Monitoring land use change by multi-temporal landsat remote sensing imagery

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 37, Issue , 2008, Pages 1037-1042

  Tayyebi, A ^a   Delavar, M R ^a   Saeedi, S ^a   Amini, J ^a   Alinia, H ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Artificial Intelligence; Change Detection; GIS; Land Use; Remote Sensing

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; ECONOMICS; FUZZY INFERENCE; GEOGRAPHIC INFORMATION SYSTEMS; HOUSING; NEURAL NETWORKS; PARAMETERIZATION; REMOTE SENSING; ROADS AND STREETS; SPACE OPTICS;

BACK PROPAGATION NEURAL NETWORKS; CHANGE DETECTION; ENVIRONMENTAL VARIABLES; LAND USE CHANGE MODELING; LAND-USE CHANGE MODELLING; SOCIAL AND ENVIRONMENTAL; SUPERVISED CLASSIFICATION; UNIVERSAL TRANSVERSE MERCATOR;

LAND USE;

EID: 85017298498     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Fisher, M. M.; Abrahart, R. J. 2000, Neurocomputing, - tools for geographers. In: Openshaw, S.; Abrahart, R. J. (eds), Geocomputation, New York: Taylor & Francis, Chap.8, p. 187-217.
2. Gong, P., 1996, Integrated analysis of spatial data from multiple sources: using evidential reasoning and artificial neural network techniques for geological mapping, Photogrammetric Engineering and Remote Sensing, Vol. 62, p. 513-523.
3. Li, X. and Yeh, A. G. O., 2001. Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environment and Planning A, 33(8), pp. 1445-1462.
4. Li, X. and Yeh, A. G. O., 2002. Urban simulation using neural networks and cellular automata for land use planning. Proc. ISPRS Commission IV on Geospatial Theory, Processing and Applications, Ottawa, CA. Vol. XXXIV, Part 4, ISSN 1682-1750.
5. Liu, X. H., 2000. Change detection for urban growth modeling: an artificial neural network approach. In Proc. 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4): Problems, Prospects and Research Needs. Banff, Alberta, CA.
6. Pijanowski, B. C., Machemer, T., Gage, S., Long, D., Cooper, W., & Edens, T. 1995. A land transformation model: integration of policy, socioecono mics and ecological succession to examine pollution patterns in watershed (Report to the Environmental Protection Agency, Research Triangle Park, North Carolina, pp. 72).
7. Pijanowski, B. C., and Gage, S. H., Long, D. T., & Cooper, W. C. 2000. A land transformation model: integrating policy, socioeconomics and environmental drivers using a geographic information system; In L. Harris, & J. Sanderson (Eds.), Landscape ecology: a top down approach. Lewis Publishers, Boca Raton.
8. Pijanowskia, B. C., and Brown, D. G., Shellitoc, B. A., and Manikd, G. A., 2002. Using neural networks and GIS to forecast land use changes: a land transformation model. Computers, Environment and Urban Systems, 26(6), pp. 553-575.
9. Skapura, D. 1996. Building Neural Networks. New York: ACM Press.
10. White R. W., 1989. The artificial intelligence of urban dynamics: Neural network modeling of urban structure. Papers of the Regional Science Association, 67, pp. 43-53.
11. Yeh, A. G., 2003; Li, X. Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning, Photogrammetric Engineering and Remote Sensing, v. 69, p. 1043-1052.
12. Zell, A., Mamier, G., Vogt, M., Mache, N., Hübner, R., Döring, S., Herrmann, K., Soyez, T., Schmalzl, M., Sommer, T., Hatzigeorgiou, A., Posselt, D., Schreiner, T., Kett, B., & Clemente, G. 1996. Stuttgart neural network simulator v4.2. [ftp.informatik.uni-stuttgart.de in /pub/SNNS/SNNSv4.1.tar.Z]. Stuttgart: Institute for Parallel and Distributed High Performance Systems, University of Stuttgart.