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Öğe Cellular automata modeling approaches to forecast urban growth for adana, Turkey: A comparative approach(Elsevier, 2016) Berberoglu, Suha; Akın Tanrıöver, Anıl; Clarke, Keith C.The aim of this study was to assess the application of cellular automata in urban modeling to give insights into a wide variety of urban phenomena, using the most commonly used urban modeling approaches including: Markov Chain, SLEUTH, Dinamica EGO modelling with the Logistic Regression (LR), Regression Tree (RT) and Artificial Neural Networks (ANN). The effectiveness of these approaches in forecasting the urban growth was assessed in the example of Adana as a fast growing City in Turkey for the year 2023. Different models have their own merits and advantages, the empirical results and findings of various approaches provided a guide for urban sprawl modeling. The accuracy figures to assess the models were derived using Allocation and Disagreement maps together with Kappa statistics. Calibration data were from remotely sensed images recorded in 1967, 1977, 1987, 1998 and 2007. SLEUTH, Markov Chain and RT models resulted in overall Kappa accuracy measures of 75%, 72% and 71% respectively, measured over the past data using hindcasting. LR and ANN yielded the least accurate results with an overall Kappa accuracy of 66%. Different modeling approaches have their own merits. However, the SLEUTH model was the most accurate for handling the variability in the present urban development. (C) 2016 Elsevier B.V. All rights reserved.Öğe The impact of historical exclusion on the calibration of the SLEUTH urban growth model(Elsevier Science Bv, 2014) Akın Tanrıöver, Anıl; Clarke, Keith C.; Berberoglu, SuhaThis paper aims to emphasize the importance of the calibration process in urban growth modeling studies. The application of cellular automata (CA) in urban modeling can give insights into a wide variety of urban phenomena. The SLEUTH model, being as a well-tested CA, was utilized. Calibration data for the model were acquired from different sources of remotely sensed data recorded in 1967, 1977, 1987, 1998 and 2007. In this context three different excluded maps representing different scenarios were utilized during the calibration process in order to analyze the effects of different policies on urban growth. Each calibration scenario yielded its own parameter values. Thirteen calibration metrics for each scenario were derived. Integrating different exclusion layers to the beginning of the calibration process has reduced the number of possible growth patterns. The overall growth characteristics of Adana were similar for all calibration results and defined as organic growth except for the fact that the spatial allocation and the amount of potential urban pixels were different. (C) 2013 Elsevier B.V. All rights reserved.Öğe Monitoring the Mediterranean type forests and land-use/cover changes using appropriate landscape metrics and hybrid classification approach in Eastern Mediterranean of Turkey(Springer, 2020) Ersoy Mirici, Merve; Satir, Onur; Berberoglu, SuhaMonitoring the Land-Use/Cover Change (LUCC) is an important tool to evaluate the reasons for environmental changes in ecologically sensitive landscapes like natural forestlands. Rural landscapes are of vital importance for ecosystem productivity, ecosystem services, and biological diversity to continue sustainably. The purpose of this paper was to detect LUCC and its effects on landscape ecology through landscape metrics in the Eastern Mediterranean of Turkey. In this study, a hybrid classification approach was used to classify the Land-Use/Cover (LUC) and detailed forest tree diversity considering topography, plant density, and satellite waveband reflectance values. To this extent, detailed LUC classification, LUCC analyses from 2003 to 2014, habitat quality differences by generating landscape metrics in two levels are called landscape and class-level metrics were carried out in the study area. Habitat quality evaluation on forest formation scale using a hybrid classification approach provided a great advantage and made it possible to examine the landscape metrics of the plant types within the scope of temporal change. The study method was implemented in seven stages including: (1) classification of forest-no forestlands with the K-Means algorithm, (2) creating a data set of reflected signals over stand types, (3) determining the rules and thresholds of decision tree algorithm, (4) object-based classification of agricultural, rocky, and settlement areas, (5) obtaining the land-cover maps for 2003 and 2014, (6) post-classification change detection analyses, and (7) assessing the habitat quality via landscape metrics. The results indicated that forest areas increased by 10.73%, while bare soil decreased by 17.70% in 12 years. The habitat quality increased in the same period in the study area according to the results of class area, mean shape index, mean patch size index, edge density, patch number, and Shannon's diversity index values.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, AhmetPercent 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.Öğe Urban change analysis and future growth of Istanbul(Springer, 2015) Akın Tanrıöver, Anıl; Sunar, Filiz; Berberoglu, SuhaThis study is aimed at analyzing urban change within Istanbul and assessing the city's future growth potential using appropriate approach modeling for the year 2040. Urban growth is a major driving force of land-use change, and spatial and temporal components of urbanization can be identified through accurate spatial modeling. In this context, widely used urban modeling approaches, such as the Markov chain and logistic regression based on cellular automata (CA), were used to simulate urban growth within Istanbul. The distance from each pixel to the urban and road classes, elevation, and slope, together with municipality and land use maps (as an excluded layer), were identified as factors. Calibration data were obtained from remotely sensed data recorded in 1972, 1986, and 2013. Validation was performed by overlaying the simulated and actual 2013 urban maps, and a kappa index of agreement was derived. The results indicate that urban expansion will influence mainly forest areas during the time period of 2013-2040. The urban expansion was predicted as 429 and 327 km(2) with the Markov chain and logistic regression models, respectively.
