GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing
"> Figure 1
<p>Laoshan National Forest Park is located in Nanjing, Jiangsu Province, China. (<b>a</b>) Jiangsu Province, China. (<b>b</b>) Laoshan, Nanjing. (<b>c</b>) Longitude and Latitude of Laoshan.</p> "> Figure 2
<p>The process of fire risk model construction, which consists of data collection, data processing and weight calculating, result and validation.</p> "> Figure 3
<p>The base map of altitude in Laoshan. Each level is determined by the fire risk classification.</p> "> Figure 4
<p>The base map of slope in Laoshan. Each level is determined by the fire risk classification.</p> "> Figure 5
<p>The base map of aspect in Laoshan. Each level is determined by the fire risk classification.</p> "> Figure 6
<p>The base map of TWI in Laoshan. Each level is determined by the fire risk classification.</p> "> Figure 7
<p>The base map of temperature in Laoshan. Each level is determined by the fire risk classification.</p> "> Figure 8
<p>The base map of <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics></math> in Laoshan. Each level is determined by the fire risk classification.</p> "> Figure 9
<p>The base map of distance to roads in Laoshan. Each level is determined by the fire risk classification.</p> "> Figure 10
<p>The base map of distance to populated areas in Laoshan. Each level is determined by the fire risk classification.</p> "> Figure 11
<p>The fire risk map of topographic factors. (<b>a</b>) The risk map of altitude. (<b>b</b>) The risk map of aspect. (<b>c</b>) The risk map of slope. (<b>d</b>) The risk map of TWI. The fire risk level of each factor is determined by the fire risk classification results.</p> "> Figure 12
<p>The fire risk map of <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics></math> and temperature. (<b>a</b>) The risk map of <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics></math>. (<b>b</b>) The risk map of temperature. The fire risk level of each factor is determined by the fire risk classification results.</p> "> Figure 13
<p>The fire risk map of human activity factors. (<b>a</b>) The risk map of distance to roads. (<b>b</b>) The risk map of distance to populated areas. The fire risk level of each factor is determined by the fire risk classification results.</p> "> Figure 14
<p>The forest fire risk map and proportion of each level of the risk area. (<b>a</b>) The generated forest fire risk map. (<b>b</b>) The proportion of areas of each fire risk level. The percentage represents the ratio of the area of each risk level to the area of Laoshan Forest Park.</p> "> Figure 15
<p>Forest fire risk validation.</p> ">
Abstract
:1. Introduction
- It is the first time to construct a fire risk model with actual geographic information for Nanjing Laoshan National Forest Park.
- For each influencing factor, the weights were calculated by multi-layer hierarchical analysis. The weight has passed the verification of consistency ratio, which proves the rationality of weight allocation.
- The validation result shows that the prediction accuracy of our model is close to 77%, which proves that the proposed model has good performance.
2. Study Area and Data Used
2.1. Study Area
2.2. Data Used
3. Methods
3.1. Layering and Rating of Fire Ignition Factors
3.1.1. Topographic Factors
- (a) Altitude: The altitude data are reclassified to generate a new classification standard, which is divided into the following levels: extremely high, high, moderate, low, extremely low. The corresponding weight is allocated to the five classes [44]. It is shown in Table 1. The result of altitude is obtained through the elevation extraction of DEM data by ArcGIS10.5, and Figure 3 shows the based map of altitude.
- (b) Slope: The slope of the study area is calculated by the slope function that comes with ArcGIS. Meanwhile, we also calculate the slope as a percentage. Figure 4 is the base map of the slope. The slope data is reclassified to generate a new standard, the classification standard is the same as that of altitude. The weight is allocated to the five classes in Table 2.
- (c) Aspect: The aspect matrix was calculated using the DEM. The aspect was classified into eight classes [19,45], which is shown in Table 3. In the northern hemisphere, the sunny slope has higher temperature and is prone to fire. Except for the tropic of cancer, the sun in other latitudes is in the south. Therefore, based on this characteristic, the slopes are divided into five grades. Figure 5 shows the base map of aspect and the weight of aspect has been listed in Table 4.
- (d) TWI: TWI is a hydrological analysis of DEM data. It considers the effects of topography and soil characteristics on soil moisture distribution [15]. Firstly, according to the actual topographic needs, the topography of the study area is filled with depressions. After filling depressions, the direction of water flow and the total amount of water flow needs to be calculated. Finally, the topographic humidity index is calculated based on the above-mentioned data. These calculations are completed in ArcGIS 10.5. The topographic wetness index has an important influence on fire occurrence and spread. Dry areas are prone to fire and spread rapidly, while wet areas are hard to cause fires [16,20]. Figure 6 shows the base map of TWI and the weight of factors is listed in Table 5.
