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Mapping, Measuring, and Modeling Urban Growth

  • Chapter
Geo-Spatial Technologies in Urban Environments

Abstract

Immediately after World War II, developers in the United States took advantage of market demand and government incentives to build new housing subdivisions for returning soldiers anxious to marry, begin families, and resume civilian life. New developments such as Levittown (New York), Park Forest (Illinois) and Lakewood (California)1 sprang up and were quickly filled with affordable cookie-cutter homes for veterans seeking the American Dream of suburban home ownership (Hayden 2003). The baby boom followed. As a result of the boom and international immigration, the U.S. population grew from 151 million to 300 million between 1950 and 2007. To accommodate this expanding population growth, cities and towns in the U.S. rapidly spread into their rural hinterlands.

Levittown, Park Forest and Lakewood are located respectively near New York City, Chicago, and Los Angeles.

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References

  • Alberti, M., Weeks, R. and Coe, S. (2004) Urban land-cover change analysis in central Puget Sound, Photogrammetric Engineering and Remote Sensing 70:1043–52.

    Google Scholar 

  • Alexander, D. and Tomalty, R. (2002) Smart growth and sustainable development: Challenges, solutions and policy directions, Local Environment 7:397–409.

    Article  Google Scholar 

  • Anderson, J. R., Hardy, E. E., Roach, J. T. and Witmer, R. E. (1976) A Land Use and Land Cover Classification System for Use with Remote Sensor Data, USGS Geological Survey Professional Paper 964. Washington, DC: USGS

    Google Scholar 

  • Anthony, J. (2004) Do state growth management regulations reduce sprawl?, Urban Affairs Review 39:367–97.

    Article  Google Scholar 

  • Aspinall, R.J. and Hill, M.J. (1997) Land cover change: a method for assessing the reliability of land cover changes measured from remotely sensed data, Proceedings of the 1997 International Geoscience and Remote Sensing Symposium, Singapore, August 4–8, 1997.

    Google Scholar 

  • Audirac, I., Shermyen, A. H. and Smith, M. T. (1990) Ideal urban form and visions of the good life: Florida’s growth management dilemma, Journal of the American Planning Association 56:470–82.

    Google Scholar 

  • Banner, A. V. and Lynham, T. (1981) Multitemporal analysis of Landsat data for forest cut over mapping — a trial of two procedures., Proceedings of the 7th Canadian Symposium on Remote Sensing, Winnipeg, Manitoba, Canada:233–40.

    Google Scholar 

  • Benediktsson, J. A., Swain, P. H. and Ersoy, O. K. (1990) Neural network approaches versus statistical methods in classification of multisource remote sensing data IEEE Transactions on Geoscience and Remote Sensing 28:540–51.

    Article  Google Scholar 

  • Bengston, D. N., Fletcher, J. O. and Nelson, K. C. (2004) Public policies for managing urban growth and protecting open space: policy instruments and lessons learned in the United States, Landscape and Urban Planning 69:271–86.

    Article  Google Scholar 

  • Ben-Zadok, E. (2005) Consistency, concurrency and compact development: three faces of growth management implementation in Florida, Urban Studies 42:2167–90.

    Article  Google Scholar 

  • Bernstein, J. D. (1994) Land Use Considerations in Urban Environmental Management. Washington, D.C.: Urban Development Division, World Bank.

    Book  Google Scholar 

  • Bischof, H., Shneider, W. and Pinz, A. J. (1992) Multispectral classification of Landsat-images using neural networks, IEEE Transactions on Geoscience and Remote Sensing 30:482–90.

    Article  Google Scholar 

  • Bruzzone, L. and Prieto, D. F. (2000) Automatic analysis of the difference image for unsupervised change detection, IEEE Transactions on Geoscience and Remote Sensing 38:1171–82.

    Article  Google Scholar 

  • Bruzzone, L., Prieto, D. F. and Serpico, S. B. (1999) A neural-statistical approach to multitemporal and multisource remote-sensing image classification, IEEE Transactions on Geoscience and Remote Sensing 37:1350–9.

    Article  Google Scholar 

  • Buliung, R. N. and Kanaroglou, P. S. (2006) Urban form and household activity travel behavior, Growth and Change 37:172–8.

