中国区域CODE全球电离层地图精度分析

电离层延迟是精密单点定位的主要误差源,双频用户可利用组合观测值消除其影响,单频用户只能利用电离层模型对其加以改正.因此电离层模型的精度对单频精密单点定位(single-frequency precise point positioning,SF-PPP)的精度至关重要.为分析欧洲轨道确定中心(Center Orbit Determination Europe,CODE)提供的全球电离层地图(global ionospheric map,GIM)在中国区域内的精度,在不同纬度范围内选取25个均匀分布的陆态网基准站,从STEC(slant total electron content,STEC)精度及单频动态定位精度两个角度对CODE GIM进行精度评估.结果表明STEC均方根(root mean square,RMS)7天内的平均值为6.38 TECU,应用CODE GIM进行单频动态精密单点定位的精度在水平方向达到亚米级,高程方向达到米级,在高纬度地区CODE GIM精度更高.     

坐标转换中的角度长度与面积变形定量分析

在实际测绘生产中,由坐标变换引起的不同程度的角度变形、长度变形和面积变形产生的影响不可忽略.本文通过公式推导在七参数变换中角度、长度和面积的变形及高斯投影后图斑椭球面积变形,并通过实验验证公式推导结果,得出在七参数变换中角度不发生变形,长度变形为m,面积变形为2 m,七参数变换中的变形与坐标和转换参数无关系,在高斯投影中,面积变形随着纬度和经差的增大而增大,纬度方向面积变形的变化率先增大后减小.     

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GNSS约束的MERSI/FY-3A PWV校准方法EI北大核心CSCD

精确的水汽信息对于短临天气预警和长期气候监测具有重要意义。本文针对搭载在我国第2代极轨卫星风云三号A星(FY-3A)上中分辨率光谱成像仪(medium-resolution spectral imager,MERSI)获取水汽精度较低的现状,提出一种GNSS约束的MERSI/FY-3A PWV校准方法。首先,对MERSI/FY-3A的PWV段产品进行处理得到水汽日产品,并利用GNSS和无线电探空(radiosonde,RS)数据对其进行评估;然后,根据PWV的季节分布特性,构建GNSS约束的MERSI/FY-3A PWV季节自适应校准模型;最后,分别利用GNSS、RS和再分析资料对校准后的MERSI/FY-3A水汽进行对比,验证提出方法的有效性。研究表明:本文提出的PWV校准方法,能够有效改善MERSI/FY-3A水汽日产品和旬产品的精度,改善率分别为58.63%和68.72%。该方法可为遥感水汽快速校准研究提供重要的理论依据。     

GNSS对流层水汽监测研究进展与展望EI北大核心CSCD

对流层是近地空间环境中与人类活动联系最为密切的大气层,而水汽是低层大气圈中最重要的组成部分之一。尽管水汽在对流层中所占比例较小,但在一系列天气和多种气候变化中都扮演着重要角色。随着全球导航卫星系统(GNSS)的快速发展,GNSS对流层水汽监测成为重要的研究和应用方向。本文系统介绍了GNSS多维水汽监测及其在相关方面应用的研究现状和进展。GNSS水汽监测研究方面,当前主要集中在二维大气可降水量监测和三维湿折射率/水汽密度廓线反演两部分;GNSS水汽应用研究方面,当前主要包括定位、短临降雨及旱涝监测、数值同化预报等。     

基于LiDAR点云数据的树冠空隙度指数分析

分形维数法是分析空间结构分布的一种典型方法,但它对于区分不同的分布形式还存在缺陷。针对这一问题,该文介绍了空隙度指数的定义和树冠空隙度的计算方法;以模拟的树冠点云数据为对象,提出了一种基于三维凸包和三维滑动盒算法的激光雷达(Li DAR)点云数据空隙度分析方法,详尽分析了不同冠型产生的空隙度指数差异;并利用4棵实测的树冠点云数据做检验;最后阐述了空隙度指数在树冠空间异质性分析研究中的作用,并对其应用范围和前景作了展望。结果表明:划分尺度相同时,在一定的尺度范围内,锥型树冠、半球型和半椭球型树冠的差别可以通过空隙度指数曲线有效地区分,实测树冠的结果也体现了空隙度指数对于判断树冠空间结构的有效性。     

三维坐标转换的通用整体最小二乘算法

三维坐标转换模型属于非线性EIV（errors-in-variables）模型,现有整体最小二乘算法均设定了某些特殊假设条件,如仅适用于小角度或者属于非统计意义上的数值解,并且不能用于结构性的系数矩阵等,算法适用性受到极大限制。本文提出了三维坐标转换模型的通用加权整体最小二乘算法,该算法适用于任意旋转角度以及一般性的权矩阵情况下的三维坐标转换模型,并且将结构性系数矩阵、同时包含随机和非随机元素的系数矩阵等情况纳入到了统一的坐标转换模型算法。实例计算表明,本文提出的算法具有通用性,适用于实际应用中的各类三维坐标转换模型。     

Impact of GLONASS pseudorange inter-channel biases on satellite clock corrections

GLONASS carrier phase and pseudorange observations suffer from inter-channel biases (ICBs) because of frequency division multiple access (FDMA). Therefore, we analyze the effect of GLONASS pseudorange inter-channel biases on the GLONASS clock corrections. Different Analysis Centers (AC) eliminate the impact of GLONASS pseudorange ICBs in different ways. This leads to significant differences in the satellite and AC-specific offsets in the GLONASS clock corrections. Satellite and AC-specific offset differences are strongly correlated with frequency. Furthermore, the GLONASS pseudorange ICBs also leads to day-boundary jumps in the GLONASS clock corrections for the same analysis center between adjacent days. This in turn will influence the accuracy of the combined GPS/GLONASS precise point positioning (PPP) at the day-boundary. To solve these problems, a GNSS clock correction combination method based on the Kalman filter is proposed. During the combination, the AC-specific offsets and the satellite and AC-specific offsets can be estimated. The test results show the feasibility and effectiveness of the proposed clock combination method. The combined clock corrections can effectively weaken the influence of clock day-boundary jumps on combined GPS/GLONASS kinematic PPP. Furthermore, these combined clock corrections can improve the accuracy of the combined GPS/GLONASS static PPP single-day solutions when compared to the accuracy of each analysis center alone.     

黄河干流龙羊峡-寺沟峡段灾害地质遥感地质调查

1、龙羊峡库区的沙漠不是今天风力搬运的产物,它是第四纪初期形成的古沙岸。2、龙羊峡-寺沟峡段黄河两岸区的滑坡、崩塌共有210处,其中滑坡137处,崩滑42处,崩塌23处,未滑动危体8处,它们受断裂的控制;3、该区段较大和较严重的泥石流51处,其中极严重泥石流4处,较严重泥石流23处,中等程度泥石流24处,主要分布在贵德、尖扎、循化,共和四个盆地。     

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.