Image-based reflectance conversion of ASTER and IKONOS imagery as precursor to structural assessment of plantation forests in KwaZulu-Natal, South Africa

  • MT Gebreslasie
  • FB Ahmed
  • JAN van Aardt

Abstract

Reflectance-converted imagery is a requirement for establishing temporally robust remote sensing algorithms, given the reduction of time-specific atmospheric effects. Thus, in this study image-based atmospheric correction methods for ASTER and IKONOS imagery for retrieving surface reflectance of plantation forests in KwaZulu-Natal, South Africa were evaluated. This effort formed part of a larger initiative that focused on retrieval of forest structural attributes from resultant reflectance imagery. Atmospheric correction methods in this study included the apparent reflectance model (AR), dark object subtraction model (DOS), and the cosine approximation model (COST). Spectral signatures derived from different image-based models for ASTER and  IKONOS were inspected visually as first departure. This was followed by comparison of the total accuracy and Kappa index computed from supervised classification of images that were derived from different image-based atmospheric correction of ASTER and IKONOS imagery. The classification accuracy of DOS images derived from ASTER and IKONOS imagery exhibited percentages of 93.3% and 94.7%, respectively. Classification accuracies for images from AR and COST, on  the other hand, resulted in lower accuracy values of 87.9% and 83.6% for ASTER and 90.5% and 92.8% for IKONOS, respectively. We concluded that the image-based DOS model was better suited to  atmospheric correction for ASTER and IKONOS imagery in this study area and for the purpose of forest structural assessment. This has important implications for the operational use of similar imagery types for forest inventory approaches.

Keywords: ASTER; IKONOS; image-based atmospheric correction; plantation forests; surface reflectance

Southern Forests 2009, 71(4): 259–265

Author Biographies

MT Gebreslasie
School of Environmental Sciences, University of KwaZulu-Natal, King George V Avenue, Glenwood, Durban 4041, South Africa; Council for Scientific and Industrial Research, Natural Resources and the Environment, Ecosystems, Earth Observation, PO Box 395, Pretoria 0001, South Africa
FB Ahmed
School of Environmental Sciences, University of KwaZulu-Natal, King George V Avenue, Glenwood, Durban 4041, South Africa
JAN van Aardt
Laboratory for Imaging Algorithms and Systems, Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14623, USA
Section
Articles

Journal Identifiers


eISSN: 2070-2639
print ISSN: 2070-2620