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Measuring Allocation Errors in Land Change Models in Amazonia. Luiz Diniz, Merret Buurman , Pedro Andrade, Gilberto Câmara , Edzer Pebesma. Merret Buurman GeoInfo , Campos do Jordão , 25 November 2013. Measuring Allocation Errors in Land Change Models in Amazonia. Luiz Diniz

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Measuring allocation errors in land change models in amazonia

Measuring Allocation Errors in Land Change Models in Amazonia

Luiz Diniz, MerretBuurman, Pedro Andrade, Gilberto Câmara, EdzerPebesma

MerretBuurmanGeoInfo, Campos do Jordão, 25 November 2013


Measuring Allocation Errors in Land Change Models in Amazonia

LuizDiniz

MerretBuurman

Pedro Andrade

Gilberto Câmara

EdzerPebesma

+


Why?“


Land change modelling
Land changemodelling

  • Simulation

  • 2001

  • 2002

  • 2003

  • 2004

  • Observed reality


Land change modelling1
Land changemodelling

  • 2004

Bigresponsability

Need toevaluateresults

This canonlybedoneafterwards!


(1) Goodnessof fit metric

(2) Evaluation ofmodels


(1) Goodnessof fit metric


Two complementary views
Twocomplementaryviews…

Costanza:Multiple resolutions

Pontius et al.:Need toconsiderpersistence

Costanza, R., Model Goodness of Fit - a Multiple Resolution Procedure.

EcologicalModelling, 1989. 47(3-4): p. 199-215.

Pontius Jr, R.G., E. Shusas, and M. McEachern, Detecting important categorical

land changes while accounting for persistence. Agriculture, Ecosystems &

Environment, 2004. 101(2): p. 251-268.


Two complementary views1
Twocomplementaryviews…

Costanza:Multiple resolutions

Pontius et al.:Need toconsiderpersistence

Costanza, R., Model Goodness of Fit - a Multiple Resolution Procedure.

EcologicalModelling, 1989. 47(3-4): p. 199-215.

Pontius Jr, R.G., E. Shusas, and M. McEachern, Detecting important categorical

land changes while accounting for persistence. Agriculture, Ecosystems &

Environment, 2004. 101(2): p. 251-268.


Multiple resolutions
Multiple resolutions


Multiple resolutions1
Multiple resolutions


Multiple resolutions2
Multiple resolutions


Multiple resolutions3
Multiple resolutions


Multiple resolutions4
Multiple resolutions


Multiple resolutions5
Multiple resolutions


Multiple resolutions6
Multiple resolutions


Multiple resolutions7
Multiple resolutions


Two complementary views2
Twocomplementaryviews…

Costanza:Multiple resolutions

Pontius et al.:Need toconsiderpersistence


Two complementary views3
Twocomplementaryviews…

Costanza:Multiple resolutions

Pontius et al.:Need toconsiderpersistence


Need to consider persistence
Need toconsiderpersistence

Manycases: Most oftheareadoes not change

Focus: Predictingthechangedarea

Example:

99% oftheareaunchanged

All thechangepredictedatwronglocations

 98 % oftheareais „correct“!


Combined into one
Combinedintoone

Change-focusing multiple-resolution goodnessof fit


What do we evaluate
What do weevaluate?


What do we evaluate1
What do weevaluate?


What do we evaluate2
What do weevaluate?

Equaltotal

amount!


Goodness of fit metric
Goodnessof fit metric

  • (1) Inside samplingwindow: Computethedifference in amountofchangebetweenbothgrids


Goodness of fit metric1
Goodnessof fit metric

(2) Sumthisupfor all samplingwindows


Goodness of fit metric2
Goodnessof fit metric

  • (3) Dividebytwicethe total amountofchange

    • Whytwice? In theprevioussteps, every „wrong“ allocation was countedtwice, becausetoomuchchange in onecellautomaticallymeanstoolittlechange in another, due totheequalityofdemand in bothgrids.


Goodness of fit metric3
Goodnessof fit metric

(4) Subtractfromonetogetgoodness

… andrepeatfor all otherresolutions


Goodness of fit metric4
Goodnessof fit metric

Fw= Goodness of fit at resolution w.

tw= Number of sampling windows at resolution w.

w= Resolution (a sampling window has w2cells).

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)



Models
Models

SimAmazonia

2001  2050

BAU and GOV

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


Models1
Models

SimAmazonia

2001  2050

BAU and GOV

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

Laurance

2000  2020

Optimistic

Non-Opt.

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

438-439.

 Comparewith PRODES 2011 (25x25km)


Why so weak
Why so weak?

Neighborhoodmodel: capturesonlyexistingregions (not newfrontiers)

SimilarityNeighborhoodmodel & SimAmazonia: Same reason?  Comparemaps!


Why so weak1
Why so weak?

Neighborhoodmodel: capturesonlyexistingregions (not newfrontiers)

SimilarityNeighborhoodmodel & SimAmazonia: Same reason?  Comparemaps!

Yes! Location ofnewfrontiersdifficulttopredict


Why so weak2
Why so weak?

  • Laurance

    • Overestimatesroads

    • Assumes same impactofroadseverywhere

    • Underestimatesprotectedareas


Parque

do Xingu

Indigenousareas (FUNAI)


Conclusion
Conclusion

Predictingthelocationsoffuturedeforestation:More difficultthanexpected!

Problem: Policyrecommendationbased on thosepredictions

Ourhope: Next generationofdeforestationmodels will capturebetterthecomplex human decision-making


Conclusion1
Conclusion

Predictingthelocationsoffuturedeforestation:More difficultthanexpected!

Problem: Policyrecommendationbased on thosepredictions

Ourhope: Next generationofdeforestationmodels will capturebetterthecomplex human decision-making

Obrigada!


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