A Comparative Study on the Drivers of Forest Fires in Different Countries in the Cross-Border Area between China, North Korea and Russia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Overarching Study Design
2.3. Data Source and Processing
2.3.1. Fire Point Data Extraction and the Creation of Random Points
2.3.2. Driving Factors
Climatic Factors
Topographic Factors
Vegetation Factors
Human Activity Factors
2.4. Methodology
2.4.1. Logistic Regression Model
2.4.2. Model Variable Selection
2.4.3. Prediction Accuracy of the Models
2.4.4. Evaluating the Relative Importance of the Driving Factors
2.4.5. Generation of a Forest Fire Occurrence Probability Map and Fire Risk Classification
3. Results
3.1. Fitting Results of the Forest Fire Occurrence Probability Prediction Model
3.1.1. Multicollinearity Test Results
3.1.2. Model Parameter Fitting Results
3.1.3. Model Prediction Accuracy Results
3.2. Comparison of the Relative Importance of Forest Fire Occurrence Drivers in Different Countries
3.3. Spatial Distribution of Forest Fire Probability and Fire Risk Division
4. Discussion
4.1. Applicability of the Logistic Regression Model in the Study Area
4.2. Comparative Analysis of the Relative Impact of Different Variables on the Occurrence of Forest Fires in Different Countries in the Cross-border Area between China, North Korea and Russia
4.2.1. Relative Impact of Climatic Factors
4.2.2. Relative Impact of Topography and Vegetation Factors
4.2.3. Relative Impact of Human Activity Factors
4.3. Spatial Distribution of High-Risk Areas for Forest Fire and Forest Fire Prevention and Control
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Area | Natural Conditions | Human Conditions and Forest Fire Management System | Fire Regime |
---|---|---|---|
Chinese side | The total area is 150,000 square kilometers, and the forest coverage rate is 55%. The terrain is low in the north and high in the south, with an average elevation of 450 m. The area has a temperate humid and semi-humid continental monsoon climate. The average temperature in January is between −21 °C and −18 °C, and the average temperature in July is between 21 °C and 22 °C. The annual precipitation is 500–650 mm. | The total population is approximately 4.7 million. When fighting large-scale forest fires, China generally uses firefighting aircraft to create artificial rain or to spray chemical agents, and cooperate with ground-based forest firefighting forces to carry out ground–air integrated firefighting [37]. For high-risk areas of fire, manpower is deployed in advance, and protective forest belts are built to prevent the spread of wildfires. | According to the monitoring results of MOD14A1, a total of 4001 forest fires occurred in the fire season (March–November) from 2001 to 2020. |
North Korean side | The total area is 48,500 square kilometers, and the forest coverage rate is 78%. The overall terrain is high, with an average elevation of 920 m. The area has a temperate monsoon climate. The annual average temperature is between 2 °C and 5 °C. The annual precipitation is 650–700 mm. | The total population is approximately 4.3 million. To date, most of North Korea’s firefighting has been with manpower. Under the mobilization of the government, the general public actively participate in firefighting; however, due to the lack of modern firefighting facilities, this often requires significant manpower [38]. | According to the monitoring results of MOD14A1, a total of 8143 forest fires occurred in the fire season (March–November) from 2001 to 2020. |
