EVALUATION OF SHAPE CHARACTERISTICS OF ...of the vegetation cover rate and NDVI value of each grid s...

ASPRS 2008 Annual Conference Portland, Oregon April 28 - May 2, 2008

EVALUATION OF SHAPE CHARACTERISTICS OF VEGETATION DISTRIBUTION IN THE CENTRAL PART OF TOKYO

BY USING HIGH-RESOLUTION SATELLITE IMAGES

Hideki Hashiba Assistant Professor

Department of Civil Engineering, College of Science and Technology,

Nihon University, 1-8-14 Kanda Surugadai, Chiyoda-ku,

Tokyo 101-8308, JAPAN [email protected]

ABSTRACT The city environment in the central part of the capital of Japan, Tokyo has been investigated by using high-resolution satellite images. The accumulation of environmental information necessary to create a good quality, city environment has been advanced. Up to now, the environmental information has been examined for the amount of distribution and the distribution shape of small-scale vegetation in the city. However, when a small scale vegetation region in the city is extracted from the high-resolution satellite image, it is necessary to improve the extraction accuracy by decreasing the miss-extraction rate. Moreover, a quantitative evaluation technique of the city environment by the distribution shape of vegetation in the city has not been firmly established. In this study, the extraction accuracy improvement of the vegetation area in the city region was examined by using the high-resolution satellite image that had been observed by the Quickbird satellite. The extraction accuracy admittedly improved by uniting the extraction result by NDVI value with the extraction result by clustering method. Moreover, the shape factor calculated from the area of the vegetation region and the surroundings length of the area was calculated from the extraction result, and the vegetation ratio and the distribution shape were evaluated. As a result, even if the vegetation ratio was the same, the difference of the shape characteristic of distribution was able to be evaluated quantitatively by the place in the city.

INTRODUCTION

The demand to improve the urban environment has been increasing. In order to bring this improvement about, various methods have been investigated and evaluated. Many environmental elements are involved in the environmental problems of urban areas, such as abnormal temperature rises due to the heat island effect. The ecology of vegetation such as trees and green spaces in urban areas is considered to greatly affect their climate change and human physiology. Currently, in large cities such as Tokyo, a great quantity of vegetation has been maintained in nature parks, green spaces, and along roadsides by central and local governments. Moreover, a great quantity of vegetation is arranged in gardens and green spaces by the private sector. This is considered to play an important role in the urban environment. However, only a few studies report on the detailed characteristics of vegetation distribution quantity and morphology throughout the Tokyo metropolitan area. Although biomass is used as an evaluation unit by which to evaluate the quantity of trees in a forest region, it is not used to evaluate the quantity of vegetation and trees in urban areas; that quantity is currently evaluated simply by the vegetation cover area, and a method by which to evaluate geometric distribution characteristics has not been sufficiently established. Even in the Green Corridor Plan of Tokyo, continuity of vegetation distribution is not sufficiently evaluated. Therefore, a new method by which to evaluate vegetation in the environment is clearly required. The vegetation in parks in urban areas has been mainly investigated using aerial photography and field surveys. Investigation of all green spaces in urban areas using aerial photographs is difficult because those covering wide areas with high spatial resolution are challenging to produce under the same conditions, and investigation by field survey often requires a great deal of labor. Recently, high spatial resolution multiple spectrum image data obtained via high resolution satellite have been actively used for environmental evaluation. If the data are applied to the investigation of the urban environment, various kinds of environmental information on vegetation in urban areas can be evaluated. Namely, geometric distribution characteristics and distribution quantity can be easily investigated and various effective methods by which to evaluate

the environment can be easily established. The results obtained in this study may contribute to providing detailed environmental information on vegetation throughout the Tokyo metropolitan area.

