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
Related authors
J. Svensson, A. Virkkula, O. Meinander, N. Kivekäs, H.-R. Hannula, O. Järvinen, J. I. Peltoniemi, M. Gritsevich, A. Heikkilä, A. Kontu, A.-P. Hyvärinen, K. Neitola, D. Brus, P. Dagsson-Waldhauserova, K. Anttila, T. Hakala, H. Kaartinen, M. Vehkamäki, G. de Leeuw, and H. Lihavainen
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-1227-2015, https://doi.org/10.5194/tcd-9-1227-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
Soot's (including black carbon and organics) negative effect on a natural snow pack is experimentally addressed in this paper through a series of experiments. Soot concentrations in the snow in the range of 200-200 000 ppb verify the negative effects on the albedo, the physical snow characteristics, as well as increasing the melt rate of the snow pack. Our experimental data generally agrees when compared with the Snow, Ice and Aerosol Radiation model.
Laura Thölix, Leif Backman, Minttu Havu, Esko Karvinen, Jesse Soininen, Justine Trémeau, Olli Nevalainen, Joyson Ahongshangbam, Leena Järvi, and Liisa Kulmala
EGUsphere, https://doi.org/10.5194/egusphere-2024-1453, https://doi.org/10.5194/egusphere-2024-1453, 2024
Short summary
Short summary
Cities seek carbon neutrality and are interested in the sinks of urban vegetation. Measurements are difficult to do which leads to the need for modeling carbon cycle. In this study, we examined the performance of models in estimating carbon sequestration rates in lawns, park trees, and urban forests in Helsinki, Finland. We found that models simulated seasonal and annual variations well. Trees had larger carbon sequestration rates compared with lawns and irrigation often increased carbon sink.
Yao Gao, Eleanor J. Burke, Sarah E. Chadburn, Maarit Raivonen, Mika Aurela, Lawrence B. Flanagan, Krzysztof Fortuniak, Elyn Humphreys, Annalea Lohila, Tingting Li, Tiina Markkanen, Olli Nevalainen, Mats B. Nilsson, Włodzimierz Pawlak, Aki Tsuruta, Huiyi Yang, and Tuula Aalto
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-229, https://doi.org/10.5194/bg-2022-229, 2022
Manuscript not accepted for further review
Short summary
Short summary
We coupled a process-based peatland CH4 emission model HIMMELI with a state-of-art land surface model JULES. The performance of the coupled model was evaluated at six northern wetland sites. The coupled model is considered to be more appropriate in simulating wetland CH4 emission. In order to improve the simulated CH4 emission, the model requires better representation of the peat soil carbon and hydrologic processes in JULES and the methane production and transportation processes in HIMMELI.
Maiju Linkosalmi, Juha-Pekka Tuovinen, Olli Nevalainen, Mikko Peltoniemi, Cemal M. Taniş, Ali N. Arslan, Juuso Rainne, Annalea Lohila, Tuomas Laurila, and Mika Aurela
Biogeosciences, 19, 4747–4765, https://doi.org/10.5194/bg-19-4747-2022, https://doi.org/10.5194/bg-19-4747-2022, 2022
Short summary
Short summary
Vegetation greenness was monitored with digital cameras in three northern peatlands during five growing seasons. The greenness index derived from the images was highest at the most nutrient-rich site. Greenness indicated the main phases of phenology and correlated with CO2 uptake, though this was mainly related to the common seasonal cycle. The cameras and Sentinel-2 satellite showed consistent results, but more frequent satellite data are needed for reliable detection of phenological phases.
Olli Nevalainen, Olli Niemitalo, Istem Fer, Antti Juntunen, Tuomas Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, Liisa Kulmala, Åsa Stam, Otto Kuusela, Stephanie Gerin, Toni Viskari, Julius Vira, Jari Hyväluoma, Juha-Pekka Tuovinen, Annalea Lohila, Tuomas Laurila, Jussi Heinonsalo, Tuula Aalto, Iivari Kunttu, and Jari Liski
Geosci. Instrum. Method. Data Syst., 11, 93–109, https://doi.org/10.5194/gi-11-93-2022, https://doi.org/10.5194/gi-11-93-2022, 2022
Short summary
Short summary
Better monitoring of soil carbon sequestration is needed to understand the best carbon farming practices in different soils and climate conditions. We, the Field Observatory Network (FiON), have therefore established a methodology for monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, and modeling. To disseminate our work, we built a website called the Field Observatory (fieldobservatory.org).
