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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models
Benjamin T. Fraser, Russell G. Congalton
Remote Sensing (2021) Vol. 13, Iss. 23, pp. 4873-4873
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review
André Duarte, Nuno Borralho, Pedro Cabral, et al.
Forests (2022) Vol. 13, Iss. 6, pp. 911-911
Open Access | Times Cited: 76
Towards operational UAV-based forest health monitoring: Species identification and crown condition assessment by means of deep learning
Simon Ecke, Florian Stehr, Julian Frey, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108785-108785
Open Access | Times Cited: 12
Assessing ecological connectivity in the Serra do Cando and Serra do Candán area of Galicia: A multitemporal classification and least-cost path modelling approach
Carlos Peco-Costas, Carolina Acuña-Alonso, Mario García-Ontiyuelo, et al.
Ecological Informatics (2025), pp. 103049-103049
Open Access | Times Cited: 1
Machine learning assisted remote forestry health assessment: a comprehensive state of the art review
Juan Sebastián Estrada, Andrés Fuentes, Pedro Reszka, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 20
Automated Parts-Based Model for Recognizing Human–Object Interactions from Aerial Imagery with Fully Convolutional Network
Yazeed Yasin Ghadi, Manahil Waheed, Tamara Al Shloul, et al.
Remote Sensing (2022) Vol. 14, Iss. 6, pp. 1492-1492
Open Access | Times Cited: 21
Precision Detection and Assessment of Ash Death and Decline Caused by the Emerald Ash Borer Using Drones and Deep Learning
Sruthi Keerthi Valicharla, Xin Li, Jennifer Greenleaf, et al.
Plants (2023) Vol. 12, Iss. 4, pp. 798-798
Open Access | Times Cited: 13
Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes
Christine L. Bunyon, Benjamin T. Fraser, Amanda McQuaid, et al.
Remote Sensing (2023) Vol. 15, Iss. 11, pp. 2839-2839
Open Access | Times Cited: 12
Integrating UAV LiDAR and multispectral data to assess forest status and map disturbance severity in a West African forest patch
Chima Iheaturu, Samuel Hepner, Jonathan L. Batchelor, et al.
Ecological Informatics (2024) Vol. 84, pp. 102876-102876
Open Access | Times Cited: 4
Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and Göttinger Forest
Julia Anwander, Melanie Brandmeier, Sebastian Paczkowski, et al.
Remote Sensing (2024) Vol. 16, Iss. 3, pp. 561-561
Open Access | Times Cited: 3
Where is the Eastern Larch Beetle? An Exploration of Different Detection Methods in Northern Wisconsin
Holly Francart, Amanda M. McGraw, Joseph Knight, et al.
Forests (2025) Vol. 16, Iss. 3, pp. 403-403
Open Access
Preliminary Machine Learning-Based Classification of Ink Disease in Chestnut Orchards Using High-Resolution Multispectral Imagery from Unmanned Aerial Vehicles: A Comparison of Vegetation Indices and Classifiers
Lorenzo Arcidiaco, Roberto Danti, Manuela Corongiu, et al.
Forests (2025) Vol. 16, Iss. 5, pp. 754-754
Open Access
UAV-Based Characterization of Tree-Attributes and Multispectral Indices in an Uneven-Aged Mixed Conifer-Broadleaf Forest
Eduardo Daniel Vivar-Vivar, Marin Pompa-García, José Alexis Martínez-Rivas, et al.
Remote Sensing (2022) Vol. 14, Iss. 12, pp. 2775-2775
Open Access | Times Cited: 13
UAV-assisted seeding and monitoring of reforestation sites: a review
I. Stamatopoulos, Tinh Cong Le, Fügen Daver
Australian Forestry (2024) Vol. 87, Iss. 2, pp. 90-98
Open Access | Times Cited: 2
A Survey of Changes in Grasslands within the Tonle Sap Lake Landscape: 2004–2023
Monysocheata Chea, Benjamin T. Fraser, Sonsak Nay, et al.
(2024)
Open Access | Times Cited: 2
Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning
Améline Vallet, Stéphane Dupuy, Matthieu Verlynde, et al.
Scientific Data (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 2
Monitoring ink disease epidemics in chestnut and cork oak forests in central Italy with remote sensing
Alessandro Sebastiani, Matteo Bertozzi, Andrea Vannini, et al.
Remote Sensing Applications Society and Environment (2024) Vol. 36, pp. 101329-101329
Open Access | Times Cited: 2
RPAS-Based forest plantation health monitoring: an overview of recent progress
Sally Deborah Pereira da Silva, Lúcio de Paula Amaral, Roberta Aparecida Fantinel, et al.
International Journal of Remote Sensing (2024) Vol. 45, Iss. 24, pp. 9131-9161
Closed Access | Times Cited: 2
Exploring forest changes in an Ips typographus L. outbreak area: insights from multi-temporal multispectral UAS remote sensing
Madeleine Östersund, Eija Honkavaara, Raquel Alves de Oliveira, et al.
European Journal of Forest Research (2024) Vol. 143, Iss. 6, pp. 1871-1892
Open Access | Times Cited: 2
Species-specific machine learning models for UAV-based forest health monitoring: Revealing the importance of the BNDVI
Simon Ecke, Florian Stehr, Jan Dempewolf, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 135, pp. 104257-104257
Open Access | Times Cited: 2
A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning
Lei Zuo, Shan Gao, Yachao Li, et al.
Remote Sensing (2022) Vol. 14, Iss. 6, pp. 1304-1304
Open Access | Times Cited: 8
Evaluating Predictive Models of Tree Foliar Moisture Content for Application to Multispectral UAS Data: A Laboratory Study
Lauren E. Lad, Wade T. Tinkham, Aaron M. Sparks, et al.
Remote Sensing (2023) Vol. 15, Iss. 24, pp. 5703-5703
Open Access | Times Cited: 4
Detection of oak decline using radiative transfer modelling and machine learning from multispectral and thermal RPAS imagery
A. Hornero, Pablo J. Zarco‐Tejada, I. Marengo, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 127, pp. 103679-103679
Open Access | Times Cited: 1
A Survey of Changes in Grasslands within the Tonle Sap Lake Landscape from 2004 to 2023
Monysocheata Chea, Benjamin T. Fraser, Sonsak Nay, et al.
Diversity (2024) Vol. 16, Iss. 8, pp. 448-448
Open Access | Times Cited: 1
Toward an enhanced characterization of seedling stands using remote sensing
Mohammad Imangholiloo
Dissertationes Forestales (2024) Vol. 2024
Open Access | Times Cited: 1
Estimation of Damaged Regions by the Bark Beetle in a Mexican Forest Using UAV Images and Deep Learning
Gildardo Godinez-Garrido, Juan-Carlos González-Islas, Angelina González-Rosas, et al.
Sustainability (2024) Vol. 16, Iss. 23, pp. 10731-10731
Open Access | Times Cited: 1
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