气候变化领域集成服务门户(CCPortal): Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning

CCPortal

DOI	10.1016/j.foreco.2018.12.020
	Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning
	Xu Q.; Li B.; Maltamo M.; Tokola T.; Hou Z.
发表日期	2019
ISSN	0378-1127
起始页码	205
结束页码	212
卷号	434
英文摘要	Biomass inventories that employ airborne laser scanning (ALS) require models that can predict tree diameter at breast height (DBH) from ALS-derived tree dimensions, as ALS can usually not directly measure DBH due to scanning angle, inadequate point density and canopy obstruction. Although some work has been done in using correlation as a measure of dependence to describe the linear relationship between variable means, none has investigated the copula-based measure of dependence for the prediction of DBH from ALS-derived height and crown diameter. Following the application of a locally-estimated copula method to 79 sample plots in eastern Finland, we compared the performance of the copula method with a baseline local regression (LOESS) model and an ordinary least squares (OLS) model. We found that the copula method outperformed the OLS model by decreasing 30% of the root-mean-squared error (RMSE). The copula method performed slightly better than the LOESS model for the original sample, but the results of the bootstrap samples showed that the variance in RMSE was sixteen times lower in the copula method than the LOESS model, suggesting that the copula had a more consistent and robust model performance across the 10,000 bootstrap samples. Moreover, while the LOESS model only predicts the conditional mean of the response variable, the copula method can also predict median and other quantiles. © 2018
英文关键词	Copula; Individual tree detection; Marginal distribution; Nearest neighbour; Quantile regression
语种	英语
scopus关键词	Biology; Forecasting; Laser applications; Least squares approximations; Mean square error; Scanning; Sediments; Copula; Individual tree detections; Marginal distribution; Nearest neighbour; Quantile regression; Forestry; airborne sensing; allometry; bootstrapping; forest inventory; laser method; prediction; regression analysis; tree; Biology; DBH; Forecasts; Forestry; Loess; Scanning; Sediments; Tree Dimensions; Finland
来源期刊	Forest Ecology and Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156216
作者单位	University of Nevada Reno, Department of Natural Resources and Environmental Science, Reno, NV, United States; University of Illinois at Urbana-Champaign, Department of Statistics, Champaign, IL, United States; University of Eastern Finland, Faculty of Science and Forestry, School of Forest Sciences, P.O. Box 111, Joensuu, FI-80101, Finland; University of Minnesota, Department of Forest Resources, Saint Paul, MN, United States
推荐引用方式 GB/T 7714	Xu Q.,Li B.,Maltamo M.,et al. Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning[J],2019,434.
APA	Xu Q.,Li B.,Maltamo M.,Tokola T.,&Hou Z..(2019).Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning.Forest Ecology and Management,434.
MLA	Xu Q.,et al."Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning".Forest Ecology and Management 434(2019).