Abstract
Hyperspectral light detection and ranging (LiDAR) (HSL) combines the characteristics of hyperspectral imaging and LiDAR techniques into a single instrument without any data registration. It provides more information than hyperspectral imaging or LiDAR alone in the extraction of vegetation physiological and biochemical parameters. However, the laser pulse intensity is affected by the incident angle, and its effect on HSL has not yet been fully explored. It is important for employing HSL to investigate vegetation properties. The aim of this paper is to study the incident angle effect of leaf reflectance with HSL and build a model about this impact. In this paper, we studied the angle effect of leaf reflectance from indoor HSL measurements of individual leaves from four typical tree species in Beijing. We observed that (a) the increasing of incident angle decreases the leaf reflectance; (b) the leaf spectrum observed by HSL from 650 to 1000 nm with 10 nm spectral resolution (36 channels) are consistent with those that measured by Analytica Spectra Devices (ASD) spectrometer (R2 = 0.9472 ~ 0.9897); (c) the specular reflection is significant in the red bands, and clear non-Lambertian characteristics are observed. In the near-infrared, there is little specular reflection, but it follows the Lambert-scattering law. We divided the whole band (650–1000 nm) into six bands and established an empirical model to correct the influence of angle effect on the reflectance of the leaf for HSL applications. In the future, the calibration of HSL measurements applied for other targets will be studied by rigorous experiments and modelling.
Highlights
Hyperspectral imager is a passive and non-invasive remote sensing sensor with nanometer resolution, which obtains abundant and detailed spectral information of a target [1]
In previous studies in remote sensing, lots of results prove that the performance of traditional Light detection and ranging (LiDAR) and hyperspectral data fusion is better than any single sensor, such as land classification, and vegetation classification [11,12]
The spectra of a variety of different leaf incidents were studied by a hyperspectral LiDAR
Summary
Hyperspectral imager is a passive and non-invasive remote sensing sensor with nanometer (nm) resolution, which obtains abundant and detailed spectral information of a target [1]. Light detection and ranging (LiDAR) is an efficient active remote sensing technology, which can accurately measure the distance to the target object and generate three-dimensional point cloud. It has advanced the development in 3D information acquisition and topographic and geomorphic information acquisition [5,6]. Due to the limitation of laser light sources, traditional LiDAR sensors usually work in a single wavelength; they cannot provide abundant spectral information [7,8,9]. The fusion of hyperspectral and LiDAR sensors has become the research hotspot in the community [1]
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