Monitoring of urban sprawl using minimum distance ...

ASIA LIFE SCIENCES Supplement 9: 245-261, 2013 The Asian International Journal of Life Sciences

Monitoring of urban sprawl using minimum distance supervised classification algorithm in Rustenburg, South Africa SAMMY K. BETT1, L OBINA GERTRUDE PALAMULENI1* and TABUKELI MUSINGI RUHIIGA1 This paper reports results on urban sprawl in Rustenburg, South Africa. The primary objective of this study was to derive the most accurate and useful land cover information to identify evidence of urban sprawl in Rustenburg. An attempt was made to map and monitor accurately the distribution, rate and spatial extent of land cover change using multitemporal Landsat and Spot data in Rustenburg Town for 1992, 2002 and 2009. A minimum distance supervised classification algorithm was used to classify four land cover classes and post-classification method adopted for change detection. In addition, land consumption rate was computed to compare the rate of population growth and the consumption of land for development. The overall classification accuracies averaged 88% for the three years. The overall accuracy of land cover change maps ranged from 78 to 88%. Between 1992 and 2009, the amount of built up areas increased from 2 to 9% of the total area, while thicket bushland and scrub forest (TBS) land cover type decreased from 88 to 76%. Land consumption rate for TBS decreased from 244 to 47% at the expense of built up areas. Trends in land cover change for Rustenburg town depict land cover transition from a monocentric to a poly-centric type model of a town. Results quantify land cover change patterns and demonstrate landscape transformations in the mining town which could be used as input for monitoring and management of sustainable urban growth. Keywords: urban sprawl, natural habitat, population growth, land use, land consumption, minimum distance supervised classification algorithm, Rustenburg, South Africa

1

Department of Geography and Environmental Sciences, Faculty of Agriculture, Science and Technology, North West University, South Africa.

Received 06 June 2013; Accepted 23 August 2013 ©Rushing Water Publishers Ltd. 2013

Printed in the Philippines

Bett, Palamuleni & Ruhiiga 2013 INTRODUCTION The concept of urban sprawl suffers from difficulties in definition since it depends upon the perspective of who presents the definition. Hoffhine et al. (2003) argued that the sprawl phenomenon seeks to describe rather than define. Sprawl is a pattern and pace of land development in which the rate of land consumed for urban purposes exceeds the rate of population growth for a given area over a specified period of time which results in an inefficient and consumptive use of land and its associated resources. Urban sprawl is an occurrence that has several radical consequences. Some environmental issues of global concern resulting from this phenomenon include decrease in water quality, decrease in air quality, less groundwater, loss of farmland, loss of wetlands, loss of green space, light pollution, loss of wildlife and heat island effect (Fiore 2007). South Africa is no exception to the environmental issues that the world is experiencing concerning urban sprawl. In South Africa, urban growth problems could be attributed to population growth and increased economic development. Consequently, these activities have led to increased water demand, and degradation of natural resources (Turton 2003). Since the early 1990’s, the main urban centres in North West Province have been growing at an unpredictable rate resulting into inadequate growth management. There has been generally a greater influx of people into the province leading to the expansion of urban areas. Land-use in urban areas is segregated and usually entails single use zone typical of traditional urban planning practice in many countries. This means that residential, commercial and industrial areas are separated from one another. Often there are large undeveloped, empty spaces between the areas. Because these areas are separated, movement from one to the other can only be done with a car. As a result, urban sprawl has become a matter with concern for many parts of the world, not only because of the intensity of the process but also because of its great environmental, social and economic impact. The use of remote sensinghas enhanced the rationality of the decision making process by improving data accuracy through the use of satellite imagery and accessibility. Information such as vast infrastructure change and often unrecorded land use change can be acquired and analysed with the use of remote sensing which provides a powerful tool for studying human land use, including those that are related to, built-up land cover mapping. A significant amount of work is reported in Helmer et al. (2005), Seto et al. (2003), Yang et al. (2003), del Mar Lopez et al. (2001); urban growth modeling - Herold et al. (2003), Hoffhine et al. (2003) and urban sprawl - Clapman (2003) and Sutton (2003). The presence of humans on earth and its use of the land have significantly altered the earth surface in a manner that has had a profound effect upon the natural environment. In addition the research focuses on Rustenburg town with satellite images of different time periods. The evolution of industrial revolution and subsequently, modern technology has brought about decentralization, particularly from the early 1990s in Rustenburg. This provided better economic opportunities for people in this town due to the rise of urbanization and decentralized employment. As a result, towns have expanded over to the rural areas along their boundaries and thus urban sprawl began increasing. Burchfield et

