World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
8239
World Ranking
7566
National Ranking
56

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
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
Xinlian Liang
Xinlian Liang Finnish Geospatial Research Institute
Xiaowei Yu
Xiaowei Yu Finnish Geospatial Research Institute
Anttoni Jaakkola
Anttoni Jaakkola Finnish Geospatial Research Institute

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Computer Science in the USA opens doors to a variety of online degree options, each with its own unique benefits and career outcomes. Many students opt for an online data science degree, which is designed for those interested in data analytics, artificial intelligence, and big data technologies. This pathway is particularly popular for its direct application to the tech and business sectors.

For individuals interested in the intersection of technology and the building industry, pursuing an accelerated construction management degree online can fast-track a management career in construction projects, leveraging digital skills in a rapidly growing field.

Business-minded learners can consider an mba online cheap option to combine tech expertise with business leadership skills, all while saving on tuition costs.

If you’re seeking flexibility or aiming for a fast track, there are diverse online master's programs—including one-year formats—that allow you to quickly advance in your chosen field.

Best Scientists Citing Eija Honkavaara

Trending Scientists