Investigating Urban Growth and the Dynamics of Urban Land Cover Change Using Remote Sensing Data and Landscape Metrics

2020 ◽  
pp. 1-15
Author(s):  
Opeyemi A. Zubair
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Growth ◽  
Landscape Metrics ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Sensing Data ◽  
Urban Land Cover ◽  
Urban Land Cover Change

scholarly journals Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery

2021 ◽  
Vol 10 (8) ◽  
pp. 533
Author(s):  
Bin Hu ◽  
Yongyang Xu ◽  
Xiao Huang ◽  
Qimin Cheng ◽  
Qing Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Urban Land ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Sensing Data ◽  
Urban Land Cover ◽  
Sar Data

Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery.

Evaluating urban land cover change in the Hurghada area, Egypt, by using GIS and remote sensing

2011 ◽  
Vol 33 (1) ◽  
pp. 41-68 ◽  
Author(s):  
Samir Kamh ◽  
Mahmoud Ashmawy ◽  
Adamantios Kilias ◽  
Basile Christaras
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Land ◽  
Gis And Remote Sensing ◽  
Urban Land Cover ◽  
Urban Land Cover Change

scholarly journals Urban Growth and Its Impact on Urban Land Cover Change in Akure South Local Government, Ondo State, Nigeria

2021 ◽  
pp. 32-40
Author(s):  
Dada Ibilewa ◽  
Mustapha Aliyu ◽  
Usman O. Alalu ◽  
Taiwo Hassan Abdulrasheed
Keyword(s):  
Local Government ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Growth ◽  
Agricultural Land ◽  
Urban Land ◽  
Landsat Images ◽  
Rock Outcrops ◽  
Urban Land Cover ◽  
Urban Land Cover Change

Urban Growth and its Impact on Urban land cover change in Akure South Local Government area was investigated to bridge the knowledge gap created by data deficiency on the nature, scope, and magnitude of urban threat on the land use/land cover type, most especially the agricultural land in the area. This was done through the analysis of Landsat images of three epochs from 2000 through 2010 to 2020. The processing of the satellite images was done in ArcGIS 10.8, while the analysis and 2030 projection were done in Microsoft office excel using the result from the analysis. QGIS was used to remove the scan lines error on the 2010 image. The result showed increasing urban growth (built-up area), reducing vegetation and farmlands, and increasing rock outcrops. The changes vary among the different classification characteristics. Both farmlands and vegetation increased in the first epoch and reduced in the second epoch due to man's urbanization and other socio-economic activities. The increasing change in the second epoch was higher in built-up areas while rock outcrops increased throughout the study period. The research was able to assess the magnitude of farmland and vegetation that have been converted for urban uses over time. It also proved the efficiency of Remote Sensing and GIS technology in urban growth studies.

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