Aneece Itiya P, Epstein Howard, Lerdau Manuel
Department of Environmental Sciences University of Virginia Charlottesville VA USA.
Ecol Evol. 2017 Apr 6;7(10):3475-3488. doi: 10.1002/ece3.2876. eCollection 2017 May.
Advances in remote sensing technology can help estimate biodiversity at large spatial extents. To assess whether we could use hyperspectral visible near-infrared (VNIR) spectra to estimate species diversity, we examined the correlations between species diversity and spectral diversity in early-successional abandoned agricultural fields in the Ridge and Valley ecoregion of north-central Virginia at the Blandy Experimental Farm. We established plant community plots and collected vegetation surveys and ground-level hyperspectral data from 350 to 1,025 nm wavelengths. We related spectral diversity (standard deviations across spectra) with species diversity (Shannon-Weiner index) and evaluated whether these correlations differed among spectral regions throughout the visible and near-infrared wavelength regions, and across different spectral transformation techniques. We found positive correlations in the visible regions using band depth data, positive correlations in the near-infrared region using first derivatives of spectra, and weak to no correlations in the red-edge region using either of the two spectral transformation techniques. To investigate the role of pigment variability in these correlations, we estimated chlorophyll, carotenoid, and anthocyanin concentrations of five dominant species in the plots using spectral vegetation indices. Although interspecific variability in pigment levels exceeded intraspecific variability, chlorophyll was more varied within species than carotenoids and anthocyanins, contributing to the lack of correlation between species diversity and spectral diversity in the red-edge region. Interspecific differences in pigment levels, however, made it possible to differentiate these species remotely, contributing to the species-spectral diversity correlations. VNIR spectra can be used to estimate species diversity, but the relationships depend on the spectral region examined and the spectral transformation technique used.
遥感技术的进步有助于在大空间范围内估计生物多样性。为了评估我们是否可以使用高光谱可见近红外（VNIR）光谱来估计物种多样性，我们在弗吉尼亚州中北部岭谷生态区的布兰迪实验农场，研究了早期演替废弃农田中物种多样性与光谱多样性之间的相关性。我们建立了植物群落样地，并收集了350至1025纳米波长的植被调查和地面高光谱数据。我们将光谱多样性（光谱的标准差）与物种多样性（香农-维纳指数）相关联，并评估这些相关性在整个可见光和近红外波长区域的不同光谱区域之间，以及不同光谱变换技术之间是否存在差异。我们发现，使用波段深度数据在可见光区域存在正相关，使用光谱一阶导数在近红外区域存在正相关，而使用两种光谱变换技术中的任何一种在红边区域的相关性较弱或不存在相关性。为了研究色素变异性在这些相关性中的作用，我们使用光谱植被指数估计了样地中五个优势物种的叶绿素、类胡萝卜素和花青素浓度。尽管种间色素水平的变异性超过了种内变异性，但叶绿素在种内的变化比类胡萝卜素和花青素更大，这导致了红边区域物种多样性与光谱多样性之间缺乏相关性。然而，色素水平的种间差异使得能够远程区分这些物种，从而形成了物种与光谱多样性的相关性。VNIR光谱可用于估计物种多样性，但这种关系取决于所研究的光谱区域和所使用的光谱变换技术。
