Towards Formulation of a Space-borne System for Early Warning of Floods: Can Cost-Effectiveness Outweigh Prediction Uncertainty?

The three most important components necessary for functioning of an operational flood warning system are: (1) a rainfall measuring system; (2) a soil moisture updating system; and, (3) a surface discharge measuring system. Although surface based networks for these systems can be largely inadequate in many parts of the world, this inadequacy particularly affects the tropics, which are most vulnerable to flooding hazards. Furthermore, the tropical regions comprise developing countries lacking the financial resources for such surface-based monitoring. The heritage of research conducted on evaluating the potential for measuring discharge from space has now morphed into an agenda for a mission dedicated to space-based surface discharge measurements. This mission juxtaposed with two other upcoming space-based missions: (1) for rainfall measurement (Global Precipitation Measurement, GPM), and (2) soil moisture measurement (Hydrosphere State, HYDROS), bears promise for designing a fully space-borne system for early warning of floods. Such a system, if operational, stands to offer tremendous socio-economic benefit to many flood-prone developing nations of the tropical world. However, there are two competing aspects that need careful assessment to justify the viability of such a system: (1) cost-effectiveness due to surface data scarcity; and (2) flood prediction uncertainty due to uncertainty in the remote sensing measurements. This paper presents the flood hazard mitigation opportunities offered by the assimilation of the three proposed space missions within the context of these two competing aspects. The discussion is cast from the perspective of current understanding of the prediction uncertainties associated with space-based flood prediction. A conceptual framework for a fully space-borne system for early-warning of floods is proposed. The need for retrospective validation of such a system on historical data comprising floods and its associated socio-economic impact is stressed. This proposal for a fully space-borne system, if pursued through wide interdisciplinary effort as recommended herein, promises to enhance the utility of the three space missions more than what their individual agenda can be expected to offer.     

Bayesian Spatial Modelling of Gamma Ray Count Data

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.     

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。     

Some Observations on the Use of Data from Historical Documents in Debris-Flow Studies

The knowledge of past events is important for the assessment of debris-flow hazard. Amongst the sources of information, documents from historical archives are particularly important in sites where the debris flows cause damage to urban areas and transportation routes. The paper analyses the availability of historical documents on debris flows in Northeastern Italy and discusses factors that can influence the building of time series from archive data both at regional and single basin scales. An increased number of debris flows was observed in the studied region for the last decades. This could be due both to an increased frequency of the events and to a larger availability of information: the analysis carried out indicates that the latter factor is probably the most influencing. The importance of factors, which affect the collection of data, including the conservation of documents and the presence and fragility of the elements at risk, is stressed in view of a wise use of historical data on debris flows.     

深层地震勘探激发井深探讨

深层地震勘探野外资料采集的关键是在保证原始资料一定信噪比的基础上,同时兼顾分辨率,激发井深是决定资料品质的重要因素之一。通过对虚反射滤波作用分析,提出了充分利用虚反射能量设计最佳激发井深,增强下传地震波能量的采集方法。理论分析和试验资料表明,在高速层下4～7m的最佳激发岩性深度是深层勘探的理想井深。