Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China

Wu, Zechuan; Li, Mingze; Wang, Bin; Tian, Yuping; Qian, Ying; Liu, Jianyang

doi:10.3390/f13071021

Cited by 17 publications

(17 citation statements)

References 81 publications

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“…There is a great deal of weight assigned to meteorological elements, including mean relative humidity, daily maximum temperature, and sunlight hours. The environment's water and heat conditions as well as the moisture content of forest fuels are all impacted by meteorological elements [65,66], which is one of the main causes of forest fires. Among the vegetation factors, NDVI has an important influence.…”

Section: Investigator Methods Description Impact Factor Precisionmentioning

confidence: 99%

Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China

Tan

Feng

2023

Sustainability

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Forest fire is a primary disaster that destroys forest resources and the ecological environment, and has a serious negative impact on the safety of human life and property. Predicting the probability of forest fires and drawing forest fire risk maps can provide a reference basis for forest fire control management in Hunan Province. This study selected 19 forest fire impact factors based on satellite monitoring hotspot data, meteorological data, topographic data, vegetation data, and social and human data from 2010–2018. It used random forest, support vector machine, and gradient boosting decision tree models to predict the probability of forest fires in Hunan Province and selected the RF algorithm to create a forest fire risk map of Hunan Province to quantify the potential forest fire risk. The results show that the RF algorithm performs best compared to the SVM and GBDT algorithms with 91.68% accuracy, 91.96% precision, 92.78% recall, 92.37% F1, and 97.2% AUC. The most important drivers of forest fires in Hunan Province are meteorology and vegetation. There are obvious differences in the spatial distribution of seasonal forest fire risks in Hunan Province, and winter and spring are the seasons with high forest fire risks. The medium- and high-risk areas are mostly concentrated in the south of Hunan.

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Section: Investigator Methods Description Impact Factor Precisionmentioning

confidence: 99%

Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China

Tan

Feng

2023

Sustainability

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“…Numerous studies have investigated the influencing factors of forest fires. Wu, Li [29] conducted a study on the driving factors of forest fires in different provinces of China. They comprehensively considered anthropogenic factors, meteorological conditions, topography, and vegetation.…”

Section: A Forest Fire Impact Factorsmentioning

confidence: 99%

A Forest Fire Prediction Model Based on Cellular Automata and Machine Learning

Sun,

Li,

Chen

et al. 2024

IEEE Access

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Forest fires constitute a widespread and impactful natural disaster, annually ravaging millions of hectares of forests and posing a severe threat to human life and property. Accurate quantitative prediction of forest fire spread is essential for devising swift risk management strategies and implementing effective firefighting approaches. In response to this imperative, this study introduces a Forest Fire Spread Behavior Prediction (FFSBP) model, encompassing two integral components: the Forest Fire Spread Process Prediction (FFSPP) model and the Forest Fire Spread Results Prediction (FFSRP) model. The FFSPP model involves the prediction of the direction and speed of forest fire spread, achieved through a fusion of the Cellular Automata model and the Wang Zhengfei model. On the other hand, the FFSRP model focuses on forecasting the extent of the burned area, leveraging machine learning methods. To validate the efficacy of the proposed models, a real case study is undertaken using the "3.29 Forest Fire" incident in China. The FFSPP model is validated against this case, while the FFSRP model is assessed using a real fire dataset obtained from Montesinho National Forest Park in Portugal. Results from the validation process reveal that during the natural development period of the "3.29 Forest Fire," the FFSPP model predicts a burned area of 286.81 hm 2 , with an associated relative error of 28.94%. This relative error is notably smaller than those observed in the Farsite and Prometheus fire behavior simulation models. Additionally, the FFSRP model demonstrates commendable predictive performance, particularly in the context of small and medium-sized fire scenarios. These findings underscore the potential of the FFSBP model as a valuable tool in enhancing forest fire prediction accuracy and facilitating more robust risk mitigation and firefighting strategies.INDEX TERMS Forest fire prediction; Forest fire spread; Cellular Automata; Wang Zhengfei model; Machine learning.

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“…Dalam konteks ini, pemetaan kerentanan kebakaran hutan di Pulau Buru menggunakan metode kernel density dapat memberikan wawasan yang berharga kepada pihak-pihak yang terlibat dalam pengelolaan hutan dan mitigasi kebakaran. Metode kernel density adalah metode pemetaan spasial yang telah terbukti efektif dalam memperkirakan tingkat kerentanan terhadap kebakaran hutan berdasarkan fire hotspot (Wu et al, 2022). Fire hotspot merupakan titik-titik panas yang terdeteksi oleh alat pemantau seperti satelit, yang menunjukkan keberadaan kebakaran aktif (Kumar & Kumar, 2022).…”

Section: Pendahuluanunclassified

Pemetaan Kerentanan Kebakaran Hutan di Pulau Buru, Provinsi Maluku Berdasarkan Fire Hotspot

Abdul Muin,

Heinrich Rakuasa

2023

INSOLOGI: Jurnal Sains dan Teknologi

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Buru Island is one of the areas in Maluku Province where forest fires are common. The prolonged summer season, flammable vegetation and the practice of clearing agricultural land and settlements by burning can also trigger forest fires on Buru Island. This research uses fire hotspots in 2019-2023 obtained from the Fire Information for Resource Management System (FIRMS) and analyzed using the Kernel Density method in Arc GIS software. The results showed that in 2019 there were 224 fire hotspots spread across Buru Island, in 2020 there were 30 fire hotspots, in 2021 there were 31 fire hotspots, in 2022 there were 31 fire hotspots, in 2022 there were 32 fire hotspots and in 2023 there were 23 hotspots spread across Buru Island. The results also show that the level of forest fire vulnerability in the very low vulnerability class has an area of 543,868.30 ha or 63.76%, low vulnerability class of 201,449.44 ha (23.62%), vulnerability class of 83,344.31 (9.77%), and high vulnerability class of 24,327.01 ha or 2.85% of the total area of Buru Island. The results of the study are expected to help the local government in future forest fire disaster mitigation efforts.

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Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China

Cited by 17 publications

References 81 publications

Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China

Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China

A Forest Fire Prediction Model Based on Cellular Automata and Machine Learning

Pemetaan Kerentanan Kebakaran Hutan di Pulau Buru, Provinsi Maluku Berdasarkan Fire Hotspot

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