Luo Shezhou, Chen Jing M, Wang Cheng, Xi Xiaohuan, Zeng Hongcheng, Peng Dailiang, Li Dong
Opt Express. 2016 May 30;24(11):11578-93. doi: 10.1364/OE.24.011578.
Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.
植被叶面积指数（LAI）、高度和地上生物量是关键的生物物理参数。玉米是一种重要的全球分布作物，可靠估计这些参数对于玉米产量预测、健康监测和生态系统建模至关重要。激光雷达（LiDAR）被认为是估算植被生物物理参数的有效技术。然而，这些参数的估计精度受到多种因素的影响。在本研究中，我们首先使用原始激光雷达数据（7.32点/平方米）估算了玉米的LAI、高度和生物量（R分别为0.80、0.874和0.838），结果表明激光雷达数据能够准确估算这些生物物理参数。其次，对激光雷达点密度、采样大小和高度阈值对LAI、高度和生物量估计精度的影响进行了综合研究。我们的研究结果表明，激光雷达点密度对植被生物物理参数的估计精度有重要影响，然而，高点密度并不总是能产生高精度的估计，降低点密度也能得到合理的估计结果。此外，结果表明采样大小和高度阈值是影响生物物理参数估计精度的另外两个关键因素。因此，应确定最佳采样大小和高度阈值以提高生物物理参数的估计精度。我们的结果还表明，与高度和生物量估计相比，获得准确的玉米LAI估计需要更高的激光雷达点密度、更大的采样大小和高度阈值。总体而言，我们的结果为激光雷达数据采集以及利用激光雷达数据估算植被生物物理参数提供了有价值的指导。
