Yazar "Sagir, Cagdas" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • [ X ]
    Öğe
    Detection of Potato Fields Using Sentinel-2 and Landsat 8 Data-Based Machine Learning Models in Semi-arid Region of Central Anatolia, Türkiye
    (Springer, 2026) Tokluoglu, Emre; Kurtulus, Bedri; Sagir, Cagdas; Erdem Altin, Gunseli; Yurdakul, Elifnur; Ates, Ersin; Canoglu, Mustafa Can
    Accurate mapping of crop types is essential for agricultural monitoring, resource management, and food security. This study evaluates the performance of two ensemble machine learning algorithms-random forest (RF) and gradient tree boosting (GTB)-for classifying potato fields using multispectral satellite imagery from Landsat 8 and Sentinel-2 in the Konya Plain, T & uuml;rkiye. The methodology involved generating median composite images from the 2020 growing season (June-August), followed by feature extraction from training samples collected via ground truth, satellite, and synthetically generated data. Model performances were assessed using overall accuracy, kappa coefficient, F-score, and user's and producer's accuracy metrics. Five-fold cross-validation was employed to evaluate model generalizability. A sensitivity analysis was conducted on the number of trees, and 100 was selected for model training. Results indicate that the Sentinel-2 random forest model achieved the highest average overall accuracy (0.94) and kappa coefficient (0.86) across folds, demonstrating robust and consistent classification. Feature importance analysis showed that red-edge and SWIR bands were the most effective for model performance. Sentinel-2 imagery combined with random forest provided a reliable and efficient approach for potato classification in arid agricultural regions. These findings support the integration of remote sensing and machine learning for operational agricultural monitoring and suggest avenues for future research involving temporal analysis and multi-sensor data fusion. This approach can be extended to other crop types and regions, enhancing the use of satellite-based crop mapping in precision agriculture. It enables policymakers and agricultural agencies to implement timely, data-driven strategies for agricultural planning and food security.
  • [ X ]
    Öğe
    Investigating the Structure of a Coastal Karstic Aquifer through the Hydrogeological Characterization of Springs Using Geophysical Methods and Field Investigation, Gokova Bay, SW Turkey
    (Mdpi, 2020) Sagir, Cagdas; Kurtulus, Bedri; Soupios, Pantelis; Ayranci, Korhan; Duztas, Erkan; Aksoy, Murat Ersen; Avsar, Ozgur
    The electrical resistivity tomography method has been widely used in geophysics for many purposes such as determining geological structures, water movement, saltwater intrusion, and tectonic regime modeling. Karstic springs are important for water basin management since the karst systems are highly complex and vulnerable to exploitation and contamination. An accurate geophysical model of the subsurface is needed to reveal the spring structure. In this study, several karst springs in the Gokova Bay (SW, Turkey) were investigated to create a 3D subsurface model of the nearby karstic cavities utilizing electrical resistivity measurements. For this approach, 2D resistivity profiles were acquired and interpreted. Stratigraphically, colluvium, conglomerate, and dolomitic-limestone units were located in the field. The resistivity values of these formations were determined considering both the literature and field survey. Then, 2D profiles were interpolated to create a 3D resistivity model of the study area. Medium-large sized cavities were identified as well as their locations relative to the springs. The measured resistivities were also correlated with the corresponding geological units. The results were then used to construct a 3D model that aids to reveal the cavity geometry in the subsurface. Additionally, several faults are detected and their effect on the cavities is interpreted.
  • [ X ]
    Öğe
    SPATIAL AND TEMPORAL ASSESSMENT OF METAL(LOID) CONTAMINATION IN ASARTEPE DAM LAKE (ANKARA, TURKEY) USING POLLUTION INDICES AND MULTIVARIATE STATISTICAL ANALYSIS
    (Parlar Scientific Publications (P S P), 2019) Kurtulus, Bedri; Sagir, Cagdas; Erdem, Gunseli; Tunc, Semih Okan; Canoglu, Mustafa Can; Tunca, Evren
    This study has investigated metal(loid) contamination in Asartepe Dam Lake, which is used for irrigation in Ankara, Turkey. Contamination Factor and Degree of Contamination were applied to evaluate contamination in the lake sediment. The contamination was shown to be moderate according to a modified Degree of Contamination analysis. Chromium was found to be the highest calculated metal on the Geoaccumulation Index, and the lake was found to be moderate-to-strongly contaminated according to the same method. The Pollution Load Index for the lake sediment varied between 3.11 and 3.5. Enrichment Factors suggest a minor anthropogenic origin for metal(loid) pollution; various statistical techniques were implemented. The greatest correlation among water-borne metal(loid)s was shown by analysis to be between iron and titanium. No strong correlation was observed for sediment samples. The results show that the lake water is relatively free of metal(loid)s. However, this is not the case for the lake sediment.