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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:

Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm
Chunhua Qian, Hequn Qiang, Feng Wang, et al.
Remote Sensing (2021) Vol. 13, Iss. 24, pp. 5030-5030
Open Access | Times Cited: 17

Showing 17 citing articles:

Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects
Onisimo Mutanga, Anita Masenyama, Mbulisi Sibanda
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 198, pp. 297-309
Closed Access | Times Cited: 81
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: 9
Aboveground biomass estimation in forests with random forest and Monte Carlo-based uncertainty analysis
Zizhao Li, Shoudong Bi, Shuang Hao, et al.
Ecological Indicators (2022) Vol. 142, pp. 109246-109246
Open Access | Times Cited: 31
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
Leveraging Deep Learning Models for Targeted Aboveground Biomass Estimation in Specific Regions of Interest
Selvin Samuel Arumai Shiney, Ramachandran Geetha, Ramasamy Seetharaman, et al.
Sustainability (2024) Vol. 16, Iss. 11, pp. 4864-4864
Open Access | Times Cited: 4
Uncertainty Analysis of Remote Sensing Estimation of Chinese Fir (Cunninghamia lanceolata) Aboveground Biomass in Southern China
Yujie Hu, Liyong Fu, Bingsheng Qiu, et al.
Forests (2025) Vol. 16, Iss. 2, pp. 230-230
Open Access
Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review
Weifeng Xu, Yu-Hao Cheng, Mengyuan Luo, et al.
Forests (2025) Vol. 16, Iss. 3, pp. 449-449
Open Access
Fusion-Based Approaches and Machine Learning Algorithms for Forest Monitoring: A Systematic Review
Abdullah Al Saim, Mohamed H. Aly
Wild (2025) Vol. 2, Iss. 1, pp. 7-7
Open Access
Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud
Chiranjit Singha, Kishore Chandra Swain, Satiprasad Sahoo, et al.
Forest Science and Technology (2025), pp. 1-20
Open Access
Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data
Chen Zhao, Zhibin Sun, Huaiqing Zhang, et al.
Remote Sensing (2023) Vol. 15, Iss. 24, pp. 5653-5653
Open Access | Times Cited: 8
Comparing Algorithms for Estimation of Aboveground Biomass in Pinus yunnanensis
Tian‐Bao Huang, Guanglong Ou, Hui Xu, et al.
Forests (2023) Vol. 14, Iss. 9, pp. 1742-1742
Open Access | Times Cited: 7
Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method
Li Xu, Jinge Yu, Qingtai Shu, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 2
Co-Kriging-Guided Interpolation for Mapping Forest Aboveground Biomass by Integrating Global Ecosystem Dynamics Investigation and Sentinel-2 Data
Yingchen Wang, Hongtao Wang, Cheng Wang, et al.
Remote Sensing (2024) Vol. 16, Iss. 16, pp. 2913-2913
Open Access | Times Cited: 1
Estimating Forest Aboveground Biomass Combining Pléiades Satellite Imagery and Field Inventory Data in the Peak–Cluster Karst Region of Southwestern China
Yinming Guo, Meiping Zhu, Yangyang Wu, et al.
Forests (2023) Vol. 14, Iss. 9, pp. 1760-1760
Open Access | Times Cited: 3
Evaluation of Machine and Deep Learning Algorithms for Subtropical Forest Above-Ground Biomass Estimation Based on Multi-Source Data
Lei Wu, Jun Xue, Zilin Feng, et al.
(2024)
Closed Access
More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass
Ying Su, Matteo Mura, Xiaoman Zheng, et al.
Journal of Geophysical Research Biogeosciences (2024) Vol. 129, Iss. 6
Closed Access
Water provision benefits from karst ecosystems: An example for Watuputih groundwater basin, North Kendeng Mountain, Indonesia
Elham Sumarga, Louise Willemen, Dian Rosleine, et al.
Environmental and Sustainability Indicators (2024), pp. 100518-100518
Open Access
Advances in Karst Geomorphology and Hydrogeology Research in the Last Decade and Its Future Direction for Karst Land Use Planning
Eko Haryono
Advances in geographical and environmental sciences (2023), pp. 231-253
Closed Access
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