Biological Invasions

, Volume 17, Issue 7, pp 2027–2042 | Cite as

Mapping the occurrence of Chromolaena odorata (L.) in subtropical forest gaps using environmental and remote sensing data

  • Oupa E. Malahlela
  • Moses A. Cho
  • Onisimo Mutanga
Original Paper


Globally, subtropical forests are rich in biodiversity. However, the native biodiversity in these forests is threatened by the presence of invasive species such as Chromolaena odorata (L.) King and Robinson, which thrives in forest canopy gaps. Our study explored the utility of WorldView-2 data, an 8-band high resolution (2 m) imagery for mapping the probability of C. odorata occurrence (presence/absence) in canopy gaps of a subtropical forest patch, the Dukuduku forest, South Africa. An integrated modelling approach involving the WorldView-2 vegetation indices and ancillary environmental data was also assessed. The results showed a higher performance of the environmental data only model (deviance or D 2  = 0.52, p < 0.05, n = 77) when compared to modelling with WorldView-2 vegetation indices such as the enhanced vegetation index, simple ratio indices and red edge normalized difference vegetation index (D 2  = 0.30, p < 0.05, n = 77). The integrated model explained the highest presence/absence variance of C. odorata (D 2  = 0.71, i.e. 71 %). This model was used to derive a probability map indicating the occurrence of invasive species in forest gaps. A 2 × 2 error matrix table and the receiver operating characteristic curve derived from an independent validation dataset (n = 38) were used to assess the model accuracy. Approximately 87 % of canopy gaps containing C. odorata were correctly predicted at probability threshold of 0.3. The derived probability map of C. odorata occurrence could assist management in prioritizing target areas for eradication of the species.


Forest management Remote sensing Invasive species ROC curve 



We wish to thank the Council for Scientific and Industrial Research’s Natural Resources and Environment (CSIR-NRE) unit, of South Africa, for its financial assistance through its research grants. Many thanks also to the Department of Agriculture, Forestry and Fisheries for the support on the study area. Authors like to thank the Department of Science and Technology of South Africa for their financial support. Many thanks to Dr. R Mathieu for his leadership role at Earth Observation Group of NRE and Dr. Ramoelo for assisting with field work and insights on this paper. We appreciate the statistics tutorship assistance offered by Dr. Pravesh Debba and Nontembeko Dudeni-Tlhone of CSIR’s Built Environment (CSIR-BE). Authors wish to thank Sibuyiselo Gumede of Khula village who assisted in field data collection. To Tino, my dearest son. We wish to thank the anonymous reviewers for their comments and for improving the quality of this paper.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Oupa E. Malahlela
    • 1
    • 4
  • Moses A. Cho
    • 2
    • 3
  • Onisimo Mutanga
    • 3
  1. 1.Research and Applications Development, Earth Observation DivisionSouth African National Space AgencyPretoriaSouth Africa
  2. 2.Earth Observation Research Group, Natural Resources and EnvironmentCouncil for Scientific and Industrial ResearchPretoriaSouth Africa
  3. 3.Geography DepartmentUniversity of KwaZulu-NatalScottsville, PietermaritzburgSouth Africa
  4. 4.SilvertonSouth Africa

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