    Article  Google Scholar 

  • Burby, R. J., Nelson, A. C., Parker, D. and Handmer, J. (2001) Urban containment policy and exposure to natural hazards: is there a connection? Journal of Environmental Planning and Management 44:475–90.

    Article  Google Scholar 

  • Burchell, R. W., Downs, A., McCann, B. and Mukherji, S. (2005) Sprawl Costs: Economic Impacts of Unchecked Development. Washington, DC: Island Press.

    Google Scholar 

  • Byrne, G. F., Crapper, P. F. and Mayo, K. K. (1980) Monitoring land-cover by principal components analysis of multitemporal Landsat data, Remote Sensing of Environment 10:175–84.

    Article  Google Scholar 

  • Cakir, H. I., Khorram, S. and Nelson, S. A. C. (2006) Correspondence analysis for detecting land cover change, Remote Sensing of Environment 102:306–17.

    Article  Google Scholar 

  • Candau, J. C. and Goldstein, N. (2002) Multiple scenario urban forecasting for the California south coast region, Proceedings from the 40th Annual Conference of the Urban and Regional Information Systems Association, Chicago, IL, October 26–30.

    Google Scholar 

  • Carruthers, J. I. (2002) The impacts of state growth management programmes: A comparative analysis, Urban Studies 39:1959–82.

    Article  Google Scholar 

  • Carruthers, J. I. and Ulfarsson, G. F. (2003) Urban sprawl and the cost of public services, Environment and Planning B: Planning and Design 30:503–22.

    Article  Google Scholar 

  • Cervero, R. (2001) Efficient urbanization: economic performance and the shape of the metropolis, Urban Studies 28:1651–71.

    Article  Google Scholar 

  • Chan, J. C. W., Chan, K. P. and Yeh, A. G. O. (2001) Detecting the nature of change in an urban environment: a comparison of machine learning algorithms, Photogrammetric Engineering and Remote Sensing 67:213–25.

    Google Scholar 

  • Chen, J., Gong, P., He, C., Pu, R. and Shi, P. (2003) Land-use / land-cover change detection using improved change vector analysis, Photogrammetric Engineering and Remote Sensing 69:369–79.

    Google Scholar 

  • Chen, Z., Chen, J., Shi, P. and Tamura, M. (2003) An IHS-based change detection approach for assessment of urban expansion impact on arable land loss in China, International Journal of Remote Sensing 24:1353–60.

    Article  Google Scholar 

  • Clarke, K. C. and Gaydos, L. J. (1998) Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington / Baltimore, International Journal of Geographical Information Science 12:699–714.

    Article  Google Scholar 

  • Clarke, K. C., Hoppen, S. and Gaydos, J. (1997) A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area, Environment and Planning B: Planning and Design 24:247–61.

    Article  Google Scholar 

  • Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B. and Lambin, E. (2004) Digital change detection methods in ecosystem monitoring: a review, International Journal of Remote Sensing 25:1565–96.

    Article  Google Scholar 

  • Coppin, P. R. and Bauer, M. E. (1994) Processing of multitemporal Landsat TM imagery to optimise extraction of forest cover change features, IEEE Transactions on Geoscience and Remote Sensing 32:918–27.

    Article  Google Scholar 

  • Coppin, P. R. and Bauer, M. E. (1994) Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features, IEEE Transactions on Geoscience and Remote Sensing 32:918–27.

    Article  Google Scholar 

  • Curran, P. J. (1987) Remote sensing methodologies and geography, International Journal of Remote Sensing 8:1255–75.

    Google Scholar 

  • Dai, X. and Khorram, S. (1999) Remotely sensed change detection based on artificial neural networks, Photogrammetric Engineering and Remote Sensing 65:1187–94.

    Google Scholar 

  • Davis, C. and Schaub, T. (2005) A transboundary study of urban sprawl in the Pacific Coast region of North America: the benefits of multiple measurement methods, International Journal of Applied Earth Observation and Geoinformation 7:268–83.

    Article  Google Scholar 

  • Dawkins, C. J. and Nelson, A. C. (2002) Urban containment policies and housing prices: an international comparison with implications for future research, Land Use Policy 19:1–12.

    Article  Google Scholar 

  • Deal, B. and Schunk, D. (2004) Spatial dynamic modeling and urban land use transformation: A simulation approach to assessing the costs of urban sprawl, Ecological Economics 51:79–95.

