the use of model, gis and remote sensed data in the society · landscape approach mirrors these...

The use of model, GIS and remote sensed data in the society –

examples from land use planning and flood management

Dagmar Haase Department of Computational Landscape Ecology

METIER Training Lecture, Helsinki, November 6, 2008

Main items of the lecture

1.

Introduction2.

GIS and models in landscape and environmental planning

Example A: Floodplain forest analysis Example B: Urban habitat models for planningExample C: Water balance and land use policy responseExample D: Integrated flood risk analysisExample E: Use of historical data for landscape planning

3.

Synthesis

A

B

C

D

E

Introduction …

about my work

Major challenges for landscape management, resource protectionand land use related research are processes and pattern of

Global change (climate change, emissions, resource exploitationdemographic change and urbanisation a.o.)

Hot spots of change: (Mega)Cities, rural landscapes in Europe

Landscape approach mirrors these challenges since it is an integrative approach

My work bases on the ideas of a.o. Naveh (2001), Brandt (2001), Wu & Hobbs (2002), Ravetz (2000), Müller et al., (2007).

Modern landscape research involves both natural and social sciencecomponents, qualitative and quantitative methodologies.

Major research questions

What are the main drivers of land use and lansdscape change today, in the next future, and

how can we learn from historic changes?

How do form, pattern and heterogeneity of land uses affect environmental performance, ecosystem services and resource availability?

What models and tools can we apply to contribute to land use planning, river basin and flood(plain) management?

Discovery from data

Gilbert et al. (2009)

DataData

Selection

TargetTarget datadata

Preprocessing

ProcessedProcessed datadata

Transformation

TransformedTransformed datadata

Data

Mining

KnowledgeKnowledge patternspatterns

Interpretation/ Evaluation

KnowledgeKnowledge

Understan- ding goals

Data Selection

Cleaning Reduction

DM task algorithms parameters

Use Provision

Geospatial analytical tools

Joima et al. (2009)

Computational cartography

Statistical

computingSpatial

StatisticsVisualisation

Mapping Querying Transformations Descriptive

summaries

Raster algebra Cartographic

modellingNetwork

analysisSpatial

interpolation

Terrain analysis

Hydrological

analysisSpatial

data

miningSpatial

modelling

Geospatial

image processing

Web GISArcGIS SAGA GIS

SELES 3.2beta R Repast

Working on cycles and causalities Example: Land consumption and land cycling policy

TRANSFORMATION OF LAND USES

IMPACT ecosystems, water fluxes, soil, economy, quality of

life, political pressure

PRESSURE STATE

ASSESS- MENT of impacts

natural RESPONSE

GOVERNANCE Intervention by policy making

and planning

modified RESPONSE

DRIVERS economy, demography, living

habits, land use policies, planning instruments

GIS and models in landscape research: how we apply it

GIS data in planning

Spatially explicit or non-explicit model

Output data = spatial shape of the simulation result

Spatial sh

ape

of dynam

ics

and change

Modelling:

neighbourhood relations

of cells; HRU’s etc

Spatial shape

of a problem

or a component

( )

( )∑

∑

=

=

∗

∗∗= n

iii

n

iiii

GWKAK

verGWKAKF

1

1

filtering capacity

References

McIntosh, B.S., Giupponi, C., Voinov, A.A., Smith, C., Matthews, K.B., Monticino, M., Kolkman, M.J., Crossman, N., van Ittersum, M., Haase, D., Haase, A., Mysiak, J., Groot, J.C.J., Sieber, S., Verweij, P., Quinn, N., Waeger, P., Gaber, N., Hepting, D., Scholten, H., Sulis, A., van Delden, H., Gaddis, E., Assaf, H. 2009. Bridging the gap: developing tools for environmental policy and management, In: Jakeman, T., Rizzoli, A., Voinov, A. & Chen (eds.) 2009. State of the Art and Futures in Environmental Modelling and Software, Elsevier.

Nuissl, H., Haase, D., Wittmer, H., Lanzendorf, M. 2008. Impact assessment of land use transition in urban areas –

an integrated approach

from an environmental perspective. Land Use Policy, doi:10.1016/j.landusepol.2008.05.006.

Example A: Remnants of floodplain forests?

A GIS model approach.

A

B

C

D

E

Urban forests –

just remnants?

A

B

C

D

E

Urban forests –

just remnants?