Laura Heimsch, Annalea Lohila, Juha-Pekka Tuovinen, Henriikka Vekuri, Jussi Heinonsalo, Olli Nevalainen, Mika Korkiakoski, Jari Liski, Tuomas Laurila, and Liisa Kulmala
Biogeosciences, 18, 3467–3483, https://doi.org/10.5194/bg-18-3467-2021, https://doi.org/10.5194/bg-18-3467-2021, 2021
Short summary
Short summary
CO2 and H2O fluxes were measured at a newly established eddy covariance site in southern Finland for 2 years from 2018 to 2020. This agricultural grassland site focuses on the conversion from intensive towards more sustainable agricultural management. The first summer experienced prolonged dry periods, and notably larger fluxes were observed in the second summer. The field acted as a net carbon sink during both study years.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
E. Honkavaara, R. Näsi, R. Oliveira, N. Viljanen, J. Suomalainen, E. Khoramshahi, T. Hakala, O. Nevalainen, L. Markelin, M. Vuorinen, V. Kankaanhuhta, P. Lyytikäinen-Saarenmaa, and L. Haataja
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 429–434, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-429-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-429-2020, 2020
E. Honkavaara, T. Hakala, O. Nevalainen, N. Viljanen, T. Rosnell, E. Khoramshahi, R. Näsi, R. Oliveira, and A. Tommaselli
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 77–82, https://doi.org/10.5194/isprs-archives-XLI-B7-77-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-77-2016, 2016
J. Svensson, A. Virkkula, O. Meinander, N. Kivekäs, H.-R. Hannula, O. Järvinen, J. I. Peltoniemi, M. Gritsevich, A. Heikkilä, A. Kontu, A.-P. Hyvärinen, K. Neitola, D. Brus, P. Dagsson-Waldhauserova, K. Anttila, T. Hakala, H. Kaartinen, M. Vehkamäki, G. de Leeuw, and H. Lihavainen
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-1227-2015, https://doi.org/10.5194/tcd-9-1227-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
Soot's (including black carbon and organics) negative effect on a natural snow pack is experimentally addressed in this paper through a series of experiments. Soot concentrations in the snow in the range of 200-200 000 ppb verify the negative effects on the albedo, the physical snow characteristics, as well as increasing the melt rate of the snow pack. Our experimental data generally agrees when compared with the Snow, Ice and Aerosol Radiation model.
G. H. S. Guendehou, J. Liski, M. Tuomi, M. Moudachirou, B. Sinsin, and R. Mäkipää
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-6-3003-2013, https://doi.org/10.5194/gmdd-6-3003-2013, 2013
Revised manuscript has not been submitted
Related subject area
Biogeophysics: Environmental Optics
Assessment of carbon mass in a Mediterranean downy oak ecosystem using airborne lidar and NASA Global Ecosystem Dynamics Investigation (GEDI) data
Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations
Assessing shaded-leaf effects on photochemical reflectance index (PRI) for water stress detection in winter wheat
On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance
Spring blooms in the Baltic Sea have weakened but lengthened from 2000 to 2014
A pilot project combining multispectral proximal sensors and digital cameras for monitoring tropical pastures
Deriving seasonal dynamics in ecosystem properties of semi-arid savanna grasslands from in situ-based hyperspectral reflectance
High-resolution analysis of a North Sea phytoplankton community structure based on in situ flow cytometry observations and potential implication for remote sensing
Disparities between in situ and optically derived carbon biomass and growth rates of the prymnesiophyte Phaeocystis globosa
A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves
Physical and biogeochemical controls on light attenuation in a eutrophic, back-barrier estuary
Using a two-layered sphere model to investigate the impact of gas vacuoles on the inherent optical properties of Microcystis aeruginosa
Nitrogen food-print: N use related to meat and dairy consumption in France
Remote sensing-based estimation of gross primary production in a subalpine grassland
Remote sensing of size structure of phytoplankton communities using optical properties of the Chukchi and Bering Sea shelf region
Analysis of vegetation and land cover dynamics in north-western Morocco during the last decade using MODIS NDVI time series data
Monitoring presence and streaming patterns of Icelandic volcanic ash during its arrival to Slovenia
Maëlie Chazette, Patrick Chazette, Ilja M. Reiter, Xiaoxia Shang, Julien Totems, Jean-Philippe Orts, Irène Xueref-Remy, and Nicolas Montes
Biogeosciences, 21, 3289–3303, https://doi.org/10.5194/bg-21-3289-2024, https://doi.org/10.5194/bg-21-3289-2024, 2024
Short summary
Short summary
The approach presented is original in its coupling between field observations and airborne lidar observations. It has been applied to an instrumented reference forest site in the south of France, which is heavily impacted by climate change. It leads to the evaluation of tree heights and ends with assessments of aerial and root carbon stocks. A detailed assessment of uncertainties is presented to add a level of reliability to the scientific products delivered.