    Article  Google Scholar 

  • Dietzel, C., Herold, M., Hemphill, J. J. and Clarke, K. C. (2005) Spatio-temporal dynamics in California’s Central Valley: Empirical links to urban theory, International Journal of Geographic Information Science 19:175–95.

    Article  Google Scholar 

  • Downs, A. (2001) What does’ smart growth’ really mean? Planning 67:20–5.

    Google Scholar 

  • Du, Y., Guindon, B. and Cihlar, J. (2002) Haze detection and removal in high resolution satellite images with wavelet analysis, IEEE Transactions on Geoscience and Remote Sensing 40:210–7.

    Article  Google Scholar 

  • Duda, R. O., Hart, P. E. and Stork, D. G. (2001) Pattern Classification. New York: Wiley.

    Google Scholar 

  • Dwyer, J. F. and Childs, G. M. (2004) Movement of people across the landscape: A blurring of distinctions between areas, interests and issue affecting natural resource management, Landscape and Urban Planning 69:153–64.

    Article  Google Scholar 

  • Eastman, J. R. and Fulk, M. (1993) Long sequence time series evaluation using standardized principal components, Photogrammetric Engineering and Remote Sensing 59:991–6.

    Google Scholar 

  • Eklundh, L. and Singh, A. (1993) A comparative analysis of standardised and unstandardised principle components analysis in remote sensing, International Journal of Remote Sensing 14:1358–70.

    Google Scholar 

  • Farina, A. (1998) Principles and Methods in Landscape Ecology. London: Chapman & Hall.

    Google Scholar 

  • Fisher, P. (1999) Models of uncertainty in spatial data, in Longley, P. A., Goodchild, M. F., Maguire, D. J. and Rhind, D. W. (eds.) Geographical Information Systems: Principles, Techniques, Management, and Applications. New York, NY: John Wiley, pp. 191–205.

    Google Scholar 

  • Frenkel, A. (2004) The potential effect of national growth-management policy on urban sprawl and the depletion of open spaces and farmland, Land Use Policy 21:357–69.

    Article  Google Scholar 

  • Fung, T. (1990) An assessment of TM imagery for land-cover change detection, IEEE Transactions on Geoscience and Remote Sensing 28:681–4.

    Article  Google Scholar 

  • Fung, T. (1992) Land use and land cover change detection with Landsat MSS and SPOT HRV data in Hong Kong, Geocarto International 3:33–40.

    Article  Google Scholar 

  • Fung, T. and LeDrew, E. (1987) Application of principal components analysis to change detection, Photogrammetric Engineering and Remote Sensing 53:1649–58.

    Google Scholar 

  • Fung, T. and LeDrew, E. (1988) The determination of optimal threshold levels for change detection using various accuracy indices, Photogrammetric Engineering and Remote Sensing 54:1449–54.

    Google Scholar 

  • Hardin, P. J. (2000) Neural networks versus nonparametric neighbor-based classifiers for semisupervised classification of Landsat Thematic Mapper imagery, Optical Engineering 39:1898–908.

    Article  Google Scholar 

  • Harris, J. R. and Murray, R. (1990) IHS transform for the integration of radar imagery with other remotely sensed data, Photogrammetric Engineering and Remote Sensing 56:1631–41.

    Google Scholar 

  • Hayden, D. (2003) Building Suburbia: Greenfields and Urban Growth 1820–2000. New York: Pantheon Books.

    Google Scholar 

  • Haykin, S. (1994) Neural Networks: a Comprehensive Foundation. Upper Saddle River, NJ: Prentice-Hall.

    Google Scholar 

  • Herold, M., Couclelis, H. and Clarke, K. C. (2005) The role of spatial metrics in the analysis and modeling of urban change, Computers, Environment, and Urban Systems 29:339–69.

    Article  Google Scholar 

  • Herold, M., Scepan, L. and Clarke, K. C. (2002) The use of remote sensing and landscape metrics to describe structures and change in urban land uses, Environment and Planning A 34:1443–58.

    Article  Google Scholar 

  • Hobbs, R. J. (1999) Clark Kent or Superman: Where is the phone booth for landscape ecology? in Klopatek, J. M. and Gardner, R. H. (eds.) Landscape Ecological Analysis: Issues and Applications. New York, NY: Springer.

