Document Type
Original Article
Abstract
Integration of GIS and remote sensing is a powerful source for implementing temporal analysis. The focus of this paper was to use Landsat 5 and 8 imageries to monitor changes in the Ranya city throughout five time periods (2000, 2005, 2010, 2015, and 2020). Image classification was done using iso cluster unsupervised classification tool via ArcGIS. To calculate the kappa coefficient and assess the accuracy, random points of the outcome were compared with ground truth data using Google Earth Pro, which produced highly accurate results. According to this study, the Ranya city had the highest proportion of green space (52%) in 2000. Though, after 20 years due to rapid urbanization, Ranya's built-up regions had quickly outgrown both bare land and agricultural land, growing in size by 223.53%. However, from the year 2000 to 2020, green spaces and bare land both showed a clear downward trend, losing, respectively, 44.9% and 41.3% of their space. Being an independent administration is one of the key causes of Ranya city's rapid urbanization and subsequent development between 2000 and 2020, in addition to other aspects like the building of numerous government and residential facilities.
Keywords
Change detection; GIS; LULC; Remote sensing; Landsat
How to Cite This Article
Chomani, Kaifi and Manguri, Shwana Braim Hassan
(2024)
"Spatiotemporal Insights into Ranya's Land-Use Transformation using Time-Series Imagery,"
Polytechnic Journal: Vol. 14:
Iss.
1, Article 9.
DOI: https://doi.org/10.59341/2707-7799.1825
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