Research Article
Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey
Table 3
Selected fire ignition factors, data sources, data processing, and intervals.
| Factors | Data sources | Processing | Intervals |
| Elevation | ASTER | Clipped with study area | 0–3500 m | Slope degree | ASTER | Derived from DEM | 0–72 degree | Slope aspect | ASTER | Derived from DEM | −1–360 angle | TWI | ASTER | Derived from DEM | 2–27 | Temperature | MGM | IDW | 12–20°C | Humidity | MGM | IDW | 15–69% | Wind speed | MGM | IDW | 6–31 m/sec | Land use | Ministry of Agriculture and Forestry | Digitized | Categorical (18) | Distance from water bodies | Ministry of Agriculture and Forestry | Euclidean distance | 0–123,488 m | Distance from residential | Ministry of Agriculture and Forestry | Euclidean distance | 0–41038 m | Distance from roads | OSM | Euclidean distance | 0–13005 m | NDVI | MODIS | Clipped with study area | −0.4–0.7 | LST | MODIS | Average of multiple images | 287–317 K |
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ASTER: advanced spaceborne thermal emission and reflection radiometer; MGM: Turkish Meteorology Service (Meteoroloji Genel Müdürlüğü); OSM: open street map, MODIS: moderate resolution imaging spectroradiometer; TWI: Topographical Wetness Index; NDVI: Normalized Difference Vegetation Index; LST: land surface temperature; DEM: digital elevation model; IDW: inverse distance weighted.
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