长江三角洲城市化地区植被初级生产力的时空变化研究

doi:10.12082/dqxxkx.2018.180100

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (6): 862-870.doi: 10.12082/dqxxkx.2018.180100

• 2017年中国地理信息科学理论与方法学术年会优秀论文专辑 • 上一篇

长江三角洲城市化地区植被初级生产力的时空变化研究

裴凤松¹(), 王坤¹, 刘小平^2,^1,^*(), 吴长江¹, 周义¹, 刘利安¹

1. 江苏师范大学地理测绘与城乡规划学院,徐州 221116
2. 中山大学地理科学与规划学院,广州 510275

收稿日期:2018-02-08 修回日期:2018-03-28 出版日期:2018-06-20 发布日期:2018-07-12
通讯作者: 刘小平 E-mail:peifs@foxmail.com;liuxp3@mail.sysu.edu.cn
作者简介:
作者简介：裴凤松（1982-）,男,博士,副教授,研究方向为GIS和地理模拟。E-mail: peifs@foxmail.com
基金资助:
国家自然科学基金项目(41401438);大学生创新训练计划项目（201710320030）

Temporal-spatial Dynamics of Vegetation Primary Productivity in the Urbanized Areas in the Yangtze River Delta, China

PEI Fengsong¹(), WANG Kun¹, LIU Xiaoping^1,^2,^*(), WU Changjiang¹, ZHOU Yi¹, LIU Li'an¹

1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
2. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China

Received:2018-02-08 Revised:2018-03-28 Online:2018-06-20 Published:2018-07-12
Contact: LIU Xiaoping E-mail:peifs@foxmail.com;liuxp3@mail.sysu.edu.cn
Supported by:
National Natural Science Foundation of China, No.41401438;National Students' Project for Innovation Traing Program, No.201710320030

摘要/Abstract

摘要：

城市化过程对植被初级生产具有重要影响。以往研究主要集中于城市用地扩张对植被初级生产力的直接影响分析,而较少关注其间接效果。本文以长江三角洲地区为例,分别从地区尺度和城市尺度分别分析了2000-2013年植被初级生产力的时空变化,探讨了其与气温、降水量及城市建成区绿化覆盖率的关系。研究表明：地区尺度上,2000-2013年长江三角洲植被初级生产力呈现不断增加,其中城市建成区植被初级生产力呈现显著增加的趋势（P<0.05）;城市尺度上,城市建成区内植被初级生产力主要呈现增加的趋势,而其外围缓冲区内则与此相反。在当前气候变化背景下,这可能与城市建成区绿化覆盖率不断增加,及快速的城市扩张有关。

关键词: 城市建成区, 植被初级生产力, 绿化覆盖率, 归因分析, 长江三角洲

Abstract:

The process of urbanization, especially the urban land expansion, frequently shows an important influence on vegetation primary productivity. Past studies mainly focused on the direct impacts of urban land sprawl on the vegetation primary productivity, such as transformation of natural land use into urban impervious surface. However, little effort was exerted to understand the indirect impacts of urban land use (i.e., changes of urban greenery coverage). Taking the Yangtze River Delta, China as a case study, this paper analyzed the temporal and spatial changes of vegetation primary productivity in the study area during 2000-2013. The analysis was conducted by cities and over the whole region, respectively. The relationships between vegetation primary productivity in the urban built-up area and the corresponding size of the built-up area were further analyzed for cities by using statistical analysis. Mechanisms of the changes in vegetation primary productivity were explored from both the large and local scales. That is, correlations were examined between vegetation primary productivity and regional meteorological factors (i.e., the annual mean temperature and total precipitation), as well as greenery coverage rate over the built-up area at different cities. The results show that the vegetation primary productivity in the study area showed an overall increased trend from 2000 to 2013 at a regional scale. In particular, the average vegetation primary productivity revealed a significant increasing trend during 2000-2013 in the urban built-up areas in the Yangtze River Delta （P < 0.05）. At city scale, the vegetation primary productivity mainly revealed increased trends over the period 2000-2013 in the built-up area in most of the cities. However, the trends were reversed in the 0~10 km buffer zone of the built-up areas. Under the condition of climate change, this increase of vegetation primary productivity might be associated with the increase of urban greenery coverage in the urban built-up areas, as well as the fast urban expansion in the Yangtze River Delta.

Key words: build-up area, primary productivity, greenery coverage, attribution analysis, Yangtze River Delta

裴凤松, 王坤, 刘小平, 吴长江, 周义, 刘利安. 长江三角洲城市化地区植被初级生产力的时空变化研究[J]. 地球信息科学学报, 2018, 20(6): 862-870.

PEI Fengsong,WANG Kun,LIU Xiaoping,WU Changjiang,ZHOU Yi,LIU Li'an. Temporal-spatial Dynamics of Vegetation Primary Productivity in the Urbanized Areas in the Yangtze River Delta, China[J]. Journal of Geo-information Science, 2018, 20(6): 862-870.

