Triangulating from the cairns…
This is a 9 meter resolution map representing the coefficient of variation (Std. Deviation / Mean) in vertical vegetation density. The point cloud dataset is synthesized from 6 airborne laser scanning (LiDAR) datasets collected from August 2016 to August 2023. The data extent covers the declared forest boundary of the Grand Mesa, Uncompaghre, and Gunnison National Forests in western Colorado, USA. An elevation model and vegetation structural information was generated by functions in the LidR package for the R programming language (version 4.1, Roussel and Auty 2024). Original point cloud data delivered from the vendors to the Colorado Hazard Mapping Program was reprojected to a common coordinate system (WGS84 UTM Zone 13N, EPSG:32613). Points with the vendor-provided ground classification flag were interpolated using a TIN algorithm and subtracted from the raw heights to generate a topography-normalized point cloud before computing mean heights and standard deviation for each grid cell. Coefficient of variation was then computed as SD / Mean. These datasets were generated by Ian Breckheimer and Rachel Crafford at Rocky Mountain Biological Laboratory on behalf of the US Forest Service. References: Roussel J, Auty D, Coops NC, Tompalski P, Goodbody TR, Meador AS, Bourdon J, de Boissieu F, Achim A (2020). “lidR: An R package for analysis of Airborne Laser Scanning (ALS) data.” Remote Sensing of Environment, 251, 112061. ISSN 0034-4257, doi:10.1016/j.rse.2020.112061, https://www.sciencedirect.com/science/article/pii/S0034425720304314. Roussel J, Auty D (2024). Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. R package version 4.1.2, https://cran.r-project.org/package=lidR.
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