Estimating leaf area index of maize using airborne full-waveform lidar data

ABSTRACT

The leaf area index (LAI) is a key input parameter in ecosystem models and plays a vital role in gas–vegetation exchange processes. Several studies have recently been conducted to estimate the LAI of low-stature vegetation using airborne discrete-return light detection and ranging (lidar) data. However, few studies have been carried out to estimate the LAI of low-stature vegetation using airborne full-waveform lidar data. The objective of this research is to explore the potential of airborne full-waveform lidar for LAI estimation of maize. First, waveform processing was conducted for better extraction of waveform-derived metrics for LAI estimation. A method of faint returns retrieval was also proposed to obtain ground returns. Second, the LAIs of maize were estimated based on the Beer–Lambert law. Finally, the LAI estimates were validated using field-measured LAIs in Huailai, Hebei Province of China. Results indicated that maize LAI could be successfully retrieved with high accuracy (R² = 0.724, RMSE = 0.449) using full-waveform lidar data by the method proposed in this study.

Acknowledgements

The authors would like to thank two anonymous reviewers for the valuable comments and suggestions.

Additional information

Funding

This work was supported by Projects of International Cooperation [2013DFG21640]; the Bureau of International Cooperation, Chinese Academy of Sciences [241311KYSB20130001]; the National Natural Science Foundation of China [41171279], [41271428].