How does urbanisation impact the extent of floodplain forests (F)?

Determinants: Groundwater (GW), Topography (T), Sediment (S), Land use (LU using the proxy impervious cover)

GIS-Model:

),,,( LUGWTSfF =

)LUGW( utm21 ++−= >°>TSF

when

Haase & Gläser (submitted)

A

B

C

D

E

Urban forests –

just remnants?Classification criteria


A

B

C

D

E

Criterium Flood

sediment

Topography Ground-

water

level

Land use Natural-

ness

Inclusion

criterion

(threshold)

Occurrence no relief energy, difference to the surrounding terraces

<2.0 meters

(Vega/

Gley soils)

floodplain forest, wetlands, urban green spaces, parks, sports and leisure grounds, cemeteries

typical and relatively typical; low degree of imper-

viousness

Exclusion

criterion

(threshold)

No occurrence

relief

energy>5%

>2.0 meters

allotments, farmland, waste-

land, ruderal and succession areas, built-up areas (housing, transport, trade and industry)

relatively untypical and non typical; high degree of imper-

viousness

Range 0 …

1 0 …

5% 0 …

2.0 m


A

B

C

D

E

Urban forests –

just remnants?


A

B

C

D

E

A

B

C

D

E

References

Haase, D. 2003. Holocene floodplains and their distribution in urban areas –

functionality indicators for their retention potentials.

Landscape & Urban Planning 66, 5-18.

Haase, D., Gläser, J. Determinants of floodplain forest development illustrated by the example of the floodplain forest in the District of Leipzig. Forest Ecology and Management.

Ecosystem Services provided by urban forests

Ecosystem service Indicators Model / Tool

Proxy

Water retention water

flow

[m3/s] ABIMO, Messer f(LU, S, T, GW)

Filter ppm, ppt, mm Soil

grain

size

Matter fixation ppm, ppt Soil

grain

size

Buffering acidity, salinity ANC, BNC CaCO3

, [OH-]

Habitat No of species Biomapper

Gene Pool No of native species Statistics

Climate

regulation T [K], ETP-flow T-Index, ABIMO f(Land

use)

Recreation area, accessibility GIS, Indicés area, length

A

B

C

D

E

Climate regulation

CO2

Global scalesequestration/storage of greenhouse gases

solar radiation

latent heat

Local scalereflectance evapotranspiration

A

B

C

D

E

Climate regulation

Indicator: Climate Regulation represented by temperature value

Each land use type is assigned an average surface temperature taken from literature. The values are validated with thermal images for Leipzig.

Land use Temperature index

Continuous

urban fabric 1.2

Discontinuous

urban fabric 1.1

Industrial or

commerical

units 1.2

Forest 1.0

Parks 0.9

Water 0.8

(Kottmeier et al., 2007)

A

B

C

D

E

Climate regulation

0 5 10 15 202,5

Kilometers

¯

Temperature index0.8

1.0

1.1

1.2

Since we assume that forest land use can reduce air temperatures best by emitting “sensitive” water flows in form of water vapor the index shows how much higher the land surface temperatures of any land use x are compared to forest

Model:

Index = T (land use x) / T (forest)

A

B

C

D

E

Conclusions for Example A

Geospatial data and GIS are suitable data bases and tools to answer questions of landscape planners, water managersand urban foresters.

For implementation of the concept of Ecosystem Services the mapping of urban forests (re)gains importance.

GIS serves a a very suitable tool for visualising geospatialand landscape (ecological) and resource related contexts.

Land use data provide a helpful basis for model transfer on e.g. Ecosystem Services.

A

B

C

D

E

Example B: Urban habitat modeling and

planning of green infrastructure

A

B

C

D

E

Faunistic issues are rarely considered in urban landscape planning.

We find today an increasing isolation of urban habitats (… and >75% of urban inhabitants in the EU).

Aim: To incorporate species related nature protection and habitat suitability evaluation in urban planning innovative methodologies are necessary that work with minimum data requirements.

Hypothesis: Distribution and occurrence of species requires a defined parameter setting – although many organisms have a tolerance to their environment, the space is limited.

How habitat modeling supports planning?

