Search

Change detection is a well-established process of detaining spatial and temporal changes of entities between two or more timesteps. Current advancements in digital map processing offer vast new sources of multitemporal geodata. As the temporal aspect gains complexity, the dismantling of detected changes on a pixel-based scale becomes a costly undertaking. In efforts to establish and preserve the evolution of detected changes in long time series, this paper presents a method that allows the decomposition of pixel evolution vectors into three dimensions of change, described as directed change, change variability, and change magnitude. The three dimensions of change compile to complex change analytics per individual pixels and offer a multi-faceted analysis of landscape changes on an ordinal scale. Finally, the integration of class confidence from learned uncertainty estimates illustrates the avenue to include uncertainty into the here presented change analytics, and the three dimensions of change are visualized in complex change maps.

Digital map processing techniques have enabled new computational methods to extract geographic features from scanned map sources. These are mainly historical maps and are rich in spatiotemporal information which can be derived by descriptive metrics. River channel width is such a typical ecological metric used to calculate properties such as water discharge rates and, generally, often applied in hydrological and ecological monitoring. Measures of river channel width in previous ecological work have successfully measured width based on in situ measurements or remote sensing efforts, but poorly captured the variability in river width due to simplifications of the river network or due to sparse availability of in situ measurements. As river width can greatly vary and change over space and time, capturing its variability with modern geospatial processing techniques is a key interest in interdisciplinary fields studying spatiotemporal changes of riverine properties. This article proposes HydroWidth, a small-scale approach to measure river width continuously along a channel, capable of capturing the variability in width in simple as well as complex river structures without the use of river masks. The small-scale approach is compared to two conventional vector-based discrete river width measurement techniques to test the robustness of the methods in terms of measuring river width and its variability. Furthermore, this article evaluates the adaptability of HydroWidth by performing three experiments measuring river width variability at rivers of different structure, length, and complexity. The methods are applied on rivers of historical map sources, allowing a novel perspective of applying algorithms measuring river width on non-airborne data sources. Lastly, the computational performance results of the conventional and HydroWidth methods at each experimental site are presented and discussed.

The legacy of historic anthropogenic disturbance can significantly affect the structure and function of contemporary freshwater ecosystems. Environmental research and management that neglect anthropogenic legacy are likely to lead to a biased interpretation of present and future ecosystem dynamics. Yet, anthropogenic legacy remains poorly considered, mainly because of the challenges associated with its identification. Synthesizing past progress in legacy research, we present a conceptual framework for the systematic identification of anthropogenic legacy. We focus on the dynamic processes occurring during legacy formation (e.g., disturbance regime, ecosystem trajectories). Based on the review of relevant case studies, we discuss the historical and contemporary sources of information (e.g., communication, cartographic, paleoenvironmental sources) that can be employed for legacy identification. Finally, we provide practical examples of anthropogenic legacy identification in real‐world freshwater ecosystems. Produced in multidisciplinary collaboration, this review presents a comprehensive approach to anthropogenic legacy to foster its informed and systematic consideration in freshwater research and management. This article is categorized under: Science of Water > Water and Environmental Change Water and Life > Stresses and Pressures on Ecosystems

Finally, nature risks like biodiversity and carbon footprints have been recognised as important business risks. Beyond immediate physical risks, transition risks include scenarios with disruptive consequences. Sustainable finance regulation calls for a transparent reporting of these risks. However, the full integration into the corporate risk framework, company valuations, and portfolio management needs a consistent quantitative treatment with discriminating precision down to company level. The crucial point is the objective of enabling allocation decisions between two competing companies from the same industry. This paper proposes a bottom-up scenario-based aggregation of company impacts measured by geospatial analysis rather than a top-down breakdown from global exposures. We overlay geospatial ESG variables mainly from global remote sensing products with the spatial footprints of asset-level data. In contrast to other approaches, the footprints of local operations are translated into a financial loss quantity for each single operational site before they are aggregated across the company. As the scenarios can be defined consistent across companies, statistically justified portfolio risk measures can be evaluated that enable portfolio risk management by a selection of individual investments and credit decisions rather than an exclusion of whole industry sectors. We illustrate our approach for the case of biodiversity risks and choose the mining sector as there is significant scientific literature on its environmental impacts and on remote sensing to detect these impacts.

Change detection is a well-established process of detaining spatial and temporal changes of entities between two or more timesteps. Current advancements in digital map processing offer vast new sources of multitemporal geodata. As the temporal aspect gains complexity, the dismantling of detected changes on a pixel-based scale becomes a costly undertaking. In efforts to establish and preserve the evolution of detected changes in long time series, this paper presents a method that allows the decomposition of pixel evolution vectors into three dimensions of change, described as directed change, change variability, and change magnitude. The three dimensions of change compile to complex change analytics per individual pixels and offer a multi-faceted analysis of landscape changes on an ordinal scale. Finally, the integration of class confidence from learned uncertainty estimates illustrates the avenue to include uncertainty into the here presented change analytics, and the three dimensions of change are visualized in complex change maps.

