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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B3-2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-429-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-429-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
21 Aug 2020
| 21 Aug 2020
USING MULTITEMPORAL HYPER- AND MULTISPECTRAL UAV IMAGING FOR DETECTING BARK BEETLE INFESTATION ON NORWAY SPRUCE
E. Honkavaara
Finnish Geospatial Research Institute, FGI, Finland
R. Näsi
Finnish Geospatial Research Institute, FGI, Finland
R. Oliveira
Finnish Geospatial Research Institute, FGI, Finland
N. Viljanen
Finnish Geospatial Research Institute, FGI, Finland
J. Suomalainen
Finnish Geospatial Research Institute, FGI, Finland
E. Khoramshahi
Finnish Geospatial Research Institute, FGI, Finland
T. Hakala
Finnish Geospatial Research Institute, FGI, Finland
O. Nevalainen
Finnish Meteorological Institute, FMI, Finland
L. Markelin
Finnish Geospatial Research Institute, FGI, Finland
M. Vuorinen
Natural Resources Institute of Finland, Finland
V. Kankaanhuhta
Natural Resources Institute of Finland, Finland
P. Lyytikäinen-Saarenmaa
University of Helsinki, Finland
L. Haataja
Finnish Forest Center, Finland
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Short summary
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Short summary
Short summary
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A. Berveglieri, A. M. G. Tommaselli, G. Santos, L. D. Santos, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1625–1629, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1625-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1625-2019, 2019
M. Weinmann, R. Müller, R. Reulke, E. Honkavaara, D. Tuia, and S. Keller
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1825–1826, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1825-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1825-2019, 2019
G. T. Miyoshi, N. N. Imai, A. M. G. Tommaselli, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1875–1880, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1875-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1875-2019, 2019
É. A. S. Moriya, N. N. Imai, A. M. G. Tommaselli, A. Berveglieri, E. Honkavaara, M. A. Soares, and M. Marino
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1881–1886, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1881-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1881-2019, 2019
R. A. Oliveira, R. Näsi, O. Niemeläinen, L. Nyholm, K. Alhonoja, J. Kaivosoja, N. Viljanen, T. Hakala, S. Nezami, L. Markelin, L. Jauhiainen, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 489–494, https://doi.org/10.5194/isprs-archives-XLII-2-W13-489-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-489-2019, 2019
M. Weinmann, R. Müller, R. Reulke, E. Honkavaara, D. Tuia, and S. Keller
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 607–608, https://doi.org/10.5194/isprs-annals-IV-2-W5-607-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-607-2019, 2019
L. Markelin, J. Suomalainen, T. Hakala, R. A. Oliveira, N. Viljanen, R. Näsi, B. Scott, T. Theocharous, C. Greenwell, N. Fox, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 283–288, https://doi.org/10.5194/isprs-archives-XLII-1-283-2018, https://doi.org/10.5194/isprs-archives-XLII-1-283-2018, 2018
G. T. Miyoshi, N. N. Imai, A. M. G. Tommaselli, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 301–305, https://doi.org/10.5194/isprs-archives-XLII-1-301-2018, https://doi.org/10.5194/isprs-archives-XLII-1-301-2018, 2018
A. M. G. Tommaselli, L. D. Santos, A. Berveglieri, R. A. Oliveira, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 429–436, https://doi.org/10.5194/isprs-archives-XLII-1-429-2018, https://doi.org/10.5194/isprs-archives-XLII-1-429-2018, 2018
T. Hakala, I. Pölönen, E. Honkavaara, R. Näsi, T. Hakala, and A. Lindfors
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 411–417, https://doi.org/10.5194/isprs-archives-XLII-2-411-2018, https://doi.org/10.5194/isprs-archives-XLII-2-411-2018, 2018
R. A. Oliveira, E. Khoramshahi, J. Suomalainen, T. Hakala, N. Viljanen, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 789–795, https://doi.org/10.5194/isprs-archives-XLII-2-789-2018, https://doi.org/10.5194/isprs-archives-XLII-2-789-2018, 2018
R. Näsi, N. Viljanen, R. Oliveira, J. Kaivosoja, O. Niemeläinen, T. Hakala, L. Markelin, S. Nezami, J. Suomalainen, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1305–1310, https://doi.org/10.5194/isprs-archives-XLII-3-1305-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1305-2018, 2018
Marinka E. B. van Puijenbroek, Corjan Nolet, Alma V. de Groot, Juha M. Suomalainen, Michel J. P. M. Riksen, Frank Berendse, and Juul Limpens
Biogeosciences, 14, 5533–5549, https://doi.org/10.5194/bg-14-5533-2017, https://doi.org/10.5194/bg-14-5533-2017, 2017
Short summary
Short summary
Understanding the contribution of the vegetation and dune size to nebkha dune growth could improve model predictions on coastal dune development. We monitored a natural nebkha dune field with a drone with camera. Our results show that dune growth in summer is mainly determined by dune size, whereas in winter dune growth was determined by vegetation. In our study area the growth of exposed dunes was restricted by storm erosion, whereas growth of sheltered dunes was restricted by sand supply.
