alos/avnir2 band reflectance characteristics of …

ASPRS 2012 Annual Conference

Sacramento, California March 19-23, 2012

ALOS/AVNIR2 BAND REFLECTANCE CHARACTERISTICS OF BUILDINGS

IN LAND USE ZONES –A CASE STUDY OF NAGOYA CITY-

Yoshiyuki Yamamoto*, Assistant Professor

Yukihiro Suzuoki

Tomohito Asaka**, Assistant Professor

Sadayoshi Aoyama**, Assistant Professor

Keishi Iwashita**, Professor

Katsuteru Kudou**, Professor

*Aichi Institute of Technology of Engineering Faculty of Urban Environment Department

1247 Yachigusa, Yakusacho, Toyota, Aichi 470-0392 JAPAN

**Nihon University of Industrial Technology College of Civil Engineering Department

1-2-1 Izumicho, Narashino, Chiba 275-8575 JAPAN

[email protected]

ABSTRACT

Advanced Visible and Near Infrared Radiometer type 2 (AVNIR2) onboard the Advanced Land Observing Satellite

(ALOS) provides three visible and one near infrared bands with 10m spatial resolution. The spatial resolution of

AVNIR2 was improved from 16 m spatial resolution of the AVNIR sensor onboard the past Japanese satellite

Advanced Earth Observing Satellite (ADEOS), but mixels (mixed pixels) have been still problematic in creating

classification maps from the ALOS/AVNIR2 data, especially in urban areas. Because of their heterogeneous

land-cover types in urban areas, mixels can be seen in urban areas more often than in rural or other homogenous

natural areas. Although higher spatial resolution images provide a better interpretability, more detailed ground

features create much more diversities in each pixel, which may lead to problems for automated classification

algorithms. To prevent these problems in extracting urban built-up area from ALOS/AVNIR2 data, this paper

describes the results of analyzing the band reflectance characteristics of buildings in different land use zones of

Nagoya, Japan. Land use zones constitute the basis for improving the city environmental aspects and preventing

from the indiscriminate land use and design in the city. Nagoya city has currently twelve different land use zones.

Quantitative analyses in these land use zones showed distinguishable differences from industrial/exclusively

industrial use zones of Nagoya city. These differences in industrial/exclusively industrial use zones may bring lower

accuracies when automated supervised-classification was applied with training data mixed with none-industrial use

zones. We could clarify these problems which makes automated supervised-classification lower accuracy in urban

areas, and our future proposed research will bring important keys to reduce these mixel problems by examining

characteristics of physical properties of buildings in urban areas.

KEYWORDS: ALOS/AVNIR2, land use zones, band reflectance characteristics, buildings, urban areas

INTRODUCTION

The urban land use is spatially heterogeneous. Especially, comparing to the United States, the spatial

composition of urban areas in Japan is more heterogeneous (Sorensen, 1999). Therefore, in the urban land

classification of satellite remote sensing data, the establishment of methodology for increasing the accuracies of the

classified results is a real need. Currently, various levels of spatial resolution of satellite data can be used over the

world. Urban land-use/cover classification is still a challenge with medium or coarse spatial resolution remotely

sensed data due to the large number of mixed pixels and the spectral confusions among different land-use/cover

types (Lu et al., 2006). In 2006, the Japanese satellite ALOS was launched. The ALOS carries AVNIR-2 sensor

which has four VNIR bands that image a 70km swath at 10m spatial resolution. The spatial resolution of AVNIR2

was improved from 16 m spatial resolution of the AVNIR sensor onboard the past Japanese satellite Advanced Earth

Observing Satellite (ADEOS). From the point of view of spatial resolution, the use of the satellite data with high

spatial resolution such as GeoEye and World View is preferable. However, the disadvantage of the use of these data

is so expensive. In contrast, the ALOS data is inexpensive and covered over wide area. For improving the land cover

classified accuracy, the consideration of the statistical method for land classified could be imagined. On the other

