Spatio temporal change of urban heat island effect in Xining from Landsat image
： 2018 - 06 - 08
： 2018 - 09 - 29
： 2018 - 10 - 19
274 1 0

Abstract & Keywords
Abstract: Background, aim, and scope Withing the implementation of the western development policy, Xining entered a rapid development stage at the end of twentieth Century, and the scale of the city has been expanding, which has caused the urban heat island effect to become more and more prominent. Therefore, it is of great significance to study the spatial and temporal evolution characteristics and mitigation measures of urban heat island effect in Xining for the sustainable development of Xining. Compared with previous studies, the general urban heat island studies mostly take the urban administrative boundary as the suburb boundary. This paper takes the urban built-up area boundary as the urban and suburban boundary, and is relatively new. Materials and methods Using the Landsat satellite image from 1987 to 2015 and the same period of meteorological data and economic data, selected the single window algorithm to retrieve the surface temperature in Xining City from Landsat satellite image, combined with the thermal field variation and years of built-up area of the boundary,then the characteristics of temporal spatial evolution of Xining City urban heat island effect is analyzed in this paper. At the same time, the relationship between heat island intensity and vegetation, impervious surface, population and other factors was discussed. Finally, it puts forward the regulation measures of urban heat island effect in the process of urbanization. Result The results show that: (1) In summer in Xining city as "heat island" effect, "cold island" effect in winter. (2) The intensity of heat island in Xining city at the time is divided into three intervals, 1987~2000 years of heat island effect enhanced slowly from 2000 to 2003 is the rapid increase of 2003 to 2011, the heat island effect gradually weakened, from 2011 to 2015 the heat island effect, strength remained unchanged. (3) The same trend area of summer heat island area and trend of winter cold island, and built-up area change. Discussinon The change of the heat island area in Xining shows that 2000 has become an important time node for the change of thermal environment. Before 2000, the area of heat island in Xining increased little, but there was a sharp increase after 2000. This is due to the implementation of the western development policy. Xining has expanded rapidly in the original built-up area, making the city development from a single center to a multi center structure, and the increasing rate of greening can not catch up with the speed of development. Combined with the actual situation in Xining, this paper puts forward four relief measures for the heat island effect. (1) The artificial lakes should be dug near the industrial zone and the park. (2) Private cars should be controlled. (3) Green energy, such as abundant solar and wind energy in Qinghai Province (3) Construction the counties around Huangzhong, Huangyuan and other Xining cities to attract and disperse the population. Conclusions Research shows: (1) There was almost no change of heat island area in Xining before 2000. After 2000, the area of heat island area, especially the area of strong heat island area and medium heat island area, increased rapidly. In 2003, the area of heat island area reached the highest. From 2003 to 2011, the area of heat island gradually decreased, but increased again after 2011. (2) During the summer daytime, the intensity of urban heat island in Xining is positively correlated with the number of vehicles, built-up area, population and NDBI mean, and negatively correlated with urban greening rate, NDVI mean and BCI. (3) In winter daytime, the intensity of urban heat island in Xining is positively correlated with urban greening rate, NDVI, NDBI, BCI, and negatively correlated with the number of vehicles and built-up area. Recommendations and perspectives Due to the lack of remote sensing data and the poor quality of remote sensing data, the final screening image is not continuous in time, and the final result may lose some information. If there are more image data, better results should be obtained. In the study, it was found that during the winter, Xining was an obvious “cold island” in the daytime. Some studies have shown that the urban area in winter is “heat island” at night. However, due to the limitation of the imaging time of remote sensing data, the night thermal environment in the built area has not been studied in this paper. In future studies, we will focus on the heat island effect of winter nights in Xining.
Keywords: urban heat island effect; single window algorithm; land surface temperature retrieval; BCI; thermal field variability index; Xining City

1   研究区概况

Fig.1 Survey of study region
2   数据与方法
2.2   数据来源

2.3   研究方法
2.3.1   地表温度的遥感反演

$$\mathrm{T}={\mathrm{K}}_{2}/\mathrm{ln}\left(1+{\mathrm{K}}_{1}/{\mathrm{L}}_{\mathrm{\lambda }}\right)$$ （1）

$$\mathrm{\epsilon }=\mathrm{f}{\mathrm{\epsilon }}_{\mathrm{v}}+\left(1-\mathrm{f}\right){\mathrm{\epsilon }}_{\mathrm{i}}+{\mathrm{d}}_{\mathrm{\epsilon }}$$ （2）

$${\mathrm{d}}_{\mathrm{\epsilon }}=\left\{\begin{array}{c}0.0038f f\le 0.5\\ 0.0038\left(1-\mathrm{f}\right)f f＞0.5 \end{array}\right\$$ (3)
$$\mathrm{f}=\left(\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}-{\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)/\left({\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)$$ (4)

