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Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. Multispectral (MS) LiDAR systems operate on different wavelengths and have recently been revolutionizing the simultaneous acquisition of height and intensity information.
MS LiDAR data are rarely used in many projects conducted by NMCAs and other mapping organizations and companies. This is mainly due to the unclear practical potential benefits of LiDAR intensity for large-scale mapping from the perspective of mapping agencies, which necessitates dedicated research.
MS LiDAR sensors have the capability of 3D data acquisition in two or more wavelength channels. Sensor properties (e.g., wavelength, instrument size, and measurement range) are selected with respect to the intended application. For example, 532 nm (green), 1064 nm (NIR), and 1550 nm (short-wavelength infrared, SWIR) are the most commonly available/used laser wavelengths according to the literature.
A multispectral point cloud is a composite of three monochromatic laser scanners bolted together. Merging the point clouds of these three mentioned channels results in multispectral point cloud data.
While the geometric information provided by (monochromatic) LiDAR data supports the extraction of structural metrics, the spectral information provided by MS LiDAR systems enhances the accuracy of these characteristics and offers new critical insights in many application sectors.
Research tasks related to MS LiDAR data include:
radiometric calibration
3D classification in forestry or urban enviornments
material identification and detailed LULC map generation
The diverse applications of MSL spann across fields of ecology and forestry, objects and Land Use Land Cover (LULC) classification, change detection, bathymetry, topographic mapping, archaeology and geology, navigation, etc.
This research is funded by European Spatial Data Research (EuroSDR) and run in collaboration with TU Vienna and FGI.
Related publications:
Josef Taher, Eric Hyyppä, Matti Hyyppä, Klaara Salolahti, Xiaowei Yu, Leena Matikainen, Antero Kukko, Matti Lehtomäki, Harri Kaartinen, Sopitta Thurachen, Paula Litkey, Ville Luoma, Markus Holopainen, Gefei Kong, Hongchao Fan, Petri Rönnholm, Matti Vaaja, Antti Polvivaara, Samuli Junttila, Mikko Vastaranta, Stefano Puliti, Rasmus Astrup, Joel Kostensalo, Mari Myllymäki, Maksymilian Kulicki, Krzysztof Stereńczak, Raul de Paula Pires, Ruben Valbuena, Juan Pedro Carbonell-Rivera, Jesús Torralba, Yi-Chen Chen, Lukas Winiwarter, Markus Hollaus, Gottfried Mandlburger, Narges Takhtkeshha, Fabio Remondino, Maciej Lisiewicz, Bartłomiej Kraszewski, Xinlian Liang, Jianchang Chen, Eero Ahokas, Kirsi Karila, Eugeniu Vezeteu, Petri Manninen, Roope Näsi, Heikki Hyyti, Siiri Pyykkönen, Peilun Hu, Juha Hyyppä, 2026. Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 233, pp. 278-309
Takhtkeshha, N., Bocaux, L., Ruoppa, L., Remondino, F., Mandlburger, G., Kukko, A., Hyyppä, J., 2025. 3D Forest Semantic Segmentation Using Multispectral LiDAR and 3D Deep Learning. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, https://doi.org/10.1007/s41064-025-00369-4
Ruoppa, L., Oinonen, O., Taher, J., Lehtomäki, M., Takhtkeshha, N., Kukko, A., Kaartinen, H., Hyyppä, H., 2025. Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 228, pp. 694-722
Takhtkeshha, N., Mandlburger, G., Remondino, F., Hyyppä, J., 2024: Multispectral Light Detection and Ranging Technology and Applications: A Review. Sensors; 24(5):1669
Takhtkeshha, N., Bayrak, O.C., Mandlburger, G., Remondino, F., Kukko, A., Hyyppä, J., 2024: Automatic Annotation of 3D Multispectral LiDAR Data for Land Cover Classification, Proc. IGARSS 2024 - IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 8645-8649
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