griscom_rel_e_kali

Comparison of REL Methods for Districts of East Kalimantan, Indonesia.

Bronson Griscom, Sr. Scientist Forest CarbonJohn Kerkering, Conservation Analyst

REDDeX Conference, Cancun, July 14, 2010

Question: What is the most accurate method for predicting the amount of deforestation within districts of East Kalimantan?

Alternative methods for predicting deforestation (i.e. REL) at sub-national scale

Historical Rateof project area

Historical Rate(with adjustments)

Forward Looking

Simple Complex

“Planned” (e.g. legal license

to log/convert)

•Non-spatially explicit model (e.g. population-forest fraction)

•Spatially explicit modeling•Rate derived from “reference region.”

•Trend analysis.1

2 3

2000

1

2005

Predicted deforestation in each district = Historic rate in each district

Predicted deforestation in each district = Historic rate of reference region

ClusterAnal.

Deforestation Variables (District Mean Values)Crop Suitability IndexDeforestation Constraints IndexDistance from Converted AreasDistance from Major CitiesDistance from Navigable RiversDistance from SawmillsElevationPercent Histosol SoilsPercent Inceptisol SoilsPercent Oxisol SoilsPercent Remaining ForestPercent Ulfisol SoilsRoad DensitySlope

2

…where reference regions are determined by cluster analysis

Predicted deforestation in each district = Modeled future rate in each district

3

…using spatially explicit model at regional (province) scale.

2020

2015

Vulnerability

Projections

Prior Deforestation

2009

spatial plansoils

forest typesslope

dist. sawmillsdist. towns

dist. navigable riversdist. cities

topographydist. converted areas

dist. roads

2005

2000

“Driver” Variables

•neural network•no dynamic variables

LCM

3Here’s how…

Note: projections assume historic rate at province scale

Deforestation Variables Cramer's V

Distance from Converted Areas 0.67Elevation (DEM) 0.60Distance from Cities 0.46Distance from Navigable Rivers 0.42Distance from Roads 0.34Distance from Towns (ESRI) 0.23Distance from Sawmills 0.16Slope 0.16Population Density (GRUMP) 0.11Population Density (ICRAF) 0.07Distance from All Rivers 0.05

Forest Cover Types 0.76Soils 0.65Spatial Plan (National) 0.49Land Systems 0.46

Continuous

Categorical

Selection of Model “Drivers” 3

Deforestation Variables Cramer's V

Distance from Converted Areas 0.67Elevation (DEM) 0.60Distance from Cities 0.46Distance from Navigable Rivers 0.42Distance from Roads 0.34Distance from Towns (ESRI) 0.23Distance from Sawmills 0.16Slope 0.16Population Density (GRUMP) 0.11Population Density (ICRAF) 0.07Distance from All Rivers 0.05

Forest Cover Types 0.76Soils 0.65Spatial Plan (National) 0.49Land Systems 0.46

Continuous

Categorical

Selection of Model “Drivers” 3

Model Performance

AnalysisFigure of

MeritKappa for Location

This Analysis 0.3374 0.8330Harris et. al. 0.1869 0.7763

3

Predicted area deforested from

2006-2009

minus

Actual area deforested from

2006-2009

(as % of actual area deforested)

321

Comparison of Three Methods

Comparison of Three Methods

321

Comparison of Three Methods

321

Why do cluster reference regions seem to work?

2

Question: What is the most accurate method for predicting the amount of deforestation within districts of East Kalimantan?

•More complex doesn’t mean better.

•I suggest reference region method 2

Nested RELs National Scale:

Historic mean, with negotiated adjustments?

Sub-National Scale (e.g. State/Province): Modeled projection, to determine proportion of national emissions pie? Separate models for deforestation vs. degradation?

Project Scale: Mixed. Modeled projection for unplanned events? Book-keeping for planned events / strategies?

thanks!

Bronson Griscom

bgriscom@tnc.org