GI_Forum 2023, Volume 11, Issue 2
Nummer:
11
Jahrgang:
2023
Heft:
2
“GI_Forum” publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain. “GI_Forum” implements the policy of open access publication (CC-BY-ND-License) after a double-blind peer review process through a highly international team of established scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions.
Issue 2023-2 includes papers by researchers from a variety of disciplines, most of whom presented their work at the GI_Salzburg 2023 conference (2023 Archive - English - GI_Salzburg (https://gi-salzburg.org/en/2023-archive-english/). Topics range from best-practice analysis and city-administration perspective on how environmental data is sensed and collected in urban areas, to the study of urban growth and highlighting the importance of monitoring and managing urban expansion for sustainable development, to the use of advanced GI-technology to measure "greenness" especially in urban environments, or to detect, map and monitor natural hazards such as avalanches, landslides or lava flows.
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Inhaltsverzeichnis
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Produktdetails
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Autor:innen / Herausgeber:innen
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Schlagworte
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weiterführende Links, Online Edition
Environmental Data Sensing Through Participatory Urbanism. A best-practice Analysis and City-Administration Perspective
The number of easy-to-use artefacts for environmental data collection is rising. Various scientific projects and private initiatives are addressing how to measure urban data in dense sensor networks by applying devices to public infrastructure or by using citizens. The latter is often done to raise both public awareness and that of administrations and governments. Best practices that use or collect environmental parameters and measurements related to the quality of public spaces (e.g., noise, heat, particulate matter) are central. This work presents data points, analysis parameters and a structured overview of best practices in this field. The results show applications of best practices, ranging from permanently mounted sensors mainly driven by municipalities, to participatory urbanism approaches, where users actively collect environmental data. Additionally, the example of the City of Vienna gives an administrative perspective on incorporating co-creation approaches. Supporting factors for cooperation with private initiatives operating participatory sensing projects are shown; challenges arise when urban participation meets administrative structures.
Schlagworte: environmental data collection, participatory urbanism, sustainability, dense sensor networks, quality of public space
Gerfried Mikusch - Andreas Petz - Elisabeth Steiner - Momir Tabakovic - Hilda Tellioglu
Seite 3 - 17 | doi: https://doi.org/10.1553/giscience2023_02_s3
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Detection of Urban Expansion Using the Indices-Based Built-Up Index Derived from Landsat Imagery in Google Earth Engine
Urban expansion, particularly in the global south, has brought numerous environmental consequences which require regular investigation. The current study assessed the growth of built-up areas in the Chbar Ampov district (Phnom Penh, Cambodia) using the Indices-Based Built-Up Index (IBI) derived from Landsat 8 satellite imagery. We applied the IBI method to satellite images from 2013 to 2021 to determine changes in the built-up area over time and their correlation with land surface temperature (LST). The research revealed generally consistent urban expansion (1.68 square kilometres per year) in the study area within the period. Despite minor inconsistencies due to temporary land-use changes and cloud interference, the results support the study's major conclusions. The findings reinforce the necessity of meticulous monitoring and management of urban expansion. Dynamic analysis and practical research methods can contribute to sustainable urban development practices, thus ensuring a balance between human progress and environmental preservation.
Schlagworte: urban expansion, built-up area, change detection, remote sensing
Gulam Mohiuddin - Jan-Peter Mund - Kazi Jahidur Rahaman
Seite 18 - 31 | doi: https://doi.org/10.1553/giscience2023_02_s18
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Airborne LiDAR Enables Measurements of “Greenness” Observed from the Human Perspective
Green spaces and natural environments are essential for quality of life, as they influence health behaviours and outcomes positively. The quantification of “greenness” observable from the human perspective is of great interest, especially in urban environments where built-up areas and infill are expanding. In this paper, we present a method based on airborne laser scanning (ALS) point clouds that makes it possible to simulate a space observed from the human perspective and to quantify the surrounding vegetation. The method can be applied over various spatial scales, in urban, suburban and rural regions. We employed this new method to analyse a set of specific locations in Switzerland that are important for people to recover from everyday stress. The greenness quantification can be used to compare the perceived restorative quality of landscape characteristics with physical landscape qualities. Our approach provides a viable methodological solution for spatial planning and large-scale socio-ecological studies on the influence of natural and green spaces on health and wellbeing, and we recommend that it be applied in the natural landscape and in urban areas.
