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

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?

Situation Study Area

Pécsely Basin: 24 km²

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

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

Topography

Pécsely

Vászoly

Land UsePécsely Basin 2003

• Based on Aster satellite image

Arable landPastureVineyardForestBuild up area

Land use ha %Arable land 492.12 20.23Pasture 632.35 25.99Vineyard 271.51 11.16Forest 961.63 39.53Buid up area 75.11 3.09Total 2432.72 100

2003

Historical land use changes

Land use around Pécsely in 1955 (a) and 1971 (b)

Source: Museum of Military History, Budapest

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

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

20%

22%

26%

30%

2%20%

26%

11%

40%

3%

Arable land: equal area; smaller parcels

Heterogeneous pattern

1992

Based on landsat satellite image

2003

Based on Aster satellite image

Recent land use changes

Evolution in land use (1992 –2003)

Unchanged: 1066 ha (44%)

1992 arable land pasture vineyard forest build up area totalarable land 176 166 46 101 6 495pasture 108 168 56 181 13 526vineyard 164 220 144 102 6 636forest 39 74 24 578 0 715build up area 5 4 2 1 49 61total 492 632 272 963 74 2433

2003

5 changes in red represents 72% of all changes

Land Use Changes (in Ha)

No change

Changed to arable land

Changed to pasture

Changed to vineyard

Changed to forest

Build up area

Actual land use changes

• Arable land Pasture 166 ha

• Pasture Arable land 108 haForest 181 ha

• Vineyard Arable land 164 haPasture 220 haForest 102 ha

Statistical analysis

• Parameters• Hight

• Slope

• Soil texture

• Distance to road

• Distance to village

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

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.

Transition Probabilities

• Based on the logistic regression analysis

• Transition probability map for each type of land use conversion

Probability map arable land to pastureProbability map pasture to arable landProbability map pasture to forest

Simulation of Land Use Changes

Stochastic allocation procedure was used to generate land use pattern for different scenarios

Predictions for 2015 when the actual trend persists???

Arable landPastureVineyardsForest

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)

Hillslope Sediment routingCALCULATION OF DISTRIBUTEDPATTERN OF MEAN ANNUALTRANSPORT CAPACITY (TC)

CALCULATION OF DISTRIBUTEDPATTERN OF MEAN ANNUAL SOIL

EROSION RATES (E)

(RUSLE-based)

ROUTING OF SEDIMENTVIA FLOWPATHS TO

THE RIVER CHANNELS

RIVERCHANNEL

TC > E +SED_INPUT

TC < E +SED_INPUT

SEDIMENTTRANSFER

SEDIMENTTRANSFER +

SEDIMENTATION

SEDIMENTDELIVERY

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???

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 haPasture: 526 tot 633 haVineyards: 636 to 272 haArable land: constant

Thank You !!!