Researchers have developed a way to use satellite imagery data to create 3D images that can quickly detect changes on the Earth’s surface.
The Planetscope constellation of satellites, operated by satellite data company Planet, collects weekly and sometimes even daily images of the entire globe. Its fleet of Cubesats, or miniature satellites, has about 1,700 images of every location on Earth. Data captured by the constellation has been used to monitor the spread of wildfires, detect changes in crop health and study areas of deforestation.
A group of researchers has found a way to use this data to detect significant natural disasters in remote areas of the globe soon after they occur, giving first responders accurate information about the needs of the affected region.
This kind of global coverage is unprecedented, Associate Professor Rongjun Qin said: “There are a lot of advantages to having satellites that cover the world very quickly. We are focused on informing the community about changes in our cities, our forests and our ecosystems.
The study, published in the Journal GISscience and remote sensing, found that Planetscope’s vast datasets could be used to create 3D reconstructions, or digital surface models, of any given area. This allows the team to estimate the area affected by a natural disaster, analyze the extent of the damage, and make decisions about the amount and type of resources needed for rescue operations.
Previous remote sensing-based disaster studies have been limited by their lack of available data and coverage and their resolution, or the frequency with which images are collected or updated. Although Google Earth renders a 3D representation of the globe, there are many places where the images provided by the tool are distorted and appear out of scale, who negatively influences the accuracy of the entire program.
The Qin team’s 3D reconstructions, which take into account different elevation levels and landscapes, are accurate down to about six meters from the ground. In terms of map data, he said this was equivalent to achieving “approximately one pixel precision”.
Planetscope data is open access for educators, allowing other scientists to use the same datasets the study used to create their own simulations. According to Qin, for an area as large 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 area.
To put their method to the test, Qin’s team designed three different case studies using thousands of Planetscope images collected between 2016 and 2021. A test case showed that they could use the satellite images to make a 3D reconstruction of an urban area and a rural area in Spain. A second test case showed that they could detect 3D changes over time in an urban, forested area near Allentown, Pennsylvania.
To determine the quality of their model during post-disaster assessment, an experiment investigated a glacial area in Chamoli, India, which experienced a devastating flood last year, killing hundreds of people and destroying two power stations nearby. The team’s results showed that their model could not only recreate the altered topography that led to the disaster, but account for the volume of rocks and ice in the avalanche.
“We verified that Planetscope’s digital surface model can be used to assess mass changes for global natural disasters similar to the avalanche event,” Qin said.
Qin’s findings will help devise better ways to use satellite data, especially as the number of satellites and their various applications increase.
“This is still in its incubation phase and will still require engineering effort,” he said, “but I think it will be a big problem in industry and for scientists interested in tackling climate change.”
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