Abstract
Changes in cropland have been the dominating land use changes in Central and Eastern Europe, with cropland abandonment frequently exceeding cropland expansion. However, surprisingly little is known about the rates, spatial patterns, and determinants of cropland change in Eastern Europe. We study cropland changes between 1995 and 2005 in Argeş County in Southern Romania with two distinct modeling techniques. We apply and compare spatially explicit logistic regressions with artificial neural networks (ANN) using an integrated socioeconomic and environmental dataset. The logistic regressions allow identifying the determinants of cropland changes, but cannot deal with non-linear and complex functional relationships nor with collinearity between variables. ANNs relax some of these rigorous assumptions inherent in conventional statistical modeling, but likewise have drawbacks such as the unknown contribution of the parameters to the outcome of interest. We compare the outcomes of both modeling techniques quantitatively using several goodness-of-fit statistics. The resulting spatial predictions serve to delineate hotspots of change that indicate areas that are under more eminent threat of future abandonment. The two modeling techniques address two controversial issues of concern for land-change scientists: (1) to identify the spatial determinants that conditioned the observed changes and (2) to deal with complex functional relationships between influencing variables and land use processes. The spatially explicit insights into patterns of cropland change and in particular into hotspots of change derived from multiple methods provide useful information for decision-makers.
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Almeida CM, Gleriani JM, Castejon EF, Soareasfilho BS (2008) Using neural networks and cellular automata for modelling intra-urban land-use dynamics. Int J Geogr Inf Sci 22:943–963
Anselin L (2002) Under the hood: issues in the specification and interpretation of spatial regression models. Agric Econ 27:247–267
Besag JE (1974) Spatial interaction and the statistical analysis of lattice systems. J R Stat Soc 36:192–236
Bicik I, Jelecek L, Stepanek V (2001) Land-use changes and their social driving forces in Czechia in the 19th and 20th centuries. Land use policy 18:65–73
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Chilonda P, Otte J (2006) Indicators to monitor trends in livestock production at national, regional and international levels. Livest Res Rural Dev 18. Accessed 22 Nov 2006 at http://www.cipav.org.co/lrrd/lrrd18/8/chil18117.htm
Chomitz KM, Gray D (1996) Roads, lands use, and deforestation: a spatial model applied to Belize. World Bank Econ Rev 10:487–512
Cremene C, Groza G, Rakosy L, Schileyko AA, Baur A, Erhardt A, Baur B (2005) Alterations of Steppe-like grasslands in Eastern Europe: a threat to regional biodiversity hotspots. Conserv Biol 19:1606–1618
Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35:352–359
Elbakidze M, Angelstam P (2007) Implementing sustainable forest management in Ukraine’s Carpathian Mountains: the role of traditional village systems. For Ecol Manage 249:28–38
Fischer MM, Abrahart RJ (2000) Neurocomputing—tools for geographers. In: Openshaw S, Abrahart RJ (eds) Geocomputation. Taylor & Francis, London, pp 187–218
Gellrich M, Baur P, Koch B, Zimmermann NE (2007) Agricultural land abandonment and natural forest re-growth in the Swiss mountains: a spatially explicit economic analysis. Agric Ecosyst Environ 118:93–108
Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New York
Ioras F (2003) Trends in Romanian biodiversity conservation policy. Biodivers Conserv 12:9–23
Kuemmerle T, Radeloff VC, Perzanowski K, Hostert P (2006) Cross-border comparison of land cover and landscape pattern in Eastern Europe using a hybrid classification technique. Remote Sens Environ 103:449–464
Kuemmerle T, Hostert P, Radeloff V, van der Linden S, Perzanowski K, Kruhlov I (2008) Cross-border comparison of post-socialist farmland abandonment in the Carpathians. Ecosystems 11:614–628
Kuemmerle T, Müller D, Griffiths P, Rusu M (2009) Land use change in Southern Romania after the collapse of socialism. Reg Environ Change 9:1–12
Lerman Z, Csaki C, Feder G (2004) Evolving farm structures and land use patterns in former socialist countries. Q J Int Agr 43:309–335
Macours K, Swinnen JFM (2008) Rural–urban poverty differences in transition countries. World Dev 2008
Maddala GS (1983) Limited-dependent variables in econometrics. Cambridge University Press, New York
May RJ, Maier HR, Dandy GD, Fernando TMKG (2008) Non-linear variable selection for artificial neural networks using partical mutual information. Environ Model Softw 23(10–11):1312–1326
