Articles | Volume 12, issue 5
https://doi.org/10.5194/bg-12-1629-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-12-1629-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Technical note
| 
12 Mar 2015
Technical note |
| 12 Mar 2015
Technical Note: Multispectral lidar time series of pine canopy chlorophyll content
T. Hakala
CORRESPONDING AUTHOR
Finnish Geospatial Research Institute (FGI), Masala, Finland
O. Nevalainen
Finnish Geospatial Research Institute (FGI), Masala, Finland
S. Kaasalainen
Finnish Geospatial Research Institute (FGI), Masala, Finland
R. Mäkipää
Finnish Forest Research Institute (METLA), Vantaa, Finland
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Cited
24 citations as recorded by crossref.
- Monitoring of Canopy Stress Symptoms in New Zealand Kauri Trees Analysed with AISA Hyperspectral Data J. Meiforth et al. 10.3390/rs12060926
- Multispectral Light Detection and Ranging Technology and Applications: A Review N. Takhtkeshha et al. 10.3390/s24051669
- Multimodal hyperspectral remote sensing: an overview and perspective Y. Gu et al. 10.1007/s11432-020-3084-1
- Estimating leaf chlorophyll and nitrogen contents using active hyperspectral LiDAR and partial least square regression method K. Bi et al. 10.1117/1.JRS.13.034513
- Mapping 3D plant chlorophyll distribution from hyperspectral LiDAR by a leaf-canopyradiative transfer model L. Xu et al. 10.1016/j.jag.2024.103649
- Identifying the genus or species of individual trees using a three-wavelength airborne lidar system B. Budei et al. 10.1016/j.rse.2017.09.037
- Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion J. Sun et al. 10.1016/j.rse.2018.04.024
- Generating a hyperspectral digital surface model using a hyperspectral 2D frame camera R. Oliveira et al. 10.1016/j.isprsjprs.2018.11.025
- Application of Remote Sensing Technologies for Assessing Planted Forests Damaged by Insect Pests and Fungal Pathogens: a Review C. Stone & C. Mohammed 10.1007/s40725-017-0056-1
- Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective R. Hall et al. 10.4039/tce.2016.11
- Tango in forests – An initial experience of the use of the new Google technology in connection with forest inventory tasks J. Tomaštík et al. 10.1016/j.compag.2017.07.015
- Estimating 3D Chlorophyll Content Distribution of Trees Using an Image Fusion Method Between 2D Camera and 3D Portable Scanning Lidar F. Hosoi et al. 10.3390/rs11182134
- Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test S. Junttila et al. 10.3390/rs71013863
- Individual Tree Crown Methods for 3D Data from Remote Sensing E. Lindberg & J. Holmgren 10.1007/s40725-017-0051-6
- Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating L. Matikainen et al. 10.1016/j.isprsjprs.2017.04.005
- Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences J. Eitel et al. 10.1016/j.rse.2016.08.018
- Closing the gap between phenotyping and genotyping: review of advanced, image-based phenotyping technologies in forestry L. Bian et al. 10.1186/s13595-022-01143-x
- Estimating canopy chlorophyll in slash pine using multitemporal vegetation indices from uncrewed aerial vehicles (UAVs) Q. Luan et al. 10.1007/s11119-023-10106-9
- Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity K. Bi et al. 10.3390/rs13214203
- Quantifying vertical profiles of biochemical traits for forest plantation species using advanced remote sensing approaches X. Shen et al. 10.1016/j.rse.2020.112041
- Spectral and spatial information from a novel dual-wavelength full-waveform terrestrial laser scanner for forest ecology F. Mark Danson et al. 10.1098/rsfs.2017.0049
- Investigating the Consistency of Uncalibrated Multispectral Lidar Vegetation Indices at Different Altitudes M. Okhrimenko & C. Hopkinson 10.3390/rs11131531
- Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR Y. Chen et al. 10.1109/LGRS.2018.2854358
- Quantification of Overnight Movement of Birch (Betula pendula) Branches and Foliage with Short Interval Terrestrial Laser Scanning E. Puttonen et al. 10.3389/fpls.2016.00222
22 citations as recorded by crossref.
- Monitoring of Canopy Stress Symptoms in New Zealand Kauri Trees Analysed with AISA Hyperspectral Data J. Meiforth et al. 10.3390/rs12060926
- Multispectral Light Detection and Ranging Technology and Applications: A Review N. Takhtkeshha et al. 10.3390/s24051669
- Multimodal hyperspectral remote sensing: an overview and perspective Y. Gu et al. 10.1007/s11432-020-3084-1
- Estimating leaf chlorophyll and nitrogen contents using active hyperspectral LiDAR and partial least square regression method K. Bi et al. 10.1117/1.JRS.13.034513
- Mapping 3D plant chlorophyll distribution from hyperspectral LiDAR by a leaf-canopyradiative transfer model L. Xu et al. 10.1016/j.jag.2024.103649
- Identifying the genus or species of individual trees using a three-wavelength airborne lidar system B. Budei et al. 10.1016/j.rse.2017.09.037
- Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion J. Sun et al. 10.1016/j.rse.2018.04.024
- Generating a hyperspectral digital surface model using a hyperspectral 2D frame camera R. Oliveira et al. 10.1016/j.isprsjprs.2018.11.025
- Application of Remote Sensing Technologies for Assessing Planted Forests Damaged by Insect Pests and Fungal Pathogens: a Review C. Stone & C. Mohammed 10.1007/s40725-017-0056-1
- Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective R. Hall et al. 10.4039/tce.2016.11
- Tango in forests – An initial experience of the use of the new Google technology in connection with forest inventory tasks J. Tomaštík et al. 10.1016/j.compag.2017.07.015
- Estimating 3D Chlorophyll Content Distribution of Trees Using an Image Fusion Method Between 2D Camera and 3D Portable Scanning Lidar F. Hosoi et al. 10.3390/rs11182134
- Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test S. Junttila et al. 10.3390/rs71013863
- Individual Tree Crown Methods for 3D Data from Remote Sensing E. Lindberg & J. Holmgren 10.1007/s40725-017-0051-6
- Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating L. Matikainen et al. 10.1016/j.isprsjprs.2017.04.005
- Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences J. Eitel et al. 10.1016/j.rse.2016.08.018
- Closing the gap between phenotyping and genotyping: review of advanced, image-based phenotyping technologies in forestry L. Bian et al. 10.1186/s13595-022-01143-x
- Estimating canopy chlorophyll in slash pine using multitemporal vegetation indices from uncrewed aerial vehicles (UAVs) Q. Luan et al. 10.1007/s11119-023-10106-9
- Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity K. Bi et al. 10.3390/rs13214203
- Quantifying vertical profiles of biochemical traits for forest plantation species using advanced remote sensing approaches X. Shen et al. 10.1016/j.rse.2020.112041
- Spectral and spatial information from a novel dual-wavelength full-waveform terrestrial laser scanner for forest ecology F. Mark Danson et al. 10.1098/rsfs.2017.0049
- Investigating the Consistency of Uncalibrated Multispectral Lidar Vegetation Indices at Different Altitudes M. Okhrimenko & C. Hopkinson 10.3390/rs11131531
2 citations as recorded by crossref.
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Short summary
A hyperspectral lidar produces point clouds with multiple spectral channels (colours) for each point. We measured a pine and used the spectral content to estimate chlorophyll content. We validated these results using chemical laboratory analysis of needles taken from the pine. Our prototype has limitations, but still shows the great potential of coloured point clouds. Potential applications include forestry, security, archaeology and city modelling.
A hyperspectral lidar produces point clouds with multiple spectral channels (colours) for each...
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