Trina Merrick, Stephanie Pau, Matteo Detto, Eben N. Broadbent, Stephanie A. Bohlman, Christopher J. Still, and Angelica M. Almeyda Zambrano
Biogeosciences, 18, 6077–6091, https://doi.org/10.5194/bg-18-6077-2021, https://doi.org/10.5194/bg-18-6077-2021, 2021
Short summary
Short summary
Remote sensing measurements of forest structure promise to improve monitoring of tropical forest health. We investigated drone-based vegetation measurements' abilities to capture different structural and functional elements of a tropical forest. We found that emerging vegetation indices captured greater variability than traditional indices and one new index trends with daily change in carbon flux. These new tools can help improve understanding of tropical forest structure and function.
Xin Yang, Shishi Liu, Yinuo Liu, Xifeng Ren, and Hang Su
Biogeosciences, 16, 2937–2947, https://doi.org/10.5194/bg-16-2937-2019, https://doi.org/10.5194/bg-16-2937-2019, 2019
Short summary
Short summary
The photochemical reflectance index (PRI) derived from remotely sensed data has emerged to be a pre-visual indicator of water stress. This study evaluated the impact of the varying shaded-leaf fractions on estimating relative water content (RWC) across growth stages of winter wheat using PRI derived from hyperspectral imagery. Results showed that PRI of the pure shaded leaves may yield inaccurate estimates of plant water status, but the accuracy of RWC predictions was not significantly affected.
Sofia Cerasoli, Manuel Campagnolo, Joana Faria, Carla Nogueira, and Maria da Conceição Caldeira
Biogeosciences, 15, 5455–5471, https://doi.org/10.5194/bg-15-5455-2018, https://doi.org/10.5194/bg-15-5455-2018, 2018
Short summary
Short summary
We compared the ability of in situ spectral and satellite sensors to estimate the productivity of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify the best set of spectral predictors. In situ CO gas exchange and vegetation reflectance measurements were used for this purpose. Our results show the potential of Sentinel 2 and Landsat 8 satellites to monitor grasslands in support of a sustainable agriculture management.
Philipp M. M. Groetsch, Stefan G. H. Simis, Marieke A. Eleveld, and Steef W. M. Peters
Biogeosciences, 13, 4959–4973, https://doi.org/10.5194/bg-13-4959-2016, https://doi.org/10.5194/bg-13-4959-2016, 2016
Short summary
Short summary
Phytoplankton spring bloom phenology was derived from a 15-year time series (2000–2014) of ship-of-opportunity chlorophyll a fluorescence observations in the Baltic Sea. Bloom peak concentrations have declined over the study period, while bloom duration has increased. It is concluded that nutrient reduction efforts led to decreasing bloom intensity, while changes in Baltic Sea environmental conditions associated with global change corresponded to a lengthening spring bloom period.
