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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:

Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments
Qingxia Zhao, Shichuan Yu, Fei Zhao, et al.
Forest Ecology and Management (2018) Vol. 434, pp. 224-234
Closed Access | Times Cited: 124

Showing 1-25 of 124 citing articles:

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass
Mi Luo, Yifu Wang, Yunhong Xie, et al.
Forests (2021) Vol. 12, Iss. 2, pp. 216-216
Open Access | Times Cited: 161
Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing
Yichao Tian, Huang Hu, Guoqing Zhou, et al.
The Science of The Total Environment (2021) Vol. 781, pp. 146816-146816
Closed Access | Times Cited: 113
Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods
Dongbo Xie, Hongchao Huang, Linyan Feng, et al.
Remote Sensing (2023) Vol. 15, Iss. 13, pp. 3344-3344
Open Access | Times Cited: 51
An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products
Yuzhen Zhang, Jun Ma, Shunlin Liang, et al.
Remote Sensing (2020) Vol. 12, Iss. 24, pp. 4015-4015
Open Access | Times Cited: 109
Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests
Huiyi Su, Wenjuan Shen, Jingrui Wang, et al.
Forest Ecosystems (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 87
Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India
Ritika Srinet, Subrata Nandy, N. R. Patel
Ecological Informatics (2019) Vol. 52, pp. 94-102
Closed Access | Times Cited: 86
A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets
Yuzhen Zhang, Jun Ma, Shunlin Liang, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 234-249
Open Access | Times Cited: 55
Aboveground biomass of typical invasive mangroves and its distribution patterns using UAV-LiDAR data in a subtropical estuary: Maoling River estuary, Guangxi, China
Yichao Tian, Qiang Zhang, Huang Hu, et al.
Ecological Indicators (2022) Vol. 136, pp. 108694-108694
Open Access | Times Cited: 46
A review of forest carbon cycle models on spatiotemporal scales
Junfang Zhao, Dongsheng Liu, Yujie Zhu, et al.
Journal of Cleaner Production (2022) Vol. 339, pp. 130692-130692
Closed Access | Times Cited: 43
Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China
Nan Zhang, Mingjie Chen, Fan Yang, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4434-4434
Open Access | Times Cited: 41
Detection of Water Hyacinth (Eichhornia crassipes) in Lake Tana, Ethiopia, Using Machine Learning Algorithms
Getachew Bayable, Ji Fei Cai, Mulatie Mekonnen, et al.
Water (2023) Vol. 15, Iss. 5, pp. 880-880
Open Access | Times Cited: 24
From Industry 5.0 to Forestry 5.0: Bridging the gap with Human-Centered Artificial Intelligence
Andreas Holzinger, Janine Schweier, Christoph Gollob, et al.
Current Forestry Reports (2024) Vol. 10, Iss. 6, pp. 442-455
Open Access | Times Cited: 13
Enhancing carbon stock estimation in forests: Integrating multi-data predictors with random forest method
Gabriel E. Suárez-Fernández, J. Martínez-Sánchez, Pedro Arias
Ecological Informatics (2025), pp. 102997-102997
Open Access | Times Cited: 1
Knowledge Assisted Differential Evolution Extreme Gradient Boost algorithm for estimating mangrove aboveground biomass
Yang Shen, Zuowen Liao, Yichao Tian, et al.
Applied Soft Computing (2025), pp. 112838-112838
Closed Access | Times Cited: 1
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 | Times Cited: 1
Assessment of multi-wavelength SAR and multispectral instrument data for forest aboveground biomass mapping using random forest kriging
Lin Chen, Yeqiao Wang, Chunying Ren, et al.
Forest Ecology and Management (2019) Vol. 447, pp. 12-25
Closed Access | Times Cited: 73
Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data
Kourosh Ahmadi, Bahareh Kalantar, Vahideh Saeidi, et al.
Remote Sensing (2020) Vol. 12, Iss. 18, pp. 3019-3019
Open Access | Times Cited: 55
An improved area-based approach for estimating plot-level tree DBH from airborne LiDAR data
Zhengnan Zhang, Tiejun Wang, Andrew K. Skidmore, et al.
Forest Ecosystems (2023) Vol. 10, pp. 100089-100089
Open Access | Times Cited: 20
Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)
Xuenan Zhang, Haocong Xu, Yehong She, et al.
Plant Methods (2024) Vol. 20, Iss. 1
Open Access | Times Cited: 7
Remote Estimation of Mangrove Aboveground Carbon Stock at the Species Level Using a Low-Cost Unmanned Aerial Vehicle System
Zhen Li, Qijie Zan, Qiong Yang, et al.
Remote Sensing (2019) Vol. 11, Iss. 9, pp. 1018-1018
Open Access | Times Cited: 48
Disentangling the effects of stand and climatic variables on forest productivity of Chinese fir plantations in subtropical China using a random forest algorithm
Zhen Wang, Xiongqing Zhang, Sophan Chhin, et al.
Agricultural and Forest Meteorology (2021) Vol. 304-305, pp. 108412-108412
Open Access | Times Cited: 41
Evaluating the Performance of Hyperspectral Leaf Reflectance to Detect Water Stress and Estimation of Photosynthetic Capacities
Jingjing Zhou, Yahao Zhang, Zemin Han, et al.
Remote Sensing (2021) Vol. 13, Iss. 11, pp. 2160-2160
Open Access | Times Cited: 40
Improved estimation of forest stand volume by the integration of GEDI LiDAR data and multi-sensor imagery in the Changbai Mountains Mixed forests Ecoregion (CMMFE), northeast China
Lin Chen, Chunying Ren, Bai Zhang, et al.
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 100, pp. 102326-102326
Open Access | Times Cited: 39
Improving water status prediction of winter wheat using multi-source data with machine learning
Bo Shi, Yifan Yuan, Tingxuan Zhuang, et al.
European Journal of Agronomy (2022) Vol. 139, pp. 126548-126548
Closed Access | Times Cited: 26
Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region, Türkiye
Sinan Bulut
Ecological Informatics (2022) Vol. 74, pp. 101951-101951
Closed Access | Times Cited: 25
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