<p>Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy. &#160;As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.</p>
Abstract. Atmospheric drought due to meteorological events occurring out of seasonal norms, and consequent droughts in agriculture and wetlands cause great damage to the ecological balance. The initial effects of this situation appear on a local scale, while the aftereffects, which last for years, appear on a global scale. Monitoring and detecting drought with remote sensing technologies can contribute to the management of water resources and forest areas and enable many measures to be taken to reduce the effects of drought. Within the scope of this study, a system that automatically performs the extraction of different drought parameters depending on years has been developed. Işıklı Lake was selected as the study area and the change of water areas over the years has been extracted from satellite images. With the system developed on the Google Earth Engine platform, different parameters were analyzed over a 13-year period and their consistency was tested. As a result, it is seen that the water areas in the lake decreased by 30% between 2010 and 2022. Likewise, the systematic decrease in the parameters, especially in 2015 and afterward, indicates the drought in the region. With the proposed automatic system, it is thought that early precautions can be taken for drought scenarios that may occur in larger-scale regions.
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