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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 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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  1. Measuring Allocation Errors in Land Change Models in Amazonia Luiz Diniz, MerretBuurman, Pedro Andrade, Gilberto Câmara, EdzerPebesma MerretBuurmanGeoInfo, Campos do Jordão, 25 November 2013

  2. Measuring Allocation Errors in Land Change Models in Amazonia LuizDiniz MerretBuurman Pedro Andrade Gilberto Câmara EdzerPebesma +

  3. „Why?“

  4. Land changemodelling • Simulation • 2001 • 2002 • 2003 • 2004 • Observed reality

  5. Land changemodelling • 2004 Bigresponsability Need toevaluateresults This canonlybedoneafterwards!

  6. (1) Goodnessof fit metric (2) Evaluation ofmodels

  7. (1) Goodnessof fit metric

  8. 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.

  9. 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.

  10. Multiple resolutions

  11. Multiple resolutions

  12. Multiple resolutions

  13. Multiple resolutions

  14. Multiple resolutions

  15. Multiple resolutions

  16. Multiple resolutions

  17. Multiple resolutions

  18. Twocomplementaryviews… Costanza:Multiple resolutions Pontius et al.:Need toconsiderpersistence

  19. Twocomplementaryviews… Costanza:Multiple resolutions Pontius et al.:Need toconsiderpersistence

  20. Need toconsiderpersistence Manycases: Most oftheareadoes not change Focus: Predictingthechangedarea Example: 99% oftheareaunchanged All thechangepredictedatwronglocations  98 % oftheareais „correct“!

  21. … Combinedintoone Change-focusing multiple-resolution goodnessof fit

  22. What do weevaluate?

  23. What do weevaluate?

  24. What do weevaluate? Equaltotal amount!

  25. Goodnessof fit metric • (1) Inside samplingwindow: Computethedifference in amountofchangebetweenbothgrids

  26. Goodnessof fit metric (2) Sumthisupfor all samplingwindows

  27. Goodnessof fit metric • (3) Dividebytwicethe total amountofchange • Whytwice? In theprevioussteps, every „wrong“ allocation was countedtwice, becausetoomuchchange in onecellautomaticallymeanstoolittlechange in another, due totheequalityofdemand in bothgrids.

  28. Goodnessof fit metric (4) Subtractfromonetogetgoodness … andrepeatfor all otherresolutions

  29. 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)

  30. (2) Evaluation ofmodels

  31. 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.

  32. 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)

  33. Why so weak? Neighborhoodmodel: capturesonlyexistingregions (not newfrontiers) SimilarityNeighborhoodmodel & SimAmazonia: Same reason?  Comparemaps!

  34. Why so weak? Neighborhoodmodel: capturesonlyexistingregions (not newfrontiers) SimilarityNeighborhoodmodel & SimAmazonia: Same reason?  Comparemaps! Yes! Location ofnewfrontiersdifficulttopredict

  35. Why so weak? • Laurance • Overestimatesroads • Assumes same impactofroadseverywhere • Underestimatesprotectedareas

  36. Parque do Xingu Indigenousareas (FUNAI)

  37. Conclusion Predictingthelocationsoffuturedeforestation:More difficultthanexpected! Problem: Policyrecommendationbased on thosepredictions Ourhope: Next generationofdeforestationmodels will capturebetterthecomplex human decision-making

  38. Conclusion Predictingthelocationsoffuturedeforestation:More difficultthanexpected! Problem: Policyrecommendationbased on thosepredictions Ourhope: Next generationofdeforestationmodels will capturebetterthecomplex human decision-making Obrigada!

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