DSC2型称重式降水传感器测雨性能的分析

为更有效地利用降雨观测数据,充分发挥新型探测设备建设效益,文章对DS(2型称重降水传感器的测雨性能进行分析评估,选取北京市13个国家级地面气象观测站在2013年4—10月,称重式降水传感器与人工、翻斗观测降雨量的业务观测资料,分析称重与人工和翻斗观测在降雨总量、日降雨量等方面的差异。结果表明:在选取样本中,12个台站的总降雨量误差符合现行业务要求,三种测量在日降雨量等级判断方面基本一致。称重比人工观测的日降雨量平均偏小0.13 mm,日降雨量相关系数为0.9968,对应地,称重比翻斗观测的结果平均偏小0.17 mm,日降雨量相关系数为0.9983。     

黔西南州区域自动站气象资料绘图系统的研制

随着气象服务的不断推进,用户对气象服务产品的要求越来越高,提高气象服务产品的质量对于基层气象台站来说显得至关重要。目前,区域气象站的建设已覆盖到各乡镇,将这些区域气象站资料用直观的色斑图来显示,可有效提高气象服务质量和服务水平。系统综合使用VB、Fortran、Gr ADS等技术,实现对区域自动站气象资料的查询、处理与绘图等功能,并可分县制作绘图边界和色斑图,为决策气象服务提供精美素材。     

全球冥古宙的研究进展和存在问题

冥古宙是地球上最老的一个地质历史时期,由于缺少可靠的岩石记录一直没有得到国际地学界的重视和承认,到2004年才在建议的国际地层表中使用了冥古宙这一术语,定义从地球形成到出现地球最老岩石这段地球历史。但冥古宙与太古宙没有明确的地质界线,一些研究者提出了不同的年代界线(3.85Ga,4.0Ga,4.03Ga),目前还存在较大分歧。目前已知残存的冥古宙岩石只有两处,一处是加拿大的阿卡斯塔片麻岩(Acasta gneiss)中两个英云闪长岩和一个变质花岗闪长岩。前者锆石U-Pb年龄为4002±4Ma和4012±6Ma,后者为4031±1Ma。另一处是东南极索尼山(Mount Sones)的麻粒岩相英云闪长质片麻岩,已获得U-Pb年龄为3927±10Ma。世界上最老的表壳岩(≥3870Ma)出露在格陵兰。冥古宙年代久远,地球形成最初的600~700Ma的初始岩石经历了陨石撞击、地壳再循环、重熔等改造,甚至再循环到地幔,几乎已经消失殆尽。目前主要以年轻岩石中的碎屑锆石或继承锆石作为桥梁,来追索这些古老锆石原来母岩的类型、特征及成因。3800Ma的碎屑锆石已在全球十多个地区发现,而以西澳伊尔岗克拉通的杰克山最多最全,从3800Ma到4404Ma都有,在3840Ma,3900~3920Ma,4000~4200Ma,4260~4300Ma和4404Ma显示峰值,其中以3900~4200Ma最为发育,4404Ma是目前世界公认的最老碎屑锆石。此外在北美克拉通、南非克拉通、华北克拉通等古老克拉通以及一些年轻造山带中都发现有冥古宙的碎屑锆石,这些锆石是追索冥古宙地质事件的重要桥梁。通过对冥古宙碎屑锆石的研究提出了很多值得重视和进一步研究的课题和内容。包括早期地壳的性质,一些锆石具有与太古宙之后岩浆锆石特征相似的环带结构,因此这些碎屑锆石的母岩大部分被认为相当花岗质,来自老地壳的重熔。冥古宙碎屑锆石的Hf同位素等综合分析表明这些碎屑锆石来源于中性熔岩的结晶,或许表明早期地壳具有中性成分特征。冥古宙锆石的成因也是一个重要的课题,根据锆石中钛的含量、锆石Ti饱和温度计计算的温度以及锆石的一些结构特征,有的研究者提出冥古宙相当一部分碎屑锆石是陨石撞击导致的岩石熔融结晶产生的,是追溯地球早期撞击事件的重要手段。在4300Ma的碎屑锆石中包裹有金刚石和石墨晶体,前者表明锆石的母岩曾处于高压条件,而后者石墨的碳同位素接近有机碳,有人认为可能为生命起源的迹象。多个地区的碎屑锆石的边部Th/U比值低,U-Pb年龄为4000Ma左右,属于变质作用的产物,表明当时地壳已具有一定厚度。冥古宙碎屑锆石的研究提出了很多重要的问题,但是由于资料有限,有些认识还存在矛盾,还需要获得更多实际资料,才能对冥古宙的地质事件过程取得更可靠的科学认识。因此今后应加强可能赋存古老碎屑锆石地区的寻找,并发现更多古老碎屑锆石。对现有的古老碎屑锆石加强综合研究以及各地区的对比研究。     

