气候变化领域集成服务门户(CCPortal): Structure metrics to generalize biomass estimation from lidar across forest types from different continents

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DOI	10.1016/j.rse.2019.111597
	Structure metrics to generalize biomass estimation from lidar across forest types from different continents
	Knapp N.; Fischer R.; Cazcarra-Bes V.; Huth A.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	Forest aboveground biomass is a key variable in remote sensing based forest monitoring. Active sensor systems, such as lidar, can generate detailed canopy height products. Relationships between canopy height and biomass are commonly established via regression analysis using information from ground-truth plots. In this way, many site-specific height-biomass relationships have been proposed in the literature and applied for mapping in regional contexts. However, such relationships are only valid within the specific forest type for which they were calibrated. A generalized relationship would facilitate biomass estimation across forest types and regions. In this study, a combination of lidar-derived and ancillary structural descriptors is proposed as an approach for generalization between forest types. Each descriptor is supposed to quantify a different aspect of forest structure, i.e., mean canopy height, maximum canopy height, maximum stand density, vertical heterogeneity and wood density. Airborne discrete return lidar data covering 194 ha of forest inventory plots from five different sites including temperate and tropical forests from Africa, Europe, North, Central and South America was used. Biomass predictions using the best general model (nRMSE = 12.4%, R2 = 0.74) were found to be almost as accurate as predictions using five site-specific models (nRMSE = 11.6%, R2 = 0.78). The results further allow interpretation about the importance of the employed structure descriptors in the biomass estimation and the mechanisms behind the relationships. Understanding the relationship between canopy structure and aboveground biomass and being able to generalize it across forest types are important steps towards consistent large scale biomass mapping and monitoring using airborne and potentially also spaceborne platforms. © 2019 Elsevier Inc.
英文关键词	Aboveground biomass; Canopy height; Forest structure; Generalization; Lidar
语种	英语
scopus关键词	Biomass; Mapping; Optical radar; Regression analysis; Remote sensing; Above ground biomass; Biomass estimation; Canopy heights; Discrete-return lidar; Forest monitoring; Forest structure; Generalization; Structural descriptors; Forestry; aboveground biomass; biomass; calibration; canopy architecture; environmental monitoring; estimation method; forest inventory; lidar; remote sensing; stand structure; vegetation type; Africa; Central America; Europe; North America; South America
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/179519
作者单位	Department of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ), Leipzig, 04318, Germany; Microwaves and Radar Institute, German Aerospace Center (DLR), Oberpfaffenhofen, 82234, Germany; Institute for Environmental Systems Research, Department of Mathematics/Computer Science, University of Osnabrück, Osnabrück, 49076, Germany; German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, 04103, Germany
推荐引用方式 GB/T 7714	Knapp N.,Fischer R.,Cazcarra-Bes V.,et al. Structure metrics to generalize biomass estimation from lidar across forest types from different continents[J],2020,237.
APA	Knapp N.,Fischer R.,Cazcarra-Bes V.,&Huth A..(2020).Structure metrics to generalize biomass estimation from lidar across forest types from different continents.Remote Sensing of Environment,237.
MLA	Knapp N.,et al."Structure metrics to generalize biomass estimation from lidar across forest types from different continents".Remote Sensing of Environment 237(2020).