乔木林丧失的时空变化及驱动力分析

赵青; 黄菲; 陈晓辉; 林玉英; 邱荣祖; 巫志龙; 胡喜生

doi:10.12302/j.issn.1000-2006.202102005

乔木林丧失的时空变化及驱动力分析

赵青, 黄菲, 陈晓辉, 林玉英, 邱荣祖, 巫志龙, 胡喜生

南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (2) : 227-235.

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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

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^,^*

作者信息 +

Spatiotemporal variations and a driving force analysis of arbor forest loss

ZHAO Qing ¹ ,
HUANG Fei ¹ ,
CHEN Xiaohui ¹ ,
LIN Yuying ² ,
QIU Rongzu ¹ ,
WU Zhilong ¹ ,
HU Xisheng ¹^,^*

Author information +

文章历史 +

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。

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.

导出引用

赵青, 黄菲, 陈晓辉, 等. 乔木林丧失的时空变化及驱动力分析[J]. 南京林业大学学报（自然科学版）. 2022, 46(2): 227-235 https://doi.org/10.12302/j.issn.1000-2006.202102005

ZHAO Qing, HUANG Fei, CHEN Xiaohui, et al. Spatiotemporal variations and a driving force analysis of arbor forest loss[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2022, 46(2): 227-235 https://doi.org/10.12302/j.issn.1000-2006.202102005

中图分类号： S758

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摘要

了解森林的变化, 是评价生物多样性变化趋势和制订有效保护行动的基础, 也是公共政策及广大公众普遍关心的议题, 然而国内一直都没有可供空间分析的森林分布及变化的公开数据。Hansen等于2013年在Global Forest Watch网站上发布了基于Landsat数据的30 m分辨率的全球森林变化数据集, 第一次提供给研究者一套高分辨率的公开数据。我们解读了2000-2013年的数据, 并与已有的国家森林清查、ChinaCover、GlobeLand30等数据集进行比较。结果表明, Global Forest Watch数据质量可靠, 其与GlobeLand30数据集的森林分布有较高的空间重合度, 后者森林类别的生产者精度为87%, 用户精度为89%。在大于20%的树木覆盖度下, 估算出2000年森林面积为1,780,472 km2, 与同期国家森林清查数据1,749,092 km2相近, 解读结果表明2000-2010年间森林减少面积在37,551-42,031 km2之间, 而同期其他几套数据显示森林面积增量为2,370-433,810 km2。森林面积年减少量最大值出现在2008年, 以该年为拐点, 森林每年减少面积的变化趋势在此之前为显著增加, 在此之后为显著降低, 林地变化最活跃的区域在广西、广东、福建、江西和云南等南方省份。到2013年底, 占保护区总面积约60%以上的407个国家级保护区仅覆盖了森林面积的5.03%, 对森林保护的面积覆盖有限, 保护区对其内部的森林保护有成效, 区内森林面积净减1.39%, 低于全国平均水平(3.46%), 相当于少毁林1,856 km2, 但仍有1,200 km2的净减。

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摘要

以南京市江宁区为例,基于2004~2011年住宅用地出让数据,利用空间扩展模型和GWR模型对都市新区住宅地价空间异质性及其驱动因素进行研究。结果表明：① 空间扩展模型与GWR模型分别可解释采样区63%、61%的住宅地价变化,较全局回归模型(47%)有显著提升,更有利于研究土地市场的空间异质性。② 空间扩展模型可有效表征各解释变量及其交互项对住宅地价作用的空间结构总体趋势,其拟合效果相对较优。GWR模型则在局部参数估计方面存在优势,借助GIS可将各变量的地价作用模式可视化,从而比空间扩展模型更能有效刻画住宅地价影响因素的空间非平稳性特征,各因素对地价的平均边际贡献排序为水域> 地铁> 大学园区> CBD> 商业网点> 医院,且商业网点、医院系数值具有方向差异性。③ 距地铁站点、水域、大学园区以及CBD的距离是研究区住宅地价的关键驱动因素,各自存在特有的地价空间作用模式,可为研究区住宅土地市场细分提供科学依据。

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摘要

为揭示土地利用空间格局的动态变化,本文基于土地利用转移矩阵,从类型间的空间转移过程出发,构建了置换变化与代换变化的计算模型,并对其2种变化进行了分析。置换变化是2种类型之间以相同面积发生空间位置的置换过程,且各种类型的数量结构保持不变;代换变化则是所研究类型的数量结构保持不变,而与之转换的其他类型数量上的增加或减少;从而将单一类型的总变化进一步细分为置换变化、代换变化与净变化。以晋江流域1985年与2006年2期土地利用格局的变化为例进行分析,结果显示：在1985-2006年间,晋江流域的土地利用空间格局发生了显著变化;园地、建设用地、未利用地呈现扩张态势,草地与旱地呈现萎缩变化,水域变化不大;这6种地类的交换变化面积相对较少,以置换变化为主。水田与林地则主要表现为空间位置的交换变化,其中,水田呈现出与林地、旱地2种类型之间的置换变化,而林地则主要是园地-林地与林地-草地这2对类型间的代换变化。结果表明,通过置换变化与代换变化的分析,更细化了各种类型之间的动态变化过程。

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