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Study on the Effect of Corridor Structures on Urban Heat Island in Nanjing by Remote Sensing

Runping Shen, Sulin Tao, and Chen Yang

Abstract—Urban heat island (UHI) effect is one of features in the modern city. The land surface temperature of study area was retrieved from ETM+ data via generalized single-channel algorithm, and two urban corridor structures-the greenbelt system and water system were extracted by the GIS to investigate their relationship with Nanjing UHI. The results showed that the two corridor structures both have an obvious effect on Nanjing UHI in the summer. The cooling effect of the greenbelt system is more obvious than that of the water system. Moreover, the impacting range of the two corridor structures is limited and usually not exceeding 240 meters from the water system and 300 meters from green system.

I. INTRODUCTION

anjing, as the political, economic and cultural center of the Jiangsu province, is one of the four famous cities

with the highest temperature in China. With the rapid development of the economy, Nanjing has experienced an increase in the population density and enhanced living standard. There is a growing demand for motor vehicles and air-conditioners for cooling and warming, which makes the heat island effect more severe.

In landscape ecology, the corridor is a panhandle that is surrounded by different basements, which facilitates the migration of animals [1]. In this paper, corridor, as suggested in urban eco-planning, is a man-made corridor or complex corridor with combination of artificial and natural, such as rivers, forests, and shelterbelt lie in city and so on. This paper extracted the typical water body and green space system to quantitatively analyze the influence of two corridor structures on the surrounding’s temperature by using remote sensing data. The influence of two corridor structures on the heat island effect of Nanjing also further proved to provide the reasoning for the urban development plan. Therefore, both scientific data and logical reasoning are implemented to support the healthy development of the

Manuscript received November 14, 2010. This work was supported by

National Natural Science Foundation of China (No.41001289) and Science and Technology Program of Jiangsu (Project No. BS2006059).

Runping Shen is with the Key Laboratory of Meteorological Disaster of Ministry of Education and Remote Sensing Institute of Nanjing University of Information Science & Technology, CO 210044 P. R. China (phone: 025-58699869; e-mail: rpshen@nuist.edu.cn).

Sulin Tao is with the Key Laboratory of Meteorological Disaster of Ministry of Education and Remote Sensing Institute of Nanjing University of Information Science & Technology, CO 210044 P. R. China (e-mail: sulintao@hotmail.com).

Chen Yang is with the Key Laboratory of Meteorological Disaster of Ministry of Education and Remote Sensing Institute of Nanjing University of Information Science & Technology, CO 210044 P. R. China (e-mail: yangchens@gmail.com).

metropolitan.

II. STUDY AREA & MATERIALS

A. Study Area The city of Nanjing is the study area located at the

longitude of 118036′ to 11906′ and the latitude of 31053′ to 32019′, which covers the downtown of Nanjing, Pukou district, Jiangning district, and the southern part of Luhe district. The climate of this region is considered to be subtropical and humid, which makes this area prone to monsoons. It has four distinct seasons, which does not only consist of cold and dry weather in the winter but also hot and rainy climate in the summer. The average annual maximum temperature is 20.4℃ and average minimum temperature is 11.6℃. The extreme maximum temperature is 43℃ (Jul 13, 1934) and extreme minimum temperature is -14℃ (Jan 6, 1955). B. Remote Sensing Materials

Considering all the factors, this study used three scenes of Landsat ETM+ images (provided by WIST web site), including the images on July 30, 2000, July 17, 2001 and August 5, 2002. C. Preprocessing of Remote Sensing Data

The ETM+ data used in this article are the L1T data, which have been processed by geometric correction. In order to improve the accuracy of retrieved land surface temperature, cloud detection, and treatment, there are other necessary steps to consider. D. Radio Metric Correction

Radiometric correction is to transform the gray value (DN) of remote sensing images recorded by sensor into entrance pupil radiation intensity (Lλ

at-sensor), which can be achieved according to conversion formula (1) provided by the Science Data User’s Handbook (SDUH) from the United States Geological Survey (USGS) website and the parameter information provided by metadata documentation (METADATA_FILE) from selected data [2].

(1)

QCAL is a DN value of remote sensing images, while

other parameters are all provided by the documentation of image metadata.

