Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Dervisoglu, Adalet(3)
- Yagmur, Nur(3)
- Çakir, Günay(3)
- Avdan, Ugur(2)
- Bilgilioglu, Burhan Baha(2)
- Comert, Resul(2)
- Günen, Mehmet Akif(2)
- Musaoglu, Nebiye(2)
- Müderrisoğlu, Haldun(2)
- Tanik, Aysegul(2)
- Akgün, Aykut(1)
- Aksoy, Necmi(1)
- Alemdağ, Selçuk(1)
- Asri, Ibrahim(1)
- Avdan, Uğur(1)
- Besdok, Erkan(1)
- Bilgilioglu, Baha(1)
- Borga, M(1)
- Bostancı, Hasan Tahsin(1)
- Bozali, Nuri(1)
- Corumluoglu, Ozsen(1)
- Dağ, Serhat(1)
- Döner, Fatih(1)
- Emre Everan(1)
- Eraslan, Selim(1)
- Filho, WL(1)
- Hatipoglu, Ilter Kutlu(1)
- Isik, Fatih(1)
- Kaya, Ayberk(1)
- Kaya, Latif Gürkan(1)
- Küçük Matcı, Dilek(1)
- Magnuszewski, A(1)
- Matci, Dilek Kucuk(1)
- Matcı, Dilek Kucuk(1)
- Munoz, PDC(1)
- Nagy, GJ(1)
- Ocak, Fatih(1)
- Ozan Kiyat(1)
- Sargıncı, Murat(1)
- Sivrikaya, Fatih(1)
Yayınlar
- Yüksek Lisans TeziAçık Erişim2026Su gövdelerindeki değişimlerin otomatik olarak tespit edilmesi ve analizi
Ozan Kiyat
Uzaktan algılama tarım, orman, bitki örtüsü ve su gibi farklı alanlarda sıklıkla kullanılmaktadır. Göllerde su yüzeyindeki değişimler, ekosistemler ve sürdürülebilir su kaynaklarının yönetimi açısından elzemdir. Bu çalışma, uydu görüntüleri kullanılarak Türkiye’de farklı oluşum kökenlerine ve su kimyalarına sahip 10 gölün (Akşehir, Eber, Tuz, Salda, Manyas, İznik, Van, Beyşehir, Eğirdir, Burdur) 1984–2025 yılları arasındaki göl su yüzey alanı değişimini otomatik olarak tespit etmeyi amaçlamaktadır. Çalışmanın temel veri kaynağını Landsat 5 TM, Landsat 8 OLI/TIRS, Landsat 9 OLI-2/TIRS-2 ve Sentinel-2 MSI uydu görüntüleri oluşturmaktadır. CHIRPS yağış verileri ile ERA5-Land potansiyel buharlaşma verileri hidroklimatik değerlendirmeleri desteklemek amacıyla kullanılmıştır. Veriler, Google Earth Engine platformunda işlenmiştir. Su yüzeylerinin sınıflandırılmasında spektral indeksler ve Otsu eşikleme yöntemi kullanılmış; bu kapsamda Modifiye Edilmiş Normalize Edilmiş Fark Su İndeksi (MNDWI), Normalize Fark Bitki Örtüsü İndeksi (NDVI), Normalize Fark Su İndeksi (NDWI) ve Otomatik Su Çıkarma İndeksi (AWEI) hesaplanmıştır. Ayrıca tüm analiz yıllarında (1984, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025) Rastgele Orman (RO), Destek Vektör Makineleri (DVM), Sınıflandırma ve Regresyon Ağacı (CART) ve K-En Yakın Komşu (KNN) makine öğrenmesi algoritmaları uygulanmıştır. Göllerin su yüzey alanlarında anlamlı azalmalar tespit edilmiştir. En çarpıcı düşüşler, yüzey alanının tamamını yitiren (%100) Akşehir Gölü’nde ve alanının %93,68’ini kaybeden Tuz Gölü’nde gözlemlenmiştir. Makine öğrenmesi algoritmaları arasında RO modeli, %99,25 genel doğrulukla en yüksek sınıflandırma başarısını göstermiştir. Bu çalışma, farklı oluşum ve özelliklere sahip göllerin izlenmesi için genelleştirilebilir ve güncellenebilir bir uzaktan algılama iş akışı sunmaktadır. Ayrıca sürdürülebilir su kaynakları yönetimi için farklı disiplinlere bilimsel bir altlık sağlamaktadır.
