Browsing by Author "Donmez, Cenk"

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Evaluating Ecosystem Service Changes in a Frame of Transportation Development in Istanbul
(Springer, 2023) Satir, Onur; Yeler, Sevim Tugce; Donmez, Cenk; Paul, Carsten
Rapid urbanization and growing transportation infrastructure in cities negatively affect ecosystems and their functions. Quantifying these effects is a prerequisite for integrating environmental considerations into all phases of transportation planning. However, in many developing or newly developed countries, research is lacking that helps to understand and manage the ecological impacts of transportation construction under local conditions. Presented research contributed to filling this gap by investigating the implications of growing transportation infrastructure on three ecosystem services: local climate regulation, erosion control, and photosynthesis potential. As a case study, we used spatial indicators to quantify changes in the supply of ecosystem services caused by the development of the 3(rd) Bosporus Bridge and its connecting highway in Istanbul, Turkiye. Our results indicate a substantial decrease in ecosystem services close to the transportation infrastructure, including a decrease in local climate regulation (- 5.4%), an increase in erosion (+ 9.4%), and a decline in photosynthesis potential or vegetation health (- 28%). Additionally, hotspots of ES supply change were detected. This study provides a blueprint for planning and impact mitigation studies.
Evaluating the Efficiency of Future Crop Pattern Modelling Using the Clue-S Approach in an Agricultural Plain
(Elsevier, 2022) Akin, Anil; Erdogan, Nurdan; Berberoglu, Sueha; cilek, Ahmet; Erdogan, Akif; Donmez, Cenk; Satir, Onur
Land Use Land Cover (LULC) change detection is an essential source of information for understanding the magnitude of environmental change to implement future development strategies. Sophisticated techniques (i.e. modelling) have been applied in the last decades worldwide for accurate LULC classification and future pro-jections. However, using these techniques in heterogeneous agricultural regions to extract crop-related infor-mation is still challenging. This study aimed to evaluate the efficiency and applicability of crop pattern prediction for the year 2050 with the CLUE-S model in an agricultural plain. The model was calibrated and validated based on the LULC changes to model future changes of the crop pattern by 2050. Twelve driving factors were utilised to quantify the relationship of LULC classes. The statistical relationship among the factors was examined with a Binomial Logistic Regression approach. Additionally, the magnitude of change in agricultural crop patterns between 2015 and 2050 was calculated according to local/regional policies and incorporated to the model as scenario layer. Future model results indicated that the cotton would increase by % 45 whereas maize would decrease by % 10 compared to 2015. The model performance was evaluated using the ground truth from the field observations considering the agricultural policies through the ROC (Receiver Operating Characteristic) indicators. The mean ROC value for the agricultural crop patterns was calculated as 0.71, while ROC values for other LULC classes were over 0.90. Overall a 0.79 ROC value was achieved as the model accuracy.
Geospatial Technologies for Physical Planning: Bridging the Gap Between Earth Science and Planning
(2022) Donmez, Cenk; Satır, Onur; Şahingöz, Merve; Akın, Anıl; Berberoglu, Suha; Cilek, Ahmet
The application of geospatial information technologies has increased recently due to increase in data sources from the earth sciences. The systematic data collection, storage and processing together with data transformation require geospatial information technologies. Rapidly developing computer technology has become an effective tool in design and physical planning in international platforms. Especially, the availability of geospatial information technologies (remote sensing, GIS, spatial models and GPS) for diverse disciplines and the capability of these technologies in data conversion from two dimensions to the three dimensions provide great efficiency. Thus, this study explores how digital technologies are reshaping physical planning and design. While the potential of digital technologies is well documented within physical planning and visualization, its application within practice is far less understood. This paper highlights the role of the geospatial information technologies in encouraging a new planning and design logic that moves from the privileging of the visual to a focus on processes of formation, bridging the interface of the earth science and physical planning.
Improving the Applicability of the Swat Model To Simulate Flow and Nitrate Dynamics in a Flat Data-Scarce Agricultural Region in the Mediterranean
(Mdpi, 2020) Donmez, Cenk; Sari, Omer; Berberoglu, Suha; Cilek, Ahmet; Satir, Onur; Volk, Martin
Understanding the soil and hydrologic processes in agricultural watersheds are vital for reliable assessments of water quantity and quality to support integrated river basin management. However, deriving hydrology-relevant information is complicated in flat data-scarce agricultural watersheds due to constraints in watershed delineation, flat topography, poor natural drainage, and irregular irrigation schedules by human intervention. The study aimed to improve the applicability of the Soil and Water Assessment Tool (SWAT) model to simulate daily flow and NO3 concentrations in a flat data-scarce agricultural watershed in the Lower Seyhan Plain (LSP) in Turkey. Refined digitized stream networks, discharge data derived from fully equipped gauging stations, and satellite data (Landsat 7 ETM+, Aster GDEM, etc.) had to be integrated into the modeling process to improve the simulation quality. The model was calibrated using a 2-year (2011-2012) dataset of streamflow and NO3 using the Sequential Uncertainty Fitting (SUFI-2) approach and validated from 2013 to 2018. Daily water yields were predicted with a reasonable simulation accuracy (E values ranging from 0.53 to 0.82 and percent bias (PBIAS) from 0 to +4.1). The results proved that integrating redefined stream networks to SWAT within a Geographic Information System (GIS) environment increases the simulation capability of flow and nitrate dynamics efficiently. Automated delineation of these networks and sub-basins at low topographic transitions limits the SWAT accuracy.
Mapping Regional Forest Fire Probability Using Artificial Neural Network Model in a Mediterranean Forest Ecosystem
(Taylor & Francis Ltd, 2016) Satir, Onur; Berberoglu, Suha; Donmez, Cenk
Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = -0.43), tree cover (R = 0.93) and temperature (R = 0.42) were strongly correlated with forest fire probability in the USB region.