Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Preparation
2.2.1. Dependent Variables
2.2.2. Explanatory Variables
Climate Variables
Topographic Variables
Vegetation Variable
Socioeconomic Variables
2.3. Model
2.4. Prediction Accuracy of the Models
2.5. Mapping Forest Fire Occurrence Likelihood
3. Results
3.1. Identification of Forest Fire Driving Factors and Their Importance Ranks
3.2. Influence of the Forest Fire Driving Factors on Forest Fire Occurrence in Different Regions
3.3. Model Prediction Accuracy in Different Regions
3.4. Likelihood of Forest Fire Occurrence
4. Discussion
4.1. Forest Fire Driving Factors and Their Influence
4.2. Implications for Forest Fire Prevention
4.3. Strengths and Limitations
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix
Variable Type | Variable | Intermediate Models | Selected Frequency | ||||
---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |||
(a) Northeast region | |||||||
Climatic | Pre_year0 | 0 | |||||
Pre_year1 | 0 | ||||||
Soil_mois0 | 0 | ||||||
Soil_mois1 | / | / | / | / | / | / | |
Tem_avg | + | + | + | + | + | 5 | |
GST_avg | / | / | / | / | / | / | |
RH_avg | 0 | ||||||
RH_min | 0 | ||||||
Pre_daily | 0 | ||||||
Pres_avg | 0 | ||||||
SSD | 0 | ||||||
Win_avg | 0 | ||||||
Win_max | 0 | ||||||
Topographic | DEM | 0 | |||||
Aspect | 0 | ||||||
Slope | 0 | ||||||
Vegetation | FVC | + | + | + | + | + | 5 |
Socioeconomic | Dis_road | 0 | |||||
Dis_sett | 0 | ||||||
Pop | 0 | ||||||
GDP | 0 | ||||||
(b) North China region | |||||||
Climatic | Pre_year0 | 0 | |||||
Pre_year1 | 0 | ||||||
Soil_mois0 | + | + | + | + | 4 | ||
Soil_mois1 | / | / | / | / | / | / | |
Tem_avg | + | + | 2 | ||||
GST_avg | / | / | / | / | / | / | |
RH_avg | 0 | ||||||
RH_min | 0 | ||||||
Pre_daily | 0 | ||||||
Pres_avg | + | 1 | |||||
SSD | 0 | ||||||
Win_avg | 0 | ||||||
Win_max | 0 | ||||||
Topographic | DEM | + | 1 | ||||
Aspect | 0 | ||||||
Slope | 0 | ||||||
Vegetation | FVC | + | + | + | + | + | 5 |
Socioeconomic | Dis_road | 0 | |||||
Dis_sett | 0 | ||||||
Pop | + | 1 | |||||
GDP | 0 | ||||||
(c) Northwest region | |||||||
Climatic | Pre_year0 | 0 | |||||
Pre_year1 | + | 1 | |||||
Soil_mois0 | 0 | ||||||
Soil_mois1 | / | / | / | / | / | / | |
Tem_avg | 0 | ||||||
GST_avg | / | / | / | / | / | / | |
RH_avg | + | + | + | 3 | |||
RH_min | 0 | ||||||
Pre_daily | 0 | ||||||
Pres_avg | + | + | 2 | ||||
SSD | 0 | ||||||
Win_avg | + | 1 | |||||
Win_max | 0 | ||||||
Topographic | DEM | + | + | + | + | 4 | |
Aspect | + | 1 | |||||
Slope | 0 | ||||||
Vegetation | FVC | 0 | |||||
Socioeconomic | Dis_road | + | + | + | 3 | ||
Dis_sett | 0 | ||||||
Pop | 0 | ||||||
GDP | + | 1 | |||||
(d) Southwest region | |||||||
Climatic | Pre_year0 | + | + | + | + | + | 5 |
Pre_year1 | + | + | + | + | + | 5 | |
Soil_mois0 | + | + | + | + | + | 5 | |
Soil_mois1 | + | + | + | + | + | 5 | |
Tem_avg | + | + | + | + | + | 5 | |
GST_avg | / | / | / | / | / | / | |
RH_avg | + | + | + | + | + | 5 | |
RH_min | + | + | + | + | + | 5 | |
Pre_daily | 0 | ||||||
Pres_avg | + | + | + | + | + | 5 | |
SSD | 0 | ||||||
Win_avg | + | + | 2 | ||||
Win_max | + | + | + | 3 | |||
Topographic | DEM | + | + | + | + | + | 5 |
Aspect | 0 | ||||||
Slope | 0 | ||||||
