Regional hydrologic mapping of flows in stream networks

River low flow (LF) is an important hydrological characteristic used in management of the quantity and quality of water resources. A common way to transpose at-station low flow quantiles to ungaged locations involves development of regional multivariate regression models. In site selection studies of water control facilities, need arises to automate the mapping of LF characteristics onto the stream network in a continuous manner. This would allow estimation of LF at any desired ungaged location. A mapping algorithm is introduced that determines the value of regression model's independent variables (input components) over the drainage area of each stream pixel and calculates the flow (output). The mapping algorithm relies mainly on the digital elevation model (DEM) and its derivatives, such as flow direction and flow accumulation. Furthermore, the contribution of each independent variable of the regional model to the total flow may be plotted to represent the flow profiles along the streams. A case study involving LF mapping in rivers of Gilan province, Iran, where LF quantiles are required for issuing water withdrawal permits as well as maintaining water quality standards, is also described in the paper. Overall, the maps and profiles of flow statistics in the region of interest provide convenient visualization and assessment tools for water resource and environmental engineers. The transposition algorithm may also be applied in regional mapping of other flow characteristics such as flood or average flows.     

利用中心差分法进行GPS定速时最佳点数的选取

分析了利用中心差分法进行GPS定速时的主要误差来源,证明了当数据采样率一定时,增加中心差分法的点数可减少微分过程中的截断误差,但同时会放大导出相位率的观测误差,得出中心差分法的最佳点数应使这两种误差之和最小的结论。实验结果表明,当数据采样间隔为1s、载体平均速度和加速度为20m.s-1和0.2m.s-2时,9个点的中心差分法定速精度最高。     

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.     

Pixel Factory制作正射影像的应用探讨

通过Pixel Factory在一些大区域项目的高频率、高质量的使用,总结出了一套科学合理、高速高效的应用方法以及在应用过程中一些软件出现的普遍问题的解决办法。     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.     

Income distribution dynamics and cross-region convergence in Europe

This paper presents a continuous version of the model of distribution dynamics to analyse the transition dynamics and implied long-run behaviour of the EU-27 NUTS-2 regions over the period 1995–2003. It departs from previous research in two respects: first, by introducing kernel estimation and three-dimensional stacked conditional density plots as well as highest density regions plots for the visualisation of the transition function, based on Hyndman et al. (J Comput Graph Stat 5(4):315–336, 1996), and second, by combining Getis’ spatial filtering view with kernel estimation to explicitly account for the spatial dimension of the growth process. The results of the analysis indicate a very slow catching-up of the poorest regions with the richer ones, a process of shifting away of a small group of very rich regions, and highlight the importance of geography in understanding regional income distribution dynamics.

人工神经网络在色彩变换中的应用

给出了一种针对数字彩色硬拷贝的输出设备，建立了由CIE色度值到设备控制值色彩变换的神经网络方法。基于蒙版方程的颜色传递机理，由色度密度变量实现色彩变换。结果表明，该色彩变换对各种色调均有较明显的校正作用，且保持了高饱和色的饱和度。     

加速9202工程建设 促进我国地理信息产业的发展

加速９２０２工程建设促进我国地理信息产业的发展张清浦，张家庆（中国测绘科学研究院）一、９２０２工程的预期目标“国务院综合国情地理信息系统”（简称９２０２１程）是由国家测绘局和国务院办公厅秘书局联合研建的用于国务院领导进行宏观分析决策的多要素、多层次、...     

广域差分GPS测量及其实现

差分ＧＰＳ测量是为了削弱美国ＳＡ政策等方面的影响，提高ＧＰＳ导航定位精度的有效手段，广域差分ＧＰＳ（ＷＡＤＧＰＳ）测量则是在更大范围内为ＧＰＳ用户提供差分改正。ＷＡＤＧ－ＰＳ实际上是在一定区域里建立的卫星跟踪网。本文较为详细地介绍了差分ＧＰＳ测量和广域差分ＧＰＳ测量的基本原理、ＷＡＤＧＰＳ网的构造，讨论了各种ＤＧＰＳ网及其算法、ＧＰＳ误差源及其空间不相关性、ＷＡＤＧＰＳ中心控制站的误差模型及其算法等，并对各种模拟结果进行了分析。最后讨论了建立我国ＷＡＤＧＰＳ系统的必要性和可行性。     

数字地形图产品模式研究

数字地形图产品模式研究李黎（中国测绘科学研究院）与传统地形图一样，数字地形图将作为国家基础地理信息数据为社会所共享。它将是这一领域内的国家权威数据，具有最可靠的信息来源，值得依赖的数据质量。它不仅可为国家分析决策系统提供支持数据，也是各专业部门地理信...