Abstract
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.
Highlights
Farallon 130, Jardines del Pedregal, 01900 Alvaro Obregón, Mexico City, Mexico; Woods Hole Research Center (WHRC), 149 Woods Hole Road, Falmouth, MA 02540, USA; United Nations Development Program (UNDP), Representation in Mexico, Montes Urales 440, Lomas de Chapultepec, 11000 Miguel Hidalgo, Mexico City, Mexico; Tel.: +52-55-5004-5009; Fax: +52-55-5004-4931
We used persistent areas calculated from the INEGI series II to IV as initial reference objects
The effects of insufficient availability of multi-temporal information will propagate errors starting with the time-series metrics calculation to the image segmentation and feature extraction to the final object classification. This might limit the applicability of the here-proposed method to countries on the outer margins of the inner tropics precisely not featuring very high precipitation levels. It might result in limited options of the inner tropical countries to produce multi-year composites of land cover sets instead of annual products such as we propose for Mexico
Summary
Farallon 130, Jardines del Pedregal, 01900 Alvaro Obregón, Mexico City, Mexico; Woods Hole Research Center (WHRC), 149 Woods Hole Road, Falmouth, MA 02540, USA; United Nations Development Program (UNDP), Representation in Mexico, Montes Urales 440, Lomas de Chapultepec, 11000 Miguel Hidalgo, Mexico City, Mexico; Tel.: +52-55-5004-5009; Fax: +52-55-5004-4931. 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. Delivery for a country of a size like Mexico can only be achieved in a standardized and cost-effective. 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. The classification scheme defined 9 and 12 classes at two hierarchical levels. 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. 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
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