DEVELOPING A MULTI-VARIABLE FOREST FIRE RISK MODEL AND FIRE RISK ZONE MAPPING
Forest steppe and taiga regions with a dry climate, small fires occur frequently between large fires, and therefore groups of trees of various ages and structures are formed in these forests, which has the effect of increasing fire exposure and "fuel reserves". The main goal of this resear...
| Published in: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2023
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1485-2023 https://noa.gwlb.de/receive/cop_mods_00070518 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00068866/isprs-archives-XLVIII-1-W2-2023-1485-2023.pdf https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/1485/2023/isprs-archives-XLVIII-1-W2-2023-1485-2023.pdf |
| Summary: | Forest steppe and taiga regions with a dry climate, small fires occur frequently between large fires, and therefore groups of trees of various ages and structures are formed in these forests, which has the effect of increasing fire exposure and "fuel reserves". The main goal of this research is to develop a multi-variable forest fire risk model and to mapping forest fire risk. The study area is the forest community in Bulgan province. Forest cover maps are essential for current research of community. We used in this research Landsat OLI and Digital Elevation Model (DEM) data. Soil moisture index and land surface temperature data from Landsat satellite data and elevation data (DEM) were used to determine slope and aspect. A multi-variable forest fire risk model was developed in this research. The final output delineated fire risk zones in the study area in four categories that include high-risk, moderate-risk, low-risk, and non-fire risk zones. Accuracy assessments on the two Landsat scenes indicates that forest/non-forest maps derived using the forest index (FI) have high accuracy. |
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