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

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DiBT Department of Bioscience and Territory, University of Molise, Contrada Fonte Lappone snc, I-86090 Pesche, Italy
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Institute for World Forestry, University of Hamburg, Leuschnerstr. 91, 21031 Hamburg, Germany
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The EFI Project Centre SURF—Supporting the Global Implementation of REDD+ and FLEGT, Institute for World Forestry, University of Hamburg, Leuschnerstr. 91, 21031 Hamburg, Germany
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Author to whom correspondence should be addressed.
Academic Editor: Timothy A. Martin
Forests 2017, 8(1), 29; https://doi.org/10.3390/f8010029
Received: 18 October 2016 / Revised: 16 December 2016 / Accepted: 18 January 2017 / Published: 22 January 2017
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. View Full-Text
Keywords: REDD+; tropical forests; spatial targeting; random forests; carbon; land-use change modelling REDD+; tropical forests; spatial targeting; random forests; carbon; land-use change modelling
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Di Lallo, G.; Mundhenk, P.; Zamora López, S.E.; Marchetti, M.; Köhl, M. REDD+: Quick Assessment of Deforestation Risk Based on Available Data. Forests 2017, 8, 29.

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