Abstract:
Information on land-cover is important for verification of land use, land-use
change and forestry (LULUCF). LULUCF is significant in assessing
anthropogenic Green House Gas (GHG) emissions. This study aimed at
developing a simple and computationally efficient yet accurate methodology for
national land-cover mapping. The longstanding Landsat data freely available with
a renewed and sustainable future archive after the launch of Landsat 8 was used.
Data of two epochs namely 2000 and 2010 were selected. A chain classification
approach using maximum likelihood classification (MLC) coupled with decision
tree was used. Chain classification approach was significant in classifying
images of different seasons given that in national land-cover projects it is rare to
obtain images of the same date. Six classes recommended by IPCC were
adopted. The developed approach attained an accuracy of 86% with a kappa
coefficient of 0.8. The study concluded the freely available Landsat data,
computational efficiency of MLC and decision tree can be tapped for sustainable
land-cover mapping for GHG. This method is replicable and therefore can be
used to produce complete and comparable national land-cover products.
