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ABSTRACT Long-term terrestrial ecosystem monitoring is a critical component of documenting outcomes of land management actions, assessing progress towards management objectives, and guiding realistic long-term ecological goals, all through repeated observation and measurement. Traditional monitoring methods have evolved for specific applications in forestry, ecology, and fire and fuels management. While successful monitoring programs have clear goals, trained expertise, and rigorous sampling protocols, new advances in technology and data management can help overcome the most common pitfalls in data quality and repeatability. This paper presents Terrestrial Laser Scanning (TLS), a specific form of LiDAR (Light Detection and Ranging), as an emerging sampling method that can complement and enhance existing monitoring methods. TLS captures in high resolution the 3D structure of a terrestrial ecosystem (forest, grassland, etc.), and is increasingly efficient and affordable (<$30,000). Integrating TLS into ecosystem monitoring can standardize data collection, improve efficiency, and reduce bias and error. Streamlined data processing pipelines can rigorously analyze TLS data and incorporate constant improvements to inform management decisions and planning. The approach described in this paper utilizes portable, push-button TLS equipment that, when calibrated with initial transect sampling, captures detailed forestry, fuels, and ecological features in less than 5 minutes per plot. We also introduce an interagency automated processing pipeline and dashboard viewer for instant, user-friendly analysis, and data retrieval of hundreds of metrics. Forest metrics and inventories produced with these methods offer effective decision-support data for managers to quantify landscape-scale conditions and respond with efficient action. This protocol further supports interagency compatibility for efficient natural resource monitoring across jurisdictional boundaries with uniform data, language, methods, and data analysis. With continued improvement of scanner capabilities and affordability, these data will shape the future of terrestrial ecosystem monitoring as an important means to address the increasingly fast pace of ecological change facing natural resource managers. 

Terrestrial laser scanning (TLS) data can offer a means to estimate subcanopy fuel characteristics to support site characterization, quantification of treatment or fire effects, and inform fire modeling. Using field and TLS data within the New Jersey Pinelands National Reserve (PNR), this study explores the impact of forest phenology and density of shrub height (i.e., shrub fuel bed depth) measurements on estimating average shrub heights at the plot-level using multiple linear regression and metrics derived from ground-classified and normalized point clouds. The results highlight the importance of shrub height sampling density when these data are used to train empirical models and characterize plot-level characteristics. We document larger prediction intervals (PIs), higher root mean square error (RMSE), and lower R-squared with reduction in the number of randomly selected field reference samples available within each plot. At least 10 random shrub heights collected in situ were needed to produce accurate and precise predictions, while 20 samples were ideal. Additionally, metrics derived from leaf-on TLS data generally provided more accurate and precise predictions than those calculated from leaf-off data within the study plots and landscape. This study highlights the importance of reference data sampling density and design and data characteristics when data will be used to train empirical models for extrapolation to new sites or plots.