Browsing by Author "Satir, Onur"

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Contributions of Cultural Ess To Human Well-Being, Landscape Planning and Rural Development in Border Landscapes: Local Insights From the Bendimahi River Basin (Eastern Anatolia, Türkiye)
(Springer, 2025) Baylan, Emel; Sehribanoglu, Sanem; Satir, Onur
Despite the rise in cultural ecosystem services (CESs) research in urban or peri-urban settings and related participatory approaches, studies on the social values of CESs benefits, in the largely natural and rural border landscapes to locals' well-being are still few. This study uses the Bendimahi River Basin (Van), one of Turkiye's least developed areas along the Iranian border, as an empirical case to explore the links between the locals' social values for ecosystem benefits, landscape characteristics, and human well-being dimensions such as personal and social fulfilment and enjoyment. A questionnaire survey combined with PPGIS landscape value mapping with 348 people was employed to gather data for 15 ecosystem services with an emphasis on CES. The findings of the statistical and spatial analyses revealed that while enjoyment benefits are lowest in the landscape due to the low development, respondents have the highest well-being benefits in social fulfilment through intrinsic, future, continuity, and sense of place values. The male respondents found as experiencing both of these well-being benefits more than women in the Basin. Physical features such as accessibility, water bodies/wetlands, settlements, dynamic topography, and naturalness displayed positive influence on the locals' CES experiences. Due to their facilitation on CESs experiences, improvements in rural tourism and environmental protection are proposed as two paths for the Basin's development and for enhancing the well-being of its inhabitants. A discussion is provided on how and when to incorporate the social values of CES into landscape planning to improve participatory decision making in rural landscapes.
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.
Estimating Net Primary Productivity of Semi-Arid Crimean Pine Stands Using Biogeochemical Modelling, Remote Sensing, and Machine Learning
(Elsevier, 2023) Bulut, Sinan; Gunlu, Alkan; Satir, Onur
The aim of the paper was to predict net primary productivity (NPP) in pure Pinus nigra J.F. Arnold (Crimean pine) stands by consecutively implementing remote sensing, biogeochemical modelling, and machine learning tech-niques. In this context, NPP was estimated using Carnegie-Ames-Stanford Approach (CASA). Following, NPP was re-modelled with spectral characteristics of the P.nigra using multi-temporal remotely sensed images (Landsat 8 OLI and Sentinel-2), land use, soils and meteorological information in a total of 180 temporary sample plots. The model results were validated using litterfall samples from 30 stations for each forest stand, including needle, branch, cone, bark, male flower, and others. The highest relationship was between NPP and male flowers (r =--0.75). In addition, reflectance (R), vegetation indices (VI) and texture (TEX) values (calculated according to filter and degree) for each sample plot were calculated from each sensor. Multiple linear regression (MLR) was applied to define the best subset to model the NPP values with R, VI and TEX values using MLR, support vector machines (SVM) and deep learning (DL) methods. The best prediction accuracy was obtained in TEX data in the SVM method and Sentinel-2 sensor combination. NPP testing determination co-efficiency (R2) values were 0.95. The performance of the male flower litterfall in the validation control was promising for the modelling of NPP in Crimean pine. The TEX properties of the satellite images were well reflected by using different filters, degrees, and functions, resulting in achieving a high success.
Estimation of Soil Losses Using Various Soil Erosion Models in a Sample Plot in Mediterranean Part of Turkey
(Parlar Scientific Publications (p S P), 2017) Artun, Ozan; Dinc, Akin Oguz; Satir, Onur
The aim of this study was to investigate the relationship between soil erosion and land use/cover change (LUCC) in an erosion hot point area using the RUSLE, modified Morgan-Morgan Finney (mMMF) and revised G2 erosion models in the Mediterranean Region of Turkey. Landsat satellite images that belong to the same months from different years (1990, 2010), were used to derive LUC maps. Maximum Likelihood Classifier (MLC) was applied to all images for classification, and overall accuracy for the determination of LUC maps was >90%. Meteorological and soil data, and information obtained from land observations were used to produce past and present erosion risk maps and potential soil losses. Results from the RUSLE, modified Morgan-Morgan Finney (mMMF) and revised G2 soil erosion methods were controlled by the field works. Results had shown that RUSLE and mMMF approaches produced more reliable/realistic results than rG2. Soil erosion had increased in the last 20 years according to all soil erosion prediction models.
