scholarly journals Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia

2016 ◽  
Vol 8 (10) ◽  
pp. 838 ◽  
Author(s):  
Abdullah Alqurashi ◽  
Lalit Kumar ◽  
Priyakant Sinha
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Growth ◽  
Satellite Images ◽  
Urban Land ◽  
Urban Land Cover ◽  
Urban Land Cover Change

scholarly journals TRAJECTORY-BASED ANALYSIS OF URBAN LAND-COVER CHANGE DETECTION

2016 ◽  
Vol XLI-B7 ◽  
pp. 607-610 ◽  
Author(s):  
Y. H. Zhang ◽  
H. P. Liu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Similarity Index ◽  
Urban Land ◽  
Urban Land Cover ◽  
Long Time ◽  
Urban Land Cover Change ◽  
Land Cover Change Detection

China have occurred unprecedented urban growth over the last two decades. It is reported that the level of China’s urbanization increased from 18 % in 1978 to 41 % in 2003, and this figure may exceed 65 % by 2050. The change detection of long time serious remote sensing images is the effective way to acquire the data of urban land-cover change to understand the pattern of urbanization. In this paper, we proposed the similarity index (SI) and apply it in long time series urban land-cover change detection. First of all, we built possible change trajectories in four times based on the normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) that extracted from time series Landsat images. Secondly, we applied SI in similarity comparison between the observed change trajectory and the possible trajectories. Lastly, verifying the accuracy of the results. The overall accuracy in four periods is 85.7 % and the overall accuracy of each two years is about 90 % and kappa statistic is about 0.85. The results show that this method is effective for time series land-cover change detection.

scholarly journals TRAJECTORY-BASED ANALYSIS OF URBAN LAND-COVER CHANGE DETECTION

2016 ◽  
Vol XLI-B7 ◽  
pp. 607-610
Author(s):  
Y. H. Zhang ◽  
H. P. Liu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Similarity Index ◽  
Urban Land ◽  
Urban Land Cover ◽  
Long Time ◽  
Urban Land Cover Change ◽  
Land Cover Change Detection

China have occurred unprecedented urban growth over the last two decades. It is reported that the level of China’s urbanization increased from 18 % in 1978 to 41 % in 2003, and this figure may exceed 65 % by 2050. The change detection of long time serious remote sensing images is the effective way to acquire the data of urban land-cover change to understand the pattern of urbanization. In this paper, we proposed the similarity index (SI) and apply it in long time series urban land-cover change detection. First of all, we built possible change trajectories in four times based on the normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) that extracted from time series Landsat images. Secondly, we applied SI in similarity comparison between the observed change trajectory and the possible trajectories. Lastly, verifying the accuracy of the results. The overall accuracy in four periods is 85.7 % and the overall accuracy of each two years is about 90 % and kappa statistic is about 0.85. The results show that this method is effective for time series land-cover change detection.

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.

Sign in / Sign up

Export Citation Format

Share Document