基于CORS站的GPS平面控制网起算点兼容性研究

不同等级或者不同时期布设控制点都可能存在较大误差,如果以误差较大点做起算,都会扭曲GPS测量的精度,严重影响GPS成果可靠性。文中以某城市四等GPS控制网为例,探讨以城市CORS站分析GPS控制网起算点兼容性,并得到成功应用。     

小江断裂带地壳运动特征及地震危险性研究

利用1999~2013年间川滇地区的GPS速度场观测数据和块体模型研究小江断裂带地壳运动特征及地震危险性。结果表明:小江断裂带闭锁程度较高,应变量积累较大,在地表以下6km的闭锁程度约为0.94,滑动亏损速率约为(9.2±0.4)mm/a,整条断裂带左旋走滑速率约为(10.8~12.1)mm/a。结合小江断裂带上的历史地震数据估算,小江断裂带未来地震最大震级为7.3级,与近年来断裂带活动较为活跃相吻合。     

DORIS、GPS和SLR空间大地测量技术导出的地心运动规律

用调和分析法分析DORIS、GPS和SLR 3种空间大地测量技术导出的地心运动时间序列。结果表明:地心长期运动不显著,但存在北向运动趋势,速度小于1mm/a;相对于DORIS和GPS,SLR导出的地心运动更符合地球物理模型计算的结果,用22aSLR数据导出的地心运动在X,Y,Z方向的周年运动振幅分别为2.8±0.2mm,2.7±0.2mm和6.1±0.2mm。     

基于时间序列统计特性的森林变化监测

森林动态变化分析对揭示生态系统环境变化及植被恢复和布局重建等具有重要意义,时间序列的遥感数据为森林监测提供了基础数据。本文根据森林植被的统计学特性,在暗目标法的基础上,利用归一化植被指数NDVI实现森林样本自动选择;并融合NDVI构建了新的综合森林特征指数(Integrated Forest Z-Score,IFZ);以时间序列的IFZ分析森林动态信息,实现森林变化动态监测。以三峡大坝及周边区域森林为研究区,利用2001年至2012年每年生长季节(5月—10月)的Landsat TM影像检验本文算法。基于2002年、2006年和2010年三期7月—9月的Quick Bird影像的精度分析结果发现:研究区森林变化检测的总体精度可达96.53%,Kappa系数为0.9512。在添加NDVI指数后构建的IFZ提高了总体监测精度。其中,毁林类别的检测精度提高显著,漏检率和误检率分别为2.74%和3.64%;干扰后重建的森林类别的检测精度有一定提高,其漏检率和误检率分别为10.79%和10.51%。研究结果表明,改进暗目标法能提高森林样本的选样效率,添加NDVI的IFZ能提高森林动态变化的识别度。此外,本算法不仅能定性识别森林变化,而且能定量提供森林干扰发生时间和干扰强度。     

《重庆历史地图集·第一卷古地图》设计与研制

文章介绍了该图集编制的背景及国内编纂情况,阐述了图集需遵循的设计原则和工艺流程,构思了图集每个版块的定位及内容组成。重点对图集在编纂过程中开本设计、结构设计、内容设计、排版和整饰设计等方面进行了剖析,并根据重庆本地历史文化展现的需要,进行了对应的设计。其中的结构编排设计、内容排版设计、图名设计、文字内容结构设计,及版式中使用的中华民族文化元素和重庆本地的文化元素的有机融合等方面,在同类作品中实现了多项创新。最后,分析总结了图集编纂中的问题和解决方法,为后续相关产品的设计与编制提供参考。     

前后向平滑算法在动态精密单点定位中的应用

针对PPP初始收敛段定位精度低而造成的定位结果无法利用的问题,文章提出将前后向平滑算法应用于动态PPP解算中,以获取整个观测时段的高精度定位结果;针对PPP成果缺乏质量检核手段的问题,文章提出构造前后向位置不符值作为质量检核指标。模拟动态算例和船载动态算例表明:平滑后的动态PPP克服了前向、后向滤波初始收敛段定位精度差的问题,整体位置误差RMS在10cm内,提高了数据利用率;收敛段的前后向位置不符值RMS也在10cm内,该指标可用来对PPP成果进行质量检核。     

Semi-automatic delineation of badlands using contrast in vegetation activity: a case study in the lower Chambal valley,India

Population growth worldwide leads to an increasing pressure on the land. Recent studies reported that many areas covered by badlands are decreasing because parts of badlands are being levelled and converted into arable land. It is important to monitor these changes for environmental planning. This paper proposes a remote-sensing-based detection method which allows mapping of badland dynamics based on seasonal vegetation changes in the lower Chambal valley, India. Supervised classification was applied on three Landsat (Thematic Mapper) images, from 3 different seasons; January (winter), April (summer) and October (post-monsoon). Different band selection methods were applied to get the best classification. Validation was done by ground referencing and a GeoEye-1 satellite image. The image from January performed best with overall accuracy of 87% and 0.69 of kappa. This method opens the possibilities of using semi-automatic classification for the Chambal badlands which is so far mapped with manual interpretations only.     

Visualizing spatiotemporal trajectories of mobile social media users using space–time cube

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators.     

农产品物流监控系统设计与开发研究

对农产品物流的监控可以及时发现农产品在流通环节的问题。结合无线传感网(WSN)技术、GIS技术、GPS技术、RFID技术,集成成熟的无线通信和数据库技术,构建可视化、智能化、通用的农产品物流监控系统,为物流链管理者提供便捷、全面的监控体系。本文介绍了系统的设计与实现。     

震前电离层TEC异常探测方法的研究进展

介绍了震前电离层TEC异常探测原理的研究进展和主要的异常探测方法,详细介绍了时间序列法、Kalman滤波和小波变换在电离层异常探测中的原理和应用,总结了传统方法和新方法在电离层异常探测的应用发展情况,并分析了每种方法的优势与不足。为未来中国采用电离层异常探测开展地震预测工作提出了建议。