Author(s)

Emmanuel Donkor^1*, Edward Matthew Osei Jnr², Stephen Adu-Bredu³, Samuel A. Andam-Akorful², Efiba Vidda Senkyire Kwarteng², Lily Lisa Yevugah⁴

¹Resource Management Support Centre of Forestry Commission, Kumasi, Ghana.
²Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
³Forestry Research Institute of Ghana, Kumasi, Ghana.
⁴University of Energy and Natural Resources, Sunyani, Ghana.

Satellite image classification has been used for long time in the field of remote sensing since classification results are used in environmental research, agriculture, climate change and natural resource management. The cocoa landscape 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 assess 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 classification for each classifier. The classification results show that the highest overall accuracy and kappa statistics were produced by the support vector machine (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 likelihood (78.51%, 0.6668). The final LULC map produced under this study can be used to monitor cocoa driven deforestation especially in the gazetted forest and game reserves. This map will also be very useful in the national forest monitoring framework for the REDD + cocoa landscape project.

Support Vector Machine, Random Forest, Artificial Neural Network, Maximum Likelihood, Image Classification, Cocoa Landscape

Donkor, E. , Jnr, E. , Adu-Bredu, S. , Andam-Akorful, S. , Kwarteng, E. and Yevugah, L. (2022) 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. Journal of Geoscience and Environment Protection, 10, 265-281. doi: 10.4236/gep.2022.1011018.