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Journal of Geo-information Science >

2018 , Vol. 20 >Issue 5: 602 - 612

DOI: https://doi.org/10.12082/dqxxkx.2018.180083

Remote Sensing Analysis of Environmental Changes in Mega Cities along the Maritime Silk Road

  • FENG Suyun , 1, 2 ,
  • ZHANG Kaixuan 1 ,
  • LU Linlin , 2, *
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  • 1. School of Geomatics, Liaoning Technical University, Fuxin 123000
  • 2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth , Chinese Academy of Sciences, Beijing 100094
Corresponding author: LU Linlin, E-mail: lull@radi.ac.cn

Online published: 2018-05-20

Supported by

National Natural Science Foundation of China, No.41471369;The Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDA19030502; The International Partnership Program of Chinese Academy of Sciences, No.131C11KYSB20160061.

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《地球信息科学学报》编辑部 所有
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Abstract

With the rapid process of urbanization, it is very important for sustainable urban development that how to evaluate the changes of urban environmental quality in time and accurately, and thus make reasonable urban developmental plans. In this paper, the fine particulate matter (PM2.5) concentration data, land surface temperature (LST) data, normalized difference vegetation index (NDVI) data and supplementary information data of urban land use obtained by satellite remote sensing were obtained and synthetically used to assess the urban environment changes in mega cities along the Maritime Silk Road. The dynamic changes of urban environmental quality of 12 mega cities along the Maritime Silk Road were analyzed based on the comprehensive evaluation index (CEI) from 2000 to 2013. The results showed that, from 2000 to 2013, approximately 75 percent of the mega cities along the Maritime Silk Road showed different degrees of environmental deterioration. The area of environmental deterioration and moderately environmental deterioration accounted for 31.33 percent (4732.39 km2) of the total urban areas in the 12 mega cities. And 29.48 percent (3765.83 km2) of the total expanded urban areas from 2000 to 2013 exhibited environmental degradation or moderately environmental degradation. The rise of average land surface temperature, the sharp decrease of vegetation coverage and the increase of the fine particulate matter concentration all had an impact on the urban environmental quality changes of the mega cities along the Maritime Silk Road. Among them, the significant increase of the fine particulate matter (PM2.5) concentration in the air was one of the main manifestations for the environmental degradation of the expanded urban areas from 2000 to 2013 in mega cities along the Maritime Silk Road. These findings suggested that more attentions should be paid to urban environment issues to ensure sustainable urban development along the Maritime Silk Road.

Key words： urban environment; mega city; urban expansion; Maritime Silk Road; multi-source remote sensing data

Cite this article

FENG Suyun , ZHANG Kaixuan , LU Linlin . Remote Sensing Analysis of Environmental Changes in Mega Cities along the Maritime Silk Road[J]. Journal of Geo-information Science, 2018 , 20(5) : 602 -612 . DOI: 10.12082/dqxxkx.2018.180083

