昆明市城市化及城市热岛效应对植被净初级生产力的影响

师静; 鲁雪媛; 陈旭

doi:10.11829/j.issn.1001-0629.2022-0511

昆明市城市化及城市热岛效应对植被净初级生产力的影响

1.
贵州省林业调查规划院, 贵州贵阳 550001
2.
中国科学院、水利部成都山地灾害与环境研究所,四川成都 610041
3.
云南师范大学地理学部, 云南昆明 650500

基金项目: 农业联合专项“云南省植被净初级生产力时空分布模式及时空变异机制研究”(202101BD07001-042)；云南省教育厅重点项目“基于多源遥感的昆明市城市化对NPP影响模式研究”(2021J0151)

摘要: 城市化极大地影响了城市区域植被净初级生产力(NPP)，但城市化对城市植被NPP的时空驱动机制尚不明确。本研究将城市化对NPP的影响分离为直接影响(NPP_dir)和间接影响(NPP_ind)，基于地理探测器和地理加权回归分别分析了城市化对NPP_dir与城市热岛效应对NPP_ind的影响和驱动机制。结果表明：1)城市化是NPP时空分异性的主导因素，城市化与城市热岛效应的交互作用对NPP的解释力均大于单因子对NPP分布影响的解释力。2)不透水面丰度(IS)对城市化区域NPP有着强烈的负面影响，而城市热岛效应在城市中心城区对NPP有着正面促进作用，城市热岛效应在一定程度上补偿了城市化对NPP的负面影响。3)相对于普通最小二乘法回归(OLS)模型，地理加权回归模型(GWR)在城市化对NPP_dir和城市热岛效应对NPP_ind的相关性分析中都具有更高的拟合度。本研究对于可持续化城市发展和城市化对NPP的影响以及碳循环等研究具有一定的参考价值。

关键词:

English

Study on the impact of urbanization and urban heat island effect on net primary productivity in Kunming

1.
Guizhou Province Forestry Investigation and Planning Institute, Guiyang 550001, Guizhou, China
2.
Chengdu Institute of Mountain Disasters and Environment, Chinese Academy of Sciences and Ministry of Water Resources, Chengdu 610041, Sichuan, China
3.
Department of Geography, Yunnan Normal University, Kunming 650500, Yunnan, China

Received Date: 2022-06-21
Accepted Date: 2022-09-24
Available Online: 2023-03-07
Publish Date: 2022-12-14

Abstract: Urbanization greatly affects the net primary productivity (NPP) of vegetation in urban areas, but the spatial and temporal mechanisms of urbanization that drive the NPP of urban vegetation are unclear. In this study, the effects of urbanization on NPP were separated into direct (NPP_dir) and indirect (NPP_ind) effects, and the effects of urbanization on NPP_dir and the associated mechanisms and the effect of urban heat islands on NPP_ind were analyzed using the Geodetector tool and geographically weighted regressions (GWR), respectively. The results showed that urbanization was the dominant factor in the spatial and temporal heterogeneity of NPP, and the explanatory power of the interaction between urbanization and the urban heat island effect on NPP was greater than the explanatory power of the influence of single factors on the NPP distribution. Impervious surface abundance had a strong negative effect on NPP in urbanized areas, while the urban heat island effect had a positive contribution to NPP in urban centers. The urban heat island effect compensates for the negative impact of urbanization on NPP to a certain extent. Compared with the ordinary least squares regression model, the geographically weighted regression model had a better fit in the correlation analysis of both urbanization and NPP_dir and the urban heat island effect and NPP_ind. This study has value as a reference for further studies on sustainable urban development and the effect of urbanization on NPP and the carbon cycle.

