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）的需求日益增长，以满足从景观尺度至大陆尺度估算森林结构与组成的迫切需求。高光谱图像（hyperspectral image）可检测林冠属性，包括物种鉴别、叶片化学特性与林木病害。树木生长速率与这些可测量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）反演的高程数据，对实验样地中种植的20种热带树木的生长速率进行预测。我们旨在解答两个核心问题：其一，所有受试树种的光谱数据与生长速率之间是否存在一致的关联关系；其二，与不同林冠化学和结构属性相关的哪些光谱区域，对预测生长速率最为关键。研究结果显示，窄带指数与高程的线性组合与20种树木的标准化生长速率呈显著相关（决定系数R²=53.70%）。尽管本次分析涵盖了可见光至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相对更为重要。总体而言，本研究证实了高光谱数据具备在远大于森林清查样地实地调查范围的区域内量化树木种群动态的潜力。