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Monitoring of urban sprawl in South Africa al. (2006) further turned to urban economic theory for guidance on investigation of the causes of sprawl and found that unfortunately, there is no unified model that could isolate what determines the extent to which development is scattered or compact. In another study Johnson (2001) concluded that one way of identifying environmental impacts of urban sprawl, is to focus on those communities whose development is the source of the sprawl phenomenon. Others (Adelmann 1998, PTCEC 1999, Steiner et al. 1999) concluded that urban sprawl results in excessive removal of native vegetation, monotonous (and regionally inappropriate) residential visual environment, absence of mountain views and presence of ecologically wasteful golf courses. Figure 1 shows the study area of Rustenburg town in South Africa situated at the foot of the Magaliesberg mountain range in the North West Province and lies on the 2527 zone on the map grid and between 25oS39′0″ and 27oE13′0″ and forms part of the Bojanala Platinum District Municipality. It normally receives about 513 mm of rain per year, with most rainfall occurring mainly during midsummer. Rustenburg town is considered as the platinum district and is the densest and fastest urbanising district in the Province, where platinum mining is the driving force, spurring urbanisation.

Figure 1. Map of study area showing Rustenburg Town in South Africa. The main pressure on the environment of Rustenburg is from a set of land uses such as human settlements, mining, manufacturing, retail and wholesale, tourism, infrastructure development, energy, and waste pollution. The purpose of

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Bett, Palamuleni & Ruhiiga 2013 the study was to investigate the extent of socio-ecological impacts due to urban sprawl within Rustenburg town in the North West Province. Several objectives are specified for this proposed study: identify empirical evidence of sprawling areas in Rustenburg town, measure the extent of urban sprawl in Rustenburg town. This paper is divided into four sections the first section presents the introductory part to the research. It sets the background and provides related literature concerning the fundamental theories of urban sprawl, presents the problem statement, identifies the investigation approach, and stipulates the purpose, objectives, and the description of the study. The second part describes the materials and methods used. The third part gives results obtained from the digital satellite image analysis and illustrates the environmental changes thereof. These are discussed in the context of the original objectives, concluding the study as well as recommendation on sustainable strategies for managing sprawl.

MATERIALS AND METHODS The sources of data included remotely sensed data (Landsat and Spot image covering North West Province) ancillary data (1: 50,000 topographical maps and aerial photographs acquired). Data for the research were acquired from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM) (10 year interval–1992 to 2002) with a 30m resolution and Spot 4 (2009) with a 20 m resolution. ERDAS accuracy assessment tool was used to conduct an accuracy assessment for this study. Within the constraints for a limited number of suitable images in archive, a strategy for selecting Landsat and Spot 4 imagery for development of land cover database for the North West Province was governed by cost-free available multi-temporal images, vegetation phenology and image quality (cloudiness, haze). For this reason, autumn season satellite images were chosen, as the images are cloud free and the effect of late summer rainfall on vegetation is clearly visible on the image. Pre-processing of remotely sensed images involved operations that are required prior to the main data analysis and extraction of information. Before the extraction of information, all the images were geometrically corrected to conform to another referenced image, thus making it easy to visualize image data in GIS environment. The entire scene acquired was not used hence sub-setting was necessary for Rustenburg town so as to focus on the desired study area. Radiometric corrections involved converting the digital numbers (DN) to reflectance units (Culf et al. 1995). No radiometric normalization was done for Rustenburg as the images belonged to the same season, hence atmospheric, phenological and seasonal factors can be assumed to be comparable (Prakash and Gupta, 1998). Images were acquired before the fire season, thereby reducing atmospheric effects associated with smoke and radiation scattering. Additionally, the methods used for land cover change detection in this study involved only a general comparison of the statistics of the various land cover categories (post-classification). Where pixel-to-pixel comparison was employed, appropriate thresholds of the change detection image histograms were used (Jensen 2005).

