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Natural color images are superior to false color images for presentation and visual interpretation purposes. They are widely used in the fields of fly-through of a draped terrain, visual interpretation, and display and have been designed, for example, for nonremote sensing professional users. However, each sensor has its own purpose, application, and limitation. In addition, blue wavelengths suffer the greatest scattering in the atmosphere, which follows an inverse dependence on wavelength to the fourth power. Some of high-resolution satellites cover only two visual spectral bands (green and red bands) plus one in the near-infrared region. As a result, a true color image cannot be formed, as the blue band is necessary in the red, green, and blue combination. This has become problematic for users needing a natural color composite that has limited the application of high-resolution images. To overcome this problem, in this letter, we propose a new approach for generating pseudo natural color (PNC) composite representations from false color composite images based on the spectral similarity scale. The method is validated experimentally. The proposed “natural color generator” can be applied to change false color images into natural color images. The generated PNC images are shown to be of high quality.

Genetic aberration of EGFR is one of the major molecular characteristics of breast cancer. However, the molecular changes associated with EGFR signaling during different stages of breast cancer development have not been studied. In this study, complementary two-dimensional-DIGE and iTRAQ technologies were used to profile the expression level of proteins in 4 isogenic cell lines in the MCF10AT model of breast cancer progression following a time course of EGF stimulation. A total of 80 proteins (67 from iTRAQ, 15 from DIGE, 2 common in both) were identified to be up- or down-regulated by EGF treatment. Following EGF stimulation, the expression level of MIF, a cytokine that has been implicated in many human cancers, was decreased in MCF10A1 normal breast mammary epithelial cells, increased in MCF10AT1k preneoplastic and MCF10CA1h low grade breast cancer cells, but showed no obvious difference in the MCF10CA1a high grade cancer cells. The increase in MIF expression level following EGF treatment could also be observed in A431 cervical cancer cells. EGF-induced increases of MIF expression levels in CA1h breast cancer cells were abrogated when MEK, but not PIK3CA, was knocked down. In addition, silencing of MIF diminished the proliferation of EGF-stimulated CA1h cells when compared to control cells. Taken together, our data suggested an EGFR --MEK --MIF proliferative pathway that has never been reported previously and that this pathway "evolves" during disease progression as modeled by the MCF10AT system. Revelation of the novel relationship between MIF and EGF may contribute to an integrated understanding of the roles of these oncogenic factors during breast cancer development.

Soil‐salinisation has become an increasingly severe form of soil degradation due to climate variability and human disturbance in arid and semi‐arid areas. A cellular automata (CA) model was used by integrating a geographic inform ation system (GIS) and remote sensing. Spatial and attribute data from Changling County were analysed to form a database in the GIS. Factors for soil‐salinisation were confirmed. A soil salinity forecast formula was obtained by analysing the soil‐salinisation system in Changling County. The CA model and database were integrated to simulate and forecast the soil‐salinisation spatial‐temporal distribution and its evolution. The results indicate the CA model is an effective decision‐making support system for the prevention and control of soil‐salinisation.

The canopy hyperspectral reflectance of winter wheat infected with yellow rust at different levels of severity were measured by an ASD FieldSpec Pro FR™ spectrometer in the field and the concentrations of chlorophyll a (Chl a) in the leaves corresponding to the spectra were determined by biochemical methods in the laboratory. Correlation analyses were made between Chl a concentrations and canopy hyperspectral data of diseased wheat. Results show that foliar Chl a concentrations are strongly correlated with canopy spectrum in the visible region and the first‐order derivative spectrum in blue edge, green edge, and red edge. Linear and nonlinear models for estimating Chl a concentrations of the diseased wheat were built based on several spectral indices. Results indicate that SDr/SDg, in which SDr and SDg are the sums of the first derivative within red and green edges, outperformed the other indices in predicting Chl a concentrations. The relative estimation errors for Chl a for 12 unseen samples ar...