Keywords:

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图 1 研究区概况

Figure 1. Study area profile

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图 2 昆明市净初级生产力分布图

Figure 2. Net primary product distribution map in Kunming

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图 3 昆明市城市化对净初级生产力的直接影响(NPP_dir)和间接影响(NPP_ind)分布图

Figure 3. Distribution map of the direct (NPP_dir) and indirect (NPP_ind) effects of urbanization on net primary productivity (NPP) in Kunming City

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图 4 昆明市不透水面丰度分布图

Figure 4. Distribution maps of the impervious surface abundance (IS) in Kunming

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图 5 昆明市2001－2017年不同土地覆盖变化类型的空间分布(A)与土地利用转换类型所占面积(B)

Figure 5. The spatial distribution of different types of land cover change in Kunming from 2001 to 2017 (A) and the percentage of each type of land use conversion (B)

Veg–Imp: vegetation–impervious surface; Imp–Imp: impervious surface–impervious surface; Imp–Veg: impervious surface–vegetation.

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图 6 地表温度分布图

Figure 6. Distribution maps of the land surface temperature (LST)

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图 7 昆明市不透水面丰度对直接NPP地理加权回归系数分布

Figure 7. Distribution maps of GWR coefficients of IS to NPP_dir in Kunming

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图 8 昆明市地表温度对间接NPP地理加权回归系数分布

Figure 8. Distribution maps of GWR coefficients of LST to NPP_ind in Kunming

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图 9 昆明市地表温度对间接NPP地理加权回归正相关系数占主城区百分比及面积

Figure 9. Percentage of positive GWR correlation coefficients and the effect of LST on NPP_ind in the urban area of Kunming

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表 1 研究数据来源

Table 1 The sources of research data

数据类型 Data type	数据 Data	时间 Time	分辨率 Spatial resolution/m	来源 Source
遥感数据 Remote sensing data	Landsat5,8	2001 2005 2009 2013 2017	30	地理空间数据云 Geospatial data cloud
	MCD12Q1土地利用 Land use		500	NASA官网 NASA official website
	MOD17A3		500	NASA官网 NASA official website
气象数据 Meteorological data	平均气温、降水 Temperature, precipitation	2001 2005 2009 2013 2017	−	国家气象科学数据中心 The National Meteorological Science Data Center
气象数据 Meteorological data	太阳辐射 Solar radiation	2001 2005 2009 2013 2017	−	国家青藏高原科学数据中心 The National Qinghai-Tibet Plateau Scientific Data Center
其他数据 Other data	FROM-GLC土地利用分类 Land use classification	2017	10	http://www.globallandcover.com
	GlobeLand30土地利用分类 Land use classification	2000	30	http://data.ess.tsinghua.edu.cn/
	数字高程模型 Digital elevation model	−	30	NASA官网 NASA official website
	研究区矢量数据 Study area vector data	2018	−	GADM数据库 Database

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表 2 Landsat遥感影像信息

Table 2 Landsat image information

成像日期 Imaging date/(YYYY-MM-DD)	轨道号 Track number	传感器 Sensor	用途 Application
2001-04-03	129/043	Landsat TM	反演NDVI Reverse NDVI 反演不透水面丰度 Reverse the impervious surface abundance 反演地表温度 Reverse surface temperature
2005-04-30	129/043	Landsat TM
2009-04-09	129/043	Landsat TM
2013-04-20	129/043	Landsat OLI
2017-05-01	129/043	Landsat OLI

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表 3 全局莫兰指数

Table 3 The Globle Moran’s Index (GMI)

年份 Year	植被净初级生产力 Net primary productivity vegetation NPP-GMI	直接NPP Direct NPP NPP_dir-GMI	间接NPP Indirect NPP NPP_ind-GMI	地表温度 Land surface temperature LST-GMI	不透水面丰度 Impervious surface abundance IS-GMI
2001	0.512	–	–	0.335	0.584
2005	0.502	0.233	0.405	0.382	0.655
2009	0.499	0.190	0.398	0.320	0.599
2013	0.457	0.200	0.353	0.332	0.588
2017	0.467	0.210	0.291	0.359	0.519

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表 4 IS与LST对NPP的交互探测结果

Table 4 The interaction results for IS and LST with NPP

年份 Year	IS解释力 Explanatory power q (IS)	LST解释力 Explanatory power q (LST)	二者共同解释力 Interactive explanatory power q (IS∩LST)	交互作用 Interaction
2001	0.344	0.123	0.485	非线性增强 Nonlinear enhancement
2005	0.260	0.193	0.513	非线性增强 Nonlinear enhancement
2009	0.238	0.168	0.408	非线性增强 Nonlinear enhancement
2013	0.375	0.130	0.468	双因子增强 Bivariate enhancement
2017	0.399	0.254	0.521	双因子增强 Bivariate enhancement
以上q值均通过显著性检验(P < 0.001)。　All q values passed the significance test (P < 0.001).

