湖北省林下植被盖度时空变化特征及驱动机理研究
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1.湖北省水利水电科学研究院;2.华中农业大学资源与环境学院

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中图分类号:

S157

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湖北省水利前期工作“湖北省水土保持三级区林下植被覆盖度研究”(2021-218-006-002);湖北省水利前期工作“湖北省水利减碳增汇措施研究”(2022-218-006-001)


Characterization of spatial and temporal changes of understory vegetation coverage and its driving mechanism in Hubei province
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    摘要:

    [目的]深入研究湖北省林下植被盖度的时空变化特征及其驱动机理。[方法]以湖北省为研究区域,通过样地调查并结合DeepLabV3+语义分割方法提取了2022年度湖北省28个样方点每半月的林下绿叶植被盖度及林下枯落物盖度。基于此,采用多种机器学习模型分析了空间位置、自然环境、社会经济环境、气候条件等4类驱动因子对林下绿叶植被盖度变化的作用。[结果]研究显示林下枯落物盖度月度变化并未表现出明显的季节性特征,且各个样方点上林下枯落物盖度时空差异性较大。而林下绿叶植被盖度则呈现明显的季节性变化特征,不同植被类型下的林下绿叶植被盖度存在明显差异:通常经济林和针叶林的林下绿叶植被盖度高于落叶阔叶林和常绿阔叶林,且落叶阔叶林和常绿阔叶林的林下绿叶植被盖度差异较小。随机森林回归模型预测性能最好,均方根误差(RMSE)为11.0723,决定系数(R2)为0.732。[结论]随机森林回归模型显示同期气温、NDVI和过去一个月的降水量是林下绿叶植被盖度时空变化的关键驱动因子。本研究结果为林下植被管理与生态保护提供了科学依据。

    Abstract:

    [Objective] To investigate the spatio-temporal change characteristics and driving mechanisms of understory vegetation cover, [Methods] Hubei Province was selected as the study area, and understory green leaf vegetation cover and understory litter cover were extracted from 28 sample plots for every half month in 2022 through field survey and DeepLabV3+ semantic segmentation method. Based on this, various machine learning models were used to analyze the impact of four driving factors, including spatial location, natural environment, social and economic environment, and climate conditions, on the change of understory green leaf vegetation cover. [Results] The results showed that the monthly variation of understory litter cover did not show obvious seasonal characteristics, and there was considerable spatial and temporal heterogeneity in understory litter cover among the sample plots. In contrast, understory green leaf vegetation cover showed obvious seasonal change characteristics, and there were significant differences in understory green leaf vegetation cover among different vegetation types: the understory green leaf vegetation cover of economic forests and conifers was generally higher than that of broadleaf deciduous forests and evergreen broadleaf forests, and the differences of the understory green leaf vegetation cover between broadleaf deciduous forests and evergreen broadleaf forests were relatively small. The random forest regression model had the best prediction performance, with a root mean square error (RMSE) of 11.0723 and a coefficient of determination (R2) of 0.732. [Conclusion] The random forest regression model shows that the temperature, NDVI, and precipitation in the previous month are the key driving factors for the spatio-temporal change of understory green leaf vegetation cover. The results of this study provide scientific evidence for the management of understory vegetation and ecological protection.

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  • 收稿日期:2024-09-04
  • 最后修改日期:2024-10-25
  • 录用日期:2024-10-25
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