Measuring Allocation Errors in Land Change Models in Amazonia

Measuring Allocation Errors in Land Change Models in Amazonia

Luiz Diniz, Merret Buurman, Pedro Andrade, Gilberto Câmara, Edzer Pebesma

Merret BuurmanGeoInfo, Campos do Jordão, 25 November 2013

Luiz Diniz Merret Buurman Pedro Andrade Gilberto Câmara Edzer Pebesma

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Measuring Allocation Errors in Land Change Models in Amazonia

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Merret Buurman, 26.11.2013

„Why?“

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Land change modellingSimulation

Observed reality2001

2002

2003

2004

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Land change modelling

Big responsabilityNeed to evaluate resultsThis can only be done afterwards!

2004

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(1) Goodness of fit metric

(2) Evaluation of models

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(1) Goodness of fit metric

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Two complementary views…

Costanza:Multiple

resolutions

Pontius et al.:Need to consider

persistence

Pontius Jr, R.G., E. Shusas, and M. McEachern, Detecting important categoricalland changes while accounting for persistence. Agriculture, Ecosystems &Environment, 2004. 101(2): p. 251-268.

Costanza, R., Model Goodness of Fit - a Multiple Resolution Procedure.Ecological Modelling, 1989. 47(3-4): p. 199-215.

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Costanza:Multiple

resolutions


persistence

Pontius Jr, R.G., E. Shusas, and M. McEachern, Detecting important categoricalland changes while accounting for persistence. Agriculture, Ecosystems &Environment, 2004. 101(2): p. 251-268.

Costanza, R., Model Goodness of Fit - a Multiple Resolution Procedure.Ecological Modelling, 1989. 47(3-4): p. 199-215.

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Multiple resolutions

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Costanza:Multiple

resolutions


persistence

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Costanza:Multiple

resolutions


persistence

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Need to consider persistence

• Many cases: Most of the area does not change• Focus: Predicting the changed area

Example:• 99% of the area unchanged• All the change predicted at wrong locations• 98 % of the area is „correct“!

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… Combined into one

Change-focusing multiple-resolution

goodness of fit

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What do we evaluate?

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Equal total amount!

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Goodness of fit metric

• (1) Inside sampling window: Compute the difference in amount of change between both grids

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• (2) Sum this up for all sampling windows

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• (3) Divide by twice the total amount of change– Why twice? In the previous steps, every „wrong“

allocation was counted twice, because too much change in one cell automatically means too little change in another, due to the equality of demand in both grids.

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• (4) Subtract from one to get goodness

• … and repeat for all other resolutions

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• Fw = Goodness of fit at resolution w.

• tw = Number of sampling windows at resolution w.• w = Resolution (a sampling window has w2 cells).• arefi = Percent of change in land cover in cell i in the reference cell space.

• amodj = Change in land use/land cover in cell j in the model cell space.• i, j = Cells inside a sampling window.• u = Cells inside the cell space.• s = A sampling window.• num = Number of cells in the cell space (tw * w2)

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(2) Evaluation of models

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Models

SimAmazonia 2001 2050

BAU and GOVSoares-Filho, B., et al., Modelling conservation in the Amazon basin. Nature, 2006. 440(7083): p. 520-523.

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Models

SimAmazonia 2001 2050

BAU and GOVSoares-Filho, B., et al., Modelling conservation in the Amazon basin. Nature, 2006. 440(7083): p. 520-523.

Laurance 2000 2020Optimistic Non-Opt.

Laurance, W., et al., The future of the Brazilian Amazon. Science, 2001. 291: p.438-439.

Compare with PRODES 2011 (25x25km)

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Why so weak?

• Neighborhood model: captures only existing regions (not new frontiers)

• Similarity Neighborhood model & SimAmazonia: Same reason? Compare maps!

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Why so weak?

• Neighborhood model: captures only existing regions (not new frontiers)

• Similarity Neighborhood model & SimAmazonia: Same reason? Compare maps!

• Yes! Location of new frontiers difficult to predict

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Why so weak?

• Laurance• Overestimates roads• Assumes same impact of roads everywhere• Underestimates protected areas

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Indigenous areas (FUNAI)

Parquedo Xingu

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Conclusion

• Predicting the locations of future deforestation:More difficult than expected!

• Problem: Policy recommendation based on those predictions

• Our hope: Next generation of deforestation models will capture better the complex human decision-making

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Conclusion

• Predicting the locations of future deforestation:More difficult than expected!

• Problem: Policy recommendation based on those predictions

• Our hope: Next generation of deforestation models will capture better the complex human decision-making

Obrigada!

Measuring Allocation Errors in Land Change Models in Amazonia

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