an assessment of deforestation models for reducing ... · introduction methodology results...

Introduction Methodology Results Conclusion Other information

An Assessment of Deforestation Models forReducing Emissions from Deforestation & forest

Degradation (REDD)A Case Study of Chiquitanıa, Bolivia

Oh Seok Kim

Department of Geography, University of Southern California

O Kim (Geography@USC) Deforestation Models for REDD 1


Background

What’s REDD?

I Carbon credit is a new monetary system [UN FCCC 1998].

I A carbon offset project must prove additional carbon benefitto generate carbon credits [IPCC 2000].

- Afforestation- Reforestation- REDD: Reducing Emissions from Deforestation & Degradation

I UN-REDD

- The United Nations Collaborative Programme on ReducingEmissions from Deforestation and Forest Degradation inDeveloping Countries [UN-REDD 2008].

- Website: http://www.un-redd.org



Background

What’s REDD?

I Carbon credit is a new monetary system [UN FCCC 1998].I A carbon offset project must prove additional carbon benefit

to generate carbon credits [IPCC 2000].

- Afforestation- Reforestation

- REDD: Reducing Emissions from Deforestation & Degradation

I UN-REDD





Background

What’s REDD?

I Carbon credit is a new monetary system [UN FCCC 1998].I A carbon offset project must prove additional carbon benefit

to generate carbon credits [IPCC 2000].

- Afforestation- Reforestation- REDD: Reducing Emissions from Deforestation & Degradation

I UN-REDD





Background

Why LUCC modeling?

I In the case of REDD, the additional carbon benefit iscalculated based on business-as-usual deforestation(i.e., baseline).

I Land-use and land-cover change (LUCC) modeling cansystematically simulate business-as-usual deforestation.

I Its result can be later combined with the correspondingcarbon content’s map.



Objectives

General scope of the research

1. This paper proposes a modular framework to assess accuracyof a spatially-explicit REDD baseline by demonstrating thecomparison of two LUCC modeling approaches.

2. It compares the performance of “GEOMOD Modeling (GM)”and “Land Change Modeler (LCM)” for simulating baselinedeforestation of multiple transitions based on model structureand predictive accuracy.



Objectives

Research questions

1. How do GM’s linear extrapolation and LCM’s Markov Chaincompare in terms of quantity of LUCC (for multiple transitionmodeling)?

2. How do GM’s empirical frequency, LCM’s logistic regression,and LCM’s multilayer perceptron compare in terms of(spatial) allocation of LUCC, measured by relative operatingcharacteristics, figure of merit, and multiple resolutionanalysis?



Study area and data

Chiquitanıa, Bolivia: land-cover of 1986,1994 & 2000

0 10 205Km

Persistent ForestPersistent SavannaForest Disturbed between 1986 and 1994Forest Disturbed between 1994 and 2000Savanna Disturbed between 1986 and 1994Savanna Disturbed between 1994 and 2000Persistent Anthropogenic DisturbanceOther Transitions between 1994 and 2000



Study area and data

Environmental variables

43,700 0

41,500 0

Distance from disturbance (meter) Distance from roads (meter)a b

13,600 0

124,500 0

Distance from streams (meter)c Distance from cities (meter)d

Elevation (meter)e Slope (degree)f

870 220

61 0



Strategy

Multiple transition modeling

I Transition refers to a process in which something undergoes achange from one land-type (e.g., forest) to another (e.g.,anthropogenic).

I There is a need of modeling multiple transitions in REDDprojects since there can be numerous transitions due todifferent types of land management.



Strategy

Calibration and validation

I The common essence of these validation processes isseparating data for calibration and validation.



Model structure

Quantity of LUCC

I Linear extrapolation (GM)

I Markov Chain (LCM)



Model structure

Allocation of LUCC (or transition potential map)

I Empirical frequency (EmpFreq) equation

- Is clear to interpret the output;- Has information of variables’ contributions;- Subjective input: number of bins/width of bins.

I Logistic Regression (LogReg) equation

- Is easier to interpret the output than MLP(i.e.,linear estimation);

- Has information of variables’ contributions;- Subjective input: statistical significance test.

I Multilayer perceptron (MLP)

- Is complex and often limits interpretation(i.e.,nonlinear & black box-like estimation);

- Has no information of variables’ contributions;- Subjective input: network training parameters.



Predictive accuracy assessment

Map comparison measurements

I Relative operating characteristic (ROC) ex. equation

- Assesses the predictive accuracy of a transition potential mapby comparing the transition potential map with a referencemap that shows the occurrence of binary events;

- Can be roughly summarized by an Area Under the ROC Curve(AUC).

I Figure of merit ex. equation

- Assesses the predictive accuracy of a simulated LUCC bysimply overlaying the land-cover map of 1994, the land-covermap of 2000 and the predicted map of 2000;

- Has both visual and numeric expressions (Figure of meritstatistics).



Predictive accuracy assessment

Map comparison measurements(con’d.)

I Multiple resolution analysis ex.

- Assesses how closely in space the simulated LUCC is to thereference LUCC;

- Compares each simulation map to its reference map byaggregating pixels to coarser resolutions;

- Assumes “persistence” as the prediction of future LUCC thatexperiences no change;

- Employs a “null resolution,” and more precise LUCCsimulations have finer null resolutions.



Quantity of LUCC

Linear extrapolation vs. Markov Chain

Linear extrapolation makes sense when there is only onetransition of land-cover change, e.g., undisturbed forest todisturbed forest.

