REDD+: Quick Assessment of Deforestation Risk Based on Available Data

The evaluation of the future dynamics of deforestation is essential to creating the basis for the effective implementation of REDD+ (Reducing Emissions from Deforestation and forest Degradation) initiatives. Such evaluation is often a challenging task, especially for countries that have to cope with a critical lack of data and capacities, higher uncertainties, and competing interests. We present a new modeling approach that makes use of available and easily accessible data sources to predict the spatial location of future deforestation. This approach is based on the Random Forest algorithm, which is a machine learning technique that enables evidence-based, data-driven decisions and is therefore often used in decision-making processes. Our objective is to provide a straightforward modeling approach that, without requiring cost-intensive assessments, can be applied in the early stages of REDD+, for a stepwise implementation approach of REDD+ projects in regions with limited availability of data, capital, technical infrastructure, or human capacities. The presented model focuses on building business-as-usual scenarios to identify and rank potentially suitable areas for REDD+ interventions. For validation purposes we applied the model to data from Nicaragua.

[1]  M. Herold,et al.  Testing methodologies for REDD+: Deforestation drivers, costs and reference levels , 2013 .

[2]  D. Gritten,et al.  Predicting Future Conflict under REDD+ Implementation , 2013 .

[3]  A. Veldkamp,et al.  CLUE-CR: An integrated multi-scale model to simulate land use change scenarios in Costa Rica , 1996 .

[4]  Sean Sloan,et al.  How accurately may we project tropical forest-cover change? A validation of a forward-looking baseline for REDD , 2012 .

[5]  Historical climate trends, deforestation, and maize and bean yields in Nicaragua , 2015 .

[6]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[7]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[8]  G. Bonan.  Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests , 2008, Science.

[9]  N. Aguilar-Amuchastegui,et al.  Identifying areas of deforestation risk for REDD+ using a species modeling tool , 2014, Carbon Balance and Management.

[10]  Daniel A Griffith,et al.  Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. , 2006, Ecology.

[11]  Reduced emissions from deforestation and forest degradation (REDD): a climate change mitigation strategy on a critical track , 2009, Carbon balance and management.

[12]  M. Honzák,et al.  Targeting and implementing payments for ecosystem services: Opportunities for bundling biodiversity conservation with carbon and water services in Madagascar , 2010 .

[13]  E. Sills,et al.  Targeting areas for Reducing Emissions from Deforestation and forest Degradation (REDD+) projects in Tanzania , 2014 .

[14]  R. Pontius,et al.  Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment , 2011 .

[15]  Florian Ploeckl.  Spatial Modeling , 2019, Handbook of Cliometrics.

[16]  Eric Koomen,et al.  Comparing the input, output, and validation maps for several models of land change , 2008 .

[17]  M. Köhl,et al.  The global climate change mitigation strategy REDD: monitoring costs and uncertainties jeopardize economic benefits , 2013, Climatic Change.

[18]  R. Dudley.  A little REDD model to quickly compare possible baseline and policy scenarios for reducing emissions from deforestation and forest degradation , 2010 .

[19]  Kasper Kok,et al.  A comparison of baseline methodologies for 'Reducing Emissions from Deforestation and Degradation' , 2009, Carbon balance and management.

[20]  Application of the ‘Climafor’ Approach to Estimate Baseline Carbon Emissions of a Forest Conservation Project in the Selva Lacandona, Chiapas, Mexico , 2005 .

[21]  N. Harris,et al.  Identifying optimal areas for REDD intervention: East Kalimantan, Indonesia as a case study , 2008 .

[22]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[23]  Akihiro Nakazawa,et al.  Spatial Simulation Modelling of Future Forest Cover Change Scenarios in Luangprabang Province, Lao PDR , 2011 .

[24]  C. B. Lantican,et al.  Assessing change in national forest monitoring capacities of 99 tropical countries , 2015 .

[25]  L. Dale,et al.  FORESTS , 1994, Restoration & Management Notes.

[26]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[27]  R. B. Jackson,et al.  CO 2 emissions from forest loss , 2009 .

[28]  Martin Herold,et al.  A stepwise framework for developing REDD+ reference levels , 2012 .

[29]  M. Herold,et al.  An assessment of deforestation and forest degradation drivers in developing countries , 2012 .

[30]  A. Etter,et al.  Landscape Transformation in Tropical Latin America: Assessing Trends and Policy Implications for REDD+ , 2010 .

[31]  A. Angelsen,et al.  Designing and Implementing Effective REDD + Policies: A Forest Transition Approach , 2013, Review of Environmental Economics and Policy.

[32]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[33]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[34]  A. Karsenty,et al.  Climate Change Mitigation: Should “Avoided Deforestation” Be Rewarded? , 2009 .

[35]  M. Clark,et al.  Asymmetric forest transition driven by the interaction of socioeconomic development and environmental heterogeneity in Central America , 2012, Proceedings of the National Academy of Sciences.

[36]  N. M. Kelly,et al.  Understanding large-scale deforestation in southern Jinotega, Nicaragua from 1978 to 1999 through the examination of changes in land use and land cover. , 2009, Journal of environmental management.

[37]  Robert Gilmore Pontius,et al.  Influence of carbon mapping and land change modelling on the prediction of carbon emissions from deforestation , 2012, Environmental Conservation.

[38]  Oh Seok Kim.  An Assessment of Deforestation Models for Reducing Emissions from Deforestation and Forest Degradation (REDD) , 2010, Trans. GIS.

[39]  T. C. Whitmore,et al.  International Union for Conservation of Nature and Natural Resources , 1970, Nature.