基于多源图像的沿海城市土地利用/土地覆盖变化检测:以尼日利亚拉各斯为例

IF 0.3 Q4 REMOTE SENSING
T. Idowu, R. Waswa, K. Lasisi, M. Nyadawa, V. Okumu
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引用次数: 3

摘要

随着沿海地区人口的迅速增长、社会经济和环境问题的出现,LULC变化检测已成为实现可持续发展的环境监测的重要工具。在本研究中,采用基于对象的图像分析方法,使用后分类比较技术,对1986年至2016年沿海城市拉各斯的LULC进行了评估。该研究描述了如何成功地整合来自不同来源(陆地卫星和SENTINEL 2A)的卫星图像,用于土地利用-土地覆盖变化检测。结果显示,1986年至2016年间,裸露地区、建成区和灌木林净增加,林地、水体和湿地普遍减少。超过60000公顷的覆盖面积(约增长190%)被转化为建成区,83541公顷(835.4平方公里)的林地被损失,这表明城市化率很高,相应的森林砍伐也很严重。在同一时间段内,湿地也减少了约60%。水体的减少和裸露和建成区的稳步增加可能是由于研究区域内普遍的土地复垦活动。与1986年至2001年相比,2001年至2016年的森林砍伐率和裸露地区的增加率更高。观察到的趋势可能会继续,对于未来的管理行动,建议进行预测性研究,以提供更多的经验证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object-based land use/land cover change detection of a coastal city using Multi-Source Imagery: a case study of Lagos, Nigeria
In the wake of the burgeoning population, socio-economic and environmental issues facing coastal areas, LULC change detection has become an essential tool for environmental monitoring towards achieving sustainable development. In this study, an object-based image analysis approach using post-classification comparison technique was applied for assessing the LULC of the coastal city of Lagos from 1986 to 2016. The study describes how satellite imagery from different sources (Landsat and SENTINEL 2A) can be successfully integrated for Land use Land cover change detection. The results show that between 1986 and 2016, there were net increases in bare areas, built-up areas, and shrublands and a general decline in forestlands, waterbodies and wetlands. Over 60,000ha cover (approx. 190% increase) was converted into built-up areas while 83,541ha (835.4km2) of forestland were lost, suggesting high rates of urbanization and corresponding deforestation. About 60% loss of wetlands was also observed in the same time period. The decrease in water bodies and a steady increase in bare and built-up areas are possibly due to the prevalent land reclamation activities in the study area. Higher rates of deforestation and increase in bare areas were observed from 2001 to 2016 in comparison to 1986 to 2001. The observed trends are likely to continue, and for future management actions, predictive studies are suggested to provide more empirical evidence.
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