Analytic tools for investigating the structure of network reliability measures with regard to observation correlations

A global measure of observation correlations in a network is proposed, together with the auxiliary indices related to non-diagonal elements of the correlation matrix. Based on the above global measure, a specific representation of the correlation matrix is presented, being the result of rigorously proven theorem formulated within the present research. According to the theorem, each positive definite correlation matrix can be expressed by a scale factor and a so-called internal weight matrix. Such a representation made it possible to investigate the structure of the basic reliability measures with regard to observation correlations. Numerical examples carried out for two test networks illustrate the structure of those measures that proved to be dependent on global correlation index. Also, the levels of global correlation are proposed. It is shown that one can readily find an approximate value of the global correlation index, and hence the correlation level, for the expected values of auxiliary indices being the only knowledge about a correlation matrix of interest. The paper is an extended continuation of the previous study of authors that was confined to the elementary case termed uniform correlation. The extension covers arbitrary correlation matrices and a structure of correlation effect.     

地形因子与小微地震相关度的空间异质性分析

针对传统最小二乘回归未能顾及数据的空间特性,且无法度量模型自变量与因变量相关性的空间变异特性的问题,本文提出利用地理加权回归方法分析小微地震频次与地形因子相关度的空间异质性。以四川地区的地震监测资料、DEM为实验数据,选取地形复杂度、坡度变率、坡向变率和地面曲率为自变量,地震发生频次为因变量,构建地理加权回归模型,并进行回归系数的空间变异分析。实验分析发现,地震频次与地形因子具有一定的相关性:地形复杂度与地震频次相关性最强;坡度变率、沟壑密度、剖面曲率与地震频次的相关性依次减弱;不同空间位置的地形因子和地震频次的相关性具有较明显的空间异质性。实验结果表明,地理加权回归可以有效地度量分析地震频次与地形因子相关度的空间异质性,研究结果可为地震及次生灾害的分析与预报提供辅助决策参考。     

Land surface temperature and its impact factors in Western Sichuan Plateau,China

The understanding influence of multiple factors variations on land surface temperature (LST) remains elusive. LST was retrieved by the atmospheric correction algorithms. Based on the correlation coefficients, stepwise regression analysis was developed to examine how multiple factors variability led to LST variations. The differences in LST between impact factors vary depending on time in a day. The elevation and land use types significantly affect the LST in sunny slope or shadow areas has a significantly quadratic curve correlation or a negative linear correlation with it, the influence of slope and aspect is not very significant. LST for forestland, grassland and bare land in the sunny slope and shadow area was the cubic polynomial related to its elevation. Normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) effectively express LST in mountainous. LST and NDMI or NDVI have a significantly negative correlation, NDMI is more effective and more applicable for the expression of LST.     

青岛气温与全球海表温度的相关分析

利用奇异谱算法对国家160站气象资料中的青岛气温数据和ERSST海表温度资料进行了分析,研究了青岛气温的发展趋势和周期性变化规律,并对未来青岛市气温进行了预测,预测结果与原观测数据接近(相关度接近0.95)。同时也研究了青岛气温异常和全球海表温度的相关性,研究表明:青岛气温异常与全球海表温度异常部分地区存在超过0.79的相关性,其中最大正滑动相关系数位于9月份8N、49W点,并且青岛气温异常存在4个单位的滞后时间。     

Air Pollution Modeling from Remotely Sensed Data Using Regression Techniques

There is a need for timely information about changes in the air pollution levels in cities for adopting precautionary measures. Keeping this in view, an attempt has been made to develop a model which will be useful to obtain air quality information directly from remotely sensed data easily and quickly. For this study pixel values, vegetation indices and urbanization index from IRS P6 LISS IV and Landsat ETM+ images were used to develop regression based models with Air Pollution Index (API), which were calculated from in-situ air pollutant information. It was found that among the 12 parameters of IRS, highest correlation exists between pixel values in NIR (Near Infra-Red) band (Pearson correlation ?0.77) and Normalized Difference Vegetation Index (NDVI) (Pearson correlation ?0.68) and both have inverse relationship with API. In case of Landsat, the highest correlation was observed in SWIR (Short Wave Infra-Red) band (Pearson correlation ?0.83) and NIR (Pearson correlation ?0.78). Both single and multivariate regression models were calibrated from best correlated variables from IRS and Landsat. Among all the models, multivariate regression model from Landsat with four most correlated variables gave the most accurate air pollution image. On comparison between the API modeled and API interpolated images, 90.5 % accuracy was obtained.     

基于相关系数的遥感图像拼接线检测算法

提出了一种基于相关系数方法的遥感影像最佳拼接线的检测算法.该算法根据影像重叠区域内两幅影像对应像素的相关性来判别像素之间纹理差异,结合了影像亮度差异的信息,检测出的镶嵌线优于平分线法检测出的镶嵌线.以4幅相邻影像自动寻找镶嵌线为例进行了实验,取得了良好的效果,验证了算法的实用性与有效性.     

