Analyzing the Angle Effect of Leaf Reflectance Measured by Indoor Hyperspectral Light Detection and Ranging (LiDAR)

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.

[1]  Anttoni Jaakkola,et al.  Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods , 2011, Remote. Sens..

[2]  W. Wagner,et al.  Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner , 2006 .

[3]  Zhijie Wen,et al.  A 91-Channel Hyperspectral LiDAR for Coal/Rock Classification , 2020, IEEE Geoscience and Remote Sensing Letters.

[4]  Yuwei Chen,et al.  Two-channel Hyperspectral LiDAR with a Supercontinuum Laser Source , 2010, Sensors.

[5]  Jie Chen,et al.  Feasibility Study on Hyperspectral LiDAR for Ancient Huizhou-Style Architecture Preservation , 2019, Remote. Sens..

[6]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Zhijie Wen,et al.  Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR , 2018, IEEE Geoscience and Remote Sensing Letters.

[8]  E. Næsset,et al.  Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve , 2002 .

[9]  Dimitri Lague,et al.  3D Terrestrial LiDAR data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology , 2011, ArXiv.

[10]  Teemu Hakala,et al.  Incidence Angle Dependency of Leaf Vegetation Indices from Hyperspectral Lidar Measurements , 2016 .

[11]  Pol Coppin,et al.  The Properties of Terrestrial Laser System Intensity for Measuring Leaf Geometries: A Case Study with Conference Pear Trees (Pyrus Communis) , 2011, Sensors.

[12]  Xiaohuan Xi,et al.  Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification , 2015, Remote. Sens..

[13]  E. Bork,et al.  Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis , 2007 .

[14]  Rachel Gaulton,et al.  The potential of dual-wavelength laser scanning for estimating vegetation moisture content , 2013 .

[15]  Ning Wang,et al.  A 10-nm Spectral Resolution Hyperspectral LiDAR System Based on an Acousto-Optic Tunable Filter , 2019, Sensors.

[16]  Lorenzo Bruzzone,et al.  Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[17]  William E. Carter,et al.  Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar , 2016, Remote. Sens..

[18]  O. Lillesaeter,et al.  Spectral reflectance of partly transmitting leaves: Laboratory measurements and mathematical modeling , 1982 .

[19]  Wei Li,et al.  Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction , 2019, Remote. Sens..

[20]  M. Menenti,et al.  Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points , 2011 .

[21]  Z. Niu,et al.  Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system , 2014 .

[22]  J. Suomalainen,et al.  Full waveform hyperspectral LiDAR for terrestrial laser scanning. , 2012, Optics express.

[23]  W. Gong,et al.  Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer , 2017, Scientific Reports.

[24]  Wei Li,et al.  A Liquid Crystal Tunable Filter-Based Hyperspectral LiDAR System and Its Application on Vegetation Red Edge Detection , 2019, IEEE Geoscience and Remote Sensing Letters.