1 引言

“一带一路”是“丝绸之路经济带”和“21世纪海上丝绸之路”的简称,它高举和平发展的旗帜,积极发展与沿线国家的经济合作伙伴关系,共同打造政治互信、经济互融、文化包容的利益共同体、命运共同体和责任共同体,其促进了城市经济的发展。据联合国统计,2016年全球共计31个人口超过一千万的超大城市,其中18个分布在一带一路区域[1,2]。特别是海上丝绸之路沿线,分布着12个超大城市,共居住着18 540.8万人口。这些城市的发展及环境的变化,严重影响着当地居民的生活和健康。
在过去的几十年间,全球超大城市经历了快速的工业化和城市化,造成自然资源的减少,环境质量的下降等问题[3,4]。这些超大城市大多面临着空气污染物（如PM2.5,PM10,O3,NOX等）含量的急剧增加[5,6,7]、淡水资源缺乏[8]、优质土壤减少[9]、城市霾日数增多[10]、植被覆盖度下降[11]、城市热岛效应加剧[12]等严重的环境问题。由于综合国力的限制,一带一路沿线的大多数发展中国家将经济发展放在首位,忽视环境保护,相对于发达国家,城市化造成的环境退化问题更为严重[13,14,15]。Dewan等[13]利用卫星图像和社会经济数据评估1975-2003年孟加拉国达卡的土地利用/覆盖变化和城市扩张,研究表明城市迅速扩张导致该城市水体、耕地、植被和湿地面积的显著减少。Doygon[14]对土耳其卡赫拉曼马拉什城市扩张对橄榄林的影响进行量化研究,结果表明城市扩张是造成该区域1985-2006年橄榄园面积大幅度减少的主要原因。Zhang等[18]利用共享经济途径和城市用地动态模型研究分析出北京、天津、河北沿线城市扩张造成粮食产量、碳储存量、淡水资源储存量的急剧下降。Liao等[5]利用WRF/Chem模型对长江三角洲地区城市化对空气质量的影响的研究表明城市化造成地表PM10含量的减少,O3含量的增加。同时,环境质量的退化严重危害着人们的健康,主要表现为诱发呼吸道疾病与生理机能障碍等各种疾病。据2016年世界卫生数据统计,2012年全球约有700万人口因此失去生命[15]。
城市可持续发展是一个动态的不断平衡生态环境与人类活动的过程,既包括经济的可持续发展,又包括社会和生态环境的可持续发展[8]。及时、精确地对城市环境的变化做出评价,研究城市扩张对城市环境的影响,进而制定出合理的发展方案,对城市可持续发展至关重要。国际社会提出了一系列的环境指标,用于评价城市环境质量和可持续发展水平。例如,联合国可持续发展委员会（United Nations Commission on Sustainable Development）提出的可持续发展框架、欧洲环境署提出的城市代谢框架（Urban Metabolism Framework）、欧洲改善生活和工作条件基金会提出的城市可持续发展指标（Urban Sustainability Indicators）、城市中国计划提出的中国城市可持续发展指数（China Urban Sustainability Index）、欧盟统计局提出的城市统计（Cities Statistics）、其他的如城市蓝图（City Blueprints）、环境发展指标（Environmental Performance Index）、全球城市指标方案（Global City Indicators Programme）等。
遥感的发展使大范围的城市环境动态监测成为可能。近年来,越来越多的遥感数据被用于城市环境质量评价。如Peng等[16]利用MODIS数据分析了全球419个大城市2003-2008年城市与郊区热岛差异;Sun等[11]利用1982-2006年监测得到的归一化植被指数数据分析出城市化造成中国大部分城市地区,尤其东部地区植被覆盖度的急剧下降; Fang等[17]基于卫星遥感数据利用自适应性建模方法模拟出中国地区2013-2014年地表PM2.5浓度变化。
为综合评价海上丝绸之路沿线12个超大城市快速发展给城市环境变化带来的影响,为城市可持续发展方案的制定提供合理的决策依据,本文基于He等[3]提出的综合评价指标（Comprehensive Evaluation Index,CEI）,综合利用地表温度数据（LST）、归一化植被指数（NDVI）、PM2.5浓度分布及全球建成区分布等多源遥感数据,分析2000-2013年海上丝绸之路沿线12个超大城市地区的环境质量动态变化。

2 研究区概况及数据源

2.1 研究区概况

本文研究区域为海上丝绸之路沿线12个超大城市,包括：印度的班加罗尔、金奈、德里、加尔各答及孟买;埃及的开罗;孟加拉的达卡;中国的广州、深圳;印度尼西亚的雅加达;巴基斯坦的卡拉奇;菲律宾的马尼拉。12个超大城市位于31°~121° E, 6°~30° S之间（图1,表1）[18]。
Fig. 1 Distribution of mega cities along the Maritime Silk Road

图1 海上丝绸之路沿线超大城市分布图

Tab. 1 Population statistics of 12 mega cities

表1 12个超大城市人口统计

城市 开罗 卡拉奇 德里 孟买 班加罗尔 加尔各答
2000年城市人口/千人 13 626 10 032 15 732 16 367 5567 13 058
2016年城市人口/千人 19 128 17 121 26 454 21 357 10 456 14 980
年增长率/% 2.1 3.3 3.2 1.7 3.9 0.9
城市 达卡 金奈 广州 深圳 马尼拉 雅加达
2000年城市人口/千人 10 285 6353 7330 6550 9962 8390
2016年城市人口/千人 18 237 10 163 13 070 10 828 13 131 10 483
年增长率/% 3.6 2.9 3.6 3.1 1.7 1.4