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参考文献 25

[1]	Seto K C, Güneralp B, Hutyra L R.Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools[J]. Proceedings of the National Academy of Sciences, 2012,109(40):16083-16088. doi: 10.1073/pnas.1211658109 pmid: 22988086
[2]	Zhang D,Huang Q X,He C Y, et al.Impacts of urban expansion on ecosystem services in the Beijing-Tianjin-Hebei urban agglomeration, China: A scenario analysis based on the Shared Socioeconomic Pathways[J]. Resources Conservation & Recycling,2017,125:115-130. doi: 10.1016/j.resconrec.2017.06.003
[3]	Field C B,Behrenfeld M J,Randerson J T, et al.Primary production of the biosphere: Integrating terrestrial and oceanic components[J]. Science, 1998,281(5374):237. doi: 10.1126/science.281.5374.237 pmid: 9657713
[4]	Zhao T T,Brown D G,Bergen K M.Increasing gross primary production (GPP) in the urbanizing landscapes of southeastern Michigan[J]. Photogrammetric Engineering & Remote Sensing, 2007,73(10). doi: 10.14358/PERS.73.10.1159
[5]	Liu S S,Du W,Su H, et al.Quantifying impacts of land-use/cover change on urban vegetation gross primary production: A case study of Wuhan, China[J]. Sustainability, 2018,10(3):714. doi: 10.3390/su10030714
[6]	Imhoff M L, Bounoua L, Defries R, et al.The consequences of urban land transformation on net primary productivity in the United States[J]. Remote Sensing of Environment, 2004,89(4):434-443. doi: 10.1016/j.rse.2003.10.015
[7]	Pei F S, Li X, Liu X P, et al. Assessing the differences in net primary productivity between pre- and post-urban land development in China[J]. Agricultural and Forest Meteorology, 2013,171-172:174-186. doi: 10.1016/j.agrformet.2012.12.003
[8]	Wu S H,Zhou S L,Chen D X, et al.Determining the contributions of urbanisation and climate change to NPP variations over the last decade in the Yangtze River Delta, China[J]. Science of the Total Environment, 2014,472:397-406. doi: 10.1016/j.scitotenv.2013.10.128 pmid: 24295756
[9]	Zhao S Q, Liu S G, Zhou D C.Prevalent vegetation growth enhancement in urban environment[J]. Proceedings of the National Academy of Sciences, 2016,113:6313-6318. doi: 10.1073/pnas.1602312113 pmid: 27185955
[10]	国家统计局.中国城市统计年鉴[M].北京:中国统计出版社,2013.
	[ National Bureau of Statistics of China. China City Statistical Yearbook[M]. Beijing: China Statistical Press, 2013. ]
[11]	韩冬锐,徐新良,李静,等.长江三角洲城市群热环境安全格局及土地利用变化影响研究[J].地球信息科学学报,2017,19(1):39-49. doi: 10.3724/SP.J.1047.2017.00039
	[ Han D R, Xu X L, Li J, et al.Study on the security pattern of the heat environment and the influence of land use change in the Yangtze River Delta urban agglomeration[J]. Journal of Geo-information Science, 2017,19(1):39-49. ] doi: 10.3724/SP.J.1047.2017.00039
[12]	Pei F S, Liu X P, Guo J, et al.Assessment of the impact of cropland loss on bioenergy potential: The case of Yangtze River Delta, China[Z]. Achour B, Wu Q. 2017:123-128.
[13]	罗天祥. 中国主要森林类型生物生产力格局及其数学模型[D].北京:中国科学院,1996.
	[ Luo T X.Patterns of net primary productivity for Chinese major forest types and their mathematical models[D]. Beijing: Chinese Academy of Sciences, 1996. ]
[14]	Ruimy A, Kergoat L, Bondeau A, et al.Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency[J]. Global Change Biology, 1999,5(S1):56-64. doi: 10.1046/j.1365-2486.1999.00007.x
[15]	朴世龙,方精云,郭庆华.利用CASA 模型估算我国植被净第一性生产力[J].植物生态学报,2001,25(5):603-608. doi: 10.1088/0256-307X/18/11/313
	[ Piao S L, Fang J Y, Guo Q H, et al.Application of CASA model to the estimation of Chinese terrestrial net primary productivity[J]. Acta Phytoecologica Sinica, 2001,25(5):603-608. ] doi: 10.1088/0256-307X/18/11/313
[16]	Prince S D.A model of regional primary production for use with coarse resolution satellite data[J]. International Journal of Remote Sensing, 1991,12(6):1313-1330. doi: 10.1080/01431169108929728
[17]	Goward S N,Tucker C J,Dye D G.North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer[J]. Vegetatio, 1985,64(1):3-14. doi: 10.1007/BF00033449
[18]	Plant R E,Munk D S,Roberts B R, et al.Application of remote sensing to strategic questions in cotton management and research[J]. Journal of Cotton Science, 2001,1:30-41.
[19]	Imhoff M L, Tucker C J, Lawrence W T, et al.The use of multisource satellite and geospatial data to study the effect of urbanization on primary productivity in the United States[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000,38:2549-2556. doi: 10.1109/36.885202
[20]	Milesi C,Running S W.Global Vegetation Production and Population Distribution[C]. Boston: American Geophysical Union, 2001:B51A-18.
[21]	Ma T, Zhou C H, Pei T, et al.Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities[J]. Remote Sensing of Environment, 2012,124:99-107. doi: 10.1016/j.rse.2012.04.018
[22]	何春阳,史培军,李景刚,等.基于DMSP/OLS夜间灯光数据和统计数据的中国大陆20世纪90年代城市化空间过程重建研究[J].科学通报,2006,51(7):856-861. doi: 10.3321/j.issn:0023-074X.2006.07.017
	[ He C Y, Shi P J, Li J G, et al.Reconstruction of urbanization process in mainland China in the 1990s based on DMSP/OLS night light data and statistical data[J]. Chinese Science Bulletin. 2006,51(7):856-861. ] doi: 10.3321/j.issn:0023-074X.2006.07.017
[23]	方精云,朴世龙,贺金生,等.近20年来中国植被活动在增强[J].中国科学(C辑:生命科学),2003,33(6):554-565. doi: 10.3321/j.issn:1006-9259.2003.06.010
	[ Fang J Y, Piao S L He J S, et al. Vegetation activities have been increasing in China in recent 20 years[J]. Sciencein China SericesC: life Sciences, 2003,33(6):554-565. ] doi: 10.3321/j.issn:1006-9259.2003.06.010
[24]	李广宇,陈爽,张慧,等. 2000-2010年长三角地区植被生物量及其空间分布特征[J].生态与农村环境学报,2016,32(5):708-715.
	[ Li G Y, Chen S, Zhang H, et al.Variation of spatial estimation and distribution of vegetation biomass in Yangtze River Delta during 2000-2010[J]. Journal of Ecology and Rural Environment, 2016,32(5):708-715. ]
[25]	周坚华,胡永红,周一凡,等.城镇绿地植被固碳量遥感测算模型的设计[J].生态学报,2010,30(20):5653-5665.
	[ Zhou J H, Hu Y H, Zhou Y F, et al. A design of carbon-sink model of urban landscape vegetation driven by remote sensing[J]. Acta Ecologica Sinica, 2010,30(20):5653-5665. ]

