Understand Vibrant Planet’s baseline data and models that help you get to implementation faster
To understand your landscape’s potential risks and opportunities to plan for resilience, you need a detailed view of your landscape. One of the first and most essential steps in this process is gathering three-dimensional (3D) structure data of your vegetation. Vibrant Planet uses dozens of publicly-available and widely-used datasets to build this in-depth view of your landscape, including lidar, and data from the USDA, National Agriculture Imagery Program (NAIP), LANDFIRE, USGS, and USFS.
Lidar is one of the most powerful remote sensing datasets to gather 3D vegetation detail, but is often expensive ($.50-$1 per acre) and can be out of date as soon as vegetation changes – whether from disturbance events such as wildfire or from treatments like mechanical thinning. Because of these limitations, Vibrant Planet uses machine learning algorithms, trained on lidar, to build a Synthetic Canopy Height Model (Synthetic CHM) that supplements additional high-quality and real-time landscape assessment data. Our Synthetic CHM utilizes high spatial and temporal resolution imagery to identify individual trees and approximate their height, as well as other helpful metrics including wood product value, and aboveground biomass.
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| Satellite imagery inputted to algorithm | Resulting canopy height data produced by Synthetic CHM |
Current and specific view of vegetation |
This modeling allows you to reassess your landscape at the tree and house-level over time as changes occur on the landscape. Use this information to help speed up the arduous and ongoing process of understanding your landscape so you can move on to analyzing the best areas for a risk reduction or ecological enhancement through management.
Now that you understand one component of the underlying landscape data to help you evaluate risk, in the next section, you’ll learn how we segment the landscape based on similar qualities.