Assessing the efficiency of pixel-based and object-based image classification using deep learning in an agricultural Mediterranean plain.

Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. Image classification, a widely adopted for extracting valuable information, has seen a surge in the application of deep learning methodologies due to their effectiveness.

The use of regression tree method for Sentinel-2 satellite data to mapping percent tree cover in different forest types.

Quantifying forest systems is of importance for ecological services and economic benefits in ecosystem models. This study aims to map the percent tree cover (PTC) of various forest stands in the Buyuk Menderes Basin, located in the western part of Turkey with different characteristics in the Mediterranean and Terrestrial transition regions Sentinel-2 data with 10-m spatial resolution. In recent years, some researches have been carried out in different fields to show the capabilities and potential of Sentinel-2 satellite sensors.

Modelling climate change impacts on regional net primary productivity in Turkey.

This study projects and models the terrestrial net primary productivity (NPP) considering the representative concentration pathways (RCPs) scenarios of Turkey using remote-sensing-based biogeochemical modelling techniques. Changes in annual NPP between 2000-2010 and 2070-2080 were projected with the biogeochemical ecosystem model NASA-Carnegie Ames Stanford Approach (CASA). A multi-temporal data set, including 16-day MODIS composites with a spatial resolution of 250 m, was used within the CASA model.

Spatial and temporal evaluation of soil erosion in Turkey under climate change scenarios using the Pan-European Soil Erosion Risk Assessment (PESERA) model.

The impacts of climate change on soil erosion are mainly caused by the changes in the amount and intensity of rainfall and rising temperature. The combination of rainfall and temperature change is likely to be accompanied by negative or positive variations in agricultural and forest management. Turkey contains vast fertile plains, high mountain chains and semi-arid lands, with a climate that ranges from marine to continental and therefore is susceptible to soil erosion under climate change, particularly on high gradients and in semi-arid areas.

Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey.

Land use and land cover (LULC) changes affect several natural environmental factors, including soil erosion, hydrological balance, biodiversity, and the climate, which ultimately impact societal well-being. Therefore, LULC changes are an important aspect of land management. One method used to analyze LULC changes is the mathematical modeling approach.