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

Previous Articles Next Articles

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

References 46

[1]	WANG X G, Comita Liza S, HAO Z Q, et al. Local-Scale drivers of tree survival in a temperate forest[J]. PLoS ONE, 2018, 7(2).DOI: 10.1371/journal.pone.0029469. doi: 10.1371/journal.pone.0029469
[2]	LIU Y L, FENG Y H, ZHAO Z, et al. Socioeconomic drivers of forest loss and fragmentation: a comparison between different land use planning schemes and policy implications[J]. Land Use Policy, 2016, 54:58-68. DOI: 10.1016/j.landusepol.2016.01.016. doi: 10.1016/j.landusepol.2016.01.016
[3]	MILLER M D. The impacts of Atlanta’s urban sprawl on forest cover and fragmentation[J]. Appl Geogr, 2012, 34:171-179. DOI: 10.1016/j.apgeog.2011.11.010. doi: 10.1016/j.apgeog.2011.11.010
[4]	NEWMAN M E, MCLAREN K P, WILSON B S. Assessing deforestation and fragmentation in a tropical moist forest over 68 years; the impact of roads and legal protection in the Cockpit Country, Jamaica[J]. For Ecol Manage, 2014, 315:138-152. DOI: 10.1016/j.foreco.2013.12.033. doi: 10.1016/j.foreco.2013.12.033
[5]	LI M S, HUANG C Q, ZHU Z, et al. Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model[J]. For Ecol Manage, 2008, 257(6):1480-1488. DOI: 10.1016/j.foreco.2008.12.023. doi: 10.1016/j.foreco.2008.12.023
[6]	CARRANZA M L, HOYOS L, FRATE L, et al. Measuring forest fragmentation using multitemporal forest cover maps: forest loss and spatial pattern analysis in the Gran Chaco, central Argentina[J]. Landscape Urban Plann, 2015, 143:238-247. DOI: 10.1016/j.landurbplan.2015.08.006. doi: 10.1016/j.landurbplan.2015.08.006
[7]	TANG J, BU K, YANG J C, et al. Multitemporal analysis of forest fragmentation in the upstream region of the Nenjiang River Basin, northeast China[J]. Ecol Indic, 2012, 23:597-607. DOI: 10.1016/j.ecolind.2012.05.012. doi: 10.1016/j.ecolind.2012.05.012
[8]	BREMER L L, FARLEY K A. Does plantation forestry restore biodiversity or create green deserts: a synthesis of the effects of land-use transitions on plant species richness[J]. Biodiversity Conserv, 2010, 19(14): 3893-3915. DOI: 10.1007/s10531-010-9936-4. doi: 10.1007/s10531-010-9936-4
[9]	LINDENMAYER D B. Landscape change and the science of biodiversity conservation in tropical forests: a view from the temperate world[J]. Biol Conserv, 2009, 143(10):2405-2411. DOI: 10.1016/j.biocon.2009.12.015. doi: 10.1016/j.biocon.2009.12.015
[10]	LÜ Y H, ZHANG L W, FENG X M, et al. Recent ecological transitions in China: greening, browning and influential factors[J]. Sci Rep, 2015, 51(1):587-592. DOI: 10.1038/srep08732. doi: 10.1038/srep08732
[11]	李凌超, 刘金龙, 程宝栋, 等. 中国劳动力转移对森林转型的影响[J]. 资源科学, 2018, 40(8):1526-1538.
