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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:

Human Activity Affects Forest Fires: The Impact of Anthropogenic Factors on the Density of Forest Fires in Poland
Aleksandra Kolanek, Mariusz Szymanowski, Andrzej Raczyk
Forests (2021) Vol. 12, Iss. 6, pp. 728-728
Open Access | Times Cited: 47

Showing 1-25 of 47 citing articles:

Assessing Chilgoza Pine (Pinus gerardiana) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies
Kaleem Mehmood, Shoaib Ahmad Anees, Mi Luo, et al.
Trees Forests and People (2024) Vol. 16, pp. 100521-100521
Open Access | Times Cited: 38
Seasonal differences in the spatial patterns of wildfire drivers and susceptibility in the southwest mountains of China
Wang Wen-quan, Fengjun Zhao, Yanxia Wang, et al.
The Science of The Total Environment (2023) Vol. 869, pp. 161782-161782
Closed Access | Times Cited: 35
Ensembling machine learning models to identify forest fire-susceptible zones in Northeast India
Mriganka Shekhar Sarkar, Bishal Kumar Majhi, Bhawna Pathak, et al.
Ecological Informatics (2024) Vol. 81, pp. 102598-102598
Open Access | Times Cited: 16
Assessing Wildfire Susceptibility in Iran: Leveraging Machine Learning for Geospatial Analysis of Climatic and Anthropogenic Factors
Ehsan Masoudian, Ali Mirzaei, Hossein Bagheri
Trees Forests and People (2025), pp. 100774-100774
Open Access | Times Cited: 1
Modeling of Forest Fire Risk Areas of Amazonas Department, Peru: Comparative Evaluation of Three Machine Learning Methods
Alex J. Vergara, Sivmny V. Valqui-Reina, Dennis Cieza-Tarrillo, et al.
Forests (2025) Vol. 16, Iss. 2, pp. 273-273
Open Access | Times Cited: 1
Sustainable collaboration: Federated learning for environmentally conscious forest fire classification in Green Internet of Things (IoT)
Ali Akbar Siddique, Nada Alasbali, Maha Driss, et al.
Internet of Things (2023) Vol. 25, pp. 101013-101013
Open Access | Times Cited: 18
Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine
Tianwu Ma, Gang Wang, Rui Guo, et al.
Journal of Environmental Management (2024) Vol. 359, pp. 120966-120966
Closed Access | Times Cited: 6
Identifying Influential Spatial Drivers of Forest Fires through Geographically and Temporally Weighted Regression Coupled with a Continuous Invasive Weed Optimization Algorithm
Parham Pahlavani, Amin Raei, Behnaz Bigdeli, et al.
Fire (2024) Vol. 7, Iss. 1, pp. 33-33
Open Access | Times Cited: 4
Fire susceptibility modeling and mapping in Mediterranean forests of Turkey: a comprehensive study based on fuel, climatic, topographic, and anthropogenic factors
A. Novo, Hurem Dutal, Saeedeh Eskandari
Euro-Mediterranean Journal for Environmental Integration (2024) Vol. 9, Iss. 2, pp. 655-679
Closed Access | Times Cited: 4
Remote sensing of air pollution due to forest fires and dust storm over Balochistan (Pakistan)
Salman Tariq, Hasan Nawaz, Usman Mehmood, et al.
Atmospheric Pollution Research (2023) Vol. 14, Iss. 2, pp. 101674-101674
Open Access | Times Cited: 10
Has climate change affected the fire regimes in semi-arid areas of northeastern Iran?
Saeedeh Eskandari, Fatemeh Ahmadloo, Pedro Lago-González
Earth Science Informatics (2025) Vol. 18, Iss. 1
Closed Access
Fire Risk Mapping Using Machine Learning Method and Remote Sensing in the Mediterranean Region
Fatih Sivrikaya, Döndü Demirel
Advances in Space Research (2025)
Closed Access
Understanding Forest Fragmentation Dynamics and Identifying Drivers for Forest Cover Loss Using Random Forest Models to Develop Effective Forest Management Strategies in North-East India
Soumik Mahapatra, Bishal Kumar Majhi, Mriganka Shekhar Sarkar, et al.
Results in Engineering (2025), pp. 104640-104640
Open Access
Post-fire Sprouting Patterns of Oak Species in the Zagros Forests of Western Iran
Loghman Ghahramany, S Bolouk Azari, Ahmad Valipour
Research Square (Research Square) (2025)
Closed Access
Contribution of anthropogenic, vegetation, and topographic features to forest fire occurrence in Poland
Mariusz Ciesielski, Radomir Bałazy, Bolesław Borkowski, et al.
iForest - Biogeosciences and Forestry (2022) Vol. 15, Iss. 4, pp. 307-314
Open Access | Times Cited: 15
Wildfire risk assessment and mapping – an approach for Natura 2000 forest sites
Bilyana Borisova, Elena Todorova, Ivo Ihtimanski, et al.
Trees Forests and People (2024) Vol. 16, pp. 100532-100532
Open Access | Times Cited: 3
Hoping the best, expecting the worst: Forecasting forest fire risk in Algeria using fuzzy logic and GIS
Louiza Soualah, Abdelhafid Bouzekri, Haroun Chenchouni
Trees Forests and People (2024) Vol. 17, pp. 100614-100614
Open Access | Times Cited: 3
Evaluating the Impact of Recursive Feature Elimination on Machine Learning Models for Predicting Forest Fire-Prone Zones
Ali Rezaei Barzani, Parham Pahlavani, Omid Ghorbanzadeh, et al.
Fire (2024) Vol. 7, Iss. 12, pp. 440-440
Open Access | Times Cited: 3
Assessing Hyrcanian forest fire vulnerability: socioeconomic and environmental perspectives
Elnaz Nejatiyanpour, Omid Ghorbanzadeh, Josef Strobl, et al.
Journal of Forestry Research (2025) Vol. 36, Iss. 1
Open Access
A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia
Harikesh Singh, Li-Minn Ang, Dipak Paudyal, et al.
Technology Knowledge and Learning (2025)
Open Access
Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain
María Bugallo, M. Dolores Esteban, Manuel Francisco Marey Pérez, et al.
Journal of Environmental Management (2022) Vol. 328, pp. 116788-116788
Open Access | Times Cited: 14
Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods
Slobodan Milanović, Jan Kaczmarowski, Mariusz Ciesielski, et al.
Forests (2022) Vol. 14, Iss. 1, pp. 46-46
Open Access | Times Cited: 14
Assessing wildfire activity and forest loss in protected areas of the Amazon basin
Emmanuel Da Ponte, Fermín Alcasena, Tejas Bhagwat, et al.
Applied Geography (2023) Vol. 157, pp. 102970-102970
Closed Access | Times Cited: 8
Mapping Forest Fire-affected Areas Using Advanced Machine Learning Techniques in Damoh District of Central India
Kanak N. Moharir, Manpreet Singh, Chaitanya B. Pande, et al.
Knowledge-Based Engineering and Sciences (2024) Vol. 5, Iss. 1, pp. 62-80
Open Access | Times Cited: 2
A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires
Arpit Tiwari, Preethi Nanjundan, Ravi Ranjan Kumar, et al.
Fire Ecology (2024) Vol. 20, Iss. 1
Open Access | Times Cited: 2
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