Markku Åkerblom

1.7k total citations · 1 hit paper
22 papers, 1.3k citations indexed

About

Markku Åkerblom is a scholar working on Environmental Engineering, Nature and Landscape Conservation and Ecology. According to data from OpenAlex, Markku Åkerblom has authored 22 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Environmental Engineering, 14 papers in Nature and Landscape Conservation and 9 papers in Ecology. Recurrent topics in Markku Åkerblom's work include Remote Sensing and LiDAR Applications (17 papers), Forest ecology and management (14 papers) and Plant Water Relations and Carbon Dynamics (9 papers). Markku Åkerblom is often cited by papers focused on Remote Sensing and LiDAR Applications (17 papers), Forest ecology and management (14 papers) and Plant Water Relations and Carbon Dynamics (9 papers). Markku Åkerblom collaborates with scholars based in Finland, United Kingdom and Australia. Markku Åkerblom's co-authors include Pasi Raumonen, M. Kaasalainen, Mathias Disney, P. Lewis, Sanna Kaasalainen, Harri Kaartinen, Mikko Vastaranta, Markus Holopainen, Eric Casella and Kim Calders and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and Annals of Botany.

In The Last Decade

Markku Åkerblom

21 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

2013 606 citations Pasi Raumonen, M. Kaasalainen et al. Remote Sensing profile →

Peers

Markku Åkerblom

EE

NLC

ECOLO

GPC

IS

Juan Carlos Pinilla Suárez United Kingdom

Alvaro Lau Netherlands

Phil Wilkes United Kingdom

Benjamin Brede Netherlands

Jean-François Côté Canada

Carine Klauberg United States

Philip J. Radtke United States

Markus Holopainen Finland

Topi Tanhuanpää Finland

Martin van Leeuwen United States

Juan Carlos Pinilla Suárez United Kingdom

Markku Åkerblom

1.1k ×1.0

EE

691 ×0.9

NLC

729 ×1.5

ECOLO

388 ×1.1

GPC

278 ×0.8

IS

Citations per year, relative to Markku Åkerblom Markku Åkerblom (= 1×) peers Juan Carlos Pinilla Suárez

Countries citing papers authored by Markku Åkerblom

Since Specialization

Citations

This map shows the geographic impact of Markku Åkerblom's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Markku Åkerblom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markku Åkerblom more than expected).

Fields of papers citing papers by Markku Åkerblom

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Markku Åkerblom. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Markku Åkerblom. The network helps show where Markku Åkerblom may publish in the future.

Co-authorship network of co-authors of Markku Åkerblom

This figure shows the co-authorship network connecting the top 25 collaborators of Markku Åkerblom. A scholar is included among the top collaborators of Markku Åkerblom based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Markku Åkerblom. Markku Åkerblom is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Raumonen, Pasi, et al.. (2025). Fitting Geometric Shapes to Fuzzy Point Cloud Data. Journal of Imaging. 11(1). 7–7. 2 indexed citations

2.

Raumonen, Pasi, et al.. (2025). The Effect of Surface Material on Cylinder Point Clouds. IEEE Transactions on Instrumentation and Measurement. 74. 1–12.

3.

Terryn, Louise, Kim Calders, Markku Åkerblom, et al.. (2022). Analysing individual 3D tree structure using the R package ITSMe. Methods in Ecology and Evolution. 14(1). 231–241. 29 indexed citations

4.

Liu, Jing, Longhui Li, Markku Åkerblom, et al.. (2021). Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests. Remote Sensing. 13(16). 3325–3325. 8 indexed citations

5.

Åkerblom, Markku & Pekka Kaitaniemi. (2021). Terrestrial laser scanning: a new standard of forest measuring and modelling?. Annals of Botany. 128(6). 653–662. 38 indexed citations

6.

Åkerblom, Markku. (2020). InverseTampere/TreeQSM: Version 2.4.0. Zenodo (CERN European Organization for Nuclear Research). 2 indexed citations

7.

Liu, Weiwei, Jon Atherton, Matti Mõttus, et al.. (2019). Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements. Remote Sensing of Environment. 232. 111274–111274. 51 indexed citations

8.

Kaasalainen, Sanna, Markku Åkerblom, Olli Nevalainen, Teemu Hakala, & M. Kaasalainen. (2018). Uncertainty in multispectral lidar signals caused by incidence angle effects. Interface Focus. 8(2). 20170033–20170033. 26 indexed citations

9.

Calders, Kim, Niall Origo, Andrew Burt, et al.. (2018). Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling. Remote Sensing. 10(6). 933–933. 126 indexed citations

10.

Åkerblom, Markku, Pasi Raumonen, Eric Casella, et al.. (2018). Non-intersecting leaf insertion algorithm for tree structure models. Interface Focus. 8(2). 20170045–20170045. 48 indexed citations

11.

Järvenpää, Marko, et al.. (2017). Bayes Forest: a data-intensive generator of morphological tree clones. GigaScience. 6(10). 1–13. 7 indexed citations

12.

Åkerblom, Markku, Pasi Raumonen, Raisa Mäkipää, & M. Kaasalainen. (2017). Automatic tree species recognition with quantitative structure models. Remote Sensing of Environment. 191. 1–12. 108 indexed citations

13.

Åkerblom, Markku. (2017). InverseTampere/TreeQSM: Initial release. Zenodo (CERN European Organization for Nuclear Research). 7 indexed citations

14.

Raumonen, Pasi, Eric Casella, Kim Calders, et al.. (2015). MASSIVE-SCALE TREE MODELLING FROM TLS DATA. SHILAP Revista de lepidopterología. II-3/W4. 189–196. 113 indexed citations

15.

Järvenpää, Marko, et al.. (2015). Data-based stochastic modeling of tree growth and structure formation. Silva Fennica. 50(1). 10 indexed citations

16.

Åkerblom, Markku. (2014). 3D Forest Information. Figshare. 1 indexed citations

17.

Astrup, Rasmus, Pasi Raumonen, Jari Liski, et al.. (2014). Tree Root System Characterization and Volume Estimation by Terrestrial Laser Scanning and Quantitative Structure Modeling. Forests. 5(12). 3274–3294. 26 indexed citations

18.

Raumonen, Pasi, Eric Casella, Mathias Disney, Markku Åkerblom, & M. Kaasalainen. (2013). Fast automatic method for constructing topologically and geometrically precise tree models from TLS Data. 2 indexed citations

19.

Kaasalainen, M., et al.. (2013). Bayes trees and forests: combining precise empirical and theoretical tree models. Jukuri (Natural Resources Institute Finland (Luke)). 1 indexed citations

20.

Åkerblom, Markku, Pasi Raumonen, M. Kaasalainen, Sanna Kaasalainen, & Harri Kaartinen. (2012). Comprehensive quantitative tree models from TLS data. 6507–6510. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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