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Remote Sens. 2016, 8(7), 593;

An Operational Framework for Land Cover Classification in the Context of REDD+ Mechanisms. A Case Study from Costa Rica

Agresta S. Coop., Duque Fernán Nuñez 2, Madrid 28012, Spain
Freelance, Plaza Constitución 8, Chapinería, Madrid 28694, Spain
Carbon Decisions International (CDI), Residencial La Castilla, Paraíso de Cartago 30201, Costa Rica
DIMAP, CEI Montegancedo, Madrid 28223, Spain
AFOLU Global Services, C/Jimenez Diaz, Pozuelo Alarcón 28224, Spain
UPM, Avda Profesor Aranguren, Madrid 28040, Spain
UCR, Ciudad de la Investigación, San José 11501, Costa Rica
World Bank, 1818 H Street, NW, Washington, DC 20433, USA
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 7 April 2016 / Revised: 28 June 2016 / Accepted: 8 July 2016 / Published: 13 July 2016
Full-Text   |   PDF [3102 KB, uploaded 13 July 2016]   |  


REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD) across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented. View Full-Text
Keywords: open source; forest; deforestation; IR MAD; Random Forest; Landsat; QGIS; R; ORFEO; python open source; forest; deforestation; IR MAD; Random Forest; Landsat; QGIS; R; ORFEO; python

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Fernández-Landa, A.; Algeet-Abarquero, N.; Fernández-Moya, J.; Guillén-Climent, M.L.; Pedroni, L.; García, F.; Espejo, A.; Villegas, J.F.; Marchamalo, M.; Bonatti, J.; Escamochero, I.; Rodríguez-Noriega, P.; Papageorgiou, S.; Fernandes, E. An Operational Framework for Land Cover Classification in the Context of REDD+ Mechanisms. A Case Study from Costa Rica. Remote Sens. 2016, 8, 593.

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