World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
8239
World Ranking
7565
National Ranking
56

Eija Honkavaara publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Eija Honkavaara sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 183 publications — 40th percentile

40% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Eija Honkavaara D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Eija Honkavaara sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Eija Honkavaara is affiliated with the Finnish Geospatial Research Institute in Finland. Their research primarily focuses on environmental science, with a strong emphasis on ecology and environmental engineering. The scholar also works within related subfields such as plant science, aerospace engineering, and global and planetary change.

The main topics of their work include:

  • Remote Sensing and LiDAR Applications
  • Remote Sensing in Agriculture
  • Forest Ecology and Biodiversity Studies
  • Forest ecology and management
  • Smart Agriculture and AI
  • Forest Insect Ecology and Management
  • Fire effects on ecosystems

Frequent co-authors collaborating with Eija Honkavaara include:

  • Niko Koivumäki (32 collaborations)
  • Roope Näsi (29 collaborations)
  • Raquel Alves de Oliveira (25 collaborations)
  • Teemu Hakala (19 collaborations)
  • Juha Suomalainen (15 collaborations)

Their research has been published across several reputable venues, including frequent publications in:

  • The "International archives of the photogrammetry, remote sensing and spatial information sciences / International archives of the photogrammetry, remote sensing and spatial information sciences (19 papers)
  • Remote Sensing (16 papers)
  • Remote Sensing of Environment (4 papers)
  • Preprints.org (4 papers)
  • Agronomy (3 papers)

Notable recent papers authored or co-authored by Eija Honkavaara include:

  • "UAV in the advent of the twenties: Where we stand and what is next," 2022, ISPRS Journal of Photogrammetry and Remote Sensing
  • "Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop," 2021, Remote Sensing of Environment
  • "Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks," 2020, Remote Sensing
  • "Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions," 2022, IEEE Geoscience and Remote Sensing Magazine
  • "Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry," 2020, Remote Sensing of Environment

Best Publications

  • Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

    Helge Aasen;Eija Honkavaara;Arko Lucieer;Pablo J. Zarco-Tejada

  • Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture

    Eija Honkavaara;Heikki Saari;Jere Kaivosoja;Ilkka Pölönen

  • Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

    Olli Nevalainen;Eija Honkavaara;Sakari Tuominen;Niko Viljanen

  • Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level

    Roope Näsi;Eija Honkavaara;Päivi Marja Emilia Lyytikäinen-Saarenmaa;Minna Blomqvist

  • Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

    Tomi Rosnell;Eija Honkavaara

  • 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 Marja Emilia Lyytikäinen-Saarenmaa

  • 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

    Niko Viljanen;Eija Honkavaara;Roope Näsi;Teemu Hakala

  • Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods

    Ville V. Lehtola;Harri Kaartinen;Andreas Nüchter;Risto Kaijaluoto

  • 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

  • Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

    Somayeh Nezami;Ehsan Khoramshahi;Olli Nevalainen;Ilkka Pölönen

  • Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables

    Kimmo Nurminen;Mika Karjalainen;Xiaowei Yu;Juha Hyyppä

  • Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

    Xinlian Liang;Yunsheng Wang;Jiri Pyörälä;Matti Lehtomäki

  • Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

    Xiaowei Yu;Juha Hyyppä;Mika Karjalainen;Kimmo Nurminen

  • FACTORS AFFECTING THE QUALITY OF DTM GENERATION IN FORESTED AREAS

    Hannu Hyyppä;Juha Hyyppä;Harri Kaartinen;Sanna Kaasalainen

  • Forest Data Collection Using Terrestrial Image-Based Point Clouds From a Handheld Camera Compared to Terrestrial and Personal Laser Scanning

    Xinlian Liang;Yunsheng Wang;Anttoni Jaakkola;Antero Kukko

  • Digital Airborne Photogrammetry—A New Tool for Quantitative Remote Sensing?—A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images

    Eija Honkavaara;Roman Arbiol;Lauri Markelin;Lucas Martinez

  • Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop

    Shan Xu;Shan Xu;Shan Xu;Jon Atherton;Anu Riikonen;Chao Zhang

  • The Use of a Hand-Held Camera for Individual Tree 3D Mapping in Forest Sample Plots

    Xinlian Liang;Anttoni Jaakkola;Yunsheng Wang;Juha Hyyppä

  • INTEGRATION OF LASER SCANNING AND PHOTOGRAMMETRY

    Petri Rönnholm;Eija Honkavaara;Paula Litkey;Hannu Hyyppä

  • Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry

    Raquel Alves Oliveira;Roope Näsi;Oiva Niemeläinen;Laura Nyholm

  • Method for determination of stand attributes and a computer program for performing the method

    Pekka Savolainen;Heikki Luukkonen;Juha Hyyppä;Eija Honkavaara

Frequent Co-Authors

Teemu Hakala
Teemu Hakala Finnish Geospatial Research Institute
Juha Hyyppä
Juha Hyyppä Finnish Geospatial Research Institute
Markus Holopainen
Markus Holopainen University of Helsinki
Mikko Vastaranta
Mikko Vastaranta University of Eastern Finland
Juha Suomalainen
Juha Suomalainen Wageningen University & Research
Harri Kaartinen
Harri Kaartinen University of Turku
Antero Kukko
Antero Kukko Aalto University
Hannu Hyyppä
Hannu Hyyppä Aalto University
Xiaowei Yu
Xiaowei Yu Finnish Geospatial Research Institute
Xinlian Liang
Xinlian Liang Finnish Geospatial Research Institute

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