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Comment published on 23 June 2016, see Remote Sens. 2016, 8(7), 533.
Open AccessArticle

MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data

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National Commission for the Knowledge and Use of Biodiversity (CONABIO), Liga Periférico-Insurgentes Sur 4903, Parques del Pedregal, 14010 Tlalpan, Mexico City, Mexico
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National Commission of Natural Protected Areas (CONANP), Camino al Ajusco 200, Jardines en la Montaña, 14210 Tlalpan, Mexico City, Mexico
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National Forestry Commission (CONAFOR), Periférico Poniente 5360, San Juan de Ocotán, Zapopan, 45019 Jalisco, Mexico
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Representation in Mexico, Food and Agriculture Organization of the United Nations (FAO), Farallon 130, Jardines del Pedregal, 01900 Alvaro Obregón, Mexico City, Mexico
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Woods Hole Research Center (WHRC), 149 Woods Hole Road, Falmouth, MA 02540, USA
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United Nations Development Program (UNDP), Representation in Mexico, Montes Urales 440, Lomas de Chapultepec, 11000 Miguel Hidalgo, Mexico City, Mexico
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Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(5), 3923-3943; https://doi.org/10.3390/rs6053923
Received: 31 January 2014 / Revised: 29 March 2014 / Accepted: 9 April 2014 / Published: 30 April 2014
Estimating forest area at a national scale within the United Nations program of Reducing Emissions from Deforestation and Forest Degradation (REDD) is primarily based on land cover information using remote sensing technologies. Timely delivery for a country of a size like Mexico can only be achieved in a standardized and cost-effective manner by automatic image classification. This paper describes the operational land cover monitoring system for Mexico. It utilizes national-scale cartographic reference data, all available Landsat satellite imagery, and field inventory data for validation. Seven annual national land cover maps between 1993 and 2008 were produced. The classification scheme defined 9 and 12 classes at two hierarchical levels. Overall accuracies achieved were up to 76%. Tropical and temperate forest was classified with accuracy up to 78% and 82%, respectively. Although specifically designed for the needs of Mexico, the general process is suitable for other participating countries in the REDD+ program to comply with guidelines on standardization and transparency of methods and to assure comparability. However, reporting of change is ill-advised based on the annual land cover products and a combination of annual land cover and change detection algorithms is suggested. View Full-Text
Keywords: REDD+; MRV; activity data; land cover; baseline; monitoring; Landsat; Mexico REDD+; MRV; activity data; land cover; baseline; monitoring; Landsat; Mexico
MDPI and ACS Style

Gebhardt, S.; Wehrmann, T.; Ruiz, M.A.M.; Maeda, P.; Bishop, J.; Schramm, M.; Kopeinig, R.; Cartus, O.; Kellndorfer, J.; Ressl, R.; Santos, L.A.; Schmidt, M. MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data. Remote Sens. 2014, 6, 3923-3943.

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