|
|
| ||||||||||||||||||||||||||||||||||
• Introduction • The study area • Materials and methods • Land-use and land-cover change detection • Land-use and land-cover classes • Results and discussion • Conclusion • Acknowledgements • Competing interests • Authors’ contributions • References |
||||||||||||||||||||||||||||||||||
Urban population increase has caused significant urban landscape transrmation globally. Bere 1994, South Africa’s highly regulated urban growth was shaped by the restrictive Prevention of Illegal Squatters Act of 1951. After the abolishment of the act in the 1980s, the period of transition to democracy in the 1990s was characterised by an unprecedented urban population influx that caused a myriad of socio-economic and environmental challenges. These challenges have consequently compounded the need to monitor urban growth r the planning and optimisation of urban spaces. The limitations of traditional mapping methods, such as surveying and photogrammetry, in urban mapping are well documented. In the recent past, satellite remote sensing has emerged as one of the most viable urban mapping tools. Using post-classification comparisons, we sought to monitor major land use and land cover (LULC) changes in the city of Port Elizabeth during South Africa’s democratic transition (1990–2000). Images r 1990, 1995 and 2000 were acquired, geo-rectified and atmospherically corrected. An iterative self-organising data analysis (ISODATA) was then used to generate existing LULCs. Classes generated using ISODATA were then amalgamated to the city’s major LULCs and resultant classes were validated using aerial photographs and field visits. Results showed that ‘Built-up’ and ‘Bare surface’ LULC classes had the highest increase and decrease, respectively. There was no change in the ‘Beach or dune’ LULC, whereas ‘Green vegetation’ and ‘Water’ classes had minimal changes. This study illustrates the efficacy of remote sensing in monitoring urban change and the potential of remote sensing to aid decision-making in rapidly changing urban landscapes.
Globally, there have recently been unprecedented increases in the concentration of the population in cities, which have led to rapid urban landscape transrmations. These transrmations are often characterised by diverse biophysical and socio-economic processes related to the conversion of nonurban to urban lands and the resultant landscape heterogeneity.1,2,3 Recent studies have shown that over 50% of the global human population resides in urban areas.4,5 According to Montgomery and Hewett6, the highest rates of urbanisation and its related spatial landscape changes are expected in developing countries. In sub-Saharan Africa r instance, urban population grew from 15% in the 1950s to 32% in the 1990s. By 2030 it is projected that 54% – 60% of the sub-Saharan population will live in urban areas.5 South Africa’s urbanisation and urban landscape changes are markedly different from other countries on the African continent.7,8 Over time, most sub-Saharan urban areas have experienced a ‘natural’ expansion, with landscape shape and rm often determined by physical infrastructure, topography and geological factors.9 In Nairobi, and indeed in many other sub-Saharan cities, major urban land uses have llowed a radial pattern around roads from the city centre and within flatter surfaces suitable r construction.8 Whereas these factors may have been critical to South Africa’s urban development, her urban spatial growth and patterns have been mainly determined by a series of movement restrictions and the pre-1994 laws that date back to 1913.10,11 A notable example is the Prevention of Illegal Squatters Act of 1951 which highly regulated settlement in South Africa’s urban areas.12,13 Between 1960 and 1983, about 860 000 people were moved from urban areas under the Group Areas Act.14 Consequently, such laws significantly limited ‘natural’ urban growth through ‘influx control’.13,15 The beginning of unrestricted rural–urban movement in 1986 and the rmal end of the Group Areas Act in 1991 saw a dramatic increase in urban population numbers.16,17 From 1991 to 1996, the population of Black South Africans in urban areas increased by about 27%15 and South Africa’s urban population grew by 4.3% to 56% between 1996 and 2001.17 Like other South African cities, the unprecedented growth and transrmation of the city of Port Elizabeth during South Africa’s democratic transition led to enormous spatial planning challenges that affected social service delivery, infrastructural development and environmental degradation that led to a general decline in quality of life.9,18,19 Port Elizabeth’s sudden growth and the consequent landscape changes during this period compounded the need to monitor spatio-temporal land use and land cover (LULC) patterns.18 Generating up-to-date LULC change in dynamic urban landscapes using ground mapping techniques is often time consuming, expensive and tedious.20,21 Other techniques, like conventional aerial photography, have a long history in urban studies but have various shortcomings, including a high cost per unit area and, until recently, the data were seldom available in digital rmats.22,23,24 Repetitive coverage, consistency in image quality, cost effectiveness and the development of change detection algorithms, amongst others, have made remote sensing a viable option in urban LULC change mapping.21, 24,25,26 Such benefits are essential r understanding past, present and future drivers and patterns of urban landscape changes.9,27 Although a number of researchers have studied urban LULC change (Mundia and Aniya9, Abbot and Doughlas28, Deng et al.29, Dewan and Yamaguchi30, Kesgin and Nurlu31 and others), Hope32 has argued that relevant urban strategies and remedies require cognisance of local socio-economic and cultural characteristics, as well as a site’s uniqueness. Like other major urban areas in South Africa, the city of Port Elizabeth and its greater metropolitan area has undergone a series of transrmations. These transrmations include restricted, and hence regulated, urban growth bere the democratic transition, urban influx during the democratic transition and post-transition policy-driven urban physical development transrmations. In contrast to ‘naturally growing’ urban areas studied by the arementioned authors, these factors have combined to make the city of Port Elizabeth a globally characteristically unique setting r studying LULC change. Consequently, the purpose of this study was to analyse major LULC changes in the city of Port Elizabeth during South Africa’s democratic transition (1990–2000).
