Michael S. Watt

6.0k total citations
209 papers, 4.5k citations indexed

About

Michael S. Watt is a scholar working on Nature and Landscape Conservation, Global and Planetary Change and Ecology. According to data from OpenAlex, Michael S. Watt has authored 209 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 123 papers in Nature and Landscape Conservation, 63 papers in Global and Planetary Change and 59 papers in Ecology. Recurrent topics in Michael S. Watt's work include Forest ecology and management (96 papers), Remote Sensing and LiDAR Applications (54 papers) and Tree Root and Stability Studies (34 papers). Michael S. Watt is often cited by papers focused on Forest ecology and management (96 papers), Remote Sensing and LiDAR Applications (54 papers) and Tree Root and Stability Studies (34 papers). Michael S. Watt collaborates with scholars based in New Zealand, Australia and United States. Michael S. Watt's co-authors include Jonathan P. Dash, Grant D. Pearse, Euan G. Mason, Darren J. Kriticos, Mark O. Kimberley, Peter W. Clinton, Jean-Pierre Lasserre, Heidi S. Dungey, David J. Palmer and John R. Moore and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The Science of The Total Environment.

In The Last Decade

Michael S. Watt

202 papers receiving 4.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael S. Watt New Zealand 37 2.1k 1.4k 1.3k 1.3k 1.1k 209 4.5k
Mark O. Kimberley New Zealand 32 1.7k 0.8× 1.4k 1.0× 460 0.4× 1.2k 1.0× 686 0.6× 164 4.0k
Thomas Rötzer Germany 38 2.2k 1.0× 844 0.6× 2.0k 1.6× 3.5k 2.8× 1.1k 1.1× 108 5.8k
Alexis Achim Canada 29 1.8k 0.9× 573 0.4× 892 0.7× 1.2k 0.9× 325 0.3× 131 3.3k
Heli Peltola Finland 48 3.8k 1.8× 970 0.7× 756 0.6× 4.6k 3.6× 1.3k 1.2× 215 7.9k
Klaus von Gadow Germany 37 3.6k 1.8× 739 0.5× 1.1k 0.9× 2.6k 2.1× 458 0.4× 235 4.9k
Isabel Cañellas Spain 38 3.1k 1.5× 619 0.4× 677 0.5× 3.0k 2.4× 826 0.8× 204 4.9k
José Natalino Macedo Silva Brazil 27 2.4k 1.2× 1.2k 0.8× 666 0.5× 2.8k 2.2× 716 0.7× 83 5.0k
Raisa Mäkipää Finland 44 2.6k 1.3× 1.7k 1.2× 1.3k 1.0× 2.9k 2.3× 1.3k 1.2× 141 5.9k
Margarida Tomé Portugal 35 3.2k 1.6× 715 0.5× 1.7k 1.3× 2.5k 2.0× 517 0.5× 159 4.9k
Douglas A. Maguire United States 37 2.6k 1.3× 501 0.4× 532 0.4× 1.9k 1.5× 506 0.5× 118 3.4k

Countries citing papers authored by Michael S. Watt

Since Specialization
Citations

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

Fields of papers citing papers by Michael S. Watt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael S. Watt. 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 Michael S. Watt. The network helps show where Michael S. Watt may publish in the future.

