Campinas City, Brazil, is one of the 10 fastest growing cities in the world with just over a million people in 2010. Does the new growth show signs of urban/suburban sprawl? This study investigates a null hypothesis that growth in Campinas cannot be characterized as urban sprawl because the growth in urban land use/land coverage (LU/LC) is the same as the population growth during the same period. Land use/land coverage was measured using Landsat imagery from 1989 through 2010.
There is much literature characterizing urban sprawl, its definition, how to measure it and its impact (Ewing et al., 2002; Johnson, 2001; Harold et al., 2003). Ewing et al. (2002) cites the quote from Justice Potter Stewart on pornography that most people would be hard pressed to define it, but they know it when they see it; the same applies to urban sprawl. A general definition for urban sprawl is growth in the urban LU/LC at a faster rate than the population (Ewing et al., 2002). Bhatta et al. (2010a) reviewed much of the recent literature looking for a better definition of urban sprawl and found that like Ewing et al. (2002), there is much disagreement on an exact definition but most agree that unorganized and uneven urban growth is a basic indicator. In general, researchers agree that urban sprawl represents a costly, poor use of available resources with several negative implications for society (Ewing et al., 2002; Bhatta et al., 2010a). New areas that grow away from the city center are typically of single use, low density areas, with increased dependence on private vehicles for transportation and in general a lower access to necessary services and goods.
The city government of Campinas published in 2006 their plan for urbanization and land use for the county (Anonymous, 2011c). In The Plan the county is divided into nine Macrozones and each one has its development plan (Figure 1). The urban city center is in the Macrozone 4, which has been defined for priority urbanization. Macrozone 1 is an area under environmental protection.
Study Location
The study area is Campinas City, Campinas County, State of São Paulo, (Figure 2). The city coordinates are 22° 54 3 S, 47° 3 26 W Datum WGS84. The 2010 population was estimated at 1,080,999 (IBGE, 2010) with over 98.3 percent in the urban region. The population density was 1,359 inhabitants/ km² for 2010 (IBGE, 2010). The municipal area of Campinas covers 795.667 square kilometers.
Data Collection
Data sources are defined in Table 1. The Internet was used to look for sources for the GIS layers as well as to obtain population data from Campinas, Brazil. DIVA holds a large collection of GIS layers and several relevant layers with administrative areas, roads and rivers and water bodies were obtained. First, a small-scale GIS database of Brazil was built to learn the context around the study site before focusing on a local study. The administrative areas 1 and 2 were located but the file for the third had the same information as the second; thus the state and county boundaries were obtained, but not the city boundaries so this study is carried out at the county level.
Population estimates for the city of Campinas were available at the Brazilian Institute of Geography and Statistics (IBGE) website. The most recent census was in 2010 and population estimates were available from 1970. Landsat imagery was located at the USGS Earth Explorer website site. Nearly cloud free imagery was searched for in Path/Row 219/76, and selected when available between 1989 and 2010.
Census Population Data
The census population estimates were plotted against year, and the growth rate was calculated. In this case, growth is defined simply as the percent change between two successive estimates.
Image Analysis
ERDAS Imagine 2010 was used for the image processing. The image processing and analyses followed these steps:
1. Download full level 1 data files and uncompress
2. Stack layers of 7 bands to combine into a single file
3. Rectify the images using the image from April 2010 as the reference, and resample with a square pixel size of 30x30m
4. Reproject to a map projection UTM 23S, WSG84
5. Subsample the images to a block surrounding Campinas country using an AOI to assure they have the same coverage
6. Create and evaluate natural color (RGB=321) and NIR color composite (RGB=432) images
7. Calculate and evaluate the Transformed Normalized Difference Vegetation Index (TNDVI)
8. Display and evaluate the thermal band 6 for each image
9. Calculate the Tasseled Cap Analysis
10. Classify the images with the objective being to identify and quantify the LU/LC including suburban areas if possible
11. The accuracy assessment was only done subjectively for this paper by comparing with the LandSat images with Google Earth
TNDVI = ((NIR-Red/NIR+Red)+0.5))^(1/2)
A hybrid technique was used to classify the Landsat images. First, an unsupervised classification was done with 30 classes to generate a set of spectral signatures to evaluate for a supervised classification. After evaluating the set of 30 signatures, some were deleted and others were added. The separability analysis in ERDAS 2010 was used to evaluate the signatures. The land cover corresponding to the signatures was visually identified by comparing to the unclassified RGB images and Google Earth.
