title: A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping.
abstract: Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply.
authors.affiliation: Department of Urban Planning and Spatial Information, Feng Chia University, Taichung 40724, Taiwan. yungcchuang@fcu.edu.tw.
authors.forename: Yung-Chung Matt
authors.initials: YC
authors.lastname: Chuang
authors.affiliation: Department of Urban Planning and Spatial Information, Feng Chia University, Taichung 40724, Taiwan. ysshiu@fcu.edu.tw.
authors.forename: Yi-Shiang
authors.initials: YS
authors.lastname: Shiu
mesh_terms: D000383:Agriculture; D057231:Climate Change; D060388:Support Vector Machine; D013624:Taiwan; D013662:Tea
chemical_list: D013662:Tea
keywords: GLCM texture; WorldView-2; pixel and object-based image analysis; random forest; support vector machine; tea crops