2.2 数据源

本文测定12个超大城市地区环境质量变化,主要利用的遥感影像数据如表2所示。首先,实验所需的PM2.5浓度数据从达尔豪西大学大气成分分析组获得（http://fizz.phys.dal.ca/~atmos/martin/?page_ id=140）,具有1 km的空间分辨率。该数据记录了基于MODIS数据、多角度成像光谱辐射计（MISR）、海洋观测宽视场传感器（Sea WIFS）观测和哥达德地球观测系统化学传输模型（GEOS-Chem）计算得到的年均PM2.5浓度分布。为减少该数据误差的影响,本文计算了原始数据当年及前后年份的滑动平均值,作为研究采用的年均PM2.5浓度分布数据。
Tab. 2 The remote sensing data used in this study

表2 实验采用的遥感数据

数据 数据信息 空间分辨率 时段 数据来源
PM2.5浓度分布 MODIS+MISR+Sea WIFS传感器+GEOS-Chem传输模型 1 km 2000-2013 达尔豪西大学大气成分分析组
地表温度数据 MOD11A2 1 km 2000-2013 美国国家航天太空总署
归一化植被指数 SPOT/VGT 10-day composite 1 km 2000-2013 比利时弗拉芒技术研究院
全球建成区分布 Landsat 38 m 2000,2014 欧盟联合研究中心
地表温度数据（LST）为全球1 km地表温度/发射率8 d合成L3产品（MOD11A2）。该数据从美国国家航天太空总署（National Aeronautics and Space Administration,NASA）获取（https://modis-land.gsfc.nasa.gov/temp.html）。依据前人研究[19,20],本次实验仅使用夜间数据,然后计算得出2000-2013年年均夜间地表温度。
植被覆盖度数据（Vegetation Cover,VC）由从比利时弗拉芒技术研究院（VTIO Belgium）下载的归一化植被指数（NDVI）（http://www.vito-eodata.be/PDF/portal/Application.html#Home）计算得到,其中NDVI数据为10 d合成数据。首先,由10 d合成数据计算年均NDVI值。然后,利用式（1）分别计算2000年及2013年像元i处的VC值[21]：
V C i = N i - N min N max - N min （1）
式中：VCi是像元i处的VC值;Ni是像元i处的NDVI值;Nmin,Nmax分别为12个超大城市NDVI最小、最 大值。
实验采用欧盟联合研究中心（Joint Research Center,JRC）基于2000年和2014年收集的多时相Landsat影像处理得到的全球建成区分布产品,作为海上丝绸之路沿线城市空间分布和城市扩张动态变化的基础数据[22]。为开展城市环境分析,我们将该数据转换为WGS84地理坐标系,并重采样生成1 km的建成区密度数据。利用优化阈值法,提取每个超大城市的城市中心及郊区边界,用于超大城市环境质量变化的空间格局分析。

3 环境变化评价方法

为全面反映海上丝绸之路沿线12个超大城市地区环境质量变化情况,本文基于联合国可持续发展委员会提出的可持续发展框架,利用PM2.5数据、地表温度数据（LST）及植被覆盖度（VC）来综合评价城市环境变化。

3.1 综合评价指标计算

本文利用He等[3]提出的综合评价指标（CEI）对海上丝绸之路沿线12个超大城市地区环境质量变化进行评价。综合评价指标（CEI）计算了PM2.5数据、地表温度数据（LST）及植被覆盖度（VC）的几何平均值,采用公式如下：
CE I i = Δ P M i + 1 × Δ V C i + 1 × Δ LS T i + 1 3 （2）
式中：CEIi是像元i处的环境变化量,范围为1-101,值越大表示环境退化越严重;ΔPMi、ΔVCi和ΔLSTi分别表示2000-2013年像元i处PM2.5浓度、VC、LST归一化变化量。
ΔLSTi和ΔPMi利用式（3）计算：
Δ V i = ( V i 2013 - V i 2000 ) - mi n v ma x v - mi n v × 100 （3）
式中：ΔVi表示PM2.5或LST归一化变化量;Vi2000,Vi2013分别表示像元i处PM2.5浓度或LST在2000年和2013年的值;minv,maxv分别表示12个超大城市2000-2013年PM2.5或LST最小、最大变化量,ΔVi越大,表示环境质量变化越差。
ΔVCi计算方式如下：
Δ V C i = ma x vc - ( V C i 2013 - V C i 2000 ) ma x vc - mi n vc × 100 （4）
式中：VCi2000,VCi2013分别表示2000、2013年像元i处VC值;minvc,maxvc分别表示12个超大城市2000-2013年植被覆盖度最小、最大变化量。

