Landsat时序变化检测综述

时序变化检测已成为当前Landsat数据主流的变化检测方法。本文从检测算法对比、时序数据构建和精度评价等方面对Landsat时序变化检测进行回顾和评述,进而提出Landsat时序变化检测当前所存在的问题,及其所面临的挑战。Landsat时序变化检测算法可大致归纳为轨迹拟合法、光谱-时间轨迹法、基于模型的方法3大类,这些算法大多基于森林扰动提出;变化检测常用指标有波段型、植被指数型、线性变换型、组合型4大类,每类指标的优势不同,可综合多类指标以更全面地检测不同扰动类型。尽管Landsat时序变化检测已取得长足发展,但仍然面临诸多挑战,其中最大挑战是缺少一致性的参考数据集进行变化检测精度评价。     

典型黄土-古土壤系列浸水渗透及湿陷变形规律

为查明典型黄土-古土壤系列地层结构的水分运移及湿陷变形规律,依托西安北至机场城际轨道项目于渭北黄土塬区所进行的大型试坑浸水试验,采用现场实时观测的方法,跟踪测试了无渗水孔条件下的湿陷性土层浸水渗透及湿陷变形。结果表明：在浸水前期,渗透水流以垂直运动为主,整体形态似一不断加大的“秤砣”形,随着水分运移至埋深较大土层时（约15.0 m）,水平运动开始加大,直至达到水分入渗扩散角界限,最终,浸润区及饱和区均呈现形似倒置“漏斗”的形态;受水分扩散的影响,沉降过程可划分为：初始浸水段-剧烈湿陷段-湿陷稳定段-停水孔压消散段-固结沉降段-沉降稳定段;古土壤层阻碍了水分的垂直渗透速度,并对其上土层的侧向扩散起到一定促进作用,停水后,则起到隔绝水分向下快速扩散的作用。研究结果为认识典型黄土-古土壤系列场地水分运移及湿陷变形规律提供了参考,也可为该地区未来工程建设提供指导及借鉴。     

东北地区近百年降水时间序列变化规律的小波分析

利用1905~2005年东北地区哈尔滨、长春、沈阳和大连的降水时间序列资料,采用距平和Morlet小波分析方法,研究了东北地区降水变化的多时间尺度的周期性性变化规律,并对东北地区近期降水状况进行了预测。结果表明:近百年来东北地区年降水量呈现较显著下降趋势,整个东北地区降幅为-5.2mm/10a;长春为-12.7mm/10a;哈尔滨为-7.1mm/10a;大连为-2.7mm/10a;沈阳略为上升趋势为1.3mm/10a。东北地区的年降水量存在着区域性的多重时间尺度下的周期变化特征,2a~3a、5a~6a,10a和50a左右的长期振荡周期具有全域性;长春、哈尔滨年降水的主要控制周期是20a左右;5a~6a的短周期和50年的长周期变化也对年降水有较大影响。     

皖南二叠纪海相暗色岩系成因及有机质富集模式

皖南二叠系已成为页岩气勘探的重要目标层位,暗色岩系的硅质来源长期存在争议,有机质富集堆积机理尚不清楚。本文通过皖南地区二叠纪海相栖霞组、孤峰组和大隆组暗色岩系的岩石学特征、古生物群落、沉积特征、有机质丰度和元素地球化学特征分析,探讨其沉积背景、硅质来源和有机质富集模式。研究表明：栖霞组硅质主要来源于热液;孤峰组硅质主要来源于生物和热液;大隆组硅质主要来源于生物和热液,少量来源于生物。二叠纪属热带—亚热带温暖潮湿气候,暗色岩系沉积于具缓慢沉积速率的厌氧—贫氧的100～300 m水体环境中,有机质含量高。栖霞组和孤峰组富有机质暗色岩系的形成除受控于局限滞留的缺氧环境外,还和海底热液活动提供生物繁盛的营养元素与矿物质和上升流的作用有较为密切的关系。大隆组富有机质暗色岩系的形成与长兴期多期火山喷发带入大量营养元素和微量元素,导致初级生产力提高,生物消亡—繁盛—再消亡—再繁盛—消亡,生烃母质快速堆积,且与缺氧环境密不可分。     

