Phonon transport in periodically and quasi-periodically modulated cylindrical nanowires.

Phonon transport in periodically modulated cylindrical nanowire (PMCN) and quasi-periodically modulated cylindrical nanowire (QPMCN) is comparatively studied. It is shown that the transmission coefficient and thermal conductance for PMCN is greater than the corresponding values for QPMCN. At low frequencies, a wide stop-frequency gap due to the destructive interference between the incoming and back waves can be clearly observed here. For PMCN, such stop-frequency gap seems to be insensitive to the change of N (the periodic number). For QPMCN, however, its breadth increases with the increase of N (the Fibonacci number). When N is increased, the thermal conductance for PMCN presents a distinct change from the decrease to the constant, while QPMCN has a tendency of monotonous decrease. A brief discussion on these results is made.

General π-Electron-Assisted Strategy for Ir, Pt, Ru, Pd, Fe, Ni Single-Atom Electrocatalysts with Bifunctional Active Sites for Highly Efficient Water Splitting.

Both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) are crucial to water splitting, but require alternative active sites. Now, a general π-electron-assisted strategy to anchor single-atom sites (M=Ir, Pt, Ru, Pd, Fe, Ni) on a heterogeneous support is reported. The M atoms can simultaneously anchor on two distinct domains of the hybrid support, four-fold N/C atoms (M@NC), and centers of Co octahedra (M@Co), which are expected to serve as bifunctional electrocatalysts towards the HER and the OER. The Ir catalyst exhibits the best water-splitting performance, showing a low applied potential of 1.603 V to achieve 10 mA cm-2 in 1.0 m KOH solution with cycling over 5 h. DFT calculations indicate that the Ir@Co (Ir) sites can accelerate the OER, while the Ir@NC3 sites are responsible for the enhanced HER, clarifying the unprecedented performance of this bifunctional catalyst towards full water splitting.

[Research on Spectral Scale Effect in the Estimation of Vegetation Leaf Chlorophyll Content].

Spectral indices (SIs) method has been widely applied in the prediction of vegetation biochemical parameters. Take the diversity of spectral response of different sensors into consideration, this study aimed at researching spectral scale effect of SIs for estimating vegetation chlorophyll content (VCC). The 5 nm leaf reflectance data under 16 levels of chlorophyll content was got by the radiation transfer model PROSPECT and then simulated to multiple bandwidths spectrum (10-35 nm), using Gaussian spectral response function. Firstly, the correlation between SIs and VCC was studied. And then the sensitivity of SIs to VCC and bandwidth were analyzed and compared. Lastly, 112 samples were selected to verify the results above mentioned. The results show that Vegetation Index Based on Universal Pattern Decomposition Method (VIUPD) is the best spectral index due to its high sensitivity to VCC but low sensitivity to bandwidth, and can be successfully used to estimate VCC with coefficient of determination R2 of 0.99 and RMSE of 3.52 µg x cm(-2). Followed by VIUPD, Normalized Difference Vegetation Index (NDVI) and Simple Ratio Index (SRI) presented a comparatively good performance for VCC estimation (R2 > 0.89) with their prediction value of chlorophyll content was lower than the true value. The worse accuracy of other indices were also tested. Results demonstrate that spectral scale effect must be well-considered when estimating chlorophyll content, using SIs method. VIUPD introduced in the present study has the best performance, which reaffirms its special feature of comparatively sensor-independent and illustrates its potential ability in the area of estimating vegetation biochemical parameters based on multiple satellite data.

[Monitor of Cyanobacteria Bloom in Lake Taihu from 2001 to 2013 Based on MODIS Temporal Spectral Data].