PURPOSE

The purpose of this study is to investigate the distribution characteristics of vegetation cover rates and a normalized difference vegetation index (NDVI) value in the Tokyo metropolitan area using high resolution satellite image data. In addition, a shape factor of vegetation distribution is proposed as a new parameter by which to evaluate the vegetation distribution in urban areas, and the initial evaluation of this shape factor was performed in this study. Previously, vegetation cover rates have been used as a parameter in evaluating the vegetation in urban areas. In this study, the vegetation distribution morphology was evaluated using the shape factor obtained by dividing the vegetation distribution area by its circumference. This shape factor is one of the factors used for demonstrating the geometric distribution tendency of the ecological distribution quantity. In order to obtain accurate results, vegetation regions extracted by the land cover classification method using clustering and those extracted by the NDVI method were superposed using high resolution satellite image data. Regions extracted by both were judged to be those with the highest likelihood of being vegetation regions. Moreover, an attempt was made to extract the vegetation existing in the shadow of buildings, often observed in high resolution satellite images, by clustering limited to shadowed areas and NDVI values.

IMAGE PROCESSING AND CLASSIFICATION

Using high resolution satellite image data, land cover classification using clustering was performed to extract the vegetation regions. These were also extracted using NDVI values. Then regions extracted by both were determined as those vegetation regions with high classification accuracy. The high resolution satellite image was divided into a grid (1 km x 1 km), and the characteristics of the extracted vegetation regions were calculated in each grid square. Then the space distribution characteristics of the vegetation were evaluated based upon the distribution characteristics of the vegetation cover rate and NDVI value of each grid square. Moreover, the shape factor for evaluating geometric distribution characteristics was proposed, and the initial evaluation of this factor was performed to characterize the space distribution of the vegetation.

Figure 1. Image data of study area (the Quickbird satellite image data).


Image Data In this study, high resolution satellite image data of the Tokyo cosmopolitan area, taken by Quickbird satellite

on September 4, 2004, were used (Figure 1 and Figure 2-1). The ground resolution of the obtained panchromatic image was 0.67 m x 0.67 m. The ground resolution of the multi spectral image was 2.69 m x 2.69 m. In this study, multi spectral image data, pan-sharpened to the ground resolution of the panchromatic image, were used. Clustering

On the 4-band (blue, green, and red as visible lights, and near-infrared rays) image data, clustering was performed to classify the land into 30 classes by using ISO-data clustering method. A field survey was also performed to classify the land. Based upon the results obtained by both, the region was determined as one with the highest likelihood of being a vegetation region (Figure 2-2). NDVI value

The NDVI value was calculated by applying equation (1) to the digital numbers (DN values) of 4 and 3 bands of the multiple spectrum images. In this study, a pixel with a positive NDVI value was judged to be a vegetation region. That is, the threshold value for separating a vegetation region from a non-vegetation region was 0 (Figure 2-3).

NDVI = (DNBand4 - DNBand3) / (DNBand4 + DNBand3) ・・・・ Eq.(1) Superposition

The vegetation regions extracted by NDVI values and those extracted by clustering were superposed (Figure 2-4). A region extracted by both was determined to be a vegetation region. Namely, the result obtained based upon the statistical features of the image was superposed upon the result obtained based upon the NDVI value features calculated using spectral characteristics of the image, and a region included in both was determined as one with the highest likelihood of being a vegetation region.


Figure 2-1. Image data of one of example in study area. Figure 2-2. The extracted vegetation zone in full-sun

area by using clustering method.


Figure 2-3. The extracted vegetation zone in full-sun area by using NDVI value (white and gray area).

Figure 2-4. The vegetation regions extracted by NDVIvalues and those extracted by clustering were superposed.

Shadowed Area

Figure 3-1 shows the example of typical distribution of shadow zone in this study area. Clustering could extract only the shadowed areas in the multi spectral image (Figure 3-2). By extending the histogram of the extracted shadowed area, various differences in land cover situations could be interpreted, although these differences were not remarkable. The NDVI values were calculated only in the shadowed areas, and a pixel with a positive NDVI value was judged to be a vegetation region.

Figure 3-1. The example of typical distribution of s

shadow zone in this study area. Figure 3-2. The shadowed areas in the example area

shown in Fig.3-1 by clustering method.