- MakaleAçık Erişim2025Snow cover detection using remote sensing techniques over different climate zones of Türkiye
Çakir, Günay; Bozali, Nuri; Sivrikaya, Fatih
Snow-covered land surfaces can be easily mapped using remote sensing technologies. Accurate estimation of snow cover on the land surface allows for the construction of water resource management today. Using Landsat TM/ETM + satellite images, this study tried to assess how much snowfall covered the soil in Trabzon, Gümüşhane, and Bayburt provinces between 1999 and 2023. Satellite images were classified into three categories using the ERDAS 9.1 TM software. These classes are classified as snow-covered surfaces, other places, and data loss (cloud-shadow). When performing image analysis, it was important to verify that the cloudiness rate in the images was less than 15%. Images with cloudiness rates of more than 15% were not used. Seasonal and annual trend analysis of snow-covered areas (SCA) over three distinct regions (Trabzon, Gümüşhane, and Bayburt) were examined using the Mann-Kendall test. When three distinct study regions were examined together, Bayburt had the highest SCA rate, followed by Gümüşhane and Trabzon. In all three fields, the highest SCA was recorded in 2000, while the lowest SCA was recorded in 2017. The trends noticed that SCA on both annual and seasonal scales did not reach the statistical significance level of 0.05. Although snowfall in Trabzon, Gümüşhane, and Bayburt was beneficial in the autumn and spring seasons, no statistically significant association was found. The research concluded that the existing data are inadequate to make any statements on the impact of global warming in the area. However, the study figured out that satellite data may be effectively used to identify snowy places as a result of the study. Comparable investigations need to be undertaken in regions with varying climates, utilizing diverse remote sensing data and classification methodologies.
- MakaleAçık Erişim2024Investigation of the relationship between the buildings collapsed in February 6, 2023 Kahramanmaras earthquakes and the soil and analysis of the risk of collapse in earthquakes
Eraslan, Selim; Hatipoglu, Ilter Kutlu; Ocak, Fatih; Isik, Fatih; Zeybek, Halil Ibrahim
In this study, the building-ground relationship in the areas affected by the February 6 Kahramanmaras earthquakes was determined, and the risk of building collapse in future earthquakes was analyzed. Remote sensing techniques determined the collapsed building-soil relationship, and the frequency ratio method was applied by utilizing Geographic Information Systems techniques to assess the risk of collapse in earthquakes. According to the research results, 84% of the collapses occurred in flat areas. On the other hand, 49.7% of the collapses occurred in the 0-500 m elevation range. In particular, it was determined that 46% (110.8 km(2)) of the collapsed buildings occurred on alluvial land belonging to the Quaternary period. According to the earthquake frequency analysis of the total 108,812 km(2) area considered in this study, 43.72% of the area is at the Risky level, indicating that a large part of the region is at significant earthquake risk. Moreover, Hatay, Adana, Osmaniye, Sanliurfa, Kahramanmaras, and Malatya are the provinces where the risk of collapse of buildings in an earthquake is high. These results reveal the necessity of analyzing by considering many criteria in determining residential areas in provinces at risk of earthquakes.
- MakaleAçık Erişim2023Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener
Günen, Mehmet Akif; Besdok, Erkan
Hyperspectral images are widely used for land use/cover analysis in remote sensing due to their rich spectral information. However, these data often suffer from noise caused by various factors such as random and systematic errors, making them less useful for end-users. In this study, denoising methods (i.e., DnCNN, NGM, CSF, BM3D, and Wiener) for hyperspectral images were compared using the Pavia University hyperspectral dataset with four different noise types: Gaussian, Salt & Pepper, Poisson, and Speckle. After denoising, the k-nearest neighbor method was used to classify the image, and statistical and visual performance comparisons were performed on the classified data. Six performance metrics-Accuracy, Sensitivity, Specificity, Precision, F-Score, and G-Mean-were employed to compare the outcomes qualitatively. The findings demonstrate that DnCNN and BM3D have the best outcome performance for all four noise types. Due to their lack of sensitivity and specificity, the CSF and Wiener approaches had low performance for particular noise sources. For all noise types, the NGM approach had the worst results. The validated instruments not provide effective results when it came to denoising Salt & Pepper noise, but they managed to produce outstanding results when it came to denoising Poisson noise. In order to enhance the quality and usability of hyperspectral images for land use/cover analysis, this study emphasizes the significance of choosing an effective denoising technique.
