Bushfires are one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analysing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve years, combining remote sensing data and machine learning techniques to predict future fire trends. By utilizing Landsat imagery and integrating spectral indices like NDVI, NBR, and Burn Index, along with topographical and climatic factors, we developed a robust predictive model using XGBoost. The model achieved high accuracy, 86.13%, demonstrating its effectiveness in predicting fire severity across diverse Australian ecosystems. By analysing historical trends and integrating factors such as population density and vegetation cover, we identify areas at high risk of future severe bushfires. Additionally, this research identifies key regions at risk, providing data-driven recommendations for targeted firefighting efforts. The findings contribute valuable insights into fire management strategies, enhancing resilience to future fire events in Australia.

中文翻译：

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使用遥感和机器学习对澳大利亚的森林大火严重程度进行建模并预测未来趋势


丛林大火是给生计和环境造成巨大损失的主要自然灾害之一。了解和分析森林火灾的严重性对于有效的管理和缓解策略至关重要，有助于防止这些自然灾害造成的广泛破坏和损失。本研究对过去 12 年澳大利亚丛林大火的严重程度进行了深入分析，并结合了遥感数据和机器学习技术来预测未来的火灾趋势。通过利用 Landsat 影像并整合 NDVI、NBR 和燃烧指数等光谱指数，以及地形和气候因素，我们使用 XGBoost 开发了一个强大的预测模型。该模型达到了 86.13% 的高精度，证明了它在预测澳大利亚不同生态系统的火灾严重程度方面的有效性。通过分析历史趋势并整合人口密度和植被覆盖率等因素，我们确定了未来发生严重丛林火灾的高风险区域。此外，这项研究还确定了面临风险的关键区域，为有针对性的消防工作提供了数据驱动的建议。这些发现为火灾管理策略提供了宝贵的见解，增强了澳大利亚对未来火灾事件的抵御能力。