Publication: Wildfire Burn Area and Severity Mapping by Using GIS and Remote Sensing Data
| dc.contributor.advisor | Di, Liping | |
| dc.contributor.author | Atakul, Canan | |
| dc.date.accessioned | 2024-11-25T14:13:57Z | |
| dc.date.available | 2024-11-25T14:13:57Z | |
| dc.date.issued | 2023-08-04 | |
| dc.description.abstract | Wildfires globally have impacted the environment socially, economically, and ecologically. A precise evaluation of burn severity is fundamental for productive post-fire management and strategic planning. The primary objective of this study is to evaluate commonly used satellite indices in detecting burn severity and compare them with a widely used field index, the composite burn index (CBI). Sentinel-2 Level-1C satellite images, providing a spatial resolution of 10 meters, were utilized in this research from the European Space Agency (ESA) within the Copernicus program. The study focused on three wildfire incidents in the United States, namely Legion Lake, Fuller, and Chimney Tops 2, each with available CBI measurements. SNAP software, an open-source software developed by ESA, was employed for image analysis and metric calculations. Using ArcGIS Pro, the burn indices results, and CBI values were compared, followed by statistical analysis in Excel to assess the correlation between burn severity categories derived from the differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR), differenced Normalized Difference Vegetation Index (dNDVI), and the newly established differenced Normalized Difference Burn Index (dNDBI) with CBI measurements. The study findings revealed that the performance of satellite burn severity indices, compared to CBI, is contingent upon the specific wildfire incident. Factors such as topography, shadow, and vegetation cover significantly influenced the results. For the ChimneyTops2 fire, the dNDVI index exhibited the most substantial correlation (R²=0.72) with the CBI, suggesting its superior predictive potential for burn severity. Conversely, the Legion Lake fire demonstrated the strongest correlation (R²=0.64) between the dNDBI index and CBI, indicating its potential as the optimal predictor of burn severity. In contrast, the Fuller fire showed generally lower correlation coefficients, with the RBR index showcasing the highest correlation with the CBI (R²=0.26). The observed significant correlation between the indices and CBI measurements highlights their value in evaluating burn severity across various regions. Additionally, the created burn severity mapping using Sentinel-2 dNBR with the proposed USGS threshold was compared to the Monitoring Trends in Burn Severity (MTBS) provided burn severity map using Landsat 8 data, yielding similar results despite some limitations, such as differences in pre- and post-fire image dates. Future research should continue to explore the applicability of these indices in other wildfire-prone regions, further enhancing our understanding of wildfire dynamics and its impact on the environment. | |
| dc.format.medium | masters theses | |
| dc.identifier.uri | http://hdl.handle.net/1920/14079 | |
| dc.language.iso | en | |
| dc.rights | Copyright 2023 Canan Atakul | |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0 | |
| dc.subject | wildfire burn severity | |
| dc.subject | remote sensing | |
| dc.subject | Sentinel 2 data | |
| dc.subject | GIS | |
| dc.title | Wildfire Burn Area and Severity Mapping by Using GIS and Remote Sensing Data | |
| dc.type | Thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.discipline | Geographic and Cartographic Science | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Master's | |
| thesis.degree.name | Master of Science in Geographic and Cartographic Science |