    Google Scholar 

  • Howarth, P. J. and Boasson, E. (1983) Landsat digital enhancements for change detection in urban environments, Remote Sensing of Environment 13:149–60.

    Article  Google Scholar 

  • Howell-Moroney, M. (2004) Community characteristics, open space preservation and regionalism: Is there a connection? Journal of Urban Affairs 26:109–18.

    Article  Google Scholar 

  • Im, J. and Jensen, J.R. (2005) A change detection model based on neighborhood correlation image analysis and decision tree classification, Remote Sensing of Environment, 99:326–340.

    Article  Google Scholar 

  • Ingram, K., Knapp, E. and Robinson, J. (1981) Change detection technique development for improved urbanized area delineation, Technical memorandum CSC/TM-81/6087-Computer Sciences Corporation, Silver Springs, Maryland.

    Google Scholar 

  • Irwin, E. G. and Bockstael, N. E. (2004) Land use externalities, open space preservation, and urban sprawl, Regional Science and Urban Economics 34:705–25.

    Article  Google Scholar 

  • Jantz, C. A., Goetz, S. J. and Shelley, M. K. (2003) Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area, Environment and Planning B: Planning and Design 30:251–71.

    Google Scholar 

  • Jensen, J. R. (2005) Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River, NJ: Prentice-Hall.

    Google Scholar 

  • Jensen, J. R., Qui, F. and Ji, M. (2000) Predictive modeling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data, International Journal of Remote Sensing 20:2805–22.

    Article  Google Scholar 

  • Jensen, J. R., Rutchey, K., Koch, M. S. and Narumalani, S. (1995) Inland wetland change detection in the Everglades water conservation area 2A using a time series of normalized remotely sensed data, Photogrammetric Engineering and Remote Sensing 61:199–209.

    Google Scholar 

  • Jensen, R. R. and Hardin, P. J. (2005) Estimating urban leaf area using field measurements and satellite remote sensing data, GIScience and Remote Sensing 42:229–52.

    Google Scholar 

  • Johnson, P. E. (2001) Environmental impacts of urban sprawl: a survey of the literature and proposed research agenda, Environment and Planning A 33:717–35.

    Article  Google Scholar 

  • Johnson, R. D. and Kasischke, E. S. (1998) Change vector analysis: a technique for the multispectral monitoring of land cover and condition, International Journal of Remote Sensing 19:411–26.

    Article  Google Scholar 

  • Kashian, R. and Skidmore, M. (2002) Preserving agricultural land via property assessment policy and the willingness to pay for land preservation, Economic Development Quarterly 16:75–87.

    Article  Google Scholar 

  • Kaufmann, R. K. and Seto, K. C. (2001) Change detection, accuracy, and bias in a sequential analysis of Landsat imagery in the Pearl River Delta, China: econometric techniques, Agriculture, ecosystems, and environment 85:95–105.

    Article  Google Scholar 

  • Kawata, Y., Ohtani, A., Kusaka, T. and Ueno, S. (1990) Classification accuracy for the MOS-1 MESSR data before and after the atmospheric correction, IEEE Transactions on Geoscience and Remote Sensing 28:755–60.

    Article  Google Scholar 

  • Kline, J. D. (2000) Comparing states with and without growth management analysis based on indicators with policy implications comment, Land Use Policy 17:349–55.

    Article  Google Scholar 

  • Kline, J. D. (2006) Public demand for preserving local open space, Society and Natural Resources 19:645–59.

    Article  Google Scholar 

  • Kohonen, T. (1995) Self-Organizing Maps. Berlin: Springer.

    Google Scholar 

  • Kuby, M., Barranda, A. and Upchurch, C. (2004) Factors influencing light-rail station boardings in the United States, Transportation Research Part A: Policy and Practice 38:223–47.

    Article  Google Scholar 

  • Lewis, S. (1990) The town that said no to sprawl, Planning (APA) 56:14–9.

    Google Scholar 

  • Lindstrom, M. J. and Bartling, H. (2003) Suburban Sprawl: Culture, Theory, and Politics. Lanham, MD: Rowman and Littlefield.

