Murray Woods

2.5k total citations
42 papers, 1.6k citations indexed

About

Murray Woods is a scholar working on Environmental Engineering, Nature and Landscape Conservation and Insect Science. According to data from OpenAlex, Murray Woods has authored 42 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Environmental Engineering, 32 papers in Nature and Landscape Conservation and 21 papers in Insect Science. Recurrent topics in Murray Woods's work include Forest ecology and management (32 papers), Remote Sensing and LiDAR Applications (32 papers) and Forest Ecology and Biodiversity Studies (21 papers). Murray Woods is often cited by papers focused on Forest ecology and management (32 papers), Remote Sensing and LiDAR Applications (32 papers) and Forest Ecology and Biodiversity Studies (21 papers). Murray Woods collaborates with scholars based in Canada, United States and Finland. Murray Woods's co-authors include Doug Pitt, Joanne C. White, Nicholas C. Coops, Michael A. Wulder, Mikko Vastaranta, Kevin Lim, Paul Treitz, Margaret Penner, Andrés Varhola and Bruce D. Cook and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Remote Sensing of Environment.

In The Last Decade

Murray Woods

42 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Murray Woods Canada 20 1.4k 1.0k 664 579 399 42 1.6k
Juho Pitkänen Finland 19 1.4k 1.0× 1.1k 1.0× 705 1.1× 582 1.0× 350 0.9× 29 1.6k
Markus Holopainen Finland 20 1.4k 1.0× 877 0.8× 538 0.8× 631 1.1× 284 0.7× 47 1.6k
Carine Klauberg United States 21 1.2k 0.9× 690 0.7× 421 0.6× 588 1.0× 351 0.9× 49 1.5k
Kevin Lim Canada 10 1.6k 1.2× 1.2k 1.1× 729 1.1× 707 1.2× 307 0.8× 13 1.8k
Jari Vauhkonen Finland 25 1.9k 1.4× 1.5k 1.4× 890 1.3× 754 1.3× 469 1.2× 70 2.1k
António Ferraz United States 19 1.1k 0.8× 744 0.7× 353 0.5× 596 1.0× 350 0.9× 40 1.4k
Phil Wilkes United Kingdom 22 1.4k 1.0× 1.0k 1.0× 418 0.6× 692 1.2× 537 1.3× 41 1.8k
Juan Carlos Pinilla Suárez United Kingdom 20 1.1k 0.8× 691 0.7× 278 0.4× 729 1.3× 388 1.0× 74 1.5k
Eduardo González‐Ferreiro Spain 22 1.0k 0.8× 564 0.5× 309 0.5× 479 0.8× 397 1.0× 35 1.1k
Cédric Vega France 20 1.1k 0.8× 671 0.6× 327 0.5× 734 1.3× 222 0.6× 38 1.4k

Countries citing papers authored by Murray Woods

Since Specialization
Citations

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

Fields of papers citing papers by Murray Woods

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Murray Woods

This figure shows the co-authorship network connecting the top 25 collaborators of Murray Woods. A scholar is included among the top collaborators of Murray Woods 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 Murray Woods. Murray Woods 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.
Goodbody, Tristan R.H., Nicholas C. Coops, Martin Queinnec, et al.. (2023). sgsR: a structurally guided sampling toolbox for LiDAR-based forest inventories. Forestry An International Journal of Forest Research. 96(4). 411–424. 16 indexed citations
2.
Penner, Margaret, Joanne C. White, & Murray Woods. (2023). Automated characterization of forest canopy vertical layering for predicting forest inventory attributes by layer using airborne LiDAR data. Forestry An International Journal of Forest Research. 97(1). 59–75. 10 indexed citations
3.
Penner, Margaret, et al.. (2023). Assessing Site Productivity via Remote Sensing—Age-Independent Site Index Estimation in Even-Aged Forests. Forests. 14(8). 1541–1541. 3 indexed citations
4.
Tompalski, Piotr, Nicholas C. Coops, Joanne C. White, et al.. (2021). Estimating Changes in Forest Attributes and Enhancing Growth Projections: a Review of Existing Approaches and Future Directions Using Airborne 3D Point Cloud Data. Current Forestry Reports. 7(1). 1–24. 58 indexed citations
5.
Tompalski, Piotr, Nicholas C. Coops, Joanne C. White, et al.. (2021). Publisher Correction: Estimating Changes in Forest Attributes and Enhancing Growth Projections: a Review of Existing Approaches and Future Directions Using Airborne 3D Point Cloud Data. Current Forestry Reports. 7(1). 25–30. 10 indexed citations
6.
White, Joanne C., et al.. (2020). Evaluating the capacity of single photon lidar for terrain characterization under a range of forest conditions. Remote Sensing of Environment. 252. 112169–112169. 26 indexed citations
7.
8.
Venier, Lisa, Tom Swystun, Marc J. Mazerolle, et al.. (2019). Modelling vegetation understory cover using LiDAR metrics. PLoS ONE. 14(11). e0220096–e0220096. 28 indexed citations
9.
Li, Jili, Baoxin Hu, & Murray Woods. (2015). A Two-Level Approach for Species Identification of Coniferous Trees in Central Ontario Forests Based on Multispectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(4). 1487–1497. 3 indexed citations
10.
Penner, Margaret, Murray Woods, & Douglas G. Pitt. (2015). A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario. Forests. 6(11). 4034–4054. 29 indexed citations
11.
Pitt, Doug, et al.. (2014). Parametric vs. nonparametric LiDAR models for operational forest inventory in boreal Ontario. Canadian Journal of Remote Sensing. 39(5). 426–443. 71 indexed citations
12.
13.
Woods, Murray, et al.. (2011). Operational implementation of a LiDAR inventory in Boreal Ontario. The Forestry Chronicle. 87(4). 512–528. 122 indexed citations
14.
Penner, Margaret, et al.. (2008). Validation of empirical yield curves for natural-origin stands in boreal Ontario. The Forestry Chronicle. 84(5). 704–717. 28 indexed citations
15.
Sharma, Mahadev, John Parton, Murray Woods, et al.. (2008). Ontario's forest growth and yield modelling program: Advances resulting from the Forestry Research Partnership. The Forestry Chronicle. 84(5). 694–703. 30 indexed citations
16.
Woods, Murray, Kevin Lim, & Paul Treitz. (2008). Predicting forest stand variables from LiDAR data in the Great Lakes St. Lawrence forest of Ontario. The Forestry Chronicle. 84(6). 827–839. 84 indexed citations
17.
18.
Morneault, Andrée E., et al.. (2006). Red Oak Research and Demonstration Area in Phelps Township, North Bay, Ontario-2004 to 2005. 43. 1 indexed citations
19.
Vanderwel, Mark C., John P. Caspersen, & Murray Woods. (2006). Snag dynamics in partially harvested and unmanaged northern hardwood forests. Canadian Journal of Forest Research. 36(11). 2769–2779. 54 indexed citations
20.
Dey, Daniel C., et al.. (1996). Validation of NE-TWIGS for tolerant hardwood stands in Ontario. Psychological Assessment. 33(130). 180–194. 7 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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