Post-fire vegetation dynamic patterns and drivers in Greater Hinggan Mountains: Insights from long-term remote sensing data analysis

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-10-09 DOI:10.1016/j.ecoinf.2024.102850

Bohan Jiang , Wei Chen , Yuan Zou , Chunying Wu , Ziyi Wu , Xuechun Kang , Haiting Xiao , Tetsuro Sakai

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引用次数: 0

Abstract

Fire has become a major disturbing factor in boreal forests, and giant forest disturbances play a vital role in regulating the climate under global warming. Therefore, it is essential to investigate the spatiotemporal patterns and main drivers of post-fire vegetation recovery for forest ecological research and post-fire recovery management. However, previous studies have focused on the post-fire forest change within the entire fire perimeter, lacking separate analysis and comparison of the burned zone (BZ) and unburned zone (UNBZ). Here, we propose the utilization of Moderate Resolution Imaging Spectroradiometer land cover type and vegetation index data to monitor vegetation dynamics and explore its drivers after the most serious forest fire in the history of P.R. China in the Greater Hinggan Mountains (GHM). The temporal and spatial patterns of vegetation recovery in the BZ/UNBZ in the GHM were analyzed using the Sen & Mann-Kendall method, Hurst index and coefficient of variation, and their driving mechanisms were explored using GeoDetector and geographically weighted regression. The results showed that there were significant differences in the spatial distribution and fluctuation of vegetation between the BZ and UNBZ, and that the BZ exhibited higher productivity and vigor. Vegetation recovery was influenced by different dominant factors and changed over time, in which land surface temperature and precipitation dominated all the time, whereas topographic relief and elevation had a more significant contribution to vegetation recovery in the BZ and UNBZ, respectively. This study provides a scientific basis for the protection and management of vegetation in disturbed forested areas, particularly after fires.

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大兴安岭地区的火后植被动态模式和驱动因素：长期遥感数据分析的启示

火灾已成为北方森林的主要干扰因素，而巨大的森林干扰在全球变暖的情况下对气候的调节起着至关重要的作用。因此，研究火后植被恢复的时空模式和主要驱动因素对于森林生态研究和火后恢复管理至关重要。然而，以往的研究主要关注整个火场周边的火后森林变化，缺乏对烧毁区（BZ）和未烧毁区（UNBZ）的单独分析和比较。在此，我们提出利用中分辨率成像分光仪的土地覆被类型和植被指数数据来监测大兴安岭发生的中国历史上最严重的森林火灾后的植被动态并探讨其驱动因素。利用Sen &, Mann-Kendall方法、Hurst指数和变异系数分析了大兴安岭BZ/UNBZ植被恢复的时空格局，并利用GeoDetector和地理加权回归探讨了其驱动机制。结果表明，BZ 和 UNBZ 的植被空间分布和波动存在显著差异，BZ 表现出更高的生产力和活力。植被恢复受不同主导因子的影响，并随时间发生变化，其中地表温度和降水一直占主导地位，而地形起伏和海拔高度分别对 BZ 和 UNBZ 的植被恢复有更显著的贡献。这项研究为受干扰林区的植被保护和管理，尤其是火灾后的植被保护和管理提供了科学依据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.