Browsing by Author "Berberoglu, Suha"

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Application of Hypothetical Ecological Risk Analysis To Sustainable Usage of Possible Winter Recreation Areas in Seyhan Basin (Turkiye)
(Istanbul Univ Press, Fac Aquatic Sciences, 2022) Yeler, Okan; Aydin, Gazel Burcu; Camur-Elipek, Belgin; Berberoglu, Suha
In this study, the long-term suitability of the area proposals for winter recreation activities in the Seyhan Basin (Turkiye), which is located in the Mediterranean and Central Anatolia regions and includes a large part of the Taurus Mountains, were examined ecologically. For this purpose, the predicted global warming scenarios in the basin and the anthropogenic impacts arising from the planned recreation areas were evaluated for the upper basin (recreation areas) and lower basin (water resources, agricultural lands, and settlements) using a hypothetical risk analysis. For this purpose, multispectral images were obtained by using Landsat 8 Oli Multispectral images of the snow areas in the region in January-February-March 2019, and a hypothetical ecological risk analysis was created considering a total of 5 pressure factors originating from global climate change and anthropogenic effects. These possible factors were determined as flood (S1), drought (S2), sedimentation (S3), aquatic nutrients (S4), and tourist density (S5). The effects of these factors on a total of four features (C1: water quality, C2: fauna-flora, C3: agricultural areas, and C4: settlements) in the region were evaluated by hypothetical grading based on the literature. According to the hypothesis results obtained by the formula and statistical calculations, it was determined that the flood factor (S1) that will occur due to possible snow melt due to global climate change in the winter recreation areas in the studied region is the most significant factor limiting the sustainable usage of the Basin. For this reason, it has been emphasized in this study that the possibility of regions being exposed to the effects of climate change in the future should be taken into account, especially when planning for winter recreation areas. At the end of this study, it was concluded that the ecological balance analysis of basins is important, especially in terms of ensuring the long-term sustainable use of winter recreation areas.
Crop Yield Prediction Under Soil Salinity Using Satellite Derived Vegetation Indices
(Elsevier Science Bv, 2016) Satir, Onur; Berberoglu, Suha
Monitoring the crop yield is one of the key factors to define agricultural land management strategies. Recent developments in spatial information technologies enabled cost and energy saving in crop yield prediction. The aim of this paper was to predict yield of the three major crops and yield loss under soil salinity effect which is one of the most important limitation in many Mediterranean countries. Crop yields were estimated using vegetation indices and Stepwise Linear Regression (SLR) derived from Landsat (Thematic Mapper and Enhanced Thematic Mapper) TM/ETM satellite images. Additionally, related crop pattern of the area was mapped using multi-temporal Landsat data set using object based classification. Soil salinity was mapped using radial basis function and field measurements with a Root Mean Square Error (RMSE) accuracy of 0.96 dSm(-1). The predictions were validated using real-time field measurements. Mean percent error (MPE) for wheat, corn and cotton were 7.9%, 8.8% and 6.3% respectively. Crop yield estimates were incorporated with various degrees of soil salinity. Soil salinity ranging between 8 and 10 dSm(-1) resulted yield loss of 55%, 28%, and 15% in corn, wheat and cotton respectively. The highest soil salinity resistance was observed only at cotton in 18 dSm(-1) with 55% yield loss. (C) 2016 Elsevier B.V. All rights reserved.
Geospatial Technologies for Physical Planning: Bridging the Gap Between Earth Science and Planning
(2022) Donmez, Cenk; Satır, Onur; Şahingöz, Merve; Akın, Anıl; Berberoglu, Suha; Cilek, Ahmet
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.
Improving the Applicability of the Swat Model To Simulate Flow and Nitrate Dynamics in a Flat Data-Scarce Agricultural Region in the Mediterranean
(Mdpi, 2020) Donmez, Cenk; Sari, Omer; Berberoglu, Suha; Cilek, Ahmet; Satir, Onur; Volk, Martin
Understanding the soil and hydrologic processes in agricultural watersheds are vital for reliable assessments of water quantity and quality to support integrated river basin management. However, deriving hydrology-relevant information is complicated in flat data-scarce agricultural watersheds due to constraints in watershed delineation, flat topography, poor natural drainage, and irregular irrigation schedules by human intervention. The study aimed to improve the applicability of the Soil and Water Assessment Tool (SWAT) model to simulate daily flow and NO3 concentrations in a flat data-scarce agricultural watershed in the Lower Seyhan Plain (LSP) in Turkey. Refined digitized stream networks, discharge data derived from fully equipped gauging stations, and satellite data (Landsat 7 ETM+, Aster GDEM, etc.) had to be integrated into the modeling process to improve the simulation quality. The model was calibrated using a 2-year (2011-2012) dataset of streamflow and NO3 using the Sequential Uncertainty Fitting (SUFI-2) approach and validated from 2013 to 2018. Daily water yields were predicted with a reasonable simulation accuracy (E values ranging from 0.53 to 0.82 and percent bias (PBIAS) from 0 to +4.1). The results proved that integrating redefined stream networks to SWAT within a Geographic Information System (GIS) environment increases the simulation capability of flow and nitrate dynamics efficiently. Automated delineation of these networks and sub-basins at low topographic transitions limits the SWAT accuracy.
