Geostatistical approaches to refinement of digital elevation data

Data refinement refers to the processes by which a dataset’s resolution, in particular, the spatial one, is refined, and is thus synonymous to spatial downscaling. Spatial resolution indicates measurement scale and can be seen as an index for regular data support. As a type of change of scale, data refinement is useful for many scenarios where spatial scales of existing data, desired analyses, or specific applications need to be made commensurate and refined. As spatial data are related to certain data support, they can be conceived of as support-specific realizations of random fields, suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finer-resolution ones. In this paper, geostatistical methods for downscaling are described, and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China, with the latter’s majority grid cells used as surrogate reference data. It was found that proper structural modeling is important for achieving increased accuracy in data refinement; here, structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter. It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means.     

Near real-time high-resolution airborne camera,AEROCam, for precision agriculture

Precision agriculture often relies on high-resolution imagery to delineate the variability within a field. Airborne Environmental Research Observational Camera (AEROCam) was designed to meet the needs of agriculture producers, ranchers, and researchers, who require high-resolution imagery in a near real-time environment for rapid decision support. AEROCam was developed and operated through a unique collaboration between several departments at the University of North Dakota, including the Upper Midwest Aerospace Consortium (UMAC), the School of Engineering and Mines, and flight operations at the John D. Odegard School of Aerospace Sciences. AEROCam consists of a Redlake MS4100 area-scan multi-spectral digital camera that features a 1920 × 1080 CCD array (7.4-μm detector) with 8-bit quantization. When operated at ～2 km above ground level, multispectral images with four bands in the visible and near infrared have a ground sample distance of 1 m with a horizontal extent of just over 1.6 km. Depending on the applications, flying at different altitudes can adjust the spatial resolution from 0.25 to 2 m. Rigorous spectral and radiometric calibrations allow AEROCam to be used in a variety of applications, qualitative and quantitative. Equipped with an inertial measurement unit (IMU) system, the images acquired can be geo-referenced automatically and delivered to end users near real time through our Digital Northern Great Plains system (DNGP). The images are also available to zone mapping application for precision farming (ZoneMAP), an online decision support tool for creating management zones from remote sensing imagery and data from other sources. Operational since 2004, AEROCam has flown over 250 sorties and delivered over 150,000 images to the users in the Northern Great Plains region, resulting in numerous applications in precision agriculture and resource management.     

Models for analyzing the driving force of cultivated land supply change

This paper focuses on a series of quantitative analysis models, such as grey relational analysis model, hierarchical cluster analysis model, principal component analysis model, linear regression model and elastic coefficient model. These models are used to analyze the comprehensive function and effect of driving forces systemically, including analysis on features, analysis for differentiating the primary and the secondary, analysis on comprehensive effects, analysis of elasticity, analysis of prediction. The primary and characteristic factors can be extracted by analysis of features and analysis for differentiating the primary and the secondary. Analysis on prediction and elasticity can predict the area of cultivated land in the future and find out which factors exert great influence on the cultivated land supply.     

Towards an integral strategy for modelling uncertain geospatial data

This paper endeavours to put the discussion on errors and uncertainties in geographical information systems (GISs) in a more systematic way by examining the strength and weakness of discrete objects and continous fields, the two distinct schools of spatial data modelling. In doing so, it argues that neither discrete objects nor continous fields alone provide objective and complete representations of highly complex geographical phenomena, though there are good reasons for asserting that continuous fields are better suited to modelling spatial dependence, heterogeneity and fuzzines significant in geographical reality than discrete objects. Thus, there seems to be merit in adopting an integrated model incorporating analytical capabilities of fields and generalization functions of objects, for which extended TIN (triangulated irregular network) models along with their duals (Voronoi diagrams) provide a pragmatical solution.     

