Vibrant Planet raises $17M seed round to develop Forest Restoration SaaS Meczyki.Net

for Alison WolfeThe 2018 wildfire season in California marked a turning point. During that record breaking yearShe started asking a lot of questions.

“We were in the middle of the 2018 wildfire season, with the Carr Fire, and what I thought at the time was the worst season ever,” Wolff said. “I started asking many and many people – climate scientists I worked with, land managers, utility leaders, insurance leaders – why is this becoming so disastrous? What is the future looking like? what can be done?”

Out of those discussions was born Vibrant Planet, a public-profit startup developing land tenders. It’s basically SaaS for Forest Management, which the company calls “Operating System for Forest Restoration”.

As wildfire season kicks in once again in the American West, Vibrant Planet exclusively told Meczyki.Net that it has raised a $17 million seed round led by the Ecosystem Integrity Fund and The Jeremy and Hannelore Grantham Environmental Trust. Is raised.

“I realized quickly that this is a climate-related issue,” Wolff said. “Land management is a big part of the problem, of course, because even if the climate remains stable, we will still lose a lot of forest. But climate change is definitely paying it big time,” she said.

“We need to do just that – we need to restore forests fast, and they can make it through climate change, and they can help us survive climate change.”

Valia Ventures, Earthshot Ventures — backed by Lauren Powell Jobs and Tom Steyer — Cisco, and Halogen Ventures also participated in the round. Past backers of the startup include Meta Chief Product Officer Chris Cox and Netflix’s former Chief Product Officer, Neil Hunt, who later joined Vibrant Planet in the same role.

Vibrant Planet provides access to a range of data sets, with the centerpiece being a lidar map of the state of California. Lidar is incredibly helpful when it comes to mapping forests in 3D and determining their fire risk, but it’s not a panacea. Dense forests, which often represent the greatest fire risk, are difficult to map from top to bottom, so the team trained machine-learning algorithms to fill in any gaps.

And since flying lidar is expensive, the company uses another AI tool to keep it updated, using cheap satellite imagery. (This all comes with the caveat that the data generated by AI tools is speculative – you can’t “enhance” it with 100% accuracy, no matter what police process says.)