ENVISAT Radar Altimeter Tracker Bias

In the past, errors in the determination of the orbit were dominant in radar altimeter missions, but technical advances have improved the orbit accuracy and hence, other sources of error have become more important. Sea-state bias is now the main source of error and can be divided into three sea-state dependent errors: skewness, electromagnetic bias, and tracker bias. We estimated the magnitude of the third term, by retracking ocean waveforms from ENVISAT RA-2. The retracking algorithm used is based on Maximum Likelihood Estimation. Tracker bias shows a seasonal and geographical dependence related to the distribution of significant wave height (SWH) and time origin differences. We estimated a mean value of 0.13 ± 0.07 %SWH. Temporal and regional dependent errors are introduced when using a linear retracker processing approach.     

AIC与BIC在亲体-补充量模型选择中的应用及比较

由于渔业资源评估中补充量的剧烈变动、亲体量的测量误差以及时间序列的偏差常常使亲体一补充量(SR)关系模型的确定存在很大偏差问题。本文以7种SR(Stock-Recruitment)模型的模拟数据作为观测数据，研究了AIC(Akaike Information Criterion)与BIC(Bayesian Information Criterion)在SR模型选择中的应用。作为例证，文中采用AIC和BIC对8组实际的SR数据进行了SR模型的选择，并对其结果进行了比较。参数的估计方法为最大似然法(Maximum likelihood method)。结果表明，AIC和BIC在SR模型选择中是有效的。但是，对于嵌套模型，BIC可能比AIC更有效。     

基于贝叶斯分类器的多普勒天气雷达海浪回波识别和效果检验

为了提升雷达数据质量，减少海浪回波对临近预报和数值天气预报模式的雷达数据同化的不利影响，因此需要对海浪回波进行识别和去除。识别算法主要为统计获得先验概率，分析海浪和降水回波特征分布得到似然函数，再经过贝叶斯分类器来达到识别的目的。在本次算法识别过程中65个样本数据试验的临界成功指数I_CS达到了0.692，结果表明利用贝叶斯分类器对海浪回波的识别，具有较好的识别效果，能一定程度降低海浪回波误判为降水回波的错误，提高雷达数据质量。     

长期极值统计理论及其在海洋环境参数统计分析中的应用

海洋环境极值参数(如风速、流速、波高、周期等)在海洋工程设计中具有重要意义。利用次序统计和极值理论方面的较新研究成果，从理论上证明了多种统计分布中Weibull分布是最优的，使长期极值统计建立在一个更坚实的基础上；同时引入基于序列统计的最大似然估计方法。利用大量数据．对最小二乘估计方法和最大似然估计法进行对比分析，指出最大似然估计法是精确估计．而最小二乘估计方法是保守估计。     

Least-squares variance component estimation

Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive method for the estimation of unknown variance and covariance components. LS-VCE is simple because it is based on the well-known principle of LS; it is flexible because it works with a user-defined weight matrix; and it is attractive because it allows one to directly apply the existing body of knowledge of LS theory. In this contribution, we present the LS-VCE method for different scenarios and explore its various properties. The method is described for three classes of weight matrices: a general weight matrix, a weight matrix from the unit weight matrix class; and a weight matrix derived from the class of elliptically contoured distributions. We also compare the LS-VCE method with some of the existing VCE methods. Some of them are shown to be special cases of LS-VCE. We also show how the existing body of knowledge of LS theory can be used to one’s advantage for studying various aspects of VCE, such as the precision and estimability of VCE, the use of a-priori variance component information, and the problem of nonlinear VCE. Finally, we show how the mean and the variance of the fixed effect estimator of the linear model are affected by the results of LS-VCE. Various examples are given to illustrate the theory.     