- MakaleYalnız künye2022Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset
Günen, Mehmet Akif
Wetlands are critical to the ecology because they maintain biodiversity and provide home for a variety of species. Researching, mapping, and conservation of wetlands is a challenging and time-consuming process. Because they produce temporal and geographical information, remote sensing and photogrammetric approaches are useful tools for analyzing and managing wetlands. In this study, the water areas of five different wetlands obtained with Sentinel-2 images in Turkey were classified. Although obtaining large amounts of high-dimensional dataset labeled for various land types is costly, it is a significant advantage to use it after model training in a wide range of applications. In this paper, the EuroSAT dataset was used in the validation process. Proposed deep learning–based 1D convolutional neural networks (CNN) and traditional machine learning methods (i.e., support vector machine, linear discriminant analysis, K-nearest neighborhood, canonical correlation forests, and AdaBoost.M1) were compared quantitatively (i.e., accuracy, recall, precision, specificity, F-score, and image quality assessment metrics) and qualitatively. Finally, pairwise comparison was made with chi-square-based McNemar’s test. There is a statistical difference between 1D CNN and machine learning method (except the support vector machine vs linear discriminant analysis in Test 1 area). CNN models outperform machine learning algorithms in terms of non-linear function approximation and the ability to extract and articulate data features. Since 1D CNNs can process data in a highly complex and unique feature space, they are very successful in segmenting strongly related and highly correlated discrete signals. It also has advantages over machine learning methods for water body extraction in that it can be integrated with sophisticated image pre-processing and standardization tools, is less susceptible to low-level random noise, and provides shift in variations and contrast-invariant image local transforms.
- MakaleYayıncıda açık erişim2022Analyzing and Predicting Spatiotemporal Urban Sprawl in Eskişehir Using Remote Sensing Data
Küçük Matcı, Dilek; Çömert, Resul; Avdan, Uğur
Urban areas of major cities in developing countries are expanding rapidly due to rapid population growth and industrialization. Determining the reasons for rapid urbanization and the amount of urban area that will be needed in the future is important for the planned growth of these cities. In this study, the LU/LC change of Eskisehir city center that has a rapid rate of urbanization and industrialization in Turkey between 1984 and 2020 was determined. Then, the area needed for urban and industrial areas in 2030 was investigated using the Cullullar Automata–Markov Chain (CA–MC) hybrid model simulation. Each year's LU/LC map was produced with over 80% kappa accuracies performing the pixel-based Random Forest (RF) algorithm for change analysis. In the change analysis made between 1984 and 2020, it was determined that there was a change of 117% in the urban area and 977% in the industrial area. The prediction was made for the validation of the CA–MC model from the period 2000–2010 to 2020. When compared with the 2020 LU/LC map, the model success was obtained as 0.84, 0.87, and 0.87 in the Kstandard, Klocation, and Kno Kappa metrics, respectively. By using the 2010–2020 periods in the estimation of 2030, it has been observed that the urban and industrial area will increase by 22.95%; therefore, there will be decreases in agriculture and other natural areas as in previous years.
- MakaleYalnız künye2021Long and short-term assessment of surface area changes in saline and freshwater lakes via remote sensing
Yagmur, Nur; Bilgilioglu, Burhan Baha; Dervisoglu, Adalet; Musaoglu, Nebiye; Tanik, Aysegul
Advances in remote sensing technology enable monitoring and detection of these vulnerable water bodies that bear numerous functions and ecological services beyond their intended use. As such, valuable data and information may be provided for long and short-term analyses and stored in a database for future projections. This paper initially grouped 18 natural lakes of the Konya Closed Basin of Turkey according to their salinity level. Freshwater, saline and brackish lakes have then undergone temporal analysis for every 5-year intervals via Landsat satellite images. Freshwater lake surfaces have not changed noticeably during the inspection period; however, saline waters have decreased in surface area by 32%. Beysehir Lake constituting 94% of the freshwater category and Tuz Lake representing 97% of the saline group were selected for the short-term analysis that was monthly conducted for years 2017 and 2018 with optical and SAR images to better verify cause and effect relationship.