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hand, the use of other geospatial data such as vector maps is effective for the land classification of remote sensing

data. In Japan, the Basic Act on the Advancement of Utilizing Geospatial Information (NSDI Act of Japan) came

into effect on August 29, 2007. The purpose of the NSDI Act is to advance policies concerning the Advancement of

Utilizing Geo-spatial Information (AUGI) in a comprehensive and well-planned manner by establishing basic

elements for policies on AUGI, in view of the fact that AUGI is essential in establishing the economy and society in

which the people can live their lives securely and abundantly at present and in the future (The government of Japan,

2009). Under the NSDI Act, various geospatial data has been created steadily in Japan. Especially, there are much

geospatial data on urban areas. In urban land use planning system in Japan, under the Area Division system, a city

planning area is classified into Urbanization Promotion Area (UPA) and Urbanization Control Area (UCA) so that

public investment for the development of such urban infrastructure as roads, parks and sewerage can be efficiency

made to create a high quality urban areas (MLIT, 2003). The UPA is classified into twelve categories of land use

zone which provide a pattern for land-use zoning in each type of urban areas. Therefore, the spatial composition of

urban areas in Japan is significantly impacted by the land use zone system. Under the NSDI Act, the geospatial data

on land use zone has been created and easily used. Under the condition of the establishment of geospatial data on

urban areas in Japan, it is highly significant that the use of the geospatial data for the urban land classification of

remote sensing data is examined. The purpose of this study is to examine the ALOS/AVNIR-2 band reflectance

characteristics of buildings in land use zones using the geospatial data on land use zones.

METHODOLOGY

Study Area and Land Use Zones As the study area, Nagoya city in Japan is selected. Nagoya city is located in the central Japan as shown in

Figure 1. Nagoya is the capital city of Aichi Prefecture and the fourth largest city in Japan. The city is also third

largest metropolitan region, known as the Chukyo Metropolitan Area. The city is divided into 16 administrative

wards. The area of the city is 32,645 ha and divided into the UPA (30,258ha, 93%) and the UCA (2,387ha, 7%). That

is to say that most of the study area is covered by the UPA. The UPA is defined as the area to be urbanized in ten

years and the UCA is to be not developed at all. For the UPA, twelve categories of land use zones are defined. As

shown in Figure 2, these categories are Category Ⅰ exclusively low-rise residential zone (ⅠLR), Category Ⅱ

exclusively low-rise residential zone (ⅡLR), Category Ⅰ mid/high-rise oriented residential zone (ⅠMR), Category

Ⅱ mid/high-rise oriented residential zone (ⅡMR), Category Ⅰ residential zone(ⅠR), Category Ⅱ residential zone

(ⅡR), Quasi-residential zone (QR), Neighborhood commercial zone (NC), Commercial zone (C), Quasi-industrial

zone (QI), Industrial zone (I), Exclusively industrial zone (EI). Twelve categories of land use zone provide a pattern

for land-use zoning in each type of urban area. These can be generally categorized into residential, commercial and

industrial uses. Each use zone has specifications concerning the uses of buildings which can be constructed in the

zone. In other words, land use zones are allocated according to a future vision of land-use pattern. And as shown in

Figure 3, land use zone controls volume, height of buildings as well as of them under provisions of the Building

Standard Law. These regulations are designed to prevent a mixture of buildings used for different purposes in one

area, and to ensure the suitable environment for the specified type of land use (MLIT, 2003).

Data Processing ALOS/AVNIR-2 imagery acquired on February 25, 2008 was used for this research as shown in Figure 4. Table

1 shows the ALOS/AVNIR-2 main characteristics. The processing level of the AVNIR-2 product is level 1B2R

(JAXA, 2006). The ALOS/AVNIR-2 data used for the research is non-cloudy and the atmospheric condition of the

study area on the acquisition date is very fine (Visibility is 50km). And the change in elevation in the whole study

area is much less. Therefore, the atmospheric correction was not performed for the AVNIR-2 imagery. The imagery

was corrected geometrically using DSM (Digital Surface Model) provided from Geospatial Information Authority of

Japan. The original AVNIR-2 imagery was projected to the UTM coordinate system. In the geometric correction, the

coordinate system of the imagery was projected to the Japan Plane Orthogonal coordinate system. Comparing to the

UTM coordinate system, the Japan Plane Orthogonal coordinate system is high accuracy in the horizontal position.

