ERROR MANAGEMENT IN DIGITAL ELEVATION MODELLING

The paper aims at the accuracy aspects of Digital Elevation Modelling, compares the most common data acquisition methods, data structures used to store elevation data and the biases that occur in extending the point data to derived information. General use techniques of interpolation will also be discussed, however, the greatest emphasis is given to the methods that are available as a pragmatic response to the wide spectrum of problems of error management

Geographic Information Systems and Spatial Analysis

Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Modelagem e implementação de um banco de dados geográficos para catalogar relatórios de missão de reconhecimento

Trabalho de Conclusão de Curso (especialização)—Universidade de Brasília, Instituto de Geociências, 2011.O presente trabalho apresenta um estudo para a modelagem e implementação de um Banco de Dados Geográficos, utilizando softwares livres, para o cadastramento dos Relatórios de Missão de Reconhecimento – REMIR produzidos pelas Unidades Aéreas de Reconhecimento pertencentes à Força Aérea Brasileira. Faz parte dos objetivos deste projeto o desenvolvimento de interfaces de visualização e de entrada de novos dados. Os softwares utilizados neste trabalho são o PostgreSQL com a extensão espacial Postgis, o Quantum GIS, o Star UML, o Apache e a linguagem PHP

Impacto de las tecnologías disruptivas en la percepción remota: big data, internet de las cosas e inteligencia artificial

Currently, disruptive technologies such as big data (BD), the internet of the things (IoT), artificial intelligence (AI) and cloud computing, have a great impact in the fields such as industry, finance, medicine and agriculture. Remote sensing is not alien to it. Consequently, this article highlights the big data characteristics of the images (volume, variety and velocity); the integration of remote sensors, with nearby and embedded sensors, via the internet, configuring the internet of things in remote perception, and the impact of artificial intelligence, which with its components such as artificial neural networks and intelligent software, allow Analyze images, automatically know their cloud content, and design autonomous spacecraft that send only relevant information to earth. It is concluded that the greatest potential of them is manifested when they act in an integrated manner as in the case of intelligent agriculture.En la actualidad, las tecnologías disruptivas, como big data (BD), internet de las coas (IoT), inteligencia artificial (IA) y computación en la nube, tienen un gran impacto en la industria, las finanzas, la medicina y la agricultura. La percepción remota no es ajena a ellas. En consecuencia, en este artículo se destacan las características de big data de las imágenes (volumen, variedad y velocidad); la integración de los sensores remotos, con los sensores cercanos y embebidos, vía internet, configurando el internet de las cosas en percepción remota, y el impacto de la inteligencia artificial, que con sus componentes como las redes neuronales artificiales y los software inteligentes, permiten analizar las imágenes, conocer de manera automática su contenido de nubes, y diseñar naves espaciales autónomas que envían a la Tierra solo información relevante. Se concluye que el mayor potencial de ellas se manifiesta cuando actúan de manera integrada, como en el caso de la agricultura inteligente

Efficient soil loss assessment for large basins using smart coded polygons

Soil erosion is a severe ecological problem. Most conventional methodologies for soil-erosion assessment are appropriate for small or medium river basins. This paper presents an approach to soil-erosion intensity assessment in large basins, utilizing coded polygons identified by spatially overlapping gradation levels of primary environmental factors. Efficient assessment of soil-erosion intensity is achieved by matching the coded polygons to selected polygons pre-assigned to reference groups. A case study is presented for the soil-erosion assessment of the Yellow River Basin. It is found that the calculated and observed soil-erosion intensities are in close agreement for 86% of the total area. Sensitivity analysis indicates that acceptable results are obtained using a 5% sample of the original 9,921 coded polygons, thus reducing substantially the computational load. Direct comparisons between the polygon codes in the reference and test groups show that uncertainty is reduced with respect to previous methods. This is confirmed by the reduction in information entropy from 7.49 to 1.33. The proposed approach should be of particular use in the cost-effective assessment of soil erosion in large basins.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000338909700005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Environmental SciencesSCI(E)[email protected]; [email protected]

