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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:

Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables
Sea Jin Kim, Chul-Hee Lim, Gang Sun Kim, et al.
Remote Sensing (2019) Vol. 11, Iss. 1, pp. 86-86
Open Access | Times Cited: 115

Showing 26-50 of 115 citing articles:

Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review
Santos D. Chicas, Jonas Østergaard Nielsen
Natural Hazards (2022) Vol. 114, Iss. 3, pp. 2417-2434
Open Access | Times Cited: 29
Can Ensemble Techniques and Large-Scale Fire Datasets Improve Predictions of Forest Fire Probability Due to Climate Change?—A Case Study from the Republic of Korea
Hyeon Kwon Ahn, Huicheul Jung, Chul-Hee Lim
Forests (2024) Vol. 15, Iss. 3, pp. 503-503
Open Access | Times Cited: 6
Harnessing Geospatial Tools to Map the Forest Fire: Risk Zonation in Pauri Garhwal, Uttarakhand
Oishi Bhattacharya, Suman Sinha, Varun Narayan Mishra, et al.
Results in Engineering (2024), pp. 103694-103694
Open Access | Times Cited: 6
Predicting the pulse of the Amazon: Machine learning insights into deforestation dynamics
Fernanda Dias, Nicolas Suhadolnik, Heloisa A. Camargo, et al.
Journal of Environmental Management (2024) Vol. 362, pp. 121359-121359
Closed Access | Times Cited: 5
Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea
Yoojin Kang, Eunna Jang, Jungho Im, et al.
Applied Sciences (2020) Vol. 10, Iss. 22, pp. 8213-8213
Open Access | Times Cited: 34
Forest Fire Risk Prediction Based on Stacking Ensemble Learning for Yunnan Province of China
Yanzhi Li, Guohui Li, Kaifeng Wang, et al.
Fire (2023) Vol. 7, Iss. 1, pp. 13-13
Open Access | Times Cited: 13
RETRACTED ARTICLE: Wildfire risk exploration: leveraging SHAP and TabNet for precise factor analysis
Faiza Qayyum, Harun Jamil, Tariq Alsboui, et al.
Fire Ecology (2024) Vol. 20, Iss. 1
Open Access | Times Cited: 4
National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making
Meysam Moharrami, Amin Naboureh, Thimmaiah Gudiyangada Nachappa, et al.
ISPRS International Journal of Geo-Information (2020) Vol. 9, Iss. 6, pp. 393-393
Open Access | Times Cited: 31
Developing spatial agricultural drought risk index with controllable geo-spatial indicators: A case study for South Korea and Kazakhstan
Sea Jin Kim, Sugyeong Park, Soo‐Jeong Lee, et al.
International Journal of Disaster Risk Reduction (2021) Vol. 54, pp. 102056-102056
Open Access | Times Cited: 26
Exploring the multidimensional effects of human activity and land cover on fire occurrence for territorial planning
Jaime Carrasco, Mauricio Acuña, Alejandro Miranda, et al.
Journal of Environmental Management (2021) Vol. 297, pp. 113428-113428
Closed Access | Times Cited: 25
Modeling wildfire risk in western Iran based on the integration of AHP and GIS
Vahid Nasiri, Seyed Mohammad Moein Sadeghi, Rasoul Bagherabadi, et al.
Environmental Monitoring and Assessment (2022) Vol. 194, Iss. 9
Closed Access | Times Cited: 18
Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach
Hyun‐Woo Jo, Andrey Krasovskiy, Mina Hong, et al.
Remote Sensing (2023) Vol. 15, Iss. 5, pp. 1446-1446
Open Access | Times Cited: 10
Assessment of fire hazard in Southwestern Amazon
Igor José Malfetoni Ferreira, Wesley A. Campanharo, Maria Lucia F. Barbosa, et al.
Frontiers in Forests and Global Change (2023) Vol. 6
Open Access | Times Cited: 10
Prediction and driving factors of forest fire occurrence in Jilin Province, China
Bo Gao, Yanlong Shan, Xiangyu Liu, et al.
Journal of Forestry Research (2023) Vol. 35, Iss. 1
Closed Access | Times Cited: 10
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
Modeling the Spatial Distribution of Wildfire Risk in Chile Under Current and Future Climate Scenarios
John Gajardo, Marco A. Yáñez, Robert S. Padilla, et al.
Fire (2025) Vol. 8, Iss. 3, pp. 113-113
Open Access
Exploring the monthly contribution of drivers on European summer wildfires with explainable artificial intelligence (XAI)
Hanyu Li, Stenka Vulova, Alby Duarte Rocha, et al.
Ecological Indicators (2025) Vol. 176, pp. 113605-113605
Closed Access
Deep learning-based forecasting of daily maximum ozone levels and assessment of socioeconomic and health impacts in South Korea
Seyedeh Reyhaneh Shams, Yunsoo Choi, Deveshwar Singh, et al.
The Science of The Total Environment (2025) Vol. 983, pp. 179684-179684
Closed Access
Predictive modeling of forest fire using geospatial tools and strategic allocation of resources: eForestFire
Abdul Qayum, Firoz Ahmad, Rakesh Arya, et al.
Stochastic Environmental Research and Risk Assessment (2020) Vol. 34, Iss. 12, pp. 2259-2275
Closed Access | Times Cited: 27
A New Passive Microwave Tool for Operational Forest Fires Detection: A Case Study of Siberia in 2019
Costas A. Varotsos, Vladimir F. Krapivin, F.А. Mkrtchyan
Remote Sensing (2020) Vol. 12, Iss. 5, pp. 835-835
Open Access | Times Cited: 26
MODIS-FIRMS and ground-truthing-based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms
Polash Banerjee
Natural Hazards (2021) Vol. 110, Iss. 2, pp. 899-935
Open Access | Times Cited: 22
A review of forest fire and policy response for resilient adaptation under changing climate in the Eastern Himalayan region
Sonam Wangyel Wang, Chul-Hee Lim, Woo‐Kyun Lee
Forest Science and Technology (2021) Vol. 17, Iss. 4, pp. 180-188
Open Access | Times Cited: 22
Fire probability mapping and prediction from environmental data: What a comprehensive savanna-forest transition can tell us
Lucas Barros‐Rosa, Paulo Henrique Zanella de Arruda, Nadja Gomes Machado, et al.
Forest Ecology and Management (2022) Vol. 520, pp. 120354-120354
Closed Access | Times Cited: 15
Predicting the Occurrence of Forest Fire in the Central-South Region of China
Quansheng Hai, Xiufeng Han, Battsengel Vandansambuu, et al.
(2024)
Open Access | Times Cited: 3
Predicting the Occurrence of Forest Fire in the Central-South Region of China
Quansheng Hai, Xiufeng Han, Battsengel Vandansambuu, et al.
Forests (2024) Vol. 15, Iss. 5, pp. 844-844
Open Access | Times Cited: 3

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