CICYTTP   12500
CENTRO DE INVESTIGACION CIENTIFICA Y DE TRANSFERENCIA TECNOLOGICA A LA PRODUCCION
Unidad Ejecutora - UE
artículos
Título:
A land cover map of Latin America and the Caribbean in the framework of the SERENA project
Autor/es:
BLANCO, P.D; R.R. COLDITZ; G. LÓPEZ SALDAÑA; HARDTKE, L.A; LLAMAS, R; N.A. MARI; A. FISCHER; C. CARIDE; ACEÑOLAZA P; DEL VALLE, H.F; ACEÑOLAZA P; M. LILLO-SAAVEDRA; CORONATO F; S. OPAZO; F. MORELLI; J.A. ANAYA; SIONE, W
Revista:
REMOTE SENSING OF ENVIRONMENT
Editorial:
ELSEVIER SCIENCE INC
Referencias:
Lugar: Amsterdam; Año: 2013 vol. 132 p. 13 - 31
ISSN:
0034-4257
Resumen:
Land cover maps at different resolutions and mapping extents contribute
to modeling and support decision making processes. Because land cover
affects and is affected by climate change, it is listed among the 13
terrestrial essential climate variables. This paper describes the
generation of a land cover map for Latin America and the Caribbean (LAC)
for the year 2008. It was developed in the framework of the project
Latin American Network for Monitoring and Studying of Natural Resources
(SERENA), which has been developed within the GOFC-GOLD Latin American
network of remote sensing and forest fires (RedLaTIF). The SERENA land
cover map for LAC integrates: 1) the local expertise of SERENA network
members to generate the training and validation data, 2) a methodology
for land cover mapping based on decision trees using MODIS time series,
and 3) class membership estimates to account for pixel heterogeneity
issues. The discrete SERENA land cover product, derived from class
memberships, yields an overall accuracy of 84% and includes an
additional layer representing the estimated per-pixel confidence. The
study demonstrates in detail the use of class memberships to better
estimate the area of scarce classes with a scattered spatial
distribution. The land cover map is already available as a printed wall
map and will be released in digital format in the near future. The
SERENA land cover map was produced with a legend and classification
strategy similar to that used by the North American Land Change
Monitoring System (NALCMS) to generate a land cover map of the North
American continent, that will allow to combine both maps to generate
consistent data across America facilitating continental monitoring and
modeling.