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Monitoring of urban sprawl in South Africa The essence of the analysis was to assess the extent of urban sprawl in the study areas. Land cover conditions were achieved by minimum distance supervised classification technique (Jensen 2005). The minimum distance decision rule, also called spectral distance calculates the spectral distance between the measurement vector for the candidate pixel and the mean vector for each signature (Jensen 1996). The minimum distance classification is the fastest decision rule to compute and since every pixel is spectrally closer to either one sample mean or another, there are no unclassified pixels. The definition of land cover classes was based on the classification legend used in South Africa by Anderson et al. (1976). Land cover change detection was carried out using image differencing, followed by postclassification analysis. Using both methods, areas of change and of no change, areal statistics and direction of change in each land cover class were derived. For characterizing urban sprawl, four major types of land cover were identified: Thicket, bush-land and scrub forest land (TBS), barren land, built-up land and water bodies. Accuracy assessment determines the quality of the information derived from remotely sensed data. In this study, ground reference samples compiled from aerial photographs and topographic maps facilitated the identification of ground reference samples for use in the assessment of the accuracy of classified image. An error matrix generated for the Rustenburg town allowed an assessment of each land cover class accuracy and error type. The product of the accuracy assessment was a confusion matrix showing errors of omission (producer’s accuracy) and commission (user’s accuracy), overall classification accuracy and a k coefficient. The overall classification accuracy is a percentage expressed as the number of correctly classified sample pixels over the total number of sample pixels. This percentage indicates how accurate the classification is with respect to the reference data (Story and Congalton 1986). The k coefficient of agreement is a measure of the actual agreement minus chance agreement (Congalton et al. 1983).

RESULTS AND DISCUSSION Spatial distributions of land cover classes. Figure 2 and Graph 1 show the distribution of land cover classes for Rustenburg Town. Four land cover classes were identified, namely: (1) thicket, bush-land and scrub forest land (TBS); (2) barren land; (3) built-up land and (4) water bodies. In 1992 land cover classification for Rustenburg showed that barren land occupied 33 935.40 ha (10%) of the study area. TBS areas, occupied 302 711 ha (88%) of the total study area making it the dominant class. Built-up areas occupied 6 155.28 ha (2%) of the total land cover classes. Water bodies occupied 661.23 ha of the land surface representing 0.19%. Rustenburg land cover classification for 2002 shows that the TBS land cover formed the most dominant class covering 217 348 ha (63%) of the study area. The next dominant class was the barren land which occupied 99 146.79 ha (29%) of the study area. Built-up areas in Rustenburg occupied 23 884.60 ha (7%). Water bodies occupied 3 083.52 ha of the land surface representing 1%. The TBS land cover classification for Rustenburg 2009 shows that

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Figure 2. Land cover maps of 1992, 2002 and 2009 - Rustenburg Town, South Africa.

Figure 3. Distribution of land cover classes for Rustenburg Town, South Africa 1992, 2002 and 2009 the land cover formed the most dominant class covering 261 365 ha (76%). The next dominant class was the barren land which occupied 53 542.57 ha (16%) of the study area. Most of the built-up areas in Rustenburg area are a result of the mining as well as the booming tourism industry hence occupied 25 443.20 ha (7%) by 2009. Water bodies occupied 3 112.14 ha of the land surface representing 1%. Thematic accuracy assessment. Accuracy assessment was based on the correlation between topographic reference samples collected from archive topographic maps of the study area, aerial photographs and the satellite image classification to give an indication of the overall agreement between ground-truthing data and processed classifications. Based on the ground truth observations and the classification, the

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Monitoring of urban sprawl in South Africa error matrices in Table 1 and Appendix A were generated for Rustenburg. The overall mapping accuracy was 76% (user), and above 78% (producer) for Rustenburg. The plausible explanation for this accuracy could be due to the different type of satellite imagery used and how well the spatial data represented what was found on the ground during the classification stage. Accuracy assessments for Rustenburg Town land-use/land cover maps: In 1992 the overall classification accuracy of the study area was 88.5% and the kappa statistics was 0.8467. The user’s and producer’s accuracies for all classes are provided in the error matrix Table 1. Built-up land has been classified correctly yielding user accuracy of 81.48%. The plausible explanation to this accuracy could be due to the concentration of built-up structure within the central part of the study area. The error matrix shown in Table 5 was constructed using 200 reference samples. Results show that barren land was correctly classified resulting into user accuracy of 86.3%. This could be attributed to the expansion of the town as a result of mining industries while TBS land yielded a user accuracy of 87.5%. Water bodies were correctly classified with user accuracy of 100%. Table 1. Accuracy assessment for Rustenburg, South Africa 1992. Classified Data

