Monitoring Soil Characteristics and Growth of Pinus densiflora Five Years after Restoration in the Baekdudaegan Ridge

Han, Seung Hyun; Kim, Junghwan; Kang, Won Seok; Hwang, Jooho; Park, Ki Hyung; Kim, Chan-Beom

doi:10.13047/kjee.2019.33.4.453

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…We expected that combining BSH with BSP would predict post-fire tree mortality more accurately. To consider other potential variables influencing mortality patterns, we measured the following 11 soil chemical factors, one year after the fire: pH, total nitrogen (TN, %), available phosphorus (P, P 2 O 5 , ppm), organic matter 138 (OM, %); NaCl (%); electric conductivity (EC, dS•m −1 ); exchangeable cations (K + , Na + , Ca 2+ , Mg 2+ ); and 139 cation exchange capacity (CEC, cmol + /kg) [26,27]. We also analyzed inter-tree competition based on DBH and inter-tree distance [28,29].…”

Section: Experimental Design and Samplingmentioning

confidence: 99%

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

Kwon

Kim

et al. 2021

Sustainability

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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.

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Section: Experimental Design and Samplingmentioning

confidence: 99%