Yazar "Donmez, Cenk" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • Küçük Resim Yok
    Öğe
    Assessing land degradation dynamics in the Buyuk Menderes River Basin: a comprehensive spatial modelling approach
    (Springer, 2025) Kulahlioglu, Muge; Akin, Anil; Berberoglu, Suha; Sahingoz, Merve; Cilek, Ahmet; Donmez, Cenk
    Land degradation stands as a pivotal determinant for the viability of sustainable ecosystems, with its impact on biodiversity closely intertwined with the intricate interplay of natural, physical, and cultural elements within landscapes. This study evaluates land degradation (LD) within the Buyuk Menderes River Basin by employing a comprehensive suite of indicators, which have been simulated using advanced spatial modelling techniques. Specifically, indicators such as Net Primary Production (NPP), erosion and Soil Organic Carbon (SOC) have been rigorously analysed to delineate the evolving landscape dynamics. Areas affected by LD in Buyuk Menderes were identified using time-series analysis of several vegetation index data derived from satellites, climate data between 1975 and 2018 together with other environmental gradients including, soil, geomorphology, vegetation and water. NASA-CASA, PESERA and Random forest approaches were adopted for NPP, erosion and SOC modelling respectively. Modelling accuracies for each approach were acquired as 83%, 85.5% and 65%. Modelling outcomes than integrated to determine degraded lands within the landscape units, and degradation levels were presented in percentiles for each land/use land cover class. The results showed that more than 50% of the river basin faces to degradation threat due to water problems related to climate change. By incorporating such comprehensive datasets, our study aims to provide valuable insights into land degradation processes and facilitate informed decision-making for sustainable landscape planning practices.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    Geospatial technologies for physical planning: Bridging the gap between earth science and planning
    (2022) Berberoglu, Suha; Akın, Anıl; Satır, Onur; Donmez, Cenk; Cilek, Ahmet; Şahingöz, Merve
    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.
  • Küçük Resim Yok
    Öğe
    Response of the regression tree model to high resolution remote sensing data for predicting percent tree cover in a Mediterranean ecosystem
    (Springer, 2015) Donmez, Cenk; Berberoglu, Suha; Erdogan, Mehmet Akif; Akın Tanrıöver, Anıl; Cilek, Ahmet
    Percent tree cover is the percentage of the ground surface area covered by a vertical projection of the outermost perimeter of the plants. It is an important indicator to reveal the condition of forest systems and has a significant importance for ecosystem models as a main input. The aim of this study is to estimate the percent tree cover of various forest stands in a Mediterranean environment based on an empirical relationship between tree coverage and remotely sensed data in Goksu Watershed located at the Eastern Mediterranean coast of Turkey. A regression tree algorithm was used to simulate spatial fractions of Pinus nigra, Cedrus libani, Pinus brutia, Juniperus excelsa and Quercus cerris using multi-temporal LANDSAT TM/ETM data as predictor variables and land cover information. Two scenes of high resolution GeoEye-1 images were employed for training and testing the model. The predictor variables were incorporated in addition to biophysical variables estimated from the LANDSAT TM/ETMdata. Additionally, normalised difference vegetation index (NDVI) was incorporated to LANDSAT TM/ETM band settings as a biophysical variable. Stepwise linear regression (SLR) was applied for selecting the relevant bands to employ in regression tree process. SLR-selected variables produced accurate results in the model with a high correlation coefficient of 0.80. The output values ranged from 0 to 100 %. The different tree species were mapped in 30 m resolution in respect to elevation. Percent tree cover map as a final output was derived using LANDSAT TM/ETM image over Goksu Watershed and the biophysical variables. The results were tested using high spatial resolution GeoEye-1 images. Thus, the combination of the RT algorithm and higher resolution data for percent tree cover mapping were tested and examined in a complex Mediterranean environment.