Estimating Residual Dry Matter with Field Spectroscopy and UAV LiDAR at The Jack and Laura Dangermond Preserve

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. This dataset includes the spectral data (Dangermond Spectra) and analysis CSV (RDM weights, locations, spectral index values, and chm values) used to correlate RDM ground-reference measurements with co-located UAV LiDAR (UAV LiDAR Data can be found on OpenTopography: https://doi.org/10.5069/G9S180QV) and spectral 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. 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. Spectral predictors are non-photosynthetic vegetation indices developed for monitoring crop residue cover (CAI, LCAI, NDLI) calculated from the spectra provided.