Moisture Dynamics and Modeling of Ground Surface Fine Dead Combustibles in Pinus massoniana Forest in Southern Jiangxi,China
[Objective]The moisture content of the surface fine dead combustibles(SFDC,including dead leaves,thin branches,dead grass,needles,etc.)significantly influences forest fire ignition and behavior.Understanding of the SFDC is essential for early warning of forest fire in a region.This study focuses on predicting SFDC in Masson pine forests in southern Jiangxi Province.[Method]We conducted long-term field observations of SFDC in Masson pine,a prevalent vegetation type in southern Jiangxi.The study involved a comparative analysis of various predictive models,considering meteorological factors'random forest relative importance and Pearson correlation in different terrains and times.[Result]SFDC in Masson pine forests shows a notable variability,with higher moisture content on shady slopes compared to sunny slopes,especially at the early time of fire prevention periods.A strong correlation(P<0.001)exists between SFDC and meteorological factors(temperature,humidity,wind speed,sunlight).The random forest model outperformed the meteorological factor regression model in accuracy,particularly on shady slopes.Sunlight,with a lag effect,and air humidity on sunny slopes and wind speed on shady slopes were the most influential factors.[Conclusion]Meteorological factors with time lag critically affect SFDC in Masson pine forests.Improved consideration of these factors enhances the prediction accuracy of the moisture content of the SFDC,offering a reliable basis for early warning of fire risk.
moisture content of surface fine dead combustiblesprediction modelmeteorological regression modelrandom forestsouthern Jiangxi
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