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表 5 最小二乘法回归系数

Table 5 The ordinary least square regression coefficients

年份 Year	IS-NPP_dir	LST-NPP_ind	P
2005	−41.276	−5.638	0.001
2009	−79.817	−5.325	0.001
2013	−83.111	−5.970	0.001
2017	−78.702	−4.925	0.001

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表 6 OLS与GWR模型拟合度对比

Table 6 The comparison of model fits between OLS and GWR

年份 Year	参数 Parameters	IS对直接NPP的 OLS拟合度 OLS_IS-NPP_dir	IS对直接NPP的 GWR拟合度 GWR_IS-GWR	LST对间接NPP的 OLS拟合度 OLS_LST-OLS	LST对间接NPP的 GWR拟合度 GWR_LST-GWR
2005	Adjusted R²	0.582	0.643	0.358	0.613
2005	AIC	111 550.679	108 338.964	121 419.537	116 146.886
2009	Adjusted R²	0.219	0.463	0.200	0.541
2009	AIC	112 523.222	109 292.109	124 905.137	119 019.784
2013	Adjusted R²	0.150	0.484	0.314	0.520
2013	AIC	116 395.342	111 891.572	122 807.178	118 971.067
2017	Adjusted R²	0.100	0.496	0.251	0.471
2017	AIC	118 076.220	112 927.655	131 065.518	127 378.100
AIC：赤池信息量准则。　AIC: Akaike information criterion.

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参考文献(45)

[1]	IMHOFF M L, BOUNOUA L, DEFRIES R, LAERENCE W T, STUTZER D, TUCKER C J, RICHETTS T. The consequences of urban land transformation on net primary productivity in the United States. Remote Sensing of Environment, 2004, 89(4): 434-443. doi: 10.1016/j.rse.2003.10.015
[2]	TIAN G J, QIAO Z. Assessing the impact of the urbanization process on net primary productivity in China in 1989–2000. Environmental Pollution, 2014, 184: 320-326. doi: 10.1016/j.envpol.2013.09.012
[3]	WU S H, ZHOU S L, CHEN D X, WEI Z Q, DAI L, LI X G. Determining the contributions of urbanization and climate change to NPP variations over the last decade in the Yangtze River Delta, China. Science of The Total Environment, 2014, 472: 397-406. doi: 10.1016/j.scitotenv.2013.10.128
[4]	VITOUSEK P M, MOONEY H A, LUBCHENCO J, MELILLO J M. Human domination of Earth’s ecosystems. Science, 2008, 277: 494-499.
[5]	GREGG J W, JONES C G, DAWSON T E. Urbanization effects on tree growth in the vicinity of New York City. Nature, 2003, 424: 183-187. doi: 10.1038/nature01728
[6]	CARREIRO M M, TRIPLER C E. Forest remnants along urban-rural gradients: Examining their potential for global change research. Ecosystems, 2005, 8(5): 568-582. doi: 10.1007/s10021-003-0172-6
[7]	ZHOU D C, ZHAO S Q, ZHANG L X, LIU S G. Remotely sensed assessment of urbanization effects on vegetation phenology in China’s 32 major cities. Remote Sensing of Environment, 2016, 176: 272-281. doi: 10.1016/j.rse.2016.02.010
[8]	YAN Y C, LIU X P, WANG F Y, LI X, OU J P, WEN Y Y, LIANG X. Assessing the impacts of urban sprawl on net primary productivity using fusion of Landsat and MODIS data. Science of The Total Environment, 2018, 613: 1417-1429.
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昆明市城市化及城市热岛效应对植被净初级生产力的影响

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Study on the impact of urbanization and urban heat island effect on net primary productivity in Kunming

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