Policy-Driven Urban Expansion and Land Use/Land Cover Change in Ewa, Honolulu (2002–2022): Remote Sensing and Machine Learning Analysis of Transit-Oriented Development Impacts

Abstract

While prior studies on LULC change in the Ewa region of O’ahu Hawai’i have explored the policy implications and the rapid infrastructure changes on land use, very few studies have attempted to fully integrate both of these changes in a comprehensive, long-term study of island geographies. Most of the past work has focused on general trends or short-term fluctuations, without considering the play of nuanced interactions between urbanization policies, transit-oriented development, and constraints of Hawai’i’s finite land resources. To fill these gaps, this study examines LULC changes in Ewa, Honolulu between 2002 and 2022, which emphasizes the impacts of strategic urban policies and infrastructure development, such as the Honolulu Skyline Rail Transit System. Using Landsat 7 satellite imagery and random forest machine learning classifier, in Google Earth Engine, LULC is classified into urban, forest, vegetation, barren, and water with classification accuracy of over 85%. The results highlight trends of significant urban growth especially after 2010, and highlight key issues of tension between housing demands and environmental sustainability in O’ahu. This study highlights the potential of integrated remote sensing and policy analysis for informing sustainable development in land-constrained island settings, and advocates for planning frameworks that more effectively balance growth, ecosystem stewardship, and community welfare.