3.2 环境质量评估

He等[3]提出综合评价指标（CEI）,应用于国内城市地区的环境变化监测,并用生态足迹、自然资源消耗等多种统计数据对监测结果进行了验证,发现它和统计数据分析结果具有一致性,能够较好的反映城市环境质量变化。本文计算了海上丝绸之路沿线12个超大城市地区的CEI及其均值和标准差,结合He等[23]提出的分类方法,将研究区域环境质量变化划分为5种类型：改善、逐步改善、未变、逐步恶化、恶化（表3）。然后依据式（5）分别计算超大城市地区5种环境质量区域的面积百分比：
P n = Are a n Are a 2013 × 100 % （5）
式中：Arean表示研究区域第n（n=1,2,3,4,5）类环境质量所占的城市面积;Area2013为2013年城市总面积。如：P1=Area1/Area2013,表示研究区域环境质量 改善所占的百分比。其中,Area1表示研究区域环境质量改善的城市面积;Area2013表示2013年城市 总面积。
本研究分别对海上丝绸之路沿线超大城市的城市行政区、城市用地和2000-2013年城市扩张用地的环境变化进行了分析。城市用地指各超大城市行政边界以内的依据欧盟全球建成区分布产品提取的建成区区域。城市扩张用地指2000-2013年新增的建成区区域。
Tab. 3 Classification criteria for comprehensive evaluation indexes in study area

表3 研究区域综合评价指标分类标准

类型
改善 逐步改善 未变 逐步恶化 恶化
分类依据 <u-1.5δ u-1.5δ~u-0.5δ u-0.5δ~u+0.5δ u+0.5δ~u+1.5δ >u+1.5δ
综合评价指标 <33.93 33.93~42.56 42.56~51.20 51.20~59.83 >59.83

注：u,δ分别为12个超大城市地区环境质量综合评价指标（CEI）的均值和标准差

4 环境变化分析

4.1 城市环境变化空间格局

图2-4分别展示了12个超大城市2000-2013年PM2.5、LST及VC归一化变化结果。实验表明,2000-2013年,约80%的城市植被覆盖度处于下降趋势;其中约50%的城市2013年夜间平均地表温度较于2000年有所上升;12个超大城市PM2.5浓度均有所增加。12个超大城市中,加尔各答ΔPM2.5浓度最小值为92,远高于其他城市地区,其次是达卡与金奈,分别为75和46;开罗、达卡及马尼拉ΔLST最小值分别为38、37和36;班加罗尔ΔVC最小值为41较于其他城市均较高,其次是孟买和金奈分别为37和36。
Fig. 2 The normalized variation of PM2.5 concentrations from 2000 to 2013 in 12 mega cities

图2 12个超大城市2000-2013年PM2.5浓度归一化变化

Fig. 3 The normalized variation of LST from 2000 to 2013 in 12 mega cities

图3 12个超大城市2000-2013年LST归一化变化

Fig. 4 The normalized variation of VC from 2000 to 2013 in 12 mega cities

图4 12个超大城市2000-2013年VC归一化变化

4.2 城市用地环境质量变化分析

研究表明,2000-2013年约75%的城市出现不同程度的环境退化现象。在整个研究区域,城市环境恶化及逐步恶化面积占城市建成区总面积的31.33%（4732.39 km2）,改善的仅占3.39%（511.84 km2）。12个超大城市中,加尔各答、达卡、广州、班加罗尔及深圳环境退化较为严重。其中,加尔各答环境退化最为严重,整个城市中心及城郊地区环境质量均处于退化状态（图5）,城市恶化面积占该城市总面积的89.51%（795.08 km2）（图6,表4）。2000-2013年该城市地区平均ΔPM2.5浓度为94.98,远高于其它城市地区,ΔLST也相对较高,约为47.42,ΔVC为55.56。其次是达卡恶化面积占该城市总面积的52.40%（约240.06 km2）,主要集中在中部地区,其中,约80%的中心城市地区环境质量处于恶化状态,城郊地区环境质量变化也不容乐观。该城市地区2000-2013年平均ΔPM2.5浓度仅次于加尔各答约为79.89,ΔLST为53.60仅次于孟买,ΔVC为54.93。广州、班加罗尔和深圳逐步恶化和恶化的城市面积占相应城市面积的40%~70%,3个城市地区平均ΔPM2.5浓度处于50-68之间,ΔVC处于52-64之间,其中班加罗尔及广州环境恶化及逐步恶化现象主要出现在中部的城市中心区域,而深圳环境退化现象主要出现在西部偏北地区,约50%的城市中心环境质量处于恶化或逐步恶化状态,相对而言,城郊地区环境质量变化较为平缓。
Tab. 4 Environmental quality changes from 2000 to 2013 in 12 mega cities

