TITLE:
A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region
AUTHORS:
Omoleomo Olutoyin Omo-Irabor
KEYWORDS:
Land Cover, Supervised and Unsupervised Classification Algorithms, Landsat Images, Change Detection, Niger Delta
JOURNAL NAME:
Journal of Geographic Information System,
Vol.8 No.2,
April
8,
2016
ABSTRACT: A critical problem
associated with the southern part of Nigeria is the rapid alteration of the
landscape as a result of logging, agricultural practices, human migration and
expansion, oil exploration, exploitation and production activities. These
processes have had both positive and negative effects on the economic and
socio-political development of the country in general. The negative impacts
have led not only to the degradation of the ecosystem but also posing hazards
to human health and polluting surface and ground water resources. This has
created the need for the development of a rapid, cost effective and efficient
land use/land cover (LULC) classification technique to monitor the biophysical
dynamics in the region. Due to the complex land cover patterns existing in the
study area and the occasionally indistinguishable relationship between land
cover and spectral signals, this paper introduces a combined use of
unsupervised and supervised image classification for detecting land use/land
cover (LULC) classes. With the continuous conflict over the impact of oil
activities in the area, this work provides a procedure for detecting LULC
change, which is an important factor to consider in the design of an
environmental decision-making framework. Results from the use of this technique
on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the
pros and cons of the two methods and the effects of their overall accuracy on
post-classification change detection.