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

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

doi:10.7717/peerj.7593

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

PeerJ. 2019 Sep 17:7:e7593. doi: 10.7717/peerj.7593. eCollection 2019.

Authors

Yaohui Zhu^{1

2

3}, Chunjiang Zhao^{1

2

3}, Hao Yang^{2

3}, Guijun Yang^{2

3}, Liang Han^{2

4}, Zhenhai Li^{2

3}, Haikuan Feng^{2

3}, Bo Xu^{2

3}, Jintao Wu^{2

3}, Lei Lei^{2

3}

Affiliations

¹ School of Information Science and Technology, Beijing Forestry University, Beijing, China.
² Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.
³ National Engineering Research Center for Information Technology in Agriculture, Beijing, China.
⁴ College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong, China.

Abstract

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/m², NRMSE = 11.12%) and the PLSR method (R ² = 0.86, RMSE = 72.28 g/m², 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/m², 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/m², 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.

Keywords: Above-ground biomass; LiDAR; Maize; Multispectral image; Unmanned aerial vehicle.

Grants and funding

This work was supported by the National Key Research and Development Program of China (2017YFE0122500), the Natural Science Foundation of China (41771469 and 41571323), the Beijing Natural Science Foundation (6182011), the Beijing Academy of Agriculture and Forestry Sciences (KJCX20170423), and the Special Funds for Technology innovation capacity platform building sponsored by the Beijing Academy of Agriculture and Forestry Sciences (PT2019-29). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.