A Clustering Approach for Remotely Sensed Data in the Western United States

Abstract

The increasing frequency and scale of wildfires carry significant ecological, socioeconomic, and environmental implications, prompting the need for a deeper grasp of wildfire characteristics. Essential meteorological factors like temperature, humidity, and precipitation wield a crucial impact on fire behavior and the estimation of burned areas. This study aims to unravel the interconnections between meteorological conditions and fire attributes within the Salmon-Challis National Forest located in east-central Idaho, USA. Through the utilization of remotely sensed data from the Fire Monitoring, Mapping, and Modeling system (Fire M3) alongside meteorological variables recorded between 2010 and 2020, an exploration is conducted into varied meteorological patterns associated with wildfire events. By integrating the computed burned area into the clustering process, valuable insights are gained into the specific influences of fire weather conditions on the extent of burned areas. The Salmon-Challis National Forest, encompassing more than 4.3 million acres and encompassing the largest wilderness area in the Continental United States, emerges as a pivotal research site for wildfire investigations. This work elucidates the data attributes employed for clustering and visualization, along with the algorithms employed. Additionally, the study presents research findings and delineates potential future applications, ultimately contributing to the advancement of fire management and mitigation strategies in regions prone to wildfires.