Kıyılar kara ve deniz sınırını oluşturan, belirli bir canlı ekosistemini ihtiva eden alanlardır. Suların iklim değişimine bağlı olarak çekilmesi veya yükselmesi, gelgit hareketleri, tropik ekosistemlerde hava olaylarına bağlı olarak meydana gelen fırtına, hortum, kasırga vb. iklim olaylarında, deniz alanlarının karalardan ayrıldığı kıyı çizgisinin belirlenmesi önem arz etmektedir. Bu çalışma kapsamında Sentinel-2A uzaktan algılama görüntüsü üzerinde, makine öğrenmesi tabanlı mean-shift, rastgele orman (RO) ve destek vektör makinaları (DVM) yöntemleri uygulanmış olup, bölütleme sonrası sonuç görüntüleri doğruluk ve IoU metrikleri ile değerlendirilmiştir. Genel doğruluklar, mean-shift, rastgele orman ve destek vektör makinaları için sırasıyla %97.23, %99.15 ve %98.68 bulunmuştur.

The frequency and severity of floods and droughts have increased due to climate change, along with mismanagement, and increasing demand for water resources due to population growth. Due to this increasing demand, dependence on groundwater resources has been needed unprecedentedly. In addition, overuse and unsustainable irrigation have severely stressed groundwater, which is a limited resource. This study has been used multi-criteria decision-making methods to determine the spatio-temporal variation of groundwater potential zones for ensuring effective groundwater management and security. Geographic Information Systems (GIS) and Remote Sensing (RS) technologies are used to integrate 10 thematic layers, i.e., precipitation, soil, geology, elevation, lineament density, slope, drainage density, land use/land cover (LULC), topographic wetness index (TWI), and topographic roughness index (TRI). The groundwater potential areas, with a high spatial resolution of 10 m, were delineated using the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) in the study area of the Harran Plain, Türkiye. The integrity of the mapped potential zones was authenticated by well measurements and application of the Receiver Operating Characteristic (ROC) analysis, which yielded an Area Under Curve (AUC) value of 72.7% for AHP and significantly improved 97.6% for Fuzzy-AHP. The results obtained have significant implications for future efforts in mapping areas critical for sustainable groundwater use, agricultural irrigation, watershed management, and research on the relationship between groundwater dynamics and climate change, particularly in arid and semi-arid regions.

This study evaluates the urban green spaces at Harran University's Osmanbey Campus using UAV technology and the Green Leaf Index (GLI). By employing Structure-from-Motion (SfM) photogrammetry, a highly detailed orthophoto of the campus was generated, while the GLI helped to identify and measure the green areas accurately. The analysis revealed that the Total Green Space Area on the campus is 8.8 hectares, within a Total Urban Area of 46.4 hectares. This results in a Green Space Ratio (GSR) of 18.97%. This percentage indicates that nearly 19% of the campus' urban area is covered by green spaces, which represents a moderate yet meaningful level of vegetation that enhances the environmental quality and overall well-being of the campus community. The findings underscore the value of incorporating UAV-based metrics into urban green space assessments and suggest that increasing the GSR to around or above 20% could provide even greater ecological and social benefits.