Based on fire susceptibility index (FSI), an improved index is developed. Improved fire susceptibility index (IFSI) considers both live fuel and dead fuel using remotely sensed data with meteorological data and is conducive to the normalisation of data processing or to multifactor analysis. The weights of IFSI between live fuel and dead fuel may be derived from a fuel type map by experience based on ground observation data. IFSI has been validated with fire potential index (FPI) using success‐rate verification (SRV) method based on historical fire hotspot data. The forest fire prediction accuracy of IFSI is very close to that of FPI, FPI is better in low fire risk ranges and IFSI is better in high fire risk ranges.

Models and methods of ecological capital assessment based on remote sensing (RS) technologies are introduced in this paper, including the selection of RS measurement indexes, models of vegetation biomass measurement, retrieval methods for net primary production and vegetation coverage. On this basis, the present ecological capital gross in typical research area of Inner Mongolia were assessed and compared according to ecological capital assessment models with the help of multi‐scale remote sensing observation data such as IKONOS, ETM+ and MODIS. The results indicate that the largest average ratio to the total ecological capital is farmland (45%), the second is forest (24%), the third is grassland (23%), and the smallest is shrubland (8%). Therefore, it can be proved that an ecological capital assessment model based on RS methods was reasonable.

The spatio-temporal distribution of vegetation is an important component of the urban/suburban environment. Therefore, correct estimation of vegetation cover in urban/suburban areas is fundamental in land use studies. In this study, the potential of extracting fractional vegetation cover (FVC) from remotely sensed data and ground measurements is explored. Based on the assumption that pixel has a mosaic structure, sub-pixel models for FVC estimation are first introduced. Then a combined approach of using different sub-pixel models for FVC estimation based on land cover classification is proposed. The experimental result, derived from a case study in Haidian district, Beijing, indicates that the accuracy of FVC estimation using the proposed method can be up to 80.7%. The results suggest that this method may be generally useful for FVC estimation in urban and suburban areas.

A remote sensing approach towards modelling the land cover pattern for controlling local soil erosion based on the universal soil loss equation (USLE) was developed. The approach was used to analyse the possible impact of land cover changes on local ecological security. Then, land cover pattern optimization aiming at the reduction of local soil erosion was taken. Results show that this method can be applied to local ecological security management.

The preservation of spectral characteristics and the retention of high spatial resolution are two key issues in image fusion. Different applications may require different balances between the two. However, the limitation is that only one fused result is possible for most of the commonly used fusion methods; users have no control over how much spatial detail or spectral information should be retained. Both quantitative and qualitative criteria, including mean, standard deviation, entropy, average gradient, spectral distortion, correlation coefficient and visual assessment, were used to evaluate the performance of the proposed fuser. A SPOT (Satellite Pour l'Observation de la Terre) panchromatic image and Landsat Thematic Mapper (TM) images were employed in the evaluation. The results show that the proposed method is slightly better than, or comparable to, commonly used image fusion methods, including Intensity–Hue–Saturation (IHS), principal component analysis (PCA), and two other wavelet‐based fusion meth...

Land surface temperature (LST) of Beijing area was retrieved from Landsat TM thermal band data utilizing a radiative transfer equation and the urban heat island (HUI) effects of Beijing and its relationship with land cover and normalized difference vegetation index (NDVI) were discussed. The result of LST showed that the urban LST was evidently higher than the suburban one. The average urban LST was found to 4. 5°C and 9°C higher than the suburban and outer suburban temperature, respectively, which demonstrated the prominent UHI effects in Beijing. Prominent negative correlation between LST and NDVI was found in the urban area, which suggested the low percent vegetation cover in the urban area was the main cause of the urban heat island.

A two‐layer remote sensing model for estimating daily evapotranspiration over a large area was developed. The model followed an energy balance approach, where evapotranspiration is estimated as a residual when net radiation, sensible heat flux and ground flux are known. The accuracy of the model outputs was determined using harmonized surface conditions derived from measurements from ground stations. An accurate agreement (r 2 = 0.844, n = 48) between estimates and ground‐based measurement was obtained.