Vegetation | FVC | + | + | + | + | + | 5 |
Socioeconomic | Dis_road | 0 | |||||
Dis_sett | 0 | ||||||
Pop | + | + | + | + | + | 5 | |
GDP | + | + | + | + | + | 5 | |
(e) Mid-south region | |||||||
Climatic | Pre_year0 | + | + | 2 | |||
Pre_year1 | + | 1 | |||||
Soil_mois0 | + | 1 | |||||
Soil_mois1 | / | / | / | / | / | / | |
Tem_avg | + | + | + | + | + | 5 | |
GST_avg | / | / | / | / | / | / | |
RH_avg | + | 1 | |||||
RH_min | + | + | + | + | + | 5 | |
Pre_daily | + | 1 | |||||
Pres_avg | + | 1 | |||||
SSD | 0 | ||||||
Win_avg | 0 | ||||||
Win_max | 0 | ||||||
Topographic | DEM | 0 | |||||
Aspect | 0 | ||||||
Slope | 0 | ||||||
Vegetation | FVC | + | + | + | + | + | 5 |
Socioeconomic | Dis_road | 0 | |||||
Dis_sett | 0 | ||||||
Pop | 0 | ||||||
GDP | + | 1 | |||||
(f) East China region | |||||||
Climatic | Pre_year0 | 0 | |||||
Pre_year1 | 0 | ||||||
Soil_mois0 | 0 | ||||||
Soil_mois1 | / | / | / | / | / | / | |
Tem_avg | + | + | + | + | + | 5 | |
GST_avg | / | / | / | / | / | / | |
RH_avg | 0 | ||||||
RH_min | 0 | ||||||
Pre_daily | 0 | ||||||
Pres_avg | 0 | ||||||
SSD | 0 | ||||||
Win_avg | 0 | ||||||
Win_max | 0 | ||||||
Topographic | DEM | 0 | |||||
Aspect | 0 | ||||||
Slope | 0 | ||||||
Vegetation | FVC | + | + | + | + | + | 5 |
Socioeconomic | Dis_road | 0 | |||||
Dis_sett | 0 | ||||||
Pop | 0 | ||||||
GDP | 0 | ||||||
(g) The whole study area | |||||||
Climatic | Pre_year0 | 0 | |||||
Pre_year1 | 0 | ||||||
Soil_mois0 | 0 | ||||||
Soil_mois1 | / | / | / | / | / | / | |
Tem_avg | 0 | ||||||
GST_avg | / | / | / | / | / | / | |
RH_avg | 0 | ||||||
RH_min | + | + | + | + | + | 5 | |
Pre_daily | 0 | ||||||
Pres_avg | 0 | ||||||
SSD | 0 | ||||||
Win_avg | 0 | ||||||
Win_max | 0 | ||||||
Topographic | DEM | 0 | |||||
Aspect | 0 | ||||||
Slope | 0 | ||||||
Vegetation | FVC | + | + | + | + | + | 5 |
Socioeconomic | Dis_road | 0 | |||||
Dis_sett | 0 | ||||||
Pop | 0 | ||||||
GDP | 0 |
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Study Area | Province | Main Climate Types | Topography | Dominant Vegetation Types | Socioeconomic Conditions |
---|---|---|---|---|---|
Northeast region | Heilongjiang, Jilin, and Liaoning | Middle temperate monsoon climate | Dominant terrain is plains and mountains. The elevation in most areas is below 500 m. | Temperate coniferous broadleaved mixed forests and cold temperate coniferous forest. Forest coverage is 41.59% [39]. | The total population is 108.75 million. The per capita GDP is ¥49,891 yuan [40]. |
North China region | Inner Mongolia, Shanxi, Beijing, Tianjin, and Hebei | Middle temperate continental climate and warm temperate monsoon climate | The dominant terrain types are plateaus and hills. The elevation in most areas is below 2000 m. | Warm temperate deciduous broadleaved forest and temperate grassland. Forest coverage is 21.09% [39]. | The total population is 174.79 million. The per capita GDP is ¥64,194 yuan [40]. |
East China region | Shandong, Jiangsu, Anhui, Zhejiang, Shanghai, Jiangxi, and Fujian | Warm temperate monsoon climate and subtropical monsoon climate | The dominant terrain types are plains and mountains. The elevation in most areas is below 1000 m. | Warm temperate deciduous broadleaved forest and subtropical evergreen broadleaved forest. Forest coverage is 40.64% [39]. | The total population is 408.98 million. The per capita GDP is ¥78,271 yuan [40] (China Statistical Yearbook, 2018). |