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International Conference on Information Science and Technology March 26-28, 2011 Nanjing, Jiangsu, China

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E. Cloud Detection and Remote Processing

The cloud detection is to eliminate the impact of clouds on remotely sensed images. There is a certain cloud in selected images in both 2000 and 2002. A study on the removal of the clouds in the two images uses detection cloud area based on a prior knowledge [3], and the removal of cloud method is based on image processing and analyzing theory. Based on the standard image in 2001 to remove the cloud in the images of 2000 and 2002, the features of objectives on the land surface covered by clouds can be represented. It can enhance the availability of ETM+ images. Cloud detection and removal processing is represented in Figure 1.

Fig. 1. Cloud detection and removal processing

1) Interpretation of cloud information. Use the first band

of ETM+ to get the radiance features in the cloud region. 2) Detection of cloud. Split the ETM+ data according to

the threshold information acquired from interpreted cloud information to obtain the cloud detection area. The cloud detection area is w, the cloud area is 1, and the other areas are 0.

3) Calculation on removal of clouds. By using the removal cloud model (2), it removes the clouds in the images of the year 2000 and 2002. In the model (2), f(x, y) is a standard image, g(x, y) is the image waiting for removing cloud, and f’(x, y) is the processed image; α is the adjustment coefficient which is the gray scale ratio of the pending image and standard image.

(2) F. Atmospheric Correction

Transformation from gray value into entrance pupil radiation intensity has been achieved after radiometric correction. Taking into account the accuracy of land surface emissivity, the entrance pupil radiance of air top sensor (band 1-5) were transformed into the true value of the object surface radiation to calculate the surface emissivity. Therefore, atmospheric correction for the three scenes of 1-5 bands of remote sensing images is needed in order to be made. Dark pixel correction method was chosen for the atmospheric correction to images.

III. LAND COVER TYPES AND CORRIDOR STRUCTURE INFORMATION INTERPREATION

The structure and properties of urban surface is the main factor affecting the urban heat island, which constitutes the basis of that [4]. To study the relationship between urban heat island effect and urban corridor structure, the decision tree classification and supervised classification methods were used to classify the ETM+ data, from which we can

get an extraction of land cover types of the study area. The image can be divided into forest, grassland, farmland, water body, building land, and bare land. The key point in supervised classification is to extract the woodland, grassland, and farmland. The water body, construction sites, and bare land can be extracted by the decision tree using NDVI (normalized difference vegetation index), MNDWI (modified normalized water index), NDBI (normalized construction index), and ETM+ 3 band methods.

NDVI (3) is often used to highlight vegetation information.

(3)

MNDWI (4) is an improved normalized water index [5], which can extract more precise information on water bodies.

(4)

NDBI (5) is a normalized construction index [6], which is used to extract urban building area.

(5)

The decision tree is shown in Figure 2, where the thresholds of parameters a1, a2, a3 and a4 are in Table I

Fig. 2. Classification Tree

TABLE I THRESHOLD PARAMETER OF DECISION TREE IN THE IMPLEMENTATION

Image Type Parameter a1

Parameter a2

Parameter a3

Parametera4

2000.07.30ETM+ 0.47 0.71 -0.88 45 2001.07.17ETM+ 0.46 0.69 -0.70 56 2002.08.05ETM+ 0.46 0.69 -0.70 70

Classification accuracy analysis in 2000, 2001 and 2002 showed overall accuracy to be 97.6327%, 96.3822% and 97.7021%, respectively. Kappa coefficients were 0.9705, 0.9546 and 0.9709, respectively. The accuracy meets the

σ Factor

Cloud Detecti

on

Cloud Interpretation

Recovery Calculati

on

Recovery

Image

ETM+ Image

NDVI>a1

Vegetation Non-vegetation

MNDWI>a2

Water Body Non-water

NDBI>a3 LBand3>a4

Supervised Classification

Building land Bare land Farmland Grassland Forest

Yes No

Yes No

Yes No

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requirement for further research. The typical greenbelt and water regions were extracted

from the results map of the corresponding classification. The bodies of water include the Qinhuai River, New Qinhuai River, and Xuanwu Lake. The greenbelt region consists of Zijin Hill, Mufu Hill, Yuhuatai Hill, General Hill, Wulong Hill, and Yangmeitang. Selecting the main city average temperature in Nanjing as baseline, nine 60m buffer zones from the extracted water and greenbelt were constructed to study the heat island effect respectively.

