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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-1/W1-2023
https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-145-2023
© 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-W1-2023-145-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
25 May 2023
| 25 May 2023
REAL-TIME LIDAR-INERTIAL POSITIONING AND MAPPING FOR FORESTRY AUTOMATION
T. Faitli
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, FI-02150 Espoo, Finland
T. Hakala
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, FI-02150 Espoo, Finland
H. Kaartinen
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, FI-02150 Espoo, Finland
J. Hyyppä
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, FI-02150 Espoo, Finland
Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, FI-02150 Espoo, Finland
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M. Katoh, S. Deng, Y. Takenaka, K. Cheung, K. Oono, M. Horisawa, J. Hyyppä, X. Yu, X. Liang, and Y. Wang
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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
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 119–122, https://doi.org/10.5194/isprs-archives-XLII-3-W3-119-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-119-2017, 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
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
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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
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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. 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.
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