Vibrant Planet Data Commons makes meaning of the best data to increase the pace and scale of forest restoration, starting in the western United States.
With a team of natural resource professionals, data scientists and creative communicators, the organization is designed to build consensus between interests and generate public will for action.
The Landscape Disturbance-Succession Simulator (LDSim) shows that forest stewardship can restore historical conditions in far fewer than 100 years—the time it would otherwise take for nature to take its course if natural disturbances were permitted to resume.
The most accurate forest structure information is derived from Lidar information. Unfortunately, up-to-date Lidar data is not always available for many landscapes and is often expensive to acquire. In the absence of Lidar information, forest structure metrics can be derived by models that operate on more readily available satellite data, from sources such as NAIP and Sentinel-2. Our partners at Vibrant Planet are using high resolution NAIP imagery data, combined with temporally matched Lidar data to build models to predict canopy height and canopy cover. These models also use terrain features and sentinel-2 monthly median data to take into account seasonal impact.
In partnership with Pyrologix and Vibrant Planet, we are developing data products that measure current and future wildfire risk under a variety climate change scenarios and forest health treatment procedures.
Good Machine is working with us to explore the potential for new techniques that could vastly decrease the cost while also significantly increasing the quantity of forestry data collected. The goal of developing new data collection methods is to expand climate-smart forest management and watershed restoration.
In partnership with the Climate Smart Wood Group and Vibrant Planet, Vibrant Planet Data Commons is developing a simple, user-friendly web application that covers the contiguous U.S. to allow the Architecture, Engineering, and Construction (AEC) community to differentiate between wood products based on forest carbon and associated ecosystem impacts. The data shared will enable an improved understanding of the carbon benefits and burdens associated with varying approaches to timber production, along with an ability to effectively differentiate timber produced from different ownerships and regions of the USA. Forest carbon, disturbance classification, and timber output data will be shared in a timeseries for each landowner type in each county, going back in time as far as 1990 or 2000.
We make meaning of the best data to increase the pace and scale of forest restoration in the western United States. We believe we can restore community and wildland resilience. But we can’t do it without better, accessible data. Please support our work.