室内定位多传感器的空间配准

针对卫星信号到达地面时较弱、不能穿透建筑物的问题,该文对无法使用卫星定位时的辅助手段之一——室内定位技术作了研究。室内定位多传感器信息融合需要在统一坐标框架下进行,传统的空间配准解决方案多为简单的"松组合"加工。该文给出了多传感器空间配准数学模型,通过构造极大似然函数估计传感器间的位置偏差,采用粗估计与高斯牛顿迭代估计的两步法求解,并用仿真算例验证了该文所采用算法的有效性与正确性。     

Markov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image

Forest encroachment (FE) is a problem in Andaman and Nicobar Islands (ANI) in India for environment and planning. Small gaps created in the forest slowly expand its periphery disturbing the biodiversity. Therefore, intrusion of poachers, slash and burn and other factors causing FE must be carefully detected and monitored. Remote sensing offers a great opportunity to accomplish this task because of its synoptic view. Conventional classification methods with remotely sensed images are problematic because of small size of FE and mixed landcover composition. This study presents an application of super-resolution mapping (SRM) based on Markov random field for detection of FE using ASTER (15 m) images. The SRM results were validated using multispectral IRS LISS-IV (5.8 m) image. Non-contiguous FE patches of various sizes and shapes are characterized using the spatial contextual information. The novelty of this approach lies in the identification and separability of small FE pockets which could not be achieved with pixel-based maximum likelihood classifier (MLC). The SRM parameters were optimized and found comparable to previous studies. Classification accuracy obtained with SRM at scale factor 3 is κ = 0.62 that is superior to accuracy of MLC (κ = 0.51). SRM is a promising tool for detection and monitoring of FE at Rutland Island in ANI, India.     

Cosmo-SkyMed X-band SAR data for classification of ice-free areas and glacier facies on Potter Peninsula,King George Island Antarctica

The climate change phenomena have been influencing terrestrial and glacial ecosystems around the planet. Maritime Antarctica is especially sensitive to these climate variations and over the last 50 years increasing global air temperatures have caused extensive glacial retreat. The objective of this study is to evaluate the potential use of the SAR technology in monitoring the surface dynamics of the Potter Peninsula, King George Island, maritime Antarctica. An image generated by the SAR satellite COSMO-SkyMed, obtained on 2 February 2011, was used to extract the backscattering values of targets on the surface for further processing and classification, using a supervised statistic classifier of maximum likelihood for the determination of the surface classes. The average backscattering of water bodies presented high similarity, which made its separation unattainable. On the other hand, the surface classes’ bare ice and wet snow over the glacier presented distinct average backscattering values, which allowed an efficient and precise classification using only this parameter. The classification process showed satisfactory results for periglacial environments, presenting high fidelity to the field data.     

Petroleum exploration models—Estimation and applications

Three statistical models of oil and gas exploration prevail in the literature: field size distribution, exploration function, and effectiveness of exploration models. They may have many functional forms and may be used for prediction and planning results of future exploration activity. A review of American and Soviet literature of exploration models is presented with special emphasis on functions suitable for countries which, like Poland, are not rich in oil and gas. Two new procedures are proposed: a method for assessing field size distribution and a method for simultaneous interval estimation of parameters of the exploration function. The methods complement existing ones as they are suited to areas that are not rich in hydrocarbons. Examples of application to exploration of two Polish petroliferous basins are given.     

Stochastic partial differential equations in groundwater hydrology

Many problems in hydraulics and hydrology are described by linear, time dependent partial differential equations, linearity being, of course, an assumption based on necessity.Solutions to such equations have been obtained in the past based purely on deterministic consideration. The derivation of such a solution requires that the initial conditions, the boundary conditions, and the parameters contained within the equations be stipulated in exact terms. It is obvious that the solution so derived is a function of these specified, values.There are at least four ways in which randomness enters the problem. i) the random initial value problem; ii) the random boundary value problem; iii) the random forcing problem when the non-homogeneous part becomes random and iv) the random parameter problem.Such randomness is inherent in the environment surrounding the system, the environment being endowed with a large number of degrees of freedom.This paper considers the problem of groundwater flow in a phreatic aquifer fed by rainfall. The goveming equations are linear second order partial differential equations. Explicit form solutions to this randomly forced equation have been derived in well defined regular boundaries. The paper also provides a derivation of low order moment equations. It contains a discussion on the parameter estimation problem for stochastic partial differential equations.