Urban Expansion Trends, Prediction and Its Impact on Agricultural Lands in Erbil Using GIS and Remote Sensing

Abstract

The surrounding agricultural lands in the city have been decreasing daily due to the expansion of urbanisation above it and the increase in the urbanisation rate in the study area, as the population growth exerted increasing pressures on the city. Furthermore, the increase in population increases the demand for land for housing and other human services, which will impact agricultural lands. In addition, the lack of proper planning in the city contributes to expanding urbanisation at the expense of agricultural land. This study aims to study the urban expansion in the direction of agricultural lands in Erbil from the year 2000 until 2020, reveal the reasons for the urban expansion in the city and put an end to the trespassers on the lands and it has negative impact on the lack of agricultural areas and the encroachment of urbanisation on it. Landsat TM 5 and Landsat 8 OLI will be used to identify and develop urban growth and its impacts on agriculture and some Remote sensing Data and GIS from 2000 to 2020 with 10 years difference to find the changes in these years and also provide a predicted map for Erbil governorate. The study recommended the necessity of preparing a strategic plan for the use of agricultural lands that regulates the urban development process of the population centres and achieves the appropriate and sustainable use of agricultural lands and their preservation. Encouraging the investment of lands and cultivation of crops to meet the population's need for vegetables and other crops. The findings of this study will help decision-makers develop future urbanisation policies, and it is worthwhile to investigate them further. The prediction model will demonstrate whether built-up areas will continue to grow or not and whether the average agricultural areas will continue to shrink based on regression analysis. Planning effective urban environmental management can benefit from this type of forecast of the LULC picture in the future.

Keywords

• change detection
• change prediction
• lcm
• ca markov
• gis
• remote sensing