In this study, we have evaluated the use of unmanned air vehicle (UAV) photogrammetry for the monitoring of a wheat experiment under field conditions, filtered a digital surface model (DSM) to derive the wheat plant heights, and compared the results with the field measurements. The images were acquired with the use of a low-cost UAV Walkera QR350 and GoProHero3+ action camera in May 2015. In total, 477 images were acquired for quality assessment of the proposed method, and a reference dataset was collected with terrestrial fieldwork. For a comparison of field measurements with DSM-derived plant heights, the maximum calculated plant height in the plot was selected. The mean, median, and standard deviation were calculated as 4.66, 3.75, and 13.78 cm. Regarding the statistical t-test between the field measurements and plant heights from the DSM, the t-value was calculated as 1.82, and the p-value was 0.071. Because the t-value was larger than 0.50, the values between the traditional method and our approach were highly correlated.

Remote sensing and geographic information systems (GIS) are effective tools for acquiring fast and accurate information about the Earth's resources and for providing a medium for the efficient flow of information. Moreover, these systems are particularly suitable for determining land use types and monitoring their variation through time. In this study, changes in land cover and land use types in the last 23 years were determined for the town of Kemer (in the province of Antalya) by means of remote sensing and GIS. Kemer is located within the western Mediterranean region of Turkey and is of national and international importance due to its natural, cultural, historical and tourism significance. Stereoscopic interpretation of aerial photographs and visual interpretation of high resolution IKONOS images were conducted and land cover type changes over 23 years were determined using the Coordination of Information on the Environment (CORINE) methodology. In this context, eight different land cover types were examined: and their changes were analysed using data from 1981, 1992 and 2004. Results indicate that urban land use increased approximately 10 times, from 86.3 ha to 931.0 ha, and tourism and secondary residence land use increased nearly 140 times, from 5.3 ha to 715.8 ha. In contrast, decreases of 65.8% in complex cultivation patterns, 37.2% in coastal dunes and beaches, and 2.1% in permanent crops were observed. This land use change reflects the large scale net loss of natural resources in the area and this trend does not suggest sustainable natural resource management. Over the last quarter of a century, the most significant reason for changes in land use in the Kemer area appears to be caused by the pressure from the tourism and secondary residence sectors.

Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML), Random Forest (RF), and Support Vector Machines (SVM) classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.