表4 12个超大城市用地环境质量变化

城市 2000年城市用地/km2 扩增
面积/km2		 恶化 逐步恶化 未变 逐步改善 改善
面积/km2 百分比/% 面积/km2 百分比/% 面积/km2 百分比/% 面积/km2 百分比/% 面积/km2 百分比/%
班加罗尔 235.26 368.57 27.53 4.56 361.27 59.83 196.11 32.48 18.92 3.13 0.00 0.00
开罗 228.48 1098.38 0.00 0.00 0.00 0.00 49.11 3.70 1225.70 92.38 52.05 3.92
金奈 44.56 254.85 0.00 0.00 5.23 1.75 184.26 61.54 57.58 19.23 52.34 17.48
德里 249.07 1361.00 0.00 0.00 0.00 0.00 171.73 10.67 1405.58 87.30 32.76 2.03
达卡 131.48 326.65 240.06 52.40 193.33 42.20 23.82 5.20 0.92 0.20 0.00 0.00
广州 710.99 2591.42 237.56 7.19 1804.73 54.65 1110.82 33.64 130.52 3.95 18.78 0.57
雅加达 148.56 2445.84 0.00 0.00 3.52 0.14 775.59 29.90 1565.27 60.33 250.02 9.63
卡拉奇 64.31 410.15 0.00 0.00 0.00 0.00 173.94 36.66 286.99 60.49 13.53 2.85
加尔各答 93.53 824.28 795.08 89.51 93.15 10.49 0.00 0.00 0.00 0.00 0.00 0.00
马尼拉 52.12 888.83 0.00 0.00 40.59 4.31 618.07 65.69 264.76 28.14 17.53 1.86
孟买 32.56 504.72 0.00 0.00 60.23 11.21 388.14 72.24 57.36 10.68 31.55 5.87
深圳 368.57 1698.49 30.92 1.50 839.19 40.60 954.03 46.15 199.64 9.66 43.28 2.09
Fig.5 Environmental quality changes from 2000 to 2013 in 12 mega cities

图5 2000-2013年12个超大城市用地环境质量变化

4.3 城市扩张用地环境质量变化分析

2000-2013年12个超大城市扩张用地约为 12 773.18 km2,是2000年建成区总面积（2329.91 km2）的5倍,占2013年城市建成区面积的84.57%。12个超大城市中,加尔各答、达卡、广州及班加罗尔城市扩张用地环境质量恶化及逐步恶化面积均占据了相应城市扩张用地的60%以上（图7）。尤其加尔各答最为突出,无论是2000-2013年城市用地环境质量变化分析结果,还是城市扩张用地环境质量变化分析结果均显示,该城市环境质量处于逐步退化状态（图6-7）,退化率达到了100%。雅加达、德里及开罗2000-2013年城市扩张用地环境质量变化较为平缓,3个城市地区平均ΔPM2.5浓度分别为30.69、37.46、26.15,相对较低;ΔLST分别为33.79、28.72、41.25;ΔVC分别为57.77、51.04、51.05。虽然德里城市地区2000-2013年平均PM2.5浓度变化相对较小,但空气中PM2.5浓度从2000年起均超过了95 ug/m3,在2010年甚至达到了120 ug/m3,这严重威胁着当地居民的健康。
Fig.6 Analysis of environmental quality changes from 2000 to 2013 in 12 mega cities

图6 2000-2013年12个超大城市用地环境质量变化分析

Fig. 7 Analysis of environmental quality changes of expanded urban areas from 2000 to 2013 in 12 mega cities

图7 2000-2013年12个超大城市扩张用地环境质量变化分析

为探究2000-2013年12个超大城市扩张用地呈现不同环境变化的原因,分析了ΔPM2.5浓度、ΔVC、ΔLST与CEI之间的相关性（表5）。实验得出ΔPM2.5与CEI相关性最高,约为0.919;ΔLST、ΔVC与CEI之间的相关系数分别为0.453,0.290。这表明PM2.5浓度的增加是海上丝绸之路沿线超大城市扩张用地环境退化的主要原因。
Tab. 5 Correlation coefficients between normalized variation of VC, LST, PM2.5 concentration and CEI

