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2014 | 33 | 1 |
Tytuł artykułu

Land-cover modelling using corine land cover data and multi-layer perceptron

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Słowa kluczowe
EN
Wydawca
-
Rocznik
Tom
33
Numer
1
Opis fizyczny
p.5-23,fig.,ref.
Twórcy
autor
  • Adam Mickiewicz University in Poznan, Dziegielowa 27, 61-680 Poznan, Poland
Bibliografia
  • Agarwal C., Green G.M., Grove J.M., Evans T.P., Schweik C.M., 2001. A review and assessment of land use change models: Dynamics of space, time, and human choice. Center for the Study of Institutions, Population, and Environmental Change, Indiana University and USDA Forest Service, Bloomington and South Burlington.
  • Atkinson P., Tatnall A., 1997. Neural networks in remote sensing. International Journal of Remote Sensing 18(4): 699-709. [CrossRef]
  • Barredo J.I., Kasanko M., McCormick N., Lavalle C., 2003. Modelling dynamic spatial processes: simulation of urban future scenarios through cellular automata. Landscape Urban Plan. 64: 145-160. [CrossRef]
  • Briassoulis H., 2000. Analysis of land use change: Theoretical and modeling approaches. In: S. Loveridge (ed.), The web book of regional science http://www.rri.wvu.edu/regscweb.htm. West Virginia University, Morgantown
  • Brown D.G., Lusch D.P., Duda K.A. 1998. Supervised classification of glaciated landscape types using digital elevation data. Geomorphology 21(3-4): 233-250. [CrossRef]
  • Burrough P.A., McDonnell R.A., 1998. Principles of Geographical Information Systems. Oxford University Press, Oxford, 333 p.
  • Carstensen L.W., 1987. A Measure of Similarity for Cellular Maps, The American Cartographer 14(4): 345-358. [CrossRef]
  • Ciołkosz A., Bielecka E., 2005. Pokrycie terenu w Polsce; Bazy danych CORINE Land Cover, Biblioteka Monitoringu Środowiska, Warszawa.
  • Cohen J., 1968. Weighed kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin 70 (4): 213-220. [CrossRef]
  • Czyż T., 2000. Zróżnicowanie regionalne i nowa organizacja terytorialna Polski. In: Ilnicki, D., Ciok, S. (Eds.), Przekształcenia regionalnych struktur funkcjonalno-przestrzennych, Wrocław, pp. 55-69.
  • Drummond S., Joshi A., Sudduth K. 1998. Application of neural networks: precision farming. IEEE Transactions on Neural Networks, 211-215
  • Eastman J.R., 2012. Idrisi Selva Manual. Clark University, Worcester, 324 p.
  • EEA [European Environmental Agency], 2013. CORINE Land Cover database. Online: http://www.eea.europa.eu/data-and-maps (accessed 1 March 2013). European Commission, 1993. CORINE Land cover map and technical guide. European Union Directorate-General Environment, Nuclear Safety and Civil Protection. Luxembourg.
  • Evans B.J., 1997. Dynamic display of spatial data-reliability: does it benefit the map user? Computers, Geosciences. 23 (4): 409-422.
  • Feranec J., Jaffrain G., Soukup T., Hazeu G., 2010. Determining changes and flows in European landscapes 1990-2000 using CORINE land cover data. Applied Geography 30(1): 19-35. [Web of Science]
  • Foley J., Defries R., Asner G.P., Barford C., Bonan G., Carpenter S.R., Chapin F.S., Coe M.T., Daily G.C., Gibbs H.K., Helkowski J.H., Holloway T., Howard E., Kucharik C.J., Monfreda, C., Patz J., Prentice C., Ramankutty N., Snyder P.K., 2005. Global consequences of land use. Science (New York, N.Y.) 309(5734): 570-574.
  • Fukushima K., Miyake S., Takayuki 1983. Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, SMC 13(5): 826-834.
  • Haase D., Schwarz N., 2009. Simulation models on human nature interactions in urban landscapes: a review including spatial economics, system dynamics, cellular automata and agent-based approaches. Living Reviews in Landscape Research 3.
  • Kolendowicz L., Bednorz E., 2010. Topoclimatic differentiation of the area of the Słowiński National Park, northern Poland. Quaestiones Geographicae 29(1): 49-56.
  • Lambin E.F., Turner B.L., Geist H.J., Agbola S.B., Angelsen A., Bruce J.W., Coomes O.T., Dirzo R., Fischer G., Folke C., George P.S., Homewood K., Imbernon J., Leemans R., Li, X., Moran E.F., Mortimore M., Ramakrishnan P.S., Richards J.F., Skånes H., Will S., Stone G.D., Svedin U., Veldkamp T.A., Vogel C., Xu J., 2001. The causes of land Global Environmental Change 11(4): 261-269.
  • Lee C.L., Huang S.L., Chan S.L., 2008. Biophysical and system approaches for simulating land-use change. Landscape and Urban Planning 86(2): 187-203. doi:10.1016/j. landurbplan.2008.02.006 [CrossRef] [Web of Science]
  • Łowicki D. 2008. Land use changes in Poland during transformation: Case study of Wielkopolska region. Landscape and Urban Planning 87(4): 279-288. [CrossRef] [Web of Science]
  • Łowicki D., Mizgajski A., 2005. Zmiany krajobrazu kulturowego Wielkopolski w okresie transformacji i opisujące je kategorie użytkowania terenu (Changes in the cultural landscape of Wielkopolska in the transformation period (1989-2000) and land use categories determining them). Przegląd Geograficzny 77(4): 551-568.
