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Land‐use modelling in Switzerland using land u se s tatistic data

Land‐use modelling in Switzerland using land u se s tatistic data. Context. Socio-economic processes are strong drivers of land- use change across Europe Land abandonment has been a dominant process Urbanisation is increasing at a rapid rate

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Land‐use modelling in Switzerland using land u se s tatistic data

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  1. Land‐use modelling in Switzerlandusing land use statistic data

  2. Context • Socio-economicprocessesare strong driversof land-usechangeacross Europe • Land abandonmenthasbeen a dominant process • Urbanisation isincreasing at a rapid rate • Increasingpushestowardsrenewableenergysources = • Unknownextentandlocationoflandusechanges • Unknownimpact on landscapeservices

  3. Land coverchangescenarios • Over-archingprocesses • Population growth • Economicgrowth • Political change • newenergypolicy • conservationpolicy, etc • Storylinesforfuturescenariosto 2035 • Relatedto IPCC storylinesfordevelopment (A1, A2, B1, B2) • Drivers oflandcoverchange • Land abandonment • Urban sprawl • Land useintensification

  4. Globalisation, High global economic growth but low Swiss growth (A1) Heterogeneous world, regionally centered growth, (comparatively) high economic growth for Switzerland (A2) Lessintervention ¨Drivingforces Population Economy …. More global More regional Globalisation but emphasis on services, high ecological concerns. Low Swiss growth (B1) Self-sufficiency, Regionally centered development, high ecological concerns (B2) More intervention

  5. Low population growth • Average technological innovation • Increased food importation • Low levels of policy-led restrictions on development • High population growth • High per capita urban demand • Low support for subsidies • Low to no policy-led restrictions on development Globalisation, High global economic growth but low Swiss growth (A1) Heterogeneous world, regionally centered growth, (comparatively) high economic growth for Switzerland (A2) Lessintervention ¨Drivingforces Population Economy …. More global More regional Globalisation but emphasis on services, high ecological concerns. Low Swiss growth (B1) Self-sufficiency, Regionally centered development, high ecological concerns (B2) More intervention • Medium population growth • Average technological innovation • Strong support for local agriculture • Strong policy-led restrictions on development • Strong support for subsidies • Very low - no population growth • Low technological innovation • Increased food importation • Policy-led restrictions on development • Support for subsidies

  6. Base dataset Swiss land-use statistics (Arealstatistik der Schweiz) • Aerial photography interpretation • 100m grid = each point represents 1ha • 72 categories of land-use/cover in theme areas • Settlement and urban • Agricultural areas • Wooded areas • Unproductive • 3 time points • 1979/85 • 1992/97 • 2004/2009 1997 2009 1985

  7. Land use/land cover types classification Closed Canopy Forest Open Forest/ Scrub Overgrown Areas Urban Areas PastureAgriculture ArableAgriculture

  8. Land coverchangescenarios • Agriculturalchange • Land abandonment, marginal open areastoforest • Agriculturalintensification • Urbanisation • high densityhousing • newsettlements Arealstatistik 1985 1997 Land usedemand Initial State 2009 Land-usesuitability 1ha resolution Dyna-CLUEModellingframework (P. Verburg, University of Amsterdam) Land usesuitability Mapsof land-usechangescenarios Environmental data

  9. Explanatory variables • Biogeographical (Static, 1ha) • Continentality index CSD/DEM25 (Zimmermann & Kienast 1999) • Yearly moisture index CSD/DEM25 (Zimmermann & Kienast 1999) • Yearly direct solar radiation CSD/DEM25 (Zimmermann & Kienast 1999) • Precipitation average growing season CSD/DEM25 (Zimmermann & Kienast 1999) • No. of summer precipitation days CSD/DEM25 (Zimmermann & Kienast 1999) • Elevation DEM100 • Slope DEM100 • Sine of aspect (east) DEM100 • Cos of aspect (north) DEM100 • Soil permeability Soil suitability maps BLW 2012 • Soil stoniness Soil suitability maps BLW 2012 • Soil suitability for agriculture Soil suitability maps BLW 2012 • Socio-economic (temporally variable, per Gemeinde) • Taxable income per tax paying resident Federal Office for Statistics • Percentage inhabitants employed in primary sector Federal Office for Statistics • Public Transport accessibility Federal Office for Spatial Planning • Infrastructure (temporally variable, 1ha) • Distance to major roads Vector25 • Distance to access roads Vector25 • Neighbourhood variables • No. of neighbours in classes (Urban, closed forest, agriculture) • Distance to forest

  10. Model suitability for land use type Explanatory variables/ Environmental data Landcover (AS) Logistic Regression Modelling Maximum Entropy Random Forests Land usesuitability

  11. Logistic regression • Cross correlationanalysis, removalofhighlycorrelatedexplanatory variables • Sampling withineach land-use type • Unequalacrosslandcovertypes • ~5% of total points • Sampling presenceandabsence • Minimum 1km apart toavoidspatialautocorrelationissues • Small classes (overgrown) fewersamples • Capturingwithinclassvariability – geographicaland environmental space • Model averaging • Every combinationofexplanatory variables to find best fit model (AIC) • Averagingprocesstodeterminecoefficientforeachexplanatory variable

  12. Quantification of Scenarios • Population growth scenarios defined by the Swiss Federal Statistics Office • Per capita urban demand (Swiss Federal Statistics Office) • Mean • Upper and lower 95% CI bounds • Agricultural demand related to population and level of imports • Land cover change restrictions representing policy and planning • Conversion restrictions • Spatial restrictions • Common to all scenarios • Forests and current National Parks/protected areas are ‘sacred’

  13. Quantificationof Scenarios Trend Scenario • Linear Interpolation of 1985-1997-2009 trend in growth (orreduction) oflanduseclasses A1 (Global/Low Intervention) • BfS ‘Low’ populationgrowthscenario, mean urban areademand per capita • Nofurtherspatialrestrictions A2 (Regional/Low Intervention) • BfS ‘High’ populationgrowthscenario, high urban areademand per capita • Weightingof urban suitabilitytoreflectimprovedpublictransportconnectivity in regional areas • Nofurtherspatialrestrictions B1 (Global/High Intervention) • ‘Stagnation’ scenarioforpopulationgrowth (nogrowth), low urban areademand per capita • Restrictions on urbanisationthroughexisitingbuildingzones (‘Bauzone’) • Restrictions on conversionfrompasturetoovergrownabove 900m asl • IncreaseddemandforAgriculture B2 (Regional/High Intervention) • BfS ‘Medium’ populationgrowthscenario, mean per captia urban areademand • Restrictions on conversionfrompasturetoovergrownabove 900m asl • Weightingof urban suitabilitytoreflectimprovedpublictransportconnectivity in regional areas • Urban growthpermitted outside of ‘Bauzone’ -regionalisation

  14. A1 Global/low intervention

  15. Maps

  16. Results: land cover transitions Land abandonment Urbanisation Reforestation

  17. Summary Key Results • Strongest scenario is A2 (regionalisation, low intervention) • Strong trend to urban sprawl, especially in lowlands • Land use intensification in lowlands • Land abandonment in Alps • Concentration of growth despite weighting for regionalisation • Population growth is key driver of land cover change, but • Planning/Policy restrictions can have mitigating control • Conservation policy to prevent land abandonment • Building zone controls

  18. Swiss Federal Institute for Snow, ForestandLandscaperesearch, WSL Thanks Questions?

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