N. Saarinen, M. Vastaranta, R. Näsi, T. Rosnell, T. Hakala, E. Honkavaara, M. A. Wulder, V. Luoma, A. M. G. Tommaselli, N. N. Imai, E. A. W. Ribeiro, R. B. Guimarães, M. Holopainen, and J. Hyyppä
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 171–175, https://doi.org/10.5194/isprs-archives-XLII-3-W3-171-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-171-2017, 2017
S. Tuominen, R. Näsi, E. Honkavaara, A. Balazs, T. Hakala, N. Viljanen, I. Pölönen, H. Saari, and J. Reinikainen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 185–194, https://doi.org/10.5194/isprs-archives-XLII-3-W3-185-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-185-2017, 2017
A. Berveglieri, A. M. G. Tommaselli, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 19–25, https://doi.org/10.5194/isprs-archives-XLII-3-W3-19-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-19-2017, 2017
N. N. Imai, E. A. S. Moriya, E. Honkavaara, G. T. Miyoshi, M. V. A. de Moraes, A. M. G. Tommaselli, and R. Näsi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 73–79, https://doi.org/10.5194/isprs-archives-XLII-3-W3-73-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-73-2017, 2017
W. Liu, J. Atherton, M. Mõttus, A. MacArthur, H. Teemu, K. Maseyk, I. Robinson, E. Honkavaara, and A. Porcar-Castell
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 107–111, https://doi.org/10.5194/isprs-archives-XLII-3-W3-107-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-107-2017, 2017
L. Markelin, E. Honkavaara, R. Näsi, N. Viljanen, T. Rosnell, T. Hakala, M. Vastaranta, T. Koivisto, and M. Holopainen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 113–118, https://doi.org/10.5194/isprs-archives-XLII-3-W3-113-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-113-2017, 2017
G. T. Miyoshi, N. N. Imai, M. V. A. de Moraes, A. M. G. Tommaselli, and R. Näsi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 123–128, https://doi.org/10.5194/isprs-archives-XLII-3-W3-123-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-123-2017, 2017
R. Näsi, N. Viljanen, J. Kaivosoja, T. Hakala, M. Pandžić, L. Markelin, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 137–141, https://doi.org/10.5194/isprs-archives-XLII-3-W3-137-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-137-2017, 2017
M. H. D. Franceschini, H. Bartholomeus, D. van Apeldoorn, J. Suomalainen, and L. Kooistra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 109–112, https://doi.org/10.5194/isprs-archives-XLII-2-W6-109-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W6-109-2017, 2017
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
R. Näsi, E. Honkavaara, S. Tuominen, H. Saari, I. Pölönen, T. Hakala, N. Viljanen, J. Soukkamäki, I. Näkki, H. Ojanen, and J. Reinikainen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 1143–1148, https://doi.org/10.5194/isprs-archives-XLI-B1-1143-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-1143-2016, 2016
A. M. G. Tommaselli, A. Berveglieri, R. A. Oliveira, L. Y. Nagai, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3-W4, 109–115, https://doi.org/10.5194/isprs-archives-XL-3-W4-109-2016, https://doi.org/10.5194/isprs-archives-XL-3-W4-109-2016, 2016
J. I. Peltoniemi, M. Gritsevich, T. Hakala, P. Dagsson-Waldhauserová, Ó. Arnalds, K. Anttila, H.-R. Hannula, N. Kivekäs, H. Lihavainen, O. Meinander, J. Svensson, A. Virkkula, and G. de Leeuw
The Cryosphere, 9, 2323–2337, https://doi.org/10.5194/tc-9-2323-2015, https://doi.org/10.5194/tc-9-2323-2015, 2015
Short summary
Short summary
Light-absorbing impurities change the reflectance of snow in different ways. Some particles are heated by the Sun and they sink out of sight. During the process, snow may look darker than pure snow when observed by nadir, but at larger view zenith angles the snow may look as white as clean snow. Thus an observer on the ground may overestimate the albedo, while a satellite underestimates the albedo. Climate studies need to examine how the contaminants behave in snow, not only their total amounts.
J. Joutsensaari, P. Yli-Pirilä, H. Korhonen, A. Arola, J. D. Blande, J. Heijari, M. Kivimäenpää, S. Mikkonen, L. Hao, P. Miettinen, P. Lyytikäinen-Saarenmaa, C. L. Faiola, A. Laaksonen, and J. K. Holopainen
Atmos. Chem. Phys., 15, 12139–12157, https://doi.org/10.5194/acp-15-12139-2015, https://doi.org/10.5194/acp-15-12139-2015, 2015
Short summary
Short summary
Global warming will induce large-scale insect outbreaks in boreal forests. Our results from field and laboratory experiments, satellite observations and global-scale modelling suggest that more frequent insect outbreaks, in addition to temperature-dependent increases in VOC emissions, could result in substantial increases in biogenic SOA formation and therefore affect both aerosol direct and indirect forcing of climate at regional scales. This should be considered in future climate predictions.
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
Short summary
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.
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
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