Forest Fire Risk Estimation and Modeling Using GIS and Remote Sensing Techniques: The Case Of Asebot Monastery, Oromia, Ethiopia
| dc.contributor.advisor | Hamere Yohannes (PhD) | |
| dc.contributor.author | Birhanu Melkam | |
| dc.date.accessioned | 2024-03-12T15:28:02Z | |
| dc.date.available | 2024-03-12T15:28:02Z | |
| dc.date.issued | 2024-02 | |
| dc.description.abstract | Forest fires are a major threat to the environment, human health, and property. The Asebot forest area is particularly vulnerable to forest fires due to its unique geographical and environmental characteristics. To mitigate the risk of forest fires in this region, it is essential to identify the influential factors of forest fire risks and estimate post-fire degradation. This was achieved by using post-fire satellite images of 2021 and modeling the location of potential fire susceptibility in the Asebot forest area. Forest fires are a major threat to ecosystems and human populations, and early detection and monitoring are crucial for effective fire management. In this study, the weighted overlay analysis technique is being used for multi-criteria decision-making. The goal is to estimate the post-fire and model forest fire risk susceptibility. To perform this analysis, each data set is converted to raster format and reclassified to a common scale using ArcGIS spatial analysis. Pair-wise comparisons of factor layers are conducted to determine their relative importance or weight. Remote sensing techniques have become increasingly important for detecting and monitoring forest fires, as they offer a cost-effective and efficient way to gather data over large areas. In this study, the two commonly used remote sensing techniques are employed to estimate fire severity: Normalized Difference Vegetation Index (NDVI) and Burn Severity Index (NBR). The study found that approximately 38.419% of the area had very high and high burn severity, as classified by the Normalized Burn Ratio (NBR) index whereas, burn severity concerning NDVI, which is very high and high burn severity covered 52.277% of the study area. The findings of the study indicate that the area under consideration has varying levels of forest fire risk. The model produced in this study reveals that a substantial portion of the area is classified as having a high to very high risk of forest fires, with over 22% falling into these categories | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/2421 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Forest fire | |
| dc.subject | multi-criteria decision-making | |
| dc.subject | RS | |
| dc.subject | GIS | |
| dc.subject | fire risk map | |
| dc.subject | prediction | |
| dc.subject | Forest fire susceptibility | |
| dc.title | Forest Fire Risk Estimation and Modeling Using GIS and Remote Sensing Techniques: The Case Of Asebot Monastery, Oromia, Ethiopia | |
| dc.type | Thesis |