Traction image method for irregular free surface boundaries in finite difference seismic wave simulation

In this study, we propose a new numerical method, named as Traction Image method, to accurately and efficiently implement the traction-free boundary conditions in finite difference simulation in the presence of surface topography. In this algorithm, the computational domain is discretized by boundary-conforming grids, in which the irregular surface is transformed into a 'flat' surface in computational space. Thus, the artefact of staircase approximation to arbitrarily irregular surface can be avoided. Such boundary-conforming gridding is equivalent to a curvilinear coordinate system, in which the first-order partial differential velocity-stress equations are numerically updated by an optimized high-order non-staggered finite difference scheme, that is, DRP/opt MacCormack scheme. To satisfy the free surface boundary conditions, we extend the Stress Image method for planar surface to Traction Image method for arbitrarily irregular surface by antisymmetrically setting the values of normal traction on the grid points above the free surface. This Traction Image method can be efficiently implemented. To validate this new method, we perform numerical tests to several complex models by comparing our results with those computed by other independent accurate methods. Although some of the testing examples have extremely sloped topography, all tested results show an excellent agreement between our results and those from the reference solutions, confirming the validity of our method for modelling seismic waves in the heterogeneous media with arbitrary shape topography. Numerical tests also demonstrate the efficiency of this method. We find about 10 grid points per shortest wavelength is enough to maintain the global accuracy of the simulation. Although the current study is for 2-D P-SV problem, it can be easily extended to 3-D problem.     

Driving forces of tropical deforestation: The role of remote sensing and spatial models

Remote sensing technologies are increasingly used to monitor landscape change in many parts of the world. While the availability of extensive and timely imagery from various satellite sensors can aid in identifying the rates and patterns of deforestation, modelling techniques can evaluate the socioeconomic and biophysical forces driving deforestation processes. This paper briefly reviews some emerging spatial methodologies aimed at identifying driving forces of land use change and applies one such methodology to understand deforestation in Mexico. Satellite image classification, change analysis and econometric modelling are used to identify the rates, hotspots and drivers of deforestation in a case study of the southern Yucatán peninsular region, an enumerated global hotspot of biodiversity and tropical deforestation. In particular, the relative roles of biophysical and socioeconomic factors in driving regional deforestation rates are evaluated. Such methodological approaches can be applied to other regions of the forested tropics and contribute insights to conservation planning and policy.     

Potential of High-Resolution Satellite Data in the Context of Vulnerability of Buildings

High-resolution space-borne remote sensing data are investigated for their potential to extract relevant parameters for a vulnerability analysis of buildings in European countries. For an evaluation of large earthquake scenarios, the number of parameters in models for vulnerability is reduced to a minimum of relevant information such as the type of building (age, material, number of storeys) and the geological and spatial context. Building-related parameters can be derived from remote sensing data either directly (e.g. height) or indirectly based on the recognition of the urban structure type in which the buildings are located. With the potential of a fully- or semi-automatic inventory of the buildings and their parameters, high-resolution satellite data and techniques for their processing are a useful supporting tool for the assessment of vulnerability.     

基于遥感技术的中国北方沙漠及沙质荒漠化土地区划研究

根据影响沙漠、沙质荒漠化土地的地质地貌、植被、气候和人类活动等特征,通过确定地域分异、发生学、多级序列等区划原则,建立了以地质地貌类型组合、气候为沙漠、沙质荒漠化土地区(Ⅰ级区)划分和以沙质荒漠化土地类型空间分布特征为沙漠、沙质荒漠化土地亚区(Ⅱ级区)划分的指标体系。采用遥感方法,将中国北方沙漠、沙质荒漠化土地划分为贺兰山以西干旱沙漠、沙质荒漠化区;贺兰山-大兴安岭干旱、半干旱沙漠、沙质荒漠化区和大兴安岭以东半干旱、半湿润沙质荒漠化区等3个区和17个亚区。并对沙漠、沙质荒漠化土地区进行了现状分析。     

遥感技术在煤炭地质中的应用现状及前景

介绍了遥感技术在煤炭资源调查和评价、煤矿区地质灾害调查和评估、生态环境及污染调查评价和监测以及煤矿三维可视化仿真规划系统建设方面的应用研究现状及所取得的主要成果，并结合遥感技术的发展方向，展望了其在煤炭地质中的应用前景。     

SPOT5影像严格几何纠正方法研究

根据SPOT卫星的成像原理和特点,讨论了对SPOT5卫星影像按共线方程法进行有控制点的几何纠正的方法,经对关中、陕北和陕南三种地形的影像纠正结果进行对比检验,证明了该方法精度达到了较高水平。     

图像数据挖掘软件原型系统的设计与开发

提出了图像数据挖掘软件原型系统的设计与开发的试验框架,阐述了其设计与开发方法,介绍了各个模块的功能、设计与开发情况。     

遥控飞艇数字航空摄影系统

提出快速获得航空影像并及时制作DOM、DLG的一种手段.介绍遥控飞艇数字摄影系统的工作原理,探讨存在的问题.     

基于高光谱卫星遥感数据的UPDM分析方法

提出了一种用于处理多高光谱卫星数据的UPDM分析方法。研究结果证明．该方法应用干Landsat／TM(ETM^-)、Terra／MODIS和ADEOS—II／GLI等高光谱卫星传感器时．光谱重构均方根误差小于0．029适用于研究高光谱卫星遥感数据。     

图像平滑算法比较研究及改进策略

图像平滑作为去除图像中含有噪声的图像增强处理技术，是各种与图像有关软件中必不可少的功能模块。文中在分析噪声模型的基础上，对均值平滑、中值平滑、边缘保持平滑等技术从图像处理空间域中的算法原理、实现方法及效率等方面进行了详细比较。针对图像中含有的不同噪声模型，通过多次试验，得出了值得从事图像处理研究者参考的结论。这将有助于提高图像处理软件开发的效率。