UAV LiDAR Survey of RDM at Jack and Laura Dangermond Preserve, CA 2024

Residual Dry Matter (RDM) is the non-green/non-photosynthetic plant material left on the ground at the end of the growing season in rangelands across California. It is a landscape-scale estimate of aboveground biomass (lbs/acre or kgs/ha) used by agencies to guide grazing and fire fuel management across the Western United States. Estimating RDM through traditional field methods is labor, cost, and time-intensive, making large-scale sampling challenging. Studies have suggested using remote sensing to quantify non-photosynthetic vegetation biomass across rangelands and grasslands, however, little is known about the accuracy and applicability of remote sensing technologies to quantify RDM directly. This dataset includes UAV LiDAR data used to correlate RDM ground-reference measurements with co-located UAV LiDAR data using random forest regression and linear regression models. This study was conducted on September 19-20, 2024 at the Jack and Laura Dangermond Preserve, a 24,000-acre preserve with extensive grazed and ungrazed rangelands on the Central California Coast that are managed by The Nature Conservancy. UAV LiDAR predictors are canopy height model metrics, derived from gridded UAV LiDAR Point Cloud Data. These metrics are ‘zonal statistics’ for each RDM hoop and are: chm_max, chm_range, chm_mean, chm_std, chm_sum, chm_median, chm_90percentile. There are two files for each study area: a pre-clipping and post-clipping laz file. Pre-clip is the flight conducted before clipping the plot RDM to create a Digital Surface Model or DSM. The Post-clip flight is conducted after clipping the RDM plot to create a Digital Terrain Model or DTM of bare soil. The difference between the two datasets was used to create a canopy height model or nDSM.

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