气候变化领域集成服务门户(CCPortal): Prediction of leaf traits of lianas and trees via the integration of wavelet spectra in the visible-near infrared and thermal infrared domains

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DOI	10.1016/j.rse.2021.112406
	Prediction of leaf traits of lianas and trees via the integration of wavelet spectra in the visible-near infrared and thermal infrared domains
	Guzmán Q. J.A.; Sanchez-Azofeifa G.A.
发表日期	2021
ISSN	00344257
卷号	259
英文摘要	Predicting leaf traits using models based on spectroscopic data can provide essential information to advance ecological research and future Earth system models. Most current models are based on Partial Least Squares Regression (PLSR) algorithms that attempt to predict a set of leaf traits of several plant groups using leaf spectra. However, PLSR models tend to be inconsistent in describing the importance of absorption features when used to predict leaf traits. Likewise, the effect of contrasting absorption features of different plant groups on the prediction and evaluation of PLSR models is not well understood. Hence, this study focuses on using wavelet spectra to overcome current PLSR's limitation and improve leaf trait predictions. Specifically, we explored the use of visible–near-infrared (0.45–1.0 μm) and mid- long-wave infrared spectra (2.55–11 μm) to predict three-leaf traits of lianas and trees: Leaf Mass Area (LMA), Water Content (WC), and Equivalent Water Thickness (EWT). We also compare the effect of life forms on the prediction of traits by using sun leaves collected from 14 liana species and 21 tree species (n = 700) from a Neotropical Dry Forest. On each leaf, reflectance measurements were performed for both selected spectral regions; then, leaf traits were estimated from a leaf segment. Leaf reflectance was first resampled and then processed using continuous wavelet transformation (CWT) to derive the wavelet spectra. PLSR models linking the leaf traits and the reflectance or wavelet spectra were compared. Our results reveal that PLSR models based on wavelet spectra require fewer components to predict traits (13–16) than those based on reflectance (25–29). In addition, PLSR models' performance (e.g., R2) of testing datasets tend to be higher for models based on wavelet spectra (LMA = 0.83; WC = 0.77; EWT = 0.68) than reflectance (LMA = 0.78; WC = 0.76; EWT = 0.49). Wavelet spectra models also seem to better characterize absorption features that drive the variability of leaf traits than models based on reflectance. However, life forms play an essential role in model performance, where the prediction of lianas' traits presenting lower R2 (R2 = 0.61 ± 0.25) than trees' traits (R2 = 0.69 ± 0.15) regardless of the type of spectra or leaf trait. Our findings highlight the use of wavelet spectra to overcome limitations of the PLSR models for predicting leaf traits and the need to explore potential bias associated with plant groups on the model evaluations. © 2021 Elsevier Inc.
英文关键词	Leaf mass area; Leaf spectroscopy; Life forms; Partial least-squares regression; Thermal infrared; Wavelet analysis
语种	英语
scopus关键词	Digital storage; Forecasting; Forestry; Infrared devices; Least squares approximations; Petroleum reservoir evaluation; Plants (botany); Reflection; Wavelet analysis; Leaf mass areas; Leaf spectroscopies; Leaf traits; Life-forms; Model-based OPC; Partial least square regression; Partial least squares regression models; Thermal-infrared; Wavelet spectrum; Wavelet-analysis; Infrared radiation; algorithm; leaf area index; least squares method; prediction; satellite altimetry; visible spectrum; water content; wavelet analysis
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/178870
作者单位	Centre for Earth Observation Sciences (CEOS), Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada
推荐引用方式 GB/T 7714	Guzmán Q. J.A.,Sanchez-Azofeifa G.A.. Prediction of leaf traits of lianas and trees via the integration of wavelet spectra in the visible-near infrared and thermal infrared domains[J],2021,259.
APA	Guzmán Q. J.A.,&Sanchez-Azofeifa G.A..(2021).Prediction of leaf traits of lianas and trees via the integration of wavelet spectra in the visible-near infrared and thermal infrared domains.Remote Sensing of Environment,259.
MLA	Guzmán Q. J.A.,et al."Prediction of leaf traits of lianas and trees via the integration of wavelet spectra in the visible-near infrared and thermal infrared domains".Remote Sensing of Environment 259(2021).