Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments
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
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发表时间：
2020-07
影响因子：
13.5
通讯作者：
A. S. Streher;R. Torres;L. Morellato;T. S. F. Silva
中科院分区：
文献类型：
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作者：
A. S. Streher;R. Torres;L. Morellato;T. S. F. Silva
对于大多数生态系统，特别是非森林和/或季节性干旱的热带植被，仍然缺乏对具有重要生态意义的叶性状（如叶质量/面积（LMA）和叶干物质含量（LDMC））的光谱估计准确性的普遍评估。在这里，我们测试的能力，使用叶片反射光谱估计LMA和LDMC和分类植物的生长形式在thecerrado和campo rupestres季节性干燥的非森林植被类型的巴西东南部，填补了现有的空白，在这种环境中的叶片光学特性和植物性状的评估。我们测量了1648个个体的植物，包括草，草本植物，灌木和树木的叶片反射光谱，开发偏最小二乘回归（PLSR）模型连接LMA和LDMC叶光谱（400-2500 nm），并确定了最大的歧视权力之间的增长形式使用Bhattacharyya距离的光谱区域。我们准确地预测了叶功能性状，并确定了不同的生长形式。LMA总体上比LDMC（RMSE = 9.75%）更准确地预测（RMSE = 8.58%）。我们的模型，包括所有采样的植物是不偏向于任何特定的生长形式，但生长形式的具体模型产生了更高的准确性，并表明木本植物的叶性状可以更准确地估计比草和杂类草，独立的测量性状。我们观察到一个大范围的LMA值（31.80-620.81 g/m2）很少在热带或温带森林中观察到，并证明，超过300 g/m2的值不能准确估计。我们的研究结果表明，光谱学可能有一个内在的饱和点，和/或PLSR，目前的方法选择估计性状从植物光谱，是无法模拟整个范围的LMA值。这一发现具有非常重要的意义，我们的能力，使用现场，空中和轨道光谱方法，以获得可推广的功能信息。因此，我们强调需要增加光谱采样和研究工作在干燥的非森林环境，环境压力导致叶适应和分配策略，是非常不同的森林生态系统。我们的研究结果还证实，叶片反射光谱可以提供重要的信息，叶片代谢，结构和化学成分的差异。这些信息使我们能够准确地区分植物生长形式在这些环境中，无论缺乏变化的叶经济性状，鼓励进一步通过遥感方法的生态学家，并允许更全面的评估植物功能多样性。
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 thecerradoandcampo rupestreseasonally 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/m2could 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.