Seasonal Characteristics and Interannual Variability of Monthly Scale Low-Frequency Oscillation in a Low-Order Global Spectral Model

Analysis is done of five-year low-pass filtered data by a five-layer low-order global spectral model, indicating that although any non-seasonal external forcing is not considered in the model atmosphere,monthly-scale anomaly takes place which is of remarkable seasonality and interannual variability.Analysis also shows that for the same seasonal external forcing the model atmosphere can exhibit two climatic states,similar in the departure pattern but opposite in sign, indicating that the anomaly is but the manifestation of the adverse states, which supports the theory of multi-equilibria proposed by Charney and Devore(1979) once again.Finally, the source for the low-frequency oscillation of the global atmosphere is found to be the convective heat source / sink inside the tropical atmosphere as discussed before in our study.Therefore, the key approach to the exploration of atmospheric steady low-frequency oscillation and the associated climatic effect lies in the examination of the distribution of convective heat sources / sinks and the variation in the tropical atmosphere.

Scale invariant features extraction for stereo vision

Stable local feature detection is a fundamental component of many stereo vision problems such as 3-D reconstruction, object localization, and object tracking. A robust method for extracting scale-invariant feature points is presented. First, the Harris corners in three-level pyramid are extracted. Then, the points detected at the highest level of the pyramid are correctly propagated to the lower level by pyramid based scale invariant （PBSI） method. The corners detected repeatedly in different levels are chosen as final feature points. Finally, the characteristic scale is obtained based on maximum entropy method. The experimental results show that the algorithm has low computation cost, strong antinoise capability, and excellent performance in the presence of significant scale changes.

Identifying Scale-location Specific Control on Vegetation Distribution in Mountain-hill Region

vegetation continuous The scale-location specific control on distribution was investigated through wavelet transforms approaches in subtropical mountain-hill region, Fujian, China. The Normalized Difference Vegetation Index （NDVI） was calculated as an indicator of vegetation greenness using Chinese Environmental Disaster Reduction Satellite images along latitudinal and longitudinal transects. Four scales of variations were identified from the local wavelet spectrum of NDVI, with much stronger wavelet variances observed at larger scales. The characteristic scale of vegetation distribution within mountainous and hilly regions in Southeast China was around 20 km. Significantly strong wavelet coherency was generally examined in regions with very diverse topography, typically characterized as small mountains and hills fractured by rivers and residents. The continuous wavelet based approaches provided valuable insight on the hierarchical structure and its corresponding characteristic scales of ecosystems, which might be applied in defining proper levels in multilevel models and optimal bandwidths in Geographically Weighted Regression.

The effects of vegetation on runoff and soil loss:Multidimensional structure analysis and scale characteristics

This review summarizes the effects of vegetation on runoff and soil loss in three dimensions: vertical vegetation structures(aboveground vegetation cover, surface litter layer and underground roots), plant diversity, vegetation patterns and their scale characteristics. Quantitative relationships between vegetation factors with runoff and soil loss are described. A framework for describing relationships involving vegetation, erosion and scale is proposed. The relative importance of each vegetation dimension for various erosion processes changes across scales. With the development of erosion features(i.e., splash, interrill, rill and gully), the main factor of vertical vegetation structures in controlling runoff and soil loss changes from aboveground biomass to roots. Plant diversity levels are correlated with vertical vegetation structures and play a key role at small scales, while vegetation patterns also maintain a critical function across scales(i.e., patch, slope, catchment and basin/region). Several topics for future study are proposed in this review, such as to determine efficient vegetation architectures for ecological restoration, to consider the dynamics of vegetation patterns, and to identify the interactions involving the three dimensions of vegetation.

Parameterization of ocean wave-induced mixing processes for finite water depth

Three dimensional wave-induced mixing plays an important role in shallow water area. A quite direct approach through the Reynolds average upon characteristic length scale is proposed to parameterize the horizontal and vertical shallow water mixing. Comparison of finite depth case with infinite depth results indicates that the difference of the wave-induced mixing strength is evident. In the shallow water condition, the infinite water depth approximation overestimates the mixing strength in the lower layers. The nonzero horizontal wave-induced mixing presents anisotropic property near the shore. The Prandtl＇s mixing length theory underestimated the wave-induced mixing in the previous studies.

