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

Volumn 19, Issue 2, 2005, Pages 197-215

  Pijanowski, Bryan C ^a   Pithadia, Snehal ^a   Shellito, Bradley A ^b   Alexandridis, Konstantinos ^a  

^a PURDUE UNIVERSITY   (United States)

^b YOUNGSTOWN STATE UNIVERSITY   (United States)

Author keywords

GIS; Kappa; Landscape pattern metrics; Neural nets

Indexed keywords

ARTIFICIAL NEURAL NETWORK; GIS; LAND USE CHANGE; LANDSCAPE CHANGE; METROPOLITAN AREA; NUMERICAL MODEL;

MIDWEST; NORTH AMERICA; UNITED STATES; WESTERN HEMISPHERE; WORLD;

EID: 12944258552     PISSN: 13658816     EISSN: None     Source Type: Journal    
DOI: 10.1080/13658810410001713416     Document Type: Article

References (21)

1. 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, US Geological Survey, Professional Paper 964, p 28, Reston, VA.
2. Bishop, C., 1999, Neural networks for pattern recognition (Oxford University Press: Oxford, England).
3. Boone, R.B. and Krohn, W.B., 2002, Modeling tools and accuracy assessment. In Predicting species occurrences: Issues of accuracy and scale, edited by J.M. Scott, P.J. Heglund, M.L. Morrison, J.B. Haufler, M.G. Raphael, W.A. Wall and F.B. Samson (Washington DC: Island Press), pp. 265-270.
4. Environmental Systems Research Institute, 2000, Arc info online documentation ArcDOC version 8.3. Cell-based modeling with GRID, Environmental System Research Institute technical documentation, Redlands, California.
5. Fielding, A.H., 2002, What are the appropriate characteristics of an accuracy measure? In Predicting species occurrences: Issues of accuracy and scale, edited by J.M. Scott, P.J. Heglund, M.L. Morrison, J.B. Haufler, M.G. Raphael, W.A. Wall and F.B. Samson (Washington DC: Island Press), pp. 271-280.
6. Gesit, H. and Lambin, E., 2002, Proximate causes and underlying driving forces of tropical deforestation. BioScience, 52, pp. 143-150.
7. Mcgarigal, K., Cushman, S.A., Neel, M.C. and Ene, E., 2002, FRAGSTATS: Spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site:www.umass.edu/landeco/research/fragstats/fragstats.html
8. Lambin, E., Geist, H. and Lepers, E., 2003, Dynamics of land-use and land-cover change in the tropical regions. Annual Review of Environmental Resources, 28, pp. 205-241.
9. Ojima, D., Galvin, K. and Turner, B. II, 1994, The global impact of land-use change. BioScience, 44, pp. 300-304.
10. Pijanowski, B.C., Gage, S.H., Long, D.T. and Cooper, W., 2000, A land transformation model: Integrating policy, socioeconomics and environmental drivers using a Geographic Information System. In Landscape ecology: A top down approach, edited by L. Harris and J. Sanderson, pp. 184-198.
11. Pijanowski, B.C., Brown, D., Shellito, B. and Manik, G., 2002a, Using neural networks and GIS to forecast land use changes: A land transformation model. Computers, Environment and Urban Systems, 26, pp. 553-575.
12. Pijanowski, B.C., Shellito, B., Pithadia, S. and Alexandridis, K., 2002b, Using artificial neural networks, geographic information systems and remote sensing to model urban sprawl in coastal watersheds along eastern Lake Michigan. Lakes and Reservoirs, 7, pp. 271-285.
13. Pontius, R.G., Jr, 2000, Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering & Remote Sensing, 66, pp. 1011-1016.
14. Pontius, R.G., Jr, 2002, Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering & Remote Sensing, 68 pp. 1041-1049.
15. Reed, R.D. and Marks II, R.J., 1999, Neural smithing: Supervised learning in feedforward artificial neural networks (Cambridge, Massachusetts: MIT Press).
16. Rumelhart, D., Hinton, G. and Williams, R., 1986, Learning internal representations by error propagation. In Parallel distributed processing: explorations in the microstructures of cognition, edited by D.E. Rumelhart and J.L. McClelland (Cambridge, Massachusetts: MIT Press).
17. Sousa, S., Caeiro, S. and Painho, M., 2002, Assessment of map similarity of categorical maps using Kappa statistics: The case of Sado estuary. In ESIG 2002 (Tagus Park, Oeiras: Associação de Utilizadores de Informação Geográfica).
18. Sawaya, K., Yuan, F. and Bauer, M., 2001, Monitoring landscape change with Landsat classifications. ASPRS Proceedings, St. Louis, Mo. April 21-26, 2001.
19. Skapura, D., 1996, Building neural networks (New York: ACM Press).
20. Veldkamp, A. and Lambin, E., 2001 . Predicting land use change. Agriculture, Ecosystems and Environment, 85, pp. 1-6.
21. Walker, R., 2003, Evaluating the performance of spatially explicit models. Photogrammetric Engineering & Remote Sensing, 69, pp. 127-1278.