A

B

C

D

E

ENFA (Ecological Niche Factor Analysis)

Ecological Niche = ∑

cells with a certain probability of presenceData ...

for the species (Presence/Absence data)Boolean values0 = species does not occur (absent)1 = species occurs (present)

for the environment (EGV)continuous values of every variable

fundamental niche: Hyper volume in a n-dimensional space given though environmental parameter settings (Hutchinson, 1957)

environmental parameter settings are specific for every species

physiologic optimum (Walter, 1970)

A

B

C

D

E

Sylvia communis

open/fallow land

Picus viridus

urban green, parks, forest

Bufo bufo

urban greenspaces

test species for an urban environment

Quercus robur

wetland, nutrient richness

A

B

C

D

E

urban structure fauna: species

parks, fallow land birds (Picus viridus)

GIS: ArcView, ArcInfo, Erdas Imagine, FRAGSTATS, Biomapper

ENFA (Ecological Niche Factor Analysis)

Methodology to create HS-maps

HS-maps: classification of the HSI summing up all cells with median values than: HSI: 0 ≤

HSI ≤

1

probability of the species occurrencefor DS in urban landscape planning

A

B

C

D

E

Methodological approach

Vorher: (Auflösung 2 x 2 m) Nachher: (Auflösung 30 x 30 m)

Rasterkonvertierung (SUMMARY)

Berechnung neuer Rasterwerte (Mittelwertsberechnung – „Mean“)30 m

30 m

30 m

30 m

Raster - „fishnet“ (30 x 30 m)2x2m 30x30m

SUMMARY MEAN

GIS and data integration


Spatial input data

A

B

C

D

E


Landscape metrics (LSM), FRAGSTATS

A

B

C

D

E


Biomapper tool (Hirzel, 2001)

A

B

C

D

E

Picus viridus – urban parks, forest

Sylvia communis – urban fallow land

A

B

C

D

E

Picus viridus

urban green, parks, forest

Model Predictability: 67 -

71 %

Sylvia communis

open/fallow land

A

B

C

D

E

Priority setting for urban structures?

Picus viridus Picus viridus

A

B

C

D

E

Land use configuration (ED) most important for Green Woodpecker

Habitat requirements of Green Woodpecker and Whitethroat highly contrast; they cover green and brown sites in the city.

ENFA represents a comfortable procedure to quantify habitat preferences for different species using presence data.

Biomapper is suitable tool for the assessment of urban structures and greenery concerning HS.

Advantage for planning purposes: GIS-implementation and visualization as HS-map-series for different scales

Conclusions for Example B

A

B

C

D

E

A

B

C

D

E

References

Strohbach, M., Kabisch, N. Haase, D. Birds and the city -

urban biodiversity, land use and social status. Urban Ecosystems.

Example C: Does urban sprawl drive changes in

the water balance and policy?

A

B

C

D

E

Drivers, Pressures, State, Impact and Response

DriversDrivers

PressuresPressures

StateStateImpactImpact

ResponseResponse

V: demand for urban sprawl depending on demographic and economic dynamics as well as formal (law) and informal (norms, values) institutions

V: land use change from non urban to urban land use

V: land cover and share of

imperviousness

V: water balance in form of sealing rate, groundwater recharge, ETP and surface run-off

V: reactions by authorities and civil society

Target systems

A

B

C

D

E

How do ∆ land use and degree of imperviousness impact the urban water balance?

How do we parameterise urban water balance models?

What are the major changes of the urban water balance in the long-term and thus of interestfor landscape planning?

Major questions

A

B

C

D

E

Methodology for determining the long-term urban water balance

Effective Evapotranspiration

(employing the Bagrov

relation)

Groundwater recharge (calculated using ABIMO

model 1997)

Runoff regulation (assessment after

MESSER 1997)

Land use (historical and current

topographic maps)

Climate data

(1x1km, DWD*)Grain size, field

capacity (soil maps 1:50.000, 200.000)

Slope

(digital terrain

model

40x40m)

Groundwater level depth (geol. map)

Scanning, geo-

referencing,

digitalisation, error correction

Derivation of model

input

parameters

GIS: Intersection, merge

data

Application

of assessment

tools

Result:

Estimation of the effects of land use, intensification and surfacing on water balance

and surface-runoff

Classifying land use; derivation of degrees of

sealed surfaces (after MÜNCHOW 1999)

Selection of water balance related

processes

Data requirements and stock of data

Data processing

Determining changes to land use and impervious land

A

B

C

D

E

Modelling: vertical flow

grain size/

field capacity

groundwater level

potential Evapotranspiration

(ETp)

Precipitation (N)

