John Armston

12.3k total citations · 6 hit papers
132 papers, 7.5k citations indexed

About

John Armston is a scholar working on Environmental Engineering, Ecology and Nature and Landscape Conservation. According to data from OpenAlex, John Armston has authored 132 papers receiving a total of 7.5k indexed citations (citations by other indexed papers that have themselves been cited), including 119 papers in Environmental Engineering, 75 papers in Ecology and 56 papers in Nature and Landscape Conservation. Recurrent topics in John Armston's work include Remote Sensing and LiDAR Applications (115 papers), Remote Sensing in Agriculture (68 papers) and Forest ecology and management (55 papers). John Armston is often cited by papers focused on Remote Sensing and LiDAR Applications (115 papers), Remote Sensing in Agriculture (68 papers) and Forest ecology and management (55 papers). John Armston collaborates with scholars based in United States, Australia and United Kingdom. John Armston's co-authors include Ralph Dubayah, Hao Tang, Laura Duncanson, M. A. Hofton, Mathias Disney, Kim Calders, Steven Hancock, James R. Kellner, Richard Lucas and J. B. Blair and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Trends in Ecology & Evolution.

In The Last Decade

John Armston

131 papers receiving 7.3k citations

Hit Papers

Mapping global forest canopy height through... 2014 2026 2018 2022 2020 2020 2014 2020 2020 250 500 750
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.

  1. Mapping global forest canopy height through integration of GEDI and Landsat data
    2020 789 citations Matthew C. Hansen, Hao Tang et al. Remote Sensing of Environment profile →
  2. The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography
    2020 764 citations Ralph Dubayah, J. B. Blair et al. SHILAP Revista de lepidopterología profile →
  3. Nondestructive estimates of above‐ground biomass using terrestrial laser scanning
    2014 508 citations Kim Calders, Glenn Newnham et al. Methods in Ecology and Evolution profile →
  4. Terrestrial laser scanning in forest ecology: Expanding the horizon
    2020 344 citations Kim Calders, Jennifer Adams et al. Remote Sensing of Environment profile →
  5. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine
    2020 336 citations John Armston et al. Remote Sensing of Environment profile →
  6. GEDI launches a new era of biomass inference from space
    2022 137 citations Ralph Dubayah, John Armston et al. Environmental Research Letters profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John Armston United States 42 5.5k 4.0k 3.1k 2.8k 821 132 7.5k
J. B. Blair United States 33 5.4k 1.0× 3.6k 0.9× 3.2k 1.0× 2.1k 0.8× 854 1.0× 81 6.9k
M. A. Hofton United States 35 5.0k 0.9× 3.5k 0.9× 2.8k 0.9× 2.1k 0.7× 699 0.9× 72 6.8k
M. A. Lefsky United States 34 7.2k 1.3× 5.2k 1.3× 4.7k 1.5× 3.7k 1.3× 1.6k 1.9× 70 10.2k
Mathias Disney United Kingdom 49 5.7k 1.0× 4.0k 1.0× 3.8k 1.2× 3.0k 1.1× 1.4k 1.7× 160 8.1k
Thomas Hilker United States 43 3.9k 0.7× 4.5k 1.1× 1.9k 0.6× 3.6k 1.3× 891 1.1× 83 7.4k
Geoffrey G. Parker United States 31 3.9k 0.7× 2.8k 0.7× 3.5k 1.1× 2.4k 0.8× 1.3k 1.6× 56 6.5k
Andrew T. Hudak United States 55 6.0k 1.1× 5.8k 1.4× 4.1k 1.3× 6.1k 2.2× 1.8k 2.2× 230 11.0k
Guoqing Sun United States 43 4.0k 0.7× 2.8k 0.7× 1.6k 0.5× 1.5k 0.5× 384 0.5× 180 5.5k
Yanjun Su China 43 3.1k 0.6× 2.6k 0.6× 1.4k 0.4× 1.5k 0.5× 446 0.5× 122 5.0k
Darius Culvenor Australia 32 3.7k 0.7× 3.3k 0.8× 2.4k 0.8× 2.1k 0.8× 1.0k 1.2× 54 5.8k