 像元类型 Pixel type NDVI取值范围 The value range of NDVI 地表比辐射率 Emissivity 水域 Waters NDVI﹤-0.180 $$\mathrm{\epsilon }=0.995$$ 裸土 Exposed soil -0.180≤NDVI﹤0.157 $$\mathrm{\epsilon }=0.972$$ 混合像元 Mixed pixel 0.157≤NDVI﹤0.886 $$\mathrm{\epsilon }=\mathrm{f}{\mathrm{\epsilon }}_{\mathrm{v}}+\left(1-\mathrm{f}\right){\mathrm{\epsilon }}_{\mathrm{i}}+{\mathrm{d}}_{\mathrm{\epsilon }}$$ 植被 Vegetative cove NDVI≥0.886 $$\mathrm{\epsilon }=0.986$$

$$\mathrm{\omega }=0.189\mathrm{P}+0.342$$ （5）
$$\mathrm{P}=0.6108\mathrm{*}\mathrm{e}\mathrm{x}\mathrm{p}\frac{17.27\left({\mathrm{T}}_{0}-273.15\right)}{237.3+\left({\mathrm{T}}_{0}-273.15\right)}\mathrm{*}\mathrm{R}\mathrm{H}$$ （6）

 成像日期 Image acquired date 实测值 Measured value 估算值 estimate 夏季 Summer 1987.9.9 13.7 286.85 52 281.69 0.4961 0.9346 1995.8.16 17.3 290.45 69 285.03 0.5995 0.9263 2000.8.11 17.3 290.45 63 285.03 0.5771 0.9281 2003.9.20 9.8 282.95 72 278.08 0.5069 0.9337 2011.8.10 20.3 293.45 50 287.81 0.5671 0.9289 2015.8.21 13.3 286.45 66 281.32 0.5325 0.9317 冬季 winter 1989.2.10 -4.5 268.65 47 264.06 0.3809 0.9454 1995.1.5 -7.3 265.85 36 261.51 0.3660 0.9468 2000.10.3 2.2 275.35 56 270.17 0.4178 0.9419 2006.10.31 1.3 274.45 59 269.35 0.4168 0.9419 2013.12.5 -1.5 271.65 59 266.80 0.4030 0.9433 2015.1.9 -8.3 264.85 39 260.60 0.3661 0.9468

$$\mathrm{T}\mathrm{S}=\frac{1}{C}\left\{a\left(1-\mathrm{C}-\mathrm{D}\right)+\left[\left(\mathrm{b}-1\right)\left(1-\mathrm{C}-\mathrm{D}\right)+1\right]\mathrm{T}-\mathrm{D}{\mathrm{T}}_{\mathrm{a}}\right\}$$ （7）

$$\mathrm{C}=\mathrm{\epsilon }\mathrm{\tau }$$ (8)
$$\mathrm{D}\mathrm{ }=\mathrm{ }\left(1-\mathrm{\epsilon }\right)\mathrm{ }\left[1+\left(1-\mathrm{\epsilon }\right)\mathrm{\tau }\right]$$ (9)
2.3.2   基于热场变异指数的西宁市及辖区热岛定量分析

$$\mathrm{H}\mathrm{I}\left(\mathrm{T}\right)=\left(\mathrm{T}-{\mathrm{T}}_{\mathrm{M}\mathrm{E}\mathrm{A}\mathrm{R}}\right)/{\mathrm{T}}_{\mathrm{M}\mathrm{E}\mathrm{A}\mathrm{R}}$$ （10）

 热场变异系数 Coefficient of heat field variation 热岛效应强度 Intensity of the heat island effect 生态评价指标 Ecologicalevaluating indicator ≤0 无 No 优Excellent 0～0.005 弱 Weak 良 Good 0.005～0.010 中Medium 一般Average 0.010～0.015 较强Strong 较差Fair ≥0.015 强Stronger 差 Poor
3   结果与分析
3.1   西宁市夏季“热岛”效应时空演变特征
3.1.1   夏季热岛区面积变化定量分析

