Water is an essential necessity for maintaining the life cycle on Earth. These resources are continuously changing because of human activities and climate-related factors. Hence, adherence to effective water management and consistent water policy is vital for the optimal utilization of water resources. Water resource monitoring can be achieved by precisely delineating the borders of water surfaces and quantifying the variations in their areas. Since Lake Van is the largest lake in Turkey, the largest alkaline lake in the world, and the fourth largest terminal lake in the world, it is very important to determine the changes in water surface boundaries and water surface areas. In this context, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automatic Water Extraction Index (AWEI) were calculated from Landsat-8 satellite images of 2014, 2017, 2020 and 2023 in June, July, and August using the Google Earth Engine (GEE) platform. Water pixels were separated from other details using the Canny edge detection algorithm based on the calculated indices. The Otsu thresholding method was employed to determine water surfaces, as it is the most favored technique for calculating NDWI, AWEI, and MNDWI indices from Landsat 8 images. Utilizing the Canny edge detection algorithm and Otsu threshold detection approaches yielded favorable outcomes in accurately identifying water surfaces. The AWEI demonstrated superior performance compared to the NDWI and MNDWI across all three measures. When the effectiveness of the classification techniques used to determine the water surface is analyzed, the overall accuracy, user accuracy, producer accuracy, kappa, and f score evaluation criteria obtained in 2014 using CART (Classification and Regression Tree), SVM (Support Vector Machine), and RF (Random Forest) algorithms as well as NDWI and AWEI were all 100%. In 2017, the highest producer accuracy, user accuracy, overall accuracy, kappa, and f score evaluation criteria were all 100% with the SVM algorithm and AWEI. In 2020, the SVM algorithm and NDWI produced the highest evaluation criteria values of 100% for producer accuracy, user accuracy, overall accuracy, kappa, and f score. In 2023, using the SVM and CART algorithms as well as the AWEI, the highest evaluation criteria values for producer accuracy, user accuracy, overall accuracy, kappa, and f score were 100%. This study is a case study demonstrating the successful application of machine learning with Canny edge detection and the Otsu water surfaces thresholding method.

Lakes and reservoirs, comprising surface water bodies that vary significantly seasonally, play an essential role in the global water cycle due to their ability to hold, store, and clean water. They are crucial to our planet’s ecology and climate systems. This study analyzed Harmonized Sentinel-2 images using the Google Earth Engine (GEE) cloud platform to examine the short-term changes in the surface water bodies of Çivril Lake from March 2018 to March 2023 with meteorological data and lake surface water temperature (LSWT). This study used the Sentinel-2 Level-2A archive, a cloud filter, the NDVI (normalized difference vegetation index), NDWI (normalized difference water index), MNDWI (modified NDWI), and SWI (Sentinel water index) methods on lake surfaces utilizing the GEE platform and the random forests (RFs) method to calculate the water surface areas. The information on the water surfaces collected between March 2018 and March 2023 was used to track the trend of changes in the lake’s area. The seasonal (spring, summer, autumn, and winter) yearly and monthly changes in water areas were identified. Precipitation, evaporation, and temperature are gathered meteorological parameters that impact the observed variation in surface water bodies for the same area. The correlations between the lake area reduction and the chosen meteorological parameters revealed a strong positive or negative significant association. Meteorological parameters and human activities selected during different seasons, months, and years have directly affected the shrinkage of the lake area.

Köyceğiz Lake is one of our country’s most critical coastal barrier lakes, rich in sulfur, located at the western end of the Mediterranean Region. Köyceğiz Lake, connected to the Mediterranean via the Dalyan Strait, is one of the 7 lakes in the world with this feature. In this study, water change analysis of Köyceğiz Lake was carried out by integrating the Object-Based Image Classification method with CART (Classification and Regression Tree), RF (Random Forest), and SVM (Support Vector Machine) algorithms, which are machine learning algorithms. SNIC (Simple Non-iterative Clustering) segmentation method was used, which allows a detailed analysis at the object level by dividing the image into super pixels. Sentinel 2 Harmonized images of the study area were obtained from the Google Earth Engine (GEE) platform for 2019, 2020, 2021, and 2022,and all calculations were made in GEE. When the classification accuracies of four years were examined, it was seen that the classification accuracies(OA, UA, PA, and Kappa) of the lake water area were above 92%, F-score was above 0.98 for all methods using the object-based classification method obtained by the combination of the SNIC algorithm and CART, RF, and SVM machine learning algorithms. It has been determined that the SVM algorithm has higher evaluation metrics in determining the lake water area than the CART and RF methods.

Keywords: Crop Cover Fraction, The Green Crop Tracker, RGB to HSI, Digital Camera, Object-Based Classification, Corn, Sunflower The use of automated methods to estimate crop cover fraction from digital color images has increased in recent years. Crop cover fraction can determine accurate, fast and inexpensive with this methods. A digital color images was acquired over each of the 30 sample fields in 2014 year at 2–3 week intervals. Study area has 15 sunflower fields and 15 corn fields. Digital color images were collected during 4 months, namely over the course of the growing season from sowing until harvesting to determine crop cover fraction. We used two approach to estimate crop cover fraction. In first method, the images were transformed from the RGB (red, green, blue) color space to the HSI (hue, intensity, saturation) color space. We used an object-based image analysis approach to classify the images into green vegetation and the other materials. In the second method, The Green Crop Tracker is less labor and time intensive than the object-based classification approach, is a viable alternative to ground-based methods. By comparing object-based classification method and Green Crop Tracker software 2014 growing season, results were obtained: There were high correlations between the estimations obtained by object-based classification method and Green Crop Tracker software (for 2014 R² = 0.89). The relationship between two methods for 2014-23 sunflower field was calculated R² = 0.97.

One of the critical issues of image processing, defined as obtaining useful information from the image and improving the quality of the image, is edge detection. How edge detection performance will be affected by adding morphological operators to edge detection algorithms is among the issues that have not been fully resolved. In the study, Canny and Sobel edge detection algorithms were applied to different milling cutters used in machinability. Morphological operators were applied to the determined edges, and their effects on the edges were examined. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) values were used to compare the performances of edge detection algorithms. According to MSE and PSNR results, it was seen that the Canny algorithm gave better results than the Sobel algorithm. In addition, it was concluded that the images obtained as a result of the applied morphological operations provided better performance than the images that were not applied for both Canny and Sobel algorithms.