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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume X-G-2025
https://doi.org/10.5194/isprs-annals-X-G-2025-551-2025
© Author(s) 2025. 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-annals-X-G-2025-551-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
11 Jul 2025
| 11 Jul 2025
Comparative Analysis of Ultra-Wideband and Mobile Laser Scanning Systems for Mapping Forest Trees under A Forest Canopy
Zuoya Liu
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150, Espoo, Finland
Harri Kaartinen
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150, Espoo, Finland
Teemu Hakala
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150, Espoo, Finland
Heikki Hyyti
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150, Espoo, Finland
Antero Kukko
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150, Espoo, Finland
Department of Built Environment, School of Engineering, Aalto University, P.O. Box 11000, FI-00076, Aalto, Finland
Juha Hyyppa
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150, Espoo, Finland
Department of Built Environment, School of Engineering, Aalto University, P.O. Box 11000, FI-00076, Aalto, Finland
Ruizhi Chen
School of Data Science, The Chinese University of Hong Kong, Shenzhen, China
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Tamás Faitli, Heikki Hyyti, Juha Hyyppä, Antero Kukko, and Harri Kaartinen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W4-2025, 37–42, https://doi.org/10.5194/isprs-archives-XLVIII-1-W4-2025-37-2025, https://doi.org/10.5194/isprs-archives-XLVIII-1-W4-2025-37-2025, 2025
Leena Leppänen, Antero Kukko, Aleksi Rimali, Aku Riihelä, and Priit Tisler
EGUsphere, https://doi.org/10.5194/egusphere-2025-2059, https://doi.org/10.5194/egusphere-2025-2059, 2025
This preprint is open for discussion and under review for Geoscientific Instrumentation, Methods and Data Systems (GI).
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We present field measurements collected during the FINNARP 2022 expedition at the Aboa station, located in Dronning Maud Land, Antarctica. Field observations were carried out weekly at the automatic weather station site as well as at selected overpass locations of two satellites. The measurements included continuous meteorological observations from the weather station, detailed snow pit profiles, ground-based and drone-based radiation measurements, and snow surface roughness with laser scanners.
Shannon M. Hibbard, Gordon R. Osinski, Etienne Godin, Chimira Andres, Antero Kukko, Shawn Chartrand, Anna Grau Galofre, A. Mark Jellinek, and Wendy Boucher
The Cryosphere, 19, 1695–1716, https://doi.org/10.5194/tc-19-1695-2025, https://doi.org/10.5194/tc-19-1695-2025, 2025
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This study investigates enigmatic ring forms found on Axel Heiberg Island (Umingmat Nunaat) in Nunavut, Canada. These ring forms comprised a series of ridges and troughs creating individual rings or brain-like patterns. We aim to identify how they form and assess the past climate conditions necessary for their formation. We use surface and subsurface observations and comparisons to other periglacial and glacial ring forms to infer a formation mechanism and propose a glacial origin.
Letícia F. Castanheiro, Antonio M. G. Tommaselli, Heikki Hyyti, and Antero Kukko
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 57–62, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-57-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-57-2024, 2024
Väinö Karjalainen, Niko Koivumäki, Teemu Hakala, Anand George, Jesse Muhojoki, Eric Hyyppa, Juha Suomalainen, and Eija Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-2024, 167–172, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-167-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-167-2024, 2024
Mariana Batista Campos, Leticia Ferrari Castanheiro, Dipal Shah, Yunsheng Wang, Antero Kukko, and Eetu Puttonen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-2024, 43–50, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-43-2024, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-43-2024, 2024
A.-M. Raita-Hakola, S. Rahkonen, J. Suomalainen, L. Markelin, R. Oliveira, T. Hakala, N. Koivumäki, E. Honkavaara, and I. Pölönen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1771–1778, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1771-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1771-2023, 2023
R. A. Oliveira, R. Näsi, P. Korhonen, A. Mustonen, O. Niemeläinen, N. Koivumäki, T. Hakala, J. Suomalainen, J. Kaivosoja, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1861–1866, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1861-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1861-2023, 2023
V. Karjalainen, T. Hakala, A. George, N. Koivumäki, J. Suomalainen, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 597–603, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-597-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-597-2023, 2023
L. F. Castanheiro, A. M. G. Tommaselli, T. A. C. Garcia, M. B. Campos, and A. Kukko
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 71–77, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-71-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-71-2023, 2023
T. Faitli, T. Hakala, H. Kaartinen, J. Hyyppä, and A. Kukko