Co-authorship network of co-authors of Michael S. Watt

This figure shows the co-authorship network connecting the top 25 collaborators of Michael S. Watt. A scholar is included among the top collaborators of Michael S. Watt 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 Michael S. Watt. Michael S. Watt 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.
Poblete, T., Michael S. Watt, Henning Buddenbaum, & Pablo J. Zarco‐Tejada. (2025). Chlorophyll content estimation in radiata pine using hyperspectral imagery: A comparison between empirical models, scaling-up algorithms, and radiative transfer inversions. Agricultural and Forest Meteorology. 362. 110402–110402. 2 indexed citations
2.
Latterini, Francesco, et al.. (2025). Soil Trafficability Maps: A Geospatial Tool for Reducing Soil Damage and Supporting Sustainable Forest Management in New Zealand. Land Degradation and Development. 36(16). 5602–5612. 3 indexed citations
3.
Latterini, Francesco, Marcin K. Dyderski, Rodolfo Picchio, et al.. (2025). Mapping Skid Trails and Evaluating Soil Disturbance From UAV ‐Based LiDAR Surveys in Mediterranean Forests. Land Degradation and Development. 37(3). 1082–1092. 1 indexed citations
4.
5.
Mohan, Midhun, Emma Asbridge, Stacey M. Trevathan‐Tackett, et al.. (2025). Eco-friendly structures for sustainable mangrove restoration. The Science of The Total Environment. 978. 179393–179393. 2 indexed citations
6.
Kimberley, Mark O. & Michael S. Watt. (2025). Allometric Equations for Estimating Carbon Stored by Individual Trees in a Radiata Pine Stand. Forests. 17(1). 61–61.
7.
Watt, Michael S., et al.. (2025). Predicting Tree-Level Diameter and Volume for Radiata Pine Using UAV LiDAR-Derived Metrics Across a National Trial Series in New Zealand. Remote Sensing. 17(8). 1456–1456. 2 indexed citations
8.
9.
Arachchige, Pavithra S. Pitumpe, M. Rondón, Michael S. Watt, et al.. (2024). Current status of mangrove conservation efforts in Qatar: A review. Regional Studies in Marine Science. 79. 103822–103822. 8 indexed citations
10.
Pearse, Grant D., et al.. (2024). Developing a forest description from remote sensing: Insights from New Zealand. SHILAP Revista de lepidopterología. 11. 100183–100183. 5 indexed citations
11.
Camarretta, Nicolò, et al.. (2024). Automatic Detection of Phytophthora pluvialis Outbreaks in Radiata Pine Plantations Using Multi-Scene, Multi-Temporal Satellite Imagery. Remote Sensing. 16(2). 338–338. 5 indexed citations
12.
Hendy, Ian, Michael S. Watt, Ruth Reef, et al.. (2024). Current Status of Remote Sensing for Studying the Impacts of Hurricanes on Mangrove Forests in the Coastal United States. Remote Sensing. 16(19). 3596–3596. 9 indexed citations
13.
Watt, Michael S., et al.. (2024). Early Detection of Myrtle Rust on Pōhutukawa Using Indices Derived from Hyperspectral and Thermal Imagery. Remote Sensing. 16(6). 1050–1050. 2 indexed citations
14.
Watt, Michael S., et al.. (2024). Use of a Consumer-Grade UAV Laser Scanner to Identify Trees and Estimate Key Tree Attributes across a Point Density Range. Forests. 15(6). 899–899. 11 indexed citations
15.
Watt, Michael S., et al.. (2023). Prediction of the severity of Dothistroma needle blight in radiata pine using plant based traits and narrow band indices derived from UAV hyperspectral imagery. Agricultural and Forest Meteorology. 330. 109294–109294. 22 indexed citations
16.
Lawrence, Judy, Anita Wreford, Paula Blackett, et al.. (2023). Climate change adaptation through an integrative lens in Aotearoa New Zealand. Journal of the Royal Society of New Zealand. 54(4). 491–522. 14 indexed citations
17.
Pearse, Grant D., et al.. (2023). Unsupervised Methodology for Large-Scale Tree Seedling Mapping in Diverse Forestry Settings Using UAV-Based RGB Imagery. Remote Sensing. 15(22). 5276–5276. 10 indexed citations
18.
Kimberley, Mark O., et al.. (2015). Tree counts from airborne LiDAR.. 60(1). 38–43. 4 indexed citations
19.
Jones, Trevor, Geoffrey M. Downes, Michael S. Watt, et al.. (2013). Effect of stem bending and soil moisture on the incidence of resin pockets in radiata pine. eCite Digital Repository (University of Tasmania). 10 indexed citations
20.
Watt, Michael S., et al.. (2010). Herbicide screening trial to control dormant wilding Pinus contorta, P. mugo and Pseudotsuga menziesii during winter.. New Zealand journal of forestry science. 40. 153–159. 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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