Assessing urban growth in Greater Surabaya using Google Earth Engine: An evaluation of built-up area expansion in Indonesian secondary cities
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Published:2024
Issue:1
Volume:74
Page:127-138
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ISSN:0350-7599
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Container-title:Journal of the Geographical Institute Jovan Cvijic, SASA
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language:en
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Short-container-title:J geographical I JC
Author:
Purwono Nugroho1ORCID, Susetyo Danang1ORCID, Rijal Seftiawan2ORCID, Syaripah Gina3ORCID, Munawaroh Siti4ORCID
Affiliation:
1. National Research and Innovation Agency, Jakarta, Indonesia 2. Universitas Brawijaya, Marine Science Study Program, Faculty of Fisheries and Marine Science, East Java, Indonesia 3. Universitas Pendidikan Indonesia, Department of Geography Education, West Java, Indonesia 4. Universitas Padjadjaran, Faculty of Geological Engineering, West Java, Indonesia
Abstract
Urbanization in Indonesia's cities is increasing, leading to various impacts, including negative consequences due to insufficient investment in local public infrastructure. Urbanization assessment primarily relies on examining changes in built-up areas over the past decade. These changes serve as an indicator that can be effectively derived from remote sensing data. In our study, we applied remote sensing data from the Google Earth Engine (GEE) catalog to delve into the urbanization dynamics within Greater Surabaya area, Indonesia. We employed satellite imagery from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI TIRS) for 2012 and 2022. We used Support Vector Machine (SVM) classification techniques to construct precise urban expansion models. Our analysis revealed distinct urban expansion trends in Mojokerto and Sidoarjo, which contrast with the relatively stable urban development trends in northern Surabaya due to the construction of toll roads. The findings provide valuable inputs for urban management, necessitating targeted interventions and strategies to address the urbanization disparities between these two areas. It underscores the critical importance of resource allocation, infrastructure development, and urban planning initiatives, with a specific focus on Gresik, to ensure sustainable urban growth and mitigate potential challenges associated with rapid expansion.
Publisher
National Library of Serbia
Reference34 articles.
1. Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326-5350. https://doi.org/10.1109/JSTARS.2020.3021052 2. Aryal, A., Bhatta, K. P., Adhikari, S., & Baral, H. (2023). Scrutinizing Urbanization in Kathmandu Using Google Earth Engine Together with Proximity-Based Scenario Modelling. Land, 12(1), Article 25. https://doi.org/10.3390/land12010025 3. Belward, A. S. & Skøien, J. O. (2015). Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 115-128. https://doi.org/10.1016/j.isprsjprs.2014.03.009 4. Dorodjatoen, A. M. H. (2009). The Emergence of Jakarta-Bandung Mega-Urban Region and Its Future Challenges. Journal of Regional and City Planning, 20(1), 15-33. https://www.researchgate.net/publication/302957191_THE_EMERGENCE_OF_JAKARTA-BANDUNG_MEGA-URBAN_REGION_AND_ITS_FUTURE_CHALLENGES 5. Elhamdouni, D., Arioua, A., & Karaoui, I. (2022). Monitoring urban expansion using SVM classification approach in Khenifra city (Morocco). Modeling Earth Systems and Environment, 8(1), 293-298. https://doi.org/10.1007/s40808-021-01092-w
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