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 145–150, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-145-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-145-2023, 2023
V. V. Lehtola, H. Hyyti, and T. Malkamäki
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W1-2022, 137–143, https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-137-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-137-2022, 2022
L. Zhu, E. Hattula, J. Raninen, and J. Hyyppä
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 561–568, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-561-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-561-2022, 2022
J. Suomalainen, R. A. Oliveira, T. Hakala, N. Koivumäki, L. Markelin, R. Näsi, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 67–72, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-67-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-67-2022, 2022
P. Rönnholm, S. Wittke, M. Ingman, P. Putkiranta, H. Kauhanen, H. Kaartinen, and M. T. Vaaja
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 633–639, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-633-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-633-2022, 2022
C. Jiang, Y. Chen, B. Xu, J. Jia, H. Sun, Z. He, T. Wang, and J. Hyyppä
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-3-W1-2022, 61–66, https://doi.org/10.5194/isprs-archives-XLVI-3-W1-2022-61-2022, https://doi.org/10.5194/isprs-archives-XLVI-3-W1-2022-61-2022, 2022
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
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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.
J.-P. Virtanen, A. Julin, H. Handolin, T. Rantanen, M. Maksimainen, J. Hyyppä, and H. Hyyppä
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4-W1-2020, 159–165, https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-159-2020, https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-159-2020, 2020
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
M. Campos, P. Litkey, Y. Wang, Y. Chen, H. Hyyti, J. Hyyppä, and E. Puttonen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 27–31, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-27-2020, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-27-2020, 2020
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. Kukko, H. Kaartinen, G. Osinski, and J. Hyyppä
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 749–756, https://doi.org/10.5194/isprs-annals-V-2-2020-749-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-749-2020, 2020
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
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
Anna Grau Galofre, A. Mark Jellinek, Gordon R. Osinski, Michael Zanetti, and Antero Kukko
The Cryosphere, 12, 1461–1478, https://doi.org/10.5194/tc-12-1461-2018, https://doi.org/10.5194/tc-12-1461-2018, 2018
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Water accumulated at the base of ice sheets is the main driver of glacier acceleration and loss of ice mass in Arctic regions. Previously glaciated landscapes sculpted by this water carry information about how ice sheets collapse and ultimately disappear. The search for these landscapes took us to the high Arctic, to explore channels that formed under kilometers of ice during the last ice age. In this work we describe how subglacial channels look and how they helped to drain an ice sheet.
Eliisa S. Lotsari, Mikel Calle, Gerardo Benito, Antero Kukko, Harri Kaartinen, Juha Hyyppä, Hannu Hyyppä, and Petteri Alho
Earth Surf. Dynam., 6, 163–185, https://doi.org/10.5194/esurf-6-163-2018, https://doi.org/10.5194/esurf-6-163-2018, 2018
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This paper analyses the timing of topographical changes of a gravel bed ephemeral river channel during consecutive and moderate- and low-magnitude floods by applying a morphodynamic model calibrated with pre- and post-event surveys using RTK-GPS and mobile laser scanning. The channel acted as a braided river during lower flows but as a meandering river during higher flows. The channel changes can be greater during the long-lasting receding phase than during the rising phase of the floods.
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. Junttila, M. Vastaranta, R. Linnakoski, J. Sugano, H. Kaartinen, A. Kukko, M. Holopainen, H. Hyyppä, and J. Hyyppä
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 81–85, https://doi.org/10.5194/isprs-archives-XLII-3-W3-81-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-81-2017, 2017
M. Katoh, S. Deng, Y. Takenaka, K. Cheung, K. Oono, M. Horisawa, J. Hyyppä, X. Yu, X. Liang, and Y. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 95–100, https://doi.org/10.5194/isprs-archives-XLII-3-W3-95-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-95-2017, 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
L. Matikainen, K. Karila, J. Hyyppä, E. Puttonen, P. Litkey, and E. Ahokas
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
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
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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
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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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