The Planetscope satellite constellation, operated by the 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. The data the constellation captures has been used to monitor the spread of wildfires, detect changes in crop health and survey areas of deforestation.

A group of researchers have found a way to utilise this data to detect significant natural disasters in remote regions of the globe soon after they happen, giving first responders accurate information about the needs of the region affected. 

This kind of global coverage is unprecedented, said associate professor Rongjun Qin: “There are a lot of great benefits in terms of having satellites cover the globe very quickly. We’re focused on informing the community about changes to our cities, forests and ecosystems.”

The study, published in the Journal GIScience 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 impacted by a natural disaster, analyse the extent of the damage and make decisions regarding the amount and type of resources needed for the rescue operations.

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. Although Google Earth renders a 3D representation of the globe, there are a lot of places where the images the tool provides are distorted, and appear out of scale, which negatively influences the entire program’s accuracy.

Qin team’s 3D reconstructions, which take into account different elevation levels and landscapes, are accurate down to about six metres from the ground. In terms of mapping data, he said it’s akin to achieving “almost approximately one-pixel accuracy.”

Planetscope’s data is open access to educators, allowing other scientists to use the same datasets the study used to create their own simulations. According to Qin, 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.

To put their method to the test, Qin’s team devised three different case studies using thousands of Planetscope images collected between 2016 and 2021. One test case showed that they could use the satellite images to make a 3D reconstruction of an urban and a rural area in Spain. A second test case showed that they could detect 3D changes over time in an urban and a forested area near Allentown, Pennsylvania.

To determine how good their model was at post-disaster assessment, one experiment investigated a glacial area in Chamoli, India, which last year experienced a devastating flood, causing the deaths of hundreds of people and destroying two nearby power plants. The team’s results showed that their model could not only recreate the changed topography that led to the disaster, but 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,” said Qin.

Qin’s findings will help engineer better ways to utilise 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.”

The Planetscope satellite constellation, operated by the 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. The data the constellation captures has been used to monitor the spread of wildfires, detect changes in crop health and survey areas of deforestation.

A group of researchers have found a way to utilise this data to detect significant natural disasters in remote regions of the globe soon after they happen, giving first responders accurate information about the needs of the region affected. 

This kind of global coverage is unprecedented, said associate professor Rongjun Qin: “There are a lot of great benefits in terms of having satellites cover the globe very quickly. We’re focused on informing the community about changes to our cities, forests and ecosystems.”

The study, published in the Journal GIScience 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 impacted by a natural disaster, analyse the extent of the damage and make decisions regarding the amount and type of resources needed for the rescue operations.

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. Although Google Earth renders a 3D representation of the globe, there are a lot of places where the images the tool provides are distorted, and appear out of scale, which negatively influences the entire program’s accuracy.

Qin team’s 3D reconstructions, which take into account different elevation levels and landscapes, are accurate down to about six metres from the ground. In terms of mapping data, he said it’s akin to achieving “almost approximately one-pixel accuracy.”

Planetscope’s data is open access to educators, allowing other scientists to use the same datasets the study used to create their own simulations. According to Qin, 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.

To put their method to the test, Qin’s team devised three different case studies using thousands of Planetscope images collected between 2016 and 2021. One test case showed that they could use the satellite images to make a 3D reconstruction of an urban and a rural area in Spain. A second test case showed that they could detect 3D changes over time in an urban and a forested area near Allentown, Pennsylvania.

To determine how good their model was at post-disaster assessment, one experiment investigated a glacial area in Chamoli, India, which last year experienced a devastating flood, causing the deaths of hundreds of people and destroying two nearby power plants. The team’s results showed that their model could not only recreate the changed topography that led to the disaster, but 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,” said Qin.

Qin’s findings will help engineer better ways to utilise 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.”