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TASK 6: Estimation of climate change and land use contribution to past forest cover change Research aims: disentangling land use and climate effects for the past forest cover trajectories at different spatial and temporal scales Compare drivers in Swiss Alps and Polish Carpathians.

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slide1

TASK 6: Estimation of climate change and land use contribution to past forest cover change

Research aims:

  • disentangling land use and climate effects for the past forest cover trajectories at different spatial and temporal scales
  • Compare drivers in Swiss Alps and Polish Carpathians
slide2

Potential drivers

Scaleofanalysis

climate/

topography

context

100m raster/30 m raster

Target variable:

Loss/gain (binary)

  • Administrative units
  • Communes
  • Districts

Target variable:

change in forest proportion (abs/rel)

socioeconomics

Test different combinationsofdrivers at different spatialresolutions

socio economic d ata list of variables pl ch
Socio-economicdata – list of variables PL/CH
  • Not fullsetofvariables availablefor all periodsandstudyregions

Selectionbased on hypothesis/availability

socio economic database
Socio-economicdatabase
  • basic data:
      • shapefiles with cities and villagies boundaries (1850, 1931, 1970, 2011)
      • shapefiles with communesboundaries (1970, 2011)
  • socio-economic data:
      • tables with socio-economic data on level of cities and viligies (1850, 1931)
      • tables with socio-economic data on level of communes (1970, 2011)
socio economic database1
Socio-economicdatabase
  • Input data for models:

shapefile with socio-economicinformation on „communes” level (1850, 1931, 1970s, 2011)

slide7

Context Data

  • Contextual variables include information that are determined by their locatione.g.:
    • distance to existingforest (biological system)
    • distance to road/settlement (socio-economy)
climate data
Climate Data

Basic dataset(1931/1961-2010)

Monthlytemperatureandprecipitationdownscaledto 100m resolution

  • Historical data (1850-1930/1960)
  • CH: Calculateanomaliesto multi-proxy historical time series; monthlytemperature (Luterbacher), seasonalprecipitation (seasonal,Pauling).
  • PL: anomalieshistorical time seriesKrakow
  • Spatial Interpolation (100m/90 m grid)

Final data(1850-2010)

Meanvaluesfortemperatureandprecipitation (periods same asforfcc)

MeanannualDDsum

slide9

Correlation coefficient values: TT Krakowvs TT Carpathian Stations in January (1961-2010)

slide10

Analysis concept – CH/PL

  • analysisattwoscales:
    • Admin units (communities, districts)
    • 1 ha rastercells (CH) [~106 pixels], 30 m(PL) [~22*106 pixels]
  • General assumption:
  • climaticandtopographic variables play a role at smallerscales(pixels) whilesocio-economicsis relevant at larger scale (communes/districts)
  • Statistical approach: generalized linear regressionmodels (GLM)
drivers
Drivers

context

climate/topo

1 ha raster (n=969’700)

Target variable: forestloss/forestgain

Administrative units

Target variable: change in forestcoverproportion

socioeconomics

Test different combinationsat different scales

slide12

Drivers offorestgain

Model: GLM (binomial) stepwise, sample: 10’000 non- forestpixels at t1

Target variable: forestgain (yes/no)

Explanatory variables: exposition (northeness/eastness), altitude, slope, distancetoforestedgeat 1st time step, distancetosettlement

Adj D2

slide13

Drivers offorestloss

Model: GLM (binomial) stepwise, sample 10’000 forestpixels at t1

Target variable: forestgain (yes/no)

Explanatory variables: exposition (northeness/eastness), altitude, slope, distancetoforestedgeat 1st time step, distancetosettlement

Adj D2

explaining forest cover by topography and previous forest cover
Explainingforestcoverbytopographyandpreviousforestcover?

Model: GLM (binomial) stepwise, sample 10’000 of all pixels

Target variable: forest (yes/no)

Explanatory variables: exposition (northeness/eastness), altitude, slope, forestcover at previous time step

Adj D2

problem of spatial autocorrelation
Problem ofspatialautocorrelation

Examplemodellinggain 1850-1880

Sample size

10’000 -> 819

Model performance (Adj D2)

0.35 -> 0.3

Implementing

2 km distancethreshold

drivers1
Drivers

context

climate/topo

1 ha raster (n=969’700)

Target variable: forestloss/forestgain

Administrative units

Target variable: change in forestcoverproportion

socioeconomics

Test different combinationsat different scales

appropriate admin unit
Appropriateadminunit?

Forestcover vs. Population change ( relative changes 1940-2010)

Communities (n=199)

Districts (n=15)

Forestcoverchange

populationchange

correlationwithproportionofolderpeople (60+) at districtlevel

conclusions and open questions
Conclusionsand open questions
  • Approach allowsforcomparingspatialscales,periods.

-> changingexplanatory power of variables/ setsof variables over time andspace

  • Sampling: all points, random, stratified, autocorrelation
  • Absolute vs. relative change (forestcover/socioecon variables)
  • Takingintoaccount lag effects?