气候变化领域集成服务门户(CCPortal): Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments

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DOI	10.1016/j.rse.2020.111828
	Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments
	Streher A.S.; Torres R.D.S.; Morellato L.P.C.; Silva T.S.F.
发表日期	2020
ISSN	00344257
卷号	244
英文摘要	Generalized assessments of the accuracy of spectroscopic estimates of ecologically important leaf traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) are still lacking for most ecosystems, and particularly for non-forested and/or seasonally dry tropical vegetation. Here, we tested the ability of using leaf reflectance spectra to estimate LMA and LDMC and classify plant growth forms within the cerrado and campo rupestre seasonally dry non-forest vegetation types of Southeastern Brazil, filling an existing gap in published assessments of leaf optical properties and plant traits in such environments. We measured leaf reflectance spectra from 1648 individual plants comprising grasses, herbs, shrubs, and trees, developed partial least squares regression (PLSR) models linking LMA and LDMC to leaf spectra (400–2500 nm), and identified the spectral regions with the greatest discriminatory power among growth forms using Bhattacharyya distances. We accurately predicted leaf functional traits and identified different growth forms. LMA was overall more accurately predicted (RMSE = 8.58%) than LDMC (RMSE = 9.75%). Our model including all sampled plants was not biased towards any particular growth form, but growth-form specific models yielded higher accuracies and showed that leaf traits from woody plants can be more accurately estimated than for grasses and forbs, independently of the trait measured. We observed a large range of LMA values (31.80–620.81 g/m2) rarely observed in tropical or temperate forests, and demonstrated that values above 300 g/m2 could not be accurately estimated. Our results suggest that spectroscopy may have an intrinsic saturation point, and/or that PLSR, the current approach of choice for estimating traits from plant spectra, is not able to model the entire range of LMA values. This finding has very important implications to our ability to use field, airborne, and orbital spectroscopic methods to derive generalizable functional information. We thus highlight the need for increasing spectroscopic sampling and research efforts in drier non-forested environments, where environmental pressures lead to leaf adaptations and allocation strategies that are very different from forested ecosystems. Our findings also confirm that leaf reflectance spectra can provide important information regarding differences in leaf metabolism, structure, and chemical composition. Such information enabled us to accurately discriminate plant growth forms in these environments regardless of lack of variation in leaf economic traits, encouraging further adoption of remote sensing methods by ecologists and allowing a more comprehensive assessment of plant functional diversity. © 2020 Elsevier Inc.
英文关键词	Cerrado; LDMC; Leaf spectroscopy; LMA; Partial least squares regression (PLSR); Plant functional traits, campo rupestre
语种	英语
scopus关键词	Ecosystems; Least squares approximations; Optical properties; Reflection; Remote sensing; Spectroscopic analysis; Tropics; Vegetation; Bhattacharyya distance; Chemical compositions; Comprehensive assessment; Environmental pressures; Functional information; Leaf optical property; Partial least squares regressions (PLSR); Plant functional diversities; Forestry; accuracy assessment; cerrado; dry matter; optical property; remote sensing; seasonality; spectroscopy; tropical environment; Brazil; Poaceae
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/179329
作者单位	Universidade Estadual Paulista (Unesp), Instituto de Biociências, Departamento de Biodiversidade, Rio Claro, São Paulo, Brazil; Department of ICT and Natural Sciences, NTNU - Norwegian University of Science and Technology, Ålesund, Norway; Universidade Estadual Paulista (Unesp), Instituto de Biociências, Departamento de Biodiversidade, Phenology Lab, Rio Claro, São Paulo, Brazil; Biological and Environmental Sciences, Faculty of Natural Resources, University of Stirling. StirlingFK9 4LA, United Kingdom
推荐引用方式 GB/T 7714	Streher A.S.,Torres R.D.S.,Morellato L.P.C.,et al. Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments[J],2020,244.
APA	Streher A.S.,Torres R.D.S.,Morellato L.P.C.,&Silva T.S.F..(2020).Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments.Remote Sensing of Environment,244.
MLA	Streher A.S.,et al."Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments".Remote Sensing of Environment 244(2020).