ENSO modulates interaction between forest insect and fire disturbances in China

Bai, Maowei; Wang, Xi; Yao, Qichao; Fang, Keyan

doi:10.1016/j.nhres.2022.04.001

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…The periodic occurrence of ENSO events and their significant impact on climate play a crucial role in the occurrence and spread of forest fires. This provides a deeper understanding and hopes to optimize fire management strategies through improved prediction models [59][60][61]. While this study has made progress in developing a forest fire prediction model, we recognize that obtaining long time series, high-precision, and consistent spatial and temporal resolution data sources remains a challenge for large-scale study areas, potentially affecting the accuracy of the research.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Hai,

Han,

Vandansambuu

et al. 2024

Preprint

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Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Hai,

Han,

Vandansambuu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Looking ahead, the research agenda is ambitiously set to explore the impact of extreme climate phenomena, specifically focusing on ENSO cycles and their significant influence on forest fire dynamics [74][75][76]. This initiative aims to further refine predictive capabilities, enhancing accuracy in forecasting and thereby improving fire management strategies.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Hai,

Han,

Vandansambuu

et al. 2024

Forests

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Understanding the spatial and temporal patterns of forest fires, along with the key factors influencing their occurrence, and accurately forecasting these events are crucial for effective forest management. In the Central-South region of China, forest fires pose a significant threat to the ecological system, public safety, and economic stability. This study employs Geographic Information Systems (GISs) and the LightGBM (Light Gradient Boosting Machine) model to identify the determinants of forest fire incidents and develop a predictive model for the likelihood of forest fire occurrences, in addition to proposing a zoning strategy. The purpose of the study is to enhance our understanding of forest fire dynamics in the Central-South region of China and to provide actionable insights for mitigating the risks associated with such disasters. The findings reveal the following: (i) Spatially, fire incidents exhibit significant clustering and autocorrelation, highlighting areas with heightened likelihood. (ii) The Central-South Forest Fire Likelihood Prediction Model demonstrates high accuracy, reliability, and predictive capability, with performance metrics such as accuracy, precision, recall, and F1 scores exceeding 85% and AUC values above 89%, proving its effectiveness in forecasting the likelihood of forest fires and differentiating between fire scenarios. (iii) The likelihood of forest fires in the Central-South region of China varies across regions and seasons, with increased likelihood observed from March to May in specific provinces due to various factors, including weather conditions and leaf litter accumulation. Risks of localized fires are noted from June to August and from September to November in different areas, while certain regions continue to face heightened likelihood from December to February.

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Assessment of the effects of the biotic and abiotic harmful factors on the amount of industrial wood production with deep learning

Sevinç

2023

Environ Sci Pollut Res

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The protection and sustainability of forest assets is possible with the planned production of forest products with minimum loss. Among the products obtained from forests, industrial wood is the most important raw material for many sectors. For this reason, it is important to determine the factors affecting the industrial wood production amount in order to obtain this raw material in the most e cient way. This article aims to investigate and assess the effects of two biotic and two abiotic harm factors on the amount of industrial wood production by building a deep learning estimation model. These factors are forest res, outbreaks of insects, outbreaks of diseases, and severe weather events. The study shows that the most harmful factor decreasing the industrial wood production level is outbreaks of diseases. The second effective factor, however, appears to be severe weather events. The third and the fourth factors were determined to be outbreaks of insects and burned forest areas, respectively.

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ENSO modulates interaction between forest insect and fire disturbances in China

Cited by 7 publications

References 46 publications

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Assessment of the effects of the biotic and abiotic harmful factors on the amount of industrial wood production with deep learning

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