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。     

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.