基于SEVI的长汀县植被活力时空分异及驱动因子研究

Spatiotemporal differentiation of vegetation vitality and driving factors in Changting County based on the SEVI

摘要

为探究南方红壤区水土流失治理背景下长汀县植被活力的长期变化规律与驱动因素，以 2000—2020 年的 Landsat 影像构建长时序植被活力数据集，采用集成地形校正模型削弱山地复杂地形的影响，来提高影像的光谱一致性和分类精度。结合随机森林分类方法实现主要植被类型的高精度识别，进一步利用 Theil-Sen 趋势分析和 Mann-Kendall 显著性检验，揭示长汀县 2000—2020 年间植被活力的时空演化特征，并通过地理探测器解析其空间分异的主导驱动力及交互作用机制。结果表明：长汀县 4 类植被活力中，阔叶林活力最高，针叶林与竹林次之且数值相近，灌草丛最低，均呈波动上升趋势。重点与非重点治理区变化趋势同全县一致，重点区阔叶林活力优势更突出，区域间植被活力存在差异；全县植被活力显著提升，极显著增加区占 52.17%，重点治理区恢复速度快于非重点治理区；地形因子和植被类型是植被活力变化的主要驱动力，人类活动因子影响力逐渐增强，体现出生态治理与社会发展交互作用的动态演化。

Abstract

To investigate the long-term patterns of variation and driving factors of vegetation vitality in Changting County in the context of soil erosion control in the red soil region of southern China, a long-term vegetation vitality dataset was constructed from Landsat imagery from 2000 to 2020. The integrated topographic correction (ITC) model was applied to mitigate the impact of complex mountainous terrain, thereby enhancing the spectral consistency and classification accuracy of the imagery. High-precision identification of major vegetation types was achieved by integrating the random forest classification method. Furthermore, the Theil-Sen trend analysis and the Mann-Kendall significance test were employed to reveal the spatiotemporal evolution characteristics of vegetation vitality in Changting County from 2000 to 2020. The geodetector was utilized to analyze the dominant driving forces and interaction mechanisms underlying its spatial heterogeneity. The results indicate that among the four vegetation types, broad-leaved forests exhibited the highest vitality, followed by coniferous forests and bamboo forests with similar values, while shrub-grass communities showed the lowest vitality. All vegetation types demonstrated a fluctuating upward trend. The trends in key treatment areas and non-key treatment areas aligned with the overall county pattern. However, the vitality advantage of broad-leaved forests was more pronounced in key treatment areas, and regional differences in vegetation vitality were observed. Vegetation vitality across the county improved significantly, with areas showing a highly significant increase accounting for 52.17%. The recovery rate in key treatment areas exceeded that in non-key treatment areas. Topographic factors and vegetation type were the primary drivers of vegetation vitality change, while the influence of human activity factors has gradually increased, reflecting the dynamic interplay between ecological governance and socio-economic development.

关键词

阴影消除植被指数 / 植被活力 / 地形校正 / 时空演化 / 地理探测器

Key words

shadow-eliminated vegetation index / vegetation vitality / topographic correction / spatiotemporal evolution / geodetector

作者贡献声明

张李洋负责论文结构设计，数据收集、处理与分析，论文撰写。江洪提供经费和资源支持，负责论文构思、指导和修改。岳晓峰参与数据收集、处理和论文的修改。刘家明参与数据收集和处理。黄楠鸿参与数据收集和处理。

AI使用声明

本文的英文题名、摘要和关键词采用腾讯元宝翻译生成，并进行了部分修改。

利益冲突声明

作者声明，不存在已知的可能影响本论文所报告工作的竞争性经济利益或个人关系。

伦理声明

本文不涉及人类或动物研究。

数据可用性

支持论文结论所需的所有数据均已包含在稿件中。如需获取与本文相关的其他数据，可向通信作者提出申请。

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