Ecol Appl. 2016;24(7):1651-69. doi: 10.1890/13-2110.1.
The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air- and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, Marea), and isotopic composition (δ15N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle-leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7–4.4% for nitrogen (Nmass), 42–54% for carbon (Cmass), 17–58% for fiber (acid-digestible fiber, ADF), 7–44% for lignin (acid-digestible lignin, ADL), 3–31% for cellulose, 17–265 g/m2 for Marea, and −9.4‰ to 0.8‰ for δ15N. The calibrations were developed using a partial least-squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R2 and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for Nmass, R2 = 0.77 (0.73) and RMSE = 0.88% (0.95%) for Cmass, R2 = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R2 = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R2 = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R2 = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ15N, and R2 = 0.88 (0.87) with RMSE = 17.2 g/m2 (22.8 g/m2) for Marea. This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost-effective, easily replicated method.
植物冠层的形态和生化特性是光合作用能力和养分循环的重要预测因子。在叶片和冠层尺度上的遥感研究已经证明,利用反射光谱学可以表征植被冠层的生化状态,包括使用空气和星载成像光谱仪进行叶片和冠层水平的测量。我们开发了一套准确和精确的光谱校准方法,用于确定温带和北方树种的叶片化学物质(氮、碳和纤维成分的含量)、形态(叶面积质量,Marea)和同位素组成(δ15N),这些信息是通过对干燥和研磨后的叶片材料的光谱进行分析得到的。该数据集由阔叶和针叶松树种的叶片组成,其值的范围很广,用标准分析方法确定:氮(Nmass)含量为 0.7–4.4%,碳(Cmass)含量为 42–54%,纤维(酸可消化纤维,ADF)含量为 17–58%,木质素(酸可消化木质素,ADL)含量为 7–44%,纤维素含量为 3–31%,Marea 为 17–265 g/m2,δ15N 为-9.4‰至 0.8‰。这些校准是使用偏最小二乘回归(PLSR)建模方法结合一种新的不确定性分析开发的。我们的 PLSR 模型产生的模型校准(独立验证在括号中显示)的 R2 和均方根误差(RMSE)值分别为 0.98(0.97)和 0.10%(0.13%),用于 Nmass;R2 = 0.77(0.73)和 RMSE = 0.88%(0.95%),用于 Cmass;R2 = 0.89(0.84)和 RMSE = 2.8%(3.4%),用于 ADF;R2 = 0.77(0.69)和 RMSE = 2.4%(3.9%),用于 ADL;R2 = 0.77(0.72)和 RMSE = 1.4%(1.9%),用于叶纤维素;R2 = 0.62(0.60)和 RMSE = 0.91‰(1.5‰),用于 δ15N;R2 = 0.88(0.87),RMSE = 17.2 g/m2(22.8 g/m2),用于 Marea。本研究证明了快速准确估计森林冠层关键叶片特征的潜力,这些特征对生态研究和建模活动很重要,并且一个单一的校准方程可以在广泛的北方温带和北方树种和叶片形态范围内有效使用。该结果为使用快速、经济有效、易于复制的方法在物种之间和内部以及跨生态梯度之间描述重要的变异性提供了基础。