Items where Author is "Coops, Nicholas C."
Up a level
Help
This graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
Help
A word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
M
Murray, B. A., Coops, N. C., White, J. C., Dick, A., Barbeito, I., & Ragab, A. (2025). Individual tree species prediction using airborne laser scanning data and derived point-cloud metrics within a dual-stream deep learning approach. International Journal of Applied Earth Observation and Geoinformation, 144, 104877 (14 pages). Available
Murray, B., Coops, N. C., White, J. C., Dick, A., & Ragab, A. (2025). Tree species proportion prediction using airborne laser scanning and Sentinel-2 data within a deep learning based dual-stream data fusion approach. International Journal of Remote Sensing, 1-29. External link
Murray, B. A., Coops, N. C., Winiwarter, L., White, J. C., Dick, A., Barbeito, I., & Ragab, A. (2024). Estimating tree species composition from airborne laser scanning data using point-based deep learning models. ISPRS Journal of Photogrammetry and Remote Sensing, 207, 282-297. External link
S
Seely, H., Coops, N. C., White, J. M., Montwé, D., & Ragab, A. (2026). Addressing the small data problem in forestry: self-supervised learning for aboveground biomass estimation. Forestry An International Journal of Forest Research, 99(2), 19 pages. External link
Seely, H., Coops, N. C., White, J. C., Montwé, D., & Ragab, A. (2025). Forest aboveground biomass estimation using deep learning data fusion of ALS, multispectral, and topographic data. International Journal of Remote Sensing, 1-39. External link
Seely, H., Coops, N. C., White, J. C., Montwé, D., Winiwarter, L., & Ragab, A. (2023). Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest. Science of Remote Sensing, 8, 100110 (17 pages). Available