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Sustainable management of biodiversity benefit from cost-effective multi-temporal classification schemes afforded by remote sensing techniques. This study compared classification accuracies of woody plant species ( n  = 27) and three coexisting land cover types using dry and wet seasons data. Random Forest (RF), Support Vector Machine (SVM) and Deep Neural Network (DNN), were applied to Sentinel-2A and SPOT-6 images. The results showed higher overall classification accuracies for wet season data (65%–72%) for both images and classifiers (DNN, RF and SVM), compared to dry season classification (52%–59%). Near infrared region bands, available in both Sentinel-2A and SPOT-6 imagery, produced high performance for both wet (83%) and dry (80%) seasons. Overall, the findings show potential of multispectral remote-sensing for woody plant species diversity in different seasons. Such a study should be extended to higher frequency species diversity classification, to capture dynamics that may manifest at short time intervals of the year.

A bi-seasonal classification of woody plant species using Sentinel-2A and SPOT-6 in a localised species-rich savanna environment