Due to many factors in the physical properties of the ground surface, the corresponding interferometric coherence values change dynamically over time. Among these factors, the roles of the vegetation and its temporal variation have not yet been revealed so far. In this paper, synthetic aperture radar (Sentinel-1) data and optical remote sensing (Landsat TM) images over four whole seasons are employed to reveal the relationship between the interferometric coherence and the normalized difference vegetation index (NDVI) at five sites that have ground deformation due to mining in Henan province, China. The result showed: (1) As for the village area with few vegetation cover, the related coherence values are significantly higher than that in the farm land area with high densities of vegetation in the spring and summer, which indicates that the subsidence by mining in few vegetation cover area is easier to be monitored; (2) Linear regression coefficients (R 2) between the interfereometric coherence values and the NDVI values is 0.62, which indicate the interferometric coherence values and the nDVi values change reversely in both farm land and village areas over the year. it suggests months between November and March with lower NDVI value are more suitable for deformation detecting. Therefore, the interfereometric coherence values can be used to detect the density of vegetation, while NDVI values can be reference for elucidating when the traditional differential interferometric synthetic aperture radar (DInSAR) could be effectively used. DInSAR leverages the phase difference between two correlated synthetic aperture radar (SAR) images to accurately detect large scale surface displacements and is widely used for mine deformation monitoring 1-4. However, as the technique suffers from a number of limitations, including spatial decorrelation, thermal noise, Doppler centroid shift, and temporal decorrelation, it is not appropriate in certain situations 5-7. Some research address the limitations of traditional DInSAR disturbed by the agricultural activities, especially the high density of crop vegetation, in the test of the polarimetric InSAR (POLInSAR) technique for its ability to increase interferometric coherence 8,9. Therefore, it is necessary to elucidate the deformation monitoring conditions under which the traditional DInSAR can be effectively used. The coherence is also taken as the main parameter in target classification 10,11 , forest change detection 12-14 , and lake study 15,16. The extent of temporal changes in the scatterers is a key factor affecting interferometric coherence 11,17. In DInSAR deformation measurements, the interferometric coherence is used for selecting the stable scatterers to achieve better accuracy 5. Compared with other scatterers, vegetation has a larger impact on SAR image coherence. In the seasons when vegetation is growing, the temporal decorrelation phenomena is
In order to evaluate water quality for a large water distribution network comprehensively, a two-stage classification method was used and the clustering methods, self-organizing map (SOM), K-means method and fuzzy c-mean (FCM), were represented. With these clustering methods, the pipes of a large real water distribution network were divided into some groups considering one or more water quality indicators synchronously. The water quality indicators of residual chlorine, water age, THMs, TAAs, TOC and BDOC are used in this paper. Residual chlorine and water age are two main water quality indicators. THMs and TAAs can represents the disinfection byproducts information. And TOC and BDOC are used to represents biological stability. According to the clustering results, the status of water quality of the water network was analysed. The results showed that the classification of SOM could express the comprehensive water quality in a water distribution network (WDN) directly and vividly by high-dimension water quality indicator projection to a low dimensional topology grid and that two-stage classification method has higher efficiency in comparison to the traditional clustering method. Water quality comprehensive evaluation was of significance for locating water quality monitoring, water network rehabilitation and expansion.
Theileria sergenti is a tick-borne parasite found in many parts of the world. The major piroplasm surface protein (MPSP), a conserved protein in all Theileria species, has been used as a marker for epidemiological and phylogenetic studies of benign Theileria species. In this study, Chinese species of T. sergenti were characterized by allele-specific polymerase chain reaction (PCR) and DNA sequence analysis of the MPSP gene. Using universal or allele-specific primer sets for PCR amplification of the MPSP gene, 98 of 288 cattle blood samples, collected from 6 provinces in China, were found to be positive. Among the positive samples, only 3 allelic MPSP gene types (Chitose [C]-, Ikeda [I]-, and buffeli [B]-type) were successfully amplified. Moreover, the results revealed that the majority of the parasites sampled in this study were C- and I-type (prevalence of 84 and 69%, respectively), whereas the B-type was less common (prevalence of 36%). Co-infections with C-, I-, and B-type T. sergenti also were found. An additional known allele, Thai-type, was not detected. Phylogenetic analysis based on the MPSP gene sequences, including 3 standard stocks generated in the laboratory ( T. sergenti Wenchuan, T. sergenti Ningxian, and T. sergenti Liaoyang), revealed that the isolates of Chinese sergenti were comprised of at least 4 allelic MPSP gene types, i.e., C-, I-, B1-, and B2-type, and these parasites with 6 MPSP types 1-5 and 7 were present in China.
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