Abstract
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 (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), 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.
Highlights
Vegetation height, leaf area index (LAI) and aboveground biomass (AGB) are key biophysical parameters for vegetation growth, yield forecasting, health monitoring, climate change and ecosystem modeling [1,2,3]
We found that the optimal sampling size and height threshold were different for estimating the different biophysical parameters
For all predictive models, the RMSE from the LOOCV (RMSEcv) values derived from leave-one-out cross-validation (LOOCV) crossvalidation were in agreement with their root mean squared error (RMSE) values, which showed that the estimation models of biophysical parameters developed using Light Detection and Ranging (LiDAR) data had good predictive ability
Summary
Vegetation height, leaf area index (LAI) and aboveground biomass (AGB) are key biophysical parameters for vegetation growth, yield forecasting, health monitoring, climate change and ecosystem modeling [1,2,3]. The accuracy of these models primarily depends on the accuracy of the key model input parameters [4]. The estimations of corn height, LAI and biomass have great significance in corn growth monitoring, yield forecasting and ecological modeling
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