The geographical differences and similarities of radon affected areas in England

The geographical distribution of radon gas is very uneven. The gas occurs naturally in all buildings at concentrations which can vary from below the United Kingdom national average of 20 Bq m(^-3) to more than 2,000 Bq m(^-3). Five counties have been identified by the NRPB as 'Affected Areas' where more than 1% of homes have radon levels in excess of the current Action Level of 200 Bq m(^-3) (Miles et al., 1992). These counties are Cornwall, Devon, Somerset, Derbyshire and Northamptonshire. The level of radon gas in buildings is largely dependent on the underlying geology but geology does not always provide a full answer as to why spatial variations in radon occur. The implication of land capability on indoor radon levels in the five Affected Areas has been assessed using ARC/INFO and in Northamptonshire die influence of social factors (population density, social class and the proportion of households consisting only of pensioners) has been analysed. There are some similarities in the results for the Affected Areas (especially between the counties located in the south-west of die country) as well as some striking differences (for example, the relationship between urban areas and radon levels differs in all the Affected Areas). Results in Somerset and Northamptonshire are strongly influenced by one or more dominant radon category or land capability grade. In general, higher radon levels are associated with poor quality agricultural land and, in Northamptonshire, with high population density at ward level. The areas of Northamptonshire which have above average proportions in social classes I and II (1991 Census) are more likely to be associated with low radon levels (at district level), whereas areas with high proportions of households consisting only of pensioners tend to be associated with areas where more than 10% of homes are above the Action Level (at ward level)

Probabilistic uncertainty in an interoperable framework

This thesis provides an interoperable language for quantifying uncertainty using probability theory. A general introduction to interoperability and uncertainty is given, with particular emphasis on the geospatial domain. Existing interoperable standards used within the geospatial sciences are reviewed, including Geography Markup Language (GML), Observations and Measurements (O&M) and the Web Processing Service (WPS) specifications. The importance of uncertainty in geospatial data is identified and probability theory is examined as a mechanism for quantifying these uncertainties. The Uncertainty Markup Language (UncertML) is presented as a solution to the lack of an interoperable standard for quantifying uncertainty. UncertML is capable of describing uncertainty using statistics, probability distributions or a series of realisations. The capabilities of UncertML are demonstrated through a series of XML examples. This thesis then provides a series of example use cases where UncertML is integrated with existing standards in a variety of applications. The Sensor Observation Service - a service for querying and retrieving sensor-observed data - is extended to provide a standardised method for quantifying the inherent uncertainties in sensor observations. The INTAMAP project demonstrates how UncertML can be used to aid uncertainty propagation using a WPS by allowing UncertML as input and output data. The flexibility of UncertML is demonstrated with an extension to the GML geometry schemas to allow positional uncertainty to be quantified. Further applications and developments of UncertML are discussed

THE DEVELOPMENT OF A HOLISTIC EXPERT SYSTEM FOR INTEGRATED COASTAL ZONE MANAGEMENT

Coastal data and information comprise a massive and complex resource, which is vital to the practice of Integrated Coastal Zone Management (ICZM), an increasingly important application. ICZM is just as complex, but uses the holistic paradigm to deal with the sophistication. The application domain and its resource require a tool of matching characteristics, which is facilitated by the current wide availability of high performance computing. An object-oriented expert system, COAMES, has been constructed to prove this concept. The application of expert systems to ICZM in particular has been flagged as a viable challenge and yet very few have taken it up. COAMES uses the Dempster- Shafer theory of evidence to reason with uncertainty and importantly introduces the power of ignorance and integration to model the holistic approach. In addition, object orientation enables a modular approach, embodied in the inference engine - knowledge base separation. Two case studies have been developed to test COAMES. In both case studies, knowledge has been successfully used to drive data and actions using metadata. Thus a holism of data, information and knowledge has been achieved. Also, a technological holism has been proved through the effective classification of landforms on the rapidly eroding Holderness coast. A holism across disciplines and CZM institutions has been effected by intelligent metadata management of a Fal Estuary dataset. Finally, the differing spatial and temporal scales that the two case studies operate at implicitly demonstrate a holism of scale, though explicit means of managing scale were suggested. In all cases the same knowledge structure was used to effectively manage and disseminate coastal data, information and knowledge