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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:

Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review
Adeel Ahmad, Hammad Gilani, Sajid Rashid Ahmad
Forests (2021) Vol. 12, Iss. 7, pp. 914-914
Open Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

A brief overview and perspective of using airborne Lidar data for forest biomass estimation
Dengsheng Lu, Xiandie Jiang
International Journal of Image and Data Fusion (2024) Vol. 15, Iss. 1, pp. 1-24
Closed Access | Times Cited: 14
Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches
Muhammad Nouman Khan, Yumin Tan, Ahmad Ali Gul, et al.
Forests (2024) Vol. 15, Iss. 6, pp. 1055-1055
Open Access | Times Cited: 10
Modelling above ground biomass for a mixed-tree urban arboretum forest based on a LiDAR-derived canopy height model and field-sampled data
Jigme Thinley, Catherine Marina Pickering, Christopher E. Ndehedehe
GEOMATICA (2025), pp. 100047-100047
Open Access | Times Cited: 1
Enhancing aboveground biomass estimation in Moso bamboo forests: the role of on-year and off-year phenomena in remote sensing
Nan Li, Mianda Hu, Jingyi Xie, et al.
Frontiers in Forests and Global Change (2025) Vol. 8
Open Access | Times Cited: 1
Estimating Aboveground Biomass of Two Different Forest Types in Myanmar from Sentinel-2 Data with Machine Learning and Geostatistical Algorithms
Phyo Wai, Huiyi Su, Mingshi Li
Remote Sensing (2022) Vol. 14, Iss. 9, pp. 2146-2146
Open Access | Times Cited: 35
Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review
Bhuwan Thapa, Sarah Taylor Lovell, Jeffrey J. Wilson
Agroforestry Systems (2023) Vol. 97, Iss. 6, pp. 1097-1111
Closed Access | Times Cited: 20
Big data-based urban greenness in Chinese megalopolises and possible contribution to air quality control
Wenjie Wang, Panli Tian, Jinghua Zhang, et al.
The Science of The Total Environment (2022) Vol. 824, pp. 153834-153834
Open Access | Times Cited: 23
Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data
Li Xu, Qingtai Shu, Huyan Fu, et al.
Forests (2023) Vol. 14, Iss. 5, pp. 876-876
Open Access | Times Cited: 13
Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods
Caiwang Zheng, Amr Abd‐Elrahman, Vance M. Whitaker, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4511-4511
Open Access | Times Cited: 19
Improved annual forest cover maps in Oklahoma from analyses of PALSAR-2, Landsat, and LiDAR data sets during 2015–2021
Yuan Yao, Xiangming Xiao, Yuanwei Qin, et al.
Frontiers of Earth Science (2025)
Closed Access
Mapping individual tree crowns to extract morphological attributes in urban areas using unmanned aerial vehicle-based LiDAR and RGB data
Geonung Park, Bong-Geun Song, Kyunghun Park
Ecological Informatics (2025), pp. 103165-103165
Open Access
Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation
Min Peng, Mingrui Xu, Jialong Zhang, et al.
Research Square (Research Square) (2025)
Closed Access
Fine-scale carbon stocks mapping in the mangrove forests of Tumaco, Colombia using machine learning and remote sensing approaches
Laura Lozano-Arias, Bryan Ernesto Gallego Pérez, John Josephraj Selvaraj
Revista de Teledetección (2025), Iss. 66
Open Access
Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation
Min Peng, Mingrui Xu, Jialong Zhang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects
Clement Atzberger, Markus Immitzer, Kyle S. Hemes, et al.
Remote Sensing of Environment (2025) Vol. 327, pp. 114774-114774
Open Access
Deep learning and machine learning enable broad-scale woodland height, cover, and biomass mapping
Michael J. Campbell, Jessie F. Eastburn, Simon Brewer, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2025) Vol. 226, pp. 187-203
Closed Access
Estimation of National Forest Aboveground Biomass from Multi-Source Remotely Sensed Dataset with Machine Learning Algorithms in China
Zhi Tang, Xiaosheng Xia, Yonghua Huang, et al.
Remote Sensing (2022) Vol. 14, Iss. 21, pp. 5487-5487
Open Access | Times Cited: 16
Using vegetation and chlorophyll indices to model above ground biomass over time in an urban arboretum in subtropical queensland
Jigme Thinley, Catherine Marina Pickering, Christopher E. Ndehedehe
Remote Sensing Applications Society and Environment (2024) Vol. 34, pp. 101202-101202
Open Access | Times Cited: 3
Mapping Forest Aboveground Biomass with MODIS and Fengyun-3C VIRR Imageries in Yunnan Province, Southwest China Using Linear Regression, K-Nearest Neighbor and Random Forest
Huafang Chen, Zhihao Qin, Deli Zhai, et al.
Remote Sensing (2022) Vol. 14, Iss. 21, pp. 5456-5456
Open Access | Times Cited: 14
Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data
Chao Zhang, Tongtong Song, Runhe Shi, et al.
Remote Sensing (2023) Vol. 15, Iss. 6, pp. 1575-1575
Open Access | Times Cited: 8
Utilizing LiDAR to quantify aboveground tree biomass within an urban university
Maggi Schick, Robert Griffin, Emil Cherrington, et al.
Urban forestry & urban greening (2023) Vol. 89, pp. 128098-128098
Open Access | Times Cited: 8
Improved Crop Biomass Algorithm with Piecewise Function (iCBA-PF) for Maize Using Multi-Source UAV Data
Lin Meng, Dameng Yin, Minghan Cheng, et al.
Drones (2023) Vol. 7, Iss. 4, pp. 254-254
Open Access | Times Cited: 7
Estimation of the Aboveground Biomass of Forests in Complex Mountainous Areas Using Regression Kriging
Yining Luo, Lihui Yan, Zhongfa Zhou, et al.
Forests (2024) Vol. 15, Iss. 10, pp. 1734-1734
Open Access | Times Cited: 2
Land use and land cover changes in Notwane watershed, Botswana, using extreme gradient boost (XGBoost) machine learning algorithm
James Magidi, Tsitsi Bangira, Matlhogonolo Kelepile, et al.
African Geographical Review (2024), pp. 1-21
Open Access | Times Cited: 2
Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango (Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe
Bester Tawona Mudereri, Elfatih M. Abdel‐Rahman, Shepard Ndlela, et al.
Sustainability (2022) Vol. 14, Iss. 10, pp. 5741-5741
Open Access | Times Cited: 7
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