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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLI-B7
https://doi.org/10.5194/isprs-archives-XLI-B7-77-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/isprs-archives-XLI-B7-77-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
17 Jun 2016
GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS
E. Honkavaara
Dep.of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Masala, Kirkkonummi, Finland
T. Hakala
Dep.of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Masala, Kirkkonummi, Finland
O. Nevalainen
Dep.of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Masala, Kirkkonummi, Finland
N. Viljanen
Dep.of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Masala, Kirkkonummi, Finland
T. Rosnell
Dep.of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Masala, Kirkkonummi, Finland
E. Khoramshahi
Dep.of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Masala, Kirkkonummi, Finland
R. Näsi
Dep.of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Masala, Kirkkonummi, Finland
R. Oliveira
UNESP, University of São Paulo State, Presidente Prudente - SP, Brazil
A. Tommaselli
UNESP, University of São Paulo State, Presidente Prudente - SP, Brazil
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Short summary
Short summary
Cities aim for carbon neutrality and seek to understand urban vegetation's role as a carbon sink. Direct measurements are challenging, so models are used to estimate the urban carbon cycle. We evaluated model performance at estimating carbon sequestration in lawns, park trees, and urban forests in Helsinki, Finland. Models captured seasonal and annual variations well. Trees had higher sequestration rates than lawns, and irrigation often enhanced carbon sinks.
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 1073–1080, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1073-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1073-2023, 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
T. A. C. Garcia, A. M. G. Tommaselli, L. F. Castanheiro, and M. B. Campos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 155–160, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-155-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-155-2023, 2023
A. M. G. Tommaselli, T. A. C. Garcia, L. F. Castanheiro, M. B. Campos, and G. H. Santos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 503–509, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-503-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-503-2023, 2023
Yao Gao, Eleanor J. Burke, Sarah E. Chadburn, Maarit Raivonen, Mika Aurela, Lawrence B. Flanagan, Krzysztof Fortuniak, Elyn Humphreys, Annalea Lohila, Tingting Li, Tiina Markkanen, Olli Nevalainen, Mats B. Nilsson, Włodzimierz Pawlak, Aki Tsuruta, Huiyi Yang, and Tuula Aalto
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-229, https://doi.org/10.5194/bg-2022-229, 2022
Manuscript not accepted for further review
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We coupled a process-based peatland CH4 emission model HIMMELI with a state-of-art land surface model JULES. The performance of the coupled model was evaluated at six northern wetland sites. The coupled model is considered to be more appropriate in simulating wetland CH4 emission. In order to improve the simulated CH4 emission, the model requires better representation of the peat soil carbon and hydrologic processes in JULES and the methane production and transportation processes in HIMMELI.
Maiju Linkosalmi, Juha-Pekka Tuovinen, Olli Nevalainen, Mikko Peltoniemi, Cemal M. Taniş, Ali N. Arslan, Juuso Rainne, Annalea Lohila, Tuomas Laurila, and Mika Aurela
Biogeosciences, 19, 4747–4765, https://doi.org/10.5194/bg-19-4747-2022, https://doi.org/10.5194/bg-19-4747-2022, 2022
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Vegetation greenness was monitored with digital cameras in three northern peatlands during five growing seasons. The greenness index derived from the images was highest at the most nutrient-rich site. Greenness indicated the main phases of phenology and correlated with CO2 uptake, though this was mainly related to the common seasonal cycle. The cameras and Sentinel-2 satellite showed consistent results, but more frequent satellite data are needed for reliable detection of phenological phases.
