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    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.
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    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.
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    Modelling tree canopy cover and evaluating the driving factors based on remotely sensed data and machine learning
    (Elsevier Gmbh, 2023) Akin, Anil; Cilek, Ahmet; Middel, Ariane
    Quantifying urban tree cover is important to ensure sustainable urban ecosystem. This study calculates urban percent tree cover (PTC) for Bursa city, Turkey from Sentinel-2 data and evaluates the driving factors of PTC using an Artificial Neural Network-Multi Layer Perception (ANN-MLP) approach. For the PTC calculation, a Regression Tree (RT) analysis was performed using several vegetation indices (NDVI, LAI, fCOVER, MSAVI2, and MCARI) to improve accuracy. Socio-economic, topographic, and biophysical variables were incorporated into the ANN-MLP approach to evaluate the factors that drive urban PTC. A PTC prediction map was generated with an accuracy of 0.95 and a coefficient of determination of 0.87. The ANN-MLP training process yielded a correlation coefficient value of 0.71 and an R-square of 0.82 was achieved between the predicted ANN-MLP and observed tree cover maps. A priority tree cover map was generated considering statistical relationships between the factors and the ANN-MLP prediction map in addition to visual interpretations at the urban scale. Results demonstrate that, unlike other urban forms, PTC has a statistically negative relationship with the gross dwelling density (R2 =0.31). Topographic variables including slope and DEM were positively correlated with PTC with the R2 value of 0.80 and 0.72 respectively. The integration of remote sensing data with vegetation indices and driving factors yielded accurate prediction for identifying and evaluating the variability in the urban PTC.
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    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.