TBS

Barren Land

Built-up Land

Water Bodies

Row total

User’s Accuracy

TBS

42

3

3

0

48

87.5%

Barren Land

4

44

3

0

51

86.3%

Built-up Land

4

3

44

3

54

81.5%

Water Bodies

0

0

0

47

47

100%

Column total

50

50

50

50

200

---

Producer’s accuracy

84%

88%

88%

94%

---

88.5%

Barren land included areas with human-induced effects that result in exposing soil surface layers and changes in topsoil which comprises areas with active excavation and opencast mines and quarries. Out of 50 reference points, 44 were accurately classified, yielding 86.27% of accurate classification. However, 5.9% was classified as built-up land while 7.8% was classified as TBS. These errors were mainly due to similarity between barren land, rooftops of some built-up land as well as built-up areas near the mines and quarries. Dry areas of TBS pixels were classified as barren land because the vegetation had less chlorophyll as end of winter images used in the study. In addition, natural fires in Southern African ecosystems are often ignited by lightening although human activity is responsible for the majority of the fires particularly in grasslands and savanna biomes (Silva

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Bett, Palamuleni & Ruhiiga 2013 et al. 2003). The burning season lasts from May to October, with its peak in July. Hence, barren areas (burned areas) could have been confused with TBS areas. The overall classification accuracy of the thematic map of Rustenburg area in 2002 is 88% and the kappa statistics is 0.8400. The error matrix was constructed based on the analysis made during the satellite image classification, for the overall agreement between the processed classifications and topographic map data. 200 reference samples points were used for the calculation of an error matrix. The producer’s accuracies for all classes are provided in the error matrix table in Appendix A. The user’s accuracies for barren land yielded a user accuracy of 84%, built-up land as 87.5%, TBS land as 81.8% and water bodies as 100%. These results generally mean that the overall classification accuracy is good and that there is an acceptable agreement that exists between the classification and the actual land cover categories. A total of 200 reference samples points were used for the calculation of an error matrix. The overall classification accuracy of the thematic map of Rustenburg area in 2009 is 87.5% and the kappa statistics is 0.8333. The error matrix shown in Appendix B was constructed based on the analysis made for the overall agreement between the processed classifications and topographic map data. The producer’s accuracies for all classes are provided in the error matrix table. The user’s accuracies for TBS land were correctly classified for 100% as well as the water bodies. Builtup land and barren land yielded a user accuracy of 78.9, 76.3%, respectively. Change detection. Results from post-classification analysis are presented using a series of change maps for visualisation and statistical tables to provide quantitative measures of change. The change maps use the colour from-to indices of change defined in Table 2. For this study, a pixel by pixel comparison procedure was done for the study year images through image differencing and post classification. The emphasis in this study is on built-up land in terms of location and direction of change. Measuring the extent of built-up area helps in analysing population density and urban growth, which provides some evidence that sprawl, is taking place as a pattern of development (Frenkel 2008). Platinum mining is one of the driving forces, spurring urbanisation in Rustenburg town. Land cover changes reflect the dynamics observed in the socio-economic condition of a given area. Similarly, changes in the socio-economic situation trigger land cover changes through their influence on land management techniques used and other various aspects of environmental policy (Lambin 2006). Table 2 shows the different rate of change in land cover categories between 1992 and 2009. In Rustenburg, there was a general increase of 5% in the build-up land between 1992 and 2002 and between 2002 and 2009; the town had a 0% growth over a period of 7 years (Table 2). Between 1992 and 2002 water bodies had an increase of 1% while between 2002 and 2009 water bodies retained 0% in the land cover category; this could be attributed to the change in the climatic condition (318.4mm and 255.2 mm rainfall distribution between these years) hence many water bodies have reduced in volume. The barren land cover between 2002 and 2009 had a 13% reduction while the TBS land cover increased by 13%. However, 1992 and 2009 TBS land cover had 25% reduction giving rise to other land cover classes.

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Monitoring of urban sprawl in South Africa Table 2. Trend changes in Rustenburg (South Africa) land cover categories.

