Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data

文献类型： 外文期刊

第一作者： Zhu, Yaohui

作者： Zhu, Yaohui;Zhao, Chunjiang;Zhu, Yaohui;Zhao, Chunjiang;Yang, Hao;Yang, Guijun;Han, Liang;Li, Zhenhai;Feng, Haikuan;Xu, Bo;Wu, Jintao;Lei, Lei;Zhu, Yaohui;Zhao, Chunjiang;Yang, Hao;Yang, Guijun;Li, Zhenhai;Feng, Haikuan;Xu, Bo;Wu, Jintao;Lei, Lei;Han, Liang

作者机构：

关键词： Maize; Multispectral image; LiDAR; Above-ground biomass; Unmanned aerial vehicle

期刊名称：PEERJ （ 影响因子：2.984； 五年影响因子：3.369 ）

ISSN： 2167-8359

年卷期： 2019 年 7 卷

页码：

收录情况： SCI

摘要： Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (R-2 = 0.82, RMSE = 79.80 g/m(2), NRMSE = 11.12%) and the PLSR method (R-2 = 0.86, RMSE = 72.28 g/m(2), NRMSE = 10.07%). The results indicate that PLSR more accurately estimates AGB than MLR, with R-2 increasing by 0.04, root mean square error (RMSE) decreasing by 7.52 g/m(2), and normalized root mean square error (NRMSE) decreasing by 1.05%. In addition, the AGB is more accurately estimated by combining LiDAR with multispectral data than LiDAR and multispectral data alone, with R-2 increasing by 0.13 and 0.30, respectively, RMSE decreasing by 22.89 and 54.92 g/m(2), respectively, and NRMSE decreasing by 4.46% and 7.65%, respectively. This study improves the prediction accuracy of AGB and provides a new guideline for monitoring based on the fusion of multispectral and LiDAR data.

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