Land use modelling in switzerland using land u se s tatistic data
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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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Land use modelling in switzerland using land u se s tatistic data

Land‐use modelling in Switzerlandusing land use statistic data


Context

Context

  • Socio-economicprocessesare strong driversof land-usechangeacross Europe

  • Land abandonmenthasbeen a dominant process

  • Urbanisation isincreasing at a rapid rate

  • Increasingpushestowardsrenewableenergysources

    =

  • Unknownextentandlocationoflandusechanges

  • Unknownimpact on landscapeservices


Land cover change scenarios

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


Land use modelling in switzerland using land u se s tatistic data

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


Land use modelling in switzerland using land u se s tatistic data

  • 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


Base dataset

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


Land use land cover types classification

Land use/land cover types classification

Closed Canopy Forest

Open Forest/ Scrub

Overgrown

Areas

Urban

Areas

PastureAgriculture

ArableAgriculture


Land use modelling in switzerland using land u se s tatistic data

  • 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


Explanatory variables

Explanatory variables

  • Biogeographical (Static, 1ha)

    • Continentality indexCSD/DEM25 (Zimmermann & Kienast 1999)

    • Yearly moisture index CSD/DEM25 (Zimmermann & Kienast 1999)

    • Yearly direct solar radiation CSD/DEM25 (Zimmermann & Kienast 1999)

    • Precipitation average growing seasonCSD/DEM25 (Zimmermann & Kienast 1999)

    • No. of summer precipitation daysCSD/DEM25 (Zimmermann & Kienast 1999)

    • ElevationDEM100

    • SlopeDEM100

    • Sine of aspect (east)DEM100

    • Cos of aspect (north)DEM100

    • Soil permeabilitySoil suitability maps BLW 2012

    • Soil stoninessSoil suitability maps BLW 2012

    • Soil suitability for agricultureSoil suitability maps BLW 2012

  • Socio-economic (temporally variable, per Gemeinde)

    • Taxable income per tax paying residentFederal Office for Statistics

    • Percentage inhabitants employed in primary sectorFederal Office for Statistics

    • Public Transport accessibilityFederal Office for Spatial Planning

  • Infrastructure (temporally variable, 1ha)

    • Distance to major roadsVector25

    • Distance to access roadsVector25

  • Neighbourhood variables

    • No. of neighbours in classes (Urban, closed forest, agriculture)

    • Distance to forest


Model suitability for land use type

Model suitability for land use type

Explanatory variables/ Environmental

data

Landcover (AS)

Logistic

Regression

Modelling

Maximum

Entropy

Random

Forests

Land usesuitability


Logistic regression

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


Quantification of scenarios

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’


Land use modelling in switzerland using land u se s tatistic data

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


Land use modelling in switzerland using land u se s tatistic data

A1 Global/low intervention


Land use modelling in switzerland using land u se s tatistic data

Maps


Results land cover transitions

Results: land cover transitions

Land abandonment

Urbanisation

Reforestation


Summary key results

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


Thanks

Swiss Federal Institute for Snow, ForestandLandscaperesearch, WSL

Thanks

Questions?


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