Mapping and quantifying land use and land cover changes are important for evaluating recent changes in the regional and global environment and for simulating future changes under different climate change and human land use scenarios. The normalized difference vegetation index (NDVI) and surface temperature (Ts) are two important parameters used to describe the characteristics of land cover. The NDVI-Ts feature space combines these two parameters into one variable. By and large, Ts/NDVI is synchronized to the growing season of vegetation so it can approximate the different phases and status of vegetation growth. Compared to NDVI and Ts, NDVI-Ts contains more land cover information and should be more suitable for characterizing the distribution of vegetation or land cover. The aim of this paper was to discuss the feasibility of using the NDVI-Ts feature space to better characterize the current distribution and changes in vegetation and land cover in China. We used several classification methods, including Principal Component Analysis (PCA), unsupervised classification and post-classification sustained based on digital elevation models (DEM). The results indicated that Ts/NDVI was highly sensitive and could discriminate different vegetation cover categories in China at large-scales. The accuracy of vegetation classification based on Ts/NDVI was 72.0%, a 3.3% improvement in accuracy as compared to NDVI images, using unsupervised classification, and the Kappa value increased 0.020 2. Moreover, because of the simplexity of remote sensing information, the classification based on seasonal Ts/NDVI data could not avoid completely the mixed classification phenomena. It was necessary to add other information, reflecting vegetation characteristic and its environment to implement post-classification, such as DEM. When the inversing accuracy of Ts improved, Ts/NDVI data can precisely describe the status of vegetation or land cover in China and improve monitoring of land use changes. This technique has great research potential and practical value.

A contour tree is a good graphical tool for representing the spatial relations of contour lines and has many applications in map generalization, map annotation, terrain analysis, etc. A new method for generating contour trees by introducing a Voronoi-based interior adjacency concept is proposed in this paper. The immediate interior adjacency set is employed to identify all of the children contours of each contour without contour elevations. It has advantages over existing methods such as the geometric method and the region growing-based method.

The study results showed: (1)9 land cover types obtained from remote sensing data were: temperate deciduous broadleaf forest, temperate upland deciduous leaflet forest, single crops+temperate typical steppe, temperate deciduous scrub and low forest, temperate meadow steppe, temperate typical steppe, temperate desert steppe, meadow+graminaceous swamp and water body; (2)the macro spatially pattern of regional land cover revealed by images of 3 different spatial resolutions was consistent, but the boundry of land cover type, the shape and the number of patches of each type differed greatly; (3)comparison of four indices (Fractal dimension, Frgmentation, Diversity, and Dominance) showed that the structure of the landscape changed greatly with change of spatial resolution as revealed by images. Fractal dimension of ach cover type showed the most obvious variation, which indicated the change of spatial resolution contributed most to the patch complexity. This paper sets up a basis for further study at different scales on landscape change processes.

Change vector analysis (CVA) and principal component analysis in NDVI time-trajectories space are powerful tools to analyze land-cover change. The magnitude of the change vector indicates amplitude of the change, while its direction indicates the nature of the change. CVA is applied to two remotely sensed indicators of land surface conditions, NDVI and spatial structure, in order to improve the capability to detect and categorize land-cover change. The magnitude and type of changes are calculated in China from 1989 to 1999. Through the research, the main conclusions are drawn as follows: 1) The changes of NDVI are quite different between eastern China and western China, and the change range in the east is bigger than that in the west. The trend in NDVI time series is smoothly increasing, the increases happen mostly in Taiwan, Fujian, Sichuan and Henan provinces and the decreases occur in Yunnan and Xinjiang. 2) The spatial structure index can indicate changes in the seasonal ecosystem dynamics for spatially heterogeneous landscapes. Most of spatial structure changes, which occurred in southern China, correlated with vegetation growth processes and strike of mountains.

It is a very complicated problem to estimate evapotranspiration (ET) over a large area of land surface. In this paper, the evapotranspiration estimation models for dense vegetation and bare soil are presented, based on the information of parameters like vegetation cover-degree and surface albedo. Combined with vegetation cover-degree data, a model for regional evapotranspiration estimation over the heterogeneous landscape is derived. Through a case study using remote sensing data over Northwest China, the accuracy of the model for regional evapotranspiration estimation is checked. The result shows that the accuracy of the model is satisfactory. The features of evapotranspiration over Northwest China are also discussed with the application of the model.