快速序贯算法

本文讨论了序贯算法的基本原理，与ｋａｌｍａｎ滤波的关系，和序贯算法的渐近性态，得到无先验信息的序贯算法不受初值的影响。实例分析表明，此序贯算法可适于方程系数阵的求逆、求解和病态方程的快速解算。     

Modelling combined open channel flow by artificial neural networks

Combined open channel flow is encountered in many hydraulic engineering structures and processes, such as irrigation ditches and wastewater treatment facilities. Extensive experimental studies have conducted to investigate combined flow characteristics. Nevertheless, there is no simple relationship that can fully describe the velocity profiles in a turbulent flow. The artificial neural network (ANN) has great computational capability for solving various complex problems, such as function approximation. The main objective of this study is to evaluate the applicability of the ANN for simulating velocity profiles, velocity contours and estimating the discharges accordingly. The velocity profiles measured by an acoustic doppler velocimeter in the open channel of the Chihtan purification plant, Taipei, with different discharges at fixed measuring section and different depths are presented. The total number of data sets is 640 and the data sets are split into two subsets, i.e. training and validation sets. The backpropagation algorithm is used to construct the neural network. The results demonstrate that the velocity profiles can be modelled by the ANN, and the ANN constructed can nicely fit the velocity profiles and can precisely predict the discharges for the conditions investigated. Copyright © 2005 John Wiley & Sons, Ltd.     

An algorithm for finding the position of a point relative to a fixed polygonal boundary

This article describes a method that determines the position of a point relative to a fixed boundary. Its advantage over the other existing algorithms described below is that in most cases it is necessary that only part of the algorithm be executed to determine the position of the point. A possible inconvenience of using this algorithm is that the contour boundary has to be entered as data. For problems where the boundary often changes the algorithm is not suitable in its present form. The applications envisaged here are to cases where the position of a very large number of points have to be determined relative to one fixed polygonal boundary whose shape is known in advance, for example, blanking out of contours in automatic contouring, or selecting drill holes which lie in the particular part of a mineral deposit.     

On smoothing techniques for the removal of periodic noise of known period

This paper is concerned with the analysis of the linear modely(n)=X(n)+S(n)+(n) for the data sequencey(n) (n=1, 2, ..., N) whereX={x _IJ} is a knownJ × M matrix of full rankM. Here, the(n) are unknown vectors, which we wish to estimate for eachn; S(n) (n=1, 2, ..., N) is a periodic component (which we wish to estimate or remove) superimposed on the linear structureX(n); and(n) is an error vector which is specified as having zero expectation (with possible further properties). Such models commonly occur in geophysical data analysis.Modified from Technical Report No. 33, Computer Centre, The Australian National University.     

Stratigraphic analysis and the asymptotic distribution of the coefficient of cross-association

The asymptotic distribution of the coefficient of cross-association between two sequences of nominal data is investigated under the assumption of independent random sampling from a multinomial distribution slightly modified to suit geological applications. This modification is due to merging consecutive strata of the same rock type into one lithological unit, and can be described by a simple Markov chain. A distinction is made between the case in which the theoretical frequencies are known, and the situation where these have to be estimated from the observed data.     

Selection of ground motion time series and limits on scaling

A procedure to select time series for use in non-linear analyses that are intended to result in an average response of the non-linear system is proposed that is not based simply on magnitude, distance, and spectral shape. A simple model of a yielding system is used as a proxy for the non-linear behavior of a more complicated yielding system. As an example, Newmark displacements are used as a proxy for more complex slope-stability models. The candidate scaled time series are evaluated to find those that yield a response of the simple non-linear system that is near the expected response for the design event. Those scaled time series with responses near the expected value are selected as the optimum time series for defining average response even if the scale factors are larger than commonly accepted (e.g. scale factors >factor of 2).