meter resolution. While the detailed images have been used to monitor deforestation, crop health and wildfires, Rongjun Qin an associate professor of civil, environmental and geodetic engineering at Ohio State University, realized they could be used to create accurate, up-to-date 3D models of any given area, quickly providing responders with vital information about the needs of the affected region.
Satellite images can provide “very nice 3D reconstructions, but there are a lot of places where those images attached to the Earth are distorted,” Qin told Ohio State News. Two-dimensional images overlaid on a globe can make objects or locations on the map appear out of scale and degrade the program’s accuracy.
Qin’s 3D reconstructions, which factor in elevation levels and landscapes, are accurate down to about 6 meters from the ground. Additionally, PlanetScope’s constellation allows the images to be refreshed more frequently.
Previous remote-sensing-based disaster studies have been limited by their lack of available data and coverage and their resolution, or how frequently images are collected or updated. For an area as big as Ohio State’s Columbus campus (1,600 acres), it would take less than an hour to turn satellite images into an accurate 3D reconstruction of the region, Qin said.
“Remote sensing could help us estimate the area impacted by a natural disaster,” he said. “We could figure out how many people to send over for rescue operations and observe the level of damage these events actually create.”
To test their model, Qin’s team devised three different case studies, or experiments, using thousands of PlanetScope images collected between 2016 and 2021. They used the images to build 3D reconstructions of urban and rural areas to detect changes over time.
For post-disaster assessment, the team looked at images from an avalanche and flood in a glacial area near Chamoli, India. They found that their model could not only recreate the changed topography that led to the disaster, but it could account for the volume of the rocks and ice in the avalanche.
“We verified that PlanetScope’s digital surface model can be used to evaluate mass changes for similar global natural disasters to the avalanche event,” Qin said.
“Data availability, coverage, and resolution have been the major hurdles for remote sensing-based disaster monitoring,” the researchers said in their paper. “Our experiment in determining the mass loss on the Chamoli site … created an unprecedented possibility to build a sustainable monitoring program to serve the first responders to confront natural disasters that might happen everywhere and at any time point.”
Qin’s findings will help engineers develop better ways to leverage satellite data, especially as the number of satellites and their various applications grow.
“This is still in its incubation stage and will still require some engineering efforts,” he said, “but I think it’s going to be a big deal in the industry and for scientists interested in combating climate change.