Russian side | The total area is 348,000 square kilometers, and the forest coverage rate is 72%. The terrain is high in the east and low in the west, with an average elevation of 420 m. The area has a temperate oceanic monsoon climate. The average temperature in January is between −30 °C and −12 °C, and the average temperature in July is between 14 °C and 21 °C. The annual precipitation is 600–900 mm. | The total population is approximately 2.5 million. Russia abolished the national forest protection system after introducing a new Forest Code in 2007, and also takes a negative attitude towards wildfires in forest areas where people are scarce and difficult to reach [36]. | According to the monitoring results of MOD14A1, a total of 87,543 forest fires occurred in the fire season (March–November) from 2001 to 2020. |
Variable Type | Variable Name | Code | Resolution /Scale | Source |
---|---|---|---|---|
Climatic | Mean daily air temperature at sigma level 995 | Temp | 2.5°/°C | NCEP-NCAR Reanalysis 1 data and CPC Global Unified Gauge-Based Analysis of Daily Precipitation data were provided by the NOAA Physical Sciences Laboratory, Boulder, CO, USA (https://psl.noaa.gov/, accessed on 22 September 2022). |
Mean daily relative humidity at sigma level 995 | Rhum | 2.5°/% | ||
Mean daily wind velocity at sigma level 995 | Wind | 2.5°/m/s | ||
Daily total of precipitation | Pre | 0.5°/mm | ||
Topographic | Elevation | Elev | 30 m/m | ASTER GDEM was provided by the Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn/, accessed on 22 September 2022). |
Slope | Slope | 30 m/° | ||
Vegetation | Monthly Enhanced Vegetation Index | EVI | 1 km | MOD13A3—MODIS/Terra Vegetation Indices Monthly L3 Global 1 km SIN Grid dataset was acquired from the Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland (https://ladsweb.nascom.nasa.gov/, accessed on 22 September 2022). |
Annual Fractional Vegetation Cover | FVC | 1 km/% | ||
Human activity | Distance from the nearest road | Dis_road | km | Global road and building dataset was provided by the OpenStreetMap Foundation (http://download.geofabrik.de/, accessed on 22 September 2022). |
Distance from the nearest railway | Dis_railway | km | ||
Distance from the nearest building | Dis_building | km | ||
Density of population | POP | 100 m/number | Global population dataset was provided by the WorldPOP Hub (https://hub.worldpop.org/, accessed on 22 September 2022). |
Initial Variable | VIF | ||
---|---|---|---|
China | North Korea | Russia | |
Temp | 2.265 | 1.929 | 1.827 |
Rhum | 1.181 | 1.383 | 1.358 |
Wind | 1.053 | 1.167 | 1.059 |
Pre | 1.213 | 1.146 | 1.146 |
Elev | 1.187 | 1.286 | 2.449 |
Slope | 1.113 | 1.039 | 1.428 |
EVI | 2.513 | 2.188 | 1.964 |
FVC | 1.170 | 1.150 | 1.869 |
Dis_road | 1.243 | 1.364 | 1.645 |
Dis_railway | 1.224 | 1.108 | 1.776 |
Dis_building | 1.375 | 1.309 | 2.471 |
POP | 1.034 | 1.147 | 1.039 |
Variable | Country | Significant Correlation Times | Parameter Estimation | |||
---|---|---|---|---|---|---|
Coefficient | Standard Error | Wald Chi-Squared Value | Significance | |||
Temp | China | 5 | 0.0795 | 0.0053 | 223.4483 | <0.0001 |
North Korea | 5 | 0.1338 | 0.0061 | 475.5498 | <0.0001 | |
Russia | 5 | 0.0961 | 0.0052 | 342.3678 | <0.0001 | |
Rhum | China | 5 | −4.3544 | 0.2788 | 243.9326 | <0.0001 |
North Korea | 5 | −8.0400 | 0.2708 | 881.2861 | <0.0001 | |
Russia | 5 | −7.2572 | 0.3468 | 438.0091 | <0.0001 | |
Wind | China | 0 | / | / | / | / |
North Korea | 5 | 0.0648 | 0.0145 | 19.9621 | <0.0001 | |
Russia | 3 | 0.0328 | 0.0151 | 4.7469 | 0.0294 | |
Pre | China | 5 | −0.3008 | 0.0312 | 93.2182 | <0.0001 |
North Korea | 5 | −0.5385 | 0.0546 | 97.2522 | <0.0001 | |
Russia | 5 | −1.0631 | 0.0890 | 142.7199 | <0.0001 | |
Elev | China | 5 | −0.0055 | 0.0002 | 1310.0693 | <0.0001 |
North Korea | 5 | −0.0014 | <0.0001 | 308.1419 | <0.0001 | |
Russia | 5 | −0.0039 | 0.0002 | 446.3535 | <0.0001 | |
Slope | China | 5 | −0.0501 | 0.0049 | 105.7375 | <0.0001 |
North Korea | 0 | / | / | / | / | |
