Among 114 clinical
Neisseria gonorrhoeae
isolates collected in Vietnam during 2019–2020, we detected 15 of subclone sequence type 13871 of the FC428 clonal complex. Fourteen sequence type 13871 isolates with mosaic
penA
allele 60.001 were ceftriaxone or cefixime nonsusceptible, and 3/14 were azithromycin nonsusceptible. Emergence of this subclone threatens treatment effectiveness.
Investigating information on land cover changes is an indispensable task in studies related to the variation of the environment. Land cover changes can be monitored using multi-temporal satellite images at different scales. The commonly used method is the post-classification change detection which can figure out the replacement of a land cover by the others. However, the magnitude and dimension of the changes are not been always exploited. This study employs the mixture of categorical and radiometric change methods to investigate the relations between land cover classes and the change magnitude, the change direction of land covers. Applying the Change Vector Analysis (CVA) method and unsupervised classification for two Landsat images acquired at the same day of years in 2000 and in 2017 in Duy Tien district, the experimental results show that a low magnitude of change occurs in the largest area of direction I and direction IV regarding the increase of Normalized Difference Vegetation Index (NDVI), but the opposite trend of (Bare soil Index) BI in the rice field. Alternately, the high magnitude of change is seen in the build-up class which occupies the smallest area with 1700 ha. The characterized changes produced by the CVA method provide a picture of change dynamics of land cover over the period of 2000-2017 in the study area.
Coastline is the most dynamic part of seascape since its shape is affected by various factors. Coastal zone is an area with immense geological, geomorphological and ecological interest. Monitoring coastal change is very important for safe navigation, coastal resource management. This paper shows a result of monitoring coastal morphological changes using Remote Sensing and GIS. Study was carried out to obtain intensity of erosion, deposition and sand bar movement in the Red River Delta. Satellite images of ALOS/AVNIR-2 and Landsat were used for the monitoring of coastal morphological changes over the period of 1975 to 2009. Band rationing and threshold technique was used for the coastline extraction. Tidal levels at the time of image acquisition varied from -0.89m to 2.87m. Therefore, coastline from another image at a different tidal level in the same year was considered to get the corrected coastline by interpolation technique. A series of points were generated along the coastal line from 1975 image and were established as reference points to see the change in later periods. The changes were measured in Euclidean distances from these reference points. Positive values represented deposition to the sea and negative values are erosion. The result showed that the Red river delta area expanded to the sea 3500m in
Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.
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