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.     

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.     

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.     

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     

中国化肥利用率的区域分异

据全国各县近10年的化肥施用量和粮食产量数据，并从不同年份单位播种面积粮食产量和肥料施用量的变化，求出相应不施肥产量和通过施肥可能达到的最高产量，并以上述结果为基础计算出该地区化肥利用率，研究结果表明：当前我国化肥施用的地区差异明显，施用量较集中范围是180－270kg/hm^2和90－180kg/hm^2，部分地区的化肥施用已经过量；单位化肥的粮食产出率主要集中在10－30kg/kg，化肥利用率大多集中在15％－35％，且与化肥施用量有关，按地区统计，高施肥量区化肥施用明显过量，平均达339kg/hm^2，是全国平均用量（262kg/hm^2)的1．29倍，中施肥量区单位面积化肥平均施用量为252kg/hm^2，略低于全国平均水平，低施肥量区则仅为178kg/hm^2，是全国平均的67．8％，我国单位化肥的生产效率及化肥利用率均以低施肥量区最高，分别为17．5kg/kg和39．8％，中施肥量区为13．7和36。     

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。     

中国环境影响评价中公众参与的有效性有待提高

环境影响评价(EIA)作为协调经济发展与环境保护的手段，已日益成为国家环境管理的重要工具。公众参与是环境影响评价的重要组成部分。从公众参与的概念及作用出发，分析中国环境影响评价中公众参与的机制，认为存在着参与对象选取不合理、参与形式双向性差、调查内容设置不科学、意见整理分析方法不完善及公众参与时段滞后等影响公众参与有效性提高的问题，最后提出注重参与对象的代表性，形式的有效性，内容的科学性，分析的定量化以及权利的保障等，以提高中国环评公众参与的有效性。     

辽阳化纤公司198#住宅楼短螺旋钻孔灌注桩施工及效益分析

介绍了辽化198#住宅楼短螺旋钻孔灌注桩施工方法，认为该工艺具有二效快、质量好、节约材料、降低二程成本，在适当条件下，采用这种方法可收到好的效益。     

改性碳酸氢铵的结构特征及其肥效增效机理研究

利用有机-无机(矿物)控释材料对碳酸氢铵进行改性，制成改性碳酸氢铵。盆栽试验结果显示，在等重条件下，改性碳酸氢铵较普通碳酸氢铵有较大幅度的增产。运用X射线衍射分析及红外光谱对改性碳酸氢铵的结构进行研究，发现其晶体结构发生较大的变化，差热分析结果也表明，改性碳酸氢铵的热分解温度比普通碳酸氢铵提高30℃。这种晶体结构的变化增加了改性碳酸氢氨的稳定性，从而延长了其肥效并提高了N的利用率。