- MakaleAçık Erişim2021Satellite-derived shallow wetland bathymetry using different classification algorithms and datasets
Dervisoglu, Adalet; Yagmur, Nur; Bilgilioglu, Burhan Baha
Remote sensing (RS) effectively identifies, analyzes, and monitors wetlands. In addition to these two-dimensional studies, RS is used with several techniques in determining shallow water depths. The primary purpose of this study is to obtain shallow wetland bathymetry utilizing spectral reflections obtained at different water depths by field study and satellite images. Machine learning (ML) algorithms, which are widely used in remote sensing, are used in this study. Four algorithms were selected as random forest (RF), support vector machine (SVM), Neural Networks (NN), and Maximum Likelihood Classification (MLC). Since machine learning algorithms use training samples/datasets, the classification accuracy is directly related to selecting these data. The effect of pixel counts on classification was investigated by using two different training data set also. Duden (Kulu) Lake, which is a shallow wetland, was chosen as the study area. The Iterative Self-Organizing Data Analysis Technique (ISODATA) classification algorithm divided into as many clusters as possible was applied on Sentinel-2 multispectral images. All classes were rede-fined using measured spectral signatures and were created a bathymetric map. This map was used as reference data in creating training sets and the accuracy assessment of ML algorithms. When the water surface areas obtained from algorithms were compared with the bathymetric map and Normalized Difference Water Index, the best result was obtained with RF. According to the accuracy assessment results, it was seen that the number of training data affects the accuracy, and the best results were obtained with SVM and RF algorithms with training data containing more pixels (overall accuracy 93.87% and 92.64, kappa 0.89 and 0.87, respectively).
- MakaleAçık Erişim2021The Impact of Recreational Use on Land Cover at Uludağ National Park (Turkey)
Uzun, Serir; Çakır, Günay; Yıldız, Oktay; Aksoy, Necmi; Sargıncı, Murat; Toprak, Bülent; Müderrisoğlu, Haldun
The intense pressure resulting from population growth and urbanization along with technology has created the need for utilizing natural areas for tourism and recreational purposes and has necessitated the protection, development, and planning of natural resources for people to benefit from. Due to the importance of determining temporal changes within the framework of the protection-use balance, this study examined the changes occurring within the recreational areas of the Uludağ National Park between the years 1970 and 2010 from human use. As a result of the recreational facilities offered to visitors, an average of 550 000 people benefits annually from the area, which was designated as a national park in 1961. The study included the camping and picnic sites of Sarıalan, Çobankaya, Kirazlıyayla, and Karabelen situated within the park and the ski site of the first development zone. High-resolution satellite images and aerial photographs of the areas at different dates were employed. The temporal changes in the selected areas were analyzed by digital image processing with the help of the geographic information system. Upon examining the impact of the recreational use changes occurring on the land cover, it was revealed that building areas had increased by 15 ha (2.57%), road areas by 21 ha (7.89%), and bare land areas by 67 ha (.53%), while the meadowland areas had decreased by 223 ha (119.37%). The forested areas had increased by 78 ha (48.93%), woodland areas by 79 ha (59.37%) and were not adversely affected, mainly due to the protection status of the park.