Direct run-offAO = (N-ETa)*p/100

effective Evapotranspiration

(ETa)Percentage p of

direct run-off

slope

BAGROV-relation

efficiencyparameter n

Groundwater recharge

AU = N-ETa-AO

Land use/

degree of imperviousness

n

p

a

ETET

dPodETa

⎟⎟⎠

⎞⎜⎜⎝

⎛−=1

A

B

C

D

E

Mapping urban land use change

Land use classification and mapping

A

B

C

D

E

Imperviousness 1870-2003

1870 1940 1985 1997 2003

detailed 6.68 17.06 26.35 29.13 32.19classified 5.74 14.23 22.28 24.63 27.36

% of the total area

0

10

20

30

40

50

60

70

80

90

100

1870 1940 1985 1997 2003

o% im

perv

ious

ness

0

2

4

6

8

10

12

14

16

20-1

00%

impe

rvio

usne

ss

0 20 40

60 80 100

A

B

C

D

E

Imperviousness 2005

0 100

Imperviousness (%)

A

B

C

D

E

Direct runoff 1870 Direct runoff 1985

Direct runoff 1940 Direct runoff 2003

Direct runoff (mm/a)

waters

(Haase, submitted)

Recharge rate 1870 Recharge rate 1985

Recharge rate 1940 Recharge rate 2003

Recharge rate (mm/a)

waters

(Haase, submitted)

ETP 1870 ETP 1985

ETP 1940 ETP 2003

ETP (mm/a)

waters

(Haase, submitted)

Impact of sprawl: ∆

surface run-off and ∆

seeping rate 1985-2003

0

500000

1000000

1500000

2000000

2500000

3000000

1985 2003

Ao

Au

A

B

C

D

E

Long-term water balance 1870-2003

Year evapotranspiration [%] (1)

surface run-off [%] (1) seeping water rate [%] (1)

1940 100 100 100

1985 90 154 107

1997 87 170 106

2003 83 262 99

(1) referring to 1940 (1940 = 100 %)

Degree of imperviousness

area evapotrans-

piration

surface run-off seepage water rate

(%) (ha) (mm/a) (mm/a) (mm/a)

> 0 –

20 1111 351 –

550 1 -

150 51 –

300

> 20 –

40 626 251 –

450 51 -

250 51 –

250

> 40 –

60 2547 201 –

350 151 -

300 101 –

200

> 60 –

80 146 151 –

300 251 -

350 51 –

125

> 80 –

100 1842 151 –

200 351 -

450 1 –

75

Water balance of the newly sealed areas in Leipzig since 1940

A

B

C

D

E

Vertical and horizontal flow models show effects of land use, ∆ land use and degree of imperviousness on the long-term urban water balance.

There are suitable empirically based parameter sets/functions to parameterise physically based models also for urban areas. Uncertainty assessment is indispensable.

Major changes in the long-term water balance are the decrease of the run-off regulation capacity, an increase of Ao and a respective decrease of ETP which finally leads to a lowering of the water holding capacity of urban area, particularly green spaces and wetlands.

BUT: the study also shows that societal reactions on urban sprawl - first of all the attempts of both authorities and public initiatives to contain sprawl are hardly motivated or influenced by concerns about environmental problems …

… since they are working at the national level (30-ha-goal) and

that the environmental impact of sprawl elicits only indirect repercussions in society.

Conclusions for Example C

A

B

C

D

E

A

B

C

D

E

References

Haase, D., Nuissl, H., 2007. Does urban sprawl drive changes in the water balance and policy?

The case of Leipzig (Germany) 1870-

2003. Landscape and Urban Planning 80, 1-13.

Haase, D. Modelling the effects of long-term urban land use change on the urban water balance. Landscape and Environment.

Haase, D. Effects of urbanisation on the water balance –

a long- term trajectory. Environment Impact Assessment Review.

