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Future Suburban Development and the Environmental Implications of Lawns: A Case Study in New England, USA

  • Daniel Miller Runfola
  • Colin Polsky
  • Nick Giner
  • Robert Gilmore PontiusJr.
  • Craig Nicolson
Chapter
Part of the Cities and Nature book series (CITIES)

Abstract

Lawns cover more land than irrigated corn in the United States according to the most recent estimates (Milesi et al. 2009). The associated ecological ramifications – such as habitat fragmentation, water quality and availability – may be far-reaching. The way lawns are maintained, especially intensive fertilization and watering, also presents risks for water use and quality, nutrient cycling, urban climate regimes, and even human health. However, the lack of broad-extent, high-resolution land cover data has limited the ability of researchers to measure or project the extent of lawns. In this chapter, we first produce a high resolution (0.5 m) land-cover classification to quantify existing lawn extent for the year 2005 in the Plum Island Ecosystem (PIE), a collection of 26 suburban towns northeast of Boston, MA, USA. We then use this dataset in conjunction with the GEOMOD land-change model to project lawn extent under two scenarios of urban growth for the year 2030. We find that in 2005, 76 km2 of lawn – defined as grass on residential land – existed in the PIE study region. Under a Current Trends scenario, we project residential lawns may increase by 7.0 % to 81 km2, while under a Smart Growth scenario we project a 1.6 % increase to 77 km2. We estimate this could result in up to 61 million additional liters of annual water use under the Current Trends scenario, and 14 million under Smart Growth, putting additional stress on utilities that already face regular water shortages.

Keywords

Land Cover Normalize Difference Vegetation Index Urban Growth Residential Land Residential Development 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The United States’ National Science Foundation (NSF) supported this work via the following programs: Long Term Ecological Research via grants OCE-0423565 and OCE-1026859 for Plum Island Ecosystems and OCE-0620959 for Georgia Coastal Ecosystems, Coupled Natural Human Systems via grant BCS-0709685, Research Experiences for Undergraduates Site via grant SES-0849985, Decision-Making Under Uncertainty via grant SES-0951366, Urban Long Term Research Areas via grant BCS-0948984, and a supplement grant entitled “Maps and Locals (MALS)” via grant DEB-0620579. The work has also benefited from the NICHD funded University of Colorado Population Center (grant R21 HD51146) for research, administrative, and computing support. Any opinions, findings, conclusions, or recommendation expressed in this paper are those of the authors and do not necessarily reflect those of the funders.

