CORS网型结构对网络RTK服务性能的影响研究

网型单元是CORS网络RTK的基本服务单元,定量评价网内各单元的服务能力对于指导和优化CORS网络建设有着积极作用。本文提出了一种基于主参考站服务能力的网型结构评价因子,结合美国CRTN网5个测站年积日2010.93的数据,分析了网型单元中不同主参考站对网络改正数精度的影响;并针对川渝CORS网络25万km2服务范围、82个三角服务单元,分析了网型结构与区域实时差分定位服务性能的关系。结果表明,CORS站网型结构是影响网络RTK定位服务性能的主要因素之一;本文提出的网型结构因子可用于100km级参考站网络网型结构的评价指标。     

基于非差观测的网络实时动态定位方法及其在连续运行基准站跨网服务中的应用

基于非差观测的网络实时动态定位(RTK)方法通过提供每颗卫星的误差改正量,使网内用户获得与网络RTK方法等价的快速精密单点定位服务。当用户跨越连续运行基准站(CORS)网内由不同参考站组成的子网(参考子网)甚至跨越不同CORS网时,都能有效避免因所选取的主参考站变化而引起的模糊度重新初始化,从而保持观测时段内用户定位结果的连续可靠和跨CORS网服务时算法上的无缝衔接。通过对海上实测动态数据处理结果的分析,验证该方法的定位精度、初始化时间与现有网络RTK方法在量级上的一致性,以及跨不同参考子网和CORS网时实现无缝衔接的有效性。     

长距离 GNSS网络 RTK算法研究

由于区域误差的空间相关性随着测站间距离的增加而减弱,所以目前网络RTK系统使用的CORS站间距多在30km~80km,而长距离网络RTK算法可以扩大网络RTK的有效作业距离,降低系统建设和维护成本。本文对长距离网络RTK参考站和流动站的整周模糊度实时解算、流动站用户观测值误差的实时改正等算法进行了研究,并基于本文的算法开发了相关的长距离网络RTK软件,最后利用实测CORS网的数据进行长距离网络RTK算法实验。     

VRS参考站点形变对流动站定位坐标的影响分析

在国内外,很多地方的网络RTK都采用虚拟参考站(VRS)技术,有些地方建立的CORS网较早,某些基准站会发生一定的形变,针对VRS参考站点形变影响流动站定位坐标的问题,通过理论分析与严密的公式推导,并对VRS系统GPSNET中参考站坐标形变进行仿真试验。结果表明,用户流动站坐标是通过虚拟参考站转接到物理主参考站,即流动站的参考站实际上是物理主参考站,用户流动站坐标变化量与物理主参考站坐标变化量基本一致,并对参考站形变后坐标调整提出方案。     

网络RTK技术发展与应用

系统介绍了网络RTK测量技术的原理,分析了连续运行参考站系统（CORS）技术构成、数据通讯方式等,比较了网络RTK中虚拟参考站（VRS）技术和主辅站技术（MAC）的工作原理和基本流程,展望了基于陆态网络移动参考站的网络RTK技术的应用前景。     

CORS系统移动基准站的设计与实现

针对CORS系统RTK服务盲区(RTK使用困难或无法使用)的问题,提出了在CORS系统服务盲区附近建立移动的CORS基准站。通过加密区域CORS系统的基站分布,缩短站间距,改善CORS系统网络RTK在该区域的可用性。通过设计实现的CORS移动基准站系统测试表明:在精度上,移动基准站达到与常规CORS基准站同等的效果,平面x,y≤5cm,高程H≤10cm;在可用性方面,CORS移动基准站自主供电、携带方便、组网灵活,区域网络RTK可用性明显提高。在应急测绘、救灾和临时加密CORS基准站系统等中具有借鉴和实用意义。     