We report on the first demonstration of a single-fiber optical tweezer that is utilized to stabilize and control the liquid droplet for dye lasing. In order to trap a liquid droplet with a diameter of 15-30 μm, an annular core micro-structured optical fiber is adopted. By using wavelength division multiplexing technology, we couple a trapping light source (980 nm) and a pumping light source (532 nm) into the annular core of the fiber to realize the trapping, controlling, and pumping of the oil droplet. We show that the laser emission spectrum tunes along the same size as the oil droplet. The lasing threshold of the oil droplet with the diameter of 24 μm is 0.7 μJ. The presented fiber-based optical manipulation of liquid droplet micro-lasers can be easily combined with the micro-fluidic chip technology and also may extend the application of optical fiber tweezers for micro-droplet lasing technology in the biological field.

Voogt [J. A. Voogt, “Assessment of an Urban Sensor View Model for thermal anisotropy,” Remote Sensing of Environment, vol. 112, pp. 482–495, 2008.] assessed the surface-sensor-sun relations model (SUM) of thermal anisotropy. The results of that assessment suggested that there is a general tendency to underestimate overall thermal anisotropy. This paper focuses on the effects of wall direction on urban thermal anisotropy. Field experiments were conducted in Beijing. One experiment used several fixed infrared thermometers to record the diurnal temperature change on the south, north, east, and west walls as well as the roof of a building. Another experiment observed directional radiance and building component temperature using an urban scale model with different geometrical viewing conditions. Two component-dividing patterns were then employed to simulate urban thermal anisotropy using SUM model: Pattern I does not consider wall direction, whereas Pattern II divides walls into south-, north-, east-, and west-facing walls. The results show that there are obvious temperature differences among the differently facing walls. Pattern I has a tendency to underestimate the directional temperature. While Pattern II can rectify this underestimation and can simulate urban thermal anisotropy with improved accuracy. The above findings underscore the importance of wall direction in urban thermal anisotropy, especially for areas with sparse buildings, and, to some extent, explain the main reason of underestimation suggested by Voogt.

In this paper, we present an approach to learning latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: (1) ambiguous correspondences between visual features and annotated keywords; (2) incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning topic models. In particular, some imagined keywords are poured into the incomplete annotation through measuring similarity between keywords. Then, both given and imagined annotations are used to learning probabilistic topic models for automatically annotating new images. We conduct experiments on a typical Corel dataset of images and loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods (using a set of discrete blobs to represent an image). The proposed method improves word-driven probability Latent Semantic Analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range., Comment: 10 pages

Hyperspectral remote sensing is not only an important technical method in the field of global observation, but also can be used in the field of precision agriculture. Owing to hyperspectral remote sensing data whose spectrum resolution is less than 10 nm, include a mass of diagnosing spectrum information, indices derived from hyperspectral reflectance spectra have the potential to indicate the environmental stress in crops. The authors used canopy-scale, ground-based measurements of hyperspectral reflectance to demonstrate the features of winter wheat in different development phases under imposed fertility (N rate) and water stress. Correlation analysis was used to acquire the correlative coefficients between hyperspectral indices and parameters of biochemistry and biophysics. The results showed that when the volume of water is in an appropriate given level, the volume of N application increased, the canopy spectral reflectance of wheat gradually increased in infrared region. Also, when the volume of N was in an appropriate given level, the volume of water application increased, the canopy spectral reflectance of wheat gradually increased in infrared region.

Coal mine spontaneous burning has led to resources waste, meanwhile it also causes air pollution and some geological disasters. Now remote sensing is becoming an important approach of monitoring the coal fire. The thermal anomalies points are extracted by three methods in this article that is multi-temporal MNF transformation, multi-threshold density slice and differentiated method. Combining with GIS the information of the coal fire extracted are qualified compared and analyzed, then find the best method to extract the high thermal anomalies points is the difference method, and helped by the MNF transformation areas where affected by coal fires extracted are more accuracy than directly using density slice.