顾及拓扑与尖角的分类矢量数据分组压缩算法

针对现有拆分重组法对存在复杂内环以及复杂相交岛屿多边形的分类矢量数据的压缩结果中包含裂缝、重叠及大量尖锐夹角的问题,提出了一种顾及拓扑一致性与尖角的分组压缩方法。依据分类矢量数据中多边形与多边形及其内环的相交关系对矢量矢量数据分组,以修正后拆分重组法结合直接分割法和间接分割法对分类矢量数据分组压缩,压缩结果中不存在裂缝与重叠现象,维护了分类矢量数据的拓扑一致性;通过取回尖角点前后被删除的点,解决了压缩结果中尖角过多的问题。以Landsat8遥感影像分类矢量数据实验验证了文章算法,并与直接分割法、间接分割法、拆分重组法进行了对比分析,验证了本文方法的有效性。     

Use of an inside buffer method to extract the extent of urban areas from DMSP/OLS nighttime light data in North China

Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) nighttime imagery provides a valuable data source for mapping urban areas. However, the spatial extents of large cities are often overestimated because of the effect of over-glow from nighttime light if a fixed thresholding technique is used. In the work reported here, an inside buffer method was developed to solve this issue. The method is based on the fact that the area overestimated is proportional to the extent of the lit area if a fixed threshold is used to extract urban areas in a region/county. Using this method, the extents of urban areas in North China were extracted and validated by interpretations from Landsat Thematic Mapper images. The results showed that the lit areas had a significant linear relationship with the urban areas for 120 representative cities in North China in 2000, with an R² value of over 0.95. This demonstrates that the inside buffer method can be used to extract urban areas. The validation results showed that the inside buffer model developed in 2000 can be directly used to extract the extent of urban areas using more recent nighttime light imagery. This is of great value for the timely updating of urban area databases in large regions or countries.     

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

青藏高原地区云水时空变化特征及其与降水的联系

利用1984-2009年ISCCP的云量、云光学厚度(COT)、云水路径(CWP)资料,分析了青藏高原云水的分布特征、变化趋势,及其与夏秋季降水、冬春季降雪的联系.结果表明:青藏高原地区大气中的云水有着显著的季节变化与水平分布差异;青藏高原春夏季总云量、高云云量高于秋冬季,CWP、COT与总云量的分布特征具有较好的一致性.高原云量高值区位于喀喇昆仑山与高原东南部;可可西里地区由于羌塘高压的下沉作用为云量低值区.青藏高原总云量在1984-2009年间呈现减少趋势;而CWP在高原总体以增加为主,但在各区域上的变化不一致,高原东部CWP增加而西部出现较弱的减小,这与来自孟加拉湾的水汽输送增加有关.青藏高原中东部地区夏秋季降水受云量减少影响较小而与CWP的增加相一致呈增长趋势;该地区冬春季降雪略有减少,与总云量的年际变化具有正相关.     

Fifteen Years of Ocean Observations with China Argo

For 15 years since the beginning of China Argo project, China has deployed over 350 profiling floats in Pacific and Indian ocean, and constructed China Argo ocean observing network. Moreover, we have setup the Argo data receiving, processing and distributing system, and developed various Argo data products using Argo observations, which has promoted the progress of ocean data sharing in China. The abundant Argo data have become a main data source in oceanic and atmospheric basic researches and operational applications. A batch of important achievements in basic research and operational application have been brought, e.g. in aspects of tropical cyclone (typhoon), ocean circulation, meso-scale eddy, turbulence, heat/salt storage and transport and water mass, as well as in ocean, atmosphere/climate operational forecasting and predicting. With the extension of the international Argo program from “Core Argo” to “Global Argo”, we are faced with great challenges in the long-term maintaining and sustained developing of our Argo ocean observing network. It is suggested that we should take the opportunity to construct China regional Argo ocean observing network as soon as possible in adjacent northwestern Pacific and Indian ocean using Chinese BeiDou profiling floats, which will make us to take responsibility and obligation of a big country for addressing global climate changes and preventing natural disasters.     

