Understanding tree canopy and vegetation structure is key to many sustainability metrics such biodiversity, forest health, and carbon sequestration.
The process produced by Salo Sciences provides collections of high-resolution vegetation structure data derived directly from satellite imagery using AI-powered ecological modelling to provide LiDAR-like 3D information across large scales. The data are optimized for use in large-scale land management, forest restoration and reforestation planning and monitoring, and ecosystem health analytics and monitoring.
The datasets include:
- Canopy height – the distance between the ground and the top of the canopy. Canopy height is a proxy for aboveground biomass and the amount of foliage that may be consumed in a canopy fire.
- Canopy cover – the horizontal cover fraction occupied by tree canopies. Maps community type & fire regime, as well as available habitat for tree-dwelling species.
- Canopy base height – the distance between the ground and the lowest branches in the canopy. Predicts whether a surface fire will transition to a canopy fire.
- Canopy bulk density – the mass of available fuel that burns in a canopy fire—typically the leaves and small branches— divided by the volume of the crown. There are no performance metrics associated with CBD because it isn’t directly measured.
- Vertical layer count – the number of distinct vertical canopy layers. Vertical layer count is a proxy for leaf area index, and maps canopy complexity.
Together, these datasets provide an unparalleled understanding of a forest’s vegetation structure using satellite images so that this process can scale to large geographical areas.
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