Evaluating UAV LiDAR and Field Spectroscopy for Estimating Residual Dry Matter Across Conservation Grazing Lands

Abstract

Residual dry matter (RDM) is a term used in rangeland management to describe the non-photosynthetic plant material left on the soil surface at the end of the growing season. RDM measurements are used by agencies and conservation entities for managing grazing and fire fuels. Measuring the RDM using traditional methods is labor-intensive, costly, and subjective, making consistent sampling challenging. Previous studies have assessed the use of multispectral remote sensing to estimate the RDM, but with limited success across space and time. The existing approaches may be improved through the use of spectroscopic (hyperspectral) sensors, capable of capturing the cellulose and lignin present in dry grass, as well as Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) sensors, capable of capturing centimeter-scale 3D vegetation structures. Here, we evaluate the relationships between the RDM and spectral and LiDAR data across the Jack and Laura Dangermond Preserve (Santa Barbara County, CA, USA), which uses grazing and prescribed fire for rangeland management. The spectral indices did not correlate with the RDM (R2

Plant phenotyping relevance

UAV LiDARとフィールド分光法を用いて、植生残渣量（RDM）という植物状態を推定するセンサー手法の評価が研究の中心であり、単なる農業実験での routine measurement ではない。

Code and data availability

The paper's UAV LiDAR data (used to derive canopy height models for RDM estimation) is explicitly stated to be publicly available in the OpenTopography Community Dataspace. The KNB deposit containing RDM weights, field spectra, and analysis data is also mentioned, but its DOI URL is not among the allowed URLs, so only