表5 VC、LST、PM2.5浓度归一化变化量与CEI之间的相关系数

CEI ΔPM2.5 ΔLST ΔVC
CEI 1
ΔPM2.5 0.919** 1
ΔLST 0.453** 0.143** 1
ΔVC 0.290** 0.202** 0.006* 1

注：**表示在0.01的置信水平上显著相关,*表示在0.05的置信水平上相关

图8展示了12个超大城市扩张用地ΔPM2.5浓度、ΔVC、ΔLST之间的比较结果。其中,加尔各答城市扩张用地平均ΔPM2.5浓度约为开罗地区ΔPM2.5浓度的3.63倍,ΔLST、ΔVC分别为均值最小值城市地区的1.65倍和1.15倍。世卫组织数据显示[16],自进入21世纪以来,印度大城市经济蓬勃发展,同时空气污染严重,估计2012年印度共有150万人死于空气污染,占该年全球死亡人数的11.6%。对于新扩张的城市用地,马尼拉年均夜间地表温度增长最大（图8）。Estoque等[24]基于Landsat数据分析发现,雅加达和马尼拉的平均陆表温度处于增长趋势,城市热岛效应加剧。位于印度南部的超大城市班加罗尔经历了大幅植被覆盖度下降。
Fig.8 Comparisons of ΔVC, ΔLST, ΔPM2.5 concentration of expanded urban areas in 12 mega cities

图8 12个超大城市地区扩张用地ΔVC、ΔLST、ΔPM2.5浓度比较分析
注：VC、LST、PM2.5浓度分别为相应城市扩张用地归一化变化量与12个超大城市扩张用地ΔVC、ΔLST、ΔPM2.5浓度均值最小值的比值,如 VC=ΔVC/ΔVCmin,其中,ΔVCmin为12个超大城市扩张用地ΔVC均值中的最小值

5 结论

与传统的基于统计数据的环境质量变化评价方式相比较,本文采用的综合评价指标在空间上直观的展示了海上丝绸之路沿线12个超大城市地区2000-2013年环境质量变化情况。①2000-2013年城市环境质量变化分析结果显示,12个超大城市约80%的城市植被覆盖度处于下降趋势,约50%的城市夜间平均地表温度有所上升,12个城市PM2.5浓度均处于增长趋势。②2000-2013年城市用地环境质量变化研究结果表明,随着城市化进程的加快,海上丝绸之路沿线约75%的超大城市出现了不同程度的环境退化现象。城市扩张用地中,加尔各答、达卡、广州、班加罗尔及深圳环境退化较为严重,恶化及逐步恶化面积均占据相应城市扩张用地的40%以上。③VC、LST、PM2.5浓度的变化均对环境质量变化有一定影响,其中PM2.5浓度的增加是导致海上丝绸之路沿线超大城市环境退化的主要原因。综上研究结果,海上丝绸之路沿线12个超大城市地区面临着PM2.5浓度增加、地表温度上升及植被覆盖度下降的问题,从而导致了研究区域的环境质量退化现象。其中,PM2.5浓度的增加是主要因素。因此,各城市需要积极采取有效措施解决环境问题,特别是空气污染问题。
因2000年前地表温度数据的缺乏,本文仅研究了2000-2013年海上丝绸之路沿线12个超大城市建成区环境质量变化。遥感传感器的多样化发展、影像分辨率的不断提高将为城市环境变化监测提供更丰富、有效的遥感信息。本文在城市环境变化的研究中均等的考虑了各因子对环境质量的影响,发现各超大城市环境质量退化的主要因素不同。因此,在今后研究中可针对城市面临的具体环境问题,对各因子赋予不同的权重,评价城市环境的变化。此外,综合利用多源遥感数据和产品,采用时间序列分析、空间分析、回归分析等多种分析方法,精确评估城市环境质量变化时空格局及驱动因素变化,为城市可持续发展提供更好的决策依据。

The authors have declared that no competing interests exist.

