Predicting Post-Fire Tree Mortality in a Temperate Pine Forest, Korea

Semyung Kwon, Sanghyun Kim, Jeonghwan Kim, Wonseok Kang, Ki-Hyung Park, Chan-Beom Kim and Miguel M. Girona
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Semyung Kwon: Division of Forest Restoration and Resource Management, National Institute of Forest Science, Seoul 02455, Korea
Sanghyun Kim: Division of Forest Restoration and Resource Management, National Institute of Forest Science, Seoul 02455, Korea
Jeonghwan Kim: Division of Forest Restoration and Resource Management, National Institute of Forest Science, Seoul 02455, Korea
Wonseok Kang: Division of Forest Restoration and Resource Management, National Institute of Forest Science, Seoul 02455, Korea
Ki-Hyung Park: Division of Forest Restoration and Resource Management, National Institute of Forest Science, Seoul 02455, Korea
Chan-Beom Kim: Temperate Middle Part Plant Conservation Team, Sejong National Arboretum, Sejong 30106, Korea
Miguel M. Girona: Group of Research in Ecology-MRC Abitibi (GREMA), Forest Research Institute, University of Québec in Abitibi-Témiscamingue, Amos Campus, Amos, QC J9T 2L8, Canada

Sustainability, 2021, vol. 13, issue 2, 1-16

Abstract: Warmer and drier conditions in temperate regions are increasing the length of the wildfire season. Given the greater fire frequency and extent of burned areas under climate warming, greater focus has been placed on predicting post-fire tree mortality as a crucial component of sustainable forest management. This study evaluates the potential of logistic regression models to predict post-fire tree mortality in Korean red pine ( Pinus densiflora ) stands, and we propose novel means of evaluating bark injury. In the Samcheok region of Korea, we measured topography (elevation, slope, and aspect), tree characteristics (tree/crown height and diameter at breast height (DBH)), and bark injuries (bark scorch height/proportion/index) at three sites subjected to a surface fire. We determined tree status (dead or live) over three years after the initial fire. The bark scorch index (BSI) produced the best univariate model, and by combining this index with the DBH produced the highest predictive capacity in multiple logistic regression models. A three-variable model (BSI, DBH, and slope) enhanced this predictive capacity to 87%. Our logistic regression analysis accurately predicted tree mortality three years post fire. Our three-variable model provides a useful and convenient decision-making tool for land managers to optimize salvage harvesting of post-fire stands.

Keywords: bark scorch index (BSI); forest ecosystems; natural disturbances; sustainable management; tree mortality; logistic regression; Pinus densiflora; wildfire (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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