基于LCZ的城市热岛强度研究

doi:10.3724/SP.J.1047.2017.00713

摘要/Abstract

摘要：

城市热岛强度是城市热岛研究和应用中的一个重要度量指标,但其科学计算一直是该研究的难点。目前常用的基于气象站的城乡温差计算法,由于在代表城区与郊区温度的选择上存在困难,较难准确客观地计算热岛强度。为此,本文在LCZ分类体系的基础上,结合卫星遥感影像数据,将其应用于福州市的城市热岛研究中,以科学地计算城市热岛强度。研究表明,采用分层分类和人工目视解译相结合的方法能较好地实现LCZ的遥感分类,并确定出分别代表城市和郊区的地表温度,据此计算得到2015年9月27日福州的城市热岛强度为6.73 ℃,热岛效应十分显著。进一步将分类结果与遥感地表温度影像叠加,可有效地区分各地类的热特性,全面反映城市热岛的分布状况。

关键词: 城市热岛强度, LCZ, 遥感, 福州市

Abstract:

In the context of city expansion and raise of awareness of climate change, urban planners are looking for methods and tools to take the urban heat island （UHI） into account. Urban heat island intensity （UHII） is an important metric used in measuring UHI effect. Nevertheless, its quantitative measurement has not yet been clearly addressed. Due to the limitation of meteorological stations either in number or location, the traditional method of calculating the temperature difference between urban and rural areas based on the meteorological station data fails to accurately describe the UHII of a city. In order to solve this problem, a classification schema “Local Climate Zones” （LCZ） was proposed by Steward and Oke. Nowadays, the satellite remote sensing imagery is widely used to reveal urban heat island phenomenon. Therefore, this paper applied the new framework of LCZ to the study of UHII in Fuzhou City, located in the center of the Fuzhou basin, southeast China, using remote sensing technology. Fuzhou City has witnessed a rapid urban expansion since the late 1970s. The fast expansion of the city has caused severe UHI phenomenon in the city. Thus, it has become the top one furnace city in China. This study reveals that LCZ based on remote sensing technology can effectively distinguish the thermal contrasts among all LCZ classes. Such contrasts are governed largely by height and spacing of buildings, pervious surface fraction, trees density and soil wetness. In addition, the LCZ can fully disclose the distribution patterns of UHI. In this study, we revealed a UHII_LCZ of 6.73℃ for Fuzhou city on 27 September 2015, which indicates a significant UHI in the city.

Key words: urban heat island intensity, local climate zones, remote sensing, Fuzhou

林中立, 徐涵秋. 基于LCZ的城市热岛强度研究[J]. 地球信息科学学报, 2017, 19(5): 713-722.DOI:10.3724/SP.J.1047.2017.00713

LIN Zhongli,XU Hanqiu. A Study of Urban Heat Island Intensity Based on “Local Climate Zones”[J]. Journal of Geo-information Science, 2017, 19(5): 713-722.DOI:10.3724/SP.J.1047.2017.00713

图/表 8

图1

表1

LCZ分类体系[16]"

定义	土地覆盖类型	定义
密集混合的高层建筑（10层以上）;几乎无树木;不透水路面;建筑材质为混凝土、钢材、石头和玻璃		茂密的落叶林和（或）常绿林;地表覆盖大量可透水面（低矮的植被）;区域功能为天然林、苗圃林或城市公园
密集混合的中层建筑（3-9层）;几乎无树木;不透水路面;建筑材质为石头、砖、瓦片和混凝土		稀疏的落叶林和（或）常绿林;地表覆盖大量可透水面（低矮的植被）;区域功能为天然林、苗圃林或城市公园
密集混合的低层建筑（1-3层）;几乎无树木;不透水路面;建筑材质为石头、砖、瓦片和混凝土		开阔分布的灌木、矮树丛和矮小的树木;地表覆盖大量可透水面（裸土或沙）;区域功能为天然灌木林地或农用地
开阔分布的高层建筑（10层以上）;地表覆盖大量可透水面（低矮的植被、稀疏的树木）;建筑材质为混凝土、钢材、石头和玻璃		草地或草本植物/作物。几乎无树木;区域功能为草地、农用地或城市公园
开阔分布的中层建筑（3-9层）;地表覆盖大量可透水面（低矮的植被、稀疏的树木）;建筑材质为混凝土、钢材、石头和玻璃		岩石或不透水路面;几乎无植被;区域功能为天然荒漠（岩石）或城市交通运输干道
开阔分布的低层建筑（1-3层）;地表覆盖大量可透水面（低矮的植被、稀疏的树木）;建筑材质为木头、砖、石头、瓦片和混凝土		土或沙;几乎无植被;区域功能为天然沙漠或农用地
密集混合的单层建筑;几乎无树木;夯实的土质路面;轻质建筑材质（木头,茅草和波纹状板材）		大面积开阔的水体,如海和湖;或小面积水体,如河、水库和池塘
开阔分布的低层大型建筑（1-3层）;几乎无树木;不透水道面;建筑材质为钢材、混凝土、金属和石头	土地覆盖的可变特性（因气候变化,农业耕作和季节循环所引起的土地覆盖特性的变化）
自然环境中零散的中、小型建筑;地表覆盖大量可透水面（低矮的植被、稀疏的树木）	b 光秃的树木	冬季少叶落叶林
自然环境中零散的中、小型建筑;地表覆盖大量可透水面（低矮的植被、稀疏的树木）	s 积雪覆盖	积雪覆盖厚度大于10 cm
中低层工业建筑（塔、贮水池、堆积物）;几乎无树木;不透水路面或夯实的土质路面;建筑材质为金属、钢材和混凝土	d 干燥地表	焦土（如火烧迹地）
中低层工业建筑（塔、贮水池、堆积物）;几乎无树木;不透水路面或夯实的土质路面;建筑材质为金属、钢材和混凝土	w 湿润地表	浸水土壤

