scholarly journals Problematic Soil Risk Assessment Approach for Sustainable Spatial Suitability of Urban Land Use in New Cities by Using GIS and Remote Sensing: A Case Study of New East Port Said City

2021 ◽  
Vol 16 (4) ◽  
pp. 615-628
Author(s):  
Dina Saleh ◽  
Abeer Abd El Kawy
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Land Use ◽  
Urban Land Use ◽  
Gis And Remote Sensing ◽  
Assessment Approach ◽  
Soil Risk Assessment ◽  
Port Said ◽  
Problematic Soil

scholarly journals GIS and remote sensing as tools for the simulation of urban land‐use change

2005 ◽  
Vol 26 (4) ◽  
pp. 759-774 ◽  
Author(s):  
Cláudia Maria De Almeida ◽  
Antonio Miguel Vieira Monteiro ◽  
Gilberto Câmara ◽  
Britaldo Silveira Soares‐Filho ◽  
Gustavo Coutinho Cerqueira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Gis And Remote Sensing ◽  
Urban Land Use Change

scholarly journals Predicting land use change base on GIS and remote sensing

2020 ◽  
Vol 61 (2) ◽  
pp. 106-115 ◽  
Author(s):  
Nga Thu Thi Nguyen ◽  
Hanh Thi Tong ◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Gis And Remote Sensing ◽  
The Neural Network ◽  
Rapid Changes ◽  
Urban Planning And Development ◽  
Land Use Development

The process of urbanization in Vietnam is developing rapidly, corresponding to the rapid changes in land use. Forecasting land use development assists policymakers in making accurate decisions in urban planning and development. The objective of this paper is to forecast and analyze land use development trends based on Landsat images in 2006, 2010, and 2016 with the help of remote sensing and GIS, a case study at Gio Linh district, Quang Tri province. The analysis results show that the rapid urbanization in Gio Linh district has led to a rapid decrease in the forest area from 28.14% compared to the total area, in 2016 to 13.5%, in 2026 to 2.67% and 2036 to 0.33%. The forecast processing was done on the neural network built-in module in the Selva version of IDRISI. The accuracy achieved is 80.5%.

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