located at 33°57’29”S and 25°36’00”E on the south-eastern seaboard of South Africa (Figure 1). Port Elizabeth is the second oldest city in South Africa and has since been incorporated into the greater Nelson Mandela Bay Metropolitan area that includes the towns of Dispatch and Uitenhage. With an area of approximately 335 km2 and a population of approximately 1 million (Statistics South Africa 2007 Community Survey33), it is the fifth largest city in South Africa. Population density varies across the city; higher-income low-density suburbs and farms at the city’s periphery have 10–30 people/km2, while the inner city, high-rise suburbs, low-income areas and inrmal settlements have over 3000 people/km2. Major land uses within the city are ‘residential’, ‘industrial’ and ‘retail’, whereas ‘small-scale to medium-scale animal and crop farming’ predominates at the city’s periphery. The city is a major industrial hub and is home to one of the major seaports in South Africa. The post-1994 era and the growing industrial, retail and tourism sectors have been the major causes of unprecedented growth of the city.
Land-use and land-cover change detection Change detection is premised on multidate spatial representation resulting from environmental conditions and human activities on multiple imagery dates.26,34 It is possible to detect surface cover changes as a result of distinguishable reflectance values of LULC, which are often distinguishable from changes caused by soil moisture, solar illumination and atmospheric conditions, amongst others, at the time of image acquisition.25,35,36 According to Chen37, several change detection techniques, including multidate composite image change detection,38 image change algebra,35 image regression,39 on-screen digitising,40 post-classification comparisons41 and fuzzy sets and fuzzy logic42 have been developed (see Mas25 and Lu35 r a detailed review of existing change detection techniques). Because the quality of change detection is often determined by thematic, spectral and spatio-temporal limitations, appropriate selection from an array of existing techniques is of paramount importance.36 In keeping with the increasing popularity of the post-classification approach,26,43,445,45 r this study we adopted a multitemporal comparison of delineated classes. In this approach, each of the image spectral classifications are perrmed independently and the resultant areas within the thematic maps are compared.26,29,44 The main advantages of this method are the detailed inrmation that can be gained from the change matrix produced and the limited multitemporal imagery impact arising from calibration, atmospheric and environmental differences.29,35 However, the original land covers often need to be reclassified or reweighted, and the quality of the resultant classes is dependent on the choice of training data.29,35 According to Deng29, sensor, radiometric and spatial resolution commonalities are critical factors in multitemporal change detection. To eliminate multitemporal imagery inconsistencies arising from multiple seasons, the sun’s inclination and phenological differences, it is paramount that the images used represent similar seasons or conditions.29,46,47,48 To facilitate comparability and to determine multitemporal changes in major land-cover types in Port Elizabeth, similar dimensional extents around the city were extracted from three sets of Landsat 5 Thematic Mapper data (Table 1). Precise registration of multitemporal image data sets is of critical importance to a change detection process.49,50 To eliminate change detection errors arising from image misregistration, it is generally recommended that the accuracy of images registered be less than a pixel,25 although, less than half a pixel is regarded as ideal.29,51 In this study, the chosen images and respective aerial photos were rectified to the Universal Transverse Mercator projection and World Geodetic System 1984 datum. Fifty invariable points of features common to the 1:50 000 topographic maps and the images were selected r geo-registration. Additional evenly distributed GPS readings of ground control points common to the 1990 image and aerial photos were also collected and used r geo-rectification. With 1990 as a base image, the other images (Figure 2) were coregistered and resampled using nearest-neighbour interpolation. An accuracy of less than half a pixel was achieved in the three images. Change detection processes require spatial resolution, phenology and radiometric characteristics to be comparable. In this study, these conditions were met as images were acquired at similar seasonal (winter) conditions (Table 1). However, other factors like sensor degradation, variations in solar illumination and effects of atmospheric scattering and absorption affect the quality of change detection outcomes.25,52,53 To correct r these possible anomalies, relative dark and light pseudo-invariant features were identified and the imagery was normalised using the process described by Paolini et al.47, Jensen53 and Hartvich et al.54 Geo-rectified aerial photographs were imported into the IDRISI Kilimanjaro raster-based software55 and used to validate the resultant land-cover classes.