References

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[17]
Fang X, Zou B, Liu X, et al.Satellite-based ground PM2.5 estimation using timely structure adaptive modeling[J]. Remote Sensing of Environment, 2016,186:152-163.Although ground-level measurement of PM 2.5 is relatively accurate, this method is limited in spatial and temporal coverage due to the high costs. Recently, satellite-retrieved aerosol optical depth (AOD), with high-resolution and wide spatial-temporal coverage has been increasingly applied to estimate PM 2.5 concentrations. However, these AOD-based PM 2.5 concentrations were spontaneously estimated using the structure fixed models across an entire study period. While these ‘structure fixed’ simplifications greatly facilitated the efficiency of model developments and enhanced their generalizability, they ignored the fact that the ‘contributors’ of PM 2.5 variation are not always coherent with time. For this, we propose a timely structure adaptive modeling (TSAM) method for satellite based ground PM 2.5 estimation in this study by considering the timely variations of modeling predictors and magnitude of predictors at respective optimal spatial scales. Meanwhile, the reliability of TSAM for estimating national scale daily PM 2.5 concentrations was tested by employing the AOD data from June 1, 2013 to May 31, 2014 over China with other multi-source auxiliary data such as meteorological factors, land use etc. While the fitting degree ( R 2 ) of the daily TSAM models is 0.82, the one in 10-fold validation is 0.80, which are relatively higher than previous studies. These results are significantly better than those from structure-fixed models in this study. Additionally, the TSAM simulated PM 2.5 concentrations show that the national annual PM 2.5 concentration in China during study period is 69.7102μg/m 3 with significant seasonal changes. These concentrations exceed the Level 2 of CNAAQS in more than 70% Chinese territory. Therefore, it can be concluded that the TSAM is a promising PM 2.5 modeling method that is superior to structure-fixed modeling and could be very useful for air pollution mapping over large geographic areas.

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[18]
United Nations, Department of Economic and Social Affairs, Population Division[R]. World Urbanization Prospects: The 2014 Revision, 2014.

[19]
He C, Liu Z, Tian J, et al.Urban expansion dynamics and natural habitat loss in China: A multiscale landscape perspective[J]. Global Change Biology, 2014,20(9):2886-2902.AbstractChina's extensive urbanization has resulted in a massive loss of natural habitat, which is threatening the nation's biodiversity and socioeconomic sustainability. A timely and accurate understanding of natural habitat loss caused by urban expansion will allow more informed and effective measures to be taken for the conservation of biodiversity. However, the impact of urban expansion on natural habitats is not well-understood, primarily due to the lack of accurate spatial information regarding urban expansion across China. In this study, we proposed an approach that can be used to accurately summarize the dynamics of urban expansion in China over two recent decades (1992–2012), by integrating data on nighttime light levels, a vegetation index, and land surface temperature. The natural habitat loss during the time period was evaluated at the national, ecoregional, and local scales. The results revealed that China had experienced extremely rapid urban growth from 1992 to 2012 with an average annual growth rate of 8.74%, in contrast with the global average of 3.20%. The massive urban expansion has resulted in significant natural habitat loss in some areas in China. Special attention needs to be paid to the Pearl River Delta, where 25.79% or 151802km2 of the natural habitat and 41.99% or 76002km2 of the local wetlands were lost during 1992–2012. This raises serious concerns about species viability and biodiversity. Effective policies and regulations must be implemented and enforced to sustain regional and national development in the context of rapid urbanization.

DOI PMID

[20]
Zhou D, Zhao S, Zhang L, et al.The footprint of urban heat island effect in China[J]. Scientific Reports, 2015, 11160.Urban heat island (UHI) is one major anthropogenic modification to the Earth system that transcends its physical boundary. Using MODIS data from 2003 to 2012, we showed that the UHI effect decayed exponentially toward rural areas for majority of the 32 Chinese cities. We found an obvious urban/rural temperature “cliff”, and estimated that the footprint of UHI effect (FP, including urban area) was 2.3 and 3.9 times of urban size for the day and night, respectively, with large spatiotemporal heterogeneities. We further revealed that ignoring the FP may underestimate the UHI intensity in most cases and even alter the direction of UHI estimates for few cities. Our results provide new insights to the characteristics of UHI effect and emphasize the necessity of considering city- and time-specific FP when assessing the urbanization effects on local climate.

DOI PMID

[21]
孙久虎,刘晓萌,李佑钢,等.北运河地区植被覆盖的遥感估算及变化分析[J].水土保持研究,2006,13(6):97-99.植被覆盖度作为衡量地表植被覆盖的一个重要指标,是计算土壤侵蚀模数、分析土壤侵蚀的必要参数.根据1994年和2004年两期同时相的Landsat TM遥感图像资料,处理和分析并提取北运河地区的NDVI指数,利用像元二分模型原理定量估算植被覆盖度,得出其植被覆盖分类图.在此基础上分析了北运河地区10年来的植被覆盖变化特征.

