Mats Nilsson

3.9k total citations
45 papers, 2.9k citations indexed

About

Mats Nilsson is a scholar working on Environmental Engineering, Nature and Landscape Conservation and Ecology. According to data from OpenAlex, Mats Nilsson has authored 45 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Environmental Engineering, 30 papers in Nature and Landscape Conservation and 23 papers in Ecology. Recurrent topics in Mats Nilsson's work include Remote Sensing and LiDAR Applications (40 papers), Forest ecology and management (30 papers) and Remote Sensing in Agriculture (21 papers). Mats Nilsson is often cited by papers focused on Remote Sensing and LiDAR Applications (40 papers), Forest ecology and management (30 papers) and Remote Sensing in Agriculture (21 papers). Mats Nilsson collaborates with scholars based in Sweden, Finland and Türkiye. Mats Nilsson's co-authors include Håkan Olsson, Johan Holmgren, Göran Ståhl, Olle Hagner, Heather Reese, Terje Gobakken, Erik Næsset, Matti Maltamo, Erkki Tomppo and Mikael Egberth and has published in prestigious journals such as Remote Sensing of Environment, International Journal of Remote Sensing and Forest Ecology and Management.

In The Last Decade

Mats Nilsson

40 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mats Nilsson Sweden 21 2.4k 1.8k 1.3k 912 701 45 2.9k
Benoît St-Onge Canada 25 2.5k 1.0× 1.5k 0.8× 1.4k 1.1× 886 1.0× 679 1.0× 45 3.0k
Robert J. McGaughey United States 25 2.5k 1.0× 1.7k 0.9× 1.3k 1.0× 936 1.0× 1.2k 1.8× 47 3.2k
Ole Martin Bollandsås Norway 35 2.6k 1.1× 2.2k 1.2× 1.2k 0.9× 1.1k 1.2× 835 1.2× 88 3.3k
Håkan Olsson Sweden 32 3.2k 1.3× 2.2k 1.2× 1.7k 1.3× 1.2k 1.3× 906 1.3× 93 3.9k
Timo Tokola Finland 29 2.5k 1.0× 1.8k 1.0× 1.5k 1.1× 919 1.0× 713 1.0× 116 3.3k
Jenny Lovell Australia 20 1.9k 0.8× 1.3k 0.7× 1.2k 0.9× 585 0.6× 543 0.8× 38 2.3k
Rubén Valbuena United Kingdom 31 1.6k 0.6× 1.2k 0.6× 1.0k 0.8× 589 0.6× 898 1.3× 84 2.4k
Christopher W. Bater Canada 21 1.8k 0.7× 1.0k 0.6× 1.2k 0.9× 564 0.6× 656 0.9× 49 2.3k
Ilkka Korpela Finland 24 1.9k 0.8× 1.1k 0.6× 1.2k 0.9× 677 0.7× 320 0.5× 55 2.1k
Donald G. Leckie Canada 24 1.9k 0.8× 1.1k 0.6× 1.7k 1.3× 601 0.7× 990 1.4× 64 2.9k

Countries citing papers authored by Mats Nilsson

Since Specialization
Citations

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

Fields of papers citing papers by Mats Nilsson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mats Nilsson

This figure shows the co-authorship network connecting the top 25 collaborators of Mats Nilsson. A scholar is included among the top collaborators of Mats Nilsson 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 Mats Nilsson. Mats Nilsson 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.
2.
D’Amico, Giovanni, et al.. (2024). Data homogeneity impact in tree species classification based on Sentinel-2 multitemporal data case study in central Sweden. International Journal of Remote Sensing. 45(15). 5050–5075.
3.
Sertel, Elif, Cem Ünsalan, Jari Salo, et al.. (2023). Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches. Marmara University Open Access System. 2661–2664. 2 indexed citations
4.
Nyström, Kenneth, et al.. (2021). Updating of forest stand data by using recent digital photogrammetry in combination with older airborne laser scanning data. Scandinavian Journal of Forest Research. 36(5). 401–407. 5 indexed citations
5.
Wallerman, Jörgen, Kenneth Nyström, Mats Nilsson, et al.. (2020). Nation-Wide Mapping of Tree Growth using Repeated Airborne Laser Scanning. 4822–4825. 1 indexed citations
6.
Persson, Henrik, et al.. (2020). Combining TanDEM-X and Sentinel-2 for large-area species-wise prediction of forest biomass and volume. International Journal of Applied Earth Observation and Geoinformation. 96. 102275–102275. 21 indexed citations
7.
8.
Fransson, Johan E. S., Maurizio Santoro, Jörgen Wallerman, et al.. (2016). Estimation of forest stem volume using ALOS-2 PALSAR-2 satellite images. Chalmers Research (Chalmers University of Technology). 5327–5330. 4 indexed citations
9.
Olsson, Håkan, et al.. (2015). The potential of digital surface models based on aerial images for automated vegetation mapping. International Journal of Remote Sensing. 36(7). 1855–1870. 23 indexed citations
10.
Heiskanen, Janne, et al.. (2012). Histogram matching for the calibration ofkNN stem volume estimates. International Journal of Remote Sensing. 33(22). 7117–7131. 12 indexed citations
11.
Nordkvist, Karin, et al.. (2011). Combining optical satellite data and airborne laser scanner data for vegetation classification. Remote Sensing Letters. 3(5). 393–401. 18 indexed citations
12.
Reese, Heather, Mats Nilsson, & Håkan Olsson. (2009). Comparison of Resourcesat-1 AWiFS and SPOT-5 data over managed boreal forest stands. International Journal of Remote Sensing. 30(19). 4957–4978. 7 indexed citations
13.
Reese, Heather, et al.. (2003). Countrywide Estimates of Forest Variables Using Satellite Data and Field Data from the National Forest Inventory. AMBIO. 32(8). 542–542. 14 indexed citations
14.
Holmgren, Johan, Mats Nilsson, & Håkan Olsson. (2003). Estimation of Tree Height and Stem Volume on Plots Using Airborne Laser Scanning. Forest Science. 49(3). 419–428. 167 indexed citations
15.
Tomppo, Erkki, et al.. (2002). Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass. Remote Sensing of Environment. 82(1). 156–171. 105 indexed citations
16.
Reese, Heather, Mats Nilsson, Per Sandström, & Håkan Olsson. (2002). Applications using estimates of forest parameters derived from satellite and forest inventory data. Computers and Electronics in Agriculture. 37(1-3). 37–55. 114 indexed citations
17.
Nilsson, Mats. (2000). Five essays on forest raw materials use in an international perspective. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1 indexed citations
18.
Holmgren, Johan, et al.. (2000). Estimating Stem Volume and Basal Area in Forest Compartments by Combining Satellite Image Data with Field Data. Scandinavian Journal of Forest Research. 15(1). 103–111. 46 indexed citations
19.
Nilsson, Mats, et al.. (1999). Regional forest biomass and wood volume estimation using satellite data and ancillary data. Agricultural and Forest Meteorology. 98-99. 417–425. 116 indexed citations
20.
Nilsson, Mats. (1997). Estimation of forest variables using satellite image data and airborne Lidar. 85 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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