Evaluating UAV LiDAR and field spectroscopy for estimating residual dry matter across conservation grazing lands
Residual dry matter (RDM) is essential for managing grazing and fire risk, yet traditional field measurements are labor‑intensive and inconsistent. At the Jack and Laura Dangermond Preserve in Santa Barbara County, California, researchers evaluated hyperspectral data and UAV‑mounted LiDAR to estimate RDM across diverse rangeland conditions. Spectral indices showed almost no correlation with RDM (R² < 0.1), likely due to dense grass cover. LiDAR‑derived canopy height models performed substantially better (R² = 0.37), and accuracy increased dramatically (R² = 0.81) when modeling plots dominated by standing vegetation. These results demonstrate that UAV LiDAR can directly quantify RDM where vegetation remains upright, offering a powerful tool for improving rangeland monitoring and informing grazing and fire‑fuel management across California and similar dryland systems.
Subject Tags
- Conservation Technology
- Land management
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 < 0.1), likely due to complete areal coverage with dense grass. The LiDAR canopy height models performed better for all the samples (R2 = 0.37), with much stronger performance (R2 = 0.81) when using a stratified model to predict the RDM in plots with predominantly standing (as opposed to laying) vegetation. This study demonstrates the potential of UAV LiDAR for direct RDM quantification where vegetation is standing upright, which could help improve RDM mapping and management for rangelands in California and beyond.
Citation
Markman, B., Butterfield, H. S., Franklin, J., Coulter, L., Katkowski, M., & Sousa, D. (2025). Evaluating UAV LiDAR and Field Spectroscopy for Estimating Residual Dry Matter Across Conservation Grazing Lands. Remote Sensing, 17(14), 2352. https://doi.org/10.3390/rs17142352
TNC Authors
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Scott Butterfield
Lead Scientist, Land, California
The Nature Conservancy
Email: scott_butterfield@tnc.org -
Moses Katkowski
Director, Dangermond Preserve, California
The Nature Conservancy
Email: mkatkowski@tnc.org