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We demonstrate graphene oxide (GO)-decorated Ag dendritic nanostructures on a copper substrate for surface enhanced Raman scattering (SERS) applications. The Ag dendrites (AgD) were synthesized through a facile and low-cost galvanic replacement reaction of Ag nitrate solution and copper foils. The AgD/Cu was further decorated with GO by a dip-coating method. The GO-decorated AgD exhibited a much higher SERS activity in terms of sensitivity, signal-to-noise ratio and stability compared with non-decorated AgD, indicating that the GO/AgD/Cu substrates could be potentially useful for highly sensitive molecule detecting applications. [ABSTRACT FROM AUTHOR]

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar (ASAR) observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter (EnKF), the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model (DHSVM) was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important. [ABSTRACT FROM AUTHOR]

This paper presents a new object-oriented land cover classification method that integrates raster analysis and vector analysis. The method adopts an improved colour structure code (CSC) for segmentation and support vector machine (SVM) for classification using high resolution (HR) QuickBird data. It combines the advantages of digital image processing (efficient improved CSC segmentation), geographical information systems (GIS) (vector-based feature selection), and data mining (intelligent SVM classification) to interpret images from pixels to objects and thematic information. The improved CSC segmentation not only achieves robust and accurate results but also combines boundary information that the traditional CSC algorithm does not consider. The SVM used for classification has the advantages of solving sparse sampling, nonlinear, high-dimensional and global optimum problems, compared with other classifiers. The results demonstrate that the new object-oriented classification method significantly outperforms some other objected-oriented classification methods such as the objected-oriented method based on traditional CSC and SVM, and perfect classification results are obtained from the classification processing, including not only the classification method, but also preprocessing, sample selection and post-processing. [ABSTRACT FROM AUTHOR]

A highly sensitive Au-graphene structure D-type fiber surface plasmon resonance biosensor is presented in this study to specifically detect biomolecules. The method of growing graphene is employed directly on the copper, and then a gold film of optimum thickness is sputtered, and the copper foil is etched to obtain the structure. This method makes the contact closer between the gold layer and the graphene layer to improve surface plasmon resonance performance. The performance of this type of surface plasmon resonance (SPR) sensor has been previously verified both theoretically and experimentally. With the proposed Au-graphene structure D-type fiber biosensor, the SPR behaviors are obtained and discussed. In the detection of ethanol solution, a red shift of 40 nm is found between the refractive index of 1.3330 and 1.3657. By calculation, the sensitivity of the sensor we designed is 1223 nm/RIU. Besides, the proposed sensor can detect the nucleotide bonding between the double-stranded DNA helix structures. Thus, our sensors can distinguish between mismatched DNA sequences. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) image segmentation is an essential step in geographic object-based image analysis (GEOBIA) to ultimately derive “meaningful objects”. While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA). Specifically, a minimum spanning tree (MST) algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR) algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the “reverse searching-forward processing” chain based on message passing interface (MPI) parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2) and different selected landscapes (residential/industrial, residential/agriculture) covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral), while the accuracy is comparable with that of the FNEA method. [ABSTRACT FROM AUTHOR]