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    An alternative assessment for transit-oriented developments (TODs) with land use/land cover (LULC)
    (Springer, 2023) Yildirim, Yalcin; Akin, Anil
    Cities experience rapid growth, and various drivers shape this growth. Notably, developing countries experience challenges on land use and transportation conflict. The idea of transit-oriented developments (TODs) is a solution for this concern of urbanization while there is no consensus on defining and identifying them. This study concentrated on the dynamics of land use/land cover (LULC) effects on transit-oriented developments (TODs) by utilizing light rail stations from a metropolitan area in Turkey, as this is the first attempt in this geography. Spatial data covers of high-resolution Sentinel 2A remotely sensed data and Google Earth images acquired in 2021. The study performed the analyses within a 500-m light rail train station buffer. The results reveal that TODs include more urbanized, open space, street connection, commercials, and dense population as expected from the TOD concept. Local officials, practitioners, and developers should be involved with the TOD implementation and policy phases to establish tangible developments in urbanization.
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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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    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.
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    How can research-based studio experience assist in tackling natural disasters?
    (Springer, 2024) Yildirim, Yalcin; Cirak, Buse Nur; Akin, Anil
    Natural disasters threaten human life in various ways, and a better understanding of their components reduces environmental and societal ramifications. This study seeks a pedagogical approach to contribute to such knowledge at a junior landscape architecture studio by concentrating on research-based experience. Assigning five disaster categories, including flood, drought and food, climate change, earthquake, and disaster-resilient society, the students obtained enriched knowledge on the studio process while they had difficulty in applying theoretical aspects of natural disasters to their projects, mainly gearing from analysis (upper scale) to design (lower scale) solutions. The study results propose that natural disaster education and awareness efforts should be integrated into design- and planning-related disciplines sooner rather than later.
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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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    Simulating the impact of natural disasters on urban development in a sample of earthquake
    (Springer, 2023) Satir, Onur; Kemec, Serkan; Yeler, Okan; Akin, Anil; Bostan, Pinar; Mirici, Merve Ersoy
    Natural disasters have been increased in areas, where people live densely, day by day. Istanbul 1999, Van 2011, and Izmir 2020 earthquakes were just some of the tragic events in the near past in Turkiye. The aim of this study was to define Van 2011 earthquakes effects as a sample on urban development by using land use/land cover projecting techniques. In this case, Van urban development (in urban macroform scale) was simulated without Van 2011 earthquakes based on existing urban development using the Cellular Automata Markov Chain (CA-MARKOV) approach for the year 2018. Effects of the earthquake were determined on urban development by comparing modeling results with observed 2018 built up areas. So that significant physical and social driving factors were evaluated including road distance, slope, hillshade, ground stability, and land use ability, and weighting values on urban development were calculated under the influence of the natural disaster. Van urban built up areas were mapped using high-spatial resolution remote sensing instruments such as SPOT, ASTER, RapidEye, and Gokturk 2 satellite dataset for 1988-2002-2011, and 2018 images applying an object-based classification approach (OBC). First of all, the model was validated using 1988, 2002, and 2011 urban development maps. The Kappa accuracy was found to be 0.85, respectively, for the model. Defined urbanization drivers were applied to the 2002-2011 time period to simulate 2018 urban areas without any earthquake. The results indicated that urban areas were affected by earthquakes. If there was no earthquake, urban development to the periphery would be 30% less. Additionally, 10% more built up areas would be constructed on ground sensitive areas, and only 2% of the new constructions would be established on suitable lands. Today this ratio is around 8%. As a result, urban development has been a trend to move from flat land to slight slopes and has been moved away from roads and settlements. It was determined that the spread into the city was accelerated as well as spread toward the periphery due to the earthquake.
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    Transit-oriented development (TOD) or development-oriented transit (DOT): how does it matter for sustainable urban growth?
    (Taylor & Francis Inc, 2025) Yildirim, Yalcin; Akin, Anil
    Urban growth patterns occur in various ways with tangible practical consequences. A key dilemma with regard to development and transportation revolves around whether the former follows the latter or vice versa. This study assessed this nexus with transit-oriented development (TOD) and development-oriented development (DOT) cases by using the land use/land cover (LU/LC) classification technique in a developing country example of a city in Turkey. Spatial data included high-resolution Sentinel 2A, Rapid Eye, and two SPOT satellite images and CORONA air photos acquired in 2021, 2013, 2000, 1989, and 1979, respectively, within three buffer zones (500, 750, and 1000 m) for both development forms. While the study acknowledges unorthodox TOD selection criteria and generalizability concerns, the results demonstrate that the TOD drastically reduced green areas (as opposed to ideal TOD requirements), while the DOT excessively grew commercial land uses and street networks. Both developments commonly impaired agricultural, vacant, and water land use. Such transformations direct local governments, transit agencies, and city planners to resolve ideal sustainable urban growth patterns by using remote sensing data and quantitative analysis.