The effect of Land Use Cover Change model resolution on scale of aggregation AAG Meeting _ March 2006 Amélie Davis & Dr. Bryan Pijanowski

March 8, 2006 AAG MEETING Chicago, Illinois Outline ► Agricultural expansion scenarios for East African countries based on Land Transformation Model (LTM) results and UN Population estimates ► Urbanization scenarios based on Multi- Criteria Evaluation (MCE) and UN Population estimates ► Scaling issues and aggregation errors

March 8, 2006 AAG MEETING Chicago, Illinois Outline (for me) Show ag expansion movie (1km) Urb exp  new technique _ MCE _ best suited because only base map, urban cells are minimal when compared to non urban cell background  patterns hard to capture : Solutions: use 90m cell resolution MCE steps Show NRB Dar es sallam examples Problems of scales with patterns and aggregation

March 8, 2006 AAG MEETING Chicago, Illinois LTM for Agricultural Expansion Land use change forecast model using neural networks Forecast Agricultural expansion based on future UN population estimates Drivers:___

March 8, 2006 AAG MEETING Chicago, Illinois Diagram of Steps Figure out which drivers to include Figure out the number or urban cells per country Calculate ratio people per Ag Cells in landscape Use ratio to extrapolate pop urban to 2050 by 5 year increments Run LTM on East Africa to forecast agricultural expansion  pick best PCM Project Agricultural use using LTM neural network outputs Mask out water bodies, parks and agriculture Include Urban expansion (use different method)

March 8, 2006 AAG MEETING Chicago, Illinois More details on steps For partial countries, use percentage of the total acreage present on landuse base UN pop data comes in 5 year increments  put data from 1950 to 2030 into SPSS do a curve fit  plug back for yearly data. 3 countries have no urban cells in land use base: Mozambique, Ethiopia, and Central African Republic. Somalia has 4 cells  all 4 countries aren’t modeled for urban. Central Af. Republic has no ag  no change Too few urban cells compared to entire landscape  carve out boxes around urban clusters, run that.  use percentage of urban in those boxes compared to entire country

March 8, 2006 AAG MEETING Chicago, Illinois Drivers _ below for Nairobi Dist to Capital Dist Towns + villages Dist Roads_a Dist to Roads_c Dist Roads_b fcl sum urb

March 8, 2006 AAG MEETING Chicago, Illinois Agriculture Forecast: 2005 (left) 2030 (right)

March 8, 2006 AAG MEETING Chicago, Illinois Results for East Africa 2005 to 2030 in 5year increments Notice that Uganda “runs out” of agricultural space by 2020

March 8, 2006 AAG MEETING Chicago, Illinois Spatial Resolution Run LTM on the capitals of Uganda, Tanzania and Kenya At 250m resolution (scaled up from 90m spatial resolution) AND at resolutions of 100m, 200m, 300m… 1km  effect on accuracy?

March 8, 2006 AAG MEETING Chicago, Illinois Spatial Patterns Nairobi KampalaDar Es Salaam Aggregated Dispersed Clumped

March 8, 2006 AAG MEETING Chicago, Illinois LTM Results Visualized  Best results for the tight knit urban landscape (Dar Es Salaam) PCM:63% Kappa:0.61 PCM:42% Kappa:0.39 PCM:79% Kappa:0.79

March 8, 2006 AAG MEETING Chicago, Illinois LTM Accuracy PCM = number of urban cells which are precited at same location on LTM run and original land use map. Lowest PCM for most fragmented urban landscape (Kampala, Uganda) Highest for most compact city (Dar Es Salaam, Tanzania)

March 8, 2006 AAG MEETING Chicago, Illinois Problem of Scales _ for Nairobi Lowest MSE for 750m Highest for 650m Highest (65.66%) to lowest (62.5%) PCMs at cycles: 350m, 550, 600, 250, 150, 300, 200, 1000, 500, 650, 850, 450, 800, 100, 700, 750, 400, 900 (lowest)  no stand-out pattern

March 8, 2006 AAG MEETING Chicago, Illinois Mini-Conclusions Surprisingly spatial resolution change has no apparent effect Certain land uses (here urban) are too scarce in some landscapes to be modeled with the potential model (one land use compared to land use change), the number of zeros overwhelm the LTM.  Need new approach / tools

March 8, 2006 AAG MEETING Chicago, Illinois Urbanization Simulation Use Multi-Criterion Evaluation Based on 1983 book by Voogd & 1991 paper by Carver et al. Also part of IDRISI software Similar to LTM in the use of drivers Uses expert judgment to weigh input drivers Simple implementation in GIS

March 8, 2006 AAG MEETING Chicago, Illinois 6 drivers for Urbanization Distance to urban Focal sum of urban close Focal sum of urban far Distance to roads a roads b roads c

March 8, 2006 AAG MEETING Chicago, Illinois Standardization of drivers from zero to one with larger number being more likely to urbanize. Ex: dist to road: max - # / min-max Pair wise comparison of each driver to establish their weights. Ex: distance to urban is extremely more important when compared to distance to roads Based on ‘expert’ judgment Steps for MCE

March 8, 2006 AAG MEETING Chicago, Illinois Approximate weight calculation Implement them in raster calculator Ex: multiply your drivers by their weights. Make sure you exclude the cells which cannot urbanize (already urban, parks, lakes, ocean)  Problem city boundaries are too smooth. Resemble edges of focal sum drivers  Multiply those 2 drivers by a random map Weighing of Drivers Weighing of Drivers

March 8, 2006 AAG MEETING Chicago, Illinois

March 8, 2006 AAG MEETING Chicago, Illinois East Africa Urbanization MCE map Red/orange = greatest likelihood of urbanization Problems with Ethiopia and Mozambique not enough information in drivers

March 8, 2006 AAG MEETING Chicago, Illinois Urban in 2000

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2005

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2010

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2015

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2020

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2025

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2030

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2035

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2040

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2045

March 8, 2006 AAG MEETING Chicago, Illinois Urban Forecast for 2050

March 8, 2006 AAG MEETING Chicago, Illinois Same MCE but for Nairobi at 1km spatial resolution

March 8, 2006 AAG MEETING Chicago, Illinois 1km MCE Results Original urban in red Urbanization in green

March 8, 2006 AAG MEETING Chicago, Illinois Modeling Implications Visually maximum ‘smooth’ transitioning of urbanization at 1km seems to be 5 years Patterns of urbanization with same method but different spatial resolution are different  Which model do you use?  Should weights be reevaluated for different spatial resolutions? 1km resolution 90m resolution resampled at 1km

March 8, 2006 AAG MEETING Chicago, Illinois Scaling Issues Different Patterns Different information passed on to climate model

March 8, 2006 AAG MEETING Chicago, Illinois Fractional Cover Change

March 8, 2006 AAG MEETING Chicago, Illinois Original LULC Map

March 8, 2006 AAG MEETING Chicago, Illinois Conclusions and Next Steps Merge Urbanization with Agricultural expansion maps Study the interaction of different fractional covers of urban at varying spatial resolutions of the LULCC change map with disparate spatial resolution climate grid  which scale will be best suited to couple both models? How will shape and patterns within the landscape influence this coupling?

March 8, 2006 AAG MEETING Chicago, Illinois

March 8, 2006 AAG MEETING Chicago, Illinois

March 8, 2006 AAG MEETING Chicago, Illinois Next Steps Merge Urbanization with Agricultural expansion maps