Land-use and land-cover classes A hybrid classification technique comprising unsupervised and supervised classifications was used to classify the 1990, 1995 and 2000 images. Two algorithms – iterative self-organising data analysis technique (ISODATA) and K-means – were used r unsupervised classification.43 Jensen43 recommends the use of ISODATA as it involves further refinements r splitting and merging classes. Consequently, ISODATA was applied to the three image data sets. This process was perrmed r two reasons: firstly, to obtain a summary of the number of spectral differences in the image data sets and, secondly, to use this inrmation as a basis r an amalgamated number of related LULCs through supervised classification. The ISODATA technique yielded 21 classes. The aerial photo mosaic corresponding to the imagery dates, a field survey and GPS readings of features considered invariant like roads, buildings and public spaces were used to distinguish and label the unsupervised classes and to identify training areas r a supervised classification. At least 120 pixels r each class were used to uniquely label new spectral signatures. Using the created signatures, the maximum likelihood algorithm was used to amalgamate LULC classes that were considered related. Consequently, the LULC classes were reduced from 21 using unsupervised classification to 5 using supervised classification: (1) beach or dune, (2) built-up area, (3) green vegetation, (4) bare surface and (5) water (Table 2). From the three classified images, pixel-by-pixel comparisons were used to determine multidate LULC transitions llowed by ‘from–to’ LULC comparisons to calculate the pixel number differences between the 1990 and 1995 images and between the 1995 and 2000 images. The areas of LULC surfaces were calculated by multiplying the imagery pixel spatial resolution (30 m x 30 m) and converting the surface areas of cover types into hectares. Accuracy assessment is a critical process in LULC mapping. The error matrix technique is the most commonly used method of assessing the accuracy of LULC maps.35,47 According to Congalton and Plourde56, an error matrix is based on a set of ground truth data, a classification scheme, a sampling scheme, a spatial autocorrelation and sample size and units. With the aid of the aerial photo mosaic (1:25 000), invariant feature points and topographic sheets, the accuracy of the images was assessed using the error matrix technique described by Congalton and Plourde56. As recommended by Congalton57 and Jensen43, ground truth data r accuracy evaluation were identified through stratified random sampling. In total, 600 polygons were used to assess the classification accuracy of each of the temporal classifications (Table 3).
The five amalgamated LULC classes were spectrally different. This difference allowed r spectral separation of the different LULC types using per-pixel delineation by the IDRISI remote-sensing software. The surface covered by ‘beach or dune’ had the lowest pixel count, area and percentage change during the study period (Table 4). Although the utmost care was taken during the classifications training process, there was some spectral confusion r the ‘beach or dune’ class. ‘Bare rocky shoreline’ r instance was confused r ‘built-up area’ and ‘white shoreline waves’ was confused r ‘beach or dune’ (Figures 3 and 4). The misclassification of ‘rocky shoreline’ as ’built-up area’ was present in all three images analysed, whereas the misclassification of ’white shoreline waves’ as ’beach or dune’ occurred only r the 1995 and 2000 image data sets (Figures 3 and 4). The ‘beach or dune’ LULC class was not expected to change significantly over the study period. However, the inconsistent pixel percentage coverage of 2.3, 2.4 and 2.3 in 1990, 1995 and 2000, respectively, r the ‘beach or dune’ LULC class can be attributed to the presence of waves during image acquisition and the consequent spectral confusion r the ‘beach or dune’ cover class (Figure 3). The occurrence of spectral confusion is not unique to this study; according to Deng et al.29 and Lo and Choi58, a complex mosaic of urban land-cover types and the consequent mixed pixel problem is often a challenge in urban LULC classification.