The land use zones information used for this research was vector data with shape file format as shown in Figure 5.

For calculating the AVNIR-2 band characteristics for the land use zones, the vector data typed land use zones

information was converted into a raster image with 10m of the pixel resolution which was equivalent to the spatial

resolution of the AVNIR-2 imagery. And by overlaying the land use zones imagery on the AVNIR-2 imagery, the

AVNIR-2 band reflectance characteristics for each land use zones were calculated.



Figure 1. This shows the location of Nagoya city as the study area.

Table 1. The main specification of ALOS/AVNIR-2

Item Specification

Observation wavelength Band1: 0.42 - 0.50 µm (Visible Blue)

Band2: 0.52 - 0.60 µm (Visible Green)

Band3: 0.61 - 0.69 µm (Visible Red)

Band4: 0.76 - 0.89 µm (Near Infrared)

Spatial resolution (IFOV) 10 m (Approx. 14.28 µrad)

Observation width > 70 km (Approx. 5.8 degree)

Number of detectors 7100 / band

Pointing angle ± 44 degree

AD bit 8bit / pixel



Figure 2. Japanese land use zones defines twelve categories. Twelve categories of land use zones provide a pattern

for land-use zoning in each type of urban area. These can be generally categorized into residential, commercial and

industrial uses. Each Land Use Zone has specifications concerning the uses of buildings which can be constructed in

the zone. (MLIT, 2003).



Figure 3. Control of building use by land use zones (MLIT, 2003).

Figure 4. This shows ALOS/AVNIR-2 imagery. The color composite The yellow line shows administrative

boundary of Nagoya city.



Figure 5. This shows the distribution of the land use zones defined in the study area.

RESULTS

Table 2 shows the minimum (min), maximum (max), mean and standard deviation (sd) of DN values of each

ALOS/AVNIR-2 Band data for each land use zone and whole study area. As the main characteristics of the results, it

was indicated that the mean and standard deviation of DN values of visible Bands (Band1,2,3) for the industrial uses

(QI, I, EI) was higher than the others. Especially, the unique results for I and EI could be seen remarkably. And the

results for visible Bands were similar in some degree. But comparing the results of visible Bands to those of near

infrared Band (Band4), it was very different between the band reflectance characteristics of visible Bands and that of

near infrared Band. In the results of Band4, the mean and standard deviation of DN values for the industrial uses are

not necessarily very high in comparison to those for other zones. In the residential uses, the standard deviation of

DN value of all Bands for IIR was slightly higher than those of other land use zones. Also, among the results for

Band4, the mean and standard deviation of DN values for the commercial uses (NC, C) were lower than those of

other land use zones.



Table 2. Statistical analysis results of ALOS AVNIR-2 band reflectance characteristics for land use zones

Land

Use

Zones

Area (ha)