Ionospheric and receiver DCB-constrained multi-GNSS single-frequency PPP integrated with MEMS inertial measurements

Single-frequency precise point positioning (SF-PPP) is a potential precise positioning technique due to the advantages of the high accuracy in positioning after convergence and the low cost in operation. However, there are still challenges limiting its applications at present, such as the long convergence time, the low reliability, and the poor satellite availability and continuity in kinematic applications. In recent years, the achievements in the dual-frequency PPP have confirmed that its performance can be significantly enhanced by employing the slant ionospheric delay and receiver differential code bias (DCB) constraint model, and the multi-constellation Global Navigation Satellite Systems (GNSS) data. Accordingly, we introduce the slant ionospheric delay and receiver DCB constraint model, and the multi-GNSS data in SF-PPP modular together. In order to further overcome the drawbacks of SF-PPP in terms of reliability, continuity, and accuracy in the signal easily blocking environments, the inertial measurements are also adopted in this paper. Finally, we form a new approach to tightly integrate the multi-GNSS single-frequency observations and inertial measurements together to ameliorate the performance of the ionospheric delay and receiver DCB-constrained SF-PPP. In such model, the inter-system bias between each two GNSS systems, the inter-frequency bias between each two GLONASS frequencies, the hardware errors of the inertial sensors, the slant ionospheric delays of each user-satellite pair, and the receiver DCB are estimated together with other parameters in a unique Kalman filter. To demonstrate its performance, the multi-GNSS and low-cost inertial data from a land-borne experiment are analyzed. The results indicate that visible positioning improvements in terms of accuracy, continuity, and reliability can be achieved in both open-sky and complex conditions while using the proposed model in this study compared to the conventional GPS SF-PPP.     

A two-step ionospheric modeling algorithm considering the impact of GLONASS pseudo-range inter-channel biases

The Global Navigation Satellite System presents a plausible and cost-effective way of computing the total electron content (TEC). But TEC estimated value could be seriously affected by the differential code biases (DCB) of frequency-dependent satellites and receivers. Unlike GPS and other satellite systems, GLONASS adopts a frequency-division multiplexing access mode to distinguish different satellites. This strategy leads to different wavelengths and inter-frequency biases (IFBs) for both pseudo-range and carrier phase observations, whose impacts are rarely considered in ionospheric modeling. We obtained observations from four groups of co-stations to analyze the characteristics of the GLONASS receiver P1P2 pseudo-range IFB with a double-difference method. The results showed that the GLONASS P1P2 pseudo-range IFB remained stable for a period of time and could catch up to several meters, which cannot be absorbed by the receiver DCB during ionospheric modeling. Given the characteristics of the GLONASS P1P2 pseudo-range IFB, we proposed a two-step ionosphere modeling method with the priori IFB information. The experimental analysis showed that the new algorithm can effectively eliminate the adverse effects on ionospheric model and hardware delay parameters estimation in different space environments. During high solar activity period, compared to the traditional GPS + GLONASS modeling algorithm, the absolute average deviation of TEC decreased from 2.17 to 2.07 TECu (TEC unit); simultaneously, the average RMS of GPS satellite DCB decreased from 0.225 to 0.219 ns, and the average deviation of GLONASS satellite DCB decreased from 0.253 to 0.113 ns with a great improvement in over 55%.     

智慧潍坊时空信息云平台公众版设计与实现

针对智慧城市背景下提出的新要求,本文在对天地图·潍坊已有建设成果进行总结分析的基础上,结合智慧潍坊时空信息云平台试点项目建设,对天地图·潍坊进行改版升级为智慧潍坊时空信息云平台公众版,介绍了平台架构、功能模块、应用示范等方面的内容,根据实际结果表明,该平台能够为全市公众、企事业单位提供权威、统一、可靠的时空地理信息服务.     

地面移动测量技术的发展与现状

地面移动测量技术是当前倍受关注的测量前沿技术。本文论述了地面移动测量技术的基本原理和特点，回顾了该技术的发展历史，总结了国内外研究现状，展望了今后的发展趋势与应用前景，并对研究、发展和应用这一技术提出了建议。     

苏通大桥钢箱梁制造几何控制三维高精度基准网测设

本文详细介绍了对苏通大桥主桥钢箱梁制造几何控制三维高精度基准网的测设，通过实际工程的实施对高精度三维基准网的方案设计和精度控制进行了有效的探索。