There were noticeable changes in the ‘built-up area’, ‘green vegetation’ and ‘bare surface’ LULC classes in Port Elizabeth during the 10-year period (Figure 3). The ‘built-up area’ LULC class showed a general increase during the study period with a percentage change of 4.4% from 1990 to 1995 and 8.9% from 1995 to 2000 (Table 4). This increase constituted the greatest change of all the LULC types (Table 4). In contrast to the other LULC types with significant changes, the ‘green vegetation’ class showed a higher decline in area in the 1990–1995 period than in the 1995–2000 period (Table 4). The ‘bare surface’ land-cover class declined by a cumulative 10% in the 10-year period, with 7.4% of the decline occurring in the 1995–2000 period. This rapid loss in the ‘bare surface’ land-cover class can be attributed to two factors: firstly, vegetation in close proximity to urban inrmal settlements is used as fuelwood and, secondly, vegetation clearance is a requisite r construction and settlement. The spatial increase in ‘built-up area’ at the expense of other land-cover types reflected in this study is consistent with urban demographic changes recorded in the literature. According to Kok and Collinson17, South Africa’s urban population grew by 4.3% to 56% between 1996 and 2001. Naude and Krugell59 noted that in the recent past, South Africa has recorded one of the highest rates of urban growth with an expected further increase of up to 70% by 2030. This trend is directly attributed to urban influx during and after the democratic transition and the concentration of labour and income opportunities in urban areas.7 Other reasons that spurred urbanisation and the consequent urban landscape transrmation during the study period were the significant decline in rural agro-based employment in the 1990s,<60 tolerance to increasing urban inrmal settlement and a government-led policy r inrmal settlement upgrade through the Reconstruction and Development Programme.12,28 Whereas there was a general increase in rural–urban movement depicted in the 2001 South African national population census, the largest proportion of the increase in movement was from secondary urban areas to major cities and peripheral towns.16,18 Changes in area covered by the ‘water’ land-cover class seem to have been influenced by the effects of weather on the sea (shown by white shoreline wave ams at the time of image acquisition) and the activities at the salt pans in the middle of the city (Figures 2 and 3). In the 1995 image classification, r instance, there was a significant spectral confusion between the ‘white shoreline waves’ on the southern coastline and the ‘beach or dune’ LULC class (Figure 3). The empty and water-filled salt pans are clearly visible in Figure 3a and 3b.
A rapid increase in urban settlement between 1990 and 2000 was the key driver of LULC change in Port Elizabeth. Like many other urban areas in South Africa, the dynamic urban landscape is directly attributed to pre-1994 laws and the new government’s policies on the provision of social and physical infrastructure. Using remote sensing, this study has shown that built-up areas in the Nelson Mandela Bay Metropolitan increased by 13788.4 ha (representing a change of 13.3%). The gain in the ‘built-up area’ class was mainly attributed to a decline in the amalgamated classes, that is, open farms, dry vegetation, very sparse vegetation and bare areas that declined by 10375 ha (representing a 10% decrease) and the ‘green vegetation’ class that declined by 3.2%. The changes in the ‘built-up area’ and ‘bare surface’ LULC classes were more significant during the 1995 to 2000 period, whilst the change in the area covered by ‘green vegetation’ was more significant during the 1990 to 1995 period. This study has shown that remote-sensing techniques offer a viable option r creating land-use inventory and monitoring systems in fast growing urban settings. These systems can be used to make optimal urban land use decisions. Whereas census-based literature has reported a general increase in South Africa’s urban population during this period, this study has attempted to fill the gap in the literature on physical urban spatial trends.