- Kitap BölümüYalnız künye2020Assessing Contribution of Climate Change on Wetlands by Using Multi-temporal Satellite Data
Musaoglu, Nebiye; Dervisoglu, Adalet; Yagmur, Nur; Bilgilioglu, Baha; Tuzcu, Aylin; Tanik, Aysegul; Filho, WL; Nagy, GJ; Borga, M; Munoz, PDC
Numerous natural lentic systems of the world are under the threat of drying due to either natural or anthropogenic activities. Natural circumstances are basically due to climate change effects remarkably sensed within the past decades. However, human-induced activities in some cases dominate over natural events due to demand of water particularly for irrigation and domestic needs. This study investigates the changes that have occurred on the water surfaces of 3 significant water bodies located in the Konya Closed Basin of Turkey. Geographically inner Anatolia region of Turkey receives half of the annual average precipitation of the overall country; on the contrary, the region is known to be an important agricultural area. The majority of fertile arable land is spared to wet agriculture necessitating vast amounts of water. As expected, all the 3 water bodies have incredibly lost their water within years. In the multi-temporal analysis of the study area, Landsat 5 TM, 7 ETM+ and 8 OLI satellite images belonging to every 10-years interval between 1987 and 2017, and corresponding meteorological data were used. Water losses in the 3 ecosystems, one is a salt production lake, Tuz (Salt) Lake, the other is a Ramsar Site (Meke Maar), and the last one is an internationally recognized wetland (Akgol), are detected. Resultantly, it is observed that only Tuz Lake has been artificially fed in the last decade as its economic value is higher than the other two systems. Meke Maar and Akgol Wetland have high ecological value; however, their original functions vanished as they have almost lost their water. It is important to state that humans’ need of water is essential; but, unconscious utilization and wrong irrigation applications lead to water scarcity. On the other hand, climate change accelerates the reduction of water resources. Satellite data coupled with ground truth measurements are accepted as modern technological tools to detect short and long term temporal changes in the water surfaces of such vulnerable water systems especially for attracting the attention of decision-makers responsible of sustainable management of water resources.
- MakaleAçık Erişim2020Comparison of tree-based classification algorithms in mapping burned forest areas; [Primerjava drevesnih algoritmov razvrščanja pri zajemu območij pogorelih gozdov]
Matci, Dilek Kucuk; Comert, Resul; Avdan, Ugur
In this study, we compared the performance of tree-based classification algorithms – Random Forest (RF), Rotation Forest (RotF), J48, The Alternating Decision Tree (ADTree), Forest by Penalising Attributes (Forest PA), Logical Analysis of Data Algorithm (LADTree) and Functional Trees (FT) – for mapping burned forest areas within the Mediterranean region in Turkey. Object-based image analysis (OBIA) was performed to pan-sharpened the Landsat 8 images. Four different burned areas, namely Kumluca, Adrasan, Anamur, and Alanya, were used as study areas. Kumluca, Anamur, and Alanya regions were used as training areas, and Adrasan region was used as the test area. Obtained results were evaluated with confusion matrix and statistically significant analysis. According to the results, FT and RotF produced more accurate results than other algorithms. Also, the results obtained with these algorithms are statistically significant.
- MakaleYalnız künye2020Medium scale earthflow susceptibility modelling by remote sensing and geographical information systems based multivariate statistics approach: an example from Northeastern Turkey
Dağ, Serhat; Akgün, Aykut; Kaya, Ayberk; Alemdağ, Selçuk; Bostancı, Hasan Tahsin
The aim of the present study was to produce an earthflow susceptibility map for the city center and environs of the province of Rize located at the Northeastern part of Turkey. The study area is the rainiest region of Turkey, and due to the triggering effect of precipitation earthflows are frequently observed in and around the study area. Besides this point, weathered rock units and steep topography accompany with precipitation for the occurrence of earthflow cases. Considering this point, an earthflow susceptibility mapping was inferred to be a necessity for the area. Parameters, such as lithology, slope gradient, slope aspect, topographical wetness index (TWI), stream power index (SPI), slope curvature, and sediment capacity index (LS) were considered to be earthflow conditioning parameters. A multi-temporal earthflow inventory map for a period of 4 years (2011–2015) was initially generated by way of remote sensing approach and field surveys. Earthflow susceptibility map was produced using the logistic regression method after which the produced susceptibility map was validated. The mapped earthflows were separated into two groups prior to modelling and validation. The first group was for training and the second group was for validation steps. The accuracy of the model was measured by fitting them to a validation set of mapped earthflows. Area under curvature (AUC) approach was applied for validation purposes. The prediction capability of the earthflow susceptibility map produced can be regarded as acceptable in accordance with the AUC values of 0.62 for the logistic regression model. Based on these results, the obtained earthflow susceptibility map can be used to mitigate hazards related to landslides and to aid in land-use planning for the Rize city center.
