南京林业大学学报(自然科学版) ›› 2022, Vol. 46 ›› Issue (2): 227-235.doi: 10.12302/j.issn.1000-2006.202102005

• 研究论文 • 上一篇    下一篇

乔木林丧失的时空变化及驱动力分析

赵青1(), 黄菲1, 陈晓辉1, 林玉英2, 邱荣祖1, 巫志龙1, 胡喜生1,*()   

  1. 1.福建农林大学交通与土木工程学院,福建 福州 350108
    2.福建师范大学旅游学院,福建 福州 350117
  • 收稿日期:2021-02-01 接受日期:2021-05-23 出版日期:2022-03-30 发布日期:2022-04-08
  • 通讯作者: 胡喜生
  • 基金资助:
    国家自然科学基金项目(31971639);国家自然科学基金项目(41901221);福建省自然科学基金项目(2019J01406)

Spatiotemporal variations and a driving force analysis of arbor forest loss

ZHAO Qing1(), HUANG Fei1, CHEN Xiaohui1, LIN Yuying2, QIU Rongzu1, WU Zhilong1, HU Xisheng1,*()   

  1. 1. College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
    2. College of Tourism, Fujian Normal University, Fuzhou 350117, China
  • Received:2021-02-01 Accepted:2021-05-23 Online:2022-03-30 Published:2022-04-08
  • Contact: HU Xisheng

摘要:

【目的】乔木林是森林生态系统的主体,对调节气候、保持水土等生态功能起着决定性作用。本研究的主要目的是了解乔木林丧失时空变化趋势,并探索乔木林丧失驱动因子。【方法】以我国30省市自治区为研究对象,基于Sen+Mann-Kendall显著性检验法和标准差椭圆法(SDE),从时间和空间两个维度分析2005—2018年乔木林丧失的动态变化;借助探索性回归分析法筛选乔木林(树高>5 m)丧失的主要驱动因子,在此基础上,利用地理加权回归(GWR)模型探讨乔木林丧失驱动因子作用的时空分异格局。【结果】①2005—2018年全国乔木林丧失面积呈现上升趋势,丧失量年均增加412.451 km2;②2005—2018年乔木林丧失重心迁移路径不规则变化且丧失严重区域向南部集聚;③乔木林丧失率与人均GDP主要呈负相关关系;与城镇居民人均可支配收入正相关区域明显扩大但影响降低;与城镇化率主要表现为正相关关系且影响程度有所下降;与道路密度则主要表现为负相关关系,其对乔木林丧失的负面影响并不明显。【结论】在我国森林资源整体持续向好的背景下,乔木林的丧失存在明显的区域差异特征,东北林区及三北防护林工程实施区域的乔木丧失量较小并呈现显著减弱趋势,而东南林区,如湖南、江西、广东、广西等省区的乔木林丧失量较大且仍然呈现较显著增加的趋势。

关键词: 乔木林丧失量, Sen+Mann-Kendall, 标准差椭圆法(SDE), 地理加权回归(GWR), 时空变化, 驱动力

Abstract:

【Objective】As the main body of forest ecosystems, arbor forests play a decisive role regarding ecological functions such as climate regulation and water and soil conservation. The major objective of this study was to understand the spatiotemporal variation trend of arbor forest loss and to explore the driving factors responsible for arbor forest loss in China. 【Method】Based on the Sen+Mann-Kendall significance test and the standard deviation ellipse method, this study analyzed the spatio-temporal changes in tree loss from 2005 to 2018. The exploratory regression analysis was used to screen the main driving factors of tree loss (tree height > 5 m), and the spatiotemporal pattern of the driving factors contributing to tree loss was investigated using a geographically weighted regression model. 【Result】(1) The loss of arbor forest in China showed an increasing trend from 2005 to 2018, with an annual increase of 412.451 km2. (2) From 2005 to 2018, the migration path of arbor forest loss characterized by the gravity center of the forest changed irregularly, and the areas with serious arbor forest loss concentrated in the south. (3) Per capita gross domestic product was mainly negatively correlated with arbor forest loss; the positive correlation area of per capita disposable income of urban residents increased markedly, but the influence decreased; the urbanization rate showed a positive correlation with arbor forest loss, and the degree of influence decreased; road density showed a negative correlation with arbor forest loss, and its negative effect on the loss of arbor forest was not significant. 【Conclusion】In the context of the overall continuous improvement of forest resources in China, however, there are obvious regional differences regarding the loss of arbor forests. This study found that the loss of arbor forests in the northeast forest region and the Three Northern Shelterbelt Project implementation regions was small and showed a significant reducing trend, whereas in the Southeast forest region, the loss of arboreal forests in Hunan, Jiangxi, Guangdong, Guangxi, and other provinces was relatively large and still showed a trend of the significant increase.

Key words: loss of arbor forest, Sen+Mann-Kendall, standard deviation ellipse method(SDE), geographically weighted regression(GWR), spatiotemporal variation, driving force

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