Remote Sensing-Based Urban Sprawl Modeling Using Multilayer Perceptron Neural Network Markov Chain in Baghdad, Iraq

Wafaa Majeed Mutashar Al-Hameedi,Biswajit Nath,Cheechouyang Faichia,Abdulla-Al Kafy,Bazel Al-Shaibah,Jie Chen,Gao Hu,Ali Al-Aizari

doi:10.3390/rs13204034

Abstract

The global and regional land use/cover changes (LUCCs) are experiencing widespread changes, particularly in Baghdad City, the oldest city of Iraq, where it lacks ecological restoration and environmental management actions at present. To date, multiple land uses are experiencing urban construction-related land expansion, population increase, and socioeconomic development. Comprehensive evaluation and understanding of the effect of urban sprawl and its rapid LUCC are of great importance to managing land surface resources for sustainable development. The present research applied remote sensing data, such as Landsat-5 Thematic Mapper and Landsat-8 Operation Land Imager, on selected images between July and August from 1985 to 2020 with the use of multiple types of software to explore, classify, and analyze the historical and future LUCCs in Baghdad City. Three historical LUCC maps from 1985, 2000, and 2020 were created and analyzed. The result shows that urban construction land expands quickly, and agricultural land and natural vegetation have had a large loss of coverage during the last 35 years. The change analysis derived from previous land use was used as a change direction for future simulation, where natural and anthropogenic factors were selected as the drivers’ variables in the process of multilayer perceptron neural network Markov chain model. The future land use/cover change (FLUCC) modeling results from 2030 to 2050 show that agriculture is the only land use type with a massive decreasing trend from 1985 to 2050 compared with other categories. The entire change in urban sprawl derived from historical and FLUCC in each period shows that urban construction land increases the fastest between 2020 and 2030. The rapid urbanization along with unplanned urban growth and rising population migration from rural to urban is the main driver of all transformation in land use. These findings facilitate sustainable ecological development in Baghdad City and theoretically support environmental decision making.

Highlights

Introductionland use/cover changes (LUCCs) is defined as the land adaptation from its original use into another land use type in the same area due to the complex association between human activity and environmental physiology, which is the main key effect of regional and global environments [10,11]
If the accuracy of each class and image analysis is over 85%, they are considered suitable for land use/cover changes (LUCCs) prediction [59]
multilayer perceptron neural network (MLPNN) and Markov chain model (MCM) were selected for the spatial simulation of future LUCC (FLUCC)

Summary

Introduction

LUCC is defined as the land adaptation from its original use into another land use type in the same area due to the complex association between human activity and environmental physiology, which is the main key effect of regional and global environments [10,11] These variations straightly contradict the principle of sustainability by leading to several negative impacts, such as fragmentation of the agroforest landscape, landscape degradation, impact to the land surface, increase in energy consumption and emission of greenhouse gases, loss of biodiversity, soil resource degradation, and climate change on the regional and global scales [12,13]. Numerous models have been developed to simulate FLUCC These models enable the provision of suitable tools for identifying the spatial patterns in land use/land cover (LULC) [19].

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Conclusion