    Google Scholar 

  • Li, X. and Yeh, A. G. O. (1998) Principal component analysis of stacked multitemporal images for the monitoring of rapid urban expansion in the Pearl River delta, International Journal of Remote Sensing 19:1501–18.

    Article  Google Scholar 

  • Liu, X. and Lathrop, R. G., Jr. (2002) Urban change detection based on an artificial neural network, International Journal of Remote Sensing 23:2513–8.

    Article  Google Scholar 

  • Liu, Y., Nishiyama, S. and Yano, T. (2004) Analysis of four change detection algorithms in bi-temporal space with a case study, International Journal of Remote Sensing 25:2121–39.

    Article  Google Scholar 

  • Madhavan, B. B., Kubo, S., Kurisaki, N. and Sivakumar, T. V. L. N. (2001) Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing, International Journal of Remote Sensing 22:789–806.

    Article  Google Scholar 

  • Maktav, D. and Erbek, F. S. (2005) Analysis of urban growth using multitemporal satellite data in Istanbul, Turkey, International Journal of Remote Sensing 26:797–810.

    Article  Google Scholar 

  • Masek, J. G., Lindsay, F. E. and Goward, S. N. (2000) Dynamics of urban growth in the Washington DC metropolitan area, 1973–1996, from Landsat observations, International Journal of Remote Sensing 21:3473–86.

    Article  Google Scholar 

  • Mattson, G. A. (2002) Small Towns, Sprawl and the Politics of Policy Choices: The Florida Experience. Lanham, MD: University Press of America.

    Google Scholar 

  • Millward, A. A., Piwowar, J. M. and Howarth, P. J. (2006) Time-series analysis of medium-resolution, multisensor satellite data for identifying landscape change, Photogrammetric Engineering and Remote Sensing 72:653–63.

    Google Scholar 

  • Morisette, J. F. and Khorram, S. (2000) Accuracy assessment curves for satellite based change detection, Photogrammetric Engineering and Remote Sensing 66:875–80.

    Google Scholar 

  • Morris, D. E. (2005) It’s a Sprawl World After All. Gabriola Island, Canada: New Society Publishers.

    Google Scholar 

  • Mundia, C. N. and Aniya, M. (2005) Analysis of land use/cover changes and urban expansion in Nairobi city using remote sensing and GIS, International Journal of Remote Sensing 26:2831–49.

    Google Scholar 

  • Nelson, A. C. (1990) Economic critique of US prime farmland preservation policies. Towards state policies that influence productive, consumptive and speculative value components of the farmland market to prevent urban sprawl and foster agricultural production in the United States, Journal of Rural Studies 6:119–42.

    Article  Google Scholar 

  • Nelson, A. C. (1992) Preserving prime farmland in the face of urbanization: lessons from Oregon, Journal of the American Planning Association 58:467–88.

    Google Scholar 

  • Nelson, A. C. (2000) Comparing states with and without growth management analysis based on indicators with policy implications, Land Use Policy 16:121–7.

    Article  Google Scholar 

  • O’Neill, R. V., Ritters, K. H., Wichham, J. D. and Jones, K. B. (1999) Landscape pattern metrics and regional assessment, Ecosystem Health 5:225–33.

    Article  Google Scholar 

  • Paolini, L., Grings, F., Sobrino, J., Jiménez Muñoz, J. and Karszenbaum, H. (2006) Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies, International Journal of Remote Sensing 27:685–704.

    Article  Google Scholar 

  • Pijanowski, B. C., Pithadia, S., Shellito, B. A. and Alexandridis, K. (2005) Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States, International Journal of Geographical Information Science 19:197–215.

    Article  Google Scholar 

  • Pohl, C. and Van Genderen, J. L. (1998) Multisensor image fusion in remote sensing: concepts, methods, and applications, International Journal of Remote Sensing 19:823–54.

    Article  Google Scholar 

  • Quarmby, N. A. and Cushnie, J. L. (1989) Monitoring urban land cover changes at the urban fringe from SPOT HRV imagery in south-east England, International Journal of Remote Sensing 10:953–63.

    Google Scholar 

  • Raad, T. and Kenworthy, J. (1998) The US and us: Canadian cities are going the way of their US counterparts into car-dependent sprawl, Alternatives 24:14–22.