IV. SURFACE TEMPERATURE RETRIEVAL

There are many methods about surface temperature retrieval, The algorithms special for the single thermal infrared band mainly are Mono-Window-Algorithm of Qin Zhihao, Generalized Single-Channel Method of Jiménez-Munoz J C & Sobrino J A, and Image-based Method [7] of Weng Q. In this paper, the former one [8], [9] were chosen. The important parameters are listed below. A. ETM+6L Band Brightness Temperature Calculation

ETM+6L band pixel brightness temperatures can be calculated using the Planck function. The remote sensing image brightness temperature in 2000, 2001 and 2002 were calculated using the algorithm provided by the Science Data User’s Handbook (SDUH) from United States Geological Survey (USGS) website. The algorithm is show below in figure (6)

(6)

In this equation, Tat-sensor stands for the pixel brightness temperature (K), while K1 and K2 are constants. For the Landsat ETM+ data, K1 = 666.09 W·m-2·sr-1·μm-1，K2 = 1282.71K; Lλ

at-sensor is entrance-pupil radiation intensity (W·m-2·sr-1·μm-1).

B. Calculation of Surface Emissivity

This study chooses the following methods to calculate the surface emissivity [9], [10]. Biomes such as forest, grassland, bare land, and farmland are grouped into natural surface, and keeping the other two categories along, There should now be only natural surface, urban area and water body on the map. The emissivity of water pixel was assigned to 0.995, and it’s calculation of natural surface pixels and urban surface pixels are as followed with equation (7) and (8)

(7)

(8) εnatural_surface and εbuilt_up separately represent the natural

and urban pixel surface emissivity. Pv is vegetation coverage, which comes from the following equation (9)

(9)

NDVI is normalized difference vegetation index, NDVIv = 0.70, NDVIs = 0.05, and when the NDVI of image pixels on the map are greater than 0.70, Pv equals to 1, or when the NDVI is lesser than 0.05, Pv equals to 0.

C. Estimation of Atmospheric Parameters From ACPC

ACPC (Atmospheric Correction Parameter Calculator) is an online atmospheric correction parameter calculator [11] provided by NCEP (National Centers for Environmental Prediction). In order to calculate the real time parameters of atmosphere, including atmospheric transmittance, atmospheric upward radiance, atmospheric downward radiance, and atmospheric water vapor content, users simply need to just type in remote sensing imaging time, latitude and longitude information, and the parameters of the surface (elevation, temperature, pressure and relative humidity).

According to ACPC model, image type, acquisition time, center coordinates, the average elevation of the study area, air pressure, temperature, and relative humidity of the ground site are inputted in order to obtain atmospheric transmittance, atmospheric radiation upstream, and downstream radiation information. The results are in Table II.

TABLE II ATMOSPHERIC PARAMETERS OF THE REMOTE SENSING IMAGE CALCULATED

IN ACPC

Map Type Atmospheric

Transmittance ( τ )

Atmospheric Downward Radiance

(Latm↓, Wm-2sr-1μm-1)

Atmospheric Upward Radiance

(Latm↑, Wm-2sr-1μm-1)

2000.07.30ETM+ 0.49 6.06 4.02 2001.07.17ETM+ 0.60 5.05 3.33 2002.08.05ETM+ 0.46 6.73 4.68

V. RESULTS AND ANALYSIS

A. Effect of Water System on Heat Island Effect in Nanjing

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0-60 60-120 120-180 180-240 240-300 300-360 360-420 420-480 480-540Buffers/(m)

Red

uctio

n of

Tem

pera

ture

/(℃ )

2000-07-30 (Waters) 2001-07-17 (Waters)2002-08-05 (Waters)

Fig. 3. Effects of Water System on the Surrounding Temperature in 2000,

2001 and 2002

The results (Figure 3) show that the water system obviously affects surrounding temperature, especially in the year 2000. The temperature goes up to 7.37℃ within the 60m water buffer zone. The trend of the curving lines