Schlagworte: GIS, green space, LiDAR, restoration, vegetation
Natalia Kolecka - Christian Ginzler - Maria Garcia Martin - Silvia Tobias
Seite 32 - 46 | doi: https://doi.org/10.1553/giscience2023_02_s32
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Tracing Individual Experiences of Everyday Greenness: Initial Results from the SpaceLog Mobile app
In this paper, we introduce a conceptual shift in how to investigate visual greenness as an important factor for individual wellbeing and self-reported health in everyday mobilities. Making use of controlled vocabularies of emotions, we discuss earlier approaches to data collection in everyday environments. We developed a new mobile diary app to collect multi-modal data that would allow not only for statistical spatial analysis, but also for a deeper analysis of individual sense-making of experiences in specific locations. From a limited first test sample, we evaluated the added value of the data collection process by conducting explorative data analyses of the visual greenness in user-generated imagery. We found that, in general, our method is able to relate different types of greenness observed on site to different emotional tags.
Schlagworte: greenspace, health geography, self-reported health, image analysis, VGI
Dominik Kremer - Andreas Neuner - Sarah Jelinski - Anna Stadlmeier - Christoph Schlieder - Blake Byron Walker
Seite 47 - 58 | doi: https://doi.org/10.1553/giscience2023_02_s47
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Assessing the Applicability of Sentinel-1 SAR Data for Semi-automatic Detection of Snow-avalanche Debris in the Southern Tyrolean Alps
Snow avalanches threaten the safety of people and infrastructure, causing casualties and damage every year. Essential information about spatial distribution and size of avalanches, for mitigation measures, hazard mapping and forecasting, is currently incomplete. Long-term avalanche monitoring over large regions can best be achieved using a space-borne synthetic aperture radar (SAR) system, which offers broad, all-weather coverage, day and night, ensuring data continuity. This study aims to assess the applicability of Sentinel-1 SAR data to semi-automatically detect avalanche debris in the western part of the Italian southern Tyrol, using the snow-rich period of January 2018 as a reference. Utilizing SAR data, avalanche debris was detected by identifying changes in backscatter caused by the rough snow in the avalanche’s run-out zone. Change detection was performed by comparing post- and pre-event Sentinel-1 SAR images and unsupervised object-based classification. 79% of avalanches within the usable portions of the SAR images were correctly detected. Further investigation is required to assess the applicability of the proposed model on a regional scale.
Schlagworte: Sentinel-1 SAR, unsupervised object classification, change detection, avalanche debris
Mattia Sartori - Zahra Dabiri
Seite 59 - 68 | doi: https://doi.org/10.1553/giscience2023_02_s59
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Remote Sensing Methods Combined with Anti-collision Systems to Reduce Avian Collisions with Wind Turbines, Facilitating the Energy Transition
In the transition to renewable energies, the presence of large birds and birds of prey leads to conflicts of interest between nature conservation and achieving energy targets, especially in the construction of wind farms. The birds at risk of collision with wind turbines are frequently protected species, which presents a barrier to gaining approval of suitable areas and to the actual siting of new wind farms. The implementation of innovative anti-collision systems, particularly AI-supported radar or stereoscopic camera systems, could reduce the risk of bird collisions with wind turbines. We present an independently controlled empirical study examining the bird detection rate and species classification accuracy of two technically different remote-sensing sensor types: a stereo-camera system and a radar scanning system. The results support the hypothesis that modern anti-collision systems have the potential to detect, recognize, track and correctly classify bird species. They further underline that anti-collision systems can be effective in reducing avian collisions with turbine blades, particularly if the all-weather radar's multi-object, close to 360⁰ scanning capability is combined with the high accuracy of AI-based image recognition from stereo-camera systems.
Schlagworte: anti-collision system, stereo camera, bird scanning radar, wind turbines, renewable energy transformation
Jan-Peter Mund - Siegfried Rieger
Seite 69 - 87 | doi: https://doi.org/10.1553/giscience2023_02_s69
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Monitoring the Evolution of the Kaiwhata Landslide in New Zealand Using Object-based Image Analysis and Sentinel-2 Time Series
Landslides are among the most serious geological hazards in mountainous and hilly areas of New Zealand, where they frequently cause significant damage and landscape changes. Monitoring the evolution of landslides and their consequences can help to mitigate hazards that could arise in later reactivation phases or in similar cases. The abundance of time-series remote sensing data has facilitated the mapping and monitoring of landslides. By applying object-based image analysis (OBIA) and using Sentinel-2 satellite data from 2017 to 2021, we aim to semi-automatically map the evolution of the Kaiwhata landslide and the subsequent impacts on the upstream area in the Wairarapa region in New Zealand. The OBIA mapping results revealed a gradual increase in the landslide area, with two major changes in June 2019 and November 2020. These major changes were followed by the formation of temporary landslide-dammed upstream lakes along the Kaiwhata river.
