Assessing the Role of Multi-Resolution Remote Sensing in Monitoring Urban Growth and Environmental Change in Rapidly Urbanizing Regions

Rapid urbanization in developing and transitional regions has created significant challenges in monitoring land use change and environmental degradation. Remote sensing (RS) has emerged as a vital tool for assessing urban growth and its ecological impacts due to its capability to provide consistent, multi-temporal, and large-scale data. However, conventional RS methods often rely on single-resolution imagery, which limits the ability to capture both detailed urban features and broader environmental trends simultaneously. Additionally, challenges such as high processing costs, skill requirements, and fragmented collaboration between Remote Sensing and Geographic Information Systems (GIS) communities further hinder comprehensive urban analysis. This study proposes a novel multi-resolution remote sensing methodology that integrates high, medium, and low-resolution satellite data for a holistic assessment of urban dynamics. The proposed approach enhances urban feature detection and environmental monitoring through spatial data fusion, supervised classification, and change detection techniques. The workflow includes data acquisition from diverse sources, pre-processing (correction, enhancement), classification using machine learning algorithms (e.g., Random Forest), accuracy assessment through ground-truth validation, and visualization via thematic maps. Experimental results demonstrate the method’s effectiveness in capturing urban sprawl, vegetation loss, and pollution indicators in rapidly urbanizing regions. The integrated multi-resolution framework outperforms single-source analysis in both accuracy and spatial coverage. This approach supports decision-making for sustainable urban planning by delivering high-quality geospatial insights. Overall, the proposed methodology bridges the gap between RS and GIS applications and serves as a scalable model for future urban environmental monitoring initiatives.