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[ Sun J H, Liu X M, Li Y G, et al.Remote sensing estimation and variation analysis of vegetation cover in North Canal area[J]. Research of Soil and Water Conservation, 2006,13(6):97-99.

[22]
Pesaresi M, Guo H, Blaes X, et al.A global human settlement layer from optical HR/VHR RS data: concept and first results[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2013,6(5):2102-2131.A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.

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[23]
He C, Ma Q, Li T, Yang Y, et al.Spatiotemporal dynamics of electric power consumption in Chinese mainland from 1995 to 2008 modeled using dmsp/ols stable nighttime lights data[J]. Journal of Geographical Sciences, 2012,22(1):125-136.电力消费(EPC ) 是为评估电力使用的基本索引之一。在 EPC 的空间与时间的动力学上获得及时、精确的数据为理解和电力资源的实际推广是关键的。在这研究，一个 EPC 模型从防卫气象学的卫星程序用稳定的夜间灯时间系列数据被开发运作的 Linescan 系统(DMSP/OLS ) 。模型被用来重建在在县的中国大陆的 EPC 的空间模式从 1995 ～ 2008 铺平。另外， EPC 的空间与时间的动力学被分析，并且下列结论被得出。(1 ) EPC 模型可靠地与约 70% 精确性在中国大陆代表了 EPC 的空间与时间的动力学。(2 ) 在中国大陆的大多数区域的 EPC 在对中等层次低，与显著时间、空间的变化；高级 EPC， 58.26% 在东方中国被集中。六城市的凝块(Beijing-Tianjin-Tangshan 区域， Shanghai-Nanjing-Hangzhou 区域，珀尔河三角洲，山东半岛，辽宁省，和四川盆中间南方) 说明了中国大陆的 10.69% 全部的区域，但是消费了 39.23% 电。(3 ) 在从 1995 ～ 2008，和 64% 大陆区域增加的中国大陆的大多数区域的 EPC 在 EPC 显示出重要增加。EPC 的中等增加从 1995 ～ 2008 在 61.62% 东方中国和 80.65% 华中被发现，而 75.69% 西方的中国没在 EPC 显示出重要增加。同时，分别地， 77.27% ， 89.35% ，和 66.72% Shanghai-Nanjing-Hangzhou 区域，珀尔河三角洲，和山东半岛在 EPC 显示出高速度的增加。EPC 的中等增加发生在 71.12% Beijing-Tianjin-Tangshan 区域和 72.13% 并且辽宁省中间南方，分别地当没有重要增加发生在 56.34% 四川盆时。

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[24]
Estoque R C, Murayama Y, Myint S W.Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia[J]. Science of the Total Environment, 2017,577:349-359.Due to its adverse impacts on urban ecological environment and the overall livability of cities, the urban heat island (UHI) phenomenon has become a major research focus in various interrelated fields, including urban climatology, urban ecology, urban planning, and urban geography. This study sought to examine the relationship between land surface temperature (LST) and the abundance and spatial pattern of impervious surface and green space in the metropolitan areas of Bangkok (Thailand), Jakarta (Indonesia), and Manila (Philippines). Landsat-8 OLI/TIRS data and various geospatial approaches, including urban-rural gradient, multiresolution grid-based, and spatial metrics-based techniques, were used to facilitate the analysis. We found a significant strong correlation between mean LST and the density of impervious surface (positive) and green space (negative) along the urban-rural gradients of the three cities, depicting a typical UHI profile. The correlation of impervious surface density with mean LST tends to increase in larger grids, whereas the correlation of green space density with mean LST tends to increase in smaller grids, indicating a stronger influence of impervious surface and green space on the variability of LST in larger and smaller areas, respectively. The size, shape complexity, and aggregation of the patches of impervious surface and green space also had significant relationships with mean LST, though aggregation had the most consistent strong correlation. On average, the mean LST of impervious surface is about 3聽掳C higher than that of green space, highlighting the important role of green spaces in mitigating UHI effects, an important urban ecosystem service. We recommend that the density and spatial pattern of urban impervious surfaces and green spaces be considered in landscape and urban planning so that urban areas and cities can have healthier and more comfortable living urban environments.

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