Shape Factor The shape factor was calculated for the vegetation region extracted using the method described, and the

geometric distribution characteristics were evaluated using it. This shape factor was calculated in each grid square using the following Equation(2) . Figure 4 shows the extracted the vegetation zone and the shape of surrounding lines in the example area. Even when the areas of vegetation regions are the same, the vegetation regions with larger shape factors were considered to bear higher density and continuity. Using the shape factor, the geometric distribution characteristics of the vegetation, such as congestion and dispersion, could be evaluated. F = A/L ・・・・・・Eq.(2)

Here, F: The Shape Factor of each 1kmx1km Grid area A: The sum total area of extracted vegetation zone in each 1km x 1km Grid zone: L: The sum total length of surrounding line of extracted vegetation zone in each 1km x 1km Grid zone.

Figure 4-1. The extracted the vegetation area (white and gray zone) in example area.

Figure 4-2. The surrounding lines of extracted vegetation area in example area.


Grid Area

In this study, the range of the image obtained by satellite was 18 km x 18 km centered at the Tokyo central area. The Tokyo central area includes the whole of the major administrative districts, such as Chiyoda-ku, Minato-ku, and Shinjuku-ku. This image included the surrounding administrative districts of Tokyo. As shown in Figure 5, this image was divided into small grid squares (1 km x 1 km), and the distribution characteristics of the vegetation cover rates, the NDVI values, and the shape factors of the vegetation were displayed in each. The number of pixels in the grid was 2,229,049 (1,493 x 1,493 pixels).

Figure 5. The divided 18x 18 Girds into small grid squares (1 km x 1 km).

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Y

EVALUATION IN EXTRACTION OF VEGETATION AREAS

Figure 6-1 shows an example of the vegetation region in a full-sun area extracted from the high resolution satellite image. In this figure, the extracted vegetation region is displayed in white. Moreover, only a shadowed area was specified by clustering. Figure 6-2 shows an example of the distribution characteristic of the NDVI values in the shadowed areas. As shown in this figure, the NDVI(>0) values were detected to be slight in the shadowed area. Therefore, the vegetation region only in the shadowed area could be extracted by use of NDVI values. As shown in this figure, the rough distribution characteristics of the vegetation region in the shadowed area could be extracted. The vegetation distribution of the shadowed area was superposed upon that of the full-sun area to display the whole vegetation distribution region. Figure 6-3 shows an example of the whole vegetation distribution region. Although the vegetation distribution in the shadowed areas involved a slight error, the vegetation distribution tendency could be indicated to some degree.


Figure 6-1. The extracted vegetation distribution in a full-sun area of example area.

Figure 6-2. The extracted vegetation distribution by NDVI values in the shadowed zone

of example area.

Figure 6-3. The whole vegetation distribution situation in example area.

Figure 6-4. The satellite image data of example area.

CHARACTERISTICS OF DISTRIBUTION AND SHAPE FACTOR OF VEGAETATION Distribution Characteristics of the Vegetation Cover Rate and NDVI Value

As shown in Figure 7-1, the vegetation distribution quantity in the Tokyo central area is larger than that of the surrounding area. Namely, sparse vegetation is observed in the surrounding area. Moreover, much small-scale vegetation is distributed in the outer area of the surrounding area. Thus, the present situation of vegetation distribution characteristics throughout the Tokyo metropolitan area was obtained. The vegetation cover rate was calculated in each grid square, and the result was expressed using a contour map. Figure 7-2 shows this contour map. In it, the interval between the contour lines denotes 2% of the vegetation cover rate. As shown in the contour map, a high vegetation cover rate to some degree is obtained in the Tokyo central area. The vegetation cover rate of the surrounding area is lower than that of the Tokyo central area. Moreover, a relatively high vegetation cover rate was obtained in the western region of the outer area, and an extremely low vegetation cover rate was obtained in the eastern region of the outer area. This eastern region with the extremely low vegetation cover rate was widely extended. The average NDVI value was calculated in each grid square (Figure 7-3). The distribution tendency of the average NDVI value is similar to that of the vegetation cover rate. Namely, the average NDVI value in the Tokyo central area is higher than that of the surrounding area, and high again in the outer area of the surrounding area. Thus, the present situation of the vegetation distribution characteristics throughout the Tokyo metropolitan area was obtained using the vegetation cover rate and the average NDVI value in each grid square.