Rebecca N. Handcock, D. L. Gobbett, Luciano A. González, Greg J. Bishop-Hurley, and Sharon L. McGavin
Biogeosciences, 13, 4673–4695, https://doi.org/10.5194/bg-13-4673-2016, https://doi.org/10.5194/bg-13-4673-2016, 2016
Short summary
Short summary
Proximal sensors can assist in managing feed in livestock production systems but raw data needs calibration to biophysical values such as biomass and ground cover. Our pilot project monitored tropical pastures for 18 months using digital cameras, multispectral sensors, soil moisture sensors, and field observations. We developed stringent data cleaning rules that are applicable to other sensor projects. Proximal sensors were found to deliver continual and timely pasture data.
T. Tagesson, R. Fensholt, S. Huber, S. Horion, I. Guiro, A. Ehammer, and J. Ardö
Biogeosciences, 12, 4621–4635, https://doi.org/10.5194/bg-12-4621-2015, https://doi.org/10.5194/bg-12-4621-2015, 2015
Short summary
Short summary
Relationships between ecosystem properties of semi-arid savanna and reflected solar radiance between 35 and 1800nm were investigated. Normalised combinations of reflectance for the near infrared, shortwave infrared, and 600 to 700nm were strongly affected by solar and viewing angle effects. Ecosystem properties of savannas were strongly correlated with reflectance at 350-1800nm, and normalised combinations of reflectance were strong predictors of the savanna ecosystem properties.
M. Thyssen, S. Alvain, A. Lefèbvre, D. Dessailly, M. Rijkeboer, N. Guiselin, V. Creach, and L.-F. Artigas
Biogeosciences, 12, 4051–4066, https://doi.org/10.5194/bg-12-4051-2015, https://doi.org/10.5194/bg-12-4051-2015, 2015
Short summary
Short summary
Phytoplankton community structure at a high spatial resolution (<3km) was studied in the North Sea during a cruise in May 2011. A first comparison with PHYSAT reflectance anomalies enables the extrapolation of the community structure rather than a dominant type at the North Sea scale and was interpreted with its hydrological characteristics. This will seriously improve our understanding of the influence of community structure on biogeochemical processes at the daily and basin scales.
L. Peperzak, H. J. van der Woerd, and K. R. Timmermans
Biogeosciences, 12, 1659–1670, https://doi.org/10.5194/bg-12-1659-2015, https://doi.org/10.5194/bg-12-1659-2015, 2015
C. Lin, S. C. Popescu, S. C. Huang, P. T. Chang, and H. L. Wen
Biogeosciences, 12, 49–66, https://doi.org/10.5194/bg-12-49-2015, https://doi.org/10.5194/bg-12-49-2015, 2015
N. K. Ganju, J. L. Miselis, and A. L. Aretxabaleta
Biogeosciences, 11, 7193–7205, https://doi.org/10.5194/bg-11-7193-2014, https://doi.org/10.5194/bg-11-7193-2014, 2014
Short summary
Short summary
Light availability to seagrass is an important factor in their success. We deployed instrumentation to measure light in Barnegat Bay, New Jersey, and found lower availability in the southern bay due to high turbidity (suspended sediment), while the northern bay has higher availability. In the northern bay, dissolved organic material and chlorophyll are most responsible for blocking light to the seagrass canopy. We also found that boat wakes do not have a large effect on sediment resuspension.
M. W. Matthews and S. Bernard
Biogeosciences, 10, 8139–8157, https://doi.org/10.5194/bg-10-8139-2013, https://doi.org/10.5194/bg-10-8139-2013, 2013
P. Chatzimpiros and S. Barles
Biogeosciences, 10, 471–481, https://doi.org/10.5194/bg-10-471-2013, https://doi.org/10.5194/bg-10-471-2013, 2013
M. Rossini, S. Cogliati, M. Meroni, M. Migliavacca, M. Galvagno, L. Busetto, E. Cremonese, T. Julitta, C. Siniscalco, U. Morra di Cella, and R. Colombo
Biogeosciences, 9, 2565–2584, https://doi.org/10.5194/bg-9-2565-2012, https://doi.org/10.5194/bg-9-2565-2012, 2012
A. Fujiwara, T. Hirawake, K. Suzuki, and S.-I. Saitoh
Biogeosciences, 8, 3567–3580, https://doi.org/10.5194/bg-8-3567-2011, https://doi.org/10.5194/bg-8-3567-2011, 2011
C. Höpfner and D. Scherer
Biogeosciences, 8, 3359–3373, https://doi.org/10.5194/bg-8-3359-2011, https://doi.org/10.5194/bg-8-3359-2011, 2011
F. Gao, S. Stanič, K. Bergant, T. Bolte, F. Coren, T.-Y. He, A. Hrabar, J. Jerman, A. Mladenovič, J. Turšič, D. Veberič, and M. Iršič Žibert
Biogeosciences, 8, 2351–2363, https://doi.org/10.5194/bg-8-2351-2011, https://doi.org/10.5194/bg-8-2351-2011, 2011
Cited articles
Asner, G. P., Knapp, D. E., Kennedy-Bowdoin, T., Jones, M. O., Martin, R. E., Boardman, J., and Field, C. B: Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging for three-dimensional studies of ecosystems, J. Appl. Remote Sens., 1, 013536, https://doi.org/10.1117/1.2794018, 2007.
