Erik Næsset

22.1k total citations · 5 hit papers
302 papers, 17.1k citations indexed

About

Erik Næsset is a scholar working on Environmental Engineering, Nature and Landscape Conservation and Insect Science. According to data from OpenAlex, Erik Næsset has authored 302 papers receiving a total of 17.1k indexed citations (citations by other indexed papers that have themselves been cited), including 277 papers in Environmental Engineering, 233 papers in Nature and Landscape Conservation and 125 papers in Insect Science. Recurrent topics in Erik Næsset's work include Remote Sensing and LiDAR Applications (270 papers), Forest ecology and management (232 papers) and Forest Ecology and Biodiversity Studies (125 papers). Erik Næsset is often cited by papers focused on Remote Sensing and LiDAR Applications (270 papers), Forest ecology and management (232 papers) and Forest Ecology and Biodiversity Studies (125 papers). Erik Næsset collaborates with scholars based in Norway, United States and Sweden. Erik Næsset's co-authors include Terje Gobakken, Ole Martin Bollandsås, Hans Ole Ørka, Svein Solberg, Ronald E. McRoberts, Liviu Theodor Ene, Göran Ståhl, Timothy G. Grégoire, Ross Nelson and Matti Maltamo and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Remote Sensing of Environment.

In The Last Decade

Erik Næsset

295 papers receiving 16.0k citations

Hit Papers

Predicting forest stand characteristics with airborne sca... 1997 2026 2006 2016 2002 2012 1997 2012 2023 250 500 750 1000

Peers

Erik Næsset
Juha Hyyppä Finland
M. A. Lefsky United States
Ralph Dubayah United States
Andrew T. Hudak United States
Mathias Disney United Kingdom
Xiaowei Yu Finland
Erik Næsset
Citations per year, relative to Erik Næsset Erik Næsset (= 1×) peers Terje Gobakken

Countries citing papers authored by Erik Næsset

Since Specialization
Citations

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

Fields of papers citing papers by Erik Næsset

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Erik Næsset

This figure shows the co-authorship network connecting the top 25 collaborators of Erik Næsset. A scholar is included among the top collaborators of Erik Næsset 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 Erik Næsset. Erik Næsset is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Saarela, Svetlana, Matti Maltamo, Petteri Packalén, et al.. (2024). Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference. Remote Sensing of Environment. 311. 114249–114249. 8 indexed citations
2.
Solberg, Svein, et al.. (2024). Biomass Change Estimated by TanDEM-X Interferometry and GEDI in a Tanzanian Forest. Remote Sensing. 16(5). 861–861. 1 indexed citations
3.
Saarela, Svetlana, Lauri Korhonen, Zhiqiang Yang, et al.. (2023). Three-phase hierarchical model-based and hybrid inference. MethodsX. 11. 102321–102321. 7 indexed citations
4.
Gobakken, Terje, et al.. (2023). Detecting the presence of natural forests using airborne laser scanning data. Forest Ecosystems. 10. 100146–100146. 1 indexed citations
5.
Næsset, Erik, et al.. (2023). Detecting the presence of standing dead trees using airborne laser scanning and optical data. Scandinavian Journal of Forest Research. 38(4). 208–220. 3 indexed citations
6.
Gobakken, Terje, et al.. (2023). Accuracy assessment of the nationwide forest attribute map of Norway constructed by using airborne laser scanning data and field data from the national forest inventory. Scandinavian Journal of Forest Research. 38(1-2). 9–22. 1 indexed citations
7.
Næsset, Erik, Terje Gobakken, Gunnar Austrheim, et al.. (2023). Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference. Remote Sensing. 15(14). 3508–3508.
8.
Ørka, Hans Ole, et al.. (2022). A framework for a forest ecological base map – An example from Norway. Ecological Indicators. 136. 108636–108636. 17 indexed citations
9.
Gobakken, Terje, et al.. (2022). Fine-Spatial Boreal–Alpine Single-Tree Albedo Measured by UAV: Experiences and Challenges. Remote Sensing. 14(6). 1482–1482. 3 indexed citations
10.
Dalponte, Michele, et al.. (2022). Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data. Remote Sensing. 14(8). 1892–1892. 4 indexed citations
11.
Saarela, Svetlana, Sören Holm, Sean P. Healey, et al.. (2022). Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation. Remote Sensing of Environment. 278. 113074–113074. 27 indexed citations
12.
Bollandsås, Ole Martin, et al.. (2021). Relationships between single-tree mountain birch summertime albedo and vegetation properties. Agricultural and Forest Meteorology. 307. 108470–108470. 16 indexed citations
13.
Mohammadi, J., Shaban Shataee, & Erik Næsset. (2020). Modeling tree species diversity by combining ALS data and digital aerial photogrammetry. SHILAP Revista de lepidopterología. 2. 100011–100011. 8 indexed citations
14.
McRoberts, Ronald E., Stephen V. Stehman, Greg C. Liknes, et al.. (2018). The effects of imperfect reference data on remote sensing-assisted estimators of land cover class proportions. ISPRS Journal of Photogrammetry and Remote Sensing. 142. 292–300. 63 indexed citations
15.
Dalponte, Michele, Liviu Theodor Ene, Terje Gobakken, Erik Næsset, & Damiano Gianelle. (2018). Predicting Selected Forest Stand Characteristics with Multispectral ALS Data. Remote Sensing. 10(4). 586–586. 32 indexed citations
16.
Ene, Liviu Theodor, Erik Næsset, Terje Gobakken, et al.. (2016). Large-scale estimation of aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data. Remote Sensing of Environment. 186. 626–636. 28 indexed citations
17.
Hansen, Endre, Terje Gobakken, & Erik Næsset. (2015). Effects of Pulse Density on Digital Terrain Models and Canopy Metrics Using Airborne Laser Scanning in a Tropical Rainforest. Remote Sensing. 7(7). 8453–8468. 37 indexed citations
18.
Hauglin, Marius, Terje Gobakken, Rasmus Astrup, Liviu Theodor Ene, & Erik Næsset. (2014). Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data. Forests. 5(3). 384–403. 40 indexed citations
19.
Bollandsås, Ole Martin, et al.. (2014). Automatic Detection of Small Single Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning. Remote Sensing. 6(10). 10152–10170. 11 indexed citations
20.
Magnussen, Steen, Erik Næsset, Terje Gobakken, & Gordon W. Frazer. (2011). A fine-scale model for area-based predictions of tree-size-related attributes derived from LiDAR canopy heights. Scandinavian Journal of Forest Research. 27(3). 312–322. 36 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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