Band1 Band2 Band3 Band4

min max mean sd min max mean sd min max mean sd min max mean sd

Res

iden

tial

ⅠLR 5138 44 255 65.8 11.1 22 255 48.2 14.5 14 255 43.1 17.2 7 255 33.8 11.9

ⅡLR 88 49 159 69.0 9.1 27 153 51.6 12.1 20 153 46.6 14.2 13 129 32.0 9.7

IMR 1007 45 255 69.2 11.5 24 255 50.9 15.1 16 255 45.5 17.6 8 255 31.4 11.2

IIMR 1883 46 255 70.1 10.5 24 255 50.7 13.1 16 255 44.7 14.7 8 255 30.0 9.4

IR 7226 45 255 69.9 11.4 23 255 50.7 14.3 15 255 44.7 16.2 6 255 29.9 10.5

IIR 3041 44 255 66.7 13.5 21 255 48.3 16.0 15 255 42.6 18.2 7 200 32.0 12.3

QR 306 45 226 72.0 12.7 23 206 53.3 15.8 15 234 47.5 18.0 9 155 29.9 10.5

Co

mm

-

erci

al NC 2511 47 255 71.8 11.3 24 255 51.5 13.4 16 255 44.8 14.4 7 171 26.3 7.9

C 2230 47 255 70.7 12.2 23 255 50.1 14.7 15 255 42.9 15.8 7 195 24.1 8.8

Indu

stri

al QI 3563 46 255 73.1 15.3 23 255 53.6 17.7 15 255 47.2 19.0 6 255 29.0 11.2

I 2612 45 255 75.5 21.5 24 255 56.5 25.1 15 255 50.1 26.9 6 255 29.3 14.0

EI 648 46 255 74.5 19.8 23 255 56.0 22.7 13 255 50.2 24.8 6 210 30.0 14.0

whole 30258 44 255 70.0 13.8 21 255 51.1 16.5 13 255 45.0 18.4 6 255 30.0 11.5

CONCLUSION AND DISCUSSION

From the analysis results of ALOS/AVNIR-2 using the land use zones information, as the ALOS/AVNIR-2 band

reflectance characteristics for land use zones, some significant findings were indicated. Especially, the distinctive

trend can be seen in the results for the industrial uses areas. As shown in Figure 5, factory can be built in only

industrial uses area. And the buildings with large area could be covered mostly in the industrial uses area. Therefore,

it is considered that the spatial extents of the buildings are larger than the spatial resolution of the ALOS/AVNIR-2

and the similar DN values are covered in the industrial uses areas in comparison with the DN values for other land

use zones. According to the ideas, the standard deviation for the industrial uses areas should be lower than those for

other land use zones, however, it was indicated that the results of the standard deviation for the industrial areas was

higher. The results of DN values in this analysis contains the DN values of the land cover categories such as bare

land except the buildings. This is because the mixel problem will be examined in the future research. These

differences in industrial/exclusively industrial use zones may bring lower accuracies when automated

supervised-classification was applied with training data mixed with none-industrial use zones. We could clarify these

problems which makes automated supervised-classification lower accuracy in urban areas, and our future proposed

research will bring important keys to reduce these mixel problems by examining characteristics of physical

properties of buildings in urban areas.

ACKNOWLEDGMENT

The authors thank the Japan Aerospace Exploration Agency (JAXA) for providing ALOS/AVNIR-2 data, the

Geospatial Information Authority of Japan for providing DSM data, the Housing and City Planning Bureau of

Nagoya City Office for providing land use zone data and BIZWORKS Corp, Inc. for providing remote sensing

software “PG-STEAMER”. This study was supported by the second Research Announcement (RA) of JAXA.



REFERENCES

JAXA (Japan Aerospace Exploration Agency), 2006. http://www.eorc.jaxa.jp/ALOS/en/doc/format.htm (Last

accessed on January 10, 2012).

JAXA (Japan Aerospace Exploration Agency), 2011. http://www.jaxa.jp/press/2011/05/20110512_daichi_e.html

(Last accessed on January 10, 2012).

Lu, D., and Q. Weng, 2006. Use of impervious surface in urban land-use classification, Remote Sensing of

Environment, 102:146-160.

MLIT (Ministry of Land, Infrastructure and Transport), 2003. Introduction of Urban Land Use Planning System in

Japan, http://www.mlit.go.jp/crd/city/plan/tochiriyou/pdf/reaf_e.pdf (Last accessed on January 10, 2012).

Sorensen, A., 1999. Land Readjustment, urban planning and urban sprawl in the Tokyo metropolitan area, Urban

Studies, 36(13): 2333-2360.

The Government of Japan, 2009. Present status of NSDI policy of Japan, In: Eighteenth United Nations Regional

Cartographic Conference for Asia and the Pacific. Bangkok-Thailand. Item 7(a) of the provisional agenda,

Country Reports, pp.1-7.

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