	LI L C, LIU J L, CHENG B D, et al. Influence of labor transfer on China’s forest transition[J]. Resour Sci, 2018, 40(8):1526-1538. DOI: 10.18402/resci.2018.08.04. doi: 10.18402/resci.2018.08.04
[12]	赵晓迪, 李凌超, 杨文涛, 等. 福建省县域森林转型社会经济影响因素分析[J]. 林业科学, 2019, 55(8):147-156.
	ZHAO X D, LI L C, YANG W T, et al. Analysis of socio-economic influencing factors of forest transition in counties of Fujian Province[J]. Sci Silvae Sin, 2019, 55(8):147-156. DOI: 10.11707/j.1001-7488.20190816. doi: 10.11707/j.1001-7488.20190816
[13]	MORTON D C, DEFRIES R S, SHIMABUKURO Y E, et al. Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon[J]. Proc Natl Acad Sci USA, 2006, 103(39):14637-14641. DOI: www.pnas.org/cgi/doi/10.1073/pnas.0606377103. doi: www.pnas.org/cgi/doi/10.1073/pnas.0606377103
[14]	CARR D L, SUTER L, BARBIERI A. Population dynamics and tropical deforestation: state of the debate and conceptual challenges[J]. Popul Environ, 2006, 27(1):89-113. DOI: 10.1007/s11111-005-0014-x. doi: 10.1007/s11111-005-0014-x
[15]	LIN Y Y, QIU R Z, YAO J X, et al. The effects of urbanization on China’s forest loss from 2000 to 2012: evidence from a panel analysis[J]. J Cleaner Prod,2019, 214:270-278. DOI: 10.1016/j.jclepro.2018.12.317. doi: 10.1016/j.jclepro.2018.12.317
[16]	GONG C F, YU S X, JOESTING H, et al. Determining socioeconomic drivers of urban forest fragmentation with historical remote sensing images[J]. Landscape Urban Plann, 2013, 117:57-65. DOI: 10.1016/j.landurbplan.2013.04.009. doi: 10.1016/j.landurbplan.2013.04.009
[17]	HOEK J V D, OZDOGAN M, BURNICKI A, et al. Evaluating forest policy implementation effectiveness with a cross-scale remote sensing analysis in a priority conservation area of southwest China[J]. Appl Geogr, 2014, 47:177-189. DOI: 10.1016/j.apgeog.2013.12.010. doi: 10.1016/j.apgeog.2013.12.010
[18]	吴恒, 朱丽艳, 王海亮, 等. 乔木树种分布格局和林分空间结构分析[J]. 林业资源管理, 2020(1): 54-61.
	WU H, ZHU L Y, WANG H L, et al. Analyses on arbor tree species distribution pattern and stand spatial structure[J]. For Resour Manage, 2020(1): 54-61. DOI: 10.13466/j.cnki.lyzygl.2020.01.008. doi: 10.13466/j.cnki.lyzygl.2020.01.008
[19]	HANSEN M C, POTAPOV P V, MOORE R, et al. High-Resolution Global maps of 21st-century forest cover change[J]. Sciince, 2013, 342(6160):850-853. DOI: 10.1126/science.1244693. doi: 10.1126/science.1244693
[20]	BRYAN B A, GAO L, YE Y Q, et al. China’s response to a national land-system sustainability emergency[J]. Nat (Int Wkly J Sci), 2018, 559(7713):193-204. DOI: 10.1038/s41586-018-0280-2. doi: 10.1038/s41586-018-0280-2
[21]	中国林业科学研究院森林生态环境与保护研究所. 林业资源分类与代码森林类型: GB/T 14721-2010[S]. 北京: 中国标准出版社, 2010:88.
[22]	王昊, 吕植, 顾垒, 等. 基于Global Forest Watch观察2000-2013年间中国森林变化[J]. 生物多样性, 2015, 23(5):575-582. doi: 10.17520/biods.2015122
	WANG H, LV Z,GUL, et al. Observations of China’s forest change(2000-2013) based on Global Forest Watch dataset[J]. Biodiversity Sci, 2015, 23(5):575-582. DOI: 10.17520/biods.2015122. doi: 10.17520/biods.2015122
