Comparative Analysis of Drone-based and GNSS Surveys in Urban Land Use Mapping

Thesis Abstract

Urban land use mapping is a critical component of sustainable city planning, resource management, and urban development, yet effective data acquisition methods remain a challenge due to technological constraints and resource limitations. Recent advances in remote sensing technologies, particularly Unmanned Aerial Vehicles (UAVs) and Global Navigation Satellite System (GNSS) surveys, offer promising alternatives for collecting accurate and timely spatial data. However, comparative analyses of these two methodologies within urban environments are limited, necessitating an empirical investigation to inform best practices and guide decision-making in land use applications. This study aims to conduct a comprehensive comparative analysis of drone-based and GNSS surveys in urban land use mapping, with specific objectives to evaluate accuracy, efficiency, cost-effectiveness, and suitability within the complexities of densely built environments. The research adopts a quantitative, cross-sectional survey design grounded in the technological adoption theory and the spatial accuracy framework to provide a structured basis for comparison. The target population comprises urban surveyors, GIS specialists, and city planning officials in a metropolitan city with diverse land types. A purposive sampling technique selected 50 professionals directly involved in land surveying processes, and a stratified sampling approach ensured representation across various urban land use zones. Data collection employed structured questionnaires, field survey data, and aerial imagery obtained through drone flights and GNSS measurements. The drone surveys were conducted using a DJI Phantom 4 Advanced UAV equipped with high-resolution RGB cameras, while GNSS data were captured using state-of-the-art GNSS receivers with Real-Time Kinematic (RTK) and Post-Processed Kinematic (PPK) capabilities. The validity and reliability of survey instruments were established through pilot testing and consistency checks, including calibration of GNSS equipment. Data analysis involved spatial accuracy assessment via Root Mean Square Error (RMSE) calculations, statistical tests such as paired t-tests and ANOVA to compare the datasets’ precision, and cost-benefit analysis to evaluate resource utilization. The study further applied Geographic Information System (GIS) overlay techniques to analyze land use classification accuracy, and regression analysis to examine the relationship between survey method parameters and mapping outcomes. Theoretical anchoring references include the Technology Acceptance Model (TAM) for understanding technology adoption and Tobler’s First Law of Geography to interpret spatial data consistency. Expected findings suggest that drone-based surveys will demonstrate superior spatial resolution and faster data acquisition per land parcel, whereas GNSS surveys will exhibit higher positional accuracy in open areas but face limitations in dense built-up zones. Cost analysis is anticipated to reveal that UAV surveys incur lower overall costs for large-scale land use mapping within urban settings when considering labor, equipment, and processing time. These results are poised to significantly contribute to the existing body of knowledge by providing empirical guidelines for selecting appropriate survey methods based on environmental context, accuracy requirements, and resource availability. In conclusion, the study will underscore the importance of context-specific survey methodology selection and advocate for the integration of UAV technology into routine urban land use assessments, especially in complex environments. Recommendations will include developing standardized protocols for drone and GNSS survey deployment, investing in capacity building for urban surveyors, and fostering policy frameworks that facilitate technology adoption. Furthermore, the study will suggest avenues for future research, such as exploring the integration of multispectral drone sensors for land cover classification and the development of hybrid survey techniques that combine UAV and GNSS advantages. Overall, this research aims to bridge the gap between technological capabilities and practical urban land management needs, ultimately enhancing spatial data quality and informing sustainable urban development strategies.