Wildfire Resilience
Estimate fuels and moisture at scales that matter for wildfire behavior modeling and decision-making
Analytics for forest fire monitoring, prevention, and mitigation planning
Climate change is rapidly reshaping wildfire regimes around the world, and testing the limits of technologies meant to manage them. CTrees is developing data, models, and analytics to support preventive action and wildfire resilience, from temperate forests to the tropics.
Map fuels in three dimensions
Forest fire behavior is governed by where fuel sits between the forest floor and the top of the canopy. CTrees uses lidar and hi-res imagery to create detailed, three-dimensional maps of complex fuel structures across forest landscapes, helping land managers anticipate risk and target treatments.
Map fuels in three dimensions
Forest fire behavior is governed by where fuel sits between the forest floor and the top of the canopy. CTrees uses lidar and hi-res imagery to create detailed, three-dimensional maps of complex fuel structures across forest landscapes, helping land managers anticipate risk and target treatments.
Observe fuel moisture as it changes
In a world of increasingly dry, disturbed forests, fuel moisture is an essential signal for fire management. CTrees’ satellite-based approach fuses optical, thermal, and microwave streams to see through smoke and cloud, providing fuel moisture data on a near-daily basis.
Observe fuel moisture as it changes
In a world of increasingly dry, disturbed forests, fuel moisture is an essential signal for fire management. CTrees’ satellite-based approach fuses optical, thermal, and microwave streams to see through smoke and cloud, providing fuel moisture data on a near-daily basis.
Forecast fire spread with AI
CTrees’ generative AI models are trained on satellite-observed fires and conditioned on the dynamic state of fuel and moisture in forests, providing hourly probability of spread from initial ignition through peak extent.
Forecast fire spread with AI
CTrees’ generative AI models are trained on satellite-observed fires and conditioned on the dynamic state of fuel and moisture in forests, providing hourly probability of spread from initial ignition through peak extent.
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Data
News, insights, and data for forest fire monitoring and prevention
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