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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.

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Showing 15 citing articles:

High-resolution forest canopy cover estimation in ecodiverse landscape using machine learning and Google Earth Engine: Validity and reliability assessment
Hamdi A. Zurqani
Remote Sensing Applications Society and Environment (2023) Vol. 33, pp. 101095-101095
Closed Access | Times Cited: 14
A data-driven, cloud-based approach for forest aboveground biomass mapping using GEDI and other earth observation data: an ecoregion-specific Investigation across the state of Alabama, USA
Janaki Sandamali, Lana L. Narine
Geocarto International (2025) Vol. 40, Iss. 1
Open Access
Assessing the contribution of mango orchards to carbon sequestration using field derived data and satellite remote sensing techniques in Shibganj, Bangladesh
Md. Asaduzzaman, M. Golam Mahboob, Sharmin Sultana, et al.
Smart Agricultural Technology (2025), pp. 100931-100931
Open Access
Ormancılıkta makine öğrenmesi kullanımı
Remzi Eker, Kamber Can Alkiş, Zennure Uçar, et al.
Turkish Journal of Forestry | Türkiye Ormancılık Dergisi (2023), pp. 150-177
Open Access | Times Cited: 6
Modeling and Mapping of Aboveground Biomass and Carbon Stock Using Sentinel-2 Imagery in Chure Region, Nepal
Ananta Poudel, Him Lal Shrestha, Niraj Mahat, et al.
International Journal of Forestry Research (2023) Vol. 2023, pp. 1-12
Open Access | Times Cited: 4
Machine learning algorithms for building height estimations using ICESat-2/ATLAS and Airborne LiDAR data
Müge AĞCA, Aslıhan YÜCEL, Efdal Kaya, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 6, pp. 5123-5134
Closed Access | Times Cited: 1
Machine learning for modeling forest canopy height and cover from multi-sensor data in Northwestern Ethiopia
Zerihun Chere, Worku Zewdie, Dereje Biru
Environmental Monitoring and Assessment (2023) Vol. 195, Iss. 12
Closed Access | Times Cited: 3
Assessing the potential impact of climate change on Kobus megaceros in South Sudan: a combination of geostatistical and species distribution modelling
Gai Alier, Rodrigue Idohou, Guillaume Hounsou-Dindin, et al.
Modeling Earth Systems and Environment (2023) Vol. 10, Iss. 2, pp. 1531-1542
Closed Access | Times Cited: 2
An automated approach for developing a regional-scale 1-m forest canopy cover dataset using machine learning and Google Earth Engine cloud computing platform
Hamdi A. Zurqani
Software Impacts (2023) Vol. 19, pp. 100607-100607
Open Access | Times Cited: 2
Modelling and Estimation of Above Ground Biomass and Carbon Stock of Pinus Roxburghii Dominated Forest Using Sentinel-2 Imagery in Shreenagar Hill, Nepal
Sagar Bashyal, Jeetendra Gautam, Bibek Subedi, et al.
(2024)
Open Access
Continuous mapping of forest canopy height using ICESat-2 data and a weighted kernel integration of multi-temporal multi-source remote sensing data aided by Google Earth Engine
Jalal Mansouri, Mohsen A. Jafari, Alireza Taheri Dehkordi
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 37, pp. 49757-49779
Closed Access
Continuous Mapping of Forest Canopy Height using ICESat-2 Data and a Weighted Kernel Integration of Multi-Temporal Multi-Source Remote Sensing Data aided by Google Earth Engine
Jalal Mansouri, Mohsen A. Jafari, Alireza Taheri Dehkordi
Research Square (Research Square) (2024)
Open Access
Advancing forest aboveground biomass mapping by integrating GEDI with other Earth Observation data using a cloud computing platform: A case study of Alabama, United States
Janaki Sandamali, Lana L. Narine
EarthArXiv (California Digital Library) (2024)
Open Access
A Novel Workflow for Mapping Forest Canopy Height by Synergizing ICESat-2 and Multi-Sensor Data
Linghui Guo, Yang Zhang, Muchao Xu, et al.
Forests (2024) Vol. 15, Iss. 12, pp. 2139-2139
Open Access
Forest Canopy Height Mapping Based on Multi-source Remote Sensing Data
Zhenpeng TIAN, Wei Zhou, Jingyi YUAN, et al.
Chinese Journal of Space Science (2023) Vol. 43, Iss. 6, pp. 1176-1176
Open Access
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