Land cover categories

1992 – 2002 Area Percent Area change (ha)

2002 – 2009 Area Percent area change (ha)

TBS Land

-85 363.0

-25.0

+44 017.0

+13.0

Barren Land

+65 211.4

+19.0

-45 604.2

-13.0

Built-up Land

+17 729.3

+5.0

+1 558.6

+0

Water Body

+2 422.3

+1.0

+28.6

+0

NB: (-) indicates decrease, (+) indicates increase

Table 3. Annual rate of change in land cover categories for Rustenburg, South Africa. Land cover categories

1992 – 2002 Area (ha) Percent area change

2002 – 2009 Area (ha) Percent area change

TBS Land

-8 536.3

-2.5

+3 081.2

+0.9

Barren Land

+6 521.1

+1.9

-3 192.3

-0.9

Built-up Land

+1 772.9

+0.5

+109.1

+0

+242.2

+0.1

+2.0

+0

Water Body

NB: (-) indicates decrease, (+) indicates increase

Between 1992 and 2002 barren land had an annual rate of change of 2% and rate of increase of barren land decelerated by 1% in the 2002 and 2009 period. This could be attributed to the 1% increase TBS, annual percentage change between 1992 to 2002 and 2002 to 2009 were -2.5 and 0.9 respectively. A plausible explanation could be that the opencast mines and quarries which were no longer in operation, undergrowth began to sprout in those areas. Over the 17-year period, built-up land increased at the rate of 0.5% between 1992 and 2009 which could be associated with migration pull into the platinum town of Rustenburg. Tables 4 and 5 show land cover change detected between 1992 and 2009. The transition of land use between 1992 and 2009 was mainly from TBS and barren land to built-up land. TBS had a net loss of 33 872.7 ha (-11%) while the other land cover classes (barren land and water bodies) had a net gain of 9 348.6 and 2 450.9 ha, respectively.

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Table 4. Land covers change detection from 1992 to 2009. Total area in land class at 1992 (ha)

Total area changed to per land class (ha)

Total area changed from per land class (ha)

TBS

+302711.0

+7473.3

+0.0

-33872.7

-11.2

Barren Land

+33935.4

+264300.8

+12705.0

+9348.6

+27.6

Built-up Land

+6155.3

+265902.5

+5382.2

+22073.2

+358.6

Water Body

+661.2

+268838.3

+0.0

+2450.9

+370.7

+343462.9

+806514.8

+18087.2

------

------

Total area

Net gain Net (Loss) change (ha) (%)

NB: (-) indicates decrease, (+) indicates increase

Table 5. Area changed into built-up between 1992, 2002 and 2009. From

Area changed to built-up land (ha) 92

Area changed to built-up land (ha) 02

Area changed to built-up land (ha) 09

Percent change per class 1992

Percent change per class 2002

Percent change per class 2009

TBS

24216.9

13040.9

31363.8

8

6

12

Barren

2036.1

5948.8

5354.3

6

6

10

Water

39.7

0

0

6

0

0

Total

26292.7

18989.7

36718.1

20

12

22

Table 5 shows the extent and nature of change in land cover transition between different land cover classes from 1992 to 2009. A total land area of 22 073.2 ha was converted from other land cover classes to built-up areas. Between 1992 and 2002, 24 216.9 ha (8%) of TBS was converted to built-up land and 2 036.1 ha (6%) of barren land was changed to built-up areas. In comparison, between 2002 and 2009, 31 363.8 ha (12%) of TBS was converted to built-up land and 5 354.3 ha (10%) of barren land was changed to built-up areas. Similarly, between 1992 and 2009, the population of Rustenburg increased by 35%, from 124,064 in 1992 to 449,776 in 2009.

