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.
Full citation
Rivera S., Lowry J.H., Hernandez A.J., Ramsey R.D., Lezama R., Velasquez M.A. (2012). A comparison between cluster busting technique and a classification tree algorithm of a moderate resolution imaging spectrometer (MODIS) land cover map of Honduras. Geocarto International, 27:17-29. 10.1080/10106049.2011.622050.