区域参考站网支撑的PPP和RTK一体化服务及其性能

GNSS区域参考站网可为大范围PPP和RTK终端用户提供快速精密定位服务,然而不同技术体制下的误差影响因素及服务模式不同,服务端数据处理方法及终端定位性能也会有所差异。本文在实现参考站非差模糊度固定的基础上,给出了基于区域参考站网的PPP及RTK一体化服务方法,并对两种技术体制的终端定位效果进行了全面评估。采用西北某省站间距约100 km的CORS站网数据进行试验分析,结果表明,采用区域参考站网解算的整数钟/UPD产品进行PPP固定解动态定位时精度较高,水平方向RMS可达0.5 cm,区域大气改正数可以显著提升定位终端的初始化速度,对于PPP和RTK,60.0%和87.7%的时段单历元即可得到固定解。需要注意的是,基于VRS模式的RTK定位等价于大气强约束,在大气建模精度较差时定位精度会显著下降,而采用虚拟大气约束的PPP-RTK定位精度几乎不受影响。     

基于多模CORS的网络RTK服务性能测试

从北斗地基增强系统建设出发,分析多模CORS实验网,提供多模网络RTK服务,增强北斗卫星导航系统的应用。根据实时测量精度、定位服务的时效性、时间可用性、空间可用性和实用性测试,评价实验网提供的网络RTK服务性能。测试结果表明通过加入北斗导航卫星系统信号,定位精度略为提高,可有效改善电离层模型,提高流动站固定的效率,可以解决困难环境的作业问题,提高作业效率。     

高精度实时位置服务的格网化VRS技术

为确保基于VRS的网络RTK定位性能,提高VRS的服务质量,本文系统地构建了面向海量并发用户服务的格网化VRS方法,建立了VRS格网的两种划分方法。顾及电离层延迟的空间相关特性,选定中纬度和低纬度两个区域的CORS网络进行试验测试,分析并确定不同区域条件下,格网化VRS的有效服务间距。结果表明:在中纬度地区和低纬度地区,格网VRS有效服务间距限值分别为12 km和7 km。与非格网化VRS(以标准定位服务结果建立)相比,可大幅度降低VRS产生和计算负荷,有效提升服务器对海量用户的服务能力。     

Delaunay三角网在大规模CORS基准站组网中的应用和研究

连续运行参考系统(CORS)的核心技术——GPS网络实时动态定位(RTK)技术已经成为GPS高精度定位中一种重要技术,其定位精度与站间距离密切相关。在构建了站间距离和网络分布适中的Delaunay三角网后,探讨了基准站点删除与插入等网络局部更新以及参考站最优选择问题,提出了构网和确定流动站所在三角形的优化算法,实验证明该算法高效快捷、简便可靠。     

BOOK REVIEWS

Book reviews in this article:
INTERNATIONAL YEARBOOK OF CARTOGRAPHY (Vol. 1, 1961). Editor, E duard I mhof .
TOPOGRAPHICAL INSTRUCTIONS OF THE UNITED STATES GEOLOGICAL SURVEY. Published in the form of a series of illustrated booklets.
PHOTOGRAMMETRIC RECTIFICATION (Chapter 3C3).
MULTIPLEX PLOTTER PROCEDURES (Chapter 3F4).
KELSH PLOTTER PROCEDURES (Chapter 3F5)
PLANIMETRIC MAP COMPILATION WITH TRIMETROGON PHOTOGRAPHS (Chapter 3G1).
COLOUR-SEPARATION SCRIBING (Chapter 4B).
METHOD OF DETERMINING THE RESOLVING POWER OF LENSES FOR CAMERAS–REVISED BRITISH STANDARD.
PHOTOGRAMMETRIC DICTIONARY: in English, French, German, Italian, Polish, Spanish and Swedish.     

