A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region
A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region作者机构：Department of Earth Sciences College of Science Federal University of Petroleum ResourcesEffurun Nigeria
出 版 物：《Journal of Geographic Information System》 (地理信息系统（英文）)
年 卷 期：2016年第8卷第2期
页 面：163-170页
学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学]
主 题：Land Cover Supervised and Unsupervised Classification Algorithms Landsat Images Change Detection Niger Delta
摘 要：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.
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