Mertens B, Lambin EF (2000) Land-cover change trajectories in southern Cameroon. Ann Assoc Am Geogr 90:467–494
Müller D, Mburu J (2009) Forecasting hotspots of forest clearing in Kakamega Forest, Western Kenya. For Ecol Manage 257:968–977
Müller D, Munroe DK (2008) Changing rural landscapes in Albania: cropland abandonment and forest clearing in the postsocialist transition. Ann Assoc Am Geogr 98:855–876
Müller D, Kuemmerle T, Rusu M, Griffith P (2009) Lost in transition: determinants of cropland abandonment in postsocialist Romania. J Land Use Sci 4:109–128
Munroe DK, Müller D (2007) Issues in spatially explicit statistical land-use/cover change (LUCC) models: examples from western Honduras and the central highlands of Vietnam. Land use policy 24:521–530
Nagendra H, Southworth J, Tucker C (2003) Accessibility as a determinant of landscape transformation in western Honduras: linking pattern and process. Landscape Ecol 18:141–158
NIS (2004) Database of localities. National Institute of Statistics, Bucharest
OECD (2001) Multifunctionality: towards an analytical framework. Organisation for Economic Co-operation and Development (OECD), Paris
Openshaw S (1998) Neural network, genetic, and fuzzy logic models of spatial interaction. Environ Plan A 30:1857–1872
Oszlanyi J, Grodzinska K, Badea O, Shparyk Y (2004) Nature conservation in Central and Eastern Europe with a special emphasis on the Carpathian Mountains. Environ Pollut 130:127–134
Palang H, Printsmann A, Gyuró ÉK, Urbanc M, Skowronek E, Woloszyn W (2006) The forgotten rural landscapes of Central and Eastern Europe. Landscape Ecol l 21:347–357
Perz SG, Skole DL (2003) Social determinants of secondary forests in the Brazilian Amazon. Soc Sci Res 32:25–60
Peterson U, Aunap R (1998) Changes in agricultural land use in Estonia in the 1990s detected with multitemporal Landsat MSS imagery. Landsc Urban Plan 41:193–201
Pijanowski BC, Brown DG, Shellito BA, Manik GA (2002) Using neural networks and GIS to forecast land use changes: a land transformation model. Comput Environ Urban Syst 26:553–575
Pijanowski BC, Pithadia S, Shellito BA, Alexandridis K (2005) Calibrating a neural network-based urban change model for two metropolitan areas in the upper midwest of the united states. Int J Geogr Inf Sci 19:197–215
Pijanowski BC, Alexandridis KT, Müller D (2006) Modelling urbanization patterns in two diverse regions of the world. J Land Use Sci 1(2):83–108
Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In IEEE International Conference on Neural Networks, San Francisco
Salazar-Ruiz E, Ordieres JB, Vergara EP, Capuz-Rizo SF (2008) Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US). Environ Model Softw 23:1056–1069
Schneider LC, Pontius RGJ (2001) Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85:83–94
Slater JA, Garvey G, Johnston C, Haase J, Heady B, Kroenung G, Little J (2006) The SRTM data “finishing” process and products. Photogramm Eng Remote Sensing 72:237–247
Tasser E, Tappeiner U (2002) Impact of land use changes on mountain vegetation. Appl Veg Sci 5:173–184
Verburg PH (2006) Simulating feedbacks in land use and land cover change models. Landscape Ecol 21:1171–1183
Verburg PH, Schulp CJE, Witte N, Veldkamp A (2006) Downscaling of land use change scenarios to assess the dynamics of European landscapes. Agric Ecosyst Environ 114:39–56
Wieland R, Mirschel W (2008) Adaptive fuzzy modeling versus artificial neural networks. Environ Model Softw 2:215–224
Williams RL (2000) A note on robust variance estimation for cluster-correlated data. Biometrics 56:645–646
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, Wieland J, Gatter J (2000) SNNS—Stuttgart Neural Network Simulator. Institute for Parallel and Distributed High Performance Systems, University of Stuttgart, Wilhelm Schickard Institute for Computer Science, University of Tübingen, Stuttgart
Acknowledgments
We are very grateful for the valuable comments of two reviewers and Eleanor Milne from the Office for Integration and Modeling that significantly improved the paper. We thank Tobias Kuemmerle and Patrick Griffiths for the remote sensing analysis of land cover changes. We also thank the Global Land Project and the guest editors for organizing this special issue. Funding of the data collection from the Deutsche Forschungsgemeinschaft (DFG) under the Emmy-Noether Programme is gratefully acknowledged.
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Lakes, T., Müller, D. & Krüger, C. Cropland change in southern Romania: a comparison of logistic regressions and artificial neural networks. Landscape Ecol 24, 1195–1206 (2009). https://doi.org/10.1007/s10980-009-9404-2
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DOI: https://doi.org/10.1007/s10980-009-9404-2