Evaluating Ecosystem Service Changes in a Frame of Transportation Development in Istanbul
(Springer, 2023) Satir, Onur; Yeler, Sevim Tugce; Donmez, Cenk; Paul, Carsten
Rapid urbanization and growing transportation infrastructure in cities negatively affect ecosystems and their functions. Quantifying these effects is a prerequisite for integrating environmental considerations into all phases of transportation planning. However, in many developing or newly developed countries, research is lacking that helps to understand and manage the ecological impacts of transportation construction under local conditions. Presented research contributed to filling this gap by investigating the implications of growing transportation infrastructure on three ecosystem services: local climate regulation, erosion control, and photosynthesis potential. As a case study, we used spatial indicators to quantify changes in the supply of ecosystem services caused by the development of the 3(rd) Bosporus Bridge and its connecting highway in Istanbul, Turkiye. Our results indicate a substantial decrease in ecosystem services close to the transportation infrastructure, including a decrease in local climate regulation (- 5.4%), an increase in erosion (+ 9.4%), and a decline in photosynthesis potential or vegetation health (- 28%). Additionally, hotspots of ES supply change were detected. This study provides a blueprint for planning and impact mitigation studies.
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.
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.
The Influence of Mycorrhizal Species on Sour Orange (Citrus Aurantium L.) Growth Under Saline Soil Conditions
(Univ Agriculture, Fac veterinary Science, 2016) Satir, Neslihan Y.; Ortas, Ibrahim; Satir, Onur
A two-staged experiment was conducted to investigate the effects of indigenous and predefined mycorrhizae inoculation on sour orange (Citrus aurantium L.) growth under saline soil conditions. In first stage, indigenous mycorrhizae that existed in the rhizosphere of Mediterranean halophytic plants propagated by using a trap culture method. Trifolium sp. was used as the host plant. In the second stage, the effects of propagated indigenous mycorrhizae and predefined morphological species (Glomus clarum, G. caledonium and G. mosseae) on citrus plant growth were evaluated with high levels of salt (2000 mu mhos/cm NaCl) under greenhouse conditions. These species are produced in the method of grafting on a regular basis exists Cukurova University. Andesitic tuff: soil: compost (6:3:1) mixture were used as growth media. Shoot and root dry matter, root infections, spore production, and concentrations of N, P, K, Zn, Mn and Cu in plant tissues were analyzed. The results demonstrated that indigenous mycorrhizae, especially spores extracted from the rhizospheres of Euphorbia paralias and Ambrosia maritima, had a significant effect on citrus growth and nutrient uptake. Citrus plants inoculated with G. clarum and G. caledonium grew more efficiently than those inoculated with G. mosseae.
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.
Mapping the Land-Use Suitability for Urban Sprawl Using Remote Sensing and Gis Under Different Scenarios
(intech Europe, 2016) Satir, Onur
Urbanization is one of the important issues in fast developing countries, such as China, Turkey, Brazil, and South Africa. Therefore, sustainable urbanization strategies come into question while designing the cities. In this point, land-use suitability mapping for urban areas is of importance. Spatial information sciences, such as geographical information systems (GIS) and remote sensing are applied widely for mapping landuse suitability. In this study, Van City, which is the most crowded city in eastern Turkey, was evaluated by applying three different scenarios called ecological, economic, and sustainable. The multi-criteria evaluation technique was used in GIS environment in the mapping stage. Distance from roads, distance from urban boundary, hillshade, slope, elevation, land-use cover, and land-use ability factors were used as inputs in the analysis stage. The weights of each input factor were calculated according to urban change dynamics between 2002 and 2015. As a result of the study, the weighting approach using the natural change dynamics of Van City has a great potential to define objective weights. In addition, Van City was developed orderly on agricultural lands and grasslands, and it was not a sustainable development for the region because the main income is still agriculture and animal production, so a new strategy was designed in a sustainable scenario to prevent agriculture and grassland area loss in a mutual benefit between nature and human.
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 Land Use/Cover Changes and Habitat Quality Using Landsat Dataset and Landscape Metrics Under the Immigration Effect in Subalpine Eastern Turkey
(Springer, 2016) Satir, Onur; Erdogan, Mehmet Akif
Monitoring the land use/cover change (LUCC) is a vital part of the ecological planning in fast-changing regions. Fast industrial developments, local social dynamics like internal security problems and hard life conditions in rural places can impact LUCC directly. The purpose of this paper was to detect LUCC and its effects on landscape ecology using landscape metrics (LMs) in the Tatvan region of Turkey located in eastern Anatolia. Landscape of the Tatvan region has been transformed due to two main reasons: fast-developing industries in Turkey and security problems in the eastern Anatolian regions since 1985. Landsat 5 TM 1989 and Landsat 8 OLI 2013 satellite images were used to detect LUCC for 24 years. Additionally, landscape changes were evaluated based on LMs to observe the habitat quality change. As a result of the study, settlement areas were increased almost 100 % in 24 years because of immigration from rural to urban areas. At the same time, grasslands were transformed into agricultural lands, settlement and forestlands. Therefore, agricultural activities increased by 45 %. Animal production was the main income in the 1980s, but while the rural population decreased, agricultural activities and industrial income increased around the cities thus animal production lost importance after 2010. From the results of the edge density, mean core area, total core area, mean patch area, Shannon's diversity index and shape index values, it was observed that overall habitat quality decreased in 24 years.
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.
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.