Remote sensing and monitoring of water resources: A comparative study of different indices and thresholding methods.

Water resources are essential for the ecological system and the development of civilization. Water is imperative factor for health preservation and sustaining various human activities, including industrial production, agriculture, and daily life. Remote sensing provides a cost-effective and practical means to detect and monitor water bodies, offers valuable insights into the impact of climatic events on water structures, especially in coastal lake regions. The research primarily utilizes Landsat-9 OLI-2 satellite images to evaluate the effectiveness of various water indices (WRI, NWI, MNDWI, NDWI) in combination with global automatic thresholding methods (K-Means, Zhenzhou's, Adaptive, Intermodes, Prewitt and Mendelsohn's Minimum, Maximum Entropy, Median, Concavity, Percentile, Intermeans, Kittler and Illingworth's Minimum Error, Tsai's Moments, Otsu's, Huang's fuzzy, Triangle, Mean, IsoData, Li's). The study was carried out on Lake Nazik, Lake Iznik, and Lake Beysehir, which have unique geographical characteristics, and examined the adaptability and robustness of the selected indices and thresholding methods. MNDWI consistently stands out as a robust index for water extraction, delivering accurate results across different thresholding methods in regions all three lakes. As a result of quite extensive analysis, it is obtained that MNDWI and NDWI are reliable choices for water feature extraction in various lake environments, but the specific index should consider the thresholding method and unique lake characteristics. The Minimum thresholding method stands out as the most effective thresholding technique, demonstrating impressive results across different lakes. Specifically, it achieved an average Peak Signal-to-Noise Ratio (PSNR) of 78.97 and Structural Similarity Index (SSIM) of 99.37 for Lake Nazik, 74.08 PSNR and 98.34 SSIM for Lake Iznik, and 63.96 PSNR and 93.61 SSIM for Lake Beysehir.

Is environment destiny? Spatial analysis of the relationship between geographic factors and obesity in Türkiye.

This study aims to evaluate the relationship of geographical factors, including precipitation, slope, air pollution and elevation with adult obesity prevalence in Türkiye (TR) using a cross-regional study design. Ordinary least squares (OLS) and geographically weighted regression (GWR) were performed to evaluate the spatial variation in the relationship between all geographic factors and obesity prevalence. In the model, a positive relationship was found between obesity prevalence and slope, whereas a negative significant relationship was determined between obesity prevalence and elevation (p < 0.05). These results, revealing spatially varying associations, were very useful in refining the interpretations of the statistical results on adult obesity. This research suggests that geographical factors should be considered as one of the components of the obesogenic environment. In addition, it is recommended that national and international strategies to overcome obesity should be restructured by taking into account the geographical characteristics of the region.

Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset.

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.

Determination of the suitable sites for constructing solar photovoltaic (PV) power plants in Kayseri, Turkey using GIS-based ranking and AHP methods.

The fact that traditional energy sources have limited reserves and have a negative impact on the environment increases the demand for renewable energy sources. Environmental, economic, and sustainability concerns have led researchers, investors, and policy makers to seek the potential of renewable energy sources. Suitable site selection for new-generation renewable resources is vital in large-scale projects. In this paper, geographic information systems and multicriteria decision-making (MCDM) methods were integrated to exploit and construct the best location for solar PV power plants in Kayseri, Turkey. Three main, twelve subcriteria, and their indicators related to the study area were determined. The rank-based (i.e., rank sum, rank reciprocal weights, and rank order centroid weights) and analytical hierarchical process (AHP) MCDM methods were used to determine the weights of the criteria. Thirty-three existing solar PV power plants were used to verify the success of MCDM methods. Four MCDM methods yielded effective results according to the proposed criteria, and most of the existing solar PV power plants match the convenient regions on the suitability map provided by geographic information systems-based rank reciprocal method. In addition, according to the experimental study, Sariz, Tomarza, and Incesu districts of Kayseri were the most suitable sites.