融合GNSS ZTD和气象要素的内蒙古土壤水含量模型

土壤水含量是农牧业衡量干旱的重要指标,对气候生态具有重要影响,土壤水的变化趋势对于区域的水土流失和气候变化研究等工作具有重要意义,而我国对于土壤水含量的监测起步较晚,因此有必要利用其他已有数据开展土壤水含量模型研究. 利用内蒙古已有全球导航卫星系统(GNSS)天顶对流层延迟(ZTD)数据和湿度、日照以及蒸发量数据进行土壤水含量反演模型研究. 首先将各要素与土壤水含量进行相关性分析,因土壤水含量与GNSS ZTD数据均存在观测噪声,所以应对数据进行去噪处理. 利用小波变换方法剔除噪声,去噪后土壤水含量数据与各要素相关性均有所提高,土壤水含量与湿度相关性最好,两者各实验点的平均相关性为0.645;土壤水含量与日照和蒸发量呈负相关,其平均相关性分别为?0.561、?0.547;而土壤水含量与GNSS ZTD数据相关性最小,其平均相关性为0.271. 根据各要素与土壤水含量的相关性,进行土壤水含量模型构建并进行可靠性验证. 经验证误差统计发现:实验区域NMWJ站模型精度最高,其精度为90.1%;HLAR站点模型精度最低,其精度为69.1%;各站点的平均精度为81.35%. 基于多变量要素的土壤水含量模型可为土壤水含量的趋势变化研究提供参考,通过研究土壤水含量的变化趋势,对区域进行水资源的合理分配利用从而达到节约水资源目的.      

基于MODIS的巢湖叶绿素a空间分布

利用MODIS影像数据,对水华现象出现的重要物质——巢湖水体叶绿素a信息进行反演。在对数据进行预处理后,采集影像相应位置的像元灰度值,与现实中在对应位置观测的湖水叶绿素a浓度进行相关性拟合分析,得到叶绿素a浓度与像元灰度值的相关性方程与相关系数。在建立相关性模型时采用了单波段和波段比值法,得到的最高相关系数为R2=0.881 8,表明利用这种方法反演巢湖水体叶绿素a浓度空间分布具有一定的可靠性。选择相关系数性较高的2个拟合方程反向演算MODIS影像,最后成功得到巢湖湖面当日叶绿素a浓度分布状况图,为巢湖防治水华发生提供了比较准确可靠的信息。     

Precision, Cross Correlation, and Time Correlation of GPS Phase and Code Observations

In high-precision Global Positioning System (GPS) relative positioning applications, the stochastic properties of the GPS observables play a crucial role. The misspecification of the a priori covariance matrix can lead ton non-optimal results and false decisions. In this article, the noise characteristics of seven commonly used GPS receivers are examined. The main areas of our study are the precision of the observations, the time correlation of the observations, and the correlation between the different observation types. In order to examine these characteristics, an experiment was carried out, which will be briefly discussed. As results, the autocorrelation functions, standard deviation, and correlation numbers will be given for the undifferenced observations of the seven receivers. The standard deviation of the phase observation is usually at the submillimeter level, and that of the code observations is some centimeters to decimeters. It will be also shown that none of the receivers are free from correlation between observation types and time correlation, especially the code observations, which suffer from time correlation. The results presented can help to refine stochastic models currently in use. ? 2000 John Wiley & Sons, Inc.     