- MakaleAçık Erişim2019OBJECT BASED BURNED AREA MAPPING WITH RANDOM FOREST ALGORITHM
Comert, Resul; Matcı, Dilek Kucuk; Avdan, Ugur
It is very important to map the burned forest areas economically, quickly and with the high accuracy of issues such as damage assessment studies, fire risk analysis, and management of forest regeneration processes. Remote sensing methods give advantages such as fast, easy-to-use and high accuracy for burned area mapping. Recent years machine learning algorithms have become more popular in satellite image classification, due to the effective solutions for the analysis of complex datasets which have a large number of variables. In this study, the success of object based random forest algorithm was investigated for burned forest area mapping. For this purpose, Object based image analysis (OBIA) was performed using Landsat 8 image of the Adrasan and Kumluca fires which occurred in 24 – 27 June 2016. The study consisted of five steps. In the first step, the multi-resolution image segmentation was performed for obtaining image objects from Landsat 8 spectral bands. In the second step, the image object metrics such as spectral index and layer values were calculated for all image objects. In the third step, a random forest classifier model was developed. Then, the developed model applied to the test site for classification of the burned area. Finally, the obtained results evaluated with confusion matrix based on the randomly sampled points. According to the results, we obtained 0.089 commission error (CE) with 0.014 omission error (OE). An overall accuracy was obtained as 0.99. The results show that this approach is very useful to be used to determine burned forest areas.
- MakaleAçık Erişim2016Assessing the effects of long-term recreational activities on landscape changes in Abant Natural Park, Turkey
Çakir, Günay; Müderrisoğlu, Haldun; Kaya, Latif Gürkan
Natural, protected areas offer many possibilities for recreation in rural areas such as camping, one of the most popular activities. The system established for protected areas in Turkey aims to provide a foundation for conserving areas for recreation. One such area, Abant Natural Park, is convenient for visits from Turkey’s two most populated metropolitan areas, Istanbul and Ankara. It also attracts tourists from other regions and countries. The aim of the present study was to evaluate the vegetation loss and land cover changes due to picnic/camping, transhumance, and construction over 40 years (1966–2004) in the park using data on visitor numbers, vegetation patches, corridors and connectivity. For evaluating landscape variables, remote sensing data and aerial photos were used. When aerial photos were imported into the ERDAS Imagine program, ground control points identified, and individual images orthorectified, land degradation was not found in the use of camping areas. Moreover, manmade areas (road, car park and hotel) observably increased, thus decreasing the forest lands. The findings show that the land use types that have had the greatest ecological impact are transhumance and construction of hotels, which also require infrastructure development. The intensity of the ecological effects mandates precautions to lessen the impacts and the need for continuing assessment to ensure sustainable use of the area.
- MakaleYalnız künye2015Determination of Kula basalts (geosite) in Turkey using remote sensing techniques
Corumluoglu, Ozsen; Vural, Alaaddin; Asri, Ibrahim
Kula Region (Manisa, Turkey), with its 80 volcanic cones, lava flows, basalt plateaus and basalt columns, fairy chimney formations in canyons carved by the Gediz River, shelter cities hidden in volcanic lava, footprint fossils belonging to the first humans, and geological uniqueness, has a great importance in terms of geotourism potential as it is being a natural heritage in the Aegean region of Anatolia. In this study, some remote sensing image processing techniques such as band combination, band rationing, and principal component analyses were used to depict the cultural and natural geosite known as Kula basalts in Turkey. Satellite images utilized here in this study were Landsat band images (band 1 to band 7). Principal component analyses (PCA) were performed on the first ratio group with 5/4, 5/1, and 3/7 bands and then on the second ratio group with 3/1, 4/5, and 3/2 bands. As a conclusion and outcome of this PCA, it was found that red, green, and blue (RGB) composite with PC1 and PC3 gray-level images from the first ratio group and PC2 gray-level image from the second group showed up the basalt areas better than those from other techniques studied in this research. After unsupervised classification of this final composite of PCA, it is computed that the basalt rocks in Kula cover an area of 36,774 ha totally.
