国土资源基础数据库整合与集成建库模式与实现

国土资源基础数据库建设位于国土资源信息化建设“十五”规划中五大任务之首,在空间信息基础设施的建设中有重要意义。本文结合国土资源基础数据库数据的特点和多年的基础数据库建库研究和工程实践,分析当前基础数据库建设的现状和存在的问题,提出并说明国土资源基础数据库整合与集成建库模式及实现思路。     

光谱预处理及其对星系/类星体分类结果的影响

由于噪声、畸变和观测环境等因素的影响,在天体光谱自动处理之前,需要对它进行相应的预处理.研究巡天光谱的预处理(数据格式和流量标准化)对光谱自动分析的影响.分析了同数据格式对光谱及其谱线特征的影响和格式标准化研究的必要性;通过分析光谱流量数量级的不确定性及其特点,提出了流量数量级变化的基本模型,并给出了相应的标准化方法.通过星系和类星体的分类实验,结果表明:1)采用对数波长数据格式对光谱的自动分类更有利;2)验证了所提出的流量标准化模型的合理性,以及所给流量标准化方法良好的性能.特别需要指出的是,文献中通常采用的流量标准化方法在光谱自动分类中的效果反而是较差的.     

Variational data assimilation for deriving global climate analyses from GNSS radio occultation data

A comprehensive global navigation satellite system (GNSS) based radio occultation (RO) data set is available for meteorology and climate applications since the start of GNSS RO measurements aboard the CHAllenging Mini-satellite Payload (CHAMP) satellite in February 2001. Global coverage, all-weather capability, long-term stability and accuracy not only makes this innovative use of GNSS signals a valuable supplement to the data set assimilated into numerical weather prediction (NWP) systems but also an excellent candidate for global climate monitoring. We present a 3D variational data assimilation (3D-Var) scheme developed to derive consistent global analysis fields of temperature, specific humidity, and surface pressure from GNSS RO data. The system is based on the assimilation of RO data within 6 h time windows into European Centre for Medium-Range Weather Forecasts (ECMWF) short-term (24 h, 30 h) forecasts, to derive climatologic monthly mean fields. July 2003 was used as a test-bed for assessing the system’s performance. The results show good agreement with climatologies derived from RO data only and recent NWP impact studies. These findings are encouraging for future developments to apply the approach for longer term climatologic analyses, validation of other data sets, and atmospheric variability studies.     

Two-dimensional,high-resolution modeling of urban dam-break flooding: A case study of Baldwin Hills,California

Modeling of dam-break flooding in an urban residential area in southern California is presented. Modeling is performed using BreZo, an unstructured grid, Godunov-type, finite volume model that solves the shallow-water equations. The model uses terrain data from a 1.5 m Light Detection and Ranging (LiDAR) Digital Terrain Model (DTM) and contour data depicting the reservoir and breach geometry. A spatially distributed Manning coefficient based on a landcover classification derived from digital orthophotos and vector data (e.g., parcel outlines) is also used, and the interception of flow by storm drains is modeled with sink terms in the 2D continuity equation. The model is validated with flood extent and stream flow measurements, and a sensitivity analysis is completed to identify the necessary level of data and model complexity for accuracy purposes. Results show street depressions in the land surface should be resolved by the computational mesh for flood extent and stream flow accuracy. A ca. 5 m resolution mesh that spans streets by approximately 3 cells achieves a good balance between accuracy and computational effort. Results also show that heterogeneous resistance is important for stream flow accuracy, and the interception of overland flow by storm sewers is important for flood extent accuracy. The sensitivity of predictions to several additional factors such as the reservoir level, breach geometry and DTM source (LiDAR, National Elevation Data, Shuttle Radar Topography Mission Data) is also reported.     

Intelligent data fusion for wide-area assessment of UXO contamination

The remediation of sites contaminated with unexploded ordnance (UXO) remains an area of intense focus for the Department of Defense. Under the sponsorship of SERDP, data fusion techniques are being developed for use in enhancing wide-area assessment UXO remediation efforts and a data fusion framework is being created to provide a cohesive data management and decision-making utility to allow for more efficient expenditure of time, labor and resources. An important first step in this work is the development of feature extraction utilities and feature probability density maps for eventual input to data fusion algorithms, making data fusion of estimates of data quality, UXO-related features, non-UXO backgrounds, and correlations among independent data streams possible. Utilizing data acquired during ESTCP’s Wide-Area Assessment Pilot Program, the results presented here successfully demonstrate the feasibility of automated feature extraction from light detection and ranging, orthophotography, and helicopter magnetometry wide-area assessment survey data acquired at the Pueblo Precision Bombing Range #2. These data were imported and registered to a common survey map grid and UXO-related features were extracted and utilized to construct survey site-wide probability density maps that are well-suited for input to higher level data fusion algorithms. Preliminary combination of feature maps from the various data sources yielded maps for the Pueblo site that offered a more accurate UXO assessment than any one data source alone.

空间数据共享中的数据质量问题

本文首先简要介绍空间数据共享涉及的技术和法制问题，接着重点讨论了空间数据质量对数据共享的影响，分析了现行的元数据库管理空间数据质量的方案，指出用元数据方法管理和描述空间数据质量方面显示出的优势及其不足，并展望了今后的研究发展方向。     

上海市高速公路网框架点GPS定位测量数据处理与分析

以上海市高速公路网及若干主要国道的道路框架点GPS控制网施测及观测数据处理为例,详细分析和总结了中长距离道路导线形式GPS控制网的设计布设方法、网的稳定度和数据处理经验。     

H layering in the top meter of Mars

We explore the capability of a method of mapping the depth distribution of a hydrogen-rich layer in the top meter of Mars from the neutron currents measured by the Mars Odyssey Neutron Spectrometer. Assuming the soil can be modeled by two layers of known composition having different hydrogen contents, simulations allow an inversion of the neutron data into knowledge of depth and hydrogen content of the lower layer. The determination of these variables is sensitive to the hypothesis of chemical composition of the soil. We quantify this contribution to the uncertainty in the method first in terms of individual chemical elements and then in terms of macroscopic absorption cross sections. To minimize this source of error, an average composition was inferred from Mars Exploration Rover data. Possible compositions having a wide range of macroscopic absorption cross sections were used to evaluate the uncertainty associated with our calculations. We finally compare our results to ice table depth estimates predicted by two published theoretical models at locations where the composition is relatively well known. The fit is excellent in the southern high latitudes but questionable in the northern high latitudes. Possible explanations of these differences include the high geographical variations of the neutron currents relative to the spatial width of the response function of the instrument and the overly simple model we, of necessity, used for surface layering.     

DBSTC: an effective method for discovering cluster features with different spatiotemporal densities

Spatiotemporal clustering is one of the most advanced research topics in geospatial data mining. It has been challenging to discover cluster features with different spatiotemporal densities in geographic information data set. This paper presents an effective density-based spatiotemporal clustering algorithm (DBSTC). First, we propose a method to measure the degree of similarity of a core point to the geometric center of its spatiotemporal reachable neighborhood, which can effectively solve the isolated noise point misclassification problem that exists in the shared nearest neighbor methods. Second, we propose an ordered reachable time window distribution algorithm to calculate the reachable time window for each spatiotemporal point in the data set to solve the problem of different clusters with different temporal densities. The effectiveness and advantages of the DBSTC algorithm are demonstrated in several simulated data sets. In addition, practical applications to seismic data sets demonstrate the capability of the DBSTC algorithm to uncover clusters of foreshocks and aftershocks and help to improve the understanding of the underlying mechanisms of dynamic spatiotemporal processes in digital earth.