数据类型	分辨率	数据来源
气象数据	站点	中国气象局气象数据中心
DMSP-OLS夜间灯光数据	1 km	地理国情监测云平台
NDVI数据	1 km、	http://lpdaac.usgs.gov
植被生产数据	森林样地	罗天祥^[13]
社会经济数据	-	中国城市统计年鉴

城市名	0 km		0~5 km		5~10 km
城市名	回归系数	显著性	回归系数	显著性	回归系数	显著性
常州	1.615	0.001	-0.667	0.080	-0.614	0.063
南京	1.973	0.000	0.850	0.078	0.899	0.007
南通	1.649	0.003	-0.307	0.500	0.169	0.599
苏州	2.367	0.000	0.032	0.949	-0.802	0.105
泰州	-0.640	0.381	-1.677	0.011	-0.172	0.782
无锡	0.720	0.151	-0.769	0.050	-0.944	0.004
扬州	1.421	0.004	0.393	0.144	0.895	0.009
镇江	0.807	0.063	-0.895	0.018	-0.453	0.380
上海	2.548	0.000	1.511	0.047	0.681	0.282
杭州	0.934	0.017	-0.419	0.100	-0.077	0.851
湖州	0.919	0.037	0.269	0.429	-0.566	0.259
嘉兴	0.440	0.111	-1.491	0.000	-1.202	0.010
宁波	0.668	0.093	-0.812	0.034	0.539	0.209
绍兴	1.302	0.008	0.089	0.755	0.650	0.003
台州	-0.564	0.305	-0.973	0.002	-0.618	0.127
舟山	-1.097	0.066	0.475	0.053	-0.117	0.758

	常州	南京	南通	苏州	泰州	无锡	扬州	镇江
回归系数	0.784	0.979	0.733	0.880	-0.239	0.500	0.741	0.529
显著性	0.001	0.000	0.003	0.000	0.410	0.069	0.002	0.052
	上海	杭州	湖州	嘉兴	宁波	绍兴	台州	舟山
回归系数	0.727	0.641	0.706	0.469	0.566	0.553	-0.354	-0.118
显著性	0.003	0.013	0.005	0.091	0.035	0.050	0.214	0.687

	常州	南京	南通	苏州	泰州	无锡	扬州	镇江
相关系数	0.686	0.730	0.422	0.733	-0.227	0.103	0.619	0.575
显著性	0.007	0.003	0.133	0.003	0.436	0.727	0.018	0.032
	上海	杭州	湖州	嘉兴	宁波	绍兴	台州	舟山
相关系数	0.492	0.592	0.529	0.453	0.379	-0.044	-0.332	-0.180
显著性	0.074	0.026	0.052	0.104	0.182	0.881	0.246	0.538

长江三角洲城市化地区植被初级生产力的时空变化研究

Temporal-spatial Dynamics of Vegetation Primary Productivity in the Urbanized Areas in the Yangtze River Delta, China

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