Schlagworte: Landslide, Sentinel-2, Object-based Image Analysis (OBIA), landslide-dammed lake, New Zealand
Kiarash Pooladsaz - Daniel Hölbling - Jan Brus
Seite 88 - 101 | doi: https://doi.org/10.1553/giscience2023_02_s88
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Lava Flow Mapping Using Sentinel-1 SAR Data at the Fagradalsfjall Volcano, Iceland
In March 2021, an eruption began in the Fagradalsfjall volcanic system in Iceland after a quiescence of 6,000 years, which also marked the end of more than 800 years of dormancy in the Reykjanes Peninsula. In areas of volcanic activity, lava flow mapping and analysis play a significant role in monitoring and disaster risk management, because lava flows represent potential hazards to surrounding areas. This study used object-based image analysis (OBIA) and Sentinel-1 synthetic aperture radar (SAR) data to map the extent of the Fagradalsfjall lava flow and its evolution. We investigated two images, acquired on 18 May 2021 and 30 September 2021, syn- and post-eruption respectively. The calculated lava inundation area was 2.07 km² for 18 May 2021, and 5.03 km² post-eruption. The results of the application of the OBIA method on Sentinel-1 backscatter data reveal a high potential for lava flow mapping as they show high agreement with existing reference data (accuracies from 79% to 93%). However, the outcomes should be evaluated carefully because factors such as spatial resolution, SAR geometrical distortions and the OBIA segmentation process can influence the classification results.
Schlagworte: lava flow, synthetic aperture radar (SAR), Object-based Image Analysis (OBIA), Sentinel-1, Iceland
Sofia Margarita Delgado Balaguera - Daniel Hölbling - Zahra Dabiri - Gro B. M. Pedersen - Jan Brus
Seite 102 - 110 | doi: https://doi.org/10.1553/giscience2023_02_s102
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Simulating the Effects of Increased Ski-lift Capacities on Waiting Times
Across Europe and the rest of the world, skiing enjoys great popularity. However, it is assumed that the number of ski areas that will remain climatically operable will decrease dramatically in the near future. Accordingly, overcrowding and longer waiting times at ski-lift stations are identified as an economic threat to skiing areas. In this study, we investigate the mitigation potential of increasing the ski-lift capacities in the small Austrian ski resort of Fanningberg. To analyse the relationship between waiting time, number of skiers and ski-lift capacity, we implemented an agent-based simulation model. Results indicate diminishing returns in terms of waiting-time reduction with a further increase of ski-lift capacities.
Schlagworte: applied systems analysis, spatial simulation, lift capacity, waiting time, tourism
Alina Heinrich - Eva Reibersdorfer-Adelsberger - Judith Grubinger-Preiner - Maximilian Mündler - Christian Neuwirth
Seite 111 - 119 | doi: https://doi.org/10.1553/giscience2023_02_s111
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Extending Rural Public Transport Based on Mobility Demand. A Case Study about Planning a Transport-on-demand System in Leogang (State of Salzburg, Austria)
In this project, location-allocation analysis and a spatio-temporal model of mobility demand were integrated into a decision-making tool for extending the public transport network by a transport-on-demand system. The temporal component of the mobility model was based on a survey of mobility behaviour which could be linked to spatial indicators of mobility demand (number of residents per 100m grid cell, a commuter matrix based on 250m grid cells, points of interest, and tourist accommodation). The case study focused on Leogang (Pinzgau, State of Salzburg). Twenty-three potential new locations for transport-on-demand stops were chosen in addition to the 17 existing public transport stops, so that as many demand points as possible would have access to a stop within 500 metres. The proposed stops would provide a public transport connection for the first time to 36.1% of the weighted origin–destination pairs within Leogang and between the municipality and other regions. This approach, which considers the accessibility of both origins and destinations of trips, is a versatile tool for public transport planning and is easily transferable to other municipalities with a similar structure.
Schlagworte: location-allocation, mobility transition, rural mobility
Maximilian Kranabetter
Seite 120 - 134 | doi: https://doi.org/10.1553/giscience2023_02_s120
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Ausgabe:
978-3-7001-9572-6, E-Journal, digital, 18.12.2023
Seitenzahl:
134 Seiten
Sprache:
Englisch
DOI (Link zur Online Edition):