线图形简化与移位算子的协同方法

在地图综合过程中,线的图形简化和移位算子通常是分别执行的,图形简化和移位有时都会产生新的空间冲突。本文试图把这两种算子进行协同,避免在地图综合过程中进行空间冲突的多次探测,提高地图综合数据处理的效率。本文通过把线图形简化转换为线上的点移位,并构建邻近地图目标之间的移位传播路径,在考虑空间上下文关系和地图感受规则的前提下,使移位过程能考虑到线图形简化,并尽量保持有关地图目标的空间特征。最后,以道路和其周边的建筑物群为例,验证了该算法的有效性和可行性。     

无人机影像匹配中尺度不变特征应用改进

针对尺度不变特征变换算法用于无人机影像匹配中存在匹配效率低下,误匹配较多等问题,该文提出一种特征点提取优化算法,并改进了特征点匹配策略。通过将原始影像划分格网,依据每个格网影像信息熵的大小来合理分配各格网中特征点数量,实现了基于纹理信息丰富程度的特征点均匀分布;基于Harris兴趣值进行筛选,保留了适合于摄影测量的特征点;采用一种多层次自适应的匹配策略,在尽可能保留正确匹配的同时提高了匹配正确率。基于一对由小型无人机拍摄影像的实验结果表明,所提方法在大量减少SIFT特征点的同时,保证较高的正确率,增加了匹配点的精度,且提高了算法效率。     

The use of WorldView-2 satellite imagery to model urban drainage system with low impact development (LID) Techniques

Within a wide range of best management practices for stormwater management in urban areas, there has been an increasing interest in source control measures. Source controls such as low-impact development (LID) techniques are potentially attractive as retrofit options for older developed areas that lack available land to implement conventional measures such as stormwater management ponds. Hence, distributed urban drainage models requiring detailed representation of developed drainage areas should be developed to accurately estimate the benefits that LIDs may provide. This study (1) presents a two-stage classification process on a high-resolution WorldView-2 image, and (2) demonstrates how to use the extracted land cover information in the subsequent hydrologic modelling and assessment of different LIDs’ performance. The proposed two-stage classification method achieved an overall accuracy of 80.6%, whereas a traditional pixel-based achieved 68.4% in classifying the same urban area into six land cover classes. From the classification results, the hydrologic properties of micro-subcatchments were imported in the United States Environmental Protection Agency Storm Water Management Model to assess the performance of LIDs. A reduction of run-off volume 18.2% and 37.1% was found with the implementation of porous pavement and bioretention, respectively, in a typical low-rise residential area located in the city of San Clemente, California, US. The study demonstrates the use of high-resolution remote sensing image to aid in evaluating LID retrofit options, and thus benefits in situations where detailed drainage area information is not available.     

Knowledge Transfer for Large‐Scale Urban Growth Modeling Based on Formal Concept Analysis

Cellular automata (CA) are useful for studies on urban growth and land‐use changes. Although various methods have been developed to define transition rules, modeling urban growth of large areas remains a tough challenge owing to heterogeneous geographical features. To address the problem, we present a novel method based on the combination of Formal Concept Analysis (FCA) and knowledge transfer techniques. FCA is used to solicit association rules among cities within a large area. This method can provide a theoretical basis for the knowledge transfer process. A cutting‐edge algorithm called TrAdaBoost is then integrated with the commonly‐used Logistic‐CA as the modeling framework. The proposed method is applied to the urban growth modeling of Guangdong Province, a large region with 21 cities in China, from 2005 to 2008. Compared with traditional methods, this method can achieve better results at the provincial and local levels, according to the experiments. The combination of FCA and knowledge transfer is expected to provide a useful tool for calibrating large‐scale urban CA models.     

Classifying Human Activity Patterns from Smartphone Collected GPS data: A Fuzzy Classification and Aggregation Approach

Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone‐measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone‐collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g. up to 96% in activity identification) using of this method. Two examples are provided in the Appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.     

A support vector machine classifier based on a new kernel function model for hyperspectral data

The kernel function is a key factor to determine the performance of a support vector machine (SVM) classifier. Choosing and constructing appropriate kernel function models has been a hot topic in SVM studies. But so far, its implementation can only rely on the experience and the specific sample characteristics without a unified pattern. Thus, this article explored the related theories and research findings of kernel functions, analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery, and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model (PRBF). Then, a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model, and a genetic algorithm (GA) was used to optimize the SVM parameters. On the basis of theoretical analysis, this article completed object classification experiments on the Hyperion hyperspectral imagery of experimental areas and verified the high classification accuracy of the model. The experimental results show that the effect of hyperspectral image classification based on this PRBF model is apparently better than the model established by a single global or local kernel function and thus can greatly improve the accuracy of object identification and classification. The highest overall classification accuracy and kappa coefficient reached 93.246% and 0.907, respectively, in all experiments.