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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-2/W8-2024
https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-9-2024
© Author(s) 2024. 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-2-W8-2024-9-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
14 Dec 2024
| 14 Dec 2024
Advancing forest inventory: a comparative study of low-cost MLS lidar device with professional laser scanners
Mattia Balestra
Department of Agricultural, Food and Environmental Sciences (D3A), Università Politecnica delle Marche, Ancona, Italy
Carlos Cabo
Department of Mining Exploitation and Prospecting, University of Oviedo, Campus de Mieres, Mieres, Spain
Arnadi Murtiyoso
Forest Resources Management, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zurich, Switzerland
Université de Strasbourg, INSA Strasbourg, CNRS, ICube Laboratory UMR 7357, Photogrammetry and Geomatics Group, France
Alessandro Vitali
Department of Agricultural, Food and Environmental Sciences (D3A), Università Politecnica delle Marche, Ancona, Italy
Flor Alvarez-Taboada
DRACONES Research Group, Escuela de Ingeniería Agraria y Forestal, (Campus de Ponferrada), Universidad de León, Av. Astorga s/n, 24400 Ponferrada, Spain
Alejandro Cantero-Amiano
HAZI Fundazioa, Granja Modelo de Arkaute, 01192 Vitoria-Gasteiz, Spain
Rodolfo Bolaños
IMIB-CSIC, Campus de Mieres, Asturias, Spain
Diego Laino
IMIB-CSIC, Campus de Mieres, Asturias, Spain
Roberto Pierdicca
Department of Civil, Building Engineering and Architecture (DICEA), Università Politecnica delle Marche, Ancona, Italy
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Short summary
Short summary
Alpine treelines reflect the impacts of climate and land use changes on ecosystems. Using low-cost drones and deep learning, we developed a method to map treelines at fine scales across diverse environments. Our results reveal accurate detection and delineation of trees maps over 90 ha of treeline ecotones. This efficient, adaptable approach enables enhanced ecological analyses of treeline processes, aiding global efforts to assess treeline dynamics and their responses to global change.
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Short summary
Short summary
We propose a method for the automated detection of the uppermost forest lines with the aim of supporting their monitoring through a replicable mapping that can be adopted in different geographical contexts and at different scales of analysis according to the available datasets. We adopted a trend analysis of Landsat-based wetness and greenness index time series of the last 40 years, detecting an increase in forest cover along the forest line ecotone in both the Italian Alps and the Apennines.
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A. Murtiyoso and P. Grussenmeyer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 1113–1118, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1113-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1113-2023, 2023
D. Abate, A. Agapiou, K. Toumbas, A. Lampropoulos, K. Petrides, R. Pierdicca, M. Paolanti, F. Di Stefano, A. Felicetti, E. S. Malinverni, and P. Zingaretti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 3–10, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-3-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-3-2023, 2023
E. Balloni, L. Gorgoglione, M. Paolanti, A. Mancini, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 155–162, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-155-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-155-2023, 2023
F. Matrone, A. Felicetti, M. Paolanti, and R. Pierdicca
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-M-1-2023, 207–214, https://doi.org/10.5194/isprs-annals-X-M-1-2023-207-2023, https://doi.org/10.5194/isprs-annals-X-M-1-2023-207-2023, 2023
F. Di Stefano, R. Pierdicca, E. S. Malinverni, A. Corneli, and B. Naticchia
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 131–138, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-131-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-131-2023, 2023
A. H. Qureshi, W. S. Alaloul, A. Murtiyoso, S. J. Hussain, S. Saad, and V. K. Oad
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W1-2022, 215–220, https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-215-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-215-2022, 2022
A. Murtiyoso, H. Hristova, N. Rehush, and V. C. Griess
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W1-2022, 185–190, https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-185-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-185-2022, 2022
C. R. Fol, A. Murtiyoso, and V. C. Griess
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W1-2022, 95–99, https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-95-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-95-2022, 2022
Roberto Pierdicca and Marina Paolanti
Geosci. Instrum. Method. Data Syst., 11, 195–218, https://doi.org/10.5194/gi-11-195-2022, https://doi.org/10.5194/gi-11-195-2022, 2022
Short summary
Short summary
For the processing of geomatics data, artificial intelligence (AI) offers overwhelming opportunities. The integration of AI approaches in geomatics has developed into the concept of geospatial artificial intelligence (GeoAI), which is a new paradigm for geographic knowledge discovery and beyond. This contribution outlines AI-based techniques for analysing and interpreting complex geomatics data. How AI approaches have been exploited for the interpretation of geomatic data is explained.
M. Balestra, S. Chiappini, A. Vitali, E. Tonelli, F. Malandra, A. Galli, C. Urbinati, E. S. Malinverni, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 833–839, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, 2022
C. R. Fol, A. Murtiyoso, and V. C. Griess
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 989–994, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-989-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-989-2022, 2022
E. Pellis, A. Murtiyoso, A. Masiero, G. Tucci, M. Betti, and P. Grussenmeyer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 861–867, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-861-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-861-2022, 2022
D. Abate, M. Paolanti, R. Pierdicca, A. Lampropoulos, K. Toumbas, A. Agapiou, S. Vergis, E. Malinverni, K. Petrides, A. Felicetti, and P. Zingaretti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 729–736, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-729-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-729-2022, 2022
A. H. Qureshi, W. S. Alaloul, A. Murtiyoso, S. Saad, and B. Manzoor
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 141–146, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-141-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-141-2022, 2022
A. Murtiyoso, F. Matrone, M. Martini, A. Lingua, P. Grussenmeyer, and R. Pierdicca
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 317–324, https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022, 2022
E. Pellis, A. Murtiyoso, A. Masiero, G. Tucci, M. Betti, and P. Grussenmeyer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2-W1-2022, 429–434, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-429-2022, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-429-2022, 2022
A. Murtiyoso and P. Grussenmeyer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2-W1-2022, 389–393, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-389-2022, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-389-2022, 2022
A. Murtiyoso and P. Grussenmeyer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-M-1-2021, 487–491, https://doi.org/10.5194/isprs-archives-XLVI-M-1-2021-487-2021, https://doi.org/10.5194/isprs-archives-XLVI-M-1-2021-487-2021, 2021
A. Murtiyoso, D. Suwardhi, P. Grussenmeyer, W. A. Fadilah, K. N. Fauzan, S. W. Trisyanti, Brahmantara, and H. Macher
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-M-1-2021, 493–497, https://doi.org/10.5194/isprs-archives-XLVI-M-1-2021-493-2021, https://doi.org/10.5194/isprs-archives-XLVI-M-1-2021-493-2021, 2021
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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