Markus Holopainen

9.3k total citations · 3 hit papers
160 papers, 6.8k citations indexed

About

Markus Holopainen is a scholar working on Environmental Engineering, Nature and Landscape Conservation and Insect Science. According to data from OpenAlex, Markus Holopainen has authored 160 papers receiving a total of 6.8k indexed citations (citations by other indexed papers that have themselves been cited), including 136 papers in Environmental Engineering, 103 papers in Nature and Landscape Conservation and 86 papers in Insect Science. Recurrent topics in Markus Holopainen's work include Remote Sensing and LiDAR Applications (136 papers), Forest ecology and management (103 papers) and Forest Ecology and Biodiversity Studies (86 papers). Markus Holopainen is often cited by papers focused on Remote Sensing and LiDAR Applications (136 papers), Forest ecology and management (103 papers) and Forest Ecology and Biodiversity Studies (86 papers). Markus Holopainen collaborates with scholars based in Finland, Canada and United States. Markus Holopainen's co-authors include Juha Hyyppä, Mikko Vastaranta, Xiaowei Yu, Ville Kankare, Harri Kaartinen, Xinlian Liang, Antero Kukko, Hannu Hyyppä, Ninni Saarinen and Sanna Kaasalainen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Markus Holopainen

154 papers receiving 6.5k 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.

Terrestrial laser scanning in forest inventories

2016 627 citations Xinlian Liang, Ville Kankare et al. ISPRS Journal of Photogrammetry and Remote Sensing profile →
Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

2013 606 citations Sanna Kaasalainen, Harri Kaartinen et al. Remote Sensing profile →
An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning

2012 400 citations Harri Kaartinen, Juha Hyyppä et al. Remote Sensing profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Markus Holopainen Finland	38	5.8k	3.8k	2.4k	2.4k	1.2k	160	6.8k
Mikko Vastaranta Finland	41	7.2k 1.2×	4.7k 1.3×	2.9k 1.2×	2.9k 1.2×	1.5k 1.2×	175	8.2k
Xiaowei Yu Finland	46	7.2k 1.2×	4.4k 1.2×	2.6k 1.1×	3.0k 1.3×	1000 0.8×	102	7.7k
Hannu Hyyppä Finland	45	5.9k 1.0×	3.0k 0.8×	2.5k 1.0×	2.0k 0.8×	1.1k 0.9×	192	7.4k
Kim Calders Belgium	38	4.3k 0.8×	3.3k 0.9×	1.9k 0.8×	1.4k 0.6×	1.4k 1.1×	121	5.4k
Xinlian Liang Finland	40	4.9k 0.8×	2.8k 0.7×	1.4k 0.6×	1.8k 0.8×	647 0.5×	123	5.5k
Harri Kaartinen Finland	51	7.8k 1.3×	3.6k 1.0×	2.7k 1.1×	2.4k 1.0×	1.1k 0.9×	191	9.2k
Terje Gobakken Norway	56	8.8k 1.5×	6.4k 1.7×	4.5k 1.8×	3.4k 1.4×	2.3k 1.9×	243	10.5k
Sorin Popescu United States	38	5.2k 0.9×	3.1k 0.8×	3.1k 1.3×	1.6k 0.7×	1.5k 1.2×	104	6.3k
Johan Holmgren Sweden	31	4.4k 0.8×	3.2k 0.8×	1.5k 0.6×	2.0k 0.9×	537 0.4×	69	4.6k
Mathias Disney United Kingdom	49	5.7k 1.0×	3.8k 1.0×	4.0k 1.6×	1.4k 0.6×	3.0k 2.5×	160	8.1k

Countries citing papers authored by Markus Holopainen

Since Specialization

Citations

This map shows the geographic impact of Markus Holopainen'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 Markus Holopainen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Holopainen more than expected).

Fields of papers citing papers by Markus Holopainen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Markus Holopainen. 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 Markus Holopainen. The network helps show where Markus Holopainen may publish in the future.

Co-authorship network of co-authors of Markus Holopainen

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Holopainen. A scholar is included among the top collaborators of Markus Holopainen 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 Markus Holopainen. Markus Holopainen 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

Yrttimaa, Tuomas, Samuli Junttila, Juha Hyyppä, et al.. (2024). Quantifying architectural uniqueness of Scots pine trees using terrestrial laser scanning: toward individual tree fingerprinting. Forestry An International Journal of Forest Research.

