文献类型： 外文期刊

作者： Li, Jing ¹ ; Huang, Duan ¹ ; Long, Beiping ¹ ; Shao, Yakui ⁶ ; Xiao, Mengwei ¹ ; Sun, Linhao ⁸ ; Li, Xusheng ⁹ ; Wang, Aiai ¹⁰ ; Chen, Xuanchi ¹¹ ; Li, Weike ¹² ;

作者机构： 1.East China Univ Technol, Key Lab Mine Environm Monitoring & Improving Poyan, Minist Nat Resources, Nanchang 330013, Peoples R China

2.Zhejiang Acad Agr Sci, Wenzhou Key Lab Resource Plant Innovat & Utilizat, Zhejiang Inst Subtrop Crops, Wenzhou 325005, Peoples R China

3.Cent South Univ Forestry & Technol, Sch Forestry, Changsha 410004, Peoples R China

4.East China Univ Technol, Sch Surveying & Geoinformat Engn, Nanchang 330013, Peoples R China

5.Jiangxi Prov Geol Bur, Nanchang, Peoples R China

6.Beijing Forestry Univ, Precis Forestry Key Lab Beijing, Beijing 100083, Peoples R China

7.East China Univ Technol, Jiangxi Key Lab Watershed Ecol Proc & Informat Pla, Nanchang 330013, Peoples R China

8.Zhejiang A&F Univ, China Key Lab Forestry Intelligent Monitoring & In, Hangzhou 311300, Peoples R China

9.China Geol Survey, Tianjin Ctr Geol Survey, Tianjin 300170, Peoples R China

10.Harbin Normal Univ, Sch Geog Sci, Harbin 150028, Peoples R China

11.Zhengzhou Univ, Sch Water Conservancy & Transportat, Zhengzhou 450001, Peoples R China

12.Chinese Acad Forestry, Ecol & Nat Conservat Inst, Beijing 100091, Peoples R China

关键词： global climate warming; wildfires; spatial heterogeneity; ensemble model (LR-RF-SVM); future climate scenarios

期刊名称：FORESTS （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2024 年 15 卷 11 期

页码：

收录情况： SCI

摘要： In the context of global climate warming, climate change is subtly reshaping the patterns of wildfires. Therefore, it is particularly urgent to conduct in-depth research on climate change, wildfires, and their management strategies. This study relies on detailed fire point data from 2001 to 2020, skillfully incorporating a spatial autocorrelation analysis to uncover the mysteries of spatial heterogeneity, while comprehensively considering the influences of multiple factors such as climate, terrain, vegetation, and socioeconomic conditions. To simulate fire conditions under future climates, we adopted the BCC-CSM2-MR climate model, presetting temperature and precipitation data for two scenarios: a sustainable low-development path and a high-conventional-development path. The core findings of the study include the following: (i) In terms of spatial heterogeneity exploration, global autocorrelation analysis reveals a striking pattern: within the southern forest region, 63 cities exhibiting a low-low correlation are tightly clustered in provinces such as Hubei, Anhui, and Zhejiang, while 48 cities with a high-high correlation are primarily distributed in Guangxi and Guangdong. Local autocorrelation analysis further refines this observation, indicating that 24 high-high correlated cities are highly concentrated in specific areas, 14 low-low correlated cities are located in Hainan, and there are only 3 sparsely distributed cities with a low-high correlation. (ii) During the model construction and validation process, this study innovatively adopted the LR-RF-SVM ensemble model, which demonstrated exceptional performance indicators: an accuracy of 91.97%, an AUC value of 97.09%, an F1 score of 92.13%, a precision of 90.75%, and a recall rate of 93.55%. These figures, significantly outperforming those of the single models SVM and RF, strongly validate the superiority of the ensemble learning approach. (iii) Regarding predictions under future climate scenarios, the research findings indicate that, compared to the current fire situation in southern forest areas, the spatial distribution of wildfires will exhibit a noticeable expansion trend. High-risk regions will not only encompass multiple cities in Hunan, Hubei, southern Anhui, all of Jiangxi, and Zhejiang but will also extend northward into southern forest areas that were previously considered low-risk, suggesting a gradual northward spread of fire risk. Notably, despite the relatively lower fire risk in some areas of Fujian Province under the SS585 scenario, overall, the probability of wildfires occurring in 2090 is slightly higher than that in 2030, further highlighting the persistent intensification of forest fire risk due to climate change.