USING VISUAL TECHNIQUES TO DETERMINE THE CHANGES USING VISUAL TECHNIQUES TO DETERMINE THE CHANGES | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Article 2, Volume 16, Issue 1, January 2016, Page 19-36 PDF (3.26 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2016.10005 | ||||
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| Authors | ||||
| T. Elarif Shabana1; B Shabana2; O Abu -ElNasr3; B Alnwsah3 | ||||
| 1Computer Science Dep., Faculty of Computer and Information Sciences, Ain Shams University- Egypt | ||||
| 2Misr Higher Institute for Commerce and Computer Science, Mansoura-Egypt | ||||
| 3Computer Science Dept., Faculty of Computer and Information System, Mansoura University - Egypt | ||||
| Abstract | ||||
| Building new cities at the fringes of old ones is a mandatory nowadays to lower the over increasing population in old cities, and to decrease the heavy load on the infrastructure and services. The main objective of this work was to evaluate the spatial and temporal changes in land uses within the studied area by using Remote Sensing (RS) and Geographic Information Systems (GIS) data and techniques. This is in addition to providing accurate estimations of current land uses to support decision makers with the right information for further development. Accordingly, Landsat TM images in 1984 and 1999 and Landsat 8 in 2014 were used in this study. Normalized difference vegetation difference index (NDVI) was used to map agricultural versus non - agricultural lands. Also, the modified normalized difference water index (MNDWI) was used to map dry lands versus wet lands (Fish pounds) in the area. The obtained results indicated that agricultural lands were increased by about 23.1 km2 from 1984 to 1999 and by about 30.1 km2 from 1999 to 2014. The total increase in agricultural lands in 30 years from 1984 to 2014 was about 53.2 km2. That increase in agricultural lands was due to land reclamation projects north of Nile-Delta. On the other hand, water features were increased by about 16.3 km2 from 1984 to 1999 and by about 23.0 km2 from 1999 to 2014. The total increase in Water features from 1984 to 2014 was about 39.3 km2. That increase in water features was mainly due to the development of fish pounds. Land use classification derived from the gap-filled Landsat SCL-off image acquired in 2009 was more accurate when the gap-filling was carried out by using the Landsat gap-fill plug-in ENVI than using the Matlab. The overall accuracy of the gap-filled images was not very high, where the gap-filling algorithms could not retrieve the actual pixel values but interpolate them. | ||||
| Keywords | ||||
| Land cover; land use; NDVI; MNDWI; Change Detection; Gap-fill; Remote Sensing; GIS | ||||
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