表1

表2

图2

图3

图4

表3

附表A

参考文献 37

[1]	United Nations.World urbanization prospects: the 2014 revision[M]. Population Division, Department of Economic and Social Affairs, Now York, 2015.
[2]	Georgescu M, Moustaoui M, Mahalov A, et al.Summer-time climate impacts of projected megapolitan expansion in Arizona[J]. Nature Climate Change, 2013,3(1):37-41.
[3]	Weng Q.Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009,64(4):335-344. doi: 10.1016/j.isprsjprs.2009.03.007
[4]	张艳,鲍文杰,余琦,等.超大城市热岛效应的季节变化特征及其年际差异[J].地球物理学报,2012,55(4):1121-1128. doi: 10.6038/j.issn.0001-5733.2012.04.007
	[Zhang Y, Bao W J, Yu Q, et al.Study on seasonal variations of the urban heat island and its interannual changes in a typical Chinese megacity[J]. Chinese Journal of Geophysics, 2012,55(4):1121-1128. ] doi: 10.6038/j.issn.0001-5733.2012.04.007
[5]	徐涵秋. 基于城市地表参数变化的城市热岛效应分析[J].生态学报, 2011,31(14):3890-3901.
	[Xu H Q.Analysis on urban heat island effect based on the dynamics of urban surface biophysical descriptors[J]. Acta Ecological Sinica, 2011,31(14):3890-3901. ]
[6]	Mnaley G.On the frequency of snowfall in metropolitan England[J]. Quarterly Journal of the Royal Meteorological Society, 1958,84:70-72. doi: 10.1002/qj.49708435910
[7]	Stewart I D.A systematic review and scientific critique of methodology in modern urban heat island literature[J]. International Journal of Climatology, 2011,31(2):200-217. doi: 10.1002/joc.2141
[8]	Zhao L, Lee X, Smith R B, et al.Strong contributions of local background climate to urban heat islands[J]. Nature, 2014,511(7508):216-219. doi: 10.1038/nature13462 pmid: 25008529
[9]	Zhou D, Zhao S, Liu S, et al.Surface urban heat island in China's 32 major cities: Spatial patterns and drivers[J]. Remote Sensing of Environment, 2014,152:51-61. doi: 10.1016/j.rse.2014.05.017
[10]	王郁,胡非.近10年来北京夏季城市热岛的变化及环境效应的分析研究[J].地球物理学报,2006,49(1):61-68. doi: 10.3321/j.issn:0001-5733.2006.01.009
	[Wang Y, Hu F.Variations of the urban heat island in summer of the recent 10 years over Beijing and its environment effect[J]. Chinese Journal of Geophysics, 2006,49(1):61-68. ] doi: 10.3321/j.issn:0001-5733.2006.01.009
[11]	许辉熙. 成都平原中等城市的热岛效应动态特征对比研究[J].测绘与空间地理信息, 2015,38(1):13-19.
	[Xu H X.Comparison on dynamic characteristics of urban heat island effects in medium - sized cities in Chengdu Plain[J]. Geomatics and spatial information technology, 2015,38(1):13-19. ]
[12]	Peng S, Piao S, Ciais P, et al.Surface urban heat island across 419 global big cities[J]. Environmental Science and Technology, 2012,46(2):696-703. doi: 10.1021/es2030438 pmid: 22142232
[13]	Zhou B, Rybski D, Kropp J P.On the statistics of urban heat island intensity[J]. Geophysical Research Letters, 2013,40(20):5486-5491. doi: 10.1002/2013GL057320
[14]	Tan M, Li X.Quantifying the effects of settlement size on urban heat islands in fairly uniform geographic areas[J]. Habitat International, 2015,49(4):100-106. doi: 10.1016/j.habitatint.2015.05.013
[15]	Imhoff M L, Zhang P, Wolfe R E, et al.Remote sensing of the urban heat island effect across biomes in the continental USA[J]. Remote Sensing of Environment, 2010,114(3):504-513. doi: 10.1109/IGARSS.2010.5653907
[16]	Stewart I D, Oke T R.Local climate zones for urban temperature studies[J]. Bulletin of the American Meteorological Society, 2012,93(12):1879-1900. doi: 10.1175/BAMS-D-11-00019.1
[17]	Stewart I D, Oke T R, Krayenhoff E S.Evaluation of the “local climate zone” scheme using temperature observations and model simulations[J]. International Journal of Climatology, 2014,34(4):1062-1080. doi: 10.1002/joc.3746
[18]	Alexander P J, Mills G.Local climate classification and Dublin's urban heat island[J]. Atmosphere, 2014,5(4):755-774. doi: 10.3390/atmos5040755
