Spatiotemporal variations and a driving force analysis of arbor forest loss

doi:10.12302/j.issn.1000-2006.202102005

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (2): 227-235.doi: 10.12302/j.issn.1000-2006.202102005

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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 E-mail:1360223929@qq.com;xshu@fafu.edu.com

Abstract

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

CLC Number:

S758

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, 2022, 46(2): 227-235.

Figures/Tables 6

Table 1

Trend types and proportions of arboreal forest loss (AFL)"

$β$	$Z$	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

Table 1

Fig.1

Fig.2

Table 2

Table 3

Fig.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

Spatiotemporal variations and a driving force analysis of arbor forest loss

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[1]	LI Xin, WENG Weisong, LI Mingshi. Assessing natural forest fragmentation process dynamics and its drivers in the Pacific northwest region, USA [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(3): 174-182.
[2]	LI Nan, LI Longwei, LU Dengsheng, ZHANG Yinlong, WU Ming. Ecological security dynamics and trend forecast of coastal wetlands in Hangzhou Bay [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 43(03): 107-115.
[3]	CHEN Yanfei, TANG Chendong, MA Qiang, XUE Jianhui. Landscape pattern change of Chongming Dongtan Nature Reserve of Shanghai from 2011 to 2015 [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2017, 41(01): 1-8.
[4]	WANG Zhi,ZHUANG Changwei,XU Wanggu. Spatio-temporal variation and driving forces of natural landscape patterns in the coast zone of Yancheng [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2014, 38(05): 91-96.
[5]	LYU Yingying, REN Xinyu, LI Mingshi. Assessing forest disturbance patterns over the three forested areas of Nanjing using multi-temporal TM/ETM+ imagery [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2014, 38(01): 77-82.
[6]	YANG Jing, ZHUANG Jiayao, ZHANG Jinchi. Study on the change of land use in the Xuzhou city based on RS and GIS [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2013, 37(02): 85-91.
[7]	GAN Xiao-yu1LI Jian-long2, LIU Xu, ZHANG Qing-feng. Analysis on Changes and Controlling Factors of Urban Green Spaces Based on Remote Sensing in Nanjing [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2007, 31(03): 73-77.