Markov Chain is applicable for multiple transition modeling.



Allocation of LUCC

Transition potential: Forest to anthropogenic

0.930

0.930

0.930

a Empirical frequency b Logistic regression c Multilayer perceptron

0 10 205Km

0 10 205Km

0 10 205Km



Allocation of LUCC

Transition potential: Savanna to anthropogenic

0.770

0.770

0.770


0 10 205Km

0 10 205Km

0 10 205Km



Map comparison

ROC: Forest to anthropogenic logic



Map comparison

ROC: Forest to anthropogenic (Zoom-in)



Map comparison

ROC: Savanna to anthropogenic



Map comparison

ROC: Savanna to anthropogenic (Zoom-in)



Map comparison

Figure of merit logic

0 10 205Km

0 10 205Km

0 10 205Km

Error due to observed change predicted as persistenceCorrect due to observed change predicted as changeError due to observed persistence predicted as changeCorrect due to observed persistence predicted as persistence






Map comparison

Multiple resolution analysis: EmpFreq logic



Map comparison

Multiple resolution analysis: LogReg



Map comparison

Multiple resolution analysis: MLP



Summary

AUCs: Forest to anthropogenic

AUC AUCp AUCt

EmpFreq .800 .000274 .00444LogReg .791 .000304 .00477MLP .813 .000300 .00479



Summary

AUCs: Savanna to anthropogenic

AUC AUCp AUCt

EmpFreq .706 .001415 .01088LogReg .741 .001422 .01266MLP .703 .001034 .00772



Summary

Figure of merit & null resolution

Figure of merit Null resolution

EmpFreq 6.62% 2.4∼4.8kmLogReg 8.0% 1.2∼2.4kmMLP 6.57% about 2.4km



Revisit

Research questions

1. How do GM’s linear extrapolation and LCM’s Markov Chaincompare in terms of quantity of LUCC (for multiple transitionmodeling)?

2. How do GM’s empirical frequency, LCM’s logistic regression,and LCM’s multilayer perceptron compare in terms of(spatial) allocation of LUCC, measured by relative operatingcharacteristics, figure of merit, and multiple resolutionanalysis?



Conclusion

LCM is better than GM!

Based on the model structure and predictive accuracy comparisons,the LCM seems more suitable than the GM to construct a REDDbaseline when considering multiple transitions.

1. The embedded Markov Chain can estimate the quantity ofLUCC for multiple transitions, while it is challenging for linearextrapolation to do so;

2. The LCM’s LogReg has the highest predictive accuracy inmost cases (it is important to note that MLP contains astochastic element).



Discussion

Predictive accuracy

I From a land change scientist’s point of view, the differences,measured by ROC, figure of merit, or multiple resolutionanalysis, must be statistically tested to check out howsignificantly they differ.

I It is invalid to employ a traditional t test or Mann-Whitney Utest to measure such differences since those are notindependent samples of observations.



Discussion

Carbon implication

I Most likely, a carbon mass map of rainforests will contain aconfidence interval to indicate its measurement precision.

I If the difference (in terms of carbon mass) between two LUCCmodeling approaches falls into the confidence interval of thecarbon mass measured then it seems reasonable to regard thedifference (in terms of predictive accuracy) between the twoLUCC modeling approaches as insignificant.



Appendix

Empirical frequency (EmpFreq)

R(i) =

∑Aa=1WaPa(i)∑A

a=1Wa

(1)

where R(i) is a transition potential value in pixel (i), a indicates aparticular environmental variable, A indicates the total number ofenvironmental variables, Wa is the weight of environmental variablea, and Pa(i) indicates the percent of LUCC during the calibrationinterval in the bin to which pixel i belongs for variable a.

back



Appendix

Logistic regression (LogReg)

P (y = 1|X) =exp

∑BX

1 + exp∑

BX(2)

where y is a binary event, P is the probability of the binary eventgiven column vector X, X is a column vector that give the valuesof the environmental variables, and B is a row vector that givesthe estimated coefficients.

back



Appendix

Area Under the ROC Curve

AUC =

n∑i=1

(xi+1 − xi)×

{yi +

(yi+1 − yi)

2

}(3)

where xi is the false positives for the threshold i, yi is the truepositives for threshold i, and n+ 1 is the number of thresholds.

back



Appendix

Figure of Merit

Figure of Merit statistics = B/(A+B + C) (4)

where A is a number of pixels for “error due to observed changepredicted as persistence” (or misses), B is a number of pixels for“correct due to observed change predicted as change” (or hits), Cis a number of pixels for “error due to observed persistencepredicted as change” (or false alarms). The Figure of Meritstatistics range from 0 to 100 percent, where 100 percent indicatesperfect prediction.

back



Bibliography

Intergovernmental Panel on Climate Change (IPCC) 2000Summary for Policymakers–Land Use, Land-Use Change andForestry.

United Nations Framework Convention on Climate Change(UN FCCC) 1998 Kyoto Protocol to the United NationsFramework Convention on Climate Change.

United Nations Collaborative Programme on ReducingEmissions from Deforestation and Forest Degradation inDeveloping Countries (UN-REDD) 2008 FrameworkDocument.



Thank you!!!!

Author’s information

I Research interest

- REDD

I Education

- B.A. in Geography education, B.S. in Statistics- M.A. in GISc for Development & Environment- Ph.D. Student in Geography

I Contact

- E-mail: [email protected]

I Questions?


an assessment of deforestation models for reducing ... · introduction methodology results...

Documents