Data from: A hyperspectral image can predict tropical tree growth rates in single-species stands

Remote sensing is increasingly needed to meet the critical demand for estimates of forest structure and composition at landscape to continental scales. Hyperspectral images can detect tree canopy properties, including species identity, leaf chemistry and disease. Tree growth rates are related to these measurable canopy properties but whether growth can be directly predicted from hyperspectral data remains unknown. We used a single hyperspectral image and LiDAR-derived elevation to predict growth rates for twenty tropical tree species planted in experimental plots. We asked whether a consistent relationship between spectral data and growth rates exists across all species and which spectral regions, associated with different canopy chemical and structural properties, are important for predicting growth rates. We found that a linear combination of narrowband indices and elevation is correlated with standardized growth rates across all twenty tree species (R2=53.70%). Although wavelengths from the entire visible-to-shortwave infrared spectrum were involved in our analysis, results point to relatively greater importance of visible and near-infrared regions for relating canopy reflectance to tree growth data. Overall, we demonstrate the potential for hyperspectral data to quantify tree demography over a much larger area than possible with field-based methods in forest inventory plots.遥感（Remote sensing）日益成为满足从景观到大陆尺度森林结构与组成估算关键需求的重要手段。高光谱图像（hyperspectral images）可探测林冠属性，包括物种识别、叶片化学特征与病害状况。树木生长速率与这些可测的林冠属性存在关联，但能否直接通过高光谱数据预测生长速率仍未明确。本研究利用单景高光谱图像与激光雷达（LiDAR）衍生的高程数据，对实验样地中栽植的20种热带树木的生长速率进行预测。本研究旨在解答两个核心问题：一是光谱数据与生长速率之间是否存在跨所有树种的一致关联；二是与不同林冠化学及结构属性相关的哪些光谱区域对生长速率预测具有重要意义。研究发现，窄波段指数与高程的线性组合与20个树种的标准化生长速率呈显著相关（决定系数R²=53.70%）。尽管本研究分析涵盖了从可见光到短波红外的全波段光谱，但结果表明，可见光与近红外区域在关联林冠反射率与树木生长数据方面的相对重要性更高。总体而言，本研究证实了利用高光谱数据可在远大于森林清查样地内野外实地调查所能覆盖的范围内，量化树木种群动态。