L. R. Porto, N. N. Imai, A. M. G. Tommaselli, and L. Salvador Neto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 17–22, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-17-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-17-2022, 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
L. F. Castanheiro, A. M. G. Tommaselli, M. V. Machado, G. H. Santos, I. S. Norberto, and T. T. Reis
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 181–187, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-181-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-181-2022, 2022
Olli Nevalainen, Olli Niemitalo, Istem Fer, Antti Juntunen, Tuomas Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, Liisa Kulmala, Åsa Stam, Otto Kuusela, Stephanie Gerin, Toni Viskari, Julius Vira, Jari Hyväluoma, Juha-Pekka Tuovinen, Annalea Lohila, Tuomas Laurila, Jussi Heinonsalo, Tuula Aalto, Iivari Kunttu, and Jari Liski
Geosci. Instrum. Method. Data Syst., 11, 93–109, https://doi.org/10.5194/gi-11-93-2022, https://doi.org/10.5194/gi-11-93-2022, 2022
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Better monitoring of soil carbon sequestration is needed to understand the best carbon farming practices in different soils and climate conditions. We, the Field Observatory Network (FiON), have therefore established a methodology for monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, and modeling. To disseminate our work, we built a website called the Field Observatory (fieldobservatory.org).
L. E. Christovam, M. H. Shimabukuro, M. L. B. T. Galo, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 823–828, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-823-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-823-2021, 2021
L. F. Castanheiro, A. M. G. Tommaselli, M. B. Campos, and A. Berveglieri
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 81–87, https://doi.org/10.5194/isprs-annals-V-1-2021-81-2021, https://doi.org/10.5194/isprs-annals-V-1-2021-81-2021, 2021
Laura Heimsch, Annalea Lohila, Juha-Pekka Tuovinen, Henriikka Vekuri, Jussi Heinonsalo, Olli Nevalainen, Mika Korkiakoski, Jari Liski, Tuomas Laurila, and Liisa Kulmala
Biogeosciences, 18, 3467–3483, https://doi.org/10.5194/bg-18-3467-2021, https://doi.org/10.5194/bg-18-3467-2021, 2021
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CO2 and H2O fluxes were measured at a newly established eddy covariance site in southern Finland for 2 years from 2018 to 2020. This agricultural grassland site focuses on the conversion from intensive towards more sustainable agricultural management. The first summer experienced prolonged dry periods, and notably larger fluxes were observed in the second summer. The field acted as a net carbon sink during both study years.
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.
L. F. Castanheiro, A. M. G. Tommaselli, M. B. Campos, A. Berveglieri, and G. Santos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 7–12, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-7-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-7-2020, 2020
G. T. Miyoshi, N. N. Imai, A. M. G. Tommaselli, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 49–54, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-49-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-49-2020, 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
A. M. G. Tommaselli, A. Berveglieri, N. N. Imai, G. H. Santos, E. A. S. Moriya, F. S. Y. Watanabe, and L. Salvador Neto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 389–396, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-389-2020, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-389-2020, 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
C. Sothe, L. E. C. la Rosa, C. M. de Almeida, A. Gonsamo, M. B. Schimalski, J. D. B. Castro, R. Q. Feitosa, M. Dalponte, C. L. Lima, V. Liesenberg, G. T. Miyoshi, and A. M. G. Tommaselli
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 193–199, https://doi.org/10.5194/isprs-annals-V-3-2020-193-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-193-2020, 2020
A. Berveglieri, A. M. G. Tommaselli, and L. D. Santos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W18, 7–12, https://doi.org/10.5194/isprs-archives-XLII-2-W18-7-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W18-7-2019, 2019
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
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
N. N. Imai, A. M. G. Tommaselli, A. Berveglieri, and E. A. S. Moriya
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 371–377, https://doi.org/10.5194/isprs-archives-XLII-2-W13-371-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-371-2019, 2019
M. V. Machado, A. M. G. Tommaselli, V. M. Tachibana, R. P. Martins-Neto, and M. B. Campos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 449–454, https://doi.org/10.5194/isprs-archives-XLII-2-W13-449-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-449-2019, 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
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
A. M. G. Tommaselli and F. M. Torres
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 711–715, https://doi.org/10.5194/isprs-archives-XLI-B1-711-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-711-2016, 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. Berveglieri and A. M. G. Tommaselli
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3-W4, 45–50, https://doi.org/10.5194/isprs-archives-XL-3-W4-45-2016, https://doi.org/10.5194/isprs-archives-XL-3-W4-45-2016, 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
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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.
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.
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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