Assessing the Self-Recovery Ability of Maize after Lodging Using UAV-LiDAR Data

文献类型： 外文期刊

第一作者： Hu, Xueqian

作者： Hu, Xueqian;Gu, Xiaohe;Sun, Qian;Wei, Zhonghui;Pan, Yuchun;Hu, Xueqian;Sun, Lin;Sun, Qian;Wei, Zhonghui;Chen, Liping

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关键词： maize; lodging; UAV; LiDAR; CHM; self-recovery ability; yield; agriculture

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

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年卷期： 2021 年 13 卷 12 期

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收录情况： SCI

摘要： Lodging is one of the main problems in maize production. Assessing the self-recovery ability of maize plants after lodging at different growth stages is of great significance for yield loss assessment and agricultural insurance claims. The objective of this study was to quantitatively analyse the effects of different growth stages and lodging severity on the self-recovery ability of maize plants using UAV-LiDAR data. The multi-temporal point cloud data obtained by the RIEGL VUX-1 laser scanner were used to construct the canopy height model of the lodging maize. Then the estimated canopy heights of the maize at different growth stages and lodging severity were obtained. The measured values were used to verify the accuracy of the canopy height estimation and to invert the corresponding lodging angle. After verifying the accuracy of the canopy height, the accuracy parameter of the tasselling stage was R-2 = 0.9824, root mean square error (RMSE) = 0.0613 m, and nRMSE = 3.745%. That of the filling stage was R-2 = 0.9470, RMSE = 0.1294 m, and nRMSE = 9.889%, which showed that the UAV-LiDAR could accurately estimate the height of the maize canopy. By comparing the yield, canopy height, and lodging angle of maize, it was found that the self-recovery ability of maize at the tasselling stage was stronger than that at the filling stage, but the yield reduction rate was 14.16 similar to 26.37% higher than that at the filling stage. The more serious the damage of the lodging is to the roots and support structure of the maize plant, the weaker is the self-recovery ability. Therefore, the self-recovery ability of the stem tilt was the strongest, while that of root lodging and root stem folding was the weakest. The results showed that the UAV-LiDAR could effectively assess the self-recovery ability of maize after lodging.

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