Post on 20-Dec-2015
transcript
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/