An evaluation of single and multi-date Landsat image classifications using random forest algorithm in a semi-arid savanna of Ghana, West Africa

• Land cover maps were produced using single and multi-date images. • Image classification was achieved using random forest algorithm. • Classified maps were based on a hierarchical land cover scheme. • Multi-date images resulted in improved map accuracy compared to single-date images. Accurate detection and quantification of land use and land cover (LULC) change is critical for understanding landscape patterns in heterogeneous semi-arid environments. This study investigates the performance of single-date and multi-date Landsat images as well as the relationship between different LULC schemes (simple [2 and 4 classes] and complex [6 and 9 classes]) and the resulting classification accuracy. Specifically, the random forest algorithm was applied to Landsat data comprised of different combinations of image dates (single-date and multi-date) captured in June, October, and December for multiple levels of LULC (scheme) mapping and accuracy evaluations due to its high performance when dealing with large data and heterogeneous landscapes. Results indicated that multi-date images consistently produced higher classification accuracies than single-date images. Significant negative correlations observed between the number of classes in LULC schemes and overall accuracy and kappa coefficient indicate that the more complex the LULC scheme, the lower the accuracy produced. Nevertheless, improvement in overall accuracy was negligible for simple schemes (e.g., ∼1 % for two LULC classes), while it was moderate for complex schemes (∼5 %) when using the best-performing images for multi-date (June-October-December) compared to single-date (October) classifications: however, the improvement was considerable when compared to the least performing single-date image (June, 8–15 %). These varying classification accuracies were due to differences or similarities in spectral responses of target classes in the various LULC schemes applied to the investigated images. Consequently, the resulting differences in the spatial distribution and quantification of LULC classes produced by the different approaches can affect policy and land management decisions, especially if inappropriate image dates are used for LULC mapping. Overall, the findings highlight the reliability of appropriate single-date and multi-date images for mapping LULC change using simple and complex schemes in heterogeneous semi-arid savanna landscapes.