<p><strong>Abstract.</strong> A flood, one of the most devastating natural disasters in the world, occurs when water inundates land that's normally dry. Although floods can develop in many ways, river floods (i.e. overflow by rivers or river banks) are the most common. Turkey is one of the flood-affected countries with its 20 main basins in 8 regions. One of the most aggrieved basins in Turkey is the Maritsa river basin in in Eastern Balkans, which also contains the natural border regions with Greece and Bulgaria. 65% of the Maritsa River basin, which originates from the Rila Mountains and joins the Arda and Tundzha rivers, is located in Bulgaria. When the melting snow flow or precipitation in the basin increases, the Maritsa River overflows from the slopes to the Edirne Plain and from time to time exceeds the capacity of the bed, causing floods. On the other hand, since the water level in the dams and reservoirs was kept at the highest level for production purposes, the flood repeat interval increased in the region, since 2000s. Today, it is possible to monitor and evaluate the damages of flood by obtaining very reliable information with space technology. Especially, microwave SAR images that can penetrate clouds, are of great importance in flood mapping because they provide immediate information on the extent of inundation and support the evaluation of property and environmental damages. In this study, rapid flood risk assessment in the region was performed using Landsat 8 and Sentinel 2 Normalized Difference Water Index (NDWI) time series images, and calibrated Sentinel 1 SAR images produced on Google Earth Engine (GEE) platform for 2015-2018 period. GEE is a cloud-based platform that facilitates access to high-performance computing resources to handle very large geographic data sets. The results were compared and verified using meteorological data, riverbed flow data, and digital media news. The results showed that the most affected areas were consistent with the highest measured flow rates and the magnitude of flood damages caused by two main causes in the basin (i.e. opening of shutters in Bulgarian dams or local excessive rainfall) was very different (approximately 8 times larger) from each other.</p>

<p><strong>Abstract.</strong> A forest fire is stated as an ecological disaster whether it is man-made or caused naturally. İzmir is one of the regions where forest fires are most intensified in Turkey. The study area located at Aegean region of Turkey suffered two forest fires in 2017; Menderes and Bayındır areas. This study presents the integration of remote sensing (Sentinel 2 and Landsat 8 satellite images) and GIS data to map and evaluate the forest burned areas due to both forest fires. For this purpose, different indexes such as Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR) and Normalized Burn Ratio Thermal (NBRT) Burn Index are applied besides different classification algorithms. The results showed that different vegetation types/zones are being affected. Sentinel 2 and Landsat 8 data are integrated to the GIS established with fieldwork data to analyse and also validate the results. Digital Elevation Model (DEM) data produced from ASTER satellite is also overlaid to the outcomes to emphasize the destructed forest areas. The efficiency of using two different satellites are outlined by comparing the accuracy of forest fire maps produced.</p>

Abstract. Turkey has favorable agricultural conditions (i.e. fertile soils, climate and rainfall) and can grow almost any type of crop in many regions, making it one of the leading sectors of the economy. For sustainable agriculture management, all factors affecting the agricultural products should be analyzed on a spatial-temporal basis. Therefore, nowadays space technologies such as remote sensing are important tools in providing an accurate mapping of the agricultural fields with timely monitoring and higher repetition frequency and accuracy. In this study, object based classification method was applied to 2017 Sentinel 2 Level 2A satellite image in order to map crop types in the Adana, Çukurova region in Turkey. Support Vector Machine (SVM) was used as a classifier. Texture information were incorporated to spectral wavebands of Sentinel-2 image, to increase the classification accuracy. In this context, all of the textural features of Gray-Level Co-occurrence Matrix (GLCM) were tested and Entropy, Standard deviation, and Mean textural features were found to be the most suitable among them. Multi-spectral and textural features were used as an input separately and/or in combination to evaluate the potential of texture in differentiating crop types and the accuracy of output thematic maps. As a result, with the addition of textural features, it was observed that the Overall Accuracy and Kappa coefficient increased by 7% and 8%, respectively.