Land use/land cover change detection along the coastline of Nigeria and its probable causes

The Nigerian coastline has been subjected to studies on land use/land cover changes, using satellite images, for three decades. This paper is borne out of the need to understand the dynamics of coastal management. The study aims at assessing land use-land cover changes along coastline in Nigeria from 1986 to 2016 using multi-day satellite imageries. The satellite data were used to extract land use/cover changes and to map the physical extent of the coastal areas of Nigeria for the three-time series during the same season. Urban/built up areas, water and vegetation are the three land use/cover classes of interest along the Nigerian coastline. The urban/built up area class increased from 8.9% in 1986 to 13.7% in 2000, and then 23% in 2016. On the other hand, vegetation decreased from 55% in 1986 to 49% in 2000 and then 43% in 2016. In contrast, water class increased from 36% in 1986 to 37% in 2000, and then decreased to 32.7% in 2016. Considering observations made from this study, it is therefore recommended that the appropriate government agencies, coastal managers and urban planners should promote afforestation along with other mitigation measures, to reduce the adverse effects of human develop-ment on the ecosystem.

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Assessment of land use land cover change detection in multitemporal satellite images using machine learning algorithms

10.1016/b978-0-12-824410-4.00006-4 ◽

2022 ◽

pp. 27-45

Author(s):

Mahalakshmi Murugan ◽

Rohini Selvaraj ◽

Sureshkumar Nagarajan

Keyword(s):

Machine Learning ◽

Land Use ◽

Land Cover ◽

Change Detection ◽

Land Cover Change ◽

Satellite Images ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Land Use Land Cover ◽

Land Cover Change Detection

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Application of GIS and Remote Sensing to Analyse Land Use Land Cover Change Detection and Vegetation Dynamics Using Multi-temporal Satellite Images; The Case of Mariamdehan Kebele, Tigray Region, Northern Ethiopia

Asian Journal of Geographical Research ◽

10.9734/ajgr/2019/v2i230079 ◽

2019 ◽

pp. 1-11

Author(s):

Esayas Meresa ◽

Yikunoamlak Gebrewhid

Keyword(s):

Land Use ◽

Land Cover ◽

Natural Resources ◽

Change Detection ◽

Land Cover Change ◽

Vegetation Dynamics ◽

Satellite Images ◽

Land Use Land Cover ◽

Multi Temporal ◽

Land Cover Change Detection

Detecting Land use and land cover change and vegetation condition has become a central component in current strategies for managing and monitoring of environmental changes caused by anthropogenic activities. To come up with such decisions, geoinformatics technology is providing new tools to conduct vegetation and land use land cover change detection analysis for managing and wise utilisation of natural resources as well as to provide information for policymakers in a given study area. This study examines the use of geoinformatics technology to analyse land use land cover (LULC) change and vegetation dynamics using multi-temporal satellite images for the maryamdehan kebele in the years 1984, 2005 and 2015. Both primary and secondary data were used from different sources. Satellite images of the year 1984, 2005 and 2015 were downloaded from the govis.usgs.gov website and ground control points (GCP) data were collected by handheld GPS for supervised image classification in Erdas imagine and ArcGIS environment. The findings show that six main land use land cover classes were detected and vegetation values were also computed in each period. As a result, the total area of the kebele was 3646.49 hectare, from which in 1984 forest area (40.691%), grassland (26.15%) and farmland (10.81%) were dominant classes and in 2005 settlement (52.41%), forest area (25.04%) & farmland (11.71%) and in 2015, 35.14% was covered by forest land, 30.04% by Settlement, and 14.74% by farmland. Water resource decreases from 9.3% to 0.64% in 2015 and the bare land also changes from 3.18% to 0.903% because of urban expansion and agricultural activities in the kebele. In addition, the vegetation condition looks like a sinusoidal trend from the year 1984 up to 2015 because of climate change and human interventions in the kebele. To conclude that detecting LULC change and analysis of vegetation dynamics plays a great role in land use management and wise utilisation of natural resources by applying Geoinformatics tools in the kebele and it provides information for the policymakers to prepared future plan and for sustainable development.

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