Sürdürülebilir Kalkınma Amaçları
Yayınlar
- BildiriYalnız künye2022Assessment of Rapid Urbanization Effects with Remote Sensing Techniques
Yagmur, Nur; Dervisoglu, Adalet; Bilgilioglu, B. Baha; Ahmed, M.B.; Boudhir, A.A.; Kara, I.R.; Jain, V.; Mellouli, S.
Istanbul is the most populous city in Turkey. The population, which was approximately 5.5 million in 1985, has reached 15.5 million in 2020. Population growth is the most important factor behind human activities that put pressure on the environment. An increasing population means depletion of limited resources, increasing environmental problems, and rapid urbanization. In parallel with the increase in population and urbanization, there has also been an increase in demand for housing, leading to new residential areas in almost every district of Istanbul. This study examined the transformation from vegetation areas to residential areas between 1985 and 2020 in a selected region in Buyukcekmece, one of the 39 districts of Istanbul. The relationship between land use and land cover (LULC) change in the area and Land Surface Temperature (LST) change caused by urbanization was analyzed. It is seen that the built-up area has increased from 57.1 ha to 781.4 ha in 35 years. In every five years, an increase in surface temperatures was determined in parallel with increasing urbanization, and this increase was determined as about 5.4℃ from 1985 to 2020. Also, when the temperature data of the Buyukcekmece Meteorological station is analyzed, it is seen that there has been an increase of approximately 2 ºC in air temperatures in the last five years. In addition, movements were observed in the stability of structures in rapid urbanization areas after analyzing with the PSI time series InSAR method. The main causes were determined as construction sites around the buildings and geological conditions of the ground, which are triggered by urbanization.
- 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).
- 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.