Colin M. Beier

2.1k total citations
61 papers, 1.4k citations indexed

About

Colin M. Beier is a scholar working on Global and Planetary Change, Ecology and Environmental Engineering. According to data from OpenAlex, Colin M. Beier has authored 61 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Global and Planetary Change, 21 papers in Ecology and 19 papers in Environmental Engineering. Recurrent topics in Colin M. Beier's work include Remote Sensing and LiDAR Applications (18 papers), Remote Sensing in Agriculture (11 papers) and Plant Water Relations and Carbon Dynamics (11 papers). Colin M. Beier is often cited by papers focused on Remote Sensing and LiDAR Applications (18 papers), Remote Sensing in Agriculture (11 papers) and Plant Water Relations and Carbon Dynamics (11 papers). Colin M. Beier collaborates with scholars based in United States, Canada and United Kingdom. Colin M. Beier's co-authors include F. Stuart Chapin, Timothy J. Sullivan, Jungho Im, Manqi Li, Gregory B. Lawrence, Daniel A. Bishop, Jesse Caputo, Trista Patterson, Glenn P. Juday and Martin Dovčiak and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and PLoS ONE.

In The Last Decade

Colin M. Beier

59 papers receiving 1.3k citations

Peers

Colin M. Beier

GPC

ECOLO

NLC

Muyi Kang China

Laura S. Borma Brazil

Yanzheng Yang China

Marcelo Rezende Italy

Julia E. M. S. Nabel Germany

Georg Leitinger Austria

Seyed Mohammad Moein Sadeghi Iran

Paulo Maurı́cio Lima de Alencastro Graça Brazil

Yelena Finegold Italy

Muyi Kang China

Colin M. Beier

603 ×0.9

GPC

404 ×1.0

ECOLO

433 ×1.1

NLC

240 ×0.7

165 ×0.8

Citations per year, relative to Colin M. Beier Colin M. Beier (= 1×) peers Muyi Kang

Countries citing papers authored by Colin M. Beier

Since Specialization

Citations

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

Fields of papers citing papers by Colin M. Beier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Colin M. Beier. 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 Colin M. Beier. The network helps show where Colin M. Beier may publish in the future.

Co-authorship network of co-authors of Colin M. Beier

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

All Works

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20 of 20 papers shown

Mahoney, M. J., et al.. (2025). New allometric models for the USA create a shift in forest carbon estimation, modeling, and mapping. Forest Ecology and Management. 589. 122751–122751. 2 indexed citations

Domke, Grant M., et al.. (2025). From pixels to parcels: Flexible, practical small-area uncertainty estimation for spatial averages obtained from aboveground biomass maps. Remote Sensing of Environment. 330. 114951–114951. 1 indexed citations

Jane, Stephen F., Mario Montesdeoca, Patrick McHale, et al.. (2025). Soil Mercury Accumulation Delays Fish Recovery from Atmospheric Deposition Declines. Environmental Science & Technology. 59(25). 12656–12666.

Han, Tao, et al.. (2024). Estimating merchantable and non-merchantable wood volume in slash walls using terrestrial and airborne LiDAR. Forest Ecology and Management. 569. 122211–122211.

Beier, Colin M., et al.. (2024). Declining trends in canopy disturbance across reserve forest landscapes of the northeastern US. Forest Ecology and Management. 578. 122463–122463. 1 indexed citations

Mahoney, M. J., et al.. (2023). Mapping historical forest biomass for stock-change assessments at parcel to landscape scales. Forest Ecology and Management. 546. 121348–121348. 7 indexed citations

Lawrence, Gregory B., Ivan J. Fernandez, Scott W. Bailey, et al.. (2023). Forming regional soil carbon networks to support effective climate change solutions. Soil Science Society of America Journal. 87(4). 755–766. 3 indexed citations

Vukomanovic, Jelena, Lindsey S. Smart, Jennifer Koch, et al.. (2023). Translating stakeholder narratives for participatory modeling in landscape ecology. Landscape Ecology. 38(10). 2453–2474. 5 indexed citations

Beier, Colin M., et al.. (2023). Foliar chemical composition and respiration rates of sugar maple (Acer saccharum) and American beech (Fagus grandifolia) trees across a gradient of soil acidification. Biogeochemistry. 162(3). 327–341. 2 indexed citations

10.

Mahoney, M. J., et al.. (2022). Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes. GIScience & Remote Sensing. 59(1). 1266–1280. 8 indexed citations

11.

Tamiminia, Haifa, et al.. (2022). Mapping Two Decades of New York State Forest Aboveground Biomass Change Using Remote Sensing. Remote Sensing. 14(16). 4097–4097. 5 indexed citations

12.

Mahoney, M. J., et al.. (2022). terrainr: An R package for creating immersive virtual environments. The Journal of Open Source Software. 7(69). 4060–4060. 6 indexed citations

13.

Tamiminia, Haifa, et al.. (2022). Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis. Geocarto International. 37(26). 12763–12791. 12 indexed citations

14.

Mahoney, M. J., et al.. (2022). Classification and mapping of low-statured shrubland cover types in post-agricultural landscapes of the US Northeast. International Journal of Remote Sensing. 43(19-24). 7117–7138. 8 indexed citations

15.

Tamiminia, Haifa, et al.. (2021). Comparison of Machine and Deep Learning Methods to Estimate Shrub Willow Biomass from UAS Imagery. Canadian Journal of Remote Sensing. 47(2). 209–227. 15 indexed citations

16.

McDonnell, Todd C., Timothy J. Sullivan, & Colin M. Beier. (2018). Influence of Climate on Long-Term Recovery of Adirondack Mountain Lakewater Chemistry from Atmospheric Deposition of Sulfur and Nitrogen. 22(1). 4. 2 indexed citations

17.

Wason, Jay, Martin Dovčiak, Colin M. Beier, & John J. Battles. (2017). Tree growth is more sensitive than species distributions to recent changes in climate and acidic deposition in the northeastern United States. Journal of Applied Ecology. 54(6). 1648–1657. 20 indexed citations

18.

Caputo, Jesse, Colin M. Beier, Timothy J. Sullivan, & Gregory B. Lawrence. (2016). Modeled effects of soil acidification on long-term ecological and economic outcomes for managed forests in the Adirondack region (USA). The Science of The Total Environment. 565. 401–411. 30 indexed citations

19.

Bishop, Daniel A. & Colin M. Beier. (2013). Assessing Uncertainty in High-Resolution Spatial Climate Data across the US Northeast. PLoS ONE. 8(8). e70260–e70260. 25 indexed citations

20.

Stager, J. Curt, et al.. (2009). Historical Patterns and Effects of Changes In Adirondack Climates Since the Early 20th Century. 15(2). 5. 14 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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