[23]	袁丽华, 蒋卫国, 申文明, 等. 2000-2010年黄河流域植被覆盖的时空变化[J]. 生态学报, 2013, 33(24):7798-7806.
	YUAN L H, JIANG W G, SHEN W M, et al. The spatio-temporal variations of vegetation cover in the Yellow River Basin from 2000 to 2010[J]. Acta Ecol Sin,2013, 33(24):7798-7806. DOI: 10.5846/stxb201305281212. doi: 10.5846/stxb201305281212
[24]	张莉, 陈芸芝, 汪小钦. 基于时序MODIS NDVI数据的长汀县植被趋势特征研究[J]. 福州大学学报(自然科学版), 2016, 44(5):661-667.
	ZHANG L, CHEN Y Z, WANG X Q. Vegetation trends research of Changting County based on time series of MODIS NDVI[J]. J Fuzhou Univ(Nat Sci Ed), 2016, 44(5):661-667. DOI: 10.7631/issn.1000-2243.2016.05.0661. doi: 10.7631/issn.1000-2243.2016.05.0661
[25]	蔡博峰, 于嵘. 基于遥感的植被长时序趋势特征研究进展及评价[J]. 遥感学报, 2009, 13(6):1170-1186.
	CAI B F, YU R. Advance and evaluation in the long time series vegetation trends research based on remote sensing[J]. J Remote Sens, 2009, 13(6):1170-1186. DOI: 10.11834/jrs.20090614. doi: 10.11834/jrs.20090614
[26]	邵霜霜, 师庆东. 基于FVC的新疆植被覆盖度时空变化[J]. 林业科学, 2015, 51(10):35-42.
	SHAO S S, SHI Q D. Spatial and temporal change of vegetation cover in Xinjiang based on FVC[J]. Sci Silvae Sin, 2015, 51(10):35-42. DOI: 10.11707/j.1001-7488.20151005. doi: 10.11707/j.1001-7488.20151005
[27]	王佃来, 刘文萍, 黄心渊. 基于Sen+Mann-Kendall的北京植被变化趋势分析[J]. 计算机工程与应用, 2013, 49(5):13-17.
	WANG D L, LIU W P, HUANG X Y. Trend analysis in vegetation cover in Beijing based on Sen+Mann-Kendall method[J]. Comput Eng Appl,2013, 49(5):13-17. DOI: 1002-8331 (2013) 05-0013-05. doi: 1002-8331 (2013) 05-0013-05
[28]	黄晓军, 祁明月, 李艳雨, 等. 关中地区PM_2.5时空演化及人口暴露风险[J]. 环境科学, 2020, 41(12):5245-5255.
	HUANG X J, QI M Y, LI Y L, et al. Spatio-temporal evolution and population exposure risk to PM_2.5 in the Guanzhong area[J]. Environ Sci, 2020, 41(12):5245-5255. DOI: 10.13227/j.hjkx.202004189. doi: 10.13227/j.hjkx.202004189
[29]	YUAN J F, BIAN Z F, YAN Q W, et al. An approach to the temporal and spatial characteristics of vegetation in the growing season in western China[J]. Remote Sens, 2020, 12(6):945. DOI: 10.3390/rs12060945. doi: 10.3390/rs12060945
[30]	康雄, 曹俊涛, 陈成, 等. 不同趋势法的宁夏长时序植被变化分析[J]. 测绘通报, 2020, 11(11):23-27.
	KANG X, CAO J T, CHEN C, et al. Analysis of long-term vegetation change in Ningxia with different trend methods[J]. Bull Surv Mapp, 2020, 11(11):23-27. DOI: 10.13474/j.cnki.11-2246.2020.0348. doi: 10.13474/j.cnki.11-2246.2020.0348
[31]	LEFEVER D W. Measuring geographic concentration by means of the standard deviational ellipse[J]. Am J Sociol, 1926, 32(1):88-94. DOI: 10.1086/214027. doi: 10.1086/214027
[32]	WANG B, SHI W Z, MIAO Z L. Confidence analysis of standard deviational ellipse and its extension into higher dimensional euclidean space[J]. PLoS ONE, 2017, 10(3):e118537. DOI: 10.1371/journal.pone.0118537. doi: 10.1371/journal.pone.0118537
