乔木林丧失的时空变化及驱动力分析

doi:10.12302/j.issn.1000-2006.202102005

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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

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.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. 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/Abstract

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

中图分类号:

S758

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.

ZHAO Qing, HUANG Fei, CHEN Xiaohui, LIN Yuying, QIU Rongzu, WU Zhilong, HU Xisheng. Spatiotemporal variations and a driving force analysis of arbor forest loss[J].Journal of Nanjing Forestry University (Natural Science Edition), 2022, 46(2): 227-235.DOI: 10.12302/j.issn.1000-2006.202102005.

图/表 6

表1

研究区乔木林丧失的趋势类型及占比"

$β$	$Z$	丧失类型 AFL type	占比/% proportion
$> 0$	$> 1.96$	显著增加	40.00
$> 0$	$≤ 1.96$	轻微增加	23.33
$= 0$	—	稳定不变	0.00
$< 0$	$> 1.96$	显著减弱	23.33
$< 0$	$≤ 1.96$	轻微减弱	13.33

表1

图1

图2

表2

表3

图3

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年份 year	中心坐标 center of inertia	短半轴长度/km minor axis	长半轴长度/km major axis	旋转角/( °) rotation angle	偏移距离/km distance	偏移方向 direction
2005	112.866°E、29.835°N	1 586.497	2 943.023	27.353	—	—
2006	112.738°E、30.814°N	1 564.908	2 965.529	18.934	110.570	西北
2007	111.759°E、28.001°N	1 483.498	2 435.376	29.497	328.862	西南
2008	112.225°E、28.132°N	1 448.443	2 331.758	29.522	47.839	东北
2009	112.186°E、28.099°N	1 540.953	2 286.588	34.663	5.333	西南
2010	111.883°E、28.543°N	1 381.745	2 569.417	29.569	57.696	西北
2011	112.749°E、27.619°N	1 329.107	2 147.443	31.921	133.329	东南
2012	111.458°E、27.700°N	1 442.880	2 117.023	39.833	126.857	西北
2013	111.229°E、26.758°N	1 292.750	1 764.840	49.203	107.450	西南
2014	111.322°E、26.511°N	1 265.255	1 804.882	53.654	28.919	东南
2015	111.424°E、26.133°N	1 104.111	1 613.193	66.049	43.294	东南
2016	111.139°E、26.277°N	1 182.017	1 640.450	65.886	32.564	西北
2017	111.075°E、26.651°N	1 247.947	1 677.324	64.635	42.091	西北
2018	111.092°E、26.595°N	1 264.494	1 688.204	48.479	6.417	东南

模型 model	年份 year	R²	校正R² adjusted R²	AICc
OLS	2005—2011	0.212	0.047	14.428
OLS	2012—2018	0.213	0.049	12.073
GWR	2005—2011	0.858	0.721	-8.241
GWR	2012—2018	0.793	0.615	-3.652

乔木林丧失的时空变化及驱动力分析

Spatiotemporal variations and a driving force analysis of arbor forest loss

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