3.1.2. Meteorological Factor
3.1.3. Vegetation Factor
3.1.4. Human Activity Factors
3.2. Generation of Weights for the Fire Risk Model
4. Result
4.1. Base Layer Classification
4.2. Forest Fire Risk Model of Laoshan
4.3. Generation of Forest Fire Risk Map
4.4. Validation of Forest Fire Risk Map
5. Discussion
5.1. Fire-Inducing Factors and Fire Risk Map
5.2. The Influence of Factors on Fire Risk Model
5.3. Accuracy and Application Analysis
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Altitude (m) | Weight | Risk |
---|---|---|
<100 | 5 | Extremely high |
100–200 | 4 | High |
200–300 | 3 | Moderate |
300–400 | 2 | Low |
>400 | 1 | Extremely low |
Slope () | Slope (%) | Weight | Risk |
---|---|---|---|
>30 | >58 | 5 | Extremely high |
20–30 | 36–58 | 4 | High |
10–20 | 18–36 | 3 | Moderate |
5–10 | 9–18 | 2 | Low |
<5 | <9 | 1 | Extremely low |
Aspect | Degre |
---|---|
N-NE | 0– |
NE-E | – |
E-SE | – |
SE-S | – |
S-SW | 1 – |
SW-W | – |
W-NW | – |
NW-N | – |
Aspect | Weight | Risk |
---|---|---|
S | 5 | Extremely high |
SE and E | 4 | High |
NE | 3 | Moderate |
N | 2 | Low |
W, NW, SW and FLAT | 1 | Extremely low |
TWI | Weight | Risk |
---|---|---|
<7 | 5 | Extremely high |
7–8 | 4 | High |
8–9 | 3 | Moderate |
9–10 | 2 | Low |
>10 | 1 | Extremely low |
Temperature (C) | Weight | Risk |
---|---|---|
>31 | 5 | Extremely high |
29–31 | 4 | High |
27–29 | 3 | Moderate |
25–27 | 2 | Low |
<25 | 1 | Extremely low |
Weight | Risk | |
---|---|---|
<0 | 5 | Extremely high |
0–0.15 | 4 | High |
0.15–0.3 | 3 | Moderate |
0.3–0.45 | 2 | Low |
> | 1 | Extremely low |
Distance to Roads (m) | Weight | Risk |
---|---|---|
<300 | 5 | Extremely high |
300–600 | 4 | High |
600–900 | 3 | Moderate |
900–1200 | 2 | Low |
>1200 | 1 | Extremely low |
Populated Areas | Weight | Risk |
---|---|---|
<400 | 5 | Extremely high |
400–800 | 4 | High |
800–1200 | 3 | Moderate |
1200–1600 | 2 | Low |
>1600 | 1 | Extremely low |
Factors | Factors | Weight | ||||
---|---|---|---|---|---|---|
Altitude | Slope | Aspect | TWI | |||
Altitude | 1 | 2 | 3 | 1/2 | 0.2863 | |
Topographic | Slope | 1/2 | 1 | 2 | 1/2 | 0.1820 |
Factors | Aspect | 1/3 | 1/2 | 1 | 1/4 | 0.0969 |
TWI | 2 | 2 | 4 | 1 | 0.4348 | |
DTR | DTP | |||||
Human Activity | DTR | 1 | 3 | 0.75 | ||
Factors | DTP | 1/3 | 1 | 0.25 |
Topographic | Temperature | Human Activity | Weight | ||
---|---|---|---|---|---|
Topographic | 1 | 1/2 | 1/3 | 1/3 | 0.1059 |
Temperature | 2 | 1 | 1/3 | 1/2 | 0.1636 |
3 | 3 | 1 | 2 | 0.4476 | |
Human Activity | 3 | 2 | 1/2 | 1 | 0.2829 |
Influence Factors of Forest Fire | Risk |
---|---|
0.0103 | |
0.0193 | |
0.0303 | |
0.0461 | |
0.0707 | |
0.1636 | |
0.2122 | |
0.4476 |
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Zhao, P.; Zhang, F.; Lin, H.; Xu, S. GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing. Remote Sens. 2021, 13, 3704. https://doi.org/10.3390/rs13183704
Zhao P, Zhang F, Lin H, Xu S. GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing. Remote Sensing. 2021; 13(18):3704. https://doi.org/10.3390/rs13183704
Chicago/Turabian StyleZhao, Pengcheng, Fuquan Zhang, Haifeng Lin, and Shuwen Xu. 2021. "GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing" Remote Sensing 13, no. 18: 3704. https://doi.org/10.3390/rs13183704
APA StyleZhao, P., Zhang, F., Lin, H., & Xu, S. (2021). GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing. Remote Sensing, 13(18), 3704. https://doi.org/10.3390/rs13183704