References

  1. Agarwal, C., Green, G., Grove, J., Evans, T. P., & Schweik, C. (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice, I. University, ed. Bloomington: Centre for the Study of Institutions Population and Environmental Change.Google Scholar
  2. Benz, U., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 239–258.CrossRefGoogle Scholar
  3. Berry, M. W., Flamm, R. O., Hazen, B. C., & MacIntyre, R. L. (1994). The Land-use Change Analysis System (LUCAS) for evaluating landscape management decisions. Knoxville: University of Tennessee.Google Scholar
  4. Blaschke, T., & Strobl, J. (2001). What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. Interfacing Remote Sensing and GIS, 6, 12–17.Google Scholar
  5. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2–16.CrossRefGoogle Scholar
  6. Blaschke, T., Lang, S., Lorup, E., Strobl, J., & Zeil, P. (2000). Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environmental Information for Planning, Politics and the Public, 2, 555–570.Google Scholar
  7. Burnett, C., & Blaschke, T. (2003). A multi-scale segmentation/object relationship modeling methodology for landscape analysis. Ecological Modelling, 168, 233–249.CrossRefGoogle Scholar
  8. Carrico, A. R., Fraser, J., & Bazuin, J. T. (2012). Green with envy: Psychological and social predictors of lawn fertilizer application. Environment and Behavior, 45, 427–454.CrossRefGoogle Scholar
  9. Castella, J., Trung, T., & Boissau, S. (2005). Participatory simulation of land-use changes in the northern mountains of Vietnam: The combined use of an agent-based model, a role-playing game, and a geographic information system. Ecology and Society, 10, 27.Google Scholar
  10. Chow, W. T. L., Brennan, D., & Brazel, A. J. (2011). Urban heat island research in Phoenix, Arizona: Theoretical contributions and policy applications. Bulletin of the American Meteorological Society, 93, 517–530.CrossRefGoogle Scholar
  11. Domene, E., & Saurí, D. (2006). Urbanisation and water consumption: Influencing factors in the metropolitan region of Barcelona. Urban Studies, 43, 1605.CrossRefGoogle Scholar
  12. Domene, E., Saurí, D., & Parés, M. (2005). Urbanization and sustainable resource use: The case of garden watering in the Metropolitan Region of Barcelona. Urban Geography, 26, 520–535.CrossRefGoogle Scholar
  13. Engelen, G., White, R., & Nijs, T. (2003). Environment explorer: Spatial support system for the integrated assessment of socio-economic and environmental policies in the Netherlands. Integrated Assessment, 4, 97–105.CrossRefGoogle Scholar
  14. Fissore, C., Baker, L., Hobbie, S., King, J., McFadden, J., Nelson, K., & Jakobsdottir, I. (2011). Carbon, nitrogen, and phosphorus fluxes in household ecosystems in the Minneapolis-Saint Paul, Minnesota, urban region. Ecological Applications, 21, 619–639.CrossRefGoogle Scholar
  15. Giner, N. M., Polsky, C., Pontius, R. G., Jr., & Runfola, D. M. (2013). Understanding the determinants of lawn landscapes: A fine-resolution spatial statistical analysis in suburban Boston, Massachusetts, USA. Landscape and Urban Planning, 111, 25–33.CrossRefGoogle Scholar
  16. Glennon, R. J. (2002). Water follies: Groundwater pumping and the fate of America’s fresh waters. Washington, D.C.: Island Press.Google Scholar
  17. Glennon, R. J. (2009). Unquenchable: America’s water crisis and what to do about it. Washington, D.C.: Island Press.Google Scholar
  18. Gober, P., Brazel, A., Quay, R., Myint, S., Grossman-Clarke, S., Miller, A., & Rossi, S. (2010). Using watered landscapes to manipulate urban heat island effects: How much water will it take to cool Phoenix? Journal of the American Planning Association, 76, 109–121.CrossRefGoogle Scholar
  19. Groffman, P. M., Law, N. L., Belt, K. T., Band, L. E., & Fisher, G. T. (2004). Nitrogen fluxes and retention in urban watershed ecosystems. Ecosystems, 7, 393–403.Google Scholar
  20. Hanlon, B., Short, J. R., & Vicino, T. J. (2010). Cities and suburbs: New metropolitan realities in the US. New York, NY: Routledge.Google Scholar
  21. Harris, E. M., Martin, D. G., Polsky, C., Denhardt, L., & Nehring, A. (2012a). The role of emotions in suburban yard management practices. Professional Geographer, 65, 345–361.CrossRefGoogle Scholar