- MakaleYalnız künye2015Determination to Results of Forest Management Planning Applications with Land Use Changes from 1971 to 2008 in Yalova-Turkey
Çakir, Günay; Özdemir, Mehmet
The forest conservation organizations in Turkey have recommended changing the classical forest management systems to protect forest biodiversity since 2006. This study discusses the spatio-temporal analysis of forest management activities and land use changes in Yalova Forest Ecosystems, Turkey. We focused on the Yalova City because (a) it is located in a highly urbanized area, northwestern Turkey, (b) forest is a governmental ownership and producing of many non-wood forest product and wildlife species, (c) governmental organizations are encouraged to be decreased clear-cuts to protect forest ecosystems; (d) GIS databases are available in Yalova; (e) Yalova Forestry Administration has a membership of the Mediterranean Model Forest Network in 2011, and (f) many new forest policies will be developed in Turkey after the membership of the model forest. This study used forest management plans of Yalova Forest during 1971–2008 periods and analyzed data in geographic information system (GIS). Results showed that the rate of forest area in the Yalova steadily increased from 53.4 % in 1971 to 61.2 % to 62.5 % between 1994 and 2008 respectively.
- Yüksek Lisans TeziAçık Erişim2014Uydu görüntüleri ile kentsel gelişimin zamansal ve mekansal olarak incelenmesi: Artvin şehir merkezi örneği
Emre Everan
Uzaktan Algılama teknolojileri ile geniş alanlara ait zamansal değişim bilgilerini elde etmek mümkün olabilmektedir. Günümüzde, elde edilen uydu görüntüleri değişimlerin zamansal ve mekânsal olarak izlenilmesinde ve sorunların çözülmesinde önemli birer kaynaktır. Bu çalışmada farklı zamanlara ait uydu görüntülerinin değerlendirilmesi ile Artvin şehir merkezini kapsayan çalışma alanında zamansal ve mekânsal olarak kentsel gelişim değerlendirilmiştir. Çalışma alanını kapsayan 2004 yılı IKONOS, 2011 yılı WORLDVIEW-2 uydu görüntüleri elde edilmiş olup, mevcut harita ve yer kontrol noktaları (YKN) verilerinden de yararlanılmıştır. Görsel değerlendirme yöntemleri kullanılarak gerçekleştirilen çalışmada Coğrafi Bilgi Sistemleri ile oluşturulan veri tabanına amaca uygun verilerinin kayıt edilmesi sağlanarak değişim analizleri ve sonuçlara ulaşılmıştır. 2004 – 2011 yılları arasında alansal olarak ev alanlarında yaklaşık %25,5, yol alanlarında ise %7,3 artış gerçekleşmiştir. Ayrıca, kamu bina alanlarının yaklaşık %0,3, yeşil alanların ise % 3,5 azaldığı tespit edilmiştir. Bu değişimin en büyük nedenlerinden birinin yapılan değerlendirmeler sonucu şehir merkezindeki nüfus hareketliliği olduğu düşünülmektedir. Bu kapsamda Artvin şehir merkezi ile ilgili yapılacak çalışmalara ve planlamalara ışık tutacak önemli istatistiksel bilgiler elde edilmiştir.
- MakaleYalnız künye2011Using landsat data to determine land use/land cover changes in gümüşhane, Turkey
Döner, Fatih
Sustainable land use and development require timely, accurate and detailed information of land resources as well as changes in the land resource. 'What is changing in where' question is answered by multiple observations over a period of time. Remote sensing technology provides such a monitoring requirement. The aim of this study was to analyze land use/ land cover (LU/LC) changes between 1975 and 2000 in Gümüs ̧hane, Turkey, using satellite images. Three Landsat images from 1975, 1987 and 2000 were used to determine changes. A post classification technique was used based on a hybrid classification approach (unsupervised and supervised). Images were classified into five LU/LC types; urban, agriculture, forest, pasture and other lands. It is found that significant changes in land cover occurred between the years 1975 and 2000. The results showed an increase in urban, forest and agriculture and a decrease in pasture and other lands between 1975 and 2000. In this period, urban land increased from 1.25 to 3.78% of the total area. While the area of pasture decreased from 26.74 to 23.44% of the total area, the area of forest and agriculture increased from 37.32 to 39.51% and from 8.83 to 13.27%, respectively.