Russia | 1 | / | / | / | / | |
EVI | China | 5 | −0.0005 | <0.0001 | 384.4244 | <0.0001 |
North Korea | 5 | −0.0008 | <0.0001 | 727.9396 | <0.0001 | |
Russia | 5 | −0.0007 | <0.0001 | 537.2400 | <0.0001 | |
FVC | China | 3 | −0.5526 | 0.2285 | 5.8515 | 0.0156 |
North Korea | 3 | 0.4519 | 0.1873 | 5.8202 | 0.0158 | |
Russia | 5 | −0.7643 | 0.1749 | 19.1006 | <0.0001 | |
Dis_road | China | 0 | / | / | / | / |
North Korea | 4 | −0.0996 | 0.0227 | 19.2222 | <0.0001 | |
Russia | 4 | 0.0229 | 0.0063 | 13.2112 | 0.0003 | |
Dis_railway | China | 0 | / | / | / | / |
North Korea | 0 | / | / | / | / | |
Russia | 5 | −0.0063 | 0.0008 | 64.2092 | <0.0001 | |
Dis_building | China | 4 | −0.0098 | 0.0034 | 8.5992 | 0.0034 |
North Korea | 3 | 0.0398 | 0.0122 | 10.6846 | 0.0011 | |
Russia | 3 | −0.0082 | 0.0038 | 4.5439 | 0.0330 | |
POP | China | 5 | 0.2011 | 0.0495 | 16.5201 | <0.0001 |
North Korea | 5 | −0.4052 | 0.0783 | 26.7928 | <0.0001 | |
Russia | 2 | / | / | / | / | |
Constant | China | / | 7.4033 | 0.2974 | 619.5687 | <0.0001 |
North Korea | / | 6.8816 | 0.2683 | 657.8327 | <0.0001 | |
Russia | / | 8.9320 | 0.3580 | 622.3646 | <0.0001 |
Sample | Country | Cut-Off | AUC Value | Prediction Accuracy (%) | |
---|---|---|---|---|---|
Training | Validation | ||||
Sample 1 | China | 0.4885 | 0.9141 | 85.79 | 84.81 |
North Korea | 0.4116 | 0.9003 | 82.08 | 80.19 | |
Russia | 0.4567 | 0.9092 | 83.85 | 85.59 | |
Sample 2 | China | 0.4414 | 0.9155 | 85.44 | 84.06 |
North Korea | 0.3675 | 0.9001 | 81.31 | 79.88 | |
Russia | 0.4726 | 0.9217 | 85.79 | 83.91 | |
Sample 3 | China | 0.3734 | 0.9166 | 84.48 | 83.44 |
North Korea | 0.4030 | 0.8956 | 81.21 | 81.59 | |
Russia | 0.4835 | 0.9106 | 84.71 | 85.47 | |
Sample 4 | China | 0.4045 | 0.9163 | 84.88 | 83.91 |
North Korea | 0.3911 | 0.9006 | 81.63 | 81.59 | |
Russia | 0.5276 | 0.9183 | 85.69 | 84.84 | |
Sample 5 | China | 0.4267 | 0.9136 | 85.21 | 84.19 |
North Korea | 0.3909 | 0.9006 | 81.63 | 80.50 | |
Russia | 0.4098 | 0.9121 | 83.85 | 84.97 | |
Modeling sample | China | 0.4273 | 0.9132 | ||
North Korea | 0.3834 | 0.8973 | |||
Russia | 0.4867 | 0.9153 |
Observed | Predicted | ||||||
---|---|---|---|---|---|---|---|
Modeling | Independent Test | ||||||
Non-Fire | Fire | Correct Rate | Non-Fire | Fire | Correct Rate | ||
China | Non-fire | 4052 | 738 | 84.59 | 1025 | 185 | 84.71 |
Fire | 480 | 2730 | 85.05 | 96 | 694 | 87.85 | |
Overall pct. | 84.78 | 85.95 | |||||
North Korea | Non-fire | 3792 | 1011 | 78.95 | 936 | 261 | 78.20 |
Fire | 491 | 2706 | 84.64 | 117 | 686 | 85.43 | |
Overall pct. | 81.23 | 81.10 | |||||
Russia | Non-fire | 4197 | 617 | 87.18 | 1051 | 135 | 88.62 |
Fire | 577 | 2609 | 81.89 | 160 | 654 | 80.34 | |
Overall pct. | 85.08 | 85.25 |
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Quan, D.; Quan, H.; Zhu, W.; Lin, Z.; Jin, R. A Comparative Study on the Drivers of Forest Fires in Different Countries in the Cross-Border Area between China, North Korea and Russia. Forests 2022, 13, 1939. https://doi.org/10.3390/f13111939
Quan D, Quan H, Zhu W, Lin Z, Jin R. A Comparative Study on the Drivers of Forest Fires in Different Countries in the Cross-Border Area between China, North Korea and Russia. Forests. 2022; 13(11):1939. https://doi.org/10.3390/f13111939
Chicago/Turabian StyleQuan, Donghe, Hechun Quan, Weihong Zhu, Zhehao Lin, and Ri Jin. 2022. "A Comparative Study on the Drivers of Forest Fires in Different Countries in the Cross-Border Area between China, North Korea and Russia" Forests 13, no. 11: 1939. https://doi.org/10.3390/f13111939
APA StyleQuan, D., Quan, H., Zhu, W., Lin, Z., & Jin, R. (2022). A Comparative Study on the Drivers of Forest Fires in Different Countries in the Cross-Border Area between China, North Korea and Russia. Forests, 13(11), 1939. https://doi.org/10.3390/f13111939