An Internet Tool For Forecasting Land Use Change And Land Degradation

In The Mediterranean Region

Richard Kingston & Andy Turner

University of LeedsUK

The presentation

• Some background• The problem• Aims and objectives• Work packages• Building a common spatial framework• Land use predictors• Building the web interface• Conclusions & next stages

Background

• MedAction funded by the EU– Fifth framework program– Key Action 2: Energy, Environment

and Sustainable Development

• Specifically looking at:– Policies for land use to combat

desertificationhttp://www.icis.unimaas.nl/medaction/

The problem

• Increasing desertification in the Mediterranean region is having a direct impact upon land use

• It is largely a society-driven problem combated by various EC agricultural subsidies

• A lack of coordinated action across the Mediterranean has led to a patchwork of policy actions

Overall aims and objectives

• The EC have decided that we need to:– Develop land use change scenarios at

various scales– Analyse effects of past policies in four target

areas– Analyse the costs of land degradation and

benefits of mitigation measures– Develop options for land use policies,

mitigation strategies, and incentives to combat desertification

Specific aims and objectives

• Develop a scenario based integrated land use and land degradation prediction model

• Develop an interactive internet interface to the modelling system and associated data

• Encourage experts, policy makers and the public to use the on-line modelling system and develop the way it operates, its functionality and its capabilities based on feedback from these users

Work Packages

our our workwork

Interconnections between MEDACTION Modules and Work Packages

WP 1.1European and Mediterranean

scenariosWP 4.5

Focus groupson land mana-

gement and mitigation

WP 3.1Decision Support System

WP 3.2Policy

Support System

Alentejo, Agri

Lesvos,Guadalentín

WP 2.1-2.6Comparative

analysis of policy

impacts on desertification

WP 3.3Internet tool for (EU) planners

WP 4.1-4.3Policy analysis at European scale

WP 1.3Integrated

cost-benefit analysisat various scales

M1 Development of land use change scenarios at various scales (pressure)

M3 Effects of regional land use scenarios in target areas (state)

M2 Effects of past land use policies in target areas (impact)

WP 4.4Synthesis

Development and communication of a Desertification Policy Guidance Framework

M4 Integrated policy development to combat desertification (response)

WP 1.2Regional scenarios

Work Package 3.3

• Develop an internet interface to an existing stand-alone modelling system that– allows users to select which variables

to include– enables them to try out different

types of model– search for and evaluate available data

with respect to the modelling tasks

Previous research

• Developed a means of estimating the likely impacts of climate change on agricultural land use and land degradation

• In order to– gain and raise awareness of the problems– inform political and public debate– have a way of contributing to the

development of mitigation strategies

Previous modelling challenge

• To predict contemporary agricultural land use based on a range of climatic, physical and socio-economic indicators

• Forecast the various indicators for some time in the future in order to forecast land use and provide a land use change scenario

• Translate land use change scenarios into land degradation indicators

• Combine land degradation indicators to produce a synoptic forecast of land degradation

What was required

•Highest possible level of spatial resolution•Complete coverage over the Mediterranean climate region of the EU

•Produce forecasts for about 50 years hence•Base the results on global climate change scenarios•Incorporate socio-economic data•Produce outputs as maps•Provide a modelling framework that could be refined as better data and understanding of the processes is gained

• something we are doing now

Creating the common spatial framework

• Step 1: Assemble a database of all relevant physical, socio-economic & environmental data

• Step 2: Model the relationships between land use and other data assembled

• Step 3: Obtain and make forecasts of the data• Step 4: Create and analyse maps of changes• Step 5: Translate the changes into land

degradation risk indicators• Step 6: Repeat forecasting based on different

climate change scenarios

Assembling the data

• Decided upon a grid at a 1-decimal-minute resolution with a fixed origin aligned in terms of latitude and longitude covering the entire Mediterranean climate region of the EU

• Manipulating available source data into the framework involved the use of GIS operations and/or modelling applications– Most environmental data could be manipulated

into it in a relatively straight forward manner– BUT...BUT... socio-economic data need to be

interpolated

The land use predictors

• soil type• soil quality• biomass• temperature• precipitation• height above sea level• population density

Height above Sea level

Climatic Biomass Potential

Predicting future land use

• An example rule• If a high proportion of land use

estimated/predicted now is arable and a high proportion of estimated/forecast future land use is:arable then land degradation is possibletrees then land degradation is unlikelybarren then land degradation is seriousother land use then land degradation is probable

Building a web interface

• WP 3.3 main aim is to develop a Web interface to the existing stand-alone prototype modelling system

– allow the viewing of available input data and existing model results

– allow users to alter climate change scenarios and input data and view the effects on land use change and land degradation

Work so far

• On-line data viewer– allows users to view relevant spatial

data– meta data

• Developing web-based GIS– allows users to decide on input

variables– model type

Step 1: Choose data and view in the on-line map viewer

Step 2: Run model

choose between model types

Step 3: Obtain Results

Step 5: Run another scenario?

Step 4: Submit Results to policy makes

Neural Net Fuzzy Logic

Satisfied?

Not Satisfied?

Datasets library

• Split into – socio-economic land use predictors

• e.g. distance to nearest built-up area• e.g. frequency of night-time lights

observation

– physical land use predictors• e.g. soil type• e.g. biomass data

The Data Viewer

• Extracts relevant gif image and associated meta data– drop down lists of data types– data for

• now• 50 years in the future

Web enabled GIS

• Developing in house GIS

– Java based open source– vector and raster capabilities– runs on the Web or stand alone

• http://mapkenzie.sourceforge.net/

The modelling interface

• After using the data library users then– select which variables to include– enables them to try out different types of

model• Neural networks for classification• Fuzzy logic based for subjective interpretation

– view results– re-run with different data-sets and/or models

• The modelling interface still has to be developed!

Here’s an example from some previous work

Next stages

• Update the system with new – socio-economic– environmental– physical data

• Develop the Web-based interface• Develop the modelling system• Allow users to add their own data

Conclusions

• This work is still in its early stages• Results will only be good enough to enlighten

debate – not control policy• It is a first step towards providing wider access

to land degradation data and models• It has the potential to open up the decision

making process to those who are interested• It provide an example web-based tool for

planners, decision makers and citizens interested in visualising the consequences of environmental change

Further details

MedActionhttp://www.ccg.leeds.ac.uk/medaction/richard@geog.leeds.ac.uka.turner@geog.leeds.ac.uk

Java GIShttp://geotools.sourceforge.net/http://mapkenzie.sourceforge.net/

Other exampleshttp:/www.ccg.leeds.ac.uk/atomic/