[19]	Ng Y X Y, Chua L H C, Irvine K N. A study of urban heat island using "local climate zones" - the case of Singapore[J]. British Journal of Environment and Climate Change, 2015,5(2):116-133. doi: 10.9734/BJECC/2015/13051
[20]	Lehnert M, Geletič J, Husák J, et al.Urban field classification by “local climate zones” in a medium-sized Central European city: The case of Olomouc (Czech Republic)[J]. Theoretical and Applied Climatology, 2015,122:531-541. doi: 10.1007/s00704-014-1309-6
[21]	Bechtel B, Alexander P J, Böhner J, et al.Mapping local climate zones for a worldwide database of the form and function of cities[J]. ISPRS International Journal of Geo-Information, 2015,4(1):199-219. doi: 10.3390/ijgi4010199
[22]	天气网.四大火炉新排名福州居首[N/OL]. 2014. .
[23]	Chander G, Markham B.Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003,41(11):2674-2677. doi: 10.1109/TGRS.2003.818464
[24]	Chavez P S.Image-based atmospheric corrections-revisited and improved[J]. Photogrammetric Engineering and Remote Sensing, 1996,62(9):1025-1035. doi: 10.1016/0031-0182(96)00019-3
[25]	USGS. Landsat 8 (L8) Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)[OL]. , 2013.
[26]	Stewart I D, Oke T R.Newly developed “thermal climate zones” for defining and measuring urban heat island magnitude in the canopy layerC]//[Eighth Symposium on Urban Environment, Phoenix, AZ, 2009.
[27]	Xu H.Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006,27(14):3025-3033. doi: 10.1080/01431160600589179
[28]	Rouse J W, Haas R H, Schell J A, et al.Monitoring vegetation systems in the great plains with ERTSC]//[Proceedings of the Third ERTS Symposium, Nasa SP-351, Washington DC, USA, 1973,1:309-317.
[29]	Xu H.Extraction of urban built-up land features from Landsat imagery using a thematic-oriented index combination technique[J]. Photogrammetric Engineering and Remote Sensing, 2007,73(12):1381-1391. doi: 10.14358/PERS.73.12.1381
[30]	Xu H Q.Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI)[J]. Photogrammetric Engineering & Remote Sensing, 2010,76(5):557-565.
[31]	Rikimaru A.Landsat TM data processing guide for forest canopy density mapping and monitoring modelC]//[International Tropical Timber Organization (ITTO) workshop on utilization of remote sensing in site assessment and planning for rehabilitation of logged-over forest, Bangkok, Thailand, 1996:1-8.
[32]	Carlson T N, Ripley D A.On the relation between NDVI, fractional vegetation cover, and leaf area index[J]. Remote Sensing of Environment, 1997,62(3):241-252. doi: 10.1016/S0034-4257(97)00104-1
[33]	徐涵秋,林中立,潘卫华.单通道算法地表温度反演的若干问题讨论——以Landsat系列数据为例[J].武汉大学学报·信息科学版, 2015,40(4):487-492.
	[Xu H Q, Lin Z L, Pan W H.Some issues in land surface temperature retrieval of Landsat thermal data with the single-channel algorithm[J]. Geomatics and information science of Wuhan University, 1997,62(3):241-252. ]
[34]	徐涵秋. 新型Landsat8卫星影像的反射率和地表温度反演[J].地球物理学报,2015,58(3):741-747. doi: 10.6038/cjg20150304
	[Xu H Q.Retrieval of the reflectance and the land surface temperature of the newly-launched Landsat 8 satellite[J]. Chinese Journal of Geophysics, 2015,58(3):741-747. ] doi: 10.6038/cjg20150304
[35]	Jiménez-Muñoz J C, Cristóbal J, Sobrino J A, et al. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009,47(1):339-349. doi: 10.1109/TGRS.2008.2007125
[36]	Jiménez-Muñoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research, 2003,108(D22):4688, doi: 10.1029/2003JD003480. doi: 10.1029/2003JD003480
[37]	Jimenez-Munoz J C, Sobrino J A, Skokovic D, et al. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data[J]. IEEE Geoscience and Remote Sensing Letters, 2014,11(10):1840-1843. doi: 10.1109/LGRS.2014.2312032