A Survey of the Spatio-Temporal Data Model

Traditional GIS(Geographic Information System)mostly can only describe the transient state data and does not have the ability to deal with the temporal dynamic data. When the data changes, new data will take the place of the old data, namely the transient state change into another, and the old data will disappear. Therefore, it is unable to make an analysis of the updating changes of the data and predict the development trend of the future. In this case, TGIS (Temporal Geographic Information System) emerges and expands the time dimension on the basis of traditional GIS. Spatio-temporal data model is the key to TGIS. Spatio-temporal data modeling is not only related to dynamic expression of spatio-temporal objects, but also gives an important support for spatio-temporal analysis and reasoning. This paper summarized the theories and applications status at home and abroad of spatio-temporal data model in detail, illustrated family tree of spatio-temporal data model for the nearly fifty years, discussed improvement and application status of Base State with Amendments Model, Event-based Spatio-temporal Data Model, Object-oriented Data Model and other spatio-temporal data models, and raised the existing problems of spatio-temporal data model. The current existing problems mainly includes: ①There are a lot of spatio-temporal data models put forward, but some of them only focus on semantic design and neglect the verified; ②Most of existing spatio-temporal data model are for vector data, only the Event-based Spatio-temporal Data Model is raster data structure; ③At present, the expression the time-space information of geographic entity is relative separated with spatio-temporal data model; ④Spatio-temporal data model is mainly used in cadastral management, land use and forestry data updating, less application in other fields. In the end, the future development direction was put forward of spatio-temporal data model. In the time of big data and “Internet plus”, it is necessary to explore the big data spatio-temporal data model that supports multiple data formats.     

面向矿床三维动态建模的地质勘探数据库增量更新方法

针对深部找矿过程中地质勘探数据增加和动态三维地质建模需要,提出了一种基于版本管理的矿床地质勘探数据库增量更新方法。在分析矿床地质勘探工程数据与三维地质模型映射关系的基础上,建立时序版本和建模版本作为矿床勘探数据增量更新版本标识。针对矿床地质勘探数据库增量更新的版本管理,提出了一种扩展的有向无环图版本管理模型,设计了基于关系数据库的版本管理方法并探讨了其实现过程。以安徽铜陵凤凰山矿床地质勘探数据为例,基于SQL Server数据库系统建立了实例矿床地质勘探版本数据库,以C#编程实现了实例矿床勘探数据库的版本管理,包括版本的建立、查询与显示等功能,通过勘探工程增量更新版本数据建立了三维矿体的动态修正模型,证实了方法的可行性与有效性。     

煤田资料的铀矿二次开发技术及其找矿意义——以二连盆地ZS煤田铀矿点的发现为例

在中国北方主要盆地铀矿地质调查工作中,煤田自然伽马异常作为主要矿化信息和找矿线索,为一系列铀矿床(点)的快速发现奠定了坚实基础。为进一步提高煤田资料铀矿二次开发的高效性和准确性,运用水成铀矿理论,以中国二连盆地ZS煤矿区典型铀矿点的发现为例,初步探讨了煤田资料的铀矿二次开发技术,提出了"异常筛选与选区分析、原位验证与环境判别、等时地层格架建立与沉积体系分析、综合分析与靶区定位"四位一体的煤矿区铀矿地质调查模式。这对中国北方含煤盆地铀矿地质调查工作提供了新的找矿思路和技术路线,具有较重要的指导意义。     

Estimation of mean and extreme waves in the East China Seas

Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves.     

基于客观分析的多源卫星叶绿素a浓度产品融合方法研究

本文基于Aqua/MODIS、Terra/MODIS和Envisat/MERIS多源卫星叶绿素a浓度产品,研究了客观分析融合方法,制作了西北太平洋海域(0°~50°N,100°~150°E)叶绿素a浓度融合产品,并从有效数据空间覆盖率和产品精度两个方面对融合方法进行了评价。与单传感器以及欧洲太空局发布的GSM模型业务化融合产品相比,客观分析融合产品空间覆盖率明显提高;与收集的2002-2012年间叶绿素a浓度实测数据比较,GSM模型业务化融合产品的匹配数据点为578个,偏差为-0.20 mg/m3,均方根误差为0.37 mg/m3,客观分析法融合产品的匹配数据点为1432个,偏差为-0.21 mg/m3,均方根误差为0.36 mg/m3。结果表明:本文研究的客观分析融合方法在保证融合产品精度的同时可显著提高产品的空间覆盖率,在海洋水色融合应用前景广阔。