Improving the classification of six evergreen subtropical tree species with multi-season data from leaf spectra simulated to WorldView-2 and RapidEye

ABSTRACT

Remote sensing offers a feasible means to monitor tree species at a regional level where species distribution and composition is affected by the impacts of global change. Furthermore, the temporal resolution of space-borne multispectral sensors offers the ability to combine phenologically important phases for the optimization of tree species classification. In this study, we determined whether multi-seasonal leaf-level spectral data (winter, spring, summer, and autumn) improved the classification of six evergreen tree species in the subtropical forest region of South Africa when compared to a single season, for hyperspectral data, and reflectance data simulated to the WorldView-2 (WV2) and RapidEye (RE) sensors. Classification accuracies of the test data were assessed using a Partial Least Square Random Forest algorithm. The accuracies were compared between single seasons and multi-season classification and across seasons using analysis of variance and post-hoc Tukey Honest Significant Difference tests. The average overall accuracy (OA) of the leaf-level hyperspectral data ranged from a minimum of 90 ± 3.5% in winter to a maximum of 92 ± 2.7% in summer, outperforming the simulated reflectance data for the WV2 and RE sensors with an average OA of between 8 and 10 percentage points (p < 0.02, Bonferroni corrected). The use of data from multiple seasons increased the average OA and decreased the number of species pair confusions for the simulated multispectral classifications. The producer’s and user’s accuracies of the hyperspectral classification were >82% and showed no significant change using multi-season data. Multiple seasons may therefore be beneficial to multispectral sensors with ≤8 bands, yet remains to be tested at canopy level, for other species and climatic regions.

Acknowledgements

This work was funded by the South African Department of Science and Technology (DST), the National Research Foundation (NRF), the Council for Scientific and Industrial Research (CSIR), and the Water Research Commission (WRC). We thank the iSimangaliso Wetland Park Authority and Ezemvelo KwaZulu-Natal (KZN) Wildlife for granting access and logistical support. Thanks to Dr Ricky Taylor, former ecologist at Ezemvelo KZN Wildlife, for guidance and advice. We are grateful to the following fieldwork assistants: Mr S. Gumede, Dr F. Durand, Ms R. van Deventer, Mr A. van Deventer, Mr S. Mfeka, Dr E. Adam, Mr T. Colins, Mr K. Barker, Mr O. Malahlela and the iSimangaliso bursary students Ms N. Nkosi, Ms T. Sibiya, Ms N. Gumede, Mr B. Mdamba and Mr B. Gumbi.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was funded by the Department of Science and Technology, Republic of South Africa, the National Research Foundation (NRF), the Council for Scientific and Industrial Research (CSIR), and the Water Research Commission [WRC K5/2268].