A Comparison Between Cluster Busting Technique and a Classification Tree Algorithm of a Moderate Resolution Imaging Spectrometer (MODIS) Land Cover Map of Honduras

Document Type

Article

Journal/Book Title/Conference

Geocarto International

Volume

27

Issue

1

Publisher

Taylor & Francis

Publication Date

11-10-2011

First Page

17

Last Page

29

Abstract

A national land cover map derived from moderate resolution imaging spectrometer (MODIS) imagery products was developed for Honduras, Central America. We compared two methods of image classification: a cluster busting (CB) classification technique and a classification and regression tree (CART) algorithm. Field data samples were used to validate the resulting classifications. Inthe classification process, we used: a Google Earth™ sampling scheme, a time series of MODIS's Enhanced Vegetation Index (EVI) and digital elevation data(shuttle radar topography mission, SRTM). The CART classification method provided a more accurate classification (Kappa coefficient, K = 74%, overall model accuracy = 79.6%) while compared to the CB classification (Kappa coefficient, K = 9%, overall model accuracy = 25.1%). The findings are useful to design more accurate MODIS classification protocols in tropical countries.

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