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

Abstract

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.

Keywords:

• Sentinel-2A
• SPOT-6
• wet and dry seasons
• Savanna Woody Plant species
• Non-parametric Classifiers

Acknowledgments

The University of Johannesburg (South Africa) provided the necessary financial and material support to undertake the study. We thank the National Research Foundation (NRF) of South Africa for supporting the first author through student scholarship program (Reference SFH150803134516). The authors also thank Bishop Ngobeli (Manager of the Klipriviersberg Nature Reserve) for unlimited access to the reserve and for additional transportation support to and from the reserve. We are grateful to Tumelo Molaba for all the assistance during the field survey.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials (Tables S1 and S2, Figure S1).