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

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

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

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

Benthic communities and their drivers: A spatial analysis off the Antarctic Peninsula

Multiple environmental factors control benthic community patterns, and their relative importance varies with spatial scale. Since this variation is difficult to evaluate quantitatively, extensive sampling across a broad range of spatial scales is required. Here, we present a first case study on Southern Ocean shelf benthos, in which mega-epibenthic communities and biota-environment relationships have been explored at multiple spatial scales. The analyses encompassed 20 seafloor, water-column, and sea-ice parameters, as well as abundances of 18 mega-epibenthic taxa in a total of 2799 high-resolution seabed images taken at 28 stations at 32–786 m depth off the northern Antarctic Peninsula. Based on a priori nesting of sampling stations into ecoregions, subregions, and habitats, analyses indicated most pronounced patchiness levels at finest (within transects among adjacent seabed photos) and largest (among ecoregions) spatial scale considered. Using an alternative approach, explicitly involving the spatial distances between the geo-referenced data, Moran’s Eigenvector mapping (MEM) classified the continuum of spatial scales into four categories: broad (> 60 km), meso (10–60 km), small (2–10 km), and fine (< 2 km). MEM analyses generally indicated an increase in mega-epibenthic community complexity with increasing spatial scale. Moreover, strong relationships between biota and environmental drivers were found at scales of > 2 km. In contrast, few environmental variables contributed to explaining biotic structures at finer scales. These are likely rather determined by nonmeasured environmental variables, as well as biological traits and interactions that are assumed to be most effective at small spatial scales

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

Rethinking the learning space at work and beyond: The achievement of agency across the boundaries of work-related spaces and environments

This paper focuses on the notion of the learning space at work and discusses the extent to which its different configurations allow employees to exercise personal agency within a range of learning spaces. Although the learning space at work is already the subject of extensive research, the continuous development of the learning society and the development of new types of working spaces calls for further research to advance our knowledge and understanding of the ways that individuals exercise agency and learn in the workplace. Research findings suggest that the current perception of workplace learning is strongly related to the notion of the learning space, in which individuals and teams work, learn and develop their skills. The perception of the workplace as a site only for work-specific training is gradually changing, as workplaces are now acknowledged as sites for learning in various configurations, and as contributing to the personal development and social engagement of employees. This paper argues that personal agency is constructed in the workplace, and this process involves active interrelations between agency and three dimensions of the workplace (individual, spatial and organisational), identified through both empirical and theoretical research. The discussion is supported by data from two research projects on workplace learning in the United Kingdom. This paper thus considers how different configurations of the learning space and the boundaries between a range of work-related spaces facilitate the achievement of personal agency