Figure 7-3. The contour map of the average NDVI value was calculated in each grid square.

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Y

Average NDVI value

Figure 7-2. The contour map of vegetation cover rate calculated in each grid square.

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Vegetation Ratio % ( per 1gird)

Yellow zone: Ratio>20%

Figure 7-1. The vegetation distribution in Tokyo.

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Y


Distribution Characteristics of the Shape Factor The shape factor was calculated in each grid square, and the result was expressed using a contour map. Figure 8-1

shows this contour map. In it, the interval between the contour lines denotes 0.5 of the shape factor. Even if the vegetation cover rate is the same, the larger shape factor is considered to indicate a higher degree of continuous vegetation; namely, vegetation congested in one place. As shown in the figure, the shape factor of the Tokyo central area is larger than that of the surrounding area. This is considered to be because large-scale green spaces are compactly distributed in the Tokyo central area. As shown in Figure 8-2, this consideration is proven based upon the results extracted from the high resolution satellite image data. A large shape factor was obtained in various urban areas such as artificial green spaces and large-scale parks. Moreover, the regions with large and small shape factors are mixed in the outer area of the surrounding area. As several typical samples , Figure 8-3～8-6 shows two regions with almost the same vegetation cover rates and greatly different shape factors. In this case, the vegetation distribution characteristics, such as density and continuity, are different between these two regions. Even when the areas of vegetation regions are the same, the vegetation regions with larger shape factors were considered to bear higher density and continuity. Oppositely, when the shape factor is smaller, the vegetation regions show the tendency to scattering and non-continuous distribution. Thus, the vegetation environment can be evaluated using the shape factor in addition to the conventional vegetation cover rate.


Figure 8-1. The Image of the distributed large-scale green spaces compactly ( rectangle line area in figure).

Shape Factor

Figure 8-1. The contour map of the calculated Shape factor in each grid square.

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(Extracted vegetation area) (image data) Figure 8-3b. The Cover of Grid (X,Y)=(14,7)

(vegetation rate= 36.7% , Shape Factor= 3.5 )

(Extracted vegetation area) (image data) Figure 8-3a. The Cover of Grid (X,Y)=(5,18)


(Extracted vegetation area) (image data) Figure 8-4b. The Cover of Grid (X,Y)=(4,8)

(vegetation rate= 18.2% , Shape Factor= 2.5)

(Extracted vegetation area) (image data) Figure 8-4a. The Cover of Grid (X,Y)=(16,18) (vegetation rate= 18.5% , Shape Factor= 5.1 )

(Extracted vegetation area) (image data) Figure 8-5b. The Cover of Grid (X,Y)=(8,8) (vegetation rate= 10.3% , Shape Factor= 1.8)

(Extracted vegetation area) (image data) Figure 8-5a. The Cover of Grid (X,Y)=(15,15)


(Extracted vegetation area) (image data) Figure 8-6b. The Cover of Grid (X,Y)=(4,5) (vegetation rate= 5.5% , Shape Factor= 1.1)

(Extracted vegetation area) (image data) Figure 8-6a. The Cover of Grid (X,Y)=(18,10) (vegetation rate= 5.5% , Shape Factor= 2.2 )

ASPRS 2008 Annual Conference

Portland, Oregon April 28 - May 2, 2008


SUMMARY

The vegetation distribution of an urban area was extracted based upon high resolution satellite image data. Moreover, the vegetation distribution characteristics of the Tokyo metropolitan area were evaluated using the vegetation cover rate, NDVI values, and the shape factor. The findings obtained in this study are summarized as follows: (1) Using high resolution satellite Quickbird image data, the vegetation distribution of Tokyo was obtained by a

method in which vegetation regions extracted by land cover classification using clustering and those extracted using NDVI values were superposed.

