Spatial heterogeneity analysis of vegetation recovery in the burned area of the catastrophic fire in the Greater Khingan Mountains

WANG Luyao, ZHANG Haoxia, JU Cunyong, LIU Qingyun, ZHANG Huixin

Journal of Nanjing Forestry University (Natural Sciences Edition） ›› 0

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南京林业大学学报（自然科学版）
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Journal of Nanjing Forestry University (Natural Sciences Edition） ›› 0 DOI: 10.12302/j.issn.1000-2006.202511011

Spatial heterogeneity analysis of vegetation recovery in the burned area of the catastrophic fire in the Greater Khingan Mountains

  • WANG Luyao, ZHANG Haoxia, JU Cunyong*, LIU Qingyun, ZHANG Huixin
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Abstract

【Objective】Post-fire vegetation recovery is a core topic in forestry and ecological research. Exploring the spatial heterogeneity of long-term vegetation recovery and its influencing factors provides the basis for formulating and implementing effective post-fire forest restoration measures. The "5·6" catastrophic forest fire in the Greater Khingan Mountains in 1987 burned an area of approximately 1.33 million hm² and caused a standing timber volume loss of 855,000 m³. After more than 30 years of recovery and succession, the burned area exhibits spatial heterogeneity in vegetation recovery status. To reveal the spatial heterogeneity patterns of vegetation recovery and the underlying driving mechanisms over the past 36 years, this study introduced the magnitude of vegetation recovery, defined as the difference between the current vegetation index value and that of the immediate post-fire year, as an evaluation indicator to quantify the net increase and absolute gain in vegetation recovery. 【Method】The study calculated the normalized difference vegetation index (NDVI), kernel normalized difference vegetation index (KNDVI), and enhanced vegetation index (EVI) from 2020 Landsat 8 OLI imagery of the study area. The study then performed Pearson correlation analysis between these indices and concurrent forest height, canopy height, and canopy density, and selected the optimal vegetation index. The study then calculated the magnitude of vegetation recovery, the dependent variable, using Landsat 5 TM imagery from June 1987 and Landsat 8 OLI imagery from June 2023. The study used 21 driving factors covering fire severity, topography, meteorological conditions, soil, and stand characteristics as independent variables and constructed a spatial dataset. The optimal parameters-based geographical detector evaluated the explanatory power (q) of each factor for the spatial heterogeneity of the magnitude of vegetation recovery and their interactions, and analyzed the relationship between each factor and the magnitude of vegetation recovery. A logistic function captured the nonlinear positive correlation between fire severity and the magnitude of vegetation recovery. Finally, the study divided the study area into ten zones by fire severity class and conducted zone-based optimal parameters-based geographical detector analysis to reveal how the explanatory power of other driving factors changed under different fire severity backgrounds. 【Results】(1) NDVI showed stronger correlations with forest height (r=0.294), canopy height (r=0.160), and canopy density (r=0.213) than KNDVI and EVI. NDVI was therefore selected for calculating the magnitude of vegetation recovery. (2) The spatial distributions of fire severity and the magnitude of vegetation recovery matched closely, with zones of identical fire severity class covering 77.5% of the entire burned area. (3) The global factor detector showed that fire severity was the absolute dominant factor (q=0.833, P<0.01). Growing season high temperature, annual precipitation, and dominant tree species had relatively strong explanatory power for the heterogeneity of the magnitude of vegetation recovery (q>0.1, P<0.01). Factors such as human footprint, forest category, and stand origin, which reflect the degree of human disturbance and stand background, as well as the topographic factor elevation, had significant but relatively weak effects (q<0.1, P<0.01). Aspect, drainage density, road density, and soil texture showed non-significant effects (P>0.05). (4) Fire severity and the magnitude of vegetation recovery exhibited a nonlinear positive relationship. The greatest increase occurred when fire severity ranged from 0.408 to 0.478, indicating the existence of an optimal threshold range in the regulatory effect of fire severity on vegetation recovery. (5) Factor interactions were predominantly bi-factor enhancement and nonlinear enhancement. Temperature factors exhibited more pronounced interactions.(6) Zone-based analysis showed that precipitation, elevation, and temperature factors dominated the spatial heterogeneity of vegetation recovery in low fire severity zones. As fire severity increased, the explanatory power of these factors declined. In high fire severity zones, the explanatory power of dominant tree species increased substantially, becoming the dominant factor governing the magnitude of vegetation recovery. 【Conclusion】The magnitude of vegetation recovery in the burned area of the "5·6" catastrophic fire in the Greater Khingan Mountains exhibits spatial heterogeneity. Fire severity is the dominant driving factor. The response of the magnitude of vegetation recovery to fire severity shows a nonlinear positive correlation, indicating the existence of a critical threshold range in the influence of fire severity on post-fire ecosystem recovery. The explanatory power and influence mechanisms of driving factors vary under different fire severity levels. Key driving factors include elevation, annual mean temperature, growing season high temperature, annual low temperature, annual precipitation, and growing season precipitation. Factor interactions are primarily nonlinear enhancement and bi-factor enhancement, which reinforce the explanatory power of individual factors. The study is limited by early remote sensing accuracy and historical human activity records, making it impossible to fully separate the effects of natural recovery and artificial intervention. Future research should integrate high-resolution remote sensing with long-term ground monitoring for further quantitative analysis.

Key words

vegetation recovery / fire severity / normalized difference vegetation index / optimal parameters-based geographical detector / the “5.6” catastrophic fire in the Greater Khingan Mountains

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WANG Luyao, ZHANG Haoxia, JU Cunyong, LIU Qingyun, ZHANG Huixin. Spatial heterogeneity analysis of vegetation recovery in the burned area of the catastrophic fire in the Greater Khingan Mountains[J]. Journal of Nanjing Forestry University (Natural Sciences Edition）. 0 https://doi.org/10.12302/j.issn.1000-2006.202511011

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