An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.     

Formation of Carbon-Enhanced Metal-Poor RR Lyrae Stars

Carbon-enhanced metal-poor (CEMP) stars are considered to be related to the first generation of stars, and responsible for the chemical evolution of the early Galaxy. More than half of them are in binaries, and could be explained by the binary evolution, but the formation channel of them is still not fully understood. Among the hundreds of CEMP stars, there are nine CEMP RR Lyrae stars identified, and at least seven of which are very likely not binaries. The usual binary star evolution channel is difficult to produce such a single star, particularly that of carbon enrichment. One way in which such a single star might be produced is the merger of a helium white dwarf with a Hertzsprung gap (HG) star. We use a stellar evolution program to calculate the models of the merger remnants, and find that the models can reproduce the observed distribution of these CEMP single RR Lyrae stars in terms of surface temperature, gravity, and carbon abundance. Hence, it is extremely possible that the helium white dwarf and HG star merger model is one of the formation channels of the metal-poor carbon-rich RR Lyrae stars.     

基于倾斜摄影测量的大比例尺三维测图技术

目前,城市大比例尺地形图更新主要采用全野外的测量方法,不仅耗费大量的劳动力,并且效率低下。旋翼机和倾斜摄影测量技术的快速发展,为城市大比例尺地形图的测绘与更新提供了全新的技术手段。本文以滕州市城区约10 km2的1∶500地形图测绘项目为例,采用大疆精灵4 Pro旋翼机进行数据获取,Smart 3D软件进行空三加密及三维建模,最后在EPS软件中进行三维测图,并对地形图成果进行了精度检核,验证了技术方案的可行性,为同类项目提供了有益参考。     

车载移动测量系统大比例尺测图精度控制研究

车载移动测量系统作为一种先进的测绘技术,已广泛应用于大比例尺测图项目中。SSW车载激光建模测量系统能够快速、高效、精确地完成大比例尺测图数据采集、处理等工作。不同比例尺对精度的要求不一样,而本文选择若干试验区,探讨不同的数据采集与处理方式对精度的影响,然后制订相应的精度控制方案,提高数据的精度,满足1∶1 000比例尺测图要求。     

空地技术结合地形图测图方法研究

车载移动测量系统可以快速、高精度地对测区进行三维激光扫描,但是因地物遮挡、视角限制,使得点云数据存在缺失;无人机航测具有高效率、高灵活性和低成本等优势,但是稳定性差,受天气影像严重,易导致影像不清晰或精度低。无人机航测技术可以弥补车载移动测量技术的采集盲区,后者可以发挥高精度的优点,二者技术联合应用,将极大提高测绘精度及生产效率。本文以某小区为例,进行了相关方法实验,对建筑物顶部或植被茂密处等扫描盲区,采用无人机航测补测,通过高精度激光点云对航摄影像进行纠正匹配,综合利用激光点云与航摄影像进行大比例尺测图。     

山地叶面积指数反演理论、方法与研究进展

叶面积指数LAI（Leaf Area Index）是表征叶片疏密程度和冠层结构特征的重要植被参数,在气候变化、作物生长模型以及碳、水循环研究中发挥着重要作用。遥感是获取区域及全球尺度LAI的一个重要手段,当前LAI产品主要基于遥感数据反演得到,但是多数LAI产品算法并未考虑地形特征的影响,导致山地LAI遥感反演精度不确定性大。提高山地LAI遥感反演精度亟需考虑地形因子对冠层反射率的影响,其中山地冠层反射率模型和遥感数据地形校正是提升山地LAI遥感反演精度的关键。本文围绕山地LAI遥感反演理论与方法,综合分析了国内外山地冠层反射率模型和地形校正模型的研究进展,总结了目前山地LAI遥感反演存在的问题,并讨论了未来研究的发展趋势。     

贺兰山东麓极端暴雨的中尺度特征

利用近10年宁夏逐时自动气象站降水、银川CD雷达、FY-2、探空和ECMWF再分析0.125°x 0.125°等高分辨率多源气象资料,在中尺度系统分型基础上,对比分析贺兰山东麓6次极端暴雨的中尺度特征.结果表明:(1)低空偏(东)南急流夜间增强并配合贺兰山地形,在东坡山前触发或增强了暴雨中小尺度系统,造成地形处降水增幅...     

奎屯市城乡土地一体化管理

以1∶500地籍数据库、1∶1000地形图数据库、1∶10000地形图及1∶10000土地利用数据库等不同比例尺、不同类型的数据库为奎屯市国土资源基础数据库实现奎屯市城乡土地一体化管理。