Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE

Surface roughness over the northern half of the Greenland Ice Sheet from airborne laser altimetry

This is the published version, also available here: http://dx.doi.org/10.1029/2008JF001067.Surface roughness, defined as the standard deviation of small-scale elevation fluctuations from the linear trend over 0.5 km, can be estimated from high-resolution airborne laser altimetry. Here we present results for the northern half of the Greenland Ice Sheet using laser data collected in May 1995. Roughness is smallest in the central region straddling the ice divide, increases in amplitude toward the coast, and appears to be correlated with slope of the ice surface. For most of the study region surface roughness is 8 cm or less (<2.5 cm water equivalent). In smaller regions associated with fast flow, larger values are found. Comparison of the size of small-scale topographic disturbances with the spatial noise estimated from five closely spaced ice cores drilled in northwest Greenland shows good agreement. Similar correspondence was found earlier using nine ice cores from the Summit region. These results indicate that the airborne laser altimeter provides an efficient platform for characterizing the statistical nature of the snow surface over large areas of the polar ice sheets

Management Factors Associated with Operation-Level Prevalence of Antibodies to Cache Valley Virus and Other Bunyamwera Serogroup Viruses in Sheep in the United States

A cross-sectional study was performed to identify operation-level risk factors associated with prevalence of antibody to Bunyamwera (BUN) serogroup viruses in sheep in the United States. Sera were obtained from 5150 sheep in 270 operations located in 22 states (three in the west, nine central states, and 10 in the east) and tested at a dilution of 1:20 by a plaque reduction neutralization test (PRNT) using Cache Valley virus (CVV). Antibodies that neutralized CVV were identified in 1455 (28%) sheep. Animal-level seroprevalence was higher in the east (49%) than the central (17%) and western (10%) states. A convenient subset (n = 509) of sera with antibodies that neutralized CVV was titrated and further analyzed by PRNT using all six BUN serogroup viruses that occur in the United States: CVV, Lokern virus (LOKV), Main Drain virus (MDV), Northway virus (NORV), Potosi virus (POTV), and Tensaw virus (TENV). Antibodies to CVV and LOKV were identified in sheep in all three geographic regions; MDV and POTV activity was detected in the central and eastern states, NORV activity was restricted to the west, and antibodies to TENV were not detected in any sheep. Several management factors were significantly associated with the presence of antibodies to BUN serogroup viruses. For instance, sheep housed during the lambing season inside structures that contained four walls and a roof and a door closed most of the time were more likely to be seropositive than other sheep. In contrast, herded/open-range sheep were less likely to be seropositive than their counterparts. These data can be used by producers to implement strategies to reduce the likelihood of BUN serogroup virus infection and improve the health and management practices of sheep

An adverbial approach for the formal specification of topological constraints involving regions with broad boundaries

Topological integrity constraints control the topological properties of spatial objects and the validity of their topological relationships in spatial databases. These constraints can be specified by using formal languages such as the spatial extension of the Object Constraint Language (OCL). Spatial OCL allows the expression of topological constraints involving crisp spatial objects. However, topological constraints involving spatial objects with vague shapes (e.g., regions with broad boundaries) are not supported by this language. Shape vagueness requires using appropriate topological operators (e.g., strongly Disjoint, fairly Meet) to specify valid relations between these objects; otherwise, the constraints cannot be respected. This paper addresses the problem of the lack of terminology to express topological constraints involving regions with broad boundaries. We propose an extension of Spatial OCL based on a geometric model for objects with vague shapes and an adverbial approach for topological relations between regions with broad boundaries. This extension of Spatial OCL is then tested on an agricultural database

Geospatial information infrastructures

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreflectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeflexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Local spatial regression models : a comparative analysis on soil contamination

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis

Interplay between topography, fog and vegetation in the central South Arabian mountains revealed using a novel Landsat fog detection technique

In the central South Arabian mountains of Yemen and Oman, monsoon fog interception by the endemic cloud forest is essential for ecosystem functions and services. Yet, we know little about the local factors affecting fog distributions and their cumulative effects on vegetation. To examine these relationships, we developed a novel method of high-resolution fog detection using Landsat data, and validated the results using occurrence records of eight moisture-sensitive plant species. Regression tree analysis was then used to examine the topographic factors influencing fog distributions and the topoclimatic factors influencing satellite-derived vegetation greenness. We find that topography affects fog distributions. Specifically, steep windward slopes obstruct the inland movement of fog, resulting in heterogenous fog densities and hotspots of fog interception. We find that fog distributions explain patterns of vegetation greenness, and overall, that greenness increases with fog density. The layer of fog density describes patterns of vegetation greenness more accurately than topographic variables alone, and thus, we propose that regional vegetation patterns more closely follow a fog gradient, than an altitudinal gradient as previously supposed. The layer of fog density will enable an improved understanding of how species and communities, many of which are endemic, range-restricted, and in decline, respond to local variability in topoclimatic conditions