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Examining Spatial Variation in the Effects of Japanese Red Pine ( Pinus densiflora ) on Burn Severity Using Geographically Weighted Regression

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  • Hyun-Joo Lee

    (Graduate Program, Department of Environmental Science, Konkuk University, Gwangjin-gu, Seoul 05029, Korea)

  • Eujin-Julia Kim

    (Department of Landscape Architecture, Gangneung-Wonju National University, Gangneung 25457, Korea)

  • Sang-Woo Lee

    (Department of Forestry and Landscape Architecture, Konkuk University, Gwangjin-gu, Seoul 05029, Korea)

Abstract

Burn severity has profound impacts on the response of post-fire forest ecosystems to fire events. Numerous previous studies have reported that burn severity is determined by variables such as meteorological conditions, pre-fire forest structure, and fuel characteristics. An underlying assumption of these studies was the constant effects of environmental variables on burn severity over space, and these analyses therefore did not consider the spatial dimension. This study examined spatial variation in the effects of Japanese red pine ( Pinus densiflora ) on burn severity. Specifically, this study investigated the presence of spatially varying relationships between Japanese red pine and burn severity due to changes in slope and elevation. We estimated conventional ordinary least squares (OLS) and geographically weighted regression (GWR) models and compared them using three criteria; the coefficients of determination ( R 2 ), Akaike information criterion for small samples (AICc), and Moran’s I -value. The GWR model performed considerably better than the OLS model in explaining variation in burn severity. The results provided strong evidence that the effect of Japanese red pine on burn severity was not constant but varied spatially. Elevation was a significant factor in the variation in the effects of Japanese red pine on burn severity. The influence of red pine on burn severity was considerably higher in low-elevation areas but became less important than the other variables in high-elevation areas. The results of this study can be applied to location-specific strategies for forest managers and can be adopted to improve fire simulation models to more realistically mimic the nature of fire behavior.

Suggested Citation

  • Hyun-Joo Lee & Eujin-Julia Kim & Sang-Woo Lee, 2017. "Examining Spatial Variation in the Effects of Japanese Red Pine ( Pinus densiflora ) on Burn Severity Using Geographically Weighted Regression," Sustainability, MDPI, vol. 9(5), pages 1-18, May.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:5:p:804-:d:98278
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    References listed on IDEAS

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    1. Yiannis Kamarianakis & Wei Shen & Laura Wynter, 2012. "Real‐time road traffic forecasting using regime‐switching space‐time models and adaptive LASSO," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 28(4), pages 297-315, July.
    2. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
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    Cited by:

    1. Eujin-Julia Kim & Sang-Woo Lee, 2018. "Structural Equation Model for Burn Severity with Topographic Variables and Susceptible Forest Cover," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    2. Hyun-Joo Lee & Yun Eui Choi & Sang-Woo Lee, 2018. "Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity," Sustainability, MDPI, vol. 10(2), pages 1-20, January.
    3. Nikolay Baranovskiy & Aleksey Malinin, 2020. "Mathematical Simulation of Forest Fire Impact on Industrial Facilities and Wood-Based Buildings," Sustainability, MDPI, vol. 12(13), pages 1-24, July.

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