Impact of Reference Data Sampling Density for Estimating Plot-Level Average Shrub Heights Using Terrestrial Laser Scanning Data

Fire

article

Aaron E. Maxwell¹  Michael R. Gallagher²  Natale Minicuci³  Michelle S. Bester¹  E. Louise Loudermilk⁴  Scott M. Pokswinski²  Nicholas S. Skowronski⁵ 

[1] Department of Geology and Geography, West Virginia University;USDA Forest Service, Northern Research Station;Tall Timbers Research Station;USDA Forest Service, Southern Research Station;New Mexico Consortium

关键词: forest fires;    prescribed forest fires;    terrestrial laser scanning;    TLS;    fire effects;    fire fuels;    fuel load;    forest understory characterization;   

DOI  :  10.3390/fire6030098

学科分类：环境科学（综合）

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY