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Measuring Tropical Deforestation with Error Margins: A Method for REDD Monitoring in South-Eastern Mexico

S. Couturier,J. Núñez,M. Kolb

Published 2012 in Unknown venue

ABSTRACT

In the second half of the twentieth century, high rates of land use and land cover (LULC) change with severe deforestation trends have caused ecosystem degradation and biodiversity loss all throughout the tropical and sub-tropical belts (Lambin et al. 2003). Estimating the rate of change in tropical forest cover has become a crucial component of global change monitoring. For example, the viability of worldwide schemes such as the reduction of emissions from deforestation and degradation (REDD) depends on an accurate change estimate. Much research has covered the subject of tropical deforestation and degradation (Achard et al., 2010), however, there is so far very little information on the accuracy of quantitative estimates, leaving much room for uncertainty at regional and global scales. In Mexico, for example, the national projections for the rate of deforestation in the past three decades have ranged from 260,000 to 1,600,000 ha/year according to the record of academic studies and official reports (Velazquez et al., 2002). The estimate depended on the total area under study, on remote sensing materials and ground measurements involved in the computation of change rates, but above all none of the studies did contemplate a sampling scheme that would permit the computation of error margins for the rate of change. As a consequence, the alleged recent reduction in deforestation is subject to much political controversy in Mexico. Although, recent advances in Geographic Information Science (GIS) have been made for the accuracy assessment of maps, a standard method for assessing land cover change has not yet been established.

PUBLICATION RECORD

  • Publication year

    2012

  • Venue

    Unknown venue

  • Publication date

    2012-03-14

  • Fields of study

    Geography, Environmental Science

  • Identifiers
    DOI 10.5772/31523
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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