Geo-scape,a granularity depended spatialization tool for visualizing multidimensional data sets

Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques.Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of gran...     

机车车辆滚动振动试验台的精密测量

本文针对国家重点牵引力动力实验室的机车车辆整车滚动振动试验台的特点，探讨了一套适合试验台安装和养护的精密测量方法，实践证明，这套方法能满足试验台安装所需的精度要求。     

星载雷达Radarsat数据产品选择及在地质探测中的应用

结合生产科研实践,在论述星载雷达成像方式及Radarsat 数据产品主要特点的基础上,针对以岩性识别和构造分析为主要研究内容的地质探测领域,着重讨论分析了波束模式、视角范围、卫星姿态、时相、数据处理级别等影响数据产品选择的主要因素,并提出地质探测领域Radarsat 数据产品的选择原则及参考意见,供广大遥感地质工作者在充分利用这一新型遥感数据源时参考。     

Long-term behavior and statistical characterization of BeiDou signal-in-space errors

GPS Solutions - We derive orbit and clock errors for BeiDou satellites from March 1, 2013, to September 30, 2016 by comparing broadcast ephemerides with the precise ephemerides produced by Wuhan...     

Extraction of Alteration Mineral Information from Moderate Remote Sensing Images Using MPS Method

Although alteration minerals related to metallogenesis is very important in mineral exploration, information of alteration mineral is weakly expressed in remote sensing imagery, which is often subject to interfering noise and sometimes limited in spectral and spatial resolutions. Because of easy access, moderate images are the main sources of alteration mineral information. Therefore, it is very important to develop alteration mineral information extraction methods from remote sensing images. In this paper, a combined method based on Mask, principal component analysis (PCA) and support vector machine method (SVM) was used to extract alteration mineral information from Enhanced thematic mapper plus remote sensing data with limited spectral and spatial resolutions. First, a mask image of the remote sensing imagery was created to remove interference information such as vegetation, shadow and water. Then, PCA was employed to collect sample data relating to iron, argillic, and carbonatization alteration. Finally, SVM was used to deal with alteration anomaly and build a feature extraction model of high accuracy. The Mask-PCA-SVM model is used to extract alteration mineral information from remote sensing images of Hatu area, Xinjiang Uygur Autonomous Regions, China. The results show that the new methods proposed in this paper can coincide well with known deposits occurrences, rate reached 86.51%. While, the consistent rate with known deposits of the ratio model, PCA model and Spectral angle mapper model were only 3.37, 65.08 and 69.05% respectively. This suggests that the proposed model can find the actual distribution of mineral deposits more effectively by reducing interference to a greater degree.     

A Stochastic Approach to Estimate Distribution of Built-Up Area in Regions with Thick Tree Cover

Buildings and other human-made constructions have been accepted as an indicator of human habitation and are identified as built-up area. Identification of built-up area in a region and its subsequent measurement is a key step in many fields of studies like urban planning, environmental studies, and population demography. Remote sensing techniques utilising medium resolution images (e.g. LISS III, Landsat) are extensively used for the extraction of the built-up area as high-resolution images are expensive, and its processing is difficult. Extraction of built land use from medium resolution images poses a challenge in regions like Western-Ghats, North-East regions of India, and countries in tropical region, due to the thick evergreen tree cover. The spectral signature of individual houses with a small footprint are easily overpowered by the overlapping tree canopy in a medium resolution image when the buildings are not clustered. Kerala is a typical case for this scenario. The research presented here proposes a stochastic-dasymetric process to aid in the built-up area recognition process by taking Kerala as a case study. The method utilises a set of ancillary information to derive a probability surface. The ancillary information used here includes distance from road junctions, distance from road network, population density, built-up space visible in the LISS III image, the population of the region, and the household size. The methodology employs logistic regression and Monte Carlo simulation in two sub processes. The algorithm estimates the built-up area expected in the region and distributes the estimated built-up area among pixels according to the probability estimated from the ancillary information. The output of the algorithm has two components. The first component is an example scenario of the built-up area distribution. The second component is a probability surface, where the value of each pixel denotes the probability of that pixel to have a significant built-up area within it. The algorithm is validated for regions in Kerala and found to be significant. The model correctly predicted the built-up pixel count count over a validation grid of 900 m in 95.2% of the cases. The algorithm is implemented using Python and ArcGIS.     

Analysis of the Seasonal Variation of CO<Subscript>2</Subscript> Concentration in China Based on GOSAT Satellite Data

Carbon dioxide (CO₂) is one of the major gases that contribute to the global warming. Therefore, studying the distribution of CO₂ can help people understand the carbon cycle. Based on the GOSAT retrieved CO₂ products, the temporal and spatial distribution and seasonal variation of CO₂ concentration were analyzed from 2011 to 2015. CO₂ concentration has obvious seasonal variation. It was low in summer, and was high in spring, and the annual increase was about 2 ppm. Nevertheless, the annual growth rate of CO₂ concentration in summer was higher than that in spring, it was 0.5425% in summer and was 0.46% in spring. CO₂ concentration was low in the northwest and was high in the southeast. The growth rate of CO₂ was 2.8 ppm in the northwest and was 3.42 ppm in the southeast. More human’s activities made CO₂ concentration higher in the southeast than that in other regions.     

Variations in the Ice Phenology and Water Level of Ayakekumu Lake,Tibetan Plateau,Derived from MODIS and Satellite Altimetry Data

The alpine lakes on the Tibetan Plateau (TP) are highly sensitive to variations in climate changes, and the lake ice phenology and water level are considered to be direct indicators of regional climate variability. In this study, we first used 14 years of moderate resolution imaging spectroradiometer snow cover products to analyse the freeze dates, ablation dates, and ice coverage durations. The lake level changes during 2002–2015 were estimated, derived from satellite altimetry and Hydroweb data. Unexpectedly, the freeze dates of lake ice greatly advanced, and the ablation dates were markedly delayed. The complete freezing duration lengthened by approximately 77 days. As a result of the warm-wet climate in the northern TP, the lake area expanded from 770 to 995 km² during 2002–2015, and the water levels rose by 4.2 m in total, at a rate of 0.3 m/year. The progressive expansion of Ayakekumu Lake profoundly affected the ice phenology. Larger water volume with larger thermal capacity likely led to the delaying of ablation dates, with the freezing point depression caused by decreasing salinity. Some new narrow and shallow bays located in southern and eastern Ayakekumu Lake were conducive to early freezing of ice. Additionally, the changes in air temperature, precipitation, potential evaporation, and sunshine duration may be related to the prolonged ice cover duration since 2002. In sum, accurate measurements of lake ice and water levels are critical for understanding the water resource balance and hydrologic cycle in arid or semi-arid regions of China.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.