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Library Mapping coffee plantations with Landsat imagery: an example from El Salvador

Mapping coffee plantations with Landsat imagery: an example from El Salvador

Mapping coffee plantations with Landsat imagery: an example from El Salvador

Resource information

Date of publication
December 2012
Resource Language
ISBN / Resource ID
AGRIS:US201400107038
Pages
220-242

Considering the potential of shaded coffee plantations mixed with natural vegetation for promoting biodiversity conservation, this project assessed the utility of multi-date Landsat Thematic Mapper (TM) satellite imagery for the characterization of natural vegetation versus coffee plantations in western El Salvador. For assembling a multi-temporal Landsat TM data set, we applied a regression analysis model to remove cloud cover and cloud shadows. Then, through a hybrid classification approach, a nine-class land use/land cover (LULC) map was generated. We identified two types of coffee plantations (‘open-canopy’ and ‘close-canopy’) along with natural forest/shrubland, mangrove, water bodies, sandy coastal soils, bare soil, urban areas and agriculture. Notwithstanding the small sample size of the accuracy data, our assessment revealed an overall accuracy of 76.7% (Kappa coefficient = 0.68), considering only the four classes with independent field data. The overall classification accuracy for distinguishing coffee plantations from non-mangrove natural forest was 81.6% and the classification accuracy for distinguishing ‘open-canopy’ from ‘close-canopy’ coffee plantations was 85.7%. We are encouraged by the results of this prototype study. They indicate that remote-sensing techniques can be used to distinguish different classes of coffee production systems and to differentiate coffee from natural forest.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Ortega-Huerta, Miguel Alfonso
Komar, Oliver
Price, Kevin P.
Ventura, Hugo J.

Publisher(s)
Data Provider
Geographical focus