Obtaining a Land Use/Cover Cartography in a Typical Mediterranean Agricultural Field Combining Unmanned Aerial Vehicle Data with Supervised Classifiers

The mapping of land use/cover (LULC) types is a crucial tool for natural resource management and monitoring changes in both human and physical environments. Unmanned aerial vehicles (UAVs) provide high-resolution data, enhancing the capability for accurate LULC representation at potentially very high spatial resolutions. In the present study, two widely used supervised classification methods, namely the Maximum Likelihood Classification (MLC) and Mahalanobis Distance Classification (MDC), were applied to analyze image data collected by UAVs from a typical Mediterranean site located in Greece. The study area, characterized by diverse land uses (urban, agricultural, and natural areas), served as an ideal field for comparing the two classification methods. Although both methods produced comparable results, MLC outperformed MDC, with an overall accuracy of 96.58% and a Kappa coefficient of 0.942, compared to MDC for which an overall accuracy of 92.77% and a Kappa coefficient of 0.878 were reported. This study highlights the advantages of using UAVs to produce robust information on the geospatial variability of land use/cover in a given area at very high spatial resolution in a cost-efficient, timely, and on-demand manner. Such information can help in decision- and policy-making for ensuring a more sustainable physical environment. This study’s limitations, including the small and relatively homogeneous study area, are acknowledged. Future research could potentially focus on exploring the use of advanced classification techniques, such as deep learning and more diverse Mediterranean landscapes, which would assist in enhancing the present’s approach applicability.