Flooding, one of the most destructive and recurrent natural catastrophes, causes severe loss of life and property. The destructive effect of floods has increased with climate change and unplanned urbanization. To prevent this devastation and find solutions to potential flooding, it is important to improve engineering, ecological, hydrological, and hydrogeological precautions, as well as potential flood simulations. Using hydraulic models to perform flood simulations is a common and successful approach globally. In this study, HEC-RAS (1D) was used to simulate three different flood scenarios on the Boğaçay sub-basin in Antalya, the most important tourism destination of Türkiye. Flood scenarios were developed based on the data of the floods that occurred in the region in 2003 and 2006, with measured flow rates of 1899.9 m³/s and 1450 m³/s, respectively, and with the maximum flow rate (2408 m³/s) determined by the relevant ministry. Then, the landscape composition of the region at the sub-basin scale was determined and the impacts of flood scenarios on the landscape composition around the riverbed were evaluated. The results of the analysis show that the water height will increase, ranging from 1.4 m to 3.6 m, and the landscape composition of the region will be significantly affected by this increase in water height in the three different flood scenarios. Especially in the part where the river meets the sea, 580.74 ha of urban settlement is estimated to be damaged by flooding, according to the worst-case scenario. Finally, the study will guide decision-makers to take the necessary measures under the relevant scenarios.

Pixel-based classification method is widely used with the purpose of detecting land use and land cover with remote sensing technology. Recently, object-based classification methods have begun to be used as well as pixel-based classification method on high resolution satellite imagery. In the studies conducted, it is indicated that object-based classification method has more successful results than other classification methods. While pixel-based classification method is performed according to the grey value of pixels, object-based classification process is executed by generating imagery segmentation and updatable rule sets. In this study, it was aimed to detect and map the greenhouses from object-based classification method by using high resolution satellite imagery. The study was carried out in the Antalya province which includes greenhouse intensively. The study consists of three main stages including segmentation, classification and accuracy assessment. At the first stage, which was segmentation, the most important part of the object-based imagery analysis; imagery segmentation was generated by using basic spectral bands of high resolution Worldview-2 satellite imagery. At the second stage, applying the nearest neighbour classifier to these generated segments classification process was executed, and a result map of the study area was generated. Finally, accuracy assessments were performed using land studies and digital data of the area. According to the research results, object-based greenhouse classification using high resolution satellite imagery had over 80% accuracy.

Bu çalışmada farklı özelliklere sahip insansız hava araçları kullanılarak (İHA), üç ayrı tarihte çekimi yapılan makarnalık buğday çeşidinin bitki boylarının yarı otomatik olarak hesaplanması amaçlanmıştır. Çalışma, Akdeniz bölgesi için uzaktan algılama çalışmalarında en uygun tarih olarak kabul edilen ve buğdayın vejetatif dönemden generatif döneme geçtiği nisan ayında gerçekleştirilmiştir. İnsansız hava aracı verileri nisan ayı içerisindeki üç farklı tarih aralığında temin edilmiş olup, İHA çekimleri ile eş zamanlı olarak arazide yersel ölçümler de yapılmıştır. İnsansız hava araçlarından alınan veriler 10 m yükseklikten ve uygun bindirme oranları ile elde edilmiştir. Tüm hava fotoğrafları ortomozaik görüntü, sayısal yüzey modeli (DSM) ve sayısal arazi modeli (DTM) üretimi amacıyla aynı prosedürler uygulanarak işlenmiştir. Çalışmada çok yüksek çözünürlüklü ortomozaik görüntüler üzerinden denemelere ait parsel sınırları belirlenirken, DSM ve DTM verileri kullanılarak elde edilen normalize edilmiş sayısal yüzey modeli (nDSM) ile bitki boyları hesaplanmıştır. Çalışma sonunda yarı otomatik olarak hesaplanan bitki boyları, aynı alandaki araziden ölçülen bitki boyları ile karşılaştırılmıştır. Hesaplanan bitki yükseklik değerleri ile arazi ölçüm değerleri arasında yapılan istatistiksel analizler sonucunda en yüksek ilişkiler, Phantom 3 Advanced İHA’sı için (r= 0.948) 16 Nisan 2020 tarihinde, Mavic Pro İHA’sı için (r= 0.886) 10 Nisan 2020 tarihinde ve Inspire 2 İHA’sı için ise (r= 0.924) 22 Nisan 2020 tarihinde elde edilmiştir. Araştırma sonucuna göre, bitki boyunun farklı özelliklere sahip insansız hava araçları ile güvenli bir şekilde belirlenebileceği ortaya konmuştur.

The relationship between urban green space (UGS) and population is essential for the quality of life in cities. In this study, a method is proposed to determine the relationship between UGS and population in Antalya, Türkiye, and to reveal the change in the short-term, using Sentinel-2 satellite data and object-based image analysis (OBIA). In the study, two different dated Sentinel-2 satellite data were used as the basic data set to analyze the vegetation. The Normalized Difference Vegetation Index (NDVI) was calculated for the threshold value and UGSs were analyzed according to different categories. Then, the Urban Green Space Index (UGSI) was calculated to determine the amount of green space and Per Capita Green Space (PCGS) was also calculated for this purpose. The OBIA general accuracy values of the proposed method are 93% and 94% for 2017 and 2023, respectively. The results showed that PCGS across the study area decreased by 7.33 m² in a short time. It is evaluated that the method proposed in this study, which reveals the short-term change in UGS and PCGS more quickly and at lower cost, can be used effectively in sustainable city management.