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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-1/W2-2023
https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1861-2023
© Author(s) 2023. 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-XLVIII-1-W2-2023-1861-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
14 Dec 2023
| 14 Dec 2023
HYPERSPECTRAL UAS IMAGERY FOR GRASS SWARDS BIOMASS AND NITROGEN ESTIMATION
R. A. Oliveira
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute of National Land Survey of Finland, Vuorimehentie, 02150, Espoo, Finland
R. Näsi
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute of National Land Survey of Finland, Vuorimehentie, 02150, Espoo, Finland
P. Korhonen
Natural Resources Institute Finland (Luke), Finland
A. Mustonen
Natural Resources Institute Finland (Luke), Finland
O. Niemeläinen
Natural Resources Institute Finland (Luke), Finland
N. Koivumäki
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute of National Land Survey of Finland, Vuorimehentie, 02150, Espoo, Finland
T. Hakala
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute of National Land Survey of Finland, Vuorimehentie, 02150, Espoo, Finland
J. Suomalainen
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute of National Land Survey of Finland, Vuorimehentie, 02150, Espoo, Finland
J. Kaivosoja
Natural Resources Institute Finland (Luke), Finland
E. Honkavaara
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute of National Land Survey of Finland, Vuorimehentie, 02150, Espoo, Finland
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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.
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J. I. Peltoniemi, M. Gritsevich, J. Markkanen, T. Hakala, J. Suomalainen, N. Zubko, O. Wilkman, and K. Muinonen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 375–382, https://doi.org/10.5194/isprs-annals-V-1-2020-375-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-375-2020, 2020
A. M. G. Tommaselli, M. B. Campos, L. F. Castanheiro, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W18, 167–171, https://doi.org/10.5194/isprs-archives-XLII-2-W18-167-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W18-167-2019, 2019
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
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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
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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
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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.
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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
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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
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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
H. Saari, A. Akujärvi, C. Holmlund, H. Ojanen, J. Kaivosoja, A. Nissinen, and O. Niemeläinen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 165–170, https://doi.org/10.5194/isprs-archives-XLII-3-W3-165-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-165-2017, 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.
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Email: isprs-sg@isprs.org