Northwest region | Xinjiang, Gansu, Ningxia, Qinghai, and Shaanxi | Warm temperate continental climate | The dominant terrain types, which fluctuate greatly, are deserts and high mountains. The elevation in most areas is between 500 and 5000 m. | Temperate desert and alpine vegetation on the Qinghai-Tibetan plateau. Forest coverage is 8.21% [39]. | The total population is 101.86 million. The per capita GDP is ¥45,463 yuan [40]. |
Southwest region | Tibet, Sichuan, Chongqing, Yunnan, and Guizhou | Subtropical monsoon climate and alpine climate | The terrain is complex and consists of basins, plateaus and mountains. The elevation in most areas is between 500 and 6000 m. | Alpine vegetation on the Qinghai-Tibetan plateau, subtropical evergreen broadleaved forest and tropical rainforest. Forest coverage is 25.75% [39]. | The total population is 200.95 million. The per capita GDP is ¥43,609 yuan [40]. |
Mid-south region | Henan, Hubei, Hunan, Guangxi, Guangdong, and Hainan | Subtropical monsoon climate and tropical monsoon climate | The dominant terrain types are plains and mountains. The elevation in most areas is below 1000 m. | Subtropical evergreen broadleaved forest and tropical rainforest. Forest coverage is 44.63% [39]. | The total population is 393.01 million. The per capita GDP is ¥57,664 yuan [40]. |
Variable Type | Variable Name | Code | Source | Resolution, Units | References |
---|---|---|---|---|---|
Climatic | Annual precipitation in the year before the fire | Pre_year0 | National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China (http://www.geodata.cn) | 0.25°, 0.1 mm | [18,31,42] |
Annual precipitation in the year of the fire | Pre_year1 | 0.25°, 0.1 mm | |||
Annual soil moisture in the year before the fire | Soil_mois0 | 0.25°, m3 m−3 | |||
Annual soil moisture in the year of the fire | Soil_mois1 | 0.25°, m3 m−3 | |||
Daily average ground surface temperature | GST_avg | Daily Data Set of China’s Surface Climate Data (V3.0), National Meteorological Information Centre (http://data.cma.cn) | 0.1 °C | [18,31,37,43,44] | |
Daily precipitation | Pre_daily | 0.1 mm | |||
Daily average air pressure | Pres_avg | 0.1 hPa | |||
Daily average relative humidity | RH_avg | % | |||
Daily minimum relative humidity | RH_min | % | |||
Sunshine hours | SSD | 0.1 h | |||
Daily average temperature | Tem_avg | 0.1 °C | |||
Daily average wind speed | Win_avg | 0.1 m/s | |||
Daily maximum wind speed | Win_max | 0.1 m/s | |||
Topographic | Elevation | Elevation | Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn) | 90 m, m | [28,29,45,46] |
Slope | Slope | 90 m, ° | |||
Aspect | Aspect | 90 m | |||
Vegetation | Fractional vegetation cover | FVC | Resource and Environment Data Cloud Platform (http://www.resdc.cn)(Xu,2018) | 1 km | [18] |
Socioeconomic | The distance to road and railway | Dis_road | National Catalogue Service for Geographic Information website (http://www.webmap.cn) | 1:1,000,000, m | [18,28,29,46,47] |
The distance to settlement | Dis_sett | 1:1,000,000, m | |||