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Monitoring of urban sprawl in South Africa Figure 3 depicts the pattern of land-cover change overtime. In all four categories, the area under dense TBS land and barren category has decreased with slight increases throughout the three images. The decrease in the TBS is an indication that the open TBS areas of the landscape are regenerating to closed TBS lands in some parts, and degrading to barren and built-up land in others. Between 2002 and 2009, the water bodies had increased due to the expansion of dams.An observation of a single image of a year, the percentage of area undergoing change does not appear to be very large, however, a combination of all images from different time periods, the change trajectories reveal that the landscape is highly dynamic. Land consumption rate. Land consumption rate compares the rate of population growth to the consumption of urban land for development during a given period. In this study, land consumption rate gives an insight into the loss of natural habitat because of population growth based on the town expanding from the central point to the outskirts for the different years; 1992, 2002 and 2009. Consequently, for each of the years; 1992, 2002 and 2009 for Rustenburg town, the rate of expansion was calculated in relation to population growth figures. Population growth of Rustenburg town for 1993, 1996, 2001 and 2007 was 124064, 311326, 395539 and 449776 respectively (Stats SA 2011). During 1992, Rustenburg town was not like any other town in the North West Province, in that mining activity played a major role in the expansion of the town. In relation to the population figures of 1993, barren land and built-up land use had consumption rates of 27 and 5%, respectively (Figure 4). Water bodies and TBS land consumption rate was at 1 and 244%, respectively. Development of the built-up areas in Rustenburg could be attributed to mining and industrial activities which attract more people to the area as it is known as the “platinum town” in the North West Province. Typical mining activities include ground clearing (removal of vegetative cover and topsoil), compacting of soils by heavy-duty vehicles, diversion of water courses from natural flow patterns. This contributed to an increment in the land consumption rate of barren land to 25%. However, misclassification of some of the built-up land as observed in Figure 2 in the south western part could also contribute to the increase. TBS had decreased in the land consumption rate by 189% since 1992 and water bodies retained its rate of 1% (Figure 6). By 2009, the land consumption rate had slowed down. Water bodies still maintained the 1% rate throughout even though its area extent is higher than 1992 and 2002 land cover (Graph 4). TBS land consumption rate decreased from 55% in 2002 to 47% in 2009. In 2009, the built-up land remained at the rate of 5% though it had a land consumption rate of 6% which could be attributed to the misclassification. Nonetheless, population figures depict increase between 1992 and 2009 which could be a consequence of the platinum mining. Rustenburg attracts migrant labour for the platinum mines and thus contributing to the physical expansion of the town as evident in the increased land conversion rate from other land cover classes to built-up areas.

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Figure 4. Land consumption rate in percentageRustenburg, South Africa 1992

Figure 5. Land use land cover map highlighting change in Rustenburg, South Africa.

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Figure 6. Land consumption rate in percentageRustenburg, South Africa 2002

Figure 7. Land consumption rate in percentageRustenburg, South Africa 2009

Land-cover change analysis. The information acquired from individual land-cover classifications for the 1992, 2002 and 2009 images were subsequently combined to provide a single image that identified change trajectories or sequences of land cover classes for both observation dates (Petit et al. 2001). The output is a categorical ‘change image’, where each pixel includes information on land cover for both dates. For all land-cover categories, there is a secular trend towards increased fragmentation of land cover over time, for all the zones. This is especially discernible in the surrounding landscape of Rustenburg town, where there is an increase in the number of patches. Figures 5 depict the changes over time for the town.

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Figure 8. Overlay of built-up land showing the location of change in 1992, 2002 to 2009 for Rustenburg, South Africa. The change analysis revealed that built-up land had increased in all directions. This is due to mining and industrial activity in this area which could be linked with the inflow of labor into Rustenburg. Mines have a major role in the expansion of the town and the transformation of the natural habitat. In the case of Rustenburg, mining activities have given rise to barren land in previously TBS area. In addition, there is evidence of settlement growth for mine workers consequently having a negative impact on the surrounding landscape.

CONCLUSION AND RECOMMENDATIONS The results demonstrate that remote sensing data can be used to produce accurate landscape change maps and trends in land cover trajectories. Trends in land cover change between 1992, 2002 and 2009 for Rustenburg town depict land cover transition from a mono-centric to a polycentric type model of a town. It has been shown that when urban areas grow beyond a certain size, a polycentric urban form is more efficient than a compact highly centralised monocentric town. Although such growth provides activities spread in clusters over a wide area outside the traditional central business district, the phenomenon tends to negatively affect the environment. In Rustenburg, urban sprawl has resulted into depletion of natural habitat and increased barren areas. Therefore, the negative aspects of urban sprawl can be neutralised by monitoring urban growth through proper planning so that they are not liability to the society and environment. The study recommends that government should propagate policies on open space preservation in the city. This should be implemented for environmental management and natural resource harmony. An introduction of environmental friendly city as opposed to compact cities would have a positive impact on people such as having parks for people to rejuvenate during breaks and after work, reduce

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Monitoring of urban sprawl in South Africa noise pollution and air pollution. The expansion of cities or towns requires integration of ecology knowledge into urban planning. To achieve this goal, understanding ecological patterns and processes in urban ecosystems is paramount.