  22. Harris, E. M., Polsky, C., Larson, K., Garvoille, R., Martin, D. G., Brumand, J., Ogden, L. (2012b). Explaining U.S. lawncare practices to improve management: Evidence from suburban Boston, Miami, and Phoenix. Human Ecology, online.Google Scholar
  23. Hilferink, M., & Rietveld, P. (1998). Land use scanner: An integrated GIS based model for long term projections of land use in urban and rural areas. Journal of Geographical Systems, 1, 155–177.CrossRefGoogle Scholar
  24. Hill, T. D., & Polsky, C. (2007). Suburbanization and drought: A mixed methods vulnerability assessment in rainy Massachusetts. Environmental Hazards, 7(4), 291–301.CrossRefGoogle Scholar
  25. House-Peters, L. A., & Chang, H. (2011). Urban water demand modeling: Review of concepts, methods, and organizing principles. Water Resources Research, 47, W05401.CrossRefGoogle Scholar
  26. Ipswich Utilities. (2011). Available from: http://www.ipswichutilities.org/
  27. Isaaks, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics. New York: Oxford University.Google Scholar
  28. Johnston, R. A., Shabazian, D. R., & Gao, S. (2003). UPlan: A versatile urban growth model for transportation planning. Transportation Research Record: Journal of the Transportation Research Board, 1831, 202–209.CrossRefGoogle Scholar
  29. Karl, T., Fall, R., Jordan, A., & Lindinger, W. (2001). On-line analysis of reactive VOCs from urban lawn mowing. Environmental Science & Technology, 35, 2926–2931.CrossRefGoogle Scholar
  30. Klosterman, R. E. (1999). The what if? Collaborative planning support system. Environment and Planning B, 26, 393–408.CrossRefGoogle Scholar
  31. Krahe, J., Runfola, D. M., & Polsky, C. (2012). The impact of pricing structure on residential and seasonal water consumption in suburban Boston, MA, in Department of Economics. Worcester.Google Scholar
  32. Krass, B. (2003). Combating urban sprawl in Massachusetts: Reforming the zoning act through legal challenges. Environmental Affairs, 30, 605–639.Google Scholar
  33. Landis, J., & Zhang, M. (1998). The second generation of the California urban futures model. Environment and Planning B: Planning and Design, 25, 795–824.CrossRefGoogle Scholar
  34. Lang, R. E., Blakely, E. J., & Gough, M. Z. (2005). Keys to the new metropolis: America’s big, fast-growing suburban counties. Journal of the American Planning Association, 71(4), 381–391.CrossRefGoogle Scholar
  35. Larson, K. L., Casagrande, D., Harlan, S. L., & Yabiku, S. T. (2009). Residents’ yard choices and rationales in a desert city: Social priorities, ecological impacts, and decision tradeoffs. Environmental Management, 44, 921–937.CrossRefGoogle Scholar
  36. Lo, C., & Yang, X. (2002). Drivers of land-use/land-cover changes and dynamic modeling for the Atlanta, Georgia metropolitan area. PE & RS- Photogrammetric Engineering & Remote Sensing, 68(10), 1073–1082.Google Scholar
  37. MAPC. (2010). Metropolitan area planning council. Boston: Metropolitan Area Planning Council.Google Scholar
  38. MassGIS. (2011). Office of Geographic and Environmental Information. Commonwealth of Massachusetts, Executive Office of Energy and Environmental Affairs.Google Scholar
  39. Mayer, P., & DeOreo, W. (1999). Residential end uses of water. Denver: American Water Works Association.Google Scholar
  40. Milesi, C., Running, S. W., Elvidge, C. D., Dietz, J. B., Tuttle, B. T., & Nemani, R. R. (2005). Mapping and modeling the biogeochemical cycling of turf grasses in the United States. Environmental Management, 36, 426–438.CrossRefGoogle Scholar
  41. Milesi, C., Elvidge, C., & Nemani, R. (2009). Assessing the extent of urban irrigated areas in the United States. In Remote sensing of global croplands for food security. San Fransisco: NASA Ames Ecological Forecasting Lab. Retrieved from: http://ecocast.arc.nasa.gov/pubs/pdfs/2009/Milesi_Urban_BookChapter.pdf
  42. Nielson, L., & Smith, C. L. (2005). Influence on residential yard care and water quality: Tualatin watershed, Oregon. JAWRA Journal of the American Water Resources Association, 41, 93–106.CrossRefGoogle Scholar