Example D: Integrated flood risk analysis and

assessment

A

B

C

D

E

Urban floods: the example of Dresden

A

B

C

D

E

Surface and groundwater level

A

B

C

D

EWater level

at the

gauge

„Augustus bridge“

and the

approximated

values

of a stationary

groundwater

model

Water level

of a groundwater

measuring

station

300 meters

away

(August 2002)

110 110

107107

Simulation of the superficial inundation of the city in August 2002 (hazardous flood in the entire Elbe river basin)

Coupling of groundwater, canalisation and surface water flows to determine mutual impacts

Scenario quantification

Major questions:

A

B

C

D

E

Approach 3ZM-GRIMEX

A

B

C

D

E

Task UFZ: Inundation modelling

A

B

C

D

E

Channel model

Groundwater model

DataData set Type Source Spatio-temporal

resolution

DEM Raster Environmental Agency Dresden 1 x 1m

DEM Raster Environmental Agency Dresden 50 x 50m

Valley bottom

topography Vector LVM Sachsen 2.5 –

20m

Buildings Vector Environmental Agency Dresden 1:10.000

Land use Vector Environmental Agency Dresden 1:10.000

Streamflow

of the

river

Elbe at different gauges

Time series Water board

Dresden (WSA DD) 01.01.2002 . 01.01.200415 minutes

interval

01.03.2006 -

15.05.200615 minutes

interval

Streamflow

of the

creeks

Müglitz, Weißeritz, Lockwitzbach

Time series Saxon

State Agency for

Geology

and Environment

01.08.2002 . 31.10.20021h intervall

Assigned

flooding

areas

(not

legally

binding)Vector Saxon State Agency for Geology

and Environment1 : 100,000

Legally

binding

flood

risk

zones Vector Environmental Agency Dresden 1 :10,000

Flooded

areas(detected

at 17.08.2002)Vector Environmental Agency Dresden 1 :10,000

Flooding

areas

based

on stationary

modelVector Environmental Agency Dresden 1 : 25,000

Dams Vector Environmental Agency Dresden 1 : 10,000

DataQuarterny aquifer

Urban Planning

GW model

local

models

Topography data

Elbe -

Laser-DGM (1x1m) Elbe -

DGM-W Elbe-South (5x5m)

Laser-DGM (1x1m) without buildungs Laser-DGM (1x1m) with buildings

A

B

C

D

E

Topographic and Land Use Data

A

B

C

D

E

Logic of water flow coupling

A

B

C

D

E

Logic of water flow coupling

surface, channel

groundwater

A

B

C

D

E

Logic of water flow coupling (2)

Definition of the coupling timing

Water Level of the Elbe

A

B

C

D

E

Inundation modelling 2D (space)

Maximum flooding

(flood

2002) using

surface

roughness

Manning‘s

of 0.2

Maximum flooding

(flood

2002) using

surface

roughness

Manning‘s

of

0.02

A

B

C

D

E

Inundation modelling 2D (space)

Simulation of the flood in August 2002 using TRIMR2D

Simulation of the inundated areas along the Weißeritz

creek within Dresden compared to the detected inundation area (City of Dresden).

A

B

C

D

E

Inundation modelling 2D (time)

A

B

C

D

E

Inundation modelling 2D (time)

103

105

107

109

111

113

115

Pegel m ü. NN diff_tudd 0022_004 0022_02 0022_002 004all

Uncertainty assessment

„Error Map“

–

normalised comparison of simulated and observed inundation areas. Dark blue = correct

(values < 0.1[>90%]).

)2002,(111 Aronican

daF

n

i

n

i∑∑==><

+= )2002,(

)(111

12 Aronicacba

aF n

i

n

i

n

i

n

i

∑∑∑

∑

===

=><

++= )2005,(

)(111

112 Hunterdcb

baF n

i

n

i

n

i

n

i

n

i

∑∑∑

∑∑

===

==><

++

−=

A

B

C

D

E

Impacts of groundwater flows

surface

canalisation

groundwater

A

B

C

D

E


Simulation using TrimR2D Groundwater reaction PCGEOFIM

A

B

C

D

E


A

B

C

D

E

WebGIS

“Mulde flood”

A

B

C

D

E

A

B

C

D

E

References

Meyer, V., Scheuer, S., Haase, D. 2008. A multi-criteria approach for flood risk mapping exemplified at the Mulde river, Germany. Natural Hazards, DOI: 10.1007/s11069-008-9244-4.

Haase, D., Weichel, T. & M. Volk (2003). Approaches towards the analysis and assessment of the disastrous floods in Germany in August 2002 and consequences for land use and retention areas.

Vaishar, A., Zapletalova, J. & J. Munzar

(eds.): Regional Geography and its Applications, proceedings of the 5th Moravian Geographical Conference CONGEO'03, 51-59.

