| Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level R Näsi, E Honkavaara, P Lyytikäinen-Saarenmaa, M Blomqvist, P Litkey, ... Remote Sensing 7 (11), 15467-15493, 2015 | 432 | 2015 |
| Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft R Näsi, E Honkavaara, M Blomqvist, P Lyytikäinen-Saarenmaa, T Hakala, ... Urban Forestry & Urban Greening 30, 72-83, 2018 | 217 | 2018 |
| 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 N Viljanen, E Honkavaara, R Näsi, T Hakala, O Niemeläinen, J Kaivosoja Agriculture 8 (5), 70, 2018 | 210 | 2018 |
| Estimating biomass and nitrogen amount of barley and grass using UAV and aircraft based spectral and photogrammetric 3D features R Näsi, N Viljanen, J Kaivosoja, K Alhonoja, T Hakala, L Markelin, ... Remote Sensing 10 (7), 1082, 2018 | 175 | 2018 |
| Assessing biodiversity in boreal forests with UAV-based photogrammetric point clouds and hyperspectral imaging N Saarinen, M Vastaranta, R Näsi, T Rosnell, T Hakala, E Honkavaara, ... Remote Sensing 10 (2), 338, 2018 | 99 | 2018 |
| Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry RA Oliveira, R Näsi, O Niemeläinen, L Nyholm, K Alhonoja, J Kaivosoja, ... Remote Sensing of Environment 246, 111830, 2020 | 98 | 2020 |
| Direct reflectance measurements from drones: Sensor absolute radiometric calibration and system tests for forest reflectance characterization T Hakala, L Markelin, E Honkavaara, B Scott, T Theocharous, ... Sensors 18 (5), 1417, 2018 | 87 | 2018 |
| Assessment of classifiers and remote sensing features of hyperspectral imagery and stereo-photogrammetric point clouds for recognition of tree species in a forest area of high … S Tuominen, R Näsi, E Honkavaara, A Balazs, T Hakala, N Viljanen, ... Remote Sensing 10 (5), 714, 2018 | 70 | 2018 |
| Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions X Liang, A Kukko, I Balenović, N Saarinen, S Junttila, V Kankare, ... IEEE geoscience and remote sensing magazine 10 (3), 32-71, 2022 | 64 | 2022 |
| Direct reflectance transformation methodology for drone-based hyperspectral imaging J Suomalainen, RA Oliveira, T Hakala, N Koivumäki, L Markelin, R Näsi, ... Remote Sensing of Environment 266, 112691, 2021 | 52 | 2021 |
| Characterizing seedling stands using leaf-off and leaf-on photogrammetric point clouds and hyperspectral imagery acquired from unmanned aerial vehicle M Imangholiloo, N Saarinen, L Markelin, T Rosnell, R Näsi, T Hakala, ... Forests 10 (5), 415, 2019 | 47 | 2019 |
| Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment GT Miyoshi, NN Imai, AMG Tommaselli, E Honkavaara, R Näsi, ... International journal of remote sensing 39 (15-16), 4910-4930, 2018 | 42 | 2018 |
| Using multitemporal hyper-and multispectral UAV imaging for detecting bark beetle infestation on norway spruce E Honkavaara, R Näsi, R Oliveira, N Viljanen, J Suomalainen, ... The international archives of the photogrammetry, remote sensing and spatial …, 2020 | 41 | 2020 |
| Multispectral imagery provides benefits for mapping spruce tree decline due to bark beetle infestation when acquired late in the season S Junttila, R Näsi, N Koivumäki, M Imangholiloo, N Saarinen, J Raisio, ... Remote Sensing 14 (4), 909, 2022 | 40 | 2022 |
| Reference measurements in developing UAV systems for detecting pests, weeds, and diseases J Kaivosoja, J Hautsalo, J Heikkinen, L Hiltunen, P Ruuttunen, R Näsi, ... Remote sensing 13 (7), 1238, 2021 | 35 | 2021 |
| A novel tilt correction technique for irradiance sensors and spectrometers on-board unmanned aerial vehicles J Suomalainen, T Hakala, R Alves de Oliveira, L Markelin, N Viljanen, ... Remote Sensing 10 (12), 2068, 2018 | 27 | 2018 |
| Autonomous hyperspectral UAS photogrammetry for environmental monitoring applications E Honkavaara, T Hakala, L Markelin, A Jaakkola, H Saari, H Ojanen, ... ISPRS Archives, 2014 | 26 | 2014 |
| Estimating grass sward quality and quantity parameters using drone remote sensing with deep neural networks K Karila, R Alves Oliveira, J Ek, J Kaivosoja, N Koivumäki, P Korhonen, ... Remote Sensing 14 (11), 2692, 2022 | 25 | 2022 |
| UAS based tree species identification using the novel FPI based hyperspectral cameras in visible, NIR and SWIR spectral ranges R Näsi, E Honkavaara, S Tuominen, H Saari, I Pölönen, T Hakala, ... International archives of the photogrammetry, remote sensing and spatial …, 2016 | 25 | 2016 |
| A clustering framework for monitoring circadian rhythm in structural dynamics in plants from terrestrial laser scanning time series E Puttonen, M Lehtomäki, P Litkey, R Näsi, Z Feng, X Liang, S Wittke, ... Frontiers in Plant Science 10, 430216, 2019 | 20 | 2019 |
Eija HonkavaaraResearch Professor, Finnish Geospatial Research Institute, FGI, UNITE FlagshipVerified email at nls.fi
