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Predicting land use change: comparison of models based on landowner surveys and historical land cover trends

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Abstract

To make informed planning decisions, community leaders, elected officials, scientists, and natural resource managers must be able to evaluate potential effects of policies on land use change. Many land use change models use remotely-sensed images to make predictions based on historical trends. One alternative is a survey-based approach in which landowners’ stated intentions are modeled. The objectives of our research were to: (1) develop a survey-based landowner decision model (SBM) to simulate future land use changes, (2) compare projections from the SBM with those from a trend-based model (TBM), and (3) demonstrate how two alternative policy scenarios can be incorporated into the SBM and compared. We modeled relationships between land management decisions, collected from a mail survey of private landowners, and the landscape, using remotely-sensed imagery and ownership parcel data. We found that SBM projections were within the range of TBM projections and that the SBM was less affected by errors in image classification. Our analysis of alternative policies demonstrates the importance of understanding potential effects of targeted land use policies. While policies oriented toward increasing enrollment in the Conservation Reserve Program (CRP) resulted in a large (11–13%) increase in CRP lands, policies targeting increased forest thinning on private non-industrial lands increased low-density forest projections by only 1%. The SBM approach is particularly appropriate for landscapes including many landowners, because it reflects the decision-making of the landowners whose individual actions will result in collective landscape change.

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References

  • An L, Linderman M, Qi J, Shortridge A, Liu J (2005) Exploring complexity in a human-environment system: an agent-based spatial model for multidisciplinary and multiscale integration. Ann Assoc Am Geogr 95:54–79

    Article  Google Scholar 

  • Baker JP, Hulse DW, Gregory SV, White D, Van Sickle J, Berger PA, Dole D, Schumaker NH (2004) Alternative futures for the Willamette River Basin, Oregon. Ecol Appl 14:313–324

    Article  Google Scholar 

  • Beck RN, Gessler PE (2007) Development of a Landsat time-series for application in forest status assessment in the Inland Northwest United States. West J Appl For (in press)

  • Beyers WB, Nelson PB (2000) Contemporary development forces in the non-metropolitan West: New insights from rapidly growing communities. J Rural Stud 16:459–474

    Article  Google Scholar 

  • Black AE, Strand E, Wright RG, Scott JM, Morgan P, Watson C (1998) Land use history at multiple scales: Implications for conservation planning. Landsc Urban Plan 43:49–63

    Article  Google Scholar 

  • Boody G, Vondracek B, Andow DA, Krinke M, Westra J, Zimmerman J, Welle P (2005) Multifunctional agriculture in the United States. BioScience 55:27–38

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Brown DG, Johnson KM, Loveland TR, Theobald DM (2005a) Rural land-use trends in the conterminous United States, 1950–2000. Ecol Appl 15:1851–1863

    Article  Google Scholar 

  • Brown DG, Page S, Riolo R, Zellner M, Rand W (2005b) Path dependence and the validation of agent-based spatial model of land use. Int J Geogr Inf Sci 19:153–174

    Article  Google Scholar 

  • Brown DG, Xie Y (2006) Spatial agent-based modeling. Int J Geogr Inf Sci 20:941–943

    Article  Google Scholar 

  • Brown DG, Robinson DT (2006) Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecol Soc 11:46

    Google Scholar 

  • Brown de Colstoun EC, Story MH, Thompson C, Commisso K, Smith TG, Irons JR (2003) National park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier. Remote Sens Environ 85:316–327

    Article  Google Scholar 

  • Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data. Lewis Publishers, New York

    Google Scholar 

  • Cohen JD (2000) Preventing disaster: home ignitability in the wildland—urban interface. J Forest 98:15–21

    Google Scholar 

  • Conway TM, Lathrop RG Jr (2005) Modeling the ecological consequences of land-use policies in an urbanizing region. Environ Manage 35:278–291

    Article  PubMed  Google Scholar 

  • Dellasala DA, Williams JE, Williams CD, Franklin JF (2004) Beyond smoke and mirrors: A synthesis of fire policy and science. Conserv Biol 18:976–986

    Article  Google Scholar 

  • Dunn CP, Stearns F, Guntenspergen GR, Sharpe DM (1993) Ecological benefits of the Conservation Reserve Program. Conserv Biol 7:132–139

    Article  Google Scholar 

  • Fang S, Gertner G, Wang G, Anderson A (2006) The impact of misclassification in land use maps in the prediction of landscape dynamics. Landsc Ecol 21:233–242

    Article  Google Scholar 

  • Greider T, Garkovich L (1994) Landscapes: the social construction of nature and the environment. Rural Sociol 59:1–24

