Measuring allocation errors in land change models in amazonia
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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

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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 changemodelling

  • Simulation

  • 2001

  • 2002

  • 2003

  • 2004

  • Observed reality


Land changemodelling

  • 2004

Bigresponsability

Need toevaluateresults

This canonlybedoneafterwards!


(1) Goodnessof fit metric

(2) Evaluation ofmodels


(1) Goodnessof fit metric


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.


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 resolutions


Multiple resolutions


Multiple resolutions


Multiple resolutions


Multiple resolutions


Multiple resolutions


Twocomplementaryviews…

Costanza:Multiple resolutions

Pontius et al.:Need toconsiderpersistence


Twocomplementaryviews…

Costanza:Multiple resolutions

Pontius et al.:Need toconsiderpersistence


Need toconsiderpersistence

Manycases: Most oftheareadoes not change

Focus: Predictingthechangedarea

Example:

99% oftheareaunchanged

All thechangepredictedatwronglocations

 98 % oftheareais „correct“!


… Combinedintoone

Change-focusing multiple-resolution goodnessof fit


What do weevaluate?


What do weevaluate?


What do weevaluate?

Equaltotal

amount!


Goodnessof fit metric

  • (1) Inside samplingwindow: Computethedifference in amountofchangebetweenbothgrids


Goodnessof fit metric

(2) Sumthisupfor all samplingwindows


Goodnessof fit metric

  • (3) Dividebytwicethe total amountofchange

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


Goodnessof fit metric

(4) Subtractfromonetogetgoodness

… andrepeatfor all otherresolutions


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)


(2) Evaluation ofmodels


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.


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?

Neighborhoodmodel: capturesonlyexistingregions (not newfrontiers)

SimilarityNeighborhoodmodel & SimAmazonia: Same reason?  Comparemaps!


Why so weak?

Neighborhoodmodel: capturesonlyexistingregions (not newfrontiers)

SimilarityNeighborhoodmodel & SimAmazonia: Same reason?  Comparemaps!

Yes! Location ofnewfrontiersdifficulttopredict


Why so weak?

  • Laurance

    • Overestimatesroads

    • Assumes same impactofroadseverywhere

    • Underestimatesprotectedareas


Parque

do Xingu

Indigenousareas (FUNAI)


Conclusion

Predictingthelocationsoffuturedeforestation:More difficultthanexpected!

Problem: Policyrecommendationbased on thosepredictions

Ourhope: Next generationofdeforestationmodels will capturebetterthecomplex human decision-making


Conclusion

Predictingthelocationsoffuturedeforestation:More difficultthanexpected!

Problem: Policyrecommendationbased on thosepredictions

Ourhope: Next generationofdeforestationmodels will capturebetterthecomplex human decision-making

Obrigada!


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