## 1 Introduction  
The forest leaf area index (_LAI_), the total one-sided leaf area per unit ground surface area [1], is an important biophysical parameter in ecosystem models [2]. The vertical distribution of the forest leaf area can influence the photosynthesis and carbon stock of the forest [3], and it is often represented by the foliage profile. The foliage profile plays an important role in energy exchange, the water cycle, and the carbon cycle in an ecosystem [4]. As a vertical structure parameter of the canopy, the foliage profile can estimate the forest aboveground biomass [5, 6] and net primary productivity (NPP) [7], and it has great potential to describe the distribution of light transmission in the canopy [4]. The foliage profile is also a key factor in determining biodiversity and habitat suitability [8], as different wildlife species live at different forest heights. Traditional direct foliage profile measurement methods, such as stratified sampling, and indirect ground measurement methods, such as the point-quadrat sampling [9, 10], are expensive, labour-intensive, and time-consuming. Moreover, these direct methods are destructive. Thesemethods cannot be implemented over large areas, and they cannot be used to monitor the vegetation growth conditions over a long time series. Optical remote sensing has been widely used to retrieve vegetation biophysical and biochemical parameters, such as _LAI_[11; 12] and chlorophyll content [13; 14]. Optical remote sensing can provide information on the horizontal distribution of forest parameters over large areas, but it has difficulty in acquiring the vertical structure of the forest canopy [15; 16]. As an active remote sensing technique, light detection and ranging (LiDAR) has a strong penetration ability in the forest canopy, and it can acquire the information from the lower canopy [17]. Full-waveform LiDAR records all backscatter energy; thus, it can capture richer vertical canopy structure information [16]. LiDAR has been used to retrieve many forest structure parameters, such as tree height [18], _LAI_[19; 20], and biomass [21]. The forest foliage profile has also been retrieved by LiDAR in recent studies [22; 23]. [PERSON] et al. [24] estimated the cumulative _LAI_ profile in tropical rain forests based on the Geometric Optical and Radiative Transfer (GORT) model with airborne large-footprint full-waveform LiDAR data, and the results indicated that the estimation moderately agreed with the tower-measured vertical _LAI_ profile. [PERSON] et al. [25] retrieved the foliage area volume density (FAVD) profile of a forest using a radiative transfer (RT) model and airborne small-footprint full-waveform LiDAR data. The study then obtained the forest _LAI_ which were highly correlated. [PERSON] et al. [26] compared the conifer stand foliage profiles derived from the terrestrial full-waveform LiDAR (EVI) and airborne full-waveform LiDAR (LVIS) system. The results showed that airborne LiDAR could acquire more upper canopy information and terrestrial LiDAR could capture more information about the lower canopy, and the two methods exhibited strong agreement. It is believed that data acquisition conditions, such as the scanning angle and flying altitude, could affect the accuracy of vegetation parameter estimations and determine the cost of data acquisition. Several studies have analysed the sensitivity of some vegetation structure parameters to LiDAR data acquisition conditions. [PERSON] et al. [27] analysed the effects of the airborne LiDAR flying height and scanning angle on tree height, fractional cover, and _LAI_ estimations. The results indicated that the underestimation of the forest height increased and the fractional cover decreased with the increase in flying altitude. The effect of the scanning angle was not as evident, probably due to the small scanning angle used, but the fractional cover seemed to be most affected by the scanning angle [27]. [PERSON] et al.