A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence

Abstract

Land Surface Temperature (LST) datasets play a crucial role in understanding the complex interplay between forest fires, climate variables, and vegetation dynamics. This study is divided into two primary parts: the first part investigates the predictive performance of a machine learning framework based on CatBoost and XGBoost models in estimating LST across different land cover classes in Alberta, Canada. On the test set, for LST-Day data, CatBoost and XGBoost achieved Median Absolute Errors (MedAE) of approximately 1.434 °C and 1.425 °C, respectively. For LST-Night data, also on the test set, the MedAE values were approximately 1.186 °C for CatBoost and 1.176 °C for XGBoost. The second part explores the intricate relationships between climatic variables—LST, precipitation, and relative humidity—forest fire occurrences, and vegetation dynamics in various subregions. The findings revealed complex interactions, with high LST, reduced precipitation, and humidity associated with increased forest fire activity and subsequent changes in vegetation patterns, particularly in the Central Mixedwood, Dry Mixedwood, and Montane subregions. A notable potential association was identified between high LST, reduced precipitation and humidity, and increased forest fire activity in these areas. These climate change impacts and fire events were found to influence ecological processes, altering species composition, reducing biodiversity, and potentially disrupting ecosystem services such as carbon sequestration and nutrient cycling. These insights are crucial for informing adaptive forest management strategies aimed at understanding and mitigating the cascading effects of climate change on fire regimes and vegetation dynamics in Alberta's diverse landscapes.