Population density | POP | National Earth System Science Data Center (http://www.geodata.cn) | 1 km, number/km2 | ||
Per capita gross national product | GDP | 1 km, RMB/km2 |
Aspect | Azimuth (°) |
---|---|
North | 337.5~22.5 |
Northeast | 22.5~67.5 |
East | 67.5~112.5 |
Southeast | 112.5~157.5 |
South | 157.5~202.5 |
Southwest | 202.5~247.5 |
Variable Type | Variable | NE | N | NW | SW | MS | E | The Whole Study Area |
---|---|---|---|---|---|---|---|---|
Climatic | Pre_year0 | + | ||||||
Pre_year1 | + | |||||||
Soil_mois0 | + | + | ||||||
Soil_mois1 | / | / | / | + | / | / | / | |
Tem_avg | + | + | + | + | ||||
GST_avg | / | / | / | / | / | / | / | |
RH_avg | + | + | ||||||
RH_min | + | + | + | |||||
Pre_daily | ||||||||
Pres_avg | + | |||||||
SSD | ||||||||
Win_avg | ||||||||
Win_max | + | |||||||
Topographic | DEM | + | + | |||||
Aspect | ||||||||
Slope | ||||||||
Vegetation | FVC | + | + | + | + | + | + | |
Socioeconomic | Dis_road | + | ||||||
Dis_sett | ||||||||
Pop | + | |||||||
GDP | + |
Regions | Model | AUC Value | Cut-Off | Prediction Accuracy (%) | |
---|---|---|---|---|---|
(Intermediate Model 1/2/3/4/5) | (Intermediate Model 1/2/3/4/5) | Training | Validation | ||
(Subtraining 1/2/3/4/5) | (Subvalidation 1/2/3/4/5) | ||||
Northeast region | Intermediate model | 0.978/0.981/0.971/0.969/0.981 | 0.466/0.494/0.488/0.466/0.420 | 92.8/93.0/90.3/89.2/92.5 | 93.2/92.4/89.4/87.5/93.1 |
Final model | 0.976 | 0.493 | 92 | 91.5 | |
North China region | Intermediate model | 0.974/0.969/0.969/0.970/0.971 | 0.396/0.426/0.435/0.433/0.390 | 92.7/92.5/92.7/91.8/92.7 | 91.6/92.2/91.5/93.5/91.6 |
Final model | 0.971 | 0.402 | 92.8 | 92.2 | |
East China region | Intermediate model | 0.963/0.956/0.963/0.957/0.955 | 0.470/0.397/0.415/0.457/0.462 | 90.9/86.8/89.4/89.5/88.8 | 88.4/86.3/86.7/88.0/89.6 |
Final model | 0.955 | 0.467 | 89 | 86.2 | |
Northwest region | Intermediate model | 0.974/0.981/0.959/0.979/0.960 | 0.315/0.414/0.462/0.644/0.238 | 91.0/93.6/91.0/93.6/89.7 | 86.5/84.6/88.5/90.4/90.4 |
Final model | 0.964 | 0.373 | 91.4 | 90.3 | |
Southwest region | Intermediate model | 0.971/0.966/0.969/0.968/0.968 | 0.382/0.384/0.379/0.375/0.393 | 91.5/91.0/91.0/90.6/90.6 | 90.1/91.2/90.1/90.3/90.2 |
Final model | 0.966 | 0.393 | 90.6 | 91 | |
Mid-south region | Intermediate model | 0.965/0.965/0.953/0.968/0.965 | 0.397/0.391/0.470/0.418/0.451 | 89.1/88.9/88.6/89.2/90.4 | 88.4/88.7/87.4/89.2/90.3 |
Final model | 0.979 | 0.481 | 90.2 | 87.1 | |
The whole study area | Intermediate model | 0.949/0.954/0.951/0.912/0.942 | 0.419/0.431/0.448/0.372/0.327 | 86.8/88.2/87.8/83.2/84.4 | 85.9/88.0/87.6/83.0/83.9 |
Final model | 0.944 | 0.415 | 86 | 85.8 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Ma, W.; Feng, Z.; Cheng, Z.; Chen, S.; Wang, F. Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm. Forests 2020, 11, 507. https://doi.org/10.3390/f11050507
Ma W, Feng Z, Cheng Z, Chen S, Wang F. Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm. Forests. 2020; 11(5):507. https://doi.org/10.3390/f11050507
Chicago/Turabian StyleMa, Wenyuan, Zhongke Feng, Zhuxin Cheng, Shilin Chen, and Fengge Wang. 2020. "Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm" Forests 11, no. 5: 507. https://doi.org/10.3390/f11050507