[33]	SHI Y S, MATSUNAGA T, YAMAGUCHI Y, et al. Long-term trends and spatial patterns of satellite-retrieved PM_2.5 concentrations in south and southeast Asia from 1999 to 2014[J]. Sci Total Environ,2018, 615:177-186. DOI: 10.1016/j.scitotenv.2017.09.241. doi: 10.1016/j.scitotenv.2017.09.241
[34]	夏晓圣, 汪军红, 宋伟东, 等. 2000-2019年中国PM_2.5时空演化特征[J]. 环境科学, 2020, 41(11):4832-4843.
	XIA X S, WANG J H, SONG W D, et al. Spatio-temporal evolution of PM_2.5 concentration during 2000-2019 in China[J]. Environ Sci, 2020, 41(11):4832-4843. DOI: 10.13227/j.hjkx.202004108. doi: 10.13227/j.hjkx.202004108
[35]	蔡汉, 朱权, 罗云建, 等. 快速城镇化地区耕地景观生态安全格局演变特征及其驱动机制[J]. 南京林业大学学报(自然科学版), 2020, 44(5):181-188.
	CAI H, ZHU Q, LUO Y J, et al. Evolution characteristics and driving mechanisms of cultivated landscape eco-security patterns in rapid urbanization areas[J]. J Nanjing For Univ (Nat Sci Ed), 2020, 44(5):181-188. DOI: 10.3969/j.issn.1000-2006.201907047. doi: 10.3969/j.issn.1000-2006.201907047
[36]	薛陈利, 张会琼, 邹滔, 等. 中老铁路经济廊带生态质量及其与人类活动的关系[J]. 应用生态学报, 2021, 32(2):638-648.
	XUE C L, ZHANG H Q, ZOU T, et al. Ecological quality and its relationships with human activities in China-Laos Railway Economic Belt[J]. Chin J Appl Ecol, 2021, 32(2):638-648. DOI: 10.13287/j.1001-9332.202102.011. doi: 10.13287/j.1001-9332.202102.011
[37]	隋雪艳, 吴巍, 周生路, 等. 都市新区住宅地价空间异质性驱动因素研究--基于空间扩展模型和GWR模型的对比[J]. 地理科学, 2015, 35(6):683-689. doi: 10.13249/j.cnki.sgs.2015.06.683
	SUI X Y, WU W, ZHOU S L, et al. Drive Pattern on the spatial heterogeneity of residential land price in urban district: a comparison of spatial expansion method and GWR model[J]. Sci Geogr Sin, 2015, 35(6):683-689. DOI: 10.13249/j.cnki.sgs.2015.06.003. doi: 10.13249/j.cnki.sgs.2015.06.003
[38]	白立敏, 冯兴华, 孙瑞丰, 等. 生境质量对城镇化的时空响应--以长春市为例[J]. 应用生态学报, 2020, 31(4):1267-1277.
	BAI L M, FENG X H, SUN R F, et al. Spatial and temporal responses of habitat quality to urbanization: a case study of Changchun City, Jilin Province, China[J]. Chin J Appl Ecol, 2020, 31(4):1267-1277. DOI: 10.13287/j.1001-9332.202004.012. doi: 10.13287/j.1001-9332.202004.012
[39]	薛瑞晖, 于晓平, 李东群, 等. 基于地理加权回归模型探究环境异质性对秦岭大熊猫空间利用的影响[J]. 生态学报, 2020, 40(8):2647-2654.
	XUE E H, YU X P, LI D Q, et al. Using geographically weighted regression to explore the effects of environmental heterogeneity on the space use by giant pandas in Qinling Mountains[J]. Acta Ecol Sin, 2020, 40(8):2647-2654. DOI: 10.5846/stxb201903120469. doi: 10.5846/stxb201903120469
[40]	YACKULIC C B, FAGAN M, JAIN M, et al. Biophysical and socioeconomic factors associated with forest transitions at multiple spatial and temporal scales[J]. Ecol Soc, 2011, 16(3):15. DOI: 10.5751/ES-04275-160315. doi: 10.5751/ES-04275-160315
[41]	侯孟阳, 姚顺波. 森林资源与经济增长的EKC关系检验--基于省际面板数据的实证研究[J]. 林业科学, 2019, 55(12):113-122.
