Outlier detection algorithms and their performance in GOCE gravity field processing

The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data.     

Development of a remotely sensed,historical land-cover change database for rural Chihuahua,Mexico

The purpose of this article is to describe the development of a remotely sensed, historical land-cover change database for the northwestern quarter of Chihuahua, Mexico, The database consists of multi-temporal land-cover classifications and change detection images. The database is developed to facilitate future investigations that examine urban–rural linkages as possible drivers of rural land-use and land-cover changes. To develop the needed land-cover change database, this study uses the North American Landsat Characterization (NALC) MSS triplicates because of their temporal depth and spatial breadth. Challenges exist, however, to effective classification and change detection using the NALC triplicates, including illumination differences across multiple scenes and periods caused by topographic and solar variations and the lack of ground reference data for historic periods. Therefore, creation of the database is a four step process. First, extensive pre-processing is performed to enhance comparability of multi-date images. Pre-processing includes topographic correction, mosaic creation and multi-date radiance normalization. Second, ancillary sources of land-cover data are combined with visual interpretations of enhanced images to define reference pixels used to classify the images using the maximum likelihood algorithm. Third, classification accuracy is assessed. Fourth, post-classification change detection is performed. Results indicate significant image improvements after pre-processing that permit very good overall classification (86.26% classified correctly) and change detection. To conclude, findings are presented that indicate significant changes to arid grasslands/shrublands and forest resources in mountainous regions.     

山西运城市森林火险等级预报系统

利用有关气象及森林火灾资料，分析了林林火灾的发生与气象条件、火条件及森林含水率的关系，计算了森林火险综合预报指标，建立了以预报方程，经试用效果良好。     

Particle size dependent response of aerosol counters

During an international workshop at the Institute for Experimental Physics of the University of Vienna, Austria, which was coordinated within the Committee on Nucleation and Atmospheric Aerosols (IAMAS-IUGG), 10 instruments for aerosol number concentration measurement were studied, covering a wide range of methods based on various different measuring principles. In order to investigate the detection limits of the instruments considered with respect to particle size, simultaneous number concentration measurements were performed for monodispersed aerosols with particle sizes ranging from 1.5 to 50 nm diameter and various compositions.The instruments considered show quite different response characteristics, apparently related to the different vapors used in the various counters to enlarge the particles to an optically detectable size. A strong dependence of the 50% cutoff diameter on the particle composition in correlation with the type of vapor used in the specific instrument was found. An enhanced detection efficiency for ultrafine hygroscopic sodium chloride aerosols was observed with water operated systems, an analogous trend was found for n-butanol operated systems with nonhygroscopic silver and tungsten oxide particles.     

古炭屑与古森林火?

森林火普遍发生在地质历史时期中，它对自然植被系统的演替起着重要作用。不同强度和频率的森林火培育出不同类型的植被：频繁的森林火导致草甸的形成，而热带雨林形成的原因之一是森林火的缺乏。古炭屑作为古森林火的遗迹，具有分布广泛、细胞结构保存完好以及原地埋藏等特点，为研究古森林火发生的强度和频率提供了宝贵的材料。古炭屑的出现与当时的气候因素、植被类型和地理状况有密切关系：干旱、少雨的气候常常伴随高频率森林火的发生，表现出古炭屑的数量增多；易燃物种组成的植被易发生森林火，古炭屑的出现也会增多，而耐火树种会降低森林火的强度，古炭屑则出现少。因此，从古炭屑的数量和种类的变化，可探讨古森林火的发生规律，进而推断长期地质历史时期的气候、植被、地理状况的演变。     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

ISO-834标准火灾作用后钢管混凝土的轴压刚度和抗弯刚度

火灾后钢管混凝土的轴压刚度和抗弯刚度是钢管混凝土结构变形性能和抗震分析的重要指标，也是火灾后钢管混凝土修复和加固的重要依据之一，本文利用数值方法计算了标准火灾作用后钢管混凝土轴压构件和纯弯构件荷载-变形关系曲线，分析了受火时间、材料强度、含钢率、截面尺寸等因素的影响规律，最后推导了标准火灾作用后钢管混凝土轴压刚度和抗弯刚度的简化计算公式，所得结果可供有关钢管混凝土结构工程设计时参考。     

克孜尔水库大坝的面波检测

1999年 3月 15日克孜尔地震之后对克孜尔水库坝体出现的裂缝进行了面波勘探。首先介绍了面波勘探法的基本原理 ,然后通过典型剖面检测与资料处理找出异常区、带并进行解释。试验表明面波勘探法适用于克孜尔水库大坝监测     

基于GIS的黄河上游龙羊峡库区生态环境遥感监测研究

本项研究以黄河上游生态环境严重退化的龙羊峡库区为研究区，应用遥感与GIS一体化的方法，建立了库区生态环境动态监测信息系统。在系统的支持下，利用多时相、多波段TM遥感数据，对库区近十年来生态环境变化进行监测分析，揭示了库区土地沙漠化、水土流失、土地利用／土地覆盖的现状，以及建库近十年来生态环境时空演化的过程与发展趋势，为库区生态环境治理提供科学依据。本项研究利用遥感和GIS技术，采用生态环境综合分类系统，在一次分类处理中通过多层次不同级别的组合获得了基于同一信息源的土地沙漠化、草场盖度、土地利用处地覆盖等多种环境类型信息，并利用环境信息模型对库区的土壤侵蚀进行了评价。基于GIS的分类后再处理使遥感分类精度有了一定程度的提高。研究表明，在GIS的支持下充分利用遥感信息监测与评价生态环境，是区域生态环境定量化研究的有效途径。     

附有约束条件的最小二乘影像匹配及其在交通事故中的应用

影像匹配技术在图像处理与计算机视觉中有着非常重要的作用。最小二乘影像匹配方法是常用的匹配方法之一。本文简单介绍了目前影像匹配的几种方法及其优缺点，探讨了数字相机的检校原理与方法。重点介绍附有约束条件的最小二乘影像匹配的原理及匹配过程，结合交通事故处理的实例，论证了该方法的可行性。与传统方法相比，在测量精度、自动化程度以及数据三维可视化等方面具有明显优势。