On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements

Abstract

The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWEL's novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultaneous scanning at both 1064 nm and 1548 nm by DWEL instruments provides a new spectral dimension to TLS data that joins the 3D spatial dimension of lidar as an information source. Our point cloud classification algorithm explores the utilization of both spectral and spatial attributes of individual points from DWEL scans and highlights the strengths and weaknesses of each attribute domain. The spectral and spatial attributes for vegetation element classification each perform better in different parts of vegetation (canopy interior, fine branches, coarse trunks, etc.) and under different vegetation conditions (dead or live, leaf-on or leaf-off, water content, etc.). These environmental characteristics of vegetation, convolved with the lidar instrument specifications and lidar data quality, result in the actual capabilities of spectral and spatial attributes to classify vegetation elements in 3D space. The spectral and spatial information domains thus complement each other in the classification process. The joint use of both not only enhances the classification accuracy but also reduces its variance across the multiple vegetation types we have examined, highlighting the value of the DWEL as a new source of 3D spectral information. Wider deployment of the DWEL instruments is in practice currently held back by challenges in instrument development and the demands of data processing required by coaxial dual- or multi-wavelength scanning. But the simultaneous 3D acquisition of both spectral and spatial features, offered by new multispectral scanning instruments such as the DWEL, opens doors to study biophysical and biochemical properties of forested and agricultural ecosystems at more detailed scales.

Leaf and wood classification framework for terrestrial LiDAR point clouds

“…() and Danson et al. (), proposed new equipment configurations, with two lasers of different wavelengths (Douglas et al., ), that would greatly improve the ability to separate wood and leaf points (Danson, Sasse, & Schofield, ; Hancock, Gaulton, & Danson, ; Li, Schaefer, Strahler, Schaaf, & Jupp, ; Li, Strahler, et al., ). Different materials present different reflectance, and exploiting the contrast between wavebands would assist the identification of such materials.…”

Accounting for Wood, Foliage Properties, and Laser Effective Footprint in Estimations of Leaf Area Density from Multiview-LiDAR Data

“…In our formulation, one of the critical aspects is to be able to estimate a fraction of leaf hits F, as well as the leaf volume fraction α (Equation 13). The development of algorithms and methods for leaf and wood separation is a subject of active research [24][25][26][27][28][29], which is a prerequisite of most methods aiming at retrieving wood volume [22]. One could notice that determining the leaf fraction F is less challenging than the classification of each individual hit as "leaf" and "wood", in the sense that leaf fraction can be correctly estimated from a classification method with significant omission and commission errors inside the voxel.…”

“…The value of F can be determined from one of the algorithms and methods developed to discriminate leaf and wood returns [18,[24][25][26][27][28][29].…”

Analyzing the Vertical Distribution of Crown Material in Mixed Stand Composed of Two Temperate Tree Species

“…As a result, they can be applied to data acquired using different instruments and experimental protocols. Different approaches have been explored and tested using geometrical feature extraction and machine learning [33][34][35][36][37]. Tao et al [38] have shown that circle and line fitting on 2D projections of the 3D point cloud allowed a better wood/leaf separation than approaches based on intensity returns.…”