A hyperspectral image can predict tropical tree growth rates in single-species stands.
A hyperspectral image can predict tropical tree growth rates in single-species stands.
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高光谱图像可以预测单一物种林中的热带树木生长率。
DOI：
10.1002/eap.1436
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Stephanie A. Bohlman
中科院分区：
文献类型：
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作者：
T. Caughlin;Sarah J. Graves;G. Asner;Michiel van Breugel;Jefferson S. Hall;R. Martin;M. Ashton;Stephanie A. Bohlman
越来越需要遥感技术来满足对从地貌到大陆尺度的森林结构和组成进行估计的迫切需要。高光谱图像可以探测树冠特性，包括物种身份、叶片化学和疾病。树木的生长率与这些可测量的冠层特性有关，但是否可以从高光谱数据直接预测生长仍然是未知的。我们使用了一个单一的高光谱图像和光检测和测距导出的海拔预测生长率为20种热带树种种植在实验地块。我们问光谱数据和增长率之间是否存在一致的关系，在所有物种和光谱区域，与不同的冠层化学和结构特性，是重要的预测增长率。我们发现，窄带指数和海拔的线性组合与所有20个树种的标准化生长率（R2 = 53.70%）。虽然从整个可见光到短波红外光谱的波长参与了我们的分析，结果表明，相对更重要的可见光和近红外区域的冠层反射率与树木生长数据。总的来说，我们展示了高光谱数据的潜力，以量化树木的人口在一个更大的面积比可能的森林资源清查地块的实地方法。
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 light detection and ranging-derived elevation to predict growth rates for 20 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 20 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.