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Predicting land use changes in the Lake Balaton catchment (Hungary)

Predicting land use changes in the Lake Balaton catchment (Hungary). Van Dessel Wim 1 , Poelmans Lien 1 , Gyozo Jordan 2 , Szilassi Peter 3 , Csillag Gabor 2 , Van Rompaey Anton 1. 1 Physical an Regional Geography Research Group, K.U.Leuven, Belgium

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Predicting land use changes in the Lake Balaton catchment (Hungary)

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  1. Predicting land use changes in the Lake Balaton catchment (Hungary) Van Dessel Wim1, Poelmans Lien1, Gyozo Jordan2, Szilassi Peter3, Csillag Gabor2 , Van Rompaey Anton1 1 Physical an Regional Geography Research Group, K.U.Leuven, Belgium 2 Geological Institute of Hungary, Hungarian Geological Survey, Hungary 3 Szeged University, Deparment of Geography, Hungary International Workshop: European Union Expansion: Land Use Change and Environmental Effects in Rural Areas

  2. Introduction • Land use changes: caused by socio-economic evolution (often at a macroscale): political decisions, econmic development, changing lifestyle • Land users determine the spatial pattern of these land use changes • e.g. Due to economic or political pressure a farmer can be forced to take land out or in production • Wich parcels will be chosen depends on a lot of criteria (ex. soil parameters); personal experience and motivation of the farmer • Evalutation of the quality/characteristics of the parcels • Can we model the behaviour of the farmer and simulate the spatial pattern of his decisions?

  3. Situation Study Area Pécsely Basin: 24 km²

  4. Objectives of the study • Which land use changes have recently occured and where? • Determine the landscape characteristics influencing the spatial pattern of the land use transitions • Can we use information from past changes to predict patterns future land use change? • Investigate the impact of recent and future land use changes on soil erosion and sediment yield

  5. Method • Satellite images (spatial pattern of the changes; resolution 30m) • 1992: Landsat 4 Thematic Mapper • 2003: Aster • Aerial photographs (parcel size, …) • Physiographic characteristics Digitizing test sites Supervised classification 77% accuracy

  6. Topography Pécsely Vászoly

  7. Land Use Pécsely Basin 2003 Arable land Pasture Vineyard Forest Build up area 2003 • Based on Aster satellite image

  8. Historical land use changes Land use around Pécsely in 1955 (a) and 1971 (b) Source: Museum of Military History, Budapest

  9. Historical land use changes • 1949: Start collectivisation • 1952: Opposition against collectivisation • 1955: Collectivisation • 1956: Revolution against collectivisation • 1957: Flexibilization • 1961: “Complete” collectivisation (90%) • 1968: New economic mechanism (more independent farms) • 1989: Republic Privatization • 1994: Farmers can claim their land back

  10. Recent land use changes • Comparison of satellite images and aerial photographs • Construction of land use transition maps • Analysis of the characteristics of the transition zones • Calculation of conditional transition probabilities

  11. Recent land use changes 1992 2003 Based on landsat satellite image Based on Aster satellite image Arable land: equal area; smaller parcels Heterogeneous pattern

  12. Evolution in land use (1992 –2003) No change Changed to arable land Changed to pasture Changed to vineyard Changed to forest Build up area Land Use Changes (in Ha) 5 changes in red represents 72% of all changes Unchanged: 1066 ha (44%)

  13. Actual land use changes • Arable land Pasture 166 ha • Pasture Arable land 108 ha Forest 181 ha • Vineyard Arable land 164 ha Pasture 220 ha Forest 102 ha

  14. Statistical analysis • Parameters • Hight • Slope • Soil texture • Distance to road • Distance to village

  15. Statistical analysis • The problem: which factors control land use changes ? • Relative importance of different factors ? • Prediction of future land use changes ? Which variables contribute significantly to the land use change pattern Chi-square analysis Logistic regression

  16. Conclusion Statistics • Which physical and “infrastructure” parameters determine the spatial pattern? Small differences observed between both methods because the first one handles with categorical variables and the second one with continuous variables. Chi-square analysis handles each factor separately.

  17. Transition Probabilities • Based on the logistic regression analysis • Transition probability map for each type of land use conversion Probability map arable land to pasture Probability map pasture to arable land Probability map pasture to forest

  18. Simulation of Land Use Changes Arable land Pasture Vineyards Forest Stochastic allocation procedure was used to generate land use pattern for different scenarios Predictions for 2015 when the actual trend persists???

  19. Consequences of Land Use Changes WATEM/SEDEM is a spatially distributed erosion and sediment delivery model(Van Rompaey et al., 2001, Van Oost et al., 2000, Verstraeten et al., 2002)

  20. Hillslope Sediment routing CALCULATION OF DISTRIBUTED PATTERN OF MEAN ANNUAL SOIL CALCULATION OF DISTRIBUTED EROSION RATES (E) PATTERN OF MEAN ANNUAL TRANSPORT CAPACITY (TC) (RUSLE-based) ROUTING OF SEDIMENT VIA FLOWPATHS TO THE RIVER CHANNELS TC > E + TC < E + RIVER SED_INPUT SED_INPUT CHANNEL SEDIMENT SEDIMENT SEDIMENT TRANSFER + DELIVERY TRANSFER SEDIMENTATION

  21. Results (erosion reduction) • Pécsely SY: 0.030 ton/ha year (1975 – 1994) • Kali Basin SY: 0.018 ton/ha year (1981 – 1989) Very low SDR-values as a consequence of relatively flat centre of the basin Predictions for 2015???

  22. Conclusions • 1949 – 1989: Collectivization • 1989 – 2004: Privatization Fragmentation Increase of non-cultivated areas • Driving Forces: (Chi² and Logistic Regression) Transition Probability Maps Scenario Development GEOMOPRHOLOGICAL IMPACT Forest: 715 to 963 ha Pasture: 526 tot 633 ha Vineyards: 636 to 272 ha Arable land: constant

  23. Thank You !!!

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