Error in target-based georeferencing and registration in terrestrial laser scanning

Terrestrial laser scanning (TLS) has been used widely for various applications, such as measurement of movement caused by natural hazards and Earth surface processes. In TLS surveying, registration and georeferencing are two essential steps, and their accuracy often determines the usefulness of TLS surveys. So far, evaluation of registration and georeferencing errors has been based on statistics obtained from the data processing software provided by scanner manufacturers. This paper demonstrates that these statistics are incompetent measures of the actual registration and georeferencing errors in TLS data and, thus, should no longer be used in practice. To seek a suitable replacement, an investigation of the spatial pattern and the magnitude of the actual registration and georeferencing errors in TLS data points was undertaken. This led to the development of a quantitative means of estimating the registration- or georeferencing-induced positional error in point clouds. The solutions proposed can aid in the planning of TLS surveys where a minimum accuracy requirement is known, and are of use for subsequent analysis of the uncertainty in TLS datasets

Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement

GIS-based modelling to predict potential habitats for black stork (Ciconia nigra) in Sweden

The black stork (Ciconia nigra L.) was lost from the Swedish fauna in the 1950’s. An increased understanding of the need to save endangered species has led to restoration or preservation of populations through reintroductions. To have background information about a species’ habitat requirements is important for introduction programs. A habitat model can be used to predict the requirements of the species, and provide suggestions for areas suitable for reintroduction. In this study, a Geographical Information System (GIS) is used to create a model to identify suitable habitats for a potential reintroduction project of black stork in Sweden. The geographical extent in the analysis was limited to the former distribution range of black stork in the southern part of Sweden. My results indicate several suitable black stork habitats in all counties included in the analysis, except the Baltic Sea Island of Gotland. Seven counties contained more than 18 % suitable habitats in relation to the total area of each county. I suggest that these areas should be the primary target areas for black stork reintroduction to Sweden.Den svarta storken (Ciconia nigra L.) försvann från den svenska faunan under 1950-talet. En ökad förståelse för behovet av att rädda utrotningshotade arter har lett till återställande eller bevarande av populationer genom återintroduktioner. Att ha bakgrundsinformation om en arts habitatkrav är viktigt för introduktionsprogram. En habitatmodell kan användas för att förutsäga artens krav och ge förslag på områden som är lämpliga för återintroduktion. I denna studie används ett geografiskt informationssystem (GIS) för att skapa en modell som kartlägger lämpliga habitat för en potentiell återintroduktion av svart stork i Sverige. Det geografiska området i analysen begränsades till svarta storkens tidigare utbredningsområde i södra delen av Sverige. Resultatet indikerar på flera lämpliga habitat för svart stork i samtliga län som ingick i analysen, förutom Gotland. Sju län innehöll mer än 18 % lämpliga habitat i förhållande till den totala arealen av varje län. Jag föreslår att dessa områden bör vara de primära målområdena vid en återintroduktion av svart stork till Sverige