Grass Evolutionary Lineages Can Be Identified Using Hyperspectral Leaf Reflectance
Grass Evolutionary Lineages Can Be Identified Using Hyperspectral Leaf Reflectance
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DOI：
10.1029/2023jg007852
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发表时间：
2024-02-01
影响因子：
3.7
通讯作者：
Griffith，Daniel M.
中科院分区：
文献类型：
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作者：
Slapikas，Ryan;Pau，Stephanie;Griffith，Daniel M.
高光谱遥感有可能绘制出地球表面的许多属性图，包括生物多样性的空间模式。草原是地球上最大的生物群落之一。草地生物多样性的准确测绘依赖于对物种或植物功能类型末端成员的光谱分辨。我们重点研究了在全球草类生物群中占主导地位的草类谱系的光谱分离：雄龙亚科(C4)、金龟亚科(C4)和草亚科(C3)。我们研究了来自北美大平原地区四个有代表性的草原地点的43个草种的叶片反射光谱(350-2500 nm)。我们评估了叶反射率数据在将草种分类为三个主要谱系和采集地点方面的有效性。分类的准确率很高(94%)，对物种和环境的立地差异具有很强的稳健性。我们还发现了使用多光谱传感器的信息损失，即利用当前多光谱卫星提供的光谱波段对草本植物谱系的分类精度很低(使用哨兵2和陆地卫星8波段的精度分别为85.2%和61.3%)。我们的结果表明，高光谱数据在根据系统发育信息绘制草的功能类型方面具有令人兴奋的潜力。草类的叶级高光谱可分离性与下一代卫星光谱仪在生物多样性和功能信息含量方面的潜在增加是一致的。
Hyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C4), Chloridoideae (C4), and Pooideae (C3). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf‐level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite‐based spectrometers.