Algal bloom highly impacts the ecological balance of inland lakes. Remote sensing provides real-time and large-scale observations, which plays an increasingly significant role in the monitoring of algal bloom. Various Vegetation Indices (VIs) derived from satellite images have been used to monitor algae. With threshold segmentation of VI, the area of algal bloom can be extracted from images. However, the result of threshold segmentation only reflects the condition of algae when images were generated. Compared to separated VI data obtained at a particular moment of time, temporal spectral VI data contains phonological information of algae, which may be used to evaluate algal bloom more accurately and comprehensively. This study chose MODIS NDVI data of the Lake Taihu from 2001 to 2013, and constructed temporal spectral data for each year. Then, we determined the feature temporal spectra of severe cyanobacteria bloom, moderate cyanobacteria bloom, slight cyanobacteria bloom and aquatic plants, and separated these four kinds of objects using SVM (Support Vector Machine) algorithm, getting the spatial distribution and area of them. In order to compare the results of our method with traditional threshold segmentation method, we chose 8 separated NDVI images from the temporal spectral data of 2007. With the threshold 0.2 and 0.4, cyanobacteria bloom was classified into three degrees: severe cyanobacteria bloom, moderate cyanobacteria bloom, and slight cyanobacteria bloom. By comparison, it showed that our method reflected cyanobacteria bloom more comprehensively, and could distinguish cyanobacteria and aquatic plants using the phonological information provided by NDVI temporal spectra. This study provides important information for monitoring the algal bloom trends and degrees of inland lakes, and temporal spectral method may be used in the forecast of algal bloom in the future.

Sophisticated Vegetation Classification Based on Feature Band Set Using Hyperspectral Image.

There are two major problems of sophisticated vegetation classification (SVC) using hyperspectral image. Classification results using only spectral information can hardly meet the application requirements with the needed vegetation type becoming more sophisticated. And applications of classification image are also limited due to salt and pepper noise. Therefore the SVC strategy based on construction and optimization of vegetation feature band set (FBS) is proposed. Besides spectral and texture features of original image, 30 spectral indices which are sensitive to biological parameters of vegetation are added into FBS in order to improve the separability between different kinds of vegetation. And to achieve the same goal a spectral-dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed. A spatial-dimension optimization algorithm of FBS based on neighborhood pixels' spectral angle distance (NPSAD) is proposed so that detailed information can be kept during the image smoothing process. The results of SVC experiments based on airborne hyperspectral image show that the proposed method can significantly improve the accuracy of SVC so that some widespread application prospects like identification of crop species, monitoring of invasive species and precision agriculture are expectable.

[Spectral Uncertainty of Terrestrial Objects and the Applicability of Spectral Angle Mapper Algorithm].

The spectral uncertainty of terrestrial objects causes a certain degree of spectral differences among feature spectra, which affects the accuracy of object recognition and also impacts the object recognition of spectral angle mapper algorithm (SAM). The spectral angle mapper algorithm is based on the overall similarity of the spectral curves, which was widely used in the classification of hyperspectral remotely sensed information. The spectral angle mapper algorithm does not take the spectral uncertainty of terrestrial objects into account while calculating the spectral angle between the spectral curves, and therefore does not tend to correctly identify the target objects. The applicability of the spectral angle mapper algorithm is studied for the spectral uncertainty of terrestrial objects and a modified SAM is proposed in this paper. In order to overcome the influence of the spectral uncertainty, the basic idea is to set a spectral difference value for the test spectra and the reference spectra and to calculate the spectral difference value based on derivation method according to the principle of minimum angle between the test spectra and the reference spectra. By considering the impact of the spectral uncertainty of terrestrial objects, this paper uses five kaolinite mineral spectra of USGS to calculate the spectral angle between the five kalinite mineral spectra by using local band combination and all bands to verify the improved algorithm. The calculation results and the applicability of the spectral angle mapper algorithm were analyzed. The results obtained from the experiments based on USGS mineral spectral data indicate that the modified SAM is not only helpful in characterizing and overcoming the impact of the spectral uncertainty but it can also improve the accuracy of object recognition to certain extent especially for selecting local band combination and has better applicability for the spectral uncertainty of terrestrial objects.

[Research on the model of spectral unmixing for minerals based on derivative of ratio spectroscopy].

