Modeling future urban network capacity and land use/land cover simulation using GEE and remote sensing data

• Published in: Arabian journal of geosciences 2023, Vol.16 (11), Article 628
• Main Authors: Khalid, Muhammad Nabeel, Ahmad, Muhammad Nasar, Javed, Muhammad Asif, Ahmad, Sajid Rashid
• Format: Article
• Language: English
• Subjects: Algorithms; Barren lands; Buses; Buses (vehicles); Change detection; City centres; Earth and Environmental Science; Earth science; Earth Sciences; Highways; International organizations; Land cover; Land use; Machine learning; Original Paper; Plant cover; Predictions; Public transportation; Remote sensing; Roads; Service areas; Sustainability; Sustainable development; Sustainable Development Goals; Sustainable transportation; Transportation networks; Transportation systems; Urban areas; Urban development; Urbanization; Vegetation; Vegetation cover
• ISSN: 1866-7511; 1866-7538
• DOI: 10.1007/s12517-023-11749-8

The urbanization rate is at its peak bringing economic boost and complex issues related to urban development. Many countries are promoting and implementing urban sustainability with a significant focus on promoting public transport systems. United Nations Sustainable Development Goals 2030 (UN SDG Agenda) provides a path for countries worldwide to achieve urban sustainability. The advancement of the Google Earth Engine and remote sensing data help monitor urban sustainability research. The study’s core objective was to investigate the serviceability of the proposed public bus transport system in Bahawalpur for 2030. The results for existing LULC revealed that about 18.5 km 2 of the urban area has increased from 2010 to 2020 with a decrease of 1 km 2 in vegetation and 19 km 2 in the barren land, respectively. This study also focuses on LULC prediction for 2030 based on a machine learning algorithm using Modules for Land Use Change Evaluation (MOLUSCE). The prediction map for 2030 was generated to understand the overall trend of LULC change detection. The LULC prediction results for the year 2030 indicate that urban area increased from 31.5 to 70 km 2 while vegetation cover decreased from 219 to 192 km 2 . Also, soil (barren land) was reduced from 64 to 52 km 2 . Furthermore, a detailed road network was created and service area analysis was performed for proposed bus stops. The results indicate that serviceability for the built-up area was 38% while the unserved area was observed at about 62%. The study spatially identified the area that appeared to be more vulnerable to future city expansion. The majority of the buildup area was concentrated around the city center, which was served by bus stations; nonetheless, a huge region surrounding important highways remains undeveloped. It can be concluded that the use of geospatial approach and future simulations can be useful in future planning for urban development, road planning, and achieving sustainability in transportation.