We declare that we have no financial or personal relationships which may have inappropriately influenced us in writing this article.
Competing interests J.O. was the lead researcher, perrmed most of the experiments and wrote the manuscript. P.M. perrmed the experiments and wrote the manuscript. V.K. gave technical input into the field research and the writing of the manuscript.
Authors’ contributions J.O. was the lead researcher, perrmed most of the experiments and wrote the manuscript. P.M. perrmed the experiments and wrote the manuscript. V.K. gave technical input into the field research and the writing of the manuscript.
1. Ridd MK, Liu JA. Comparison of ur algorithms r change detection in an urban environment. Remote Sens Environ. 1998;6:95–100. 2. Antrop M. Changing patterns in the urbanized countryside of Western Europe. Landscape Ecol. 2000;15:257–270. http://dx.doi.org/10.1023/A:1008151109252 3. Herold M, Goldstein NC, Clarke KC. The spatiotemporal rm of urban growth: Measurement, analysis and modeling. Remote Sens Environ. 2003;86:286–302. http://dx.doi.org/10.1016/ S0034-4257(03)00075-0 4. Moeller MS, Blaschke T. Urban change extraction from high resolution satellite image. Paper presented at: ISPRS Technical Commission II Symposium; 2006 July 12–14; Vienna, Austria. Enschede: ISPRS, 2006; p.151–156. 5. United Nations Environmental Program: Key facts about cities: Issues r the urban millennium. New York: United Nations Environmental Program; 2005. 6. Montgomery MR, Hewett PC. Urban health in developing countries: Household and neighborhood effects. Demography. 2005;42:397. http://dx.doi.org/10.135/dem.2005.0020 7. Potts D. Shall we go home? Increasing urban poverty in African cities and migration processes. Geogr J. 1995;161:245–264. http://dx.doi.org/10.2307/3059830 8. Gelderblom D. Migration patterns in South Africa. In: Falola I, Salm S, editors. Urbanization and globalization in Africa. Trenton, NJ: Africa World Press, 2004; p.117–136. 9. Mundia CN, Aniya M. Analysis of landuse/cover changes and urban expansion of Nairobi city using remote sensing and GIS. Int J Remote Sens. 2005;26:2831–2849. 10. Roberts M. The ending of apartheid: Shifting inequalities in South Africa. Geography. 1994;79:53–64. 11. Mabin A. The impact of apartheid on rural areas of South Africa. Antipode. 1991;23:33–46. http://dx.doi.org/10.111/j.1467-8330.1991.tb00401.x 12. Sihlongonyane MF. Monitoring paper part 1: Land occupation in South Africa. National Land Committee, South Africa [homepage on the Internet]. c2003 [cited 2010 Oct 25]. Available from: http://www.landaction.org/display.php?article=137 13. Seekings J. Introduction: Urban studies in South Africa after apartheid. Int J Urban Regional Res. 2000;24:882–840. http://dx.doi.org/10.1111/1468-2427.00281 14. Platzky L, Walker C. The surplus people. Johannesburg: Raven Press; 1985. 15. Christopher AJ. Urban segregation in post apartheid South Africa. Urban Stud. 2001;38:449–466. http://dx.doi.org/10.1080/00420980120080031 16. Collinson MA, Tollman SM, Kahn K. Migration, settlement change and health in post-apartheid South Africa: Triangulating health and demographic surveillance with national census data. Scand J Public Health. 2007;35:77–84. http://dx.doi.org/10.1080/14034950701356401, PMid:17676507, PMCid:2830108 17. Kok P, Collinson MA. Migration and urbanization in South Africa. Report 03-04-04. Pretoria: Statistics South Africa; 2006. 18. Pillay DL, Sebake MD. Assessing post apartheid settlement growth patterns using remote sensing and GIWS: A case study of Cape Town metropolitan area. Paper presented at: Second CSIR Biennial Conference; 2008 Nov 17–18; Pretoria, South Africa. Pretoria: CSIR, 2008; p. 1–7. 