Cargando…

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

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 te...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Zhan, Schaefer, Michael, Strahler, Alan, Schaaf, Crystal, Jupp, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829182/
https://www.ncbi.nlm.nih.gov/pubmed/29503720
http://dx.doi.org/10.1098/rsfs.2017.0039
_version_ 1783302753811955712
author Li, Zhan
Schaefer, Michael
Strahler, Alan
Schaaf, Crystal
Jupp, David
author_facet Li, Zhan
Schaefer, Michael
Strahler, Alan
Schaaf, Crystal
Jupp, David
author_sort Li, Zhan
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5829182
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher The Royal Society
record_format MEDLINE/PubMed
spelling pubmed-58291822018-03-02 On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements Li, Zhan Schaefer, Michael Strahler, Alan Schaaf, Crystal Jupp, David Interface Focus Articles 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. The Royal Society 2018-04-06 2018-02-16 /pmc/articles/PMC5829182/ /pubmed/29503720 http://dx.doi.org/10.1098/rsfs.2017.0039 Text en © 2018 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Li, Zhan
Schaefer, Michael
Strahler, Alan
Schaaf, Crystal
Jupp, David
On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements
title On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements
title_full On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements
title_fullStr On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements
title_full_unstemmed On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements
title_short On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements
title_sort on the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829182/
https://www.ncbi.nlm.nih.gov/pubmed/29503720
http://dx.doi.org/10.1098/rsfs.2017.0039
work_keys_str_mv AT lizhan ontheutilizationofnovelspectrallaserscanningforthreedimensionalclassificationofvegetationelements
AT schaefermichael ontheutilizationofnovelspectrallaserscanningforthreedimensionalclassificationofvegetationelements
AT strahleralan ontheutilizationofnovelspectrallaserscanningforthreedimensionalclassificationofvegetationelements
AT schaafcrystal ontheutilizationofnovelspectrallaserscanningforthreedimensionalclassificationofvegetationelements
AT juppdavid ontheutilizationofnovelspectrallaserscanningforthreedimensionalclassificationofvegetationelements