    Google Scholar 

  • Radeloff, V. C., Holcomb, S. S., McKeefry, J. F., Hammer, R. B. and Stewart, S. I. (2005) The wildland-urban interface in the United States, Ecological Applications 15:799–805.

    Google Scholar 

  • Ridd, M. K. and Liu, J. (1998) A comparison of four algorithms for change detection in an urban environment, Remote Sensing of Environment 63:95–100.

    Article  Google Scholar 

  • Robbins, P. and Birkenholtz, T. (2003) Turfgrass revolution: Measuring the expansion of the American Lawn, Land Use Policy 20:181–94.

    Article  Google Scholar 

  • Rogan, J., Franklin, J. and Roberts, D. A. (2002) A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery, Remote Sensing of Environment 80:143–56.

    Article  Google Scholar 

  • Rosin, P. L. (2002) Thresholding for change detection, Computer Vision and Image Understanding 86:79–95.

    Article  Google Scholar 

  • Rosin, P. L. and Ioannidis, E. (2003) Evaluation of global image thresholding for change detection, Pattern Recognition Letters 24:2345–56.

    Article  Google Scholar 

  • Rouse J.W., R.H. Haas, J.A. Schell, D.W. Deering, and Harlan, J.C. (1974) Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. College Station (TX): Texas A&M University, Remote Sensing Center. Report RSC 1978-4.

    Google Scholar 

  • Schneider, A., Seto, K. C. and Webster, D. R. (2005) Urban growth in Chengdu, Western China: applications of remote sensing to assess planning and policy outcomes, Environment and Planning B: Planning and Design 32:323–45.

    Article  Google Scholar 

  • Schott, J. R., Salvaggio, C. and Volchock, W. J. (1988) Radiometric scene normalization using pseudoinvariant features, Remote Sensing of Environment 26:1–16.

    Article  Google Scholar 

  • Schowengerdt, R. A. (1997) Remote Sensing; Models and Methods for Image Processing. New York: Academic Press.

    Google Scholar 

  • Serra, P., Pons, X. and Sauri, D. (2003) Post-classification change detection with data from different sensors: some accuracy considerations, International Journal of Remote Sensing 24:3311–40.

    Article  Google Scholar 

  • Seto, K. C. and Fragkias, M. (2005) Quantifying spatiotemporal patterns of urban land-use change in four cities of China with timer series landscape metrics, Landscape Ecology 20:871–88.

    Article  Google Scholar 

  • Singh, A. (1989) Digital change detection techniques using remotely-sensed data, International Journal of Remote Sensing 10:989–1003.

    Google Scholar 

  • Singh, A. and Harrison, A. (1985) Standardized principal components, International Journal of Remote Sensing 6:883–96.

    Google Scholar 

  • Smart Growth Network (2003) Getting to Smart Growth: 100 Policies for Implementation: International City/County Management Organization (ICMA).

    Google Scholar 

  • Smits, P. C. and Annoni, A. (2000) Toward specification-driven change detection, IEEE Transactions on Geoscience and Remote Sensing 38:1484–8.

    Article  Google Scholar 

  • Song, C., Woodcock, C. E., Seto, K. C., Pax-Lenney, M. and Macomber, S. A. (2001) Classification and change detection using Landsat TM data: when and how to correct for atmospheric effects? Remote Sensing of Environment 75:230–44.

    Article  Google Scholar 

  • Stow, D. A., Tinney, L. R. and Estes, J. E. (1980) Deriving land use/land cover change statistics from Landsat: a study of prime agricultural land, Proceedings of the 14th International Symposium on Remote Sensing of the Environment. Ann Arbor, MI: Environmental Research Institute of Michigan, pp. 1227–37.

    Google Scholar 

  • Summers, A. A., Cheshire, P. C. and Senn, L. (1999) Urban Change in the United States and Western Europe: Comparative Analysis and Policy. Washington DC: Urban Institute Press.

    Google Scholar 

  • Sunar, F. (1998) Analysis of changes in a multidate set: a case study in Ikitelli Area, Istanbul, Turkey, International Journal of Remote Sensing 19:225–35.

    Article  Google Scholar 

  • Syphard, A. D., Clarke, K. C. and Franklin, J. (2005) Using a cellular automaton model to forecast the effects of urban growth on habitat pattern in southern California, Ecological Complexity 2:185–203.

