For forest, water resource and emergency planners and managers, data science and digital twins are the new necessities for effective, defensible watershed and disaster management decisions.
Teren’s suite of actionable forest, watershed and disaster data products harmonize LiDAR data with contextual datasets to create and analyze dynamic digital twins of the natural and human worlds.
Reveal where action is needed most after a wildfire to protect human lives and infrastructure, prevent further disasters, and quickly regenerate ecosystem function.
Leveraging remotely-sensed data, Teren’s nationwide wildfire model, and deep earth sciences expertise, identify and prioritize zones at highest risk for wildfire and its indirect impacts such as flooding, sediment, and debris flows.
Process and analyze high-fidelity remotely sensed data at speed and scale to gain insights that drive confident action.
Scenario-based hydrologic modeling and quantification of flow velocities, volume, and depths to understand and mitigate risks associated with flooding & debris flows.
Analyses that quantify primary stand structure indices, wood volume, mass, and foliage 3D structure. Assess forest health, composition, stand age, and change through time at multiple resolutions.
Learn more about how Teren’s products harness technology for a robust forest ecosystem and economy.
Teren provides customizable analysis and integrations to focus on the risk factors that matter most to you.
Make data-driven treatment decisions to execute stabilization and reclamation efforts with increased efficiency and effectiveness.
Maximize public benefit while safeguarding the Values at Risk.
Gain powerful methods for quantifying ecosystem services, hydro-geologic processes, and climate impacts.
Efficiently pinpoint, anticipate, and mitigate risks from flooding, sedimentation, and debris flows.
Improve accuracy, speed, and scale of climate-adaptive hydrologic modeling.
Identify, anticipate, and mitigate risks from flooding, sedimentation, and debris flows.
Gain insights from high-fidelity, continuous terrain models produced from remotely-sensed data.