Journal of Geoscience and Environment Protection, 2022, 10, 265-281 https://www.scirp.org/journal/gep ISSN Online: 2327-4344 ISSN Print: 2327-4336 DOI: 10.4236/gep.2022.1011018 Nov. 30, 2022 265 Journal of Geoscience and Environment Protection Application of Parametric and Non Parametric Classifiers for Assessing Land Use/Land Cover Categories in Cocoa Landscape of Juaboso and Bia West Districts of Ghana Emmanuel Donkor 1* , Edward Matthew Osei Jnr 2 , Stephen Adu-Bredu 3 , Samuel A. Andam-Akorful 2 , Efiba Vidda Senkyire Kwarteng 2 , Lily Lisa Yevugah 4 1 Resource Management Support Centre of Forestry Commission, Kumasi, Ghana 2 Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 3 Forestry Research Institute of Ghana, Kumasi, Ghana 4 University of Energy and Natural Resources, Sunyani, Ghana Abstract Satellite image classification has been used for long time in the field of remote sensing since classification results are used in environmental research, agri- culture, climate change and natural resource management. The cocoa land- scape of Ghana is complex and diverse in nature, composing of mixture of closed forest, open forest, settlements, croplands and cocoa farms which make mapping the landscape difficult. The purpose of this research is to as- sess and compare the classification performances of three machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and a statistical classification algorithm: Maximum Likelihood (ML) to know which classifier is best suited for mapping the cocoa landscape of Ghana using Juaboso and Bia West districts of Ghana as study area. A representative sampling approach was adopted to collect 1246 sample points for the various Land Use/Land Cover (LULC) types. These sample points were divided at random into 869 which form 70% for classification and 377 which constitute 30% of the total sample points for validation. The Stacked sentinel-2 image, classification data and validation data storing the identities of the LULC classes were imported in R to run supervised classifi- cation for each classifier. The classification results show that the highest over- all accuracy and kappa statistics were produced by the support vector ma- chine (86.47%, 0.7902); next is the artificial neural network (85.15%, 0.7700), followed by the random forest (84.08%, 0.7559) and finally the maximum li- kelihood (78.51%, 0.6668). The final LULC map produced under this study How to cite this paper: Donkor, E., Jnr, E. M. O., Adu-Bredu, S., Andam-Akorful, S. A., Kwarteng, E. V. S., & Yevugah, L. L. (2022). Application of Parametric and Non Parametric Classifiers for Assessing Land Use/Land Cover Categories in Cocoa Land- scape of Juaboso and Bia West Districts of Ghana. Journal of Geoscience and Environ- ment Protection, 10, 265-281. https://doi.org/10.4236/gep.2022.1011018 Received: October 12, 2022 Accepted: November 27, 2022 Published: November 30, 2022 Copyright © 2022 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access