  • Macias A., Dryjer M., 2010. Forest cover dynamics in the city of Poznań from 1830 to 2004. Quaestiones Geographicae 29(3): 47-57.
  • Mackiewicz A., Parysek J.J., Ratajczak W., 1979. A multivariate study of Poland’s socio-economic spatial structure in 1975: a principal components analysis with eigenvalues obtained using modified Q R algorithm. Quaestiones Geographicae 79(5).
  • Marfai M.A., 2011. Impact of coastal inundation on ecology and agricultural land use case study in central Java, Indonesia. Quaestiones Geographicae 30(3): 19-32.
  • North M.J., Macal C.M., 2007. Managing business complexity: Discovering strategic solutions with agent-based modelling and simulation. New York: Oxford University Press.
  • Ott L., Larson R.F., Mendenhall W., 1983. Statistics: A Tool for the Social Sciences. Boston: Duxbury Press.
  • Parker D.C., Berger T., Manson S.M., 2002. Agent-based models of land-use and land-cover change. Report and Review of an International Workshop, October 4-7, 2001. LUCC Report Series No. 6, 124 p.
  • Parker D.C., Manson S.M., Janssen M.A., Hoffman M., Deadman P., 2003. Multi-agent systems for the simulation of land-use and land cover change: A review. Annals of the Association of American Geographers 93: 314-337. [CrossRef]
  • Pijanowski B.C., Brown D.G., Shellito B.A., Manik G.A., 2002. Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Computers, Environment and Urban Systems 26(6): 553-575. [CrossRef]
  • Pontius R.G., 2000. Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering, Remote Sensing 66(8): 1011-1016.
  • Pontius R.G., 2002. Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering, Remote Sensing 68(10): 1041-1049.
  • Pontius R.G., Suedmeyer B., 2004. Components of agreement in categorical maps at multiple resolutions. In: R.S. Lunetta, J.G. Lyon (eds), Remote Sensing and GIS Accuracy Assessment p. 233-251. Chapter 17 CRC Press, Boca Raton FL.
  • Ritter N., Logan T., Bryant N., 1988. Integration of neural network technologies with geographic information systems. Proceedings of the GIS symposium: integrating technology and geoscience applications (pp. 102-103). Denver, Colorado. United States Geological Survey, Washington, DC.
  • Rosenfield G.H., Fitzpatrick K.L., 1986. A Coefficient of Agreement as a Measure of Thematic Classification Accuracy. Photogrammetric Engineering and Remote Sensing 52(2): 223-227.
  • Rumelhart D., Hinton G., Williams R., 1986. Learning internal representations by error propagation. In: D.E. Rumelhart, J.L. McClelland (eds), Parallel distributed processing: explorations in the microstructures of cognition (Vol. 1; pp. 318-362). Cambridge: MIT Press.
  • Sangermano F., Eastman J.R., Zhu H., 2010. Similarity weighted instance based learning for the generation of transition potentials in land change modeling. Transactions in GIS 14(5): 569-580.
  • Schaldach R., Priess A., 2008. Integrated Models of the Land System: A Review of Modelling Approaches on the Regional to Global Scale. Living Reviews in Landscape Research (2) 1-34.
  • Schmit C., Rounsevell M.D.A., La Jeunesse I., 2006. The limitations of spatial land use data in environmental analysis. Environmental Science, Policy 9(2): 174-188. [CrossRef]
  • Schweitzer C., Priess J.A., Das S., 2011. A generic framework for land-use modelling. Environmental Modelling, Software 26(8): 1052-1055.
  • Skapura D., 1996. Building neural networks. New York: ACM Press.
  • Szlachta J., 1993. Rozwój regionalny Polski w warunkach transformacji gospodarczej, Warszawa.
  • Szpikowski J., 2002 Contemporary processes of soil erosion and the transformation of the morphology of slopes in agricultural use in the postglacial catchment of the Chwalimski Potok (upper Parseta, Drawskie Lakeland). Quaestiones Geographicae 22: 79-90.
  • Veldkamp A., Lambin E.F., 2001. Predicting land-use change: Editorial. Agriculture Ecosystem Environment 85(1-3): 1-6.
  • Verburg P.H., Veldkamp A., 2005 Editorial: Spatial modeling to explore land use dynamics. International Journal of Geographical Information Science 19: 99-102.
  • Wang S.H., Huang S.L., William W., Budd W.W., 2012. Integrated ecosystem model for simulating land use allocation. Ecological Modelling 227: 46-55.
  • Willems E., Vandevoort C.,Willekens A., Buffaria B., 2000. Landscape and land cover diversity index. Online: http://ec.europa.eu/agriculture/publi/landscape (accessed 1 August 2013).
  • Zwoliński Zb. (ed.), 2012. GIS - teledetekcja środowiska (GIS - remote sensing of the environment]), Bogucki Wyd. Naukowe, Poznań.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-13ccd14e-b610-42e2-9c51-487a899829f3
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