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

TL;DR
This paper explores the relationship between meteorological factors and wildfire characteristics in the Salmon-Challis National Forest using remotely sensed data and clustering techniques to improve fire management strategies.
Contribution
It introduces a clustering approach that integrates burned area data with meteorological variables to analyze wildfire patterns and influences in a major U.S. wilderness area.
Findings
Meteorological patterns significantly influence wildfire extent.
Clustering reveals distinct fire weather condition groups.
Insights support improved fire management strategies.
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…
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Taxonomy
TopicsFire effects on ecosystems · Fire Detection and Safety Systems · Landslides and related hazards
