Can Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient?
Can Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient?
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DOI：
10.1002/2017jg003883
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发表时间：
2017-11
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影响因子：
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通讯作者：
C. Doughty;P. Santos-Andrade;G. Goldsmith;B. Blonder;A. Shenkin;L. Bentley;C. Chavana-Bryant;
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作者：
C. Doughty;P. Santos-Andrade;G. Goldsmith;B. Blonder;A. Shenkin;L. Bentley;C. Chavana-Bryant;
高分辨率光谱学可以用来测量叶片的化学和结构特征。这样的叶片性状通常与其他性状高度相关，如光合作用，通过叶片经济学光谱。我们测量了秘鲁海拔3,300米的10个1公顷样地中约150个优势树种的日照和遮荫叶片的可见光-近红外叶片反射率(400-1,075 nm)(4284片单叶)。我们使用偏最小二乘(PLS)回归来比较叶片反射率与化学性状(如氮和磷)、结构性状(包括单位面积叶质量(LMA)、枝条密度和叶脉)以及较高水平的性状(如叶片光合作用能力、叶片拒水性和木本植物生长速率)之间的关系。使用叶片反射率的经验模型预测了叶片N和LMA(R2>；30%和%RMSE<；30%)，对叶脉、光合作用和枝条密度的预测较弱(R2在10-35%之间，%RMSE在10%-65%之间)，而不能预测叶片的拒水性或木本植物的生长速率(R2<；5%)。光合作用和枝条密度等高水平性状的预测可能是由于这些性状与LMA的相关性，而LMA是一种很容易用叶片光谱预测的性状。
High‐resolution spectroscopy can be used to measure leaf chemical and structural traits. Such leaf traits are often highly correlated to other traits, such as photosynthesis, through the leaf economics spectrum. We measured VNIR (visible‐near infrared) leaf reflectance (400–1,075 nm) of sunlit and shaded leaves in ~150 dominant species across ten, 1 ha plots along a 3,300 m elevation gradient in Peru (on 4,284 individual leaves). We used partial least squares (PLS) regression to compare leaf reflectance to chemical traits, such as nitrogen and phosphorus, structural traits, including leaf mass per area (LMA), branch wood density and leaf venation, and “higher‐level” traits such as leaf photosynthetic capacity, leaf water repellency, and woody growth rates. Empirical models using leaf reflectance predicted leaf N and LMA (r2 > 30% and %RMSE < 30%), weakly predicted leaf venation, photosynthesis, and branch density (r2 between 10 and 35% and %RMSE between 10% and 65%), and did not predict leaf water repellency or woody growth rates (r2<5%). Prediction of higher‐level traits such as photosynthesis and branch density is likely due to these traits correlations with LMA, a trait readily predicted with leaf spectroscopy.