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Bett, Palamuleni & Ruhiiga 2013 Seto, K.C. and W. Liu. 2003. Comparing ARTMAP neural network with the maximumlikelihood classifier for detecting urban change. Photogrammetric Engineering and Remote Sensing 69(9): 981-990. Silva, J.M.N., J.M.C. Pereira, A.I. Cabral, A.C.L. Sa, M.J.P. Vasconcelos, B. Mota and J.M. Grégoire. 2003. An estimate of the area burned in southern africa during the 2000 dry season using SPOT-VEGETATION satellite data. Journal of Geophysical Research 108(D13): 8498. Stats SA. 2011. Population census 1996 for North West Province, Statistics South Africa, Pretoria. [Online] Available at: http://www.statssa.gov.za (Accessed 03 October 2011). Steiner, F., L. Mcsherry, D. Brennan, M. Soden, J. Yarchin, D. Green, J.M. Mccarthy, C. Spellman, J. Jennings and K. Barré. 1999. Concepts for alternative suburban planning in the northern Phoenix area. Journal of the American Planning Association 65(2): 207-222. Story, M. and R.G. Congalton. 1986. Accuracy assessment - A user\’s perspective. Photogrammetric Engineering and Remote Sensing 52(3): 397-399. Sutton, P.C. 2003. A scale-adjusted measure of “urban sprawl” using nighttime satellite imagery. Remote Sensing of Environment 86(3): 353-369. Turton, A.R. 2003. The hydropolitical dynamics of cooperation in Southern Africa: A strategic perspective on institutional development in international river basins. Transboundary Rivers, Sovereignty and Development: Hydropolitical drivers in the Okavango River basin, pp. 83-103. Yang, L., G. Xian, J.M. Klaver and B. Deal. 2003. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering and Remote Sensing 69(9): 1003-1010.

APPENDICES Appendix A. Accuracy assessment for Rustenburg, South Africa 2002. Classified Data

TBS

Barren Land

Built-up Land

Water Bodies

Row total

User’s Accuracy(%)

TBS

45

5

3

2

55

81.82

Barren Land

2

42

5

1

50

84

Built-up Land

3

3

42

0

48

87.50

Water Bodies

0

0

0

47

47

100

Column total

50

50

50

50

200

---

Producer’s accuracy(%)

90

84

84

94

---

88%

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Monitoring of urban sprawl in South Africa

Appendix B. Accuracy assessment for Rustenburg, South Africa 2009. Classified Data

TBSH

Barren Land

Built-up Land

Water Bodies

Row total

User’s Accuracy(%)

TBSH

39

0

0

0

39

100

Barren Land

5

45

9

0

59

76.27

Built-up Land

6

5

41

0

52

78.85

Water Bodies

0

0

0

50

50

100

Column total

50

50

50

50

200

---

Producer’s accuracy (%)

78

90

82

100

---

87.50

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©Rushing Water Publishers Ltd., Philippines 2013 e papers published in Asia Life Sciences are indexed in the Biological Abstracts, CAB Abstracts, CAB Global Health, Zoological Record, SciSearch®/Science Citation Index Expanded, Journal Citation Reports/Science Edition, BIOSIS Previews, ISI Web of Science®, ISI Web of Knowledge® and are covered by the and CABI, Wallingford, Oxon, UK.

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Bett, Palamuleni & Ruhiiga 2013

Beyond Excellence© e-mails: [email protected] [email protected] http://journals.uplb.edu.ph/index.php/ALS

©Rushing Water Publishers Ltd., Philippines 2013 e papers published in Asia Life Sciences are indexed in the Biological Abstracts, CAB Abstracts, CAB Global Health, Zoological Record, SciSearch®/Science Citation Index Expanded, Journal Citation Reports/Science Edition, BIOSIS Previews, ISI Web of Science®, ISI Web of Knowledge® and are covered by the and CABI, Wallingford, Oxon, UK.

Asia Life Sciences has an Impact Factor of 0.259. Asia Life Sciences is a recipient of the Journal Accreditation Award of the Commission on Higher Education (CHED), Republic of the Philippines (2010-2013).

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