A Framework for Integrating Remote Sensing Data into Urban Land Use Planning

Thesis Abstract

Rapid urbanization and expanding metropolitan areas have heightened the necessity for accurate, efficient, and sustainable land use planning. Traditional planning approaches often rely heavily on manual surveys and static maps, which may not adequately capture the dynamic and complex nature of urban environments. This research addresses the critical gap in integrating remote sensing data effectively into urban land use planning processes by proposing a comprehensive framework that enhances decision-making through geospatial information systems (GIS), satellite imagery, and spatial analysis techniques. The primary aim of this study is to develop, validate, and demonstrate a robust, scalable framework for integrating remote sensing data into urban land use planning to improve spatial analysis, land suitability assessments, and urban growth management. Specific objectives include (1) to analyze existing methodologies and frameworks for remote sensing in urban planning, (2) to identify key indicators and metrics from remote sensing datasets pertinent to land use classification, (3) to design a conceptual model that integrates remote sensing data with GIS-based planning tools, and (4) to validate the framework through a case study in a rapidly urbanizing city. This study seeks to make a significant contribution to geospatial planning literature by providing an operational model that combines theoretical insights with practical applications. The research adopts a mixed-methods approach, combining qualitative and quantitative techniques. The qualitative component involves a comprehensive review of literatures and existing frameworks, informed by the theoretical underpinning of the Theory of Land Utilization (theory of human-environment interaction) and the Spatial Decision Support Systems (SDSS) theory, which emphasizes informed and participatory planning. The quantitative aspect employs a descriptive and inferential statistical analysis. A case study methodology is applied, focusing on the metropolitan area of a mid-sized city with a population of approximately 1.5 million residents. A stratified random sampling technique is used to select a sample population of 150 land use planning officials, GIS analysts, and urban planners for interviews and questionnaires. Remote sensing data, including multispectral satellite imagery (Landsat 8 and Sentinel-2) and aerial photography, are collected over a 12-month period to ensure temporal variation and data accuracy. Data analysis involves spatial data processing using supervised and unsupervised classification algorithms (Maximum Likelihood Classification and K-Means Clustering). The integrated framework is operationalized through a Geographic Information System (ArcGIS Pro) and custom-developed decision-support modules. Analytical techniques include regression analysis to examine relationships between remote sensing indicators and land use categories, alongside thematic analysis of qualitative data. The framework’s effectiveness is validated through a set of criteria, including accuracy assessments (overall accuracy, Kappa coefficient) and stakeholder feedback. The anticipated findings depict that the integrated framework significantly improves the precision and efficiency of land use classification, enhances spatial visualization of urban expansion, and facilitates more sustainable planning decisions. It is expected that the framework will demonstrate high classification accuracy (above 85%) and foster participatory planning processes through accessible decision-support tools. The study further anticipates identifying key remote sensing indicators, such as vegetation indices and built-up area metrics, that are critical for urban land use planning. This research contributes to theoretical and practical knowledge by bridging the gap between remote sensing technology and urban planning practice, offering a replicable and adaptable model for cities worldwide experiencing rapid urban growth. It advances understanding of how integrated geospatial data enhances land designation processes, supports sustainable urbanization, and mitigates informal development. The main conclusion underscores that effective integration of remote sensing data into urban land use planning is vital for informed, transparent, and sustainable urban development. Recommendations include adopting the proposed framework as a standard planning tool, investing in capacity building for urban planners in GIS and remote sensing technologies, and encouraging policymakers to institutionalize data-driven planning practices. Future research should explore the integration of emerging technologies such as deep learning and real-time data streams to further enhance urban land use analysis and decision-making.