Assessment of optic disc and ganglion cell layer in diabetes mellitus type 2

The purpose of this study was to compare the optic disc parameters, retinal nerve fiber (RNFL), and macular ganglion cell layers between patients with diabetes mellitus (DM) type 2 and healthy controls. In this cross-sectional study, 69 eyes of 69 diabetic patients without diabetic retinopathy and 47 eyes of 47 healthy controls were included. Optic disc parameters (i.e., rim area, disc area, cup to disc ratio, cup volume), RNFL, and macular ganglion cell-inner plexiform layers (GCL+IPL) thickness were measured by means of spectral domain optical coherence tomography. There were not statistically significant differences between the diabetic patients and healthy controls in terms of RNFL thickness (P=.32), rim area (P=.20), disc area (P=.16), cup volume (P=.12), and average macular GCL+IPL thickness (P=.11). Nevertheless, binocular RNFL thickness symmetry percentage (P=.03), average cup to disc ratio (P=.02), and superior-nasal macular GCL+IPL thickness (P=.04) were statistically significantly different in the diabetic and control groups. Diabetic patients without retinopathy have more binocular RNFL thickness asymmetry, higher cup to disc ratio, and thinner sectoral macular GCL+IPL when compared to healthy controls. Our results may support the statement that DM causes inner retinal neurodegenerative changes. © 2017 the Author(s). Published by Wolters Kluwer Health, Inc

The relationship between life satisfaction and alienation level of disabled athletes (Kayseri Case)

Background and Study Aim: The aim of this study is to investigate the relationship between life satisfaction and alienation level of disabled athletes living in Kayseri. Material and Methods: The population of the study consists of 421 disabled athletes engaged in sports. The sample consisted of 109 disabled athletes identified by simple random sampling method. The study was performed by scanning method and the data was collected by survey method. Demographic information form consisting of 5 questions, "Life Satisfaction Scale" which was developed by Diener et.al. and translated into Turkish by Köker, of which reliability and validity study had been conducted and the "Alienation Scale" developed by Dean and adopted into Turkish by Kınık were applied. The obtained data were recorded in the SPSS 23 package program. Mann Whitney U test was used for comparison of binary groups and Kruskal Wallis test was used for multiple comparisons. Spearman Correlation test was applied to determine the relationship between life satisfaction and alienation level sub-dimensions. Results: It has been determined that there is a difference between life satisfaction and alienation level sub-dimension scores of disabled athletes according to the gender and marital status, that there is a difference according to life satisfaction level score and ages of 18-23, 24-29 and 18-23 and 30 and above in terms of alienation level and irregularity sub dimensions; and ages of 18-23, 24-29 and 18-23 and 30 and above in terms of social isolation sub dimension and that there is a statistically significant difference between their life satisfaction level score according to education status and alienation level sub dimension. It has been determined that there is a negative and medium sized relation between the level of alienation and life satisfaction, weakness (r = -.491, p =.050) and the irregularity (r = -.619, p =.050) sub-dimension, and that there is a positive relation between life satisfaction and social isolation sub dimension (r=.795, p= .050). Conclusions: A medium level negative relation was determined between the level of life satisfaction and alienation with gender, age, marital status and educational status and between the level of alienation with life satisfaction and weakness and irregularity sub dimensions; and a high positive relation was determined with the social isolation sub dimension

A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data

In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objective of this study is to develop and demonstrate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near-real-time. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. The validation of the algorithm showed very few omission errors and no commission errors. It demonstrates the ability of the proposed algorithm to perform as effectively as human interpretation of the images. The validation of the permanent water surface product with an independent dataset derived from high resolution imagery, showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed 27 is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficultie

A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use, and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lake and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 137 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30&thinsp;m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh&thinsp;∕&thinsp;dA) for all lakes. Nearly all lakes showed a linear regression, and 42&thinsp;% of the lakes showed a strong linear relationship with a R2&thinsp;&gt;&thinsp;0.8, an average R2 of 0.91 and a standard deviation of 0.05. For these lakes and for lakes with a nearly constant lake area (coefficient of variation &lt;&thinsp;0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage and (3) a predominant lack of data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the US, Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97 with a standard deviation of 0.041, and a normalized RMSE of 7.42&thinsp;%. These results show a high potential for measuring lake volume dynamics using a pre-classified GSW dataset, which easily allows the method to be scaled up to an extensive global volumetric dataset. This dataset will not only provide a historical lake and reservoir volume variation record, but will also help to improve our understanding of the behaviour of lakes and reservoirs and their representation in (large-scale) hydrological models.</p

Harmonisation, Mosaicing and Production of the Global Land Cover 2000 Database.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Developing and applying a multi-purpose land cover validation dataset for Africa

The production of global land cover products has accelerated significantly over the past decade thanks to the availability of higher spatial and temporal resolution satellite data and increased computation capabilities. The quality of these products should be assessed according to internationally promoted requirements e.g., by the Committee on Earth Observation Systems-Working Group on Calibration and Validation (CEOS-WGCV) and updated accuracy should be provided with new releases (Stage-4 validation). Providing updated accuracies for the yearly maps would require considerable effort for collecting validation datasets. To save time and effort on data collection, validation datasets should be designed to suit multiple map assessments and should be easily adjustable for a timely validation of new releases of land cover products. This study introduces a validation dataset aimed to facilitate multi-purpose assessments and its applicability is demonstrated in three different assessments focusing on validating discrete and fractional land cover maps, map comparison and user-oriented map assessments. The validation dataset is generated primarily to validate the newly released 100 m spatial resolution land cover product from the Copernicus Global Land Service (CGLS-LC100). The validation dataset includes 3617 sample sites in Africa based on stratified sampling. Each site corresponds to an area of 100 m × 100 m. Within site, reference land cover information was collected at 100 subpixels of 10 m × 10 m allowing the land cover information to be suitable for different resolution and legends. Firstly, using this dataset, we validated both the discrete and fractional land cover layers of the CGLS-LC100 product. The CGLS-LC100 discrete map was found to have an overall accuracy of 74.6 ± 2.1% (at 95% confidence level) for the African continent. Fraction cover products were found to have mean absolute errors of 9.3, 8.8, 16.2, and 6.5% for trees, shrubs, herbaceous vegetation and bare ground, respectively. Secondly, for user-oriented map assessment, we assessed the accuracy of the CGLS-LC100 map from four user groups' perspectives (forest monitoring, crop monitoring, biodiversity and climate modelling). Overall accuracies for these perspectives vary between 73.7% ± 2.1% and 93.5% ± 0.9%, depending on the land cover classes of interest. Thirdly, for map comparison, we assessed the accuracy of the Globeland30-2010 map at 30 m spatial resolution. Using the subpixel level validation data, we derived 15,252 sample pixels at 30 m spatial resolution. Based on these sample pixels, the overall accuracy of the Globeland30-2010 map was found to be 66.6 ± 2.4% for Africa. The three assessments exemplify the applicability of multi-purpose validation datasets which are recommended to increase map validation efficiency and consistency. Assessments of subsequent yearly maps can be conducted by augmenting or updating the dataset with sample sites in identified change areas

Global lake responses to climate change

Climate change is one of the most severe threats to global lake ecosystems. Lake surface conditions, such as ice cover, surface temperature, evaporation and water level, respond dramatically to this threat, as observed in recent decades. In this Review, we discuss physical lake variables and their responses to climate change. Decreases in winter ice cover and increases in lake surface temperature modify lake mixing regimes and accelerate lake evaporation. Where not balanced by increased mean precipitation or inflow, higher evaporation rates will favour a decrease in lake level and surface water extent. Together with increases in extreme-precipitation events, these lake responses will impact lake ecosystems, changing water quantity and quality, food provisioning, recreational opportunities and transportation. Future research opportunities, including enhanced observation of lake variables from space (particularly for small water bodies), improved in situ lake monitoring and the development of advanced modelling techniques to predict lake processes, will improve our global understanding of lake responses to a changing climate