On the Out-of-Plane Vibration of Rotating Circular Nanoplates

A rotating axisymmetric circular nanoplate is modeled by the Mindlin plate theory.The Mindlin plate theory incorporates the nonlocal scale and strain gradient effects.The shear deformation of the circular nanoplate is considered and the nonlocal strain gradient theory is utilized to derive the governing differential equation of motion that describes the out-of-plane free vibration behaviors of the nanoplate.The differential quadrature method is used to solve the governing equation numerically,and the natural frequencies of the out-of-plane vibration of rotating nanoplates are obtained accordingly.Two kinds of boundary conditions are commonly used in practical engineering,namely the fixed and simply supported constraints,and are considered in numerical examples.The variations of natural frequencies with respect to the thickness to radius ratio,the angular velocity,the nonlocal characteristic scale and the material characteristic scale are analyzed in detail.In particular,the critical angular velocity that measures whether the rotating circular nanoplate is stable or not is obtained numerically.The presented study has reference significance for the dynamic design and control of rotating circular nanostructures in current nano-technologies and nano-devices.

Characterizing Landscape Spatial Heterogeneity in Multisensor Images with Variogram Models

Most evaluation of the consistency of multisensor images have focused on Normalized Difference Vegetation Index （NDVI） products for natural landscapes, often neglecting less vegetated urban landscapes. This gap has been filled through quantifying and evaluating spatial heterogeneity of urban and natural landscapes from QuickBird, Satellite pour l＇observation de la Terre （SPOT）, Ad- vanced Spacebome Thermal Emission and Reflection Radiometer （ASTER） and Landsat Thematic Mapper （TM） images with variogram analysis. Instead of a logarithmic relationship with pixel size observed in the corresponding aggregated images, the spatial variability decayed and the spatial structures decomposed more slowly and complexly with spatial resolution for real multisensor im- ages. As the spatial resolution increased, the proportion of spatial variability of the smaller spatial structure decreased quickly and only a larger spatial structure was observed at very coarse scales. Compared with visible band, greater spatial variability was observed in near infrared band for both densely and less densely vegetated landscapes. The influence of image size on spatial heterogeneity was highly dependent on whether the empirical sernivariogram reached its sill within the original image size. When the empirical semivariogram did not reach its sill at the original observation scale, spatial variability and mean characteristic length scale would increase with image size; otherwise they might decrease. This study could provide new insights into the knowledge of spatial heterogeneity in real multisen- sor images with consideration of their nominal spatial resolution, image size and spectral bands.

Identifying Landscape Pattern Metrics for the Hani Terrace in Yunnan, China

This study seeks to isolate a select group of landscape metrics particularly well-suited for describing the Hani Terrace in southwest of China.We examined the response of 47 landscape metrics to a large range of imagery grain sizes.Based on a correlation analysis,the original 47 metrics were placed into 21 groups such that all metrics within a group were strongly correlated with each other with a value of more than 0.9,and were represented by a single descriptor.Using these cross-sectional metrics in the context of principal components analysis,we found that five factors explained almost 93％ of the total variation in the landscape pattern.The highest loadings for these five factors were the Splitting index （SPLIT）,Patch area distribution （AREA_CV）,Shannon＇s diversity index （SHDI）,Euclidean nearest neighbor distance distribution （ENN_AM）,and Total core area （TCA）,respectively.Considering the real landscape,we added the Patch fractal dimension distribution （FRAC_MN） as the sixth landscape pattern metric.As the scale effect of landscape pattern metrics we design to investigate how a suite of commonly use landscape metrics respond to changing grain size.Based on the anlasis,we determined that the best domain of scale to characterise the Hani Terrace pattern metrics is between 40m and 45m.Through the fractal method,we found that the characteristic scale of the Hani Terrace is the same as the scale domain of metrics,among the 40m and 45m.We suggest that the majority of the patterns in the Hani Terrace landscapes,indeed for all those in southwest China,can be described effectively with these six metrics.