Characterization of Surface Geological Material in Northwest India and Adjoining Areas of Pakistan Using Normalized Difference Water Index,Land Surface Temperature and Silica Index

Land surface temperature (LST) shows negative correlation with the Normalized Difference Water Index (NDWI). Variability in the degree of correlation between LST and NDWI is ascribed to the physical character of specific geological material. Northwest India exhibits various landforms with different geological materials and has been broadly classified into four zones. Structural ridges of Aravalli Mountain of different rock compositions show strong variability both in NDWI (range 1.154, SD?=?0.0599) and in LST (range 24 °C and SD?=?2.54). Negative LST–NDWI correlation in this sector is partially linear. Western Thar Desert, having homogenous silica sand of lower emissivity shows least variability in its NDWI (range 0.88, SD?=?0.027) and moderate variability in its LST (20 °C, SD?=?2.389). Strong negative correlation of LST with NDWI is exhibited here. Band ratio Silica map in this sector shows strong positive correlation with LST. The eastern part of the Thar desert with mixed rocky knobs, and wind-blown sand shows low variability in NDWI (range 0.85) as well as LST (range 15 °C). Area in Indus–Bias–Sutlej River basin, dominated with fluvial sediments with lesser amount of windblown sediments, show low variability of NDWI (0.85) and moderate variability of LST (range 23 °C). In the areas, around Luni river higher NDWI trend is recorded, which is unrelated to present drainage trends indicating presence of palaeo-drainage. In addition, high NDWI and high LST bearing linear zones at places are interpreted as structural lineaments/faults based on pattern, moisture content and thermal high.     

Evaluation of satellite-derived agro-climate variables in the Northern Great Plains of the United States

The climate of the United States Northern Great Plains region is highly variable. Modelling of agriculture in this region and similar locations depends on the availability and quality of satellite and ground data for agro-climate variables. We evaluated tropical rainfall measuring mission (TRMM) multi-satellite preparation analysis (TMPA) precipitation, atmospheric infrared sounder (AIRS) surface air temperature, and AIRS relative air humidity (RH). A significant bias was found within the temperature and RH products and no bias but an insufficient rain event detection skill in the precipitation product (probability of detection ～0.3). A linear correction of the temperature product removed the bias as well as lowered the root mean square deviation (RMSD). The bias-corrections for RH led to increased RMSD or worse correlation. For precipitation, the correlation between the satellite product and ground data improved if cumulative precipitation or only precipitation during the growing season was used.     

基于计算智能的土地适宜性评价模型

将计算智能理论引入土地评价领域，构建了一个全新的土地适宜性评价模型。首先基于模糊逻辑和人工神经网络构造了一个模糊神经网络模型，然后采用改进的遗传算法进行训练，能够快速收敛到最优解，对初始的规则库进行修正，形成了一个自学习、自适应的评价系统。     

Estimation of Crown Closure and Tree Density Using Landsat TM Satellite Images in Mixed Forest Stands

The objective of this study was to investigate the relationship between crown closure and tree density in mixed forest stands using Landsat Thematic Mapper (TM) reflectance values (TM 1- TM 5 and TM 7) and six vegetation indices (SR, DVI, SAVI, NDVI, TVI and NLI). In this study, multiple regression analysis was used to estimate the relationships between the crown closure and tree density (number of tree stems per hectare) using reflectance values and vegetation indices (VIs). The results demonstrated that the model that used SR and DVI had the best performances in terms of crown closure (R²?=?0.674) and the model that used the DVI and SAVI had the best performances in terms of tree density (R²?=?0.702). The regression model that used TM 1, TM 3 together with TM 4 showed the performances of the crown closure (R²?=?0.610) and the regression model that used TM 1 showed the performances of the tree density (0.613). Results obtained from this research show that vegetation indices (VIs) were a better predictor of crown closure and tree density than other TM bands.     

利用最小一乘法改进的灰色模型的导航卫星钟差预报

在卫星钟差波动较大的情况下,为了克服基于最小二乘法估计灰色模型参数对卫星钟差预报精度的不足,本文利用最小一乘法对传统灰色模型进行了改进。在建模的过程中,采用以误差绝对值之和最小为优化原则,针对目标函数不可微的特点,运用线性规划的方法对灰色预报模型的模型参数进行了估计。此外,将改进后的预报模型应用到卫星钟差波动较大情况下钟差的预报中,并将预报结果与传统灰色模型的预报结果进行了对比分析。结果表明：在卫星钟差波动较大的情况下,该方法相比传统灰色模型的预报结果有显著改善,为高精度的卫星钟差预报提供了一种新思路。     

