Leaf area index (LAI) is a key parameter for the study of biogeochemical cycles in ecosystems. Remote sensing techniques have been widely used to estimate LAIs in a wide range of vegetation types. However, limited by the sensor detection capability, considerable fewer studies investigated the layered estimation of LAIs in the vertical direction, which can significantly affect the precision evaluation of vegetation biophysical and biochemical processes. This study tried to generate a kind of pseudo large footprint waveform from the small footprint full-waveform airborne LiDAR data by an aggregation approach. The layered distribution of canopy heights and LAIs were successfully retrieved based on the large footprint waveform data in an agricultural landscape of orchards with typical multi-layer vegetation covers. The Gaussian fitting was conducted on the normalized large footprint waveforms to identify the vertical positions for different vegetation layers. Then, the gap theory was applied to retrieve the layered LAIs. Statistically significant simple linear regression models were fitted between the LiDAR-retrieved and field-observed values for the canopy heights and LAIs in different layers. Satisfactory results were obtained with a root mean square error of 0.36 m for the overstorey canopy height (R2 = 0.82), 0.29 m for the understory canopy height (R2 = 0.76), 0.28 for overstorey LAI (R2 = 0.75), 0.40 for understory LAI (R2 = 0.64), and 0.38 for total LAI (R2 = 0.69), respectively. To conclude, estimating the layered LAIs in the multi-layer agriculture orchards from the pseudo large footprint waveforms is feasible and the estimation errors are acceptable, which will provide some new ideas and methods for the quantitative remote sensing with vegetation.