Groundwater study using remote sensing and geographic information systems (GIS) in the central highlands of Eritrea

Remote sensing, evaluation of digital elevation models (DEM), geographic information systems (GIS) and fieldwork techniques were combined to study the groundwater conditions in Eritrea. Remote sensing data were interpreted to produce lithological and lineament maps. DEM was used for lineament and geomorphologic mapping. Field studies permitted the study of structures and correlated them with lineament interpretations. Hydrogeological setting of springs and wells were investigated in the field, from well logs and pumping test data. All thematic layers were integrated and analysed in a GIS. Results show that groundwater occurrence is controlled by lithology, structures and landforms. Highest yields occur in basaltic rocks and are due to primary and secondary porosities. High yielding wells and springs are often related to large lineaments, lineament intersections and corresponding structural features. In metamorphic and igneous intrusive rocks with rugged landforms, groundwater occurs mainly in drainage channels with valley fill deposits. Zones of very good groundwater potential are characteristic for basaltic layers overlying lateritized crystalline rocks, flat topography with dense lineaments and structurally controlled drainage channels with valley fill deposits. The overall results demonstrate that the use of remote sensing and GIS provide potentially powerful tools to study groundwater resources and design a suitable exploration plan.The online version of the original article can be found at     

长江口水域多光谱遥感水深反演模型研究

利用Landsat-7 ETM 遥感影像反射率和实测水深值之间的相关性可以探测水深。该文介绍单波段、双波段比值和多波段3种线性回归模型以及动量BP人工神经网络水深反演模型。选择长江口北港河道上段作为研究区,利用上述模型,分两种情况进行水深反演:一是以河道全部历史样本建模;二是将河道按自然水深划分为浅水区和深水区分别建模。结果表明:神经网络模型预测精度高于线性回归模型;水深分区后线性回归和神经网络模型预测误差均有所减小。     

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。     

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。     

不等电极距情况下高密度电阻率法的反演结果分析

通过高密度电阻率法的模型实验,得出不等电极距情况对直立高阻薄板和低阻球体反演结果的影响:缺失电极对它正下方的ρ_s影响最大,而异常形态主要在水平方向有明显的变化,异常体随电极缺失的位置有趋势变化,电极缺失的数目越多,异常形态变化越大,但是从整体来看反演剖面图基本相似。