	HOU M Y, YAO S B. Testing of the EKC relationship between amount of forest resources and economic growth: an empirical study based on provincial panel data[J]. Sci Silvae Sin, 2019, 55(12):113-122. DOI: 10.11707/j.1001-7488.20191212. doi: 10.11707/j.1001-7488.20191212
[42]	曹荣青, 胡喜生, 吴承祯. 福建省2000-2012年森林丧失量时空分布动态研究[J]. 山东农业大学学报(自然科学版), 2019, 50(2):197-201.
	CAO R Q, HU X S, WU C Z. Study on temporal and spatial distribution of forest loss in Fujian Province during 2000 to 2012[J]. J Shandong Agric Univ (Nat Sci Ed), 2019, 50(2):197-201. DOI: 10.3969/j.issn.1000-2324.2019.02.004. doi: 10.3969/j.issn.1000-2324.2019.02.004
[43]	侯秀英, 黄菲, 赵青, 等. 福建省森林丧失的时空格局演化及其驱动力机制[J]. 山地学报, 2020, 38(6):829-840.
	HOU X Y, HUANG F, ZHAO Q, et al. Spatial-temporal pattern evolution and driving mechanism of forest loss in Fujian Province, China[J]. Mt Res, 2020, 38(6):829-840. DOI: 10.16089 /j.cnki.1008-2786.000559. doi: 10.16089 /j.cnki.1008-2786.000559
[44]	李奇, 朱建华, 范立红, 等. 西南地区乔木林碳储量及木材生产潜力预测[J]. 生态环境学报, 2018, 27(3):416-423.
	LI Q, ZHU J H, FAN L H, et al. Prediction of forest carbon storage and timber yield potential in southwestern China[J]. Ecol and Environ Sci, 2018, 27(3):416-423. DOI: 10.16258/j.cnki.1674-5906.2018.03.003. doi: 10.16258/j.cnki.1674-5906.2018.03.003
[45]	刘豪, 徐冬梅. 山西省乔木林碳汇动态趋势研究[J]. 林业资源管理, 2019(6):49-54,68.
	LIU H, XU D M. Study on the dynamic trend of carbon sinks in arbor forests in Shanxi Province[J]. For Resour Manage, 2019(6):49-54, 68. DOI: 10.13466/j.cnki.lyzygl.2019.06.010. doi: 10.13466/j.cnki.lyzygl.2019.06.010
[46]	冉建波, 陈兴伟. 土地利用空间格局的置换变化与代换变化分析[J]. 地球信息科学学报, 2015, 17(6):661-667. doi: 10.3724/SP.J.1047.2015.00661
	RAN J B, CHEN X W. Displacement and substitution of the land-use spatial pattern[J]. J Geo-Inf Sci, 2015, 17(6):661-667. DOI: 10.3724/SP.J.1047.2015.00661. doi: 10.3724/SP.J.1047.2015.00661

Metrics

Comments

www.nldxb.njfu.edu.cn

Edited ＆ Published by Editorial Department of Nanjing Forestry University(Natural Sciences Edition)
Address： No.159 Longpan Road,Nanjing 210037 Jiangsu,P.R.China
E-mail ：xuebao@njfu.edu.cn , xuebao@njfu.com.cn
Telephone +86-25-85428247,85427076
Distributed by China International Book Trading Corporation P.O. Box 399, Beijing ,P.R. China

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

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 46

Related Articles 7

Recommended Articles

Metrics

Comments

Author

Reader

Reviewer

[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, 62(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, 60(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, 57(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, 57(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, 56(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, 50(03): 73-77.