Project reorganization (Swiss part)

TASK 7&8: Bronwyn (content), Achilleas (tech. support), Janine (senior expert)

TASK 9 tbd UG project coordination/ communication/

administration until end of FORECOM

Forest cover time series Swiss Alpsfirst results

Time series based on historical maps for -SA (1850/1880/1940/[1970]/current)

Trends and trajectories Test reliability

Maps (the Swiss Alps)o Dufour Map Original Survey (~1850, scale 1:25 000 – 1:50 000) o Siegfried Map (edition 1880 and 1940, scale 1:25 000 – 1:50 000)o Landeskarte der Schweiz (1970s and current state, 1: 25 000)

0 40000 80000 120000 160000

1880-1940

0 40000 80000 120000 160000

1940-2012

0 40000 80000 120000 160000

1850-1880

(ha)

persistent

loss

increase

Forest transition

GR GL URI OW NW tot0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

forest cover 1850 forest cover 1880

forest cover 1940 forest cover 2012

GR

GL

URIOWNW

porti

on o

f tot

al la

ndsc

ape

Historic map comparison: Methodological challenges

Test for consistency:

• Minimal Mapping Unit• Reliability of trajectories• Comparison with

independent sources

Trajectory (1850-1880-1940-2012)

Portion of forest in 2012

1-1-1-1 48.7% 87.9%

0-1-1-1 8.2%

0-0-1-1 8.8%

0-0-0-1 23.4%

1-0-0-1 6.9% 94.7%

1-0-1-1 3.0% 99.4%

1-1-0-1 1.7%

0-1-0-1 0.6% 100%

OrthophotoHistorical Map Terrestrial Photo

Comparison-spatial overlay-identification of error types

Comparison-qualitative assessment-areas with good/bad agreement

Hypothesis generation-topography-morphologyHypothesis test

Application- Accurracy map large extent

Vectorization of forest cover information

TASK 6: Drivers of past forest cover change concept and first results for the Swiss Alps

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

1 ha raster (n=970’000)Target variable:Loss/gain (binary)

Administrative units • Communities (n=199)• Districts (n=15)• Cantons (n=5)Target variable:change in forest proportion (abs/rel)

context climate/topography

socioeconomics

Test different combinations of drivers at different spatial resolutions

Potential drivers

Scale of analysis

Topographical Data

parameter unit calculation/transformation source status

Altitude m asl DHM100 ready

Slope degreecalculated from DHM 100 ready

Norhtness (-1,1)

cos ((aspect in degrees * PI)/180) calculated from

DHM 100 ready

Eastness (-1,1)

sin ((aspect in degrees * PI)/180) calculated from

DHM 100 ready

Socioeconomic Data• An extensive sample of socioeconomic data has been compiled

for all 199 communities within FORECOM study area by Marc Herrmann (data to be jointly used in AlpPast/FORDYNCH and FORECOM)

• Parameters include information on population (inc. Age distribution), accessibility (road/railway), agriculture, employment sectors, commuters etc.

• Not full set of parameters available for all periods (most go back to 1930)

• Transferability of approach and comparability -> identify minimal set of parameters available for CH and PL

Socioeconomic Data

population agriculture economy accessibility

N people N farmsemployees per econ sector By railroad (0/1)

Age classes (0-14/15-60/60+) Farming area

By road (major roads only)

Animals (LU/small cattles)

Selection based on hypothesisAvailability Poland ?

Context Data

Contextual variables include information that is determined by location. Some variables are clearly related to the biological system (distance to forest edge) others to socio-economy (distance to road/settlement)

Climate DataBasic data set (1931-2010)

Monthly temperature and precipitation downscaled to 100m resolution

Historical data (1850-1930)• Calculate anomalies to reconstructed historical time series; monthly

temperature (Luterbacher), seasonal precipitation (seasonal,Pauling).• Spatial Interpolation (100m grid)

Final data (1850-2010)Mean values for temperature and precipitation (periods same as for fcc)

Mean annual DDsum

context climate/topo

socioeconomics

Test different combinations at different scales

1 ha raster (n=969’700)

Target variable: forest loss/forest gain

Administrative units

Target variable: change in forest cover proportion

Drivers

Drivers of forest gain

OM-sieg1st (1850-1880)

sieg1st-sieglast (1880-1940)

sieglast_today (1940-2010)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Adj D

2

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

Target variable: forest gain (yes/no)Explanatory variables: exposition (northeness/eastness), altitude, slope,

distance to forest edge at 1st time step, distance to settlement

Drivers of forest lossAd

j D2

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

Target variable: forest gain (yes/no)Explanatory variables: exposition (northeness/eastness), altitude, slope,

distance to forest edge at 1st time step, distance to settlement

OM-sieg1st (1850-1880)

sieg1st-sieglast (1880-1940)

sieglast_today (1940-2010)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Explaining forest cover by topography and previous forest cover?

Adj D

2

Model: GLM (binomial) stepwise, sample 10’000 of all pixels Target variable: forest (yes/no)Explanatory variables: exposition (northeness/eastness), altitude, slope,

forest cover at previous time step

OM (1850)

sieg1st (1880)

sieglast (1940)

today (2010)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

topo only

incl previous fcover

Problem of spatial autocorrelation

Example modelling gain 1850-1880

Sample size 10’000 -> 819

Model performance (Adj D2)0.35 -> 0.3

2 km distance threshold

context climate/topo

socioeconomics

Test different combinations at different scales

1 ha raster (n=969’700)

Target variable: forest loss/forest gain

Administrative units

Target variable: change in forest cover proportion

Drivers

Appropriate admin unit?

-40 -20 0 20 40 60 80 100 120-100

-500

50100150200250300350400

R² = 0.0694504293352704

0.000 0.100 0.200 0.300 0.400 0.500 0.600-0.500

0.000

0.500

1.000

1.500

2.000

R² = 0.388800825199352

Forest cover vs. Population change ( relative changes 1940-2010)

Communities (n=199) Districts (n=15)

Relatively strong correlation with proportion of older people (60+) at district level

Fore

st c

over

cha

nge

population change

Does population changes drive forest cover change?

• What is the appropriate resolution (admin unit)?

• Absolute vs relative changes (fc and pc)• Time lag between pop change and forest

cover change?