An Internet Tool For F orecasting Land Use Change And Land Degradation In The Mediterranean Region

1. An Internet Tool For Forecasting Land Use Change And Land Degradation In The Mediterranean Region Richard Kingston & Andy Turner University of Leeds UK

2. 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

3. 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 desertification http://www.icis.unimaas.nl/medaction/

4. The problem • Increasing desertification in the Mediterranean regionis 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

5. 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

6. 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

7. Work Packages our work

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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...socio-economic data need to be interpolated

14. The land use predictors • soil type • soil quality • biomass • temperature • precipitation • height above sea level • population density

15. Climatic Biomass Potential Height above Sea level

16. 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 possible trees then land degradation is unlikely barren then land degradation is serious other land use then land degradation is probable

17. 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 changescenarios and input data and view the effects on land use change and land degradation

18. 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

19. Step 1: Choose data and view in the on-line map viewer Step 2: Run model choose between model types Step 5: Run another scenario? Neural Net Fuzzy Logic Step 3: Obtain Results Not Satisfied? Step 4: Submit Results to policy makes Satisfied?

20. 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 usepredictors • e.g. soil type • e.g. biomass data

21. 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

23. 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/

24. 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!

25. Here’s an example from some previous work

26. 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

27. 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

28. Further details MedAction http://www.ccg.leeds.ac.uk/medaction/ richard@geog.leeds.ac.uk a.turner@geog.leeds.ac.uk Java GIS http://geotools.sourceforge.net/ http://mapkenzie.sourceforge.net/ Other examples http:/www.ccg.leeds.ac.uk/atomic/