Theoretical analysis of flamelet model for supersonic turbulent combustion

Being physically intuitionistic and computationally efficient, the flamelet model has obtained increasing attention and becomes popular in numerical simulations of supersonic combustion. However, the flamelet model was firstly built for simulations of the low-speed flows. Therefore it is still unclear whether the assumption of the fiamelet model is reasonable in supersonic combustion. This paper tries to identify the existence of the flamelet mode in supersonic combustion. Firstly, the interaction of the turbulent fluctuation and the flame is discussed, based on which the combustion regime is distinguished. Then the charac- teristic length scale of the turbulent flows and the combustion inside the scramjet combustor are calculated and compared, which are used to identify whether the assumption of the flamelet model is reasonable in supersonic flows. The results show that for premixed combustion, due to the low fluctuation velocity in the recirculafion zone and the shear layer, the assumption of the flamelet model is established for all the flight Mach numbers, while for non-premixed combustion the assumption is also established for most of the flight Mach numbers except for very high Mach number under which the slow reaction mode dominates the combustion. In order to quantitatively examine the combustion mode, numerical calculations are performed to simulate the strut-injection supersonic combustion which has been experimentally investigated in German Aerospace Center. The results show that the supersonic combustion occurs in the fully-developed turbulent regions and the assumption of the flamelet model is established in the whole flow field.

Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering

In the field of magnetic tile surface detection, artificial detection efficiency is low, and the traditional image segmentation algorithm cannot show good performance when the gray scale of the magnetic tile itself is small, or the image is affected by uneven illumination. In view of these questions, this paper puts forward a new clustering segmentation algorithm based on texture feature. This algorithm uses Gabor function spectra to represent magnetic tile surface texture and then uses a user-defined local product coefficient to modify Gabor energy spectra to get the center number of fuzzy C-means(FCM) clustering. Moreover, the user-defined Gabor energy spectra image is segmented by clustering algorithm. Finally, it extracts the magnetic tile surface defects according to the changes of regional gray characteristics. Experiments show that the algorithm effectively overcomes the noise interference and makes a good performance on accuracy and robustness, which can effectively detect crack,damage, pit and other defects on the magnetic tile surface.

Extraction of lacunarity variation index for revealing the slope pattern in the Loess Plateau of China

Lacunarity analysis is frequently used in multiscale and spatial pattern studies.However,the explanation for the lacunarity analysis results is limited mainly at a qualitative description level.In other words,this approach can be used to judge whether the spatial pattern of the objective is regular,random or aggregated in space.The lacunarity analysis,however,cannot afford many quantitative information.Therefore,this study proposed the lacunarity variation index(LVI)to reflect the rates of variation of lacunarity with the resolution.In comparison with lacunarity analysis,the simulated experiments show that the LVI analysis can distinguish the basic spatial pattern of the geography objects more clearly and detect the scale of aggregated data.The experiment showed that different slope types in the Loess Plateau display aggregated patterns,and the characteristic scales of these patterns were detected using the slope pattern in the Loess Plateau as the research data.This study can improve the spatial pattern analysis and scale detecting methods,as well as provide a new method for landscape and vegetation community pattern analyses.Lacunarity analysis is frequently used in multiscale and spatial pattern studies.However,the explanation for the lacunarity analysis results is limited mainly at a qualitative description level.In other words,this approach can be used to judge whether the spatial pattern of the objective is regular,random or aggregated in space.The lacunarity analysis,however,cannot afford many quantitative information.Therefore,this study proposed the lacunarity variation index(LVI)to reflect the rates of variation of lacunarity with the resolution.In comparison with lacunarity analysis,the simulated experiments show that the LVI analysis can distinguish the basic spatial pattern of the geography objects more clearly and detect the scale of aggregated data.The experiment showed that different slope types in the Loess Plateau display aggregated patterns,and the characteristic scales of these patterns were detected using the slope pattern in the Loess Plateau as the research data.This study can improve the spatial pattern analysis and scale detecting methods,as well as provide a new method for landscape and vegetation community pattern analyses.