应用高光谱遥感数据估算土壤表层水分的研究

土壤水分是土壤的重要组成部分，它在陆地表层和大气之间的物质和能量交换方面扮演着重要角色，寻求快速而准确的方法估算土壤水分具有重要意义。通常，从可见光一近红外对土壤表层水分的估计多是建立在土壤水分与反射率的关系之上的。而在土壤水分含量不高时，土壤水分的增加使土壤光谱反射率在整个波长范围内降低，尤其在760nm，970nm，1190nm，1450nm，1940nm和2950nm等水分吸收波段，而在土壤水分含量较高时，土壤水分的增加会使土壤光谱反射率在某些光谱波段升高。而土壤水分的估计往往是基于土壤水分与土壤水分吸收波段的吸收强度之间的线性关系上，虽然这些经验的方法对于估算某些土壤的表层水分含量是有效的，但这些关系应用于其它条件(如不同种类土壤、土壤湿度变化范围很大的情况)时却面临很多困难，这与土壤的光谱反射率是由土壤的组成成分(土壤水分、有机质、氧化铁和粘土矿物等)的含量和它们在土壤中的分布密切相关。微分技术处理“连续”的光谱是遥感中常用的数学方法，微分技术能部分消除低频光谱成分的影响。现在微分光谱已广泛地应用于研究植被的生物物理参数、矿物和有机质等。然而利用微分光谱对土壤水分反演的研究却鲜见报道。本文通过对实验室中多种不同类型的土壤进行光谱与土壤表层水分含量进行观测，探讨了通过土壤反射率与微分光谱对土壤表层水分的反演方法。4种类型的土壤光谱数据(反射率(R)，反射率倒数的对数(log(1／R))，反射率的一阶微分光谱(dR／dλ)，反射率倒数的对数的一阶微分光谱(d(log(1／R))／dλ))与土壤表层水分之间的关系在本文中得到分析，R与log(1／R)对于不同土壤类型与土壤表层水分都很敏感，说明通过R与log(1／R)反演土壤表层水分受土壤类型的影响很大，而dR／dλ，d(log(1／R))／dλ)对土壤类型却不敏感，对土壤表层水分较为敏感，说明dR／dλ和d(log(1／R))／dλ)对于反演不同类型土壤具有很大的潜力，微分光谱与土壤水分在某些波段具有显著的相关性。通过随机对9种土壤(各具有4个土壤水分)的数据建立反演土壤水分的模型，并其他9种土壤(各具有4个土壤水分)的数据进行验证模型，结果表明，dR／dλ和d(log(1／R))／dλ)能够显著提高R与log(1／R)对于不同土壤类型土壤表层水分的反演精度，由于吸收过程是非线性的，在四种类型的土壤光谱数据中，总体来说，d(log(1／R))／dλ)具有最好的能力预测不同类型土壤的表层水分含量。     

Comparison of length of day with oceanic and atmospheric angular momentum series

This is a companion paper to earlier comparisons and study of operational polar motion series, published recently in the same journal. In this contribution, four operational, publicly available, length-of-day (LOD) time series have been compared to the atmospheric angular momentum (AAM) augmented with recent oceanic angular momentum (OAM) data during September 1997–July 2000, using several intervals ranging from 3 days to almost 3 years. Additionally, the LOD of the International GNSS Service (IGS) historical series and a new LOD combination (CMB) were also analyzed. All the six LOD series showed an overall correlation exceeding 0.99 for the complete interval of almost 3 years. Even for the shortest interval of only 3 days, the correlation was still higher than 0.60. The combined AAM + OAM series with inverted barometer corrections always gave the best correlation. The Rapid Service LOD of the International Earth Rotation and Reference Systems Service (IERS) compared the best at all intervals but the shortest one, where the CMB LOD was the best with a correlation of 0.73, followed by both IGS series with a correlation of about 0.71. Prior to all the correlation analyses, in addition to the removal of all the known (conventional) LOD tidal variations with periods ranging from 5.6 days to 18.6 years and lunar fortnightly and monthly oceanic tides, small corrections of lunar fortnightly and monthly tides, semi-annual, annual periodical signals, drift and scale had to be estimated with respect to the combined AAM + OAM series.     

区域CORS网在PM2.5监测中的应用研究

在雾霾频发的冬季大气可降水量PWV与大气污染物PM_2.5之间存在一定的关联关系,本文通过对河南地区CORS网的GNSS大气可降水量与PM_2.5之间的相关性进行分析并建立了实时监测模型。试验研究发现：河南地区冬季雾霾频发期大气可降水量PWV与PM_2.5之间存在着较强的正相关关系,通过建立的PM_2.5实时监测模型验证了可以依据大气可降水量PWV对PM_2.5进行实时监测,并取得了较为理想的结果,可以为当前进行PM_2.5监测提供一种新的参考。     

航测无人机作业安全评价

针对航测无人机安全评价指标体系的构建,该文应用层次分析法,计算出各指标权重及评价指数,并设定最低安全作业条件,计算出最低安全评价指数,在此基础上,该文发现由各指标赋予权重进行整体评价的结果并不能完整地反映出无人机的客观状态,当计算最低评价指数时,其结果只能作为航测无人机适航必要条件,而不能作为充分条件,由此引入灰色关联度分析法,从系统内部探究各指标与结果的作用关系,进而得出航测无人机安全作业的临界标准条件,即安全评价指数不小于4.17,且灰色关联度不小于0.682。在此基础上,分析20个架次的无人机作业数据,结果表明,将层次分析法与灰色关联度分析相结合,通过整体评价与内部分析,对航测无人机安全作业系统进行全面评价,可以提高评价结果的准确度。     

利用影像相关分析的遥感变化检测

针对利用单一光谱特征进行影像相关分析在遥感变化检测应用中效果较差的问题,提出了一种将纹理特征与光谱特征相结合的相关系数计算模型。应用中在对多时相影像进行相同尺度棋盘分割的基础上,先计算各对应分割窗口内的相关系数,再以其中心点的坐标和相关系数值作为一个特征点,在三维空间中进行插值处理得到整个区域的相关系数空间分布图;进一步通过密度分割处理提取变化信息。本文以两期GF-1影像数据进行了变化信息提取试验,结果表明,采用组合特征相关系数的变化检测结果明显优于单一光谱相关系数的变化检测结果。组合相关系数的应用研究为利用影像相关分析方法从高分影像中提取变化信息提供了一种新的思路。     