    Article  Google Scholar 

  • Gustafson EJ, Hammer RB, Radeloff VC, Potts RS (2005) The relationship between environmental amenities and changing human settlement patterns between 1980 and 2000 in the Midwestern USA. Landsc Ecol 20:773–789

    Article  Google Scholar 

  • Hansen AJ, Garman SL, Marks B, Urban DL (1993) An approach for managing vertebrate diversity across multiple-use landscapes. Ecol Appl 3:471–496

    Article  Google Scholar 

  • Hansen AJ, Rotella JJ (2002) Biophysical factors, land use, and species viability in and around nature reserves. Conserv Biol 16:1112–1122

    Article  Google Scholar 

  • Haynes RW (2002) Forest management in the 21st century: changing numbers, changing context. J Forest 2:38–43

    Google Scholar 

  • Hessburg PF, Agee JK (2003) An environmental narrative of Inland Northwest United States forests, 1800–2000. Forest Ecol Manag 178:23–59

    Article  Google Scholar 

  • Idaho Department of Water Resources (2006) Idaho Department of Water Resources Well Information Search. http://www.idwr.idaho.gov/water/well/search.htm

  • Irwin EG, Bell KP, Geoghegan J (2003) Modeling and managing urban growth at the rural-urban fringe: a parcel-level model of residential land use change. Agr Res Econ Rev 32:83–102

    Google Scholar 

  • Jepsen JU, Topping CJ, Odderskaer P, Andersen PN (2005) Evaluating consequences of land-use strategies on wildlife populations using multiple-species predictive scenarios. Agr Ecosyst Environ 105:581–594

    Article  Google Scholar 

  • Johnson J, Maxwell B (2001) The role of the Conservation Reserve Program in controlling rural residential development. J Rural Stud 17:323–332

    Article  Google Scholar 

  • Johnson KM, Beale CL (1994) The recent revival of widespread population growth in nonmetropolitan areas of the United States. Rural Soc 59:655–667

    Article  Google Scholar 

  • Lambin EF, Geist H (eds) (2006) Land-use and land-cover change: local processes and global impacts. Springer-Verlag, Berlin, Germany

    Google Scholar 

  • Li C, Liu L, Wang J, Zhao C, Wang R (2004) Comparison of two methods of the fusion of remote sensing images with fidelity of spectral information. Proc 2004 IEEE Int 4:2561–2564

    Google Scholar 

  • Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2/3a:18–22

    Google Scholar 

  • Loomis J, Brown T, Lucero B, Peterson G (1996) Improving validity experiments of contingent valuation methods: results of efforts to reduce the disparity of hypothetical and actual willingness to pay. Land Econ 72:450–461

    Article  Google Scholar 

  • Maestas JD, Knight RL, Gilgert WC (2003) Biodiversity across a rural land-use gradient. Conserv Biol 17:1425–1434

    Article  Google Scholar 

  • Markham BL, Barker JL (1986) Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures. Landsat Tech. Note 1. Earth Observation Satellite Co., Lanham, MD

    Google Scholar 

  • Musacchio LR, Grant WE (2002) Agricultural production and wetland habitat quality in a coastal prairie ecosystem: simulated effects of alternative resource policies on land-use decisions. Ecol Model 150:23–43

    Article  Google Scholar 

  • NASA Goddard Space Flight Center (2005) Landsat 7 Science Data Users Handbook. http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html.

  • O’Neill RV, Krummel JR, Gardener RH, Sugihara G, Jackson B, DeAngelis DL, Milne BT, Turner MG, Zygmunt B, Christensen S, Dale VH, Graham RL (1988) Indices of Landscape Pattern. Landsc Ecol 1:153–162

    Article  Google Scholar 

  • Parker DC, Manson SM, Jannsen MA, Hoffman MJ, Deadman P (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93:314–347

    Article  Google Scholar 

  • Pontius RG, Huffaker D, Denman K (2004) Useful techniques of validation for spatially explicit land-change models. Ecol Model 179:445–461

    Article  Google Scholar 

  • Pontius G, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19:243–265

    Article  Google Scholar 

  • R Development Core Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: http://www.R-project.org

    Google Scholar 

  • Ryan MR, Burger LW, Kurzejeski EW (1998) The impact of CRP on avian wildlife: a review. J Prod Agric 11:61–66

    Google Scholar 

  • Santelmann MV, White D, Freemark K, Nassauer JI, Eilers JM, Vaché KB, Danielson BJ, Corry RC, Clark ME, Polasky S, Cruse RM, Sifneos J, Rustigian H, Coiner C, Wu J, Debinski D (2004) Assessing alternative futures for agriculture in Iowa, U.S.A. Landsc Ecol 19:357–374

