Improving accuracy of Landsat-8 OLI classification using image composite and multisource data with Google Earth Engine

Abstract

ABSTRACTLand use and land cover (LULC) mapping is very important for evaluation and management of natural resources. Classification of remote sensing data in mountainous and other heterogeneous environments are particularly problematic because of their high spectral heterogeneity. The aim of this study is to conduct accuracy analyses of LULC classification of Landsat-8 OLI composite images and multisource ancillary data using four machine learning classifiers on the Google Earth Engine (GEE) platform in the Golden Gate Highland National Park (GGHNP) and its near surrounding. Our results indicate that the inclusion of short-wave infrared bands increased accuracy of image composites by more than 10% in landcover types with wider range of spectral characteristics. Best performance was achieved by MaxEnt with overall accuracy and kappa statistics of 0.846 and 0.775, respectively. Support Vector Machine (SVM), however, produces relatively higher accuracy for handling different characteristics of multisource data. In elevated areas, aspect was found to outperform other topographic indices in reducing misclassification errors associated with confusion of grasslands with bare lands and water. The LULC produced from image composites can provide some reliable information on vegetation, land and water characteristics and reduce confusion associated with complex biophysical environments on vegetation spectral signatures in sub-tropical mountainous grasslands.