Characteristics of spatial-temporal changes and its driving factors in understory vegetation coverage in Hubei Province

Zhao Hui; Dong Shikang; Yang Wei; Li Lu; Yu Bing

doi:10.13961/j.cnki.stbctb.2025.01.010

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Characteristics of spatial-temporal changes and its driving factors in understory vegetation coverage in Hubei Province

- Characteristics of spatial-temporal changes and its driving factors in understory vegetation coverage in Hubei Province
- Bulletin of Soiland Water Conservation Vol. 45, Issue 1, Pages: 82-93(2025)
- 作者机构：
  
  1. 湖北省水利水电科学研究院,湖北,武汉,430070
  2. 湖北省水土保持工程技术研究中心,湖北,武汉,430070
  3. 华中农业大学资源与环境学院,湖北,武汉,430070
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2025.01.010
  CLC： Q948.1;S157
- Published：2025
- 稿件说明：
移动端阅览
Zhao Hui, Dong Shikang, Yang Wei, et al. Characteristics of spatial-temporal changes and its driving factors in understory vegetation coverage in Hubei Province[J]. Bulletin of Soiland Water Conservation, 2025, 45(1): 82-93.
DOI：

Zhao Hui, Dong Shikang, Yang Wei, et al. Characteristics of spatial-temporal changes and its driving factors in understory vegetation coverage in Hubei Province[J]. Bulletin of Soiland Water Conservation, 2025, 45(1): 82-93. DOI： 10.13961/j.cnki.stbctb.2025.01.010.

摘要

[目的

]

研究湖北省林下植被盖度的时空变化特征及其驱动因素，为该区生态环境保护与植被管理提供科学依据。[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

]

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11.072 3，决定系数(R

)为0.732。[结论

]

随机森林回归模型显示同期气温、NDVI和过去1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。

Abstract

[Objective] The temporal and spatial variation of understory vegetation coverage and its driving factors in Hubei Province were studied to provide scientific basis for ecological environment protection and vegetation management in this area. [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

namely spatial location

natural environment

social and economic environment

and climate conditions

on changes in understory green leaf vegetation cover. [Results] Monthly variations in understory litter cover did not show obvious seasonal characteristics

and there was considerable spatial and temporal heterogeneity in the 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 in understory green leaf vegetation cover between broadleaf deciduous and evergreen broadleaf forests were relatively small. The random forest regression model showed the best prediction performance

with a root mean square error (RMSE) of 11.072 3 and a coefficient of determination (R2) of 0.732. [Conclusion] The random forest regression model showed that temperature

NDVI

and precipitation in the previous month were the key driving factors for spatiotemporal changes in the understory green leaf vegetation cover.

关键词

Keywords

references

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