| Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows H Aasen, E Honkavaara, A Lucieer, PJ Zarco-Tejada Remote Sensing 10 (7), 1091, 2018 | 709 | 2018 |
| Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture E Honkavaara, H Saari, J Kaivosoja, I Pölönen, T Hakala, P Litkey, ... Remote Sensing 5 (10), 5006-5039, 2013 | 617 | 2013 |
| Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging O Nevalainen, E Honkavaara, S Tuominen, N Viljanen, T Hakala, X Yu, ... Remote sensing 9 (3), 185, 2017 | 542 | 2017 |
| 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 | 485 | 2015 |
| Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera T Rosnell, E Honkavaara Sensors 12 (1), 453-480, 2012 | 388 | 2012 |
| UAV in the advent of the twenties: Where we stand and what is next F Nex, C Armenakis, M Cramer, DA Cucci, M Gerke, E Honkavaara, ... ISPRS journal of photogrammetry and remote sensing 184, 215-242, 2022 | 334 | 2022 |
| 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 | 251 | 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 | 250 | 2018 |
| Comparison of the selected state-of-the-art 3D indoor scanning and point cloud generation methods VV Lehtola, H Kaartinen, A Nüchter, R Kaijaluoto, A Kukko, P Litkey, ... Remote sensing 9 (8), 796, 2017 | 228 | 2017 |
| Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks S Nezami, E Khoramshahi, O Nevalainen, I Pölönen, E Honkavaara Remote Sensing 12 (7), 1070, 2020 | 208 | 2020 |
| 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 | 198 | 2018 |
| Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements X Liang, Y Wang, J Pyörälä, M Lehtomäki, X Yu, H Kaartinen, A Kukko, ... Forest ecosystems 6 (1), 1-16, 2019 | 190 | 2019 |
| Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables K Nurminen, M Karjalainen, X Yu, J Hyyppä, E Honkavaara ISPRS Journal of photogrammetry and Remote Sensing 83, 104-115, 2013 | 173 | 2013 |
| Comparison of laser and stereo optical, SAR and InSAR point clouds from air-and space-borne sources in the retrieval of forest inventory attributes X Yu, J Hyyppä, M Karjalainen, K Nurminen, K Karila, M Vastaranta, ... Remote Sensing 7 (12), 15933-15954, 2015 | 162 | 2015 |
| Factors affecting the quality of DTM generation in forested areas H Hyyppä, X Yu, J Hyyppä, H Kaartinen, S Kaasalainen, E Honkavaara, ... International Archives of Photogrammetry, Remote Sensing and Spatial …, 2005 | 151 | 2005 |
| The use of a hand-held camera for individual tree 3D mapping in forest sample plots X Liang, A Jaakkola, Y Wang, J Hyyppä, E Honkavaara, J Liu, ... Remote Sensing 6 (7), 6587-6603, 2014 | 143 | 2014 |
| Digital airborne photogrammetry—A new tool for quantitative remote sensing?—A state-of-the-art review on radiometric aspects of digital photogrammetric images E Honkavaara, R Arbiol, L Markelin, L Martinez, M Cramer, S Bovet, ... Remote Sensing 1 (3), 577-605, 2009 | 142 | 2009 |
| Forest data collection using terrestrial image-based point clouds from a handheld camera compared to terrestrial and personal laser scanning X Liang, Y Wang, A Jaakkola, A Kukko, H Kaartinen, J Hyyppä, ... IEEE transactions on geoscience and remote sensing 53 (9), 5117-5132, 2015 | 138 | 2015 |
| 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 | 129 | 2020 |
| 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 | 125 | 2022 |
Teemu HakalaResearch Scientist, Finnish Geospatial Research InstituteVerified email at nls.fi
