Articles | Volume 9, issue 7
https://doi.org/10.5194/bg-9-2565-2012
© Author(s) 2012. 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-9-2565-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Remote sensing-based estimation of gross primary production in a subalpine grassland
M. Rossini
Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi Milano-Bicocca, Milan, Italy
S. Cogliati
Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi Milano-Bicocca, Milan, Italy
M. Meroni
Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi Milano-Bicocca, Milan, Italy
European Commission, DG-JRC, Institute for Environment and Sustainability, Monitoring Agricultural Resources Unit, Ispra, VA, Italy
M. Migliavacca
Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi Milano-Bicocca, Milan, Italy
European Commission, DG-JRC, Institute for Environment and Sustainability, Climate Risk Management Unit, Ispra, VA, Italy
M. Galvagno
Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi Milano-Bicocca, Milan, Italy
Agenzia Regionale per la Protezione dell'Ambiente della Valle d'Aosta, Sez. Agenti Fisici, Aosta, Italy
L. Busetto
Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi Milano-Bicocca, Milan, Italy
European Commission, DG-JRC, Institute for Environment and Sustainability, Forest Resources and Climate Unit, Ispra, VA, Italy
E. Cremonese
Agenzia Regionale per la Protezione dell'Ambiente della Valle d'Aosta, Sez. Agenti Fisici, Aosta, Italy
T. Julitta
Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi Milano-Bicocca, Milan, Italy
C. Siniscalco
Plant Biology Department, Università degli Studi di Torino, Turin, Italy
U. Morra di Cella
Agenzia Regionale per la Protezione dell'Ambiente della Valle d'Aosta, Sez. Agenti Fisici, Aosta, Italy
R. Colombo
Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi Milano-Bicocca, Milan, Italy
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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
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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.
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Rebecca N. Handcock, D. L. Gobbett, Luciano A. González, Greg J. Bishop-Hurley, and Sharon L. McGavin
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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
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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
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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
T. Hakala, O. Nevalainen, S. Kaasalainen, and R. Mäkipää
Biogeosciences, 12, 1629–1634, https://doi.org/10.5194/bg-12-1629-2015, https://doi.org/10.5194/bg-12-1629-2015, 2015
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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.
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
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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
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
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