19. Shao Y, Walsh SJ, Entwisle B, Rindfuss RR. Spatial clustering and urban settings of rural migrants in Bangkok, Thailand. Geocarto Int. 2008;23:35–52. http://dx.doi.org/10.1080/10106040701417204 20. Prakash A, Gupta RP. Land-use mapping and change detection in a coal mining area – A case in the Jharia coalfield, India. Int J Remote Sens. 1998;19:391–410. http://dx.doi.org/10.1080/014311698216053 21. Rawashdeh SA, Saleh B. Satellite monitoring of urban spatial growth in Amman Area, Jordan. J Urban Plan D-ASCE. 2006;132:211–216. http://dx.doi.org/10.1061/(ASCE)0733-9488 22. Yagoub MM. Monitoring of urban growth of a desert city through remote sensing: Al-Ain, UAE, between 1976 and 2000. Int J Remote Sens. 2004;25:1063–1076. http://dx.doi.org/10.1080/0143116031000156792 23.. Thapa K, Burtch R. Primary and secondary methods of data collection in GIS/LIS. SaLIS. 1991;51:162–170. 24. Longley PA. Geographical inrmation systems: Will developments in urban remote sensing and GIS lead to ‘better’ urban geography? Prog Hum Geogr. 2002;26:231–239. http://dx.do.org/10.1191/0309132502ph366pr 25. Mas JF. Monitoring land-cover changes: A comparison of change detection techniques. Int J Remote Sens. 1999;20:139–152. http://dx.doi.org/10.1080/014311699213659 26. Singh A. Digital change detection techniques using remotely-sensed data. Int J Remote Sens. 1989;10:989–1003. http://dx.doi.org/10.1080/01431168908903939 27. Xu C, Liu M, Yang X, Sheng S, Zhang M, Huang Z. Detecting the spatial differentiation in s ettlement change during rapid urbanization in the Nanjing metropolitan region, China. Environ Monit Assess. 2010;171:457–470. , http://dx.doi.org/10.1007/s10661-009-1291-0, PMid:20058069 28. Abbot J, Douglas D. The use of longitudinal spatial analysis of inrmal settlements in urban development planning. Devel Southern Africa. 2003;20:3–19. http://dx.doi.org/10.1080/03768835022000019301 29. Deng JS, Wang K, Deng YH, Qi GJ. PCA-based land-use change detection and analysis using multitemporal and multisensory satellite data. Int J Remote Sens. 2008;29:4823–4838. http://dx.doi.org/10.1080/01431160801950162 30. Dewan AM, Yamaguchi Y. Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005. Environ Monit Assess. 2009;150:237–249. http://dx.doi.org/10.1007/s10661-008-0226-5, PMid:18317939 31. Kesgin B, Nurlu E. Land cover changes on the coastal zone of Candarli Bay. Environ Monit Assess. 2009;157:89–96. http://dx.doi.org/10.1007/s10661-008-0517-x, PMid:18704725 32. Hope RK. Managing rapid urbanization in Africa: Some aspects of policy. J Third World Stud. 1999;16:47–59. 33. Statistics South Africa. Community survey [homepage on the Internet]. c2007 [updated 2009; cited 2011 June 27] Available from: www.statssa.gov.za/community_new/content.asp? 34. Green K, Kempka D, Lackey L. Using remote sensing to detect and monitor land-cover and land-use change. Photogramm Eng Remote Sens. 1994;60:331–337. 35. LuD, Mausel P, Brondizios E, Moran E. Change detection techniques. Int J Remote Sens. 2004;25:2365–2407. http://dx.doi.org/10.1080/0143116031000139863 36. Fuller RM, Smith GM, Devereux BJ. The characterization and measurement of land cover through remote sensing: Problems in operational applications? Int J Appl Earth Obs. 2003;4:243–253. http://dx.doi.org/10.1016/S0303-2434(03)00004-7 37.. Chen X. Using remote sensing and GIS to analyze land cover change and its impact on regional sustainable development. Int J Remote Sens. 2001;22:1–8. http://dx.doi.org/10.1080/01431160010007051 38. Eastman JR, Fulk M. Long sequence time series evaluation using standardized principal components. Photogramm Eng Remote Sens. 1993;59:991–996. 39. Singh A. Change detection in tropical rest environment of north eastern India using Landsat data. In: Eden MJ, Parry JT, editors. Remote sensing of tropical land management. London: Wiley, 1986; p. 237–254. 