  43. Pijanowski, B., Gage, S., & Long, D. (1997, February). The land transformation model. Paper presented at: Land Use Modeling Workshop, Sioux Falls.Google Scholar
  44. Polsky, C., Pontius Jr., R. G., Decatur, A., Giner, N., Rahul, R., & Runfola, D. M. (2012). Mapping lawns using an object-oriented methodology with high-resolution four-band aerial photography: The twenty-sex towns of the Ipswich and Parker River Watersheds, Massachusetts. In George Perkins Marsh Working Paper. Worcester: Clark University.Google Scholar
  45. Pinto, P., Cabral, P., Caetano, M., & Alves, M. F. (2009). Urban growth on coastal erosion vulnerable stretches. Journal of Coastal Research, 56(2), 1567–1571.Google Scholar
  46. Pontius, R. G. J., Cornell, J., & Hall, A. S. C. (2001). Modeling the spatial pattern of land-use change with GEOMOD2: Application and validation for Costa Rica. Agriculture, Ecosystems & Environment, 85, 191–203.CrossRefGoogle Scholar
  47. Priest, M., Williams, D., & Bridgman, H. (2000). Emissions from in-use lawn-mowers in Australia. Atmospheric Environment, 34, 657–664.CrossRefGoogle Scholar
  48. Robbins, P. (2007). Lawn people: How grasses, weeds, and chemicals make us who we are. Philadelphia: Temple University Press.Google Scholar
  49. Robbins, P., & Birkenholtz, T. (2003). Turfgrass revolution: Measuring the expansion of the American lawn. Land Use Policy, 20, 181–194.CrossRefGoogle Scholar
  50. Robbins, P., & Sharp, J. T. (2003). Producing and consuming chemicals: The moral economy of the American lawn. Economic Geography, 79, 425–451.CrossRefGoogle Scholar
  51. Roy Chowdhury, R., Larson, K., Grove, M., Polsky, C., Cook, E., Onsted, J., & Ogden, L. (2011). A multi-scalar approach to theorizing socio-ecological dynamics of urban residential landscapes. Cities and the Environment (CATE), 4, 6.Google Scholar
  52. Runfola, D. M., Polsky, C., Nicolson, C., Giner, N., Pontius, R. G., Jr., Krahe, J., & Decatur, A. (2013). A growing concern? Examining the influence of lawn size on residential water use in suburban Boston, MA, USA. Landscape and Urban Planning, 119, 112–123.CrossRefGoogle Scholar
  53. Runfola, D. M., & Pontius, R. G., Jr. (2013). Quantifying the temporal instability of land change transitions. International Journal of GIS, 27(9), 1696–1716.Google Scholar
  54. Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26, 525–552.CrossRefGoogle Scholar
  55. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.CrossRefGoogle Scholar
  56. Tu, J., Xiz, Z., Clark, K. C., & Frei, A. (2007). Impact of urban sprawl on water quality in Eastern Massachusetts, USA. Environmental Management, 40, 183–200.CrossRefGoogle Scholar
  57. United States Census Bureau (2011). Decennial Census. http://www.census.gov/.Google Scholar
  58. US Census (2013). http://www.census.gov/
  59. Veldkamp, A., & Fresco, L. (1996). CLUE-CR: An integrated multi-scale model to simulate land use change scenarios in Costa Rica. Ecological Modelling, 91, 231–248.CrossRefGoogle Scholar
  60. Waddell, P. (2002). Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68, 297–314.CrossRefGoogle Scholar
  61. Walker, R., Drzyzga, S. A., Li, Y., Qi, J., Caldas, M., Arima, E., & Vergara, D. (2004). A behavioral model of landscape change in the Amazon basin: The colonist case. Ecological Applications, 14, 299–312.CrossRefGoogle Scholar
  62. Wentz, E. A., & Gober, P. (2007). Determinants of small-area water consumption for the city of Phoenix, Arizona. Water Resources Management, 21, 1849–1863.CrossRefGoogle Scholar
  63. Yang, X., & Lo, C. (2003). Modelling urban growth and landscape changes in the Atlanta metropolitan area. International Journal of Geographical Information Science, 17, 463–488.CrossRefGoogle Scholar
  64. Zhou, W., Troy, A., & Grove, M. (2008). Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors, 8, 1613–1636.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daniel Miller Runfola
    • 1
  • Colin Polsky
    • 2
  • Nick Giner
    • 2
  • Robert Gilmore PontiusJr.
    • 2
  • Craig Nicolson
    • 3
  1. 1.National Center for Atmospheric ResearchUniversity of Colorado at BoulderBoulderUSA
  2. 2.Clark UniversityWorcesterUSA
  3. 3.University of Massachusetts AmherstAmherstUSA

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