griscom_rel_e_kali

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  • Let’s consider reference region method 2 because:Seems to be relatively accurate.Pretty intuitive (even though some geeky stats).Facilitates nesting to national scale.Might encourage competition to reduce deforestation rates.
  • griscom_rel_e_kali

    1. 1.
    2. 2. REDDeX Conference, Cancun, July 14, 2010<br />Comparison of REL Methods for Districts of East Kalimantan, Indonesia.<br />Bronson Griscom, Sr. Scientist Forest Carbon<br />John Kerkering, Conservation Analyst<br />
    3. 3. Question: What is the most accurate method for predicting the amount of deforestation within districts of East Kalimantan?<br />
    4. 4. Alternative methods for predicting deforestation (i.e. REL) at sub-national scale<br />Complex<br />Simple<br />Historical Rate<br />(with adjustments)<br />Forward Looking<br />Historical Rate<br />of project area<br />1<br /><ul><li>Non-spatially explicit model (e.g. population-forest fraction)
    5. 5. Trend analysis.
    6. 6. Rate derived from “reference region.”
    7. 7. Spatially explicit modeling</li></ul>3<br />“Planned” <br />(e.g. legal license <br />to log/convert)<br />2<br />
    8. 8. 1<br />Predicted deforestation in each district = <br />Historic rate in each district<br />2000<br />2005<br />
    9. 9. 2<br />Predicted deforestation in each district = <br />Historic rate of reference region <br />…where reference regions are determined by cluster analysis<br />Cluster<br />Anal.<br />
    10. 10. 3<br />Predicted deforestation in each district = <br />Modeled future rate in each district <br />…using spatially explicit model at regional (province) scale.<br />
    11. 11. Here’s how…<br />3<br />Prior Deforestation<br />Vulnerability<br />LCM<br /><ul><li>neural network</li></ul>2000<br /><ul><li>no dynamic variables</li></ul>“Driver” Variables<br />2005<br />Projections<br />2020<br />2015<br />dist. roads<br />dist. converted areas<br />spatial plan<br />soils<br />forest types<br />slope<br />dist. sawmills<br />dist. towns<br />dist. cities<br />topography<br />2009<br />Note: projections assume historic rate at province scale<br />dist. navigable rivers<br />
    12. 12. Selection of Model “Drivers”<br />3<br />
    13. 13. Selection of Model “Drivers”<br />3<br />
    14. 14. 3<br />Model Performance<br />
    15. 15. Comparison of Three Methods<br />Predicted area deforested from<br />2006-2009<br />minus<br />Actual area <br />deforested from<br />2006-2009<br />(as % of actual area deforested)<br />3<br />2<br />1<br />
    16. 16. Comparison of Three Methods<br />3<br />2<br />1<br />
    17. 17. Comparison of Three Methods<br />3<br />2<br />1<br />
    18. 18. Why do cluster reference regions <br />seem to work?<br />2<br />
    19. 19. Question: What is the most accurate method for predicting the amount of deforestation within districts of East Kalimantan?<br />
    20. 20. <ul><li>More complex doesn’t mean better.
    21. 21. I suggest reference region method </li></ul>2<br />
    22. 22. Nested RELs<br />National Scale:<br />Historic mean, with negotiated adjustments?<br />Sub-National Scale<br />(e.g. State/Province):<br />Modeled projection, to determine proportion of national emissions pie? Separate models for deforestation vs. degradation?<br />Project Scale:<br />Mixed. Modeled projection for unplanned events? Book-keeping for planned events / strategies?<br />
    23. 23. thanks!<br />Bronson Griscom<br />bgriscom@tnc.org<br />

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