Correlation of the Meishucunian Stage in South Asia

The determination of boundary points and their correlation are prerequisites for defining the PrecambrianCambrian boundary and the intercontinental correlation. A large number of good Precambrian-Cambrian boundary sections in South Asia not only possess the conditions for selection of a boundary stratotype, but also provide the basis for the long-distance correlation of those boundary sections.Through a detailed study of the latest data obtained from the typical sections in South China, the Lesser Himalaya Mountains and the Alborz Mountains. the locations of boundary points have been defined, and a correlation scheme is proposed, which contributes to the solution of the problem of worldwide correlation of boundary sections.     

Spatial and temporal distribution of precipitation based on corrected TRMM data around the Hexi Corridor, China

Accurate rainfall distribution is difficult to acquire based on limited meteorological stations, especially in remote areas like high mountains and deserts. The Hexi Corridor and its adjacent regions （including the Qilian Mountains and the Alxa Plateau） are typical districts where there are only 30 available rain gauges. Tropical Rainfall Measuring Mission （TRMM） data provide a possible solution. After precision analysis of monthly 0.25 degree resolution TRMM 3B43 data from 1998 to 2012, we find that the correlations between TRMM 3B43 estimates and rain gauge precipitation are significant overall and in each station around the Hexi Corridor; however, the biases of annual precipitation differ in different stations and are seriously overestimated in most of the sites. Thus, Inverse Distance Weighting （IDW） interpolation method was used to rectify TRMM data based on the difference between TRMM 3B43 estimates and rain gauge observations. The results show that rectified TRMM data present more details than rain gauges in remote areas where there are few stations, alt- hough they show high coherence of distribution. Precipitation decreases from southeast to northwest on an annual and seasonal scale. There are three rainfall centers （〉500 mm） including Menyuan, Qilian and Toson Lake, and two low rain- fall centers （〈50 mm） including Dunhuang and Ejin Banner. Meanwhile, precipitation in most of the study area presents an increasing trend; especially in northern Qilian Mountains （〉5 mm/a）, Badain Jaran Desert （〉2 mm/a）, Toson Lake （〉20 mm/a） and Qingtu Lake （〉20 ram/a） which shows a significant increasing trend, while precipitation in Hala Lake （〈-2 mm/a） and Tengger Desert （〈-3 mm/a） demonstrates a decreasing trend.     

SVLBI用于大地测量的卫星轨道及其跟踪网设计

SVLBI （space very long baseline interferometry） has some important potential applications in geodesy and geodynamics, for which one of the most difficult tasks is to precisely determine the orbit of an SVLBI satellite. This work studies several technologies that will possibly be able to determine the orbit of a space VLBI satellite. Then, according to the types and charac- teristics of the satellite and the requirements for geodetic study and the geometry of the GNSS （GPS, GALILEO） satellite to track the space VLBI satellite, the six Keplerian elements of the SVLBI satellite （TEST-SVLBI） are determined. A program is designed to analyze the coverage area of space of different altitudes by the stations of the network, with which the tracking network of TEST-SVLBI is designed. The efficiency of tracking TEST-SVLBI by the network is studied, and the results are presented.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

利用NOAA NDVI数据集监测冬小麦生育期的研究

探索了利用NDVI研究作物生育期的方法，对黄淮海冬麦区的返青期、抽穗期、成熟期进行了估测，并利用地面实际观测资料进行了验证。结果表明，NDVI数据对大范围农作物生育期监测是可行的。冬小麦遥感反青期由南到北依次推迟，符合春季绿波由南到北推移规律。对冬小麦遥感生育期年际变化分析表明，黄淮海平原返青期变化相对较大，而抽穗期和成熟期变化较小。根据历年月平均温度与返青期分析，冬小麦返青日期与2月份平均温度密切相关。对于局部地区，利用5d合成1km分辨率数据，且按农业生态分区分别制定生育期判别标准，估测效果将更好。