Mapping Regional Forest Fire Probability Using Artificial Neural Network Model in a Mediterranean Forest Ecosystem
(Taylor & Francis Ltd, 2016) Satir, Onur; Berberoglu, Suha; Donmez, Cenk
Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = -0.43), tree cover (R = 0.93) and temperature (R = 0.42) were strongly correlated with forest fire probability in the USB region.
Mapping the Dominant Forest Tree Distribution Using a Combined Image Classification Approach in a Complex Eastern Mediterranean Basin
(Taylor & Francis Ltd, 2017) Satir, Onur; Berberoglu, Suha; Akca, Erhan; Yeler, Okan
A land use/cover (LUC) classification strategy based on an unsupervised k-means, object-based and expert knowledge classification technique was cross-checked using Landsat satellite datasets along with ancillary data for mapping dominant forest tree species of the Goksu River Basin in the Eastern Mediterranean Region. Eight dominant forest tree species were classified as juniper (Juniperus excelsa Bieb), Taurus fir (Abies cilicica Ant. & Kotschy), Turkish pine (Pinus brutia Tenore), black pine (Pinus nigra Arnold), cedar (Cedrus libani Rich), oak (Quercus pubescens Schwarz and Quercus cerris Pall), stone pine (Pinus pinea L.) and plane (Platanus orientalis L.). The results of the combined classification approach (CCA) were compared with a traditional maximum likelihood classifier (MLC) for a better understanding of the benefits of the CCA. Kappa values of the CCA and the MLC were derived as 0.77 and 0.44 respectively.
A Methodological Overview of Risk Mapping Approaches Used in Prevention of Forest Fires From Past To Present
(Tuba-turkish Acad Sciences, 2021) Satir, Onur; Berberoglu, Suha
Natural or cultural caused forest fires were increased disaster risk in particularly regions that were populated by the human towards natural areas. It was known that the forest fires were mostly caused by the human activities. In addition to direct, stalk fire, shepherd fire, cigarette, picnic fire, sabotage, etc. as an indirect factor, global warming has created ideal conditions for the fire occur. The mapping of forest fire risk offers significant advantages in the stages of prevention, detection and response, which is a part of disaster management. The aim of this study was to reveal the availability of the most widely used forest fire risk mapping techniques from past to present, and to offer suggestions on a suitable fire risk mapping and response system for our country. In this context, we focused on usage pattern, accuracy and data structures of the fire weather index (FWI), which is one of the oldest risk mapping techniques, traditional multi-criteria spatial decision support systems that are not dependent on fire occurrence data, and data-dependent multi-criteria spatial decision support systems, simulation (simulation) based risk assessment systems. As a result, it has been suggested that easily applicable techniques such as FWI and methods suitable for automation-based system creation such as machine learning and deep learning should be integrated in a single interface, supported by remote sensing, and concerned digital data. In addition, it has been determined that there is a need for a system in which damage determinations can be made in advance or instantaneously, and prevention-intervention strategies can be determined, by supporting the regions where disaster risk is detected at local scale with simulation inputs obtained from this platform. It has been determined that the most important problems in such a system are the integration of data from different sources and the development of an artificial intelligence-based automatic action system.
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) Mirici, Merve Ersoy; Satir, Onur; Berberoglu, Suha
Monitoring 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.
Tarım Alanlarında Çok Zamanlı Uydu Verileri Kullanılarak Ürün Deseninin Obje Tabanlı Sınıflama Yöntemiyle Belirlenmesi: Aşağı Seyhan Ovası Örneği
(2017) Berberoglu, Suha; Satır, Onur; Yeler, Okan
Aşağı Seyhan Ovası (ASO) Türkiye'deki en verimli tarım havzalarından birisidir ve Çukurova'nın önemli bir kesimini kapsamaktadır. Yetiştirilen ürün miktarı Türkiye ve pek çok gelişmiş ülke ortalamasının üzerindedir. Bu durum bölgenin ideal iklim, toprak ve ulaşım olanakları gibi konumsal özelliklerinden kaynaklanmaktadır. Bu çalışmanın amacı, bölgedeki yazlık ve kışlık ürün desenlerinin çok zamanlı Landsat Uydu veri seti kullanılarak obje tabanlı sınıflandırma yöntemiyle belirlenmesidir. Arazi kullanımları ve ürün desenleri belirlenirken 2013 yılı için bir yıllık hidrolojik dönem (Ekim 2012 - Eylül 2013) yazlık ve kışlık ürün desenleri olmak üzere iki kısma ayrılmıştır. Landsat veri setleri kışlık ve yazlık tarımsal ürünlerin en yeşil oldukları ve bulutsuz olan dönemler esas alınarak belirlenmiştir. Obje tabanlı sınıflama yöntemi, tarımsal ürünlerin düzenli parseller halinde olması nedeniyle tercih edilmiştir. Sınıflama sonuçlarına göre; ASO arazi örtüsü genel Kappa doğruluk katsayısı 0.9 olarak tespit edilmiştir. Sonuçlara göre, ilk üç sırayı, kışlık ürünlerde alansal olarak, buğday, patates ve soğan, yazlık ürünlerde ise birinci ürün mısır, birinci ürün pamuk ve ikinci ürün mısır almıştır.