气候变化领域集成服务门户(CCPortal): Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification

CCPortal

DOI	10.1016/j.rse.2021.112464
	Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification
	Adnan S.; Maltamo M.; Mehtätalo L.; Ammaturo R.N.L.; Packalen P.; Valbuena R.
发表日期	2021
ISSN	00344257
卷号	260
英文摘要	McArthur's foliage height diversity (FHD) has been the gold standard in the determination of structural complexity of forests characterized by LiDAR vertical height profiles. It is based on Shannon's entropy index, which was originally designed to describe evenness in abundances among qualitative typologies, and thus the calculation of FHD involves subjective layering steps which are essentially unnatural to describe a continuous variable (X) such as height. In this contribution we aim to provide a mathematical framework for determining maximum entropy in 3D remote sensing datasets based on the Gini Coefficient of theoretical continuous distributions, intended to replace FHD as entropy measure in vertical profiles of LiDAR heights (1D, X), with extensions to variables expressing dimensions of higher order (2D or 3D, Z ∝ X2 or X3). Then we apply this framework to Boreal forests in Finland to describe landscape heterogeneity with the intention to improve the modelling of forest aboveground biomass (AGB), hypothesizing that LiDAR models of AGB should essentially be different in areas of differing structural characteristics. We carried out a pre-stratification of LiDAR data collected in 2012 using simple rules applied to the L-skewness (Lskew) and L-coefficient of variation of LiDAR echo heights (Lcv; equivalent to the Gini coefficient, GCH), determining a new threshold at GCH = 0.33 as a consequence of the newly developed mathematical proofs. We observed only moderate improvements in terms of model accuracies: RMSDs reduced from 41.7% to 38.9 or 37.0%. More remarkably, we identified critical differences in the metrics selected at each stratum, which is useful to understand what predictor variables are more important for estimating AGB at each area of a forest. We observed that higher LiDAR height percentiles are more relevant at open canopies and heterogeneous forests, whereas closed canopies in homogeneous forests obtain most accurate predictions from a combination of cover metrics and percentiles around the median. Without stratification, the overall model would neglect explained variability in the structural types of lower occurrence, and predictions from a model influenced by structural types of higher occurrence would be biased at those areas. These results are thus useful in terms of improving our understanding on the relationships underlying LiDAR-AGB models. © 2021 Elsevier Inc.
英文关键词	Airborne laser scanning; Forest aboveground biomass; Forest structure; Gini coefficient; L-moments
语种	英语
scopus关键词	Forestry; Maximum entropy methods; Optical radar; Remote sensing; 3D remote sensing; Above ground biomass; Airborne Laser scanning; Biomass models; Forest aboveground biomass; Forest structure; Gini coefficients; L-moments; Maximum-entropy; Structural type; Biomass
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178836
作者单位	University of Eastern Finland, School of Forest Sciences, PO Box 111, Joensuu, FI-80101, Finland; University of Eastern Finland, School of Computing, P.O. Box 111, Joensuu, FI-80101, Finland; University of Cambridge, Department of Plant Sciences, Forest Ecology and Conservation, Downing Street, Cambridge, CB2 3EA, United Kingdom; Bangor University, School of Natural Sciences, Thoday building, Bangor, Gwynedd LL57 2UW, United Kingdom
推荐引用方式 GB/T 7714	Adnan S.,Maltamo M.,Mehtätalo L.,et al. Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification[J],2021,260.
APA	Adnan S.,Maltamo M.,Mehtätalo L.,Ammaturo R.N.L.,Packalen P.,&Valbuena R..(2021).Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification.Remote Sensing of Environment,260.
MLA	Adnan S.,et al."Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification".Remote Sensing of Environment 260(2021).