On the Detection of Snow Cover Changes over the Australian Snowy Mountains Using a Dynamic OBIA Approach

This study detected the spatial changes in Snow Cover Area (SCA) over the Snowy Mountains in New South Wales, Australia. We applied a combination of Object-Based Image Analysis (OBIA) algorithms by segmentation, classification, and thresholding rules to extract the snow, water, vegetation, and non-vegetation land covers. For validation, the Maximum Snow Depths (MSDs) were collected at three local snow observation sites (namely Three Mile Dam, Spencer Creek, and Deep Creek) from 1984 to 2020. Multiple Landsat 5, 7, and 8 imageries extracted daily MSDs. The process was followed by applying an Estimation Scale Parameter (ESP) tool to build the local variance (LV) of object heterogeneity for each satellite scene. By matching the required segmentation parameters, the optimal separation step of the image objects was weighted for each of the image bands and the Digital Elevation Model (DEM). In the classification stage, a few land cover classes were initially assigned, and three different indices—Normalized Differential Vegetation Index (NDVI), Surface Water Index (SWI), and a Normalized Differential Snow Index (NDSI)—were created. These indices were used to adjust a few classification thresholds and ruleset functions. The resulting MSDs in all snow observation sites proves noticeable reduction trends during the study period. The SCA classified maps, with an overall accuracy of nearly 0.96, reveal non-significant trends, although with considerable fluctuations over the past 37 years. The variations concentrate in the north and south-east directions, to some extent with a similar pattern each year. Although the long-term changes in SCA are not significant, since 2006, the pattern of maximum values has decreased, with fewer fluctuations in wet and dry episodes. A preliminary analysis of climate drivers’ influences on MSD and SCA variability has also been performed. A dynamic indexing OBIA indicated that continuous processing of satellite images is an effective method of obtaining accurate spatial–temporal SCA information, which is critical for managing water resources and other geo-environmental investigations

Sero-epidemiological survey of brucellosis in small ruminants in Hamedan province, Iran

Objective: Brucellosis is a highly contagious zoonosis with global distribution. The disease remains endemic in many countries including Iran, while its seroprevalence in endemic area is not well documented. We aimed to determine the seroprevalence of brucellosis in sheep and goats in Hamedan province, west of Iran. Material and methods: A total of 3,250 blood samples from 2,550 sheep and 700 goats were collected randomly. All samples were analyzed for the presence of Brucella antibodies using Rose Bengal, Wright standard tube agglutination and 2-mercaptoethanol agglutination tests. Results: The seroprevalence rate of brucellosis in animals and flock level were found in 4.6% and 13.6% of goats and 3% and 27.9% of sheep, respectively. No evidence of correlation between gender and Brucella infection rate were found in animals (P>0.05). Statistical significant differences was seen between age groups and infection rate in goats (P=0.033, OR=2.1); unlike to sheep (P=0.373). Also, the infection rate in nomads population of sheep was higher than fix location animals (P=0.003; OR=1.9); unlike to goats (P=0.195). In animals with history of abortion and vaccination against brucellosis, seroprevalence rate was significantly lower than other (P<0.05). Conclusion: This is the first report of brucellosis in sheep and goats in Hamedan province. The design of a comprehensive control program including vaccination, screening, and culling of brucellosis-positive animals is recommended. [J Adv Vet Anim Res 2016; 3(4.000): 399-405

Abstracts of The Second Eurasian RISK-2020 Conference and Symposium

This abstract book contains abstracts of the various research ideas presented at The Second Eurasian RISK-2020 Conference and Symposium.The RISK-2020 Conference and Symposium served as a perfect venue for practitioners, engineers, researchers, scientists, managers and decision-makers from all over the world to exchange ideas and technology about the latest innovation developments dealing with risk minimization