40. Wang J, Treitz PM, Howarth PJ. Road network detection from SPOT imagery r updating geographical inrmation systems in the rural–urban fringe. Int J Geogr Inf Syst. 1992;6:141–157. http://dx.doi.org/10.1080/02693799208901901 41. Rutchey K, Velchneck L. Development of an everglades vegetation map using a SPOT image and the Global Positioning System. Photogramm Eng Remote Sens. 1994; 60:767–775. 42.. Metternicht G. Change detection assessment using fuzzy sets and remotely sensed data: An application of topographic map revisions. ISPRS J Photogramm. 1999;53:221–233. http://dx.doi.org/10.1016/S0924-2716(99)00023-4 43. Jensen JR. Introductory image processing – A remote sensing perspective. 2nd ed. Upper Saddle River, NJ: Prince-Hall; 1996. 44. Dewidar KM. Detection of land use/land cover changes r the northern part of the Nile delta (Burullus region), Egypt. Int J Remote Sens. 2004;25:4079–4089. http://dx.doi.org/10.10 80/01431160410001688312 45. ody GM. Status of land cover classification accuracy assessment. Remote Sens Environ. 2002;80:185–201. http://dx.doi.org/10.1016/S0034-4257(01)00295-4 46. Quarmby NA, Cushine JL. Monitoring urban land cover changes at the urban fringe from SPOT HRV imagery in south-east England. Int J Remote Sens. 1989;10:953–963. http://dx.doi.org/10.1080/01431168908903937 47. Paolini L, Grings F, Sobrinos JA, Munoz JC, Karszenbaum H. Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies. Int J Remote Sens. 2006;27:685–704. http://dx.doi.org/10.1080/01431160500183057 48. Coppin P, Jonkheere I, Nackaerts K, Muys B, Lambin E. Digital change detection methods in ecosystem monitoring: A review. Int J Remote Sens. 2004;25:1565–1596. http://dx.doi.org/10.1080/0143116031000101675 49. Morisette JT, Khoram S. Accuracy assessment curves r satellite-based change detection. Photogramm Eng Remote Sens. 2000;66:875–880. 50. Townshend JRG, Justice CO, Gurney C, Mcmanus J. The impact of misregistration on change detection. IEEE Trans Geosci Remote Sens. 1992;30:1054–1060. http://dx.doi. org/10.1109/36.175340 51. Milne A. Change detection analysis using Landsat imagery: A review of methodology. Paper presented at: IGARR’88. Proceedings of the International Geoscience and Remote Sensing 1988 Symposium; 1988 Sep 13–16; Edinburgh, Scotland. Noordwijk: ESA, 1988; p. 541–544. 52. Jensen JR. Introductory image processing: A remote sensing perspective. 3rd ed. Upper Saddle River, NJ: Pearson Prentice-Hall; 2005. 53. Janzen DT, Fredeen AL, Wheate R. Radiometric correction techniques and accuracy assessment r Landsat TM data in remote rested regions. Can J Remote Sens. 2006;32:330–340. ttp://dx.doi.org/10.5589/m06-028 54. Hartvich F, Koskinen K, Pellikka P. Change detection of urban areas using multitemporal Landsat TM satellite data in Turku Finland. Paper presented at: Nordic GIS Conference; 2001 Oct 08–10; Helsinki, Finland. 55. IDRISI Kilimanjaro. Version 14.02.Worcester: Clark Labs; 2004. 56. Congalton RG, Plourde L. Quality assurance and accuracy assessment of inrmation derived from remotely sensed data. In: Bossler J, editor. Manual of geospatial science and technology. London: Taylor and Francis, 2002; p. 349–361. 57. Congalton RG. A review of assessing the accuracy of classification of remotely sensed data. Remote Sens Environ. 1991;37:35–46. http://dx.doi.org/10.1016/0034-4257(91)90048-B 58.. Lo CP, Choi J. A hybrid approach to urban land/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images. Int J Remote Sens. 2004;25:2687–2700. http://dx.doi. org/10.1080/01431160310001618428 59. Naude WA, Krugell WF. Are South African cities too small? Cities. 2003;20:175–180. http://dx.doi.org/10.1016/S0264-2751(03)00011-8 60. Mather C. Beyond the farm: Rural and agricultural geography. In: Rogerson C, McCarthy J, editors. Geography in a changing South Africa. Cape Town: Oxrd University Press, 1992; p. 229–245.
|