Fig.2 The area change of different grade heat island area in Xining in summer
3.1.2   夏季热岛效应时空分布特征

Fig.3 The intensity distribution of heat island effect and the boundary of built-up area in Xining in summer
3.2   西宁市冬季热岛效应时空演变特征
3.2.1   冬季热岛区面积变化定量分析

Fig.4 The area change of different grade heat island area in Xining in winter
3.2.2   冬季热岛效应时空分布特征

Fig.5 The intensity distribution of heat island effect and the boundary of built-up area in Xining in summer
3.3热岛效应影响因子及其多元回归分析

 指标类型 Index type 车辆 Cars 建成区面积 Area of built-up area 绿化率 Greening rate NDVI均值 NDVI (mean) NDBI 均值 NDBI (mean) BCI 均值 BCI (mean) 人口数 population HI(T) 车辆 Cars 1.000 － － － － － － － 建成区面积 Area of built-up area 0.989* 1.000 － － － － － － 绿化率 Greening rate 0.948* 0.970* 1.000 － － － － － NDVI均值NDVI(mean) 0.977* 0.974* 0.929* 1.000 － － － － NDBI均值NDBI(mean) -0.824* -0.832* -0.684* -0.800* 1.000 － － － BCI均值BCI(mean) -0.495* 0.521* -0.361* -0.493* 0.838* 1.000 － － 人口 population 0.837* 0.807* 0.880* 0.802* -0.390* 0.039* 1.000 － HI(T) 0.369* 0.421* -0.471* -0.502* 0.400* -0.340* 0.282* 1.000
*：表示相关性在0.05水平上显著。
*: indicates that the correlation of the parameters reaches a significant level p≤0.05.

 指标类型 Index type 车辆 Cars 建成区面积 Area of built-up area 绿化率 Greening rate NDVI均值 NDVI (mean) NDBI 均值 NDBI (mean) BCI 均值 BCI (mean) 人口数 population HI(T) 车辆 Cars 1.000 － － － － － － － 建成区面积 Area of built-up area 0.953* 1.000 － － － － － － 绿化率 Greening rate 0.993* 0.940* 1.000 － － － － － NDVI均值NDVI(mean) 0.964* 0.872* 0.961* 1.000 － － － － NDBI均值NDBI(mean) 0.336* 0.037* 0.358* 0.456* 1.000 － － － BCI均值 BCI(mean) -0.548* -0.640* -0.561* -0.320* 0.411* 1.000 － － 人口 population 0.336* 0.037* 0.358* 0.456* 0.351* 0.111* 1.000 － HI(T) -0.177* -0.296* 0.148* 0.149* 0.349* 0.847* -0.094 1.000
*：表示相关性在0.05水平上显著。
*: indicates that the correlation of the parameters reaches a significant level p≤0.05.
4   讨论

5   结论
（1）1987—1995年，夏季白天，西宁市区和市郊温差不大，2000年以后，城市热源的分布明显增多，地表温度热源主要分布在三个区域：城市中心和居民新区等人口稠密的地区；工业园区；城区边缘地带的河谷裸土地。冬季白天，地表温度没有十分明显的高低极值，西宁市区从1989年起，就表现为一个“冷岛”。夏季的热岛区和冬季的冷岛区均随着西宁市城区发展有向东、西、南北四个方向扩展的趋势。
（2）西宁市热岛区面积变化在时间序列上有三个节点：2000年以前西宁市夏季和冬季白天各个热岛区面积变化不明显，2000年以后热岛区面积，尤其是较强热岛区面积和中热岛区面积都出现迅速增加的趋势；2003年热岛区面积达到最高，2003年以后热岛区面积渐渐回落；2011年以后又增强。
（3）夏季白天，热岛强度与西宁市区车辆数、建成区面积NDBI均值和人口数呈正相关，热岛强度与城市绿化率、城市NDVI均值、BCI指数呈负相关。冬季白天，西宁市热岛区面积与城市绿化率、NDVI均值、NDBI均值和BCI均值呈正相关，与市区车辆数、建成区面积呈负相关，相关系数分别为-0.177和-0.296，与人口的相关性不明显。

LIU Xuemei

GAO Xiaohong

JIA Wei

Natural Science Fund Project of Qinghai Provincial Science & Technology Department（2016-ZJ-907）

Journal of Earth Environment