河谷型城市热岛空间分布格局演变及对策——以西宁市区为例

doi:10.3724/SP.J.1047.2014.00592

摘要/Abstract

摘要：

城市热岛效应直接反映着城市的气候特征,这对于研究由城市化发展与环境改变引起的城市气温的变化及保护城市的生态环境具有重要的现实意义。本文利用Landsat TM影像、气象台站资料,基于GIS的空间分析技术及单窗算法,对河谷型城市西宁市的地表温度进行反演,分析了地表温度与NDVI、NDBI的空间对应关系。结果表明：西宁市存在明显的城市热岛效应,热场分布及延伸与西宁市空间扩展布局相一致,热岛范围呈逐年增长的趋势;低、中温区的热岛面积大幅度减少,高温区的热岛范围显著增加;热岛效应冬季最强,夏季次之,秋季有明显减弱的趋势。在河谷型城市的空间格局上,地表温度与NDVI呈负相关关系、与NDBI呈正相关关系。最后,依据热岛时空演化、成因分析和策略研究的思路,从不同角度提出了缓解城市热岛效应的措施和对策,为未来西宁市热环境的改善提供科学参考和决策支持。

关键词: 城市热岛, 单窗算法, 河谷型城市, 定量反演, 西宁市区

Abstract:

Urban thermal environment is an important part of urban ecosystem, and urban heat island (UHI) effect directly reflects the climate characteristics of the city. It has an important practical significance to analyze comprehensively the urban heat island for studying urban air temperature change resulting from urbanization and environment change, and protecting urban eco-environment .Taking the valley city of Xining as a case study area, six Landsat 5 TM scenes were adopted from USGS website, and meteorological station data were obtained from China Meteorological Data Sharing Service System respectively. Firstly, the six Landsat 5 TM scenes were preprocessed by using ArcGIS 10.0 and ENVI 5.0 and the meteorological station data was processed using EXCEL software. Secondly, based on mono-window algorithm, the land surface temperature (LST) of the study area was retrieved. Then, all LST retrieval results obtained from the six Landsat 5 TM scenes were normalized to 0-1 for comparing the temporal differences of varied urban heat island. Thirdly, all normalized results were divided into five equal interval levels based on density separation technology of ENVI 5.0 software. Finally, the correlations between LST and NDVI as well as LST and NDBI were analyzed respectively. The results show that an obvious urban heat island effect exists in Xining city from 1996 to 2011. In addition, the spatial distribution pattern and extension of the urban heat island is consistent with urban sprawl. The urban heat island area is respectively 54.2 km² in August 1996, 85.9 km² in July 2001, 147.1 km² in July 2007, 166.7 km² in March 2009, 148.9 km² in July 2010, and 158.7 km² in September 2011, showing an expanding trend from 1996 to 2011 in general. Specifically, the heat island area has reduced substantially in the low and medium LST sub-region, whereas the heat island area has increased significantly in the high LST sub-region. The heat island effect is the strongest in winter, and it decreases evidently in the summer, then the autumn. In the spatial pattern of valley city, the land surface temperature is negatively correlated with NDVI, and positively correlated with NDBI. According to the spatio-temporal evolution of urban heat island, with the adoption of analysis of causes and strategic thinking, the constructive strategies and measures to alleviate urban heat island effect are proposed from different aspects, thus providing a scientific reference for decision making to improve the thermal environment of Xining City in the future.

Key words: urban heat island, mono-window algorithm, quantitative inversion, Xining City, valley city

贾伟, 高小红*. 河谷型城市热岛空间分布格局演变及对策——以西宁市区为例[J]. 地球信息科学学报, 2014, 16(4): 592-601.DOI:10.3724/SP.J.1047.2014.00592

JIA Wei,GAO Xiaohong*. Analysis of Urban Heat Island Environment in a Valley City for Policy Formulation: A Case Study of Xining City in Qinghai Province of China[J]. Journal of Geo-information Science, 2014, 16(4): 592-601.DOI:10.3724/SP.J.1047.2014.00592

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日期	观测数据			反演参数				LST定量反演
日期	平均气温(K)		相对湿度		大气平均作用温度(K)	大气透射率	NDVI(均值)	平均地表温度(K)	标准差
1996-08-16 2001-07-13 2007-07-14 2009-03-13 2010-07-22 2011-09-11	290.45 291.25 289.55 267.05 293.45 287.05	69 61 54 61 64 68		285.0287 285.7697 284.1951 262.6010 287.8073 281.8796	0.926088 0.927551 0.931517 0.944797 0.923621 0.930402	0.372558 0.394763 0.448654 0.098554 0.430700 0.312257		308.43 309.66 309.02 281.87 310.56 307.59	2.72 3.11 3.01 3.64 2.86 2.41

日期	低温区面积(km²)	次中温区面积(km²)	中温区面积(km²)	次高温区面积(km²)	高温区面积(km²)
1996-08-16	46.4	142.7	105.3	35.9	18.3
2001-07-13	45.9	127.1	89.7	46.0	39.9
2007-07-14	29.4	99.4	72.7	59.3	87.8
2009-03-13	50.8	81.2	49.9	64.2	102.5
2010-07-22	31.0	80.4	88.3	56.3	92.6
2011-09-11	19.1	85.8	85.0	85.1	73.6