Integration of hyperspectral and LiDAR data for mapping small water bodies

Inland water bodies are globally threatened by environmental degradation and climate change. On the other hand, new water bodies can be designed during landscape restoration (e.g. after coal mining). Effective management of new water resources requires continuous monitoring; in situ surveys are, however, extremely time-demanding. Remote sensing has been widely used for identifying water bodies. However, the use of optical imagery is constrained by accuracy problems related to the difficulty in distinguishing water features from other surfaces with low albedo, such as tree shadows. This is especially true when mapping water bodies of different sizes. To address these problems, we evaluated the potential of integrating hyperspectral data with LiDAR (hereinafter “integrative approach”). The study area consisted of several spoil heaps containing heterogeneous water bodies with a high variability of shape and size. We utilized object-based classification (Support Vector Machine) based on: (i) hyperspectral data; (ii) LiDAR variables; (iii) integration of both datasets. Besides, we classified hyperspectral data using pixel-based approaches (K-mean, spectral angle mapper). Individual approaches (hyperspectral data, LiDAR data and integrative approach) resulted in 2–22.4 % underestimation of the water surface area (i.e, omission error) and 0.4–1.5 % overestimation (i.e., commission error).The integrative approach yielded an improved discrimination of open water surface compared to other approaches (omission error of 2 % and commission error of 0.4 %). We also evaluated the success of detecting individual ponds; the integrative approach was the only one capable of detecting the water bodies with both omission and commission errors below 10 %. Finally, the assessment of misclassification reasons showed a successful elimination of shadows in the integrative approach. Our findings demonstrate that the integration of hyperspectral and LiDAR data can greatly improve the identification of small water bodies and can be applied in practice to support mapping of restoration process.     

基于UML的农用地估价信息系统建模与实现

根据农用地估价的基本原理和方法,以ComGIS为系统开发平台,利用UML技术,从系统整体设计入手,提出了采用多层次用例描述法建立农用地估价信息系统(ALEIS)用例模型的过程,在此基础上介绍了ALEIS设计、编码与测试,最后提出了ALEIS的特点与发展趋势。在农用地估价信息系统开发过程中,利用UML对系统进行建模分析能够取得较好的效果。     

Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis

The objective of this paper is to incorporate the concept of fuzzy (linguistic) quantifiers into the GIS-based land suitability analysis via ordered weighted averaging (OWA). OWA is a multicriteria evaluation procedure (or combination operator). The nature of the OWA procedure depends on some parameters, which can be specified by means of fuzzy (linguistic) quantifiers. By changing the parameters, OWA can generate a wide range of decision strategies or scenarios. The quantifier-guided OWA procedure is illustrated using land-use suitability analysis in a region of Mexico.     

Numerical modeling and prediction of future response of permafrost to different climate change scenarios on the Qinghai–Tibet Plateau

Projecting the future distribution of permafrost under different climate change scenarios is essential, especially for the Qinghai–Tibet Plateau (QTP). The altitude-response model is used to estimate future permafrost changes on the QTP for the four RCPs (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). The simulation results show the following: (1) from now until 2070, the permafrost will experience different degrees of significant degradation under the four RCP scenarios. This will affect 25.68%, 40.54%, 45.95%, and 62.84% of the current permafrost area, respectively. (2) The permafrost changes occur at different rates during the periods 2030–2050 and 2050–2070 for the four different RCPs. (1) In RCP2.6, the permafrost area decreases a little during the period 2030–2050 but shows a small increase from 2050 to 2070. (2) In RCP4.5, the rate of permafrost loss during the period 2030–2050 (about 12.73%) is higher than between 2050 and 2070 (about 8.33%). (3) In RCP6.0, the permafrost loss rate for the period 2030–2050 (about 16.52%) is similar to that for 2050–2070 (about 16.67%). (4) In RCP8.5, there is a significant discrepancy in the rate of permafrost decrease for the periods 2030–2050 and 2050–2070: the rate is only about 3.70% for the first period but about 29.49% during the second.