The Tasseled Cap analysis was performed on the image from April 2010 and the urban and suburban areas were very identifiable. However, this analysis was not possible for the other images because ERDAS 2010 could not find the sensor information associated to the data. Integration with GIS ArcGIS v10 was used. Shapefiles of Brazil administrative areas were used to create a map of the state of São Paulo and Campinas County. The country boundary was used to clip the Landsat images to the county area. The Campinas City use plan (Anonymous, 2006) was obtained, which includes a map of their macrozones. Each macrozone is managed separately with its own land use plan. The boundaries of the macrozones were digitized from a JPG image (Campinas City Hall) using ArcGis 10 to use as another feature layer.
Results
The urban and suburban areas in Campinas County, Brazil are very clear in the NCC imagery (Figure 3). In these images red is the reflectance from green vegetation, blue is constructed surfaces (impervious) and green shades are bare soil.
The TNDVI is another good way to identify urban areas. Water would have the lowest numbers but is not separated in the present analysis. Figure 4 shows the urban areas identified by TNDVI from 1989, 2000 and 2010. Especially in the urban center (Macrozone 4) the increase in urban land cover is quite apparent.
The Tasseled Cap analysis shown in Figure 6 is quite striking. The features are very clear and even the suburban areas are clearly separated (orange colors) from the urban areas which as shown in red (when observed under zoom). The blue lines are the roads and were combined with the red urban areas to estimate impervious surfaces. Classification of a series of Tasseled Cap images and measurement of the land cover could be valuable for future work.
The urban area and some of the suburban areas were identified with the final supervised classification recoded into a binary thematic map; urban areas and non-urban areas. Figure 7 shows the urban areas identified for each image year; 1989, 2000 and 2010. Again, the increase in urban areas is evident especially in the Macrozone 4.
The amount of urban land cover has been increasing (Table 3); the rate of increase in urban coverage during the last 30 years has been over double the population growth rate (Figure 9).
Conclusions
Comparing the images during 1989-2010, and the population growth rates compared to the rate of increase in urban land coverage, it must concluded that the null hypothesis should be rejected; there is evidence that suggests that Campinas County is showing signs of Urban Sprawl. The urban area land coverage is increasing faster than the population is growing, and the growth pattern shows the leap-frogging affect where isolated areas (mostly suburban) are popping up with less access to required services. The natural color and NCC images show the distribution of the urban areas growing out from the city center. The growth has not been concentric but is in discontinuous radial extensions of the city.
M. Gregory Hammann is a Senior Director at GeoEye, Inc. and PhD student at George Mason University in the Earth Systems and Geospatial Sciences program with a concentration in Remote Sensing. MS from Oregon State University and BA from Whittier College. He lives in northern Virginia with his wife and two children.
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The formal land development plan was published by the city of Campinas in 2006 and the individual plans for each Macrozone are underway (Anonymous, 2011c). Therefore, we are observing the results of either not having a previous plan or not supporting it with the necessary laws. We could expect that if the new plan is followed, new urban growth should be more organized.
Another problem is the lack of a good accuracy assessment of the signatures used for the classification. More work is necessary to improve the signature extraction and evaluation, and to determine the user and producers accuracy statistics. In this case, the separation of the urban area from everything else was the focus, but within the urban area, many pixels are mixed at Landsat resolution. Higher resolution imagery will permit more detail to study the urban centers and better classify the green areas within the city. The open areas in the county provide recreation opportunities for people, but the green areas in the city (Urban Tree Canopy) was difficult to measure in this study. The thermal images suggest areas of increased urban heat where more green areas need to be established. In the urban center downtown areas, you can see some areas with trees and small parks but elsewhere the constructed density is very high. The housing construction style is with very small yards and many structures seem to be sharing walls (Google Earth). The more affluent areas have more green space and there are two lines of homes between the access streets vs. three in other areas of the city. The counties to the east are agricultural and can provide Campinas County with the necessary fresh goods.
The American Forests organization describes the importance of increasing the amount of tree cover in urban areas (American Forests, 2011). The Confederation of US Mayors (Anonymous, 2008) expressed the importance of improving the amount of urban tree cover in their cities and makes recommendations for humid areas in the USA (Table 4). Another important follow-on study would be to determine the urban and vegetation proportions in each of the Macrozones, and how they have changed in time compared to the LU/LC cover city plan.
The LU/LC for urban areas needs to be estimated for 1970 and 1980 to improve our understanding of the historical trends in both population and urban LU/LC growth. Next steps for this study are to use very high resolution (<4m) imagery as a source of ground truth to calibrate and validate the classifications in the Landsat imagery. The Tasseled Cap analysis appears to provide important spectral information and should be compared between the years to evaluate any improvement in the classification accuracy. Finally using very high-resolution imagery it will be possible to evaluate the Urban Tree Canopy in different sectors of the city.
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