**1. Introduction**

The remote sensing community has demonstrated the effectiveness of hyperspectral imagers and LiDAR to obtain spectral and spatial information [1–5]. The hyperspectral imager is capable of obtaining consecutive and abundant spectral profiles of targets, which has been employed in vegetation parameter extraction, food production prediction, target classification, etc. [1–5]. However, the hyperspectral imager relies on environmental illumination conditions, so poor lighting will affect the hyperspectral information acquisition. LiDAR is an active sensor invented to acquire spatial information. In LiDAR a laser source emits monochromatic laser beams to a target, and thus, the ranging information is obtained through measuring the travel time of the laser beam [6,7]. With a scanning operation, LiDAR is able to obtain spatial information from the environment. Besides this, the power of the reflected signal from the target in LiDAR can be obtained with ranging operation. With careful calibration, the power of the reflected signal is measured and termed as intensity. Researchers have carried out some investigations using the intensity of a single wavelength to obtain some textures of the targets, for instance, rock analysis in outcrop models, landcover classification, etc. [1–8].

Restricted by the monochromatic laser source, intensity information of the back-scattered laser pulse or the spectral information from a traditional single wavelength LiDAR is much less efficient than a passive spectrometer [9–11]. Recently, two approaches were investigated for the fusion of spatial and spectral data. The first approach ias to combine spectral and spatial data from two standalone instruments into the same framework, and this method was employed in forest area classification, urban species classification, automatic building extraction, and outcrop analysis [12–18]. The disadvantage is that the data registration is complicated and time-consuming, and the coordinate transformation between the two instruments will probably introduce additional errors [12–18].

The second approach refers to the integration of ranging with spectral measuring functions into a single sensor or instrument. Hyperspectral LiDAR (HSL) or Multispectral LiDAR (MSL) were developed as active sensors to obtain spectral and spatial information simultaneously. Basically, there are two solutions to develop an HSL or MSL. The first solution is to combine several monochromatic laser sources of different wavelengths together. Since more channels mean more laser sources at different spectral wavelengths, it was hard to combine tens or hundreds of monochromatic laser sources together in this framework [19–21]. The second solution is to develop the HSL through employing a super-continuum (SC) laser source replacing the above monochromatic laser sources of different wavelengths, and the SC laser source is able to emit ultra-wideband coherent laser transmissions with spectral ranging from approximately 400 nm to 2500 nm [19–21]. Scientists from the Finnish Geospatial Research Institute (FGI) proposed the SC laser source-based spectral measurement concept in 2007 [22]. The first results with the prototype instrument were presented with a discussion of improvements and applications in laser-based hyperspectral remote sensing [22]. Further, in 2010, a two-channel multispectral LiDAR with 600 nm and 800 nm spectral wavelengths was developed and demonstrated, which was capable of distinguishing between a vegetation target (Norway spruce) and inorganic material using the Normalized Difference Vegetation Index (NDVI) parameter [23]. In 2012, the first full-waveform HSL with eight spectral channels was constructed by FGI. The novel instrument produced 3D point clouds with spectral back-scattered reflectance data [24]. Then, HSL was investigated in vegetation content estimation, leaf level chlorophyll estimation, leaf biochemical content estimation, landcover classification, and artificial object classification [25–30]. However, compared with the hyperspectral imager, these HSLs had restricted and discrete spectral bands and channels. For broadening the applications of HSL, attention should be paid to develop a HSL enabling continuous spectral band collection with higher spectral resolution [31–33].

As an active instrument to acquire abundant spectral profiles, HSL usually has limited spectral bands and coverage, and a more universal and practical HSL with fine spectral resolution and coverage is of great significance for non-contact and active vegetation parameter extraction. Motivated by this, in this paper, an Acousto-optical Tunable Filter HSL (AOTF-HSL) with 10 nm spectral resolution covering 500–1000 nm was developed, and the HSL was evaluated by comparing the selected "Red Edge" (RE) vegetation parameter-related results from AOTF-HSL with those obtained using an SVC HR-1024 spectrometer.

In this research, leaves from four different plants were measured to evaluate the capacity of using the spectral from the AOTF-HSL for vegetation RE-related parameter extraction. In the vegetation research community, the important "Red Edge" position (REP) parameter is closely related to various physical and chemical parameters of vegetation, and it is commonly employed to indicate the growing states of the vegetation and monitor the plant activity [34,35]. Thus, the RE related parameters were selected as the representative for evaluating the HSL in vegetation applications. As shown in Figure 1, REP refers to the position of an inflection point of the first derivative of reflectance values, and it usually locates in the red spectrum band [33–36]. REP result comparison between the HSL and SVC spectrometer could provide a preliminary evaluation of the utility of HSL in vegetation parameter extraction.

**Figure 1.** Schematic diagram of a tunable Hyperspectral LiDAR system based on AOTF.

In addition, three most common used methods (First-order Reflectance Slope (FRS), Linear Four-point Interpolation technology (LFPIT) and Linear Extrapolation technology (LET)) were investigated in this research for fully and furtherly evaluating the HSL capacity in spectral profiles acquirement and vegetation parameters extraction.

The contribution of this paper was summarized as follows:


The remainder of this paper is organized as follows: Section 2 presents the system design of the AOTF-HSL and the REP determination methods in detail; Section 3 presents the results and analysis of the laboratory experiments concerning the RE-related parameters measurements, result comparisons between different methods and the analysis; and then the conclusions are drawn in Section 4.
