基于可见光波段包络线去除的湿地植物叶片叶绿素估算

研究采用芦苇和香蒲叶片光谱及实测叶绿素含量数据,选取波段谱带范围为可见光波段400—760nm(为了避免近红外波段受叶片水分含量的影响,降低构建模型的稳定性),利用相关分析与逐步回归分析的统计学分析方法,建立叶面尺度下不同包络线去除衍生转换光谱:BD(band depth)、CRDR(continuum-removed derivative reflectance)、BDR(band depth ratio)、NBDI(normalized band depth index)与叶绿素含量估算模型。通过对入选波段的统计表明在550—750nm,特别是700—750nm(红边)波段范围内产生了较多的有效波段,是今后进行生物参量反演的重点波段范围。舍一交叉验证结果表明芦苇、香蒲和混合样本绿素含量估测的最佳模型分别为BD、CRDR和NBDI模型,其交叉验证决定系数依次为0.87、0.83和0.81,交叉验证均方根误差RMSE依次为0.16、0.15和0.33。并在此基础上利用独立样本非参数检验和多因子方差分析,探讨相关因素对于叶绿素含量估算模型精度的影响。结果表明物种差异、数据类型差异对于叶绿素回归模型的影响较大,而光谱类型差异及光谱数据与数据类型交互作用对于回归模型精度的影响较小。

Estimation of wetland plant leaf chlorophyll content based on continuum removal in the visible domain

Increasing amounts of recycled water are being used in urban wetlands; as such, monitoring the growth of wetland plants over large areas is of great significance to assessing the restoration and reconstruction of wetlands created by recycled water. At present, remote-sensing technology is considered an important method for monitoring the growth of plants on a large scale. In this study, typical wetland plants (Phragmites australis and Typha angustifolia) growing in the South Park Wetland of Olympic Park were selected as research subjects. Spectral reflectance was determined at a domain ranging from 400 to 760 nm to avoid the influence of leaf water on the established model. Chlorophyll content was obtained from data sources. Statistical analysis, including correlation and stepwise regression analysis, was conducted to establish chlorophyll content inversion models with different derivative transformation spectrums at the leaf level for:(1) band depth (BD), (2) continuum-removed derivative reflectance (CRDR), (3) band depth ratio (BDR), and (4) normalized band depth index (NBDI). We found that 550 nm to 750 nm, particularly 700 nm to 750 nm (red edge range), was the key range to estimate biochemical parameters. Single removal cross-validation results indicated that optimal models of chlorophyll content inversion in reeds, cattails, and combined samples were BD, CRDR, and NBDI, respectively. Corresponding R² values were 0.87, 0.83, and 0.81, and the respective RMSE values were 0.16, 0.15, and 0.33, respectively. Kruskal-Wallis non-parametric tests and multi-way ANOVAs were performed to elucidate the influence of relevant factors individually and in combination with one another on the regression results of biochemical parameters of plant water. Results showed that vegetation type (reed, cattail) and data type (single or mixed species) greatly influenced the inversion model. In contrast, the spectral derivative transformation(BD, CRDR, BDR, and CRDR)and the interaction between spectral derivative transformation and data types did not significantly affect the inversion model. In this study, an estimation model of wetland plant biochemical parameters was established and functions of related factors in the estimation model were analyzed. Our results could be used as a scientific basis for non-destructive monitoring of growth in wetland plants. This study also provided a reference for the use of recycled water in restoration and management.