Countries citing papers authored by John Armston

Since Specialization
Citations

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

Fields of papers citing papers by John Armston

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Armston

This figure shows the co-authorship network connecting the top 25 collaborators of John Armston. A scholar is included among the top collaborators of John Armston 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 John Armston. John Armston 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.
Wessels, Konrad, John Armston, Laura Duncanson, et al.. (2024). Evaluation of GEDI footprint level biomass models in Southern African Savannas using airborne LiDAR and field measurements. SHILAP Revista de lepidopterología. 10. 100161–100161. 3 indexed citations
2.
Qi, Wenlu, John Armston, Atticus Stovall, et al.. (2024). Mapping large-scale pantropical forest canopy height by integrating GEDI lidar and TanDEM-X InSAR data. Remote Sensing of Environment. 318. 114534–114534. 6 indexed citations
3.
Schlund, Michael, John Armston, Martyna M. Kotowska, et al.. (2024). Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models. Remote Sensing of Environment. 313. 114384–114384. 12 indexed citations
4.
Calders, Kim, Niall Origo, Louise Terryn, et al.. (2024). Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning. Remote Sensing. 16(19). 3639–3639. 2 indexed citations
5.
Armston, John, et al.. (2024). Characterizing the structural complexity of the Earth’s forests with spaceborne lidar. Nature Communications. 15(1). 8116–8116. 18 indexed citations
6.
Babcock, Chad, John Armston, Maurizio Santoro, et al.. (2024). A geostatistical approach to enhancing national forest biomass assessments with Earth Observation to aid climate policy needs. Remote Sensing of Environment. 318. 114557–114557. 6 indexed citations
7.
Baeten, Lander, Frieke Van Coillie, Kim Calders, et al.. (2024). Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning. Forest Ecosystems. 11. 100196–100196. 2 indexed citations
8.
Calders, Kim, Hans Verbeeck, Kris Verheyen, et al.. (2023). Exploring the influence of tree species richness on vertical structure variability in young plantations using terrestrial laser scanning. Forest Ecology and Management. 554. 121662–121662. 3 indexed citations
9.
Hancock, Steven, et al.. (2023). Can ICESat-2 estimate stand-level plant structural traits? Validation of an ICESat-2 simulator. SHILAP Revista de lepidopterología. 7. 100086–100086. 4 indexed citations
10.
Duncanson, Laura, Veronika Leitold, John Armston, et al.. (2023). The effectiveness of global protected areas for climate change mitigation. Nature Communications. 14(1). 2908–2908. 82 indexed citations
11.
Wilkes, Phil, Mathias Disney, John Armston, et al.. (2023). TLS2trees : A scalable tree segmentation pipeline for TLS data. Methods in Ecology and Evolution. 14(12). 3083–3099. 19 indexed citations
12.
Thomas, Nathan, Mikhail Urbazaev, Atticus Stovall, et al.. (2023). Seasonal flooding provides limitations and opportunities for ecosystem carbon accounting from space. Environmental Research Letters. 18(8). 81002–81002. 3 indexed citations
13.
Calders, Kim, Benjamin Brede, Glenn Newnham, et al.. (2023). StrucNet: a global network for automated vegetation structure monitoring. Remote Sensing in Ecology and Conservation. 9(5). 587–598. 19 indexed citations
14.
Milenković, Milutin, Johannes Reiche, John Armston, et al.. (2022). Assessing Amazon rainforest regrowth with GEDI and ICESat-2 data. SHILAP Revista de lepidopterología. 5. 100051–100051. 25 indexed citations
15.
Demol, Miro, Hans Verbeeck, Bert Gielen, et al.. (2022). Estimating forest above‐ground biomass with terrestrial laser scanning: Current status and future directions. Methods in Ecology and Evolution. 13(8). 1628–1639. 72 indexed citations
16.
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
17.
Dubayah, Ralph, John Armston, Sean P. Healey, et al.. (2022). GEDI launches a new era of biomass inference from space. Environmental Research Letters. 17(9). 95001–95001. 137 indexed citations breakdown →
18.
Lang, Nico, Nikolai Kalischek, John Armston, et al.. (2021). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment. 268. 112760–112760. 151 indexed citations
19.
Hancock, Steven, M. A. Hofton, J. B. Blair, et al.. (2019). An open source tool to reduce geolocation uncertainty in GEDI data. AGUFM. 2019. 4 indexed citations
20.
Calders, Kim, Glenn Newnham, Andrew Burt, et al.. (2014). Nondestructive estimates of above‐ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution. 6(2). 198–208. 508 indexed citations breakdown →

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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