Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China
Forests are the largest terrestrial ecosystem with major benefits in three areas: economy, ecology, and society. However, the frequent occurrence of forest fires has seriously affected the structure and function of forests. To provide a strong scientific basis for forest fire prevention and control,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/7/1021 |
| _version_ | 1797439511855104000 |
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| author | Zechuan Wu Mingze Li Bin Wang Yuping Tian Ying Quan Jianyang Liu |
| author_facet | Zechuan Wu Mingze Li Bin Wang Yuping Tian Ying Quan Jianyang Liu |
| author_sort | Zechuan Wu |
| collection | DOAJ |
| description | Forests are the largest terrestrial ecosystem with major benefits in three areas: economy, ecology, and society. However, the frequent occurrence of forest fires has seriously affected the structure and function of forests. To provide a strong scientific basis for forest fire prevention and control, Ripley’s K(d) function and the LightGBM algorithm were used to determine the spatial pattern of forest fires in four different provinces (Heilongjiang, Jilin, Liaoning, Hebei) in China from 2019 to 2021 and the impact of driving factors on different ecosystems. In addition, this study also identified fire hotspots in the four provinces based on kernel density estimation (KDE). An artificial neural network model (ANN) was created to predict the probability of occurrence of forest fires in the study area. The results showed that the forest fires were spatially clustered, but the variable importance of different factors varied widely among the different forest ecosystems. Forest fires in Heilongjiang and Liaoning Provinces were mainly caused by human-driven factors. For Jilin, meteorological factors were important in the occurrence of fires. Topographic and vegetation factors exhibited the greatest importance in Hebei Province. The selected driving factors were input to the ANN model to predict the probability of fire occurrence in the four provinces. The ANN model accurately captured 93.17%, 90.28%, 83.16%, and 89.18% of the historical forest fires in Heilongjiang, Jilin, Liaoning, and Hebei Provinces; Precision, Recall, and F-measure based on the full dataset are 0.87, 0.88, and 0.87, respectively. The results of this study indicated that there were differences in the driving factors of fire in different forest ecosystems. Different fire management policies must be formulated in response to this spatial heterogeneity. |
| first_indexed | 2024-03-09T11:53:52Z |
| format | Article |
| id | doaj.art-f0a538970ab544f09373015e4a62b798 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T11:53:52Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-f0a538970ab544f09373015e4a62b7982023-11-30T23:11:43ZengMDPI AGForests1999-49072022-06-01137102110.3390/f13071021Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in ChinaZechuan Wu0Mingze Li1Bin Wang2Yuping Tian3Ying Quan4Jianyang Liu5Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaForests are the largest terrestrial ecosystem with major benefits in three areas: economy, ecology, and society. However, the frequent occurrence of forest fires has seriously affected the structure and function of forests. To provide a strong scientific basis for forest fire prevention and control, Ripley’s K(d) function and the LightGBM algorithm were used to determine the spatial pattern of forest fires in four different provinces (Heilongjiang, Jilin, Liaoning, Hebei) in China from 2019 to 2021 and the impact of driving factors on different ecosystems. In addition, this study also identified fire hotspots in the four provinces based on kernel density estimation (KDE). An artificial neural network model (ANN) was created to predict the probability of occurrence of forest fires in the study area. The results showed that the forest fires were spatially clustered, but the variable importance of different factors varied widely among the different forest ecosystems. Forest fires in Heilongjiang and Liaoning Provinces were mainly caused by human-driven factors. For Jilin, meteorological factors were important in the occurrence of fires. Topographic and vegetation factors exhibited the greatest importance in Hebei Province. The selected driving factors were input to the ANN model to predict the probability of fire occurrence in the four provinces. The ANN model accurately captured 93.17%, 90.28%, 83.16%, and 89.18% of the historical forest fires in Heilongjiang, Jilin, Liaoning, and Hebei Provinces; Precision, Recall, and F-measure based on the full dataset are 0.87, 0.88, and 0.87, respectively. The results of this study indicated that there were differences in the driving factors of fire in different forest ecosystems. Different fire management policies must be formulated in response to this spatial heterogeneity.https://www.mdpi.com/1999-4907/13/7/1021forest fireecosystemforest managementdriving factors |
| spellingShingle | Zechuan Wu Mingze Li Bin Wang Yuping Tian Ying Quan Jianyang Liu Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China Forests forest fire ecosystem forest management driving factors |
| title | Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China |
| title_full | Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China |
| title_fullStr | Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China |
| title_full_unstemmed | Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China |
| title_short | Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China |
| title_sort | analysis of factors related to forest fires in different forest ecosystems in china |
| topic | forest fire ecosystem forest management driving factors |
| url | https://www.mdpi.com/1999-4907/13/7/1021 |
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