Tuesday, February 23, 2010

Comparison of Two GIS-based Land Change Modules for Constructing REDD Baselines in Bolivia

AAG - 2010 Annual Meeting, Washington, DC

Comparison of Two GIS-based Land Change Modules for Constructing REDD Baselines in Bolivia

is part of the Paper Session:
Land Use and Spatial Analysis

scheduled on Sunday, 4/18/10 at 8:00 AM.

Oh Seok Kim* - Department of Geography, University of Southern California

With the increasing concerns in developing methodologies for Reducing Emissions from Deforestation and Degradation (REDD) projects, there is a need to understand the characteristics of existing Land-Use/Cover Change (LUCC) modules. This research presents a comparison of two existing approaches: GEOMOD Modeling (GM) and Land Change Modeler (LCM). The comparison uses data from a case study in Chiquitanía, Bolivia. Data from 1986 and 1994 are used to simulate land-cover of 2000; the resulting maps are compared to an observed land-cover map of 2000. GM and LCM simulate baseline deforestation at the pixel-level. The model structures of linear extrapolation and Markov chain are compared to review quantity of LUCC; and the model structures of empirical frequency, logistic regression and multilayer perceptron are compared to review allocation of LUCC. Relative operating characteristics, figure of merit and multiple resolution analysis are employed to assess predictive accuracy of multiple transition modeling. By design, GM lacks the potential to model multiple transitions, while LCM may produce different results for each simulation. Based on the model structure and predictive accuracy comparisons, the LCM's logistic regression seems the most suitable LUCC module to construct a REDD baseline in this case. However, it is crucial to emphasize that these results are strictly limited to this particular case. Thus, there is no obvious method that is most accurate, so if a REDD project employs predictive GIS-based LUCC modeling for its spatially-explicit baseline construction, it should include the framework employed in this research to establish the baseline in a scientific manner.

Thursday, February 18, 2010

Workshop at the AAG Annual Meeting

Modeling REDD-Baselines Using IDRISI's Land Change Modeler

Tuesday, April 13, 1:00pm – 4:00pm

Stefano Crema, Clark Labs, Clark University
Florencia Sangermano, Clark Labs, Clark University
Marc Steininger, Conservation International

Workshop Capacity: 30
Room: Lincoln Room 3

The implementation of REDD projects requires a best estimate of the deforestation and/or degradation baseline. The baseline is derived from the spatial modeling of the expected future trends of deforestation using maps of historical deforestation and biomass estimates.

Land Change Modeler (LCM) is a robust modeling tool that allows calibration, validation and the creation of maps of expected future deforestation trends. Clark Labs, in conjunction with Conservation International, has designed a REDD tool within LCM which guides the user through the steps of baseline development and directly produce tables and graphics necessary in the reporting process.

This workshop will cover the complexity of REDD baseline creation using real case studies to illustrate the process.

Tuesday, February 2, 2010

LCM on Google's Earth Engine interface.

Clark Labs Receives Support from Google.org to Develop an On-line Prototype of its Land Change Modeler Application to be run on Google’s Earth Engine

Worcester, MA -- Google has contracted Clark Labs to develop an on-line prototype of the Land Change Modeler application to run on Google’s Earth Engine interface. This development complements the on-line forest monitoring applications of Carnegie Institution for Science (CLASlite) and IMAZON (SAD), demonstrated on Earth Engine at the COP 15 meeting in Copenhagen last month.

The on-line prototype will contain much of the functionality of the land change modeling component of Clark Labs’ Land Change Modeler application, included within the IDRISI GIS and Image Processing software, as well as within its extension to ArcGIS. The system will allow users to analyze, model and predict deforestation and then to evaluate the amount of carbon involved.

The hope is that this on-line system will help support the implementation and evaluation of REDD (Reducing Emissions from Deforestation and Degradation) projects. The intent of REDD is to assign financial value to the carbon stored in forests such that sequestration of that carbon becomes a viable alternative to other uses such as conversion to pasture and farmland. The Land Change Modeler provides a critical ingredient for REDD projects in that it can be used to establish the business-as-usual (BAU) baseline that projects the future deforestation in the absence of a REDD project. Quantification of this projected deforestation and comparison with various governance scenarios provides the basis for evaluation of the carbon stocks involved and potential compensation.

It is hoped that the REDD program will lead to a significant reduction in CO2 emissions, enhance ecosystem services and biodiversity conservation, and provide opportunities for poverty reduction.

Implementing REDD projects will depend on modeling which requires significant data and computing resources. Google’s new Earth Engine platform will ameliorate the financial burden of implementing REDD projects.

“The objective of Earth Engine is to enable organizations such as Clark Labs to run their algorithms on-line, powered by Google’s computational capacity, with easy access to massive earth observation data sets,” indicated Dr. Amy Luers, Senior Environment Program Manager for Google.org.

At this time, many governmental and non-governmental organizations are using the Land Change Modeler as their primary analytical tool for REDD project development. Clark Labs, in conjunction with Conservation International, is currently augmenting its Land Change Modeler suite to ensure compatibility with the World Banks’ BioCarbon Fund (BioCF) methodological framework.