İnsan nüfusunun artması ve teknolojinin gelişmesiyle birlikte doğal kaynaklara olan talep her geçen gün artmaktadır. Üretim ve tüketim ihtiyaçlarını sağlamak için kullanılan doğal kaynaklar insanların dünyanın sahibi olduğuna dair düşüncelerinden dolayı tahrip edilerek düşüncesizce kullanılmaktadır. Ekolojik ayak izi ifadesi kaynak kullanımının sürdürülebilir bir biçimde gerçekleştirilebilmesi için kullanılan sayısal bir metottur. Bu metot ekolojik kaynakların ekonomik değerlerinin olduğunu belirten ekolojik ekonomi ve sürdürülebilirlik kavramlarının bir bütününü temsil etmektedir. Ekolojik ayak izi kavramı, tüketilen ekolojik verimli kaynak miktarlarını ve bu kaynakların alan bazında belirlenmesini sağlayarak ekolojik kaynak tüketimini sayısallaştırmaktadır. Son zamanlarda sürdürülebilirlik kavramının disiplinler arası değerlendirilmesi sonucunda ekolojik ayak izi kavramı genişletilerek ayak izi ailesi kavramı oluşturulmuştur. Ayak izi ailesi, ekolojik kaynakların tüketiminin kaynak bazında değerlendirilmesini sağlayarak her bir kaynağın sürdürülebilirliğini sağlamaktadır. Bu çalışmada, ekolojik kaynakların sürdürülebilir yönetiminde anahtar rol oynayan ekolojik ayak izi kavramının tarihçesi ve kaynakların yönetiminde etkin olan ayak izi ailesi hakkında detaylı bir derleme çalışması yapılmıştır.

Soil salinity is a major threat to the continuity of sustainable agriculture and food provision and the soil structure deterioration. In this context, determining, reducing and managing soil salinity is very important for creating sustainable modern agriculture. Determining soil salinity is generally carried out in the laboratory environment and devices used in land plots. Remote sensing is one of the important methods used for precise estimation and mapping of salinity. With remote sensing technology, soil salinity maps for large areas can be obtained with low cost and low effort. This study aims to compare remote sensing soil electrical conductivity from PlanetScope and ground measured data in wheat and beet fields in the farming areas of Alpu, Turkey. For that reason, electrical conductivity was measured at several points in wheat and beet fields using in-situ measurements and compared with various soil salinity indices from PlanetScope imagery acquired on the same day. Linear regression analysis was carried out to correlate the electrical conductivity data with their corresponding soil salinity spectral index values. The results show a high correlation (R² = 0.84) between soil salinity in wheat fields and some of the used indices. This study strengthens the idea that soil salinity maps can be obtained fast and accurately for large areas using remote sensing technology.

The Virtual Geographic Environment (VGEs) is a framework in which real-world information is conveyed and converted into the digital world. It contributes to a better understanding of geographic knowledge by human beings and helps decision-makers by giving them a profound view of geographic concerns like three-dimensional (3D) models. The production of 3D models is essential for the characterization, monitoring, and management of water areas. This study aims to expose the 3D model of the pond in the presence of Unmanned Aerial Vehicle (UAV) and Acoustic Doppler Profile (ACDP) bathymetry to precisely determine the actual amount of water in the pond for possible water problems in the future. Moreover, determine how the sediments carried by the mainstream and side tributaries feeding the pond affect the pond’s water volume and in which parts of the pond their accumulation is concentrated. This study will contribute to the literature by using an ACDP device for the bathymetry measurement process which is rare in the literature. In this approach, the pond's underwater topography of the pond contributed to the sustainable management of the pond by producing Digital Elevation Models (DEM), which is a sensitive indicator of climate change and man-made effects.

Salinity is one of the most critical problems for agricultural lands. Soil salinity should be monitored with fast, economical and accurate data and methods. In this study, soil salinity was estimated using remote sensing data and machine learning algorithms, where fıve different methods were used, and the results were compared. As a study area, Alpu, Turkey has been selected. Within the scope of the study, on-site measurements were made in cultivation areas where there are different agricultural products such as beets, wheat, tomatoes, and corn in the district. The results show that machine learning algorithms and Planetscope images successfully determine soil salinity. Future studies will evaluate the methods by taking samples from different product classes and wet/arid lands.