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Estimating Leaf Area Index of Maize Using Airborne Discrete-Return LiDAR Data

Author:
Nie, Sheng  Wang, Cheng  Dong, Pinliang  Xi, Xiaohuan  Luo, Shezhou  Zhou, Hangyu  


Journal:
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING


Issue Date:
2016


Abstract(summary):

The leaf area index (LAI) is an important vegetation biophysical parameter, which plays a critical role in gas-vegetation exchange processes. Several studies have recently been conducted to estimate vegetation LAI using airborne discrete-return Light Detection and Ranging (LiDAR) data. However, few studies have been carried out to estimate the LAI of low-statue vegetation, such as the maize. The objective of this research is to explore the potential of estimating LAI for maize using airborne discrete-return LiDAR data. The LAIs of maize were estimated by a method based on the Beer-Lambert law and a method based on the allometric relationship, respectively. In addition, a new height threshold method for separating ground returns from canopy returns was proposed to better estimate the LAI of maize. Moreover, the two LAI estimation methods were also evaluated using the leave-one-out cross-validation method. Results indicate that the new height threshold method performs better than the traditional height threshold method in separating grounds returns from LiDAR returns. The coefficient of variation of detrended return heights within a field was a good parameter to estimate the LAI ofmaize. In addition, results also indicate that the method based on the Beer-Lambert law (R-2 =3D 0.849, RMSE =3D 0.256) was more accurate than the method based on the allometric relationship (R-2 =3D 0.779, RMSE =3D 0.315) in low-LAI regions, while only the method based on the allometric relationship is suitable for estimating the LAI of maize in high-LAI regions.


Page:
3259---3266


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