Global coherence of GPS-measured high-frequency surface tremor motions

The traditional approach of GPS investigations is determining trends which are connected with the motion of tectonic plates. At the same time, a global GPS network provides the possibility of investigating statistical properties of high-frequency earth surface tremor in different parts of the world. Based on the results of coherence and correlation analysis of noise components of daily three-component GPS time series, representing measurements of earth surface displacements at 1097 stations, we have found that, during 2010–2011, there was a significant increase in the average level of noise coherence or correlation with dominant periods 7–9 days of surface tremor in nine regions of the earth, and in some of these regions, the average level of coherence or correlation is still high and does not return to the previous level. The increase of the average level of coherence and correlation could be detected on the graphs purely visually, while the middle time point of the time interval in which the ascending occurred is detected more precisely by a formal method based on the use of the Fisher’s ratio.     

基于影像IB算法的沈阳市热岛效应分析

本文利用2006年、2010年沈阳市地区TM遥感数据,采用影像IB算法反演地表温度,分析了沈阳市热岛效应的空间分布特征、变化现象以及地表温度与归一化植被指数(NDVI)、归一化建筑指数(NDBI)之间的相关性。研究表明沈阳市热岛效应总体呈现由市中心向四周逐渐扩张的空间特征,地表温度与归一化植被指数(ND-VI)存在紧密的负线性相关关系,地表温度与归一建筑指数(NDBI)存在正相关关系。     

2000-2012年鄂尔多斯高原植被动态及干旱响应

针对鄂尔多斯高原植被覆盖变化受干旱胁迫的状况,该文结合降水和气温的协同变化,以2000-2012年生长季的MODIS-NDVI数据和同期降水、温度和帕尔默干旱指数为依据,采用线性趋势分析、标准偏差分析和相关性分析等方法,对鄂尔多斯高原植被与气候变化的相关关系和干旱异常变化对植被动态的影响进行了研究.结果表明:鄂尔多斯高原生长季及季节(春季、夏季和秋季)植被归一化植被指数主要受降水的控制和干旱的制约,秋季归一化植被指数更多地受到夏季干旱的影响.与气象因子的空间相关分析表明,春季温度上升有利于研究区北部归一化植被指数像元的增加.在荒漠草原和沙漠地区,夏季干旱与归一化植被指数的相关关系最强.秋季降水对典型草原归一化植被指数的提升显著.     

西秦岭金矿化带中土壤与岩石实验室光谱相关性研究

通过西秦岭金矿化带中土壤与岩石实验室光谱的相关性分析与研究。探索该区表层壤覆盖条件下应用遥感信息寻找金矿床。研究表明,土壤与岩石两者光谱之间有很高的正相关性,并且在谱形上亦有相似性,使得利用该地区土壤光谱特征从遥感信息中提取出土壤覆盖层下的岩石光谱特征成为可能,为这种遥感技术找矿方法提供依据。     

基于小波相干的雾霾与GNSS对流层延迟相关性分析

为了进一步揭示雾霾与对流层延迟相关性的细部特征,基于小波相干算法构建雾霾与天顶对流层延迟（zenith tropospheric delay,ZTD）相关性分析的新方法,有效地分析了雾霾与ZTD相关性的时频空间分布特征及成因。首先,通过分析IGS（internet GNSS service）北京站2014-2017年每个月的ZTD与量化评定雾霾的空气质量指数（air quality index,AQI）的相关系数,发现两者在月尺度上相关系数绝对值大于0.4的比率占29.17%,宏观上显示了两者具有相关性。其次,采用小波相干的方法分析了北京2014-2017年期间年度ZTD与AQI的相关性,发现ZTD与AQI在一定的时频域上具有很强的相关性,并且通过了95%置信度检验。最后,由北京、长春部分时域的ZTD与AQI相关性分析可知,北京3月份ZTD与AQI总体上呈现正相关,并且AQI与温度、气压、湿度和风速具有较好的相关性;在研究时域内秸秆燃烧加剧了北京、长春的雾霾,进而影响对流层延迟。     

基于SCOT双加权二次相关的时延估计算法

在研究时延估计中二次相关法的基础上,为了进一步提升二次相关法在更低信噪比条件下的可用性,结合广义互相关中的广义权函数,对二次相关法中的两次互相关分别进行平滑相干变换(SCOT)加权处理.实验仿真结果表明,与二次相关法以及广义二次相关法相比,基于SCOT双加权二次相关的时延估计算法在信噪比低时仍具有较高的时延估值精度.