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    Analysing temporal and spatial urban sprawl change of Bursa city using landscape metrics and remote sensing
    (Springer Heidelberg, 2020) Akin, Anil; Akif Erdogan, M.
    This paper aims to analyse the effect of spatio-temporal urban dynamics of Bursa city in terms of urban development trends and landscape metrics on the land use/cover change. Four different remotely sensed data recorded in 1979, 1989, 2000, 2013 and future simulation map of 2040 were used for the analysis. SLEUTH model in the frame of cellular automata was adopted for the future development. Object-based classification approach was used to extract the land use/cover maps and determine the quantity and quality of change in order to identify the land degradation. Eight urban landscape metrics were calculated from current and future land use/cover classification data. General phases of diffusion and coalescence in urban sprawl were revealed through the metric calculations. The most devastating change was defined on the agricultural lands. Metric results indicated that the irregular urban growth leads to an increase in patch number which ended up with the land degradation. Although the urbanization pattern mostly moved from the seed area to outwards for different time periods, the new spreading centres for the individual patches were observed for the future urban development. The empirical results and findings for historical and future land use/cover provided an insight to urban sprawl characteristics and further development of sustainable urban planning studies.
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    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.