(2) The Tokyo metropolitan area was divided into a grid of 1 km x 1 km, and the vegetation distribution characteristics of each grid square were analyzed.

(3) The vegetation cover rate of the Tokyo central area is higher than that of the surrounding area. Moreover, high and low rates are mixed in the outer area of the surrounding area.

(4) The average NDVI value was calculated in each grid of 1km x 1 km. The distribution tendency of the average NDVI value is similar to that of the vegetation cover rate.

(5) The shape factor for vegetation distribution was proposed, and the factor of each grid square was calculated. Consequently, vegetation regions are found to be compactly distributed in the Tokyo central area and sparsely distributed in the surrounding area.

(6) The shape factor could quantitatively clarify the differences in vegetation distribution characteristics between grid squares where the vegetation cover rates are almost the same.

(7) Vegetation cover rates, NDVI values, and shape factors obtained based upon high resolution satellite image data could be used to evaluate the urban vegetation environment.

In this study, vegetation distribution characteristics were evaluated by comparing the results obtained based

upon high resolution satellite image data with those obtained based upon field surveys. In the future, vegetation distribution characteristics will be evaluated quantitatively upon the basis of a reference map regarding extraction accuracy. For this purpose, a detailed reference map for error evaluation will be created, and various extraction methods will be compared. Moreover, extraction accuracy of vegetation regions in shadowed areas will be improved. Subsequently, the vegetation environment in the Tokyo metropolitan area will be accurately evaluated using the shape factor of vegetation distribution, which was initially evaluated in this study, in addition to conventional vegetation cover rates and NDVI values.

ACKNOWLEDGEMENT

This research was conducted as a part of the Academically Promoted Frontier Research Program on “Suitable City based on Environmental Preservation and Disaster Prevention” at Nihon University, College of Science and Technology under a Grant from the Ministry of Education, Science and Sports, JAPAN. The images used here were provided from as follows. *Quickbird images were provided from Hitachi Software Engineering Co., Ltd. (@Hitachi Software Engineering) that is Japanese agency of Digital Globe (Includes material @ Digital Globe, Inc. ALL RIGHTS RESERVED).

REFERENCE H.Hashiba, K.Kameda, S.Tanaka, T.Sugimura (2000). Possibility of Mapping Small Trees and Plants Distribution in

Urban Area Using High Resolution Satellite Image, Journal of Map: Science of spatial representation, Japan Cartographers Association, Vol.38, No.4, p. 1-13.

H.Hashiba, K.Kameda, S.Tanaka, T.Sugimura (2003b). An Application of Digital Roof Model (DRM) for Height Measurement of Trees, IEEE, International Geoscience and Remote Sensing Society, IGARSS2003, Vol. IV: pp.2559– 2561.

H.Hashiba, T.Sugimura, S.Tanaka (2004a). Evaluation of Small-scale Vegetation in City Region from High-


resolution Satellite Images with Different Ground Resolution. IEEE, International Geoscience and Remote Sensing Society, IGARSS2003, Vol. V: pp.3140– 3143.

H.Hashiba, S.Tanaka, T.Sugimura (2004b). Detection of Three-dimensional position of vegetation in the city using high-resolution satellite image and GIS. ASPRS 2004 Fall Conference Proceedings (ASPRS Images to Decisions:Remote Sensing Foundation for GIS Applications), No.12.

H.Hashiba, T. Sugimura and S. Tanaka(2005) , Mapping detailed distribution of tree canopies by high-resolution satellite images, ASPRS 2005 Annual Conference Proceedings, PaperID:71351, pp1-9.

H. Hashiba, S.Tanaka, T.Sugimura (2006), Evaluation of city environment with vegetation covers by using several kinds of high- resolution satellite images, ASPRS 2006 Annual Conference Proceedings, paper ID 104427, pp1-9.

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