610

remains the same in three years that the drop rate of the temperature trend decrease from the 60m buffer to the 240m buffer zone, and the temperature decreased value is steady from the 300m buffer to the 540m buffer zone. As a result, we can make a conclusion that the water system has an obvious effect on the heat island effect. However, the data is also limited for water system that has a weak effect greater than 240m. B. Effect of Greenbelt on Nanjing Heat Island

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0-60 60-120 120-180 180-240 240-300 300-360 360-420 420-480 480-540Buffers/(m)

Redu

cti

on

of

Tem

pera

ture

/(℃

) 2000-07-30 (Greenbelts)2001-07-17 (Greenbelts)2002-08-05 (Greenbelts)

Fig. 4. Effects of greenbelt on surrounding temperature in 2000, 2001

and 2002

Figure 4 indicates the impact of the greenbelt regions to the surrounding temperature on July 30, 2000, July 17, 2001 and August 5, 2002. It can be seen directly from the chart that the trend of the curving lines are almost the same in the three years from 2000 to 2002. They can be considered as straight lines. In other words, the cooling effect of mainly greenbelt regions toward its surrounding temperature has a linear relationship with the thermal distance. It is obviously that these approximate straight lines appear to be very smooth, which is less steep compared to the ones in the water system. Also, the drop rates of the temperature trend steadies from the 300m buffer zone. Besides, the statistic results show that the effect of the greenbelt system on the surrounding temperature is bigger than the one of the water system. The statistics of the year 2000 show that the decreased value of temperature up to 9.66℃ within 60m greenbelt buffer zone, which is 2.29℃ higher than the temperature in the same buffer zone in the water system over the same period. This result is opposite to the study of Du Mingyi etc. [4], [12]. The reason may be due to the greenbelt covering much more area than water in Nanjing downtown. The large area of greenbelt in Zijin Mountain, Mufu Mountain, Yuhuatai, General Hill, Wulong Mountain, and Yangmeitang benefits temperatures in the summer more than the water systems like Xuanwu Lake, Qinhuai River and etc. C. Effect of the two Corridor Structures on Heat Island of

Nanjing Figure 5 is a comparison between the effects of the

average water system and greenbelt system on Nanjing’s heat island effect in three years. It shows that the urban greenbelt system has a more significant effect on cooling the urban heat field in the summer, so metropolitans should

pay attention to the layout of the greenbelt; on the other hand, the influence of the water system cannot be underestimated. Its effect on surrounding temperature is 2-7℃ lower than the average city temperature, but increasing water area in urban construction is proving to be hard enough. Most importantly, figure 5 reflects the overall temperature regulation of the water system is less effective than the greenbelt system. Not only in the same time phase and the same buffer zone that the temperature’s decrease rate is smaller, but it also reflects that the sustainability of temperature regulation by the water system to the surrounding environment is weak. The decreased temperature value in the buffer zone from 60m to 240m decreases rapidly and then levels off.

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0-60 60-120 120-180 180-240 240-300 300-360 360-420 420-480 480-540Buffers/(m)

Reduct

ion o

f Tem

pera

ture

/(℃

) Waters

Greenbelts

Fig. 5. Comparison between average effect of water system and greenbelt

system on Nanjing UHI in three years

V. SUMMARIZE

With in-depth study of the urban heat island effect, the quantitative monitoring, analysis, and evaluation of urban heat island effect has gradually become an important part of urban climate and environment studies. The land surface temperature of Nanjing was retrieved from ETM+ data of July 30, 2000, July 17, 2001 and August 5, 2002 by means of usually accepted single-channel algorithm. This article abstract covers information about Nanjing using the combination method, such as decision tree classification and supervised classification. It also analyzes the effect of the corridor structure toward the heat island effect in Nanjing by extracting water, greenbelt system at the environment of ArcGIS 9.2 with the established buffer. The result shows that greenbelt and water area system as corridor structures have significant effect on the thermal action in Nanjing. The greenbelt system benefits more than water system on cooling effect due to the larger area it occupied. However, the impacting range of the two corridor structures is limited and usually not exceeding 240 meters from the water system and 300 meters from green system.

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