This article focuses on defining hydromorphological features to be extracted from historical maps by means of digital map processing techniques. The hydromorphological features, evolving through time, can be described quantitatively by the development and application of various ecological metrics to study the spatiotemporal change of the natural and built freshwater environment. With the goal to support future revitalization efforts, this article first reviews the theory on quantifying spatiotemporal change using landscape and ecological metrics, ranging from simple shape metrics (e.g., shoreline length) to more complex hydromorphological indexes (e.g., river braiding index). Second, the hydromorphological features themselves are important to consider in terms of data quality and uncertainty as they might inherit errors due to the low-quality maps, the extraction process, or due to poor definitions used during feature extraction efforts. Errors introduced by poorly defined features can be avoided by the use of a well-structured definition system. Thus, the article concludes in a new concept categorizing hydromorphological features and the changes they can undergo. The definition framework integrates novel perspectives for defining and evaluating features from the Siegfried and old Swiss national map, including key aspects from the theory.

Die Bodenseeregion gehört zu einer der ältesten Kulturlandschaften Europas. Ihre regionale kulturelle Identität trägt zum Image sowie Identifikation seitens der Bevölkerung mit der Bodenseeregion bei. Dennoch mangelte es bislang an einer ganzheitlichen, die gesamte Bodenseeregion umfassende Betrachtung der Frage, was die kulturelle Identität der Bodenseeregion ausmacht. Das IBH-Projekt Cultmap4.0 untersuchte daher erstmals aus einer räumlichen Perspektive die Wechselwirkung zwischen regionaler Identität, Kultur und Mobilität. Unter Einbindung von Stakeholdern wie der regionalen Bevölkerung, Unternehmen, Touristinnen und Touristen sowie Akteuren aus der Regionalplanung entstanden sogenannte «Cultural Mappings» zu den Themen Kulturelle Ressourcen, Gesellschaft und Teilhabe, Wirtschaft, Arbeit und Regionalplanung sowie Tourismus und Mobilität. Das Projekt knüpft an das junge Forschungsfeld des «Cultural Mapping» an, und erweitert dieses durch die Einbindung webbasierter Methoden zur Datenerfassung und –präsentation zu «Cultural Mapping 4.0». Als Ergebnisse des Projektziels «Cultural Mapping als Methode» konnten flexible digitale Werkzeuge entwickelt werden, die sowohl in Strassenumfragen und Workshop aber auch über online Umfragen das räumliche Wissen von Stakeholdern über die kulturelle Identität der Bodenseeregion zu erfassen vermögen (z. B. die räumliche Ausdehnung des Heimatverständnisses von Einheimischen, Hotspots des Kulturkonsums von Touristinnen). Die «Cultural Mappings als Resultat» präsentieren die Ergebnisse durch Storymaps – interaktive Webinhalte aus Texten, Karten und weiteren Medien. Die entstandene Plattform Cultmap4.0 ist öffentlich zugänglich und kann künftig von den Stakeholdern als Informations- und Planungstool sowie fürs Standortmarketing genutzt werden. So schärft Cultmap4.0 langfristig ein Bewusstsein für die kulturelle Identität sowie kulturelle Ressourcen der Bodenseeregion und konnte durch die breite Einbindung relevanter Stakeholder zur Ver

Dieses Papier präsentiert erste Erkenntnisse aus dem GEOBIM-Projekt. Ziel war es zu untersuchen und aufzuzeigen, inwieweit es möglich ist, die Optimierung der Nachhaltigkeit von Bauprojekten durch den Einsatz von BIM- und GIS-Daten zu unterstützen. Die Erkenntnisse werden genutzt, um eine neue Methode zu entwickeln, die eine systematische Analyse und Nutzung vorhandener BIM- und GIS-Daten zur Optimierung der Nachhaltigkeit von Bauprojekten ermöglicht. Eine der wichtigsten Erkenntnisse aus der Kombination bestehender BIM- und GIS-Daten ist, dass eine Vielzahl von Nachhaltigkeitsaspekten einfacher visualisiert und verstanden werden konnten. So können Bauherren frühzeitig prüfen und kontrollieren, ob das geplante Gebäude wirklich ihre spezifischen Bedürfnisse unterstützt und Nachhaltigkeits- und andere Nutzeranforderungen erfüllt. Die Ergebnisse veranschaulichen die Nutzung von BIM- und GIS-Daten zur Optimierung und Visualisierung der Nachhaltigkeit von Gebäuden im Rahmen von Zertifizierungsprozessen, die die digitale Planung langfristig wertvoll machen. Um eine multifaktorielle Optimierung der Nachhaltigkeit von Immobilien zu erreichen, bedarf es auch eines hohen Verständnisses für die Komplexität in den planungsbegleitenden Prozessen. Das Projekt zeigt konkret, wie eine effiziente und nachhaltige Kombination der beiden Methoden im Rahmen von Lean Planning and Construction Management möglich ist. Dies ist von hoher Relevanz und zukünftig zwingend erforderlich, um durch den intelligenten Einsatz dieser neuen Methoden und die daraus resultierenden Synergien die volkswirtschaftlichen Kosten einer Nachhaltigkeitszertifizierung deutlich reduzieren zu können.

Modeling complex real-world variables, such as tick hazard, can face the problem that no measurements are available for the target variable itself