气候变化领域集成服务门户(CCPortal): Mapping three-dimensional variation in leaf mass per area with imaging spectroscopy and lidar in a temperate broadleaf forest

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DOI	10.1016/j.rse.2020.112043
	Mapping three-dimensional variation in leaf mass per area with imaging spectroscopy and lidar in a temperate broadleaf forest
	Chlus A.; Kruger E.L.; Townsend P.A.
发表日期	2020
ISSN	00344257
卷号	250
英文摘要	Imaging spectroscopy is a valuable tool for mapping canopy foliar traits in forested ecosystems at landscape and larger scales. Most efforts to date have involved two-dimensional mapping of traits, typically representing top-of-canopy conditions. However, traits and their associated biological functions vary through the canopy vertical profile, such that incorporating information about vertical patterns may improve modeling of ecosystem processes like primary productivity. In 2016 and 2017, we collected extensive field data in forests in Domain 5 (Great Lakes) of the National Ecological Observatory Network (NEON) to characterize the vertical variation in leaf mass per area (LMA), an important foliar trait related to plant growth and defense. Fieldwork was coincident with NEON Airborne Observation Platform (AOP) overflights which collected imaging spectroscopy and lidar data. Using imaging spectroscopy to map top-of-canopy LMA and lidar to model vertical gradients of transmittance, we developed a method to map three-dimensional patterns in LMA in temperate broadleaf forests. Partial least squares regression (PLSR) was used to estimate top-of-canopy LMA (R2: 0.57, RMSE 10.8 g m−2), which, along with lidar-derived metrics of light transmittance and height, was used in a multilevel regression to model within-canopy LMA (R2: 0.78, RMSE 8.3 g m−2). The coupled models accurately estimated LMA throughout the canopy without taking into account species composition (R2 = 0.82, RMSE: 8.5 g m−2). © 2020 Elsevier Inc.
英文关键词	Imaging spectroscopy; Leaf mass per area; Lidar; NEON AOP; Three dimensional
语种	英语
scopus关键词	Ecosystems; Forestry; Least squares approximations; Mapping; Neon; Optical radar; Airborne observations; Biological functions; Dimensional variations; Imaging spectroscopy; Partial least squares regressions (PLSR); Primary productivity; Three-dimensional patterns; Two dimensional mapping; Plants (botany); broad-leaved forest; defense mechanism; field method; forest ecosystem; lidar; primary production; temperate forest; three-dimensional modeling; two-dimensional modeling; vegetation mapping; Great Lakes [North 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/179140
作者单位	Department of Forest and Wildlife Ecology, University of Wisconsin, Madison, WI, United States
推荐引用方式 GB/T 7714	Chlus A.,Kruger E.L.,Townsend P.A.. Mapping three-dimensional variation in leaf mass per area with imaging spectroscopy and lidar in a temperate broadleaf forest[J],2020,250.
APA	Chlus A.,Kruger E.L.,&Townsend P.A..(2020).Mapping three-dimensional variation in leaf mass per area with imaging spectroscopy and lidar in a temperate broadleaf forest.Remote Sensing of Environment,250.
MLA	Chlus A.,et al."Mapping three-dimensional variation in leaf mass per area with imaging spectroscopy and lidar in a temperate broadleaf forest".Remote Sensing of Environment 250(2020).