1. Mapping the future Converting storylines to maps Nasser Olwero GMP, Bangkok April 2-6 2012

2. Scenarios They are not necessarily predictions Scenarios – Narratives of alternative environments in which today’s decisions may be played out (Adam Gordon – futuresavvy.net) – alternative pathways into the future – Plausible future – “What if” analysis

3. Introduction Landcover change scenarios Modeling landcover change – Determined by factors, but has a random component Need for scenarios tool in InVEST Terrestrial vs Marine Tier 0?

4. Land cover change modeling Quantity of change – How much is changing? – Stakeholder storylines Transition probabilities – Statistical methods – regression, Bayesian probabilities…. – Artificial intelligence – artificial neural networks – Stakeholder estimates Factors – spatial and environmental characteristics Decision rules – Drivers, actors influence – Eg development can occur only in areas less than 35% slope Constraints Transition procedures

5. What are the storylines? Landcover TypesChangeRules Broadleaved tree plantationincrease along roads, in poor soils, on hilltops, difficult to cultivate areas, in and around cfrs & lfrs, Coniferous plantationincrease along roads, in poor soils, on hilltops, difficult to cultivate areas, in and around cfrs & lfrs, Tropical high forestincreasein and around cfrs and lfrs, not in nps Degraded forestdecreasein and around cfrs and lfrs, not in nps Woodlandincreaseoutside pas

6. How do you move from story to map? Landcover TypesChangeRules Broadleaved tree plantationincrease along roads, in poor soils, on hilltops, difficult to cultivate areas, in and around cfrs & lfrs, Coniferous plantationincrease along roads, in poor soils, on hilltops, difficult to cultivate areas, in and around cfrs & lfrs, Tropical high forestincrease in and around cfrs and lfrs, not in nps Degraded forestdecrease in and around cfrs and lfrs, not in nps Woodlandincreaseoutside pas ?

7. Land cover transition (Swetnam et al 2010)

8. Land cover transition ForestAgricultureGrassland built 7 2 1 3 1 Original ForestAgricultureGrasslandBuilt Scenario New cover

9. Objectives: Cover as Objective Land cover change is driven by an objective and the objective determines the decision rule Objective can be complimentary or conflicting Single objective modeling is simpler than multi-objective Examples of objectives are: agricultural development, conservation, urbanization etc In this model, the cover type is used as a proxy for the objective. Cover types that increase represent some objectives

10. Factors Factors are criteria that increases or reduces the suitability of a parcel for a specific objective Proximity attributes that determine where change occurs – Roads [transportation, ] – Rivers [transportation, proximity to water] – Slope [access, ag suitability] – Cities [market, population pressure] Rules combined added to attributes creates a suitability layer

11. Constraints Limit the alternatives Create exceptions [‘no go’] Constraints can be simple (specific areas cannot be affected) or more complex (eg minimum area required for large scale agriculture) Constraints have varied degrees of porosity – 0 – no change – 1 – no effect on change Multiple constraint layers combined by taking the minimum value An example is protected areas

12. Criteria Evaluation Land cover change analysis is a decision strategy analysis Developing scenarios based on decision strategy helps make them more realistic and plausible

13. Multi Criteria Evaluation Slope Factors Weights w1w1 Dist to Roads Elevation w2w2 w3w3 < 35%< 5km> 1000m x 1 (std 0-1) X 2 (std 0-1) X 3 (std 0-1) Suitability(x 1 w 1 x2w2x2w2 x 2 w 2 ) * Constraints ++ = pixels with suitability values above threshold are converted Decision Rules Assigned by AHP Threshold User sets goal of conversion quantities Addition of likelihood matrix and Overrides

14. Calculating weights with AHP Its easy to assign weight for a few criteria but with more criteria, it becomes problematic AHP is used to assign weights by comparing two factors at a time – much easier A comparison matrix is prepared Eigenvectors used to compute the weights In this model AHP is used both for weighting criteria and objectives (cover types)

15. AHP in a nutshell Objective: Find areas to convert for agriculture Criteria: Soils, Rainfall, Distance to roads, Distance to Market How do you weight them? Compare each 2! SoilsRainfallRoadsMarketWeight Soils10.2856 Rainfall2/110.4525 Roads1/21/410.0965 Market1/3 3/110.1654

16. The Matrix Forest Grassland Agriculture Urban Change ProximityProximity distance Priority /Forest0172-30%000 Grassland0031-40%000 Agriculture0000501102 Urban000010151 LocationQuantity GAIN LOSS

17. Preparing Suitabilitylayers Likelihood(+) Factors(+) Proximity (+) Constraints (x) weighted Aggregate transition probability/suitability Rules

18. AHP Tool

19. Factors tool

20. Scenario Generator

21. Changing the pixel Process each cover in order of priority Calculate quantity of change from % change given in matrix Convert pixels starting from suitability values 10 down to 1 until target area change is attained If number of pixels required is less than number available in the group, select randomly. Clumping algorithm used.

22. Limitations/Issues Subjective – depends on numbers entered by stakeholders Model grows cover, doesn’t shrink Does not directly account for drivers Model assumes a cover type either increases or decreases but not both Assumes a single step transition Cover grows as a % of existing cover

23. Virungas Example Current BAU Market Green

24. Virungas Example