Example E: GIS and model based analysis of

historical land use change as base for recent planning

A

B

C

D

E

Landscape change

Hypothesis: How have land use and landscape structure changed over historical periods along the rural-urban gradient?

How have these changes to the landscape affected the ecosystem pattern and biophysical processes, particularly water and nutrient fluxes as well as biodiversity (as services provided by landscapes)?

Can general trends be concluded regarding future changes to land use and its structuring over the next decades?

Local/regional land use change

Land use transition matrix

aggregation

detection

Land development history/scenarios

interpretation

Conceptual model and scales

Haase et al., 2007

A

B

C

D

E

Equidistant Map 1879 Survey Map 1927 Top. Map TK25 1997

Digital land use data sets

Georeferencing and editing of land use classes

Final data set

Overlaying polygons

Splitting polygons

class definition

class correction

analogue digital

GIS

Bringing historical maps into GIS

Haase et al., 2007

Regulation Function

(ecosystem service)

Land usetopographic

map 1:25.000

Soil indicessoil maps 1:25.000

and 1:200.000

ClimateDWD* 1x1km

raster

TopographyDigital Terrain Model 25x25m

Land usetopographic

map 1:25.000

Soil indicessoil maps 1:25.000

and 1:200.000

ClimateDWD* 1x1km

raster

TopographyDigital Terrain Model 25x25m

Ground Water Recharge

Model ABIMO (Glugla & Fürtig, 1997)

Run-off regulation

Marks et al. (1992)

Erosion ResistanceFunction

Marks et al. (1992)

Ground Water Recharge

Model ABIMO (Glugla & Fürtig, 1997)

Run-off regulation

Marks et al. (1992)

Erosion ResistanceFunction

Marks et al. (1992)

Land useclasses

Grain sizeGroundwater level

Field capacity precipitationevapotranspir.

slopecurvature

Land useclasses



slopecurvature

Land useclasses



slopecurvature

Landscape functions analysis

Haase et al., 2007

A

B

C

D

E

City of Leipzig 1870 –

2003

Visualisation of land use change

Haase et al., 2007

Test sites in Saxony

A

B

C

D

E

Visualisation of land use change (2)

Haase et al., 2007

A

B

C

D

E

Changes of the landscape structure

Haase et al., 2007

A

B

C

D

E

City of Dresden 1880-1998

Quantification of land use change

Haase et al., 2007

year Ground water recharge rate (mm)

1-25 26-50 51-75 76-100 101-150 151-200 201-250

1879 9 8 18 17 35 13 0

1927 9 9 19 17 31 14 1

1997 10 12 23 21 24 8 0

1927 1997

Change of landscape functionality 1879

Haase et al., 2007

Landscape and Land Use Change in a lignite mining area south to Leipzig:

Future landscape concepts are based on historical data and planners’ ideas.

Future Land Use Change in the Tisza River Basin due to Global Change.

Haase et al., 2007

Using the history for shaping the future

Historical maps can be integrated into a GIS to model/show land use change quantitatively over >200 years.

Statistics can then be compiled on the development of the proportions of linear elements and areas of certain usage types.

Interpretation and future planning of landscape development can be quantitatively substantiated.

Taking into account landscape functions in the assessment brings home how important it is to consider the ‘loss’ of not only land in the meaning of total area but also resources.

Quantification of long-term land use change and its environmental impact enables us to reduce the existing uncertainty to predict future landscape change in land use change or biophysical models which often limits planning.

Conclusions for Example D

A

B

C

D

E

A

B

C

D

E

References

Haase, D., Nuissl, H., 2007. Does urban sprawl drive changes in the water balance and policy?

The case of Leipzig (Germany) 1870-

2003. Landscape and Urban Planning 80, 1-13.

Haase, D., Walz, U., Neubert, M., Rosenberg, M. 2007. Changes to Saxon landscapes -

analysing historical maps to approach current

environmental issues. Land Use Policy 24, 248-263.

Main questions raised are in close connection to what concerns landscape change and resource planning or management.

The items touched come from different fields of landscape ecology and thus comprise different components and variables of the landscape in space and time.

Models and GIS had been presented as a kind of relational and mutually feeding methodologies that support applied research andprovide planning and land use policy with quantitative tools.

At the UFZ we recently incorporate agent behavior and heuristics of decision-making as a key to better understand land use changes (ABM, role games).

Synthesis

Thank you for the attention.

the use of model, gis and remote sensed data in the society · landscape approach mirrors these...

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