SciELO - Scientific Electronic Library Online

vol.8 número41Influencia de parámetros climáticos sobre las fluctuaciones poblacionales del complejo Dendroctonus frontalis Zimmerman, 1868 y Dendroctonus mexicanus Hopkins, 1909Comparación de técnicas de rodalización en masas forestales de la Sierra Madre Occidental en el estado de Durango índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados




Links relacionados

  • No hay artículos similaresSimilares en SciELO


Revista mexicana de ciencias forestales

versión impresa ISSN 2007-1132


ACOSTA MIRELES, Miguel et al. Landsat ETM+ imaging for the estimation of the forest density in the southern region of the State of Mexico. Rev. mex. de cienc. forestales [online]. 2017, vol.8, n.41, pp.30-55. ISSN 2007-1132.

In the context of the mechanisms of climatic change mitigation, constant forest monitoring is important because forests provide crucial information. The estimation of forest stand attributes based on satellite imagery data combined with forest inventory data allows producing accurate information on forest structure, at a relative and accessible cost. However, there is still a need to use models that enable the construction of a valid relationship between remote sensing and field data. Therefore, this study aims to estimate forest attributes such as basal area (AB), volume (V) and aboveground biomass (B) by analyzing and using the relationship between spectral information, from Landsat ETM+ imagery, and tree measurements. Data from plots collected in 2010 by the National Forest and Soils Inventory (INFyS) were used as primary source of information. Results suggest that the best models to estimate AB, V and B were those that used the near infrared band (band 5) as the independent variable. Results also indicated that adjusted regression models shown statistical bases to estimate AB, V and B in a precise manner. All regression models were highly significant (5 %) with determination coefficients (R2 adj ) higher than 0.47.

Palabras llave : aboveground biomass; forest structure; Landsat; regression models; forest parameters; remote sensors.

        · resumen en Español     · texto en Español | Inglés     · Español ( pdf ) | Inglés ( pdf )