Yrttimaa, Tuomas, Ville Kankare, Ville Luoma, et al.. (2023). A method for identifying and segmenting branches of Scots pine (Pinus sylvestris L.) trees using terrestrial laser scanning. Forestry An International Journal of Forest Research. 97(4). 531–545. 1 indexed citations

Tanhuanpää, Topi, Mikko Vastaranta, Tuomas Yrttimaa, et al.. (2023). Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds. Remote Sensing. 15(2). 382–382. 3 indexed citations

Lehtomäki, Matti, Xiaowei Yu, Antero Kukko, et al.. (2023). Individual tree segmentation and species classification using high-density close-range multispectral laser scanning data. SHILAP Revista de lepidopterología. 9. 100039–100039. 30 indexed citations

Luoma, Ville, et al.. (2023). A New Approach for Feeding Multispectral Imagery into Convolutional Neural Networks Improved Classification of Seedlings. Remote Sensing. 15(21). 5233–5233. 2 indexed citations

Yrttimaa, Tuomas, Ville Kankare, Mikko Vastaranta, et al.. (2022). Assessing Structural Complexity of Individual Scots Pine Trees by Comparing Terrestrial Laser Scanning and Photogrammetric Point Clouds. Forests. 13(8). 1305–1305. 4 indexed citations

Junttila, Samuli, Roope Näsi, Niko Koivumäki, et al.. (2022). Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season. Remote Sensing. 14(4). 909–909. 28 indexed citations

Luoma, Ville, Tuomas Yrttimaa, Ville Kankare, et al.. (2021). Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning. Forests. 12(7). 835–835. 16 indexed citations

Yrttimaa, Tuomas, Ville Luoma, Ninni Saarinen, et al.. (2020). Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning. Remote Sensing. 12(17). 2672–2672. 18 indexed citations

10.

Yrttimaa, Tuomas, Ninni Saarinen, Ville Kankare, et al.. (2020). Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands. ISPRS International Journal of Geo-Information. 9(5). 309–309. 15 indexed citations

11.

Yrttimaa, Tuomas, Ninni Saarinen, Ville Kankare, et al.. (2020). Performance of terrestrial laser scanning to characterize managed Scots pine (Pinus sylvestris L.) stands is dependent on forest structural variation. ISPRS Journal of Photogrammetry and Remote Sensing. 168. 277–287. 24 indexed citations

12.

Saarinen, Ninni, et al.. (2020). Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands. Remote Sensing. 12(20). 3328–3328. 10 indexed citations

13.

Saarinen, Ninni, Lauri Markelin, Tomi Rosnell, et al.. (2019). Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle. Forests. 10(5). 415–415. 38 indexed citations

14.

Saarinen, Ninni, Ville Kankare, Jiri Pyörälä, et al.. (2019). Assessing the Effects of Sample Size on Parametrizing a Taper Curve Equation and the Resultant Stem-Volume Estimates. Forests. 10(10). 848–848. 12 indexed citations

15.

Yrttimaa, Tuomas, Ninni Saarinen, Ville Kankare, et al.. (2019). Investigating the Feasibility of Multi-Scan Terrestrial Laser Scanning to Characterize Tree Communities in Southern Boreal Forests. Remote Sensing. 11(12). 1423–1423. 30 indexed citations

16.

Yrttimaa, Tuomas, Ninni Saarinen, Ville Luoma, et al.. (2019). Detecting and characterizing downed dead wood using terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing. 151. 76–90. 30 indexed citations

17.

Vastaranta, Mikko, Xiaowei Yu, Ville Luoma, et al.. (2018). Aboveground forest biomass derived using multiple dates of WorldView-2 stereo-imagery: quantifying the improvement in estimation accuracy. International Journal of Remote Sensing. 39(23). 8766–8783. 19 indexed citations

18.

Junttila, Samuli, Mikko Vastaranta, Riikka Linnakoski, et al.. (2018). Can Leaf Water Content Be Estimated Using Multispectral Terrestrial Laser Scanning? A Case Study With Norway Spruce Seedlings. Frontiers in Plant Science. 9. 299–299. 26 indexed citations

19.

Karjalainen, Mika, Sanna Kaasalainen, Juha Hyyppä, et al.. (2010). SAR Satellite Images and Terrestrial Laser Scanning in Forest Damages Mapping in Finland. ESASP. 686. 38. 5 indexed citations

20.

Hyyppä, Juha, Anttoni Jaakkola, Hannu Hyyppä, et al.. (2009). Map updating and change detection using vehicle-based laser scanning. 1–6. 34 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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