Jan–Peter Mund

483 total citations
26 papers, 361 citations indexed

About

Jan–Peter Mund is a scholar working on Environmental Engineering, Global and Planetary Change and Nature and Landscape Conservation. According to data from OpenAlex, Jan–Peter Mund has authored 26 papers receiving a total of 361 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Environmental Engineering, 10 papers in Global and Planetary Change and 8 papers in Nature and Landscape Conservation. Recurrent topics in Jan–Peter Mund's work include Remote Sensing and LiDAR Applications (11 papers), Forest ecology and management (6 papers) and Land Use and Ecosystem Services (5 papers). Jan–Peter Mund is often cited by papers focused on Remote Sensing and LiDAR Applications (11 papers), Forest ecology and management (6 papers) and Land Use and Ecosystem Services (5 papers). Jan–Peter Mund collaborates with scholars based in Germany, Ukraine and United Kingdom. Jan–Peter Mund's co-authors include Tanja Sanders, Klaus Greve, Jörg Szarzynski, Wilhelm Barthlott, Stefan Porembski, J. Schellberg, Nataliia Kussul, Joerg Szarzynski, Joachim Post and Sergii Skakun and has published in prestigious journals such as SHILAP Revista de lepidopterología, Forest Ecology and Management and Remote Sensing.

In The Last Decade

Jan–Peter Mund

23 papers receiving 338 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jan–Peter Mund Germany 9 189 115 108 78 72 26 361
Xuejun Ouyang China 7 121 0.6× 74 0.6× 152 1.4× 94 1.2× 43 0.6× 16 374
Přemysl Janata Czechia 8 269 1.4× 67 0.6× 178 1.6× 78 1.0× 144 2.0× 11 392
K. C. Cushman United States 12 216 1.1× 218 1.9× 137 1.3× 162 2.1× 61 0.8× 25 530
Alessandro Montaghi Italy 11 211 1.1× 139 1.2× 163 1.5× 90 1.2× 33 0.5× 19 388
I‐Kuai Hung United States 11 194 1.0× 102 0.9× 165 1.5× 95 1.2× 68 0.9× 61 429
Tauheed Ullah Khan China 13 216 1.1× 131 1.1× 223 2.1× 188 2.4× 65 0.9× 25 510
Jan Trochta Czechia 7 294 1.6× 210 1.8× 124 1.1× 93 1.2× 71 1.0× 8 453
Werner Mücke Austria 12 266 1.4× 117 1.0× 199 1.8× 75 1.0× 65 0.9× 17 423
Miłosz Mielcarek Poland 10 267 1.4× 169 1.5× 177 1.6× 92 1.2× 60 0.8× 16 387
Nataliia Rehush Switzerland 9 204 1.1× 96 0.8× 127 1.2× 71 0.9× 39 0.5× 14 282

Countries citing papers authored by Jan–Peter Mund

Since Specialization
Citations

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

Fields of papers citing papers by Jan–Peter Mund

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jan–Peter Mund. 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 Jan–Peter Mund. The network helps show where Jan–Peter Mund may publish in the future.

Co-authorship network of co-authors of Jan–Peter Mund

This figure shows the co-authorship network connecting the top 25 collaborators of Jan–Peter Mund. A scholar is included among the top collaborators of Jan–Peter Mund 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 Jan–Peter Mund. Jan–Peter Mund 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.
Mund, Jan–Peter, et al.. (2025). Digital Forest Inventory Using Fused UAV and PLS Point Cloud Data. ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences. XLVIII-M-7-2025. 299–304. 1 indexed citations
2.
Miranda, Luís, et al.. (2024). Unveiling wind-thrown trees: Detection and quantification of wind-thrown tree stems on UAV-orthomosaics based on UNet and a heuristic stem reconstruction. Forest Ecology and Management. 578. 122411–122411. 3 indexed citations
3.
Richter, Rico, et al.. (2024). Tree Instance Segmentation in Urban 3D Point Clouds Using a Coarse-to-Fine Algorithm Based on Semantic Segmentation. SHILAP Revista de lepidopterología. X-4/W5-2024. 79–86. 1 indexed citations
4.
Mund, Jan–Peter, et al.. (2024). Spatiotemporal Analysis of Land Surface Temperature in Response to Land Use and Land Cover Changes: A Remote Sensing Approach. Remote Sensing. 16(7). 1286–1286. 11 indexed citations
5.
6.
Mund, Jan–Peter, et al.. (2023). Spatiotemporal Analysis of Land Surface Temperature in Response to Land Use and Land Cover Changes: A Remote Sensing Approach. SHILAP Revista de lepidopterología. 15–15. 2 indexed citations
7.
8.
Wallor, Evelyn, et al.. (2022). Further application of using a personal laser scanner and simultaneous localization and mapping technology to estimate the log’s volume and its comparison with traditional methods. International Journal of Applied Earth Observation and Geoinformation. 109. 102779–102779. 17 indexed citations
9.
Mund, Jan–Peter, et al.. (2021). Charcoal heaps volume estimation based on unmanned aerial vehicles. Southern Forests a Journal of Forest Science. 83(4). 303–309. 2 indexed citations
10.
Mund, Jan–Peter, et al.. (2021). Detection of Windthrown Tree Stems on UAV-Orthomosaics Using U-Net Convolutional Networks. Remote Sensing. 14(1). 75–75. 14 indexed citations
11.
Mund, Jan–Peter, et al.. (2021). Advantages of 360° Virtual Forest Tours to Supplement Academic Forestry Education. GI_Forum. 1. 34–44. 3 indexed citations
12.
Görgens, Eric Bastos, et al.. (2020). Automated operational logging plan considering multi-criteria optimization. Computers and Electronics in Agriculture. 170. 105253–105253. 10 indexed citations
13.
Mund, Jan–Peter & Susanne Müller. (2019). Augmented Reality and Mobile GIS as Tools for Teaching Data-collection in the Context of Forest Inventories. GI_Forum. 1. 129–143. 4 indexed citations
14.
Mund, Jan–Peter, et al.. (2019). UAV-based Tree Height Estimation in Dense Tropical Rainforest Areas in Ecuador and Brazil. GI_Forum. 1. 47–59. 2 indexed citations
15.
Hoffmann, Monika T., et al.. (2017). Urban and Peri-Urban Forest Areas in European Cities – A Comparative Remote-Sensing Study. GI_Forum. 1. 15–26. 1 indexed citations
16.
Hartmann, Fabian, et al.. (2016). UAV Workflow Optimization for the Acquisition of High-Quality Photogrammetric Point Clouds in Forestry. GI_Forum. 4(1). 72–84. 2 indexed citations
17.
Mund, Jan–Peter, et al.. (2015). Detecting Multi-layered Forest Stands Using High Density Airborne LiDAR Data. GI_Forum. 1. 178–188. 12 indexed citations
18.
Kussul, Nataliia, Dan Mandl, Jan–Peter Mund, et al.. (2012). Interoperable Infrastructure for Flood Monitoring: SensorWeb, Grid and Cloud. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 5(6). 1740–1745. 34 indexed citations
19.
Mund, Jan–Peter. (2011). The Agricultural Sector in Cambodia: Trends, Processes and Disparities. 7 indexed citations
20.
Porembski, Stefan, Jörg Szarzynski, Jan–Peter Mund, & Wilhelm Barthlott. (1996). Biodiversity and vegetation of small‐sized inselbergs in a West African rain forest (Taı, Ivory Coast). Journal of Biogeography. 23(1). 47–55. 57 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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