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Niko Viljanen
Researcher at Finnish Geodetic Institute
Publications - 33
Citations - 1429
Niko Viljanen is an academic researcher from Finnish Geodetic Institute. The author has contributed to research in topics: Hyperspectral imaging & Photogrammetry. The author has an hindex of 12, co-authored 33 publications receiving 993 citations. Previous affiliations of Niko Viljanen include National Land Survey of Finland & Aalto University.
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Journal ArticleDOI
Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
Olli Nevalainen,Eija Honkavaara,Sakari Tuominen,Niko Viljanen,Teemu Hakala,Xiaowei Yu,Juha Hyyppä,Heikki Saari,Ilkka Pölönen,Nilton Nobuhiro Imai,Antonio Maria Garcia Tommaselli +10 more
TL;DR: Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions, and are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.
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Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level
Roope Näsi,Eija Honkavaara,Päivi Lyytikäinen-Saarenmaa,Minna Blomqvist,Paula Litkey,Teemu Hakala,Niko Viljanen,Tuula Kantola,Topi-Mikko Tapio Tanhuanpää,Markus Holopainen +9 more
TL;DR: A new processing method for analyzing spectral characteristic for high spatial resolution photogrammetric and hyperspectral images in forested environments, as well as for identifying individual anomalous trees, which will be of a high practical value for forest health management.
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Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft
Roope Näsi,Eija Honkavaara,Minna Blomqvist,Päivi Lyytikäinen-Saarenmaa,Teemu Hakala,Niko Viljanen,Tuula Kantola,Markus Holopainen +7 more
TL;DR: In this paper, a hyperspectral camera based on a tunable Fabry-Perot interferometer was operated from a small, unmanned airborne vehicle (UAV) platform and a small Cessna-type aircraft platform.
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A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone
TL;DR: In this article, a machine learning technique for the estimation of canopy height and biomass of grass swards utilizing multispectral photogrammetric camera data was developed and assessed.
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Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features
Roope Näsi,Niko Viljanen,Jere Kaivosoja,Katja Alhonoja,Teemu Hakala,Lauri Markelin,Eija Honkavaara +6 more
TL;DR: It is concluded that the integration of spectral and high spatial resolution 3D features and radiometric calibration was necessary to optimize the accuracy of crop biomass and nitrogen estimation.