    Article  Google Scholar 

  • Schlaepfer MA, Runge MC, Sherman PW (2002) Ecological and evolutionary traps. Trends Ecol Evol 17:474–480

    Article  Google Scholar 

  • Scott JM, Peterson CR, Karl JW, Strand E, Svancara LK, Wright NM (2002) A gap analysis of Idaho: final report. Idaho Cooperative Fish and Wildlife Research Unit, Moscow, ID

    Google Scholar 

  • Shumway JM, Otterstrom SM (2001) Spatial patterns of migration and income change in the Mountain West: the dominance of service-based and amenity-rich counties. Prof Geogr 53:492–502

    Article  Google Scholar 

  • Smith MD, Krannich RS (2000) “Culture clash” revisited: newcomer and longer-term residents’ attitudes toward land use, development, and environmental issues in rural communities in the Rocky Mountain West. Rural Sociol 65:396–421

    Article  Google Scholar 

  • Spies TA, Reeves GH, Burnett KM, McComb WC, Johnson KN, Grant G, Ohmann JL, Garman SL, Bettinger P (2002) Assessing the ecological consequences of forest policies in a multi-ownership province in Oregon. In: Liu J, Taylor WW (eds) Integrating landscape ecology into natural resource management, Cambridge University Press, New York

    Google Scholar 

  • Theobald DM, Hobbs NT (1998) Forecasting rural land-use change: a comparison of regression- and spatial transition-based models. Geogr Environ Model 2:65–82

    Google Scholar 

  • Theobald DM, Hobbs NT (2002) A framework for evaluating land use planning alternatives: Protecting biodiversity on private land. Conserv Ecol 6:5 www.consecol.org/vol6/iss1/art5

  • Theobald DM (2005) Landscape patterns of exurban growth in the USA from 1980 to 2020. Ecol Soc 10:32 URL: www.ecologyandsociety.org/vol10/iss1/art32/

  • United States Census Bureau (2005) Table 1: ranking of Census 2000 and projected 2030 state population change. www.census.gov/population/projections/PressTab1.xls

  • Verburg PH, Kok K, Pontius Jr RG, Veldkamp A (2006) Modeling land-use and land-cover change. In: Lambin EF, Geist H (eds) Land-use and land-cover change: local processes and global impacts, Springer-Verlag, Berlin, Germany

    Google Scholar 

  • Wear DN, Bolstad P (1998) Land-use change in southern Appalachian landscape: spatial analysis and forecast evaluation. Ecosystems 1:575–594

    Article  Google Scholar 

  • White D, Minotti PG, Barczak MJ et al. (1997) Assessing risks to biodiversity from future landscape change. Conserv Biol 11:349–360

    Article  Google Scholar 

Download references

Acknowledgements

We thank the 442 survey participants and interview contributors: Ross Appelgren, Robert Barkley, Trish Heekin, Dean Johnson, Ron Mahoney, Brian Moser, Nolan Noren, Dennis Parent, and Chris Schnepf. We received assistance with LU/LC mapping, statistical, and GIS modeling methods from Roger Bivand, Mike Falkowski, Steven Sesnie, Alistair Smith, Kirk Steinhorst, Eva Strand and David Theobald. Paul Gessler provided historical Landsat imagery. We thank Nicole Nielsen-Pincus, Sam Chambers, and Drew Hawley for assisting with the landowner surveys. We thank David Theobald, Sanford Eigenbrode, Andrew Robinson, and two anonymous reviewers for insightful comments that improved the manuscript. This research was funded by the USDA McIntire-Stennis Program, National Science Foundation IGERT grant 0014304, and the University of Idaho.

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Authors and Affiliations

  1. Department of Forest Resources, University of Idaho, Moscow, ID, USA

    Amy Pocewicz, Max Nielsen-Pincus, Penelope Morgan & Jo Ellen Force

  2. Department of Fish and Wildlife Resources, University of Idaho, Moscow, ID, USA

    Caren S. Goldberg & Lisette P. Waits

  3. Department of Environmental Science, University of Idaho, Moscow, ID, USA

    Melanie H. Johnson

  4. Department of Rangeland Ecology and Management, University of Idaho, Moscow, ID, USA

    Lee Vierling

  5. The Nature Conservancy, Wyoming Field Office, 258 Main St., Suite 200, Lander, WY, 82520, USA

    Amy Pocewicz

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  1. Amy Pocewicz
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Correspondence to Amy Pocewicz.

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Pocewicz, A., Nielsen-Pincus, M., Goldberg, C.S. et al. Predicting land use change: comparison of models based on landowner surveys and historical land cover trends. Landscape Ecol 23, 195–210 (2008). https://doi.org/10.1007/s10980-007-9159-6

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