Landsat 8影像	Google Earth影像	Landsat 8影像	Google Earth影像
LCZ 1 密集高层建筑		LCZ A 茂密树木

LCZ 2 密集中层建筑		LCZ B 稀疏树木

LCZ 3 密集低层建筑		LCZ C 灌木和矮树

LCZ 5 开阔中层建筑		LCZ D 低矮植被

LCZ 8 大型低层建筑		LCZ E 道路

LCZ 10 工业厂房		LCZ F 裸土或沙

		LCZ G 水体

LCZ类型	研究区范围			建成区范围
LCZ类型	面积/km²	比例/%	LST/℃	面积/km²	比例/%
LCZ 1 密集高层建筑	21.29	2.10	37.38	19.48	8.42
LCZ 2 密集中层建筑	68.01	6.71	39.67	64.44	27.86
LCZ 3 密集低层建筑	52.62	5.19	39.23	33.52	14.49
LCZ 5 开阔中层建筑	21.88	2.16	37.89	19.24	8.32
LCZ 8 大型低层建筑	2.23	0.22	42.45	2.23	0.96
LCZ 10 工业厂房	16.79	1.66	41.82	15.14	6.54
LCZ A 茂密树木	546.41	53.92	28.24	4.61	1.99
LCZ B 稀疏树木	78.22	7.72	28.64	6.31	2.73
LCZ C 灌木和矮树	53.55	5.28	32.72	15.45	6.68
LCZ D 低矮植被	44.52	4.39	32.94	8.61	3.72
LCZ E 裸露的岩石或道路	30.47	3.01	39.23	22.33	9.65
LCZ F 裸土或沙	14.66	1.45	37.96	9.38	4.06
LCZ G 水体	62.76	6.19	29.05	10.56	4.57
合计	1013.41	100.00	-	231.33	100.00

验证数据
	LCZ类型	1	2	3	5	8	10	A	B	C	D	E	F	G	行合计	使用者精度/%
分类数据	1	84	9	0	0	1	1	0	0	0	1	4	0	15	115	73.04
	2	8	173	9	4	0	3	0	0	3	0	9	1	0	210	82.38
	3	1	15	88	0	2	4	0	1	0	0	4	6	0	121	72.73
	5	1	12	0	39	0	2	0	0	2	1	1	1	0	59	66.10
	8	0	0	0	1	13	0	0	0	0	0	0	0	0	14	92.86
	10	0	4	5	0	0	34	0	0	0	0	2	0	0	45	75.56
	A	0	0	0	0	0	0	236	14	4	0	0	0	0	254	92.91
	B	0	0	0	0	0	0	4	29	8	1	0	0	0	42	69.05
	C	1	0	0	4	0	0	0	4	52	2	0	2	3	68	76.47
	D	2	0	0	0	0	0	0	1	2	37	0	0	1	43	86.05
	E	0	4	0	0	0	0	0	0	2	0	40	5	0	51	78.43
	F	0	2	2	0	0	3	0	0	0	0	0	28	0	35	80.00
	G	0	0	0	0	0	0	0	0	0	0	1	0	142	143	99.30
	列合计	97	219	104	48	16	47	240	49	73	42	61	43	161	1200
	生产者精度/%	86.60	79.00	84.62	81.25	81.25	72.34	98.33	59.18	71.23	88.10	65.57	65.12	88.20
	总精度/%	82.93
	Kappa系数	0.806