    Article  Google Scholar 

  • Theobald, D. M. (2005) Landscape patterns of exurban growth in the USA from 1980 to 2020, Ecology and Society 10:34.

    Google Scholar 

  • Todd, W. J. (1977) Urban and regional land use change detected by using Landsat data, Journal of Research by the U.S. Geological Survey 5:529–34.

    Google Scholar 

  • Tomalty, R. (2002) Growth management in the Vancouver Region, Local Environment 7:431–45.

    Article  Google Scholar 

  • Torrens, P. M. (2000) How cellular models of urban systems work (1. Theory). Centre for Advanced Spatial Analysis Working Paper Series 28.

    Google Scholar 

  • Turner, M. G. (1989) Landscape Ecology: The Effects of Pattern on Process, Annual Review of Ecological Systems 20:171–97.

    Article  Google Scholar 

  • UNCHS (2001) The State of the World’s Cities. Nairobi, Kenya: United Nations Centre for Human Settlements.

    Google Scholar 

  • Ward, D., Phinn, S. R. and Murray, A. T. (2000) Monitoring growth in rapidly urbanizing areas using remotely sensed data, Professional Geographer 52:371–86.

    Article  Google Scholar 

  • Weber, C. and Puissant, A. (2003) Urbanization pressure and modeling of urban growth: example of the Tunis Metropolitan Area, Remote Sensing of Environment 86:341–52.

    Article  Google Scholar 

  • Weiler, S. and Theobald, D. (2003) Pioneers of rural sprawl in the Rocky Mountain West, Review of Regional Studies 33:264–83.

    Google Scholar 

  • Willson, R. W. (1995) Suburban parking requirements: a tacit policy for automobile use and sprawl, Journal of the American Planning Association 61:29–42.

    Google Scholar 

  • Xian, G. and Crane, M. (2005) Assessments of urban growth in the Tampa Bay watershed using remote sensing data, Remote Sensing of Environment 97:203–15.

    Article  Google Scholar 

  • Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y. and Huang, Z. (2006) Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing, Landscape and Urban Planning 75:69–80.

    Article  Google Scholar 

  • Yang, X. (2002) Satellite monitoring of urban spatial growth in the Atlanta metropolitan area, Photogrammetric Engineering and Remote Sensing 68:725–34.

    Google Scholar 

  • Yang, X. and Lo, C. P. (2002) Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area, International Journal of Remote Sensing 23.

    Google Scholar 

  • Yang, X. and Lo, C. P. (2003) Modelling urban growth and landscape changes in the Atlanta metropolitan area, International Journal of Geographical Information Science 17:463–88.

    Article  Google Scholar 

  • Yeh, A. G. O. and Li, X. (1997) An integrated remote sensing and GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl River Delta, China, International Planning Studies 2:193–210.

    Google Scholar 

  • Yu, X. and Ng, C. (2006) An integrated evaluation of landscape change using remote sensing and landscape metrics: a case study of Panyu, Guangzhou, International Journal of Remote Sensing 27:1075–92.

    Article  Google Scholar 

  • Yuan, D., Elvidge, C. D. and Lunetta, R. S. (eds.) (1999) Survey of multispectral methods for land cover change analysis. London: Taylor and Francis.

    Google Scholar 

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Authors and Affiliations

  1. Department of Geography, Brigham Young University, 690 SWKT, Provo, UT, 84602, USA

    Perry J. Hardin, Mark W. Jackson & Samuel M. Otterstrom

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  1. Perry J. Hardin
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  2. Mark W. Jackson
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  3. Samuel M. Otterstrom
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Editors and Affiliations

  1. Department of Geography, Geology and Anthropology, Indiana State University, Terre Haute, IN, 47809, USA

    Ryan R. Jensen , Jay D. Gatrell  & Daniel McLean ,  & 

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© 2007 Springer-Verlag Berlin Heidelberg

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Hardin, P.J., Jackson, M.W., Otterstrom, S.M. (2007). Mapping, Measuring, and Modeling Urban Growth. In: Jensen, R.R., Gatrell, J.D., McLean, D. (eds) Geo-Spatial Technologies in Urban Environments. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69417-5_8

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