Austin, A. T. and Ballaré, C. S.: Dual role of lignin in plant litter decomposition in terrestrial ecosystems, P. Natl. Acad. Sci. USA, 107, 4618–4622, https://doi.org/10.1073/pnas.0909396107, 2010.
Chen, J.: Evaluation of vegetation indices and modified simple ratio for boreal applications, Can. J. Remote Sens., 22, 229–242, 1996.
Coops, N. C., Stone, C., Culvenor, D. S., Chisholm, L. A., and Merton, R. N.: Chlorophyll content in eucalypt vegetation at the leaf and canopy scales as derived from high resolution spectral data, Tree Physiol., 23, 23–31, https://doi.org/10.1093/treephys/23.1.23, 2003.
Daughtry, C.: Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance, Remote Sens. Environ., 74, 229–239, https://doi.org/10.1016/S0034-4257(00)00113-9, 2000.
Dawson, T. P., Curran, P. J., and Plummer, S. E.: LIBERTY – modeling the effects of leaf biochemical concentration on reflectance spectra, Remote Sens. Environ., 65, 50–60, 1998.
Douglas, E. S., Strahler, A., Martel, J., Cook, T., Mendillo, C., Marshall, R., Chakrabarti, S., Schaaf, C., Woodcock, C., Li, Z., Yang, X., Culvenor, D., Jupp, D., Newnham, G., and Lovell, J.: DWEL: A Dual-Wavelength Echidna Lidar for ground-based forest scanning, 4998–5001, Int. Geosci. Remote Se., 2012.
Eitel, J. U. H., Vierling, L. A., Long, D. S., and Hunt, E. R.: Early season remote sensing of wheat nitrogen status using a green scanning laser, Agr. Forest Meteorol., 151, 1338–1345, https://doi.org/10.1016/j.agrformet.2011.05.015, 2011.
Féret, J. B., François, C., Gitelson, A., Asner, G. P., Barry, K. M., Panigada, C., Richardson, A. D., and Jacquemoud, S.: Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling, Remote Sens. Environ., 115, 2742–2750, https://doi.org/10.1016/j.rse.2011.06.016, 2011.
Gaulton, R., Danson, F. M., Ramirez, F. A., and Gunawan, O.: The potential of dual-wavelength laser scanning for estimating vegetation moisture content, Remote Sens. Environ., 132, 32–39, https://doi.org/10.1016/j.rse.2013.01.001, 2013.
Gond, V., de Pury, D. G. G., Veroustraete, F., and Ceulemans, R.: Seasonal variations in leaf area index, leaf chlorophyll, and water content; scaling-up to estimate fAPAR and carbon balance in a multilayer, multispecies temperate forest, Tree Physiol., 19, 673–679, https://doi.org/10.1093/treephys/19.10.673, 1999.
Hakala, T., Suomalainen, J., Kaasalainen, S., and Chen, Y.: Full waveform hyperspectral LiDAR for terrestrial laser scanning, Opt. Express, 20, 7119, https://doi.org/10.1364/OE.20.007119, 2012.
Hancock, S., Lewis, P., Foster, M., Disney, M., and Muller, J.-P.: Measuring forests with dual wavelength lidar: A simulation study over topography, Agr. Forest Meteorol., 161, 123–133, https://doi.org/10.1016/j.agrformet.2012.03.014, 2012.
Jones, T. G., Coops, N. C., and Sharma, T.: Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada, Remote Sens. Environ., 114, 2841–2852, https://doi.org/10.1016/j.rse.2010.07.002, 2010.
Kaasalainen, S., Krooks, A., Liski, J., Raumonen, P., Kaartinen, H., Kaasalainen, M., Puttonen, E., Anttila, K., and Mäkipää, R.: Change Detection of Tree Biomass with Terrestrial Laser Scanning and Quantitative Structure Modelling, Remote Sens., 6, 3906–3922, https://doi.org/10.3390/rs6053906, 2014.
Lausch, A., Heurich, M., Gordalla, D., Dobner, H.-J., Gwillym-Margianto, S., and Salbach, C.: Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales, Forest Ecol. Manag., 308, 76–89, https://doi.org/10.1016/j.foreco.2013.07.043, 2013.
Morsdorf, F., Nichol, C., Malthus, T., and Woodhouse, I. H.: Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling, Remote Sens. Environ., 113, 2152–2163, https://doi.org/10.1016/j.rse.2009.05.019, 2009.
Nevalainen, O., Hakala, T., Suomalainen, J., Mäkipää, R., Peltoniemi, M., Krooks, A., and Kaasalainen, S.: Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR, Agr. Forest Meteorol., 198–199, 250–258, https://doi.org/10.1016/j.agrformet.2014.08.018, 2014.
Peltoniemi, M. S., Duursma, R. A., and Medlyn, B. E.: Co-optimal distribution of leaf nitrogen and hydraulic conductance in plant canopies, Tree Physiol., 32, 510–519, https://doi.org/10.1093/treephys/tps023, 2012.
Raumonen, P., Kaasalainen, M., Åkerblom, M., Kaasalainen, S., Kaartinen, H., Vastaranta, M., Holopainen, M., Disney, M., and Lewis, P.: Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data, Remote Sens., 5, 491–520, https://doi.org/10.3390/rs5020491, 2013.
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, W. D.: Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA SP-351, 309–317, 1973
Thomas, V., Finch, D. A., McCaughey, J. H., Noland, T., Rich, L., and Treitz, P.: Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a lidar–hyperspectral approach, Agr. Forest Meteorol., 140, 287–307, https://doi.org/10.1016/j.agrformet.2006.04.008, 2006.
Wang, L. and Schjoerring, J. K.: Seasonal variation in nitrogen pools and 15N/13C natural abundances in different tissues of grassland plants, Biogeosciences, 9, 1583–1595, https://doi.org/10.5194/bg-9-1583-2012, 2012.
Wellburn, A. R.: The Spectral Determination of Chlorophylls a and b, as well as Total Carotenoids, Using Various Solvents with Spectrophotometers of Different Resolution, J. Plant Physiol., 144, 307–313, https://doi.org/10.1016/S0176-1617(11)81192-2, 1994.
Woodhouse, I. H., Nichol, C., Sinclair, P., Jack, J., Morsdorf, F., Malthus, T. J., and Patenaude, G.: A Multispectral Canopy LiDAR Demonstrator Project, IEEE Geosci. Remote Sens. Lett., 8, 839–843, https://doi.org/10.1109/LGRS.2011.2113312, 2011.
Wu, C., Niu, Z., Tang, Q., and Huang, W.: Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation, Agr. Forest Meteorol., 148, 1230–1241, https://doi.org/10.1016/j.agrformet.2008.03.005, 2008.
Zarco-Tejada, P. J., Miller, J. R., Noland, T. L., Mohammed, G. H., and Sampson, P. H.: Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data, IEEE T. Geosci. Remote, 39, 1491–1507, https://doi.org/10.1109/36.934080, 2001.
Zhang, Y., Chen, J., Miller, J., and Noland, T.: Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery, Remote Sens. Environ., 112, 3234–3247, https://doi.org/10.1016/j.rse.2008.04.005, 2008.
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...
Altmetrics
Final-revised paper
Preprint