The precise analysis of mineral abundance is a key difficulty in hyperspectral remote sensing research. In the present paper, based on linear spectral mixture model, the derivative of ratio spectroscopy (DRS) was introduced for spectral unmixing of visible to short-wave infrared (Vis-SWIR; 0.4 - 2.5 microm) reflectance data. The mixtures of different proportions of plaster and allochite were analyzed to estimate the accuracy of the spectral unmixing model based on DRS. For the best 5 strong linear bands, the Pearson correlation coefficient (PCC) of the abundances and the actual abundances were higher than 99.9%, while the root mean square error (RMSE) is less than 2.2%. The result shows that the new spectral unmixing model based on DRS is simple, of rigorous mathematical proof, and highly precise. It has a great potential in high-precision quantitative analysis of spectral mixture with fixed endmembers.

[Monitoring spatio-temporal spectral characteristics of leaves of karst plant during dehydration using a field imaging spectrometer system].

As the supplement of spaceborne and airborne imaging spectrometer system, field Imaging spectrometer system spans a very broad range of applications. Imaging spectrometer system of this new kind could provide vital information especially for which spaceborne or airborne remote sensing could not be competent, such as proximal detection of plant population, individual plant or plant organs for site-specific management in precision agriculture. A new self-developed imaging spectrometer system was utilized to monitor spatio-temporal dynamics of spectral changes of plant leaves in response to dehydration. lThe phenomenon of blue shift of red edge of plant leaves was successfully detected and visualized in the form of image series. The patterns of photochemical reflectance index (PRI) of leaves during dehydration were compared and confirmed by fluorescence parameter quantum yield. Our results show that FISS has good spectral and radiometric properties and could be used in quantitative researches and precise information mapping.

[Quantitative inversion of rock SiO2 content based on thermal infrared emissivity spectrum].

The present paper used the emissivity of non-processed rocks measured by M304, a hyperspectral Fourier transform infrared (FTIR) spectroradiometer, and SiO2 content by the X-ray fluorescence spectrometry. After continuum removal and normalization, stepwise regress method was employed to select the feature bands of rocks emissivity. And then quantitative relationship between SiO2 content and continuum removal emissivity of feature bands was analysed. Based on that, by comparing twelve SiO2 indices models, the optimal model for predicting SiO2 content was built. The result showed that the SiO2 indices can predict SiO2 content efficiently, and especially the normalization silicon dioxide index (NSDI) about 11.18 and 12.36 microm is the best; compared with regression models, NSDI is simpler and has higher practicality; the result has an important application value in rock classification and SiO2 content extraction with high precision.

[Fresh and frozen-thawed meat discrimination based on FISS imaging spectral data].

In the present paper, a self-developed Field imaging spectrometer system (FISS) was used to detect whether pork has been frozen and thawed. The preservation time of fresh pork has also been identified. Fresh and frozen-thawed pork was scanned and imaged and hyperspectral image cubes were acquired using FISS. To eliminate high-frequency random noise and baseline offset and improve the multi-collinearity, all samples were preprocessed by MNF (Minimum noise fraction) transform and first derivative. Multiple analysis models were built by using Wilks' lambda stepwise method to select proper wavelengths. Fisher LDA (linear discriminant analysis) was performed to discriminate fresh and frozen-thawed pork. Eight selected bands gave 99% correct results of fresh or frozen-thawed pork samples. For the freshness by the day, classification accuracy reached 98% with 6 selected bands, while for the freshness by the hour, classification accuracy reached 93.6% with all 28 selected bands. The results showed that FISS might be used as a screening method to identify the quality of meat.

[Study on discrimination of varieties of milk based on FISS imaging spectral data].

Using a self-developed field imaging spectrometer system (FISS), hyperspectral images of 14 typical kinds of milk were acquired, based on which the discrimination of varieties of milk was studied. Firstly, removing 2 abnormal samples, the remaining 12 kinds of milk were randomly sampled, a total of 1 200 pixel samples. To eliminating high-frequency random noises and baseline offset and decrease the multi-collinearity, all samples were preprocessed by smooth-moving average and first derivative. Secondly, multiple discriminant analysis models for milk were built using characteristic wavelengths selected by the stepwise method. Results demonstrated that the overall identification accuracy for 1 200 spectral samples put together reached 95.5%, of which the overall distinguishing rate of Mengniu, Yili and Guangming acidophilous milk was 88.3%. The discriminant models for the three kinds of acidophilous milk subset, 300 spectral samples in all, were built, with the overall distinguishing rate of 88.7%. This explicated that FISS would be useful for discriminating milk varieties, and to accomplish specific discrimination of milk varieties, it would be best for milk of the same type from different manufacturers to form a subset, which may not only reduce the model variables, improving operational efficiency and the stability of the model, but improve their overall discriminant accuracy.

[Quantitative estimation of CaO content in surface rocks using hyperspectral thermal infrared emissivity].

The objective of the present paper is to study the quantitative relationship between the CaO content and the thermal infrared emissivity spectra. The surface spectral emissivity of 23 solid rocks samples were measured in the field and the first derivative of the spectral emissivity was also calculated. Multiple linear regression (MLR), principal component analysis (PCR) and partial least squares regression (PLSR) were modeled and the regression results were compared. The results show that there is a good relationship between CaO content and thermal emissivity spectra features; emissivities become lower when CaO content increases in the 10.3-13 mm region; the first derivative spectra have a better predictive ability compared to the original emissivity spectra.

[Research on crop-weed discrimination using a field imaging spectrometer].

Discrimination of weeds from crop is the first and important step for variable herbicides application and precise physical weed control. Using a new field imaging spectrometer developed by our group, hyperspectral images in the wavelength range 380-870 nm were taken in the wild for the investigation of crop-weed discrimination. After normalizing the data to reduce or eliminate the influence of varying illuminance, stepwise forward variable selection was employed to select the proper band sets and fisher linear discriminant analysis (LDA) was performed to discriminate crop and weeds. For the case of considering each species as a different class, classification accuracy reached 85% with eight selected bands while for the case of considering overall weed species as a class, classification accuracy was higher than 91% with seven selected bands. In order to develop a low-cost device and system in future, all combinations of two and three bands were evaluated to find the best combinations. The result showed that the best three bands can achieve a performance of 89% comparable to the performance achieved by five bands selected using stepwise selection. The authors also found that "red edge" could afford abundant information in the discrimination of weed and crop.

[Evaluation of sensor spectral parameters for the simulation accuracy of the vegetation spectrum].

Hyperspectral imaging (HSI) has become one of the most promising and emerging techniques in remote sensing. Due to hundreds of co-registered bands used in HSI system, hyperspectral imagery may provide more spectral information than multi-band images. Unfortunately, original hyperspectral images are more expensive and difficult to achieve than multi-band ones. However, an abundance of spectral information has to be acquired by part of special research for the purpose of ground monitoring, which original HSI systems can easily provide. Then a solution, called hyperspectral satellite data simulation, is proposed for studies in satellite data simulation. It is also one of the most important studies to simulate satellite remote sensing data. In the method, the model with low computational complexity can simulate hyperspectral data quickly, which is based on the priori spectral knowledge of the ground objects. But the accuracy of the simulation data depends on spectral parameters of the sensor. In the present paper, the authors experiment with EO-1/ALI bands in VIS/NIR wavelengths. Then the relationship between the spectral parameters, including the number of bands, bandwidth and the peak wavelength, and the simulation accuracy of the vegetation spectrum are analyzed from their variation principles. According to the results, spectral parameters can determine the effective spectral feature of the vegetation, and impact simulation model directly. Optimal parameters are also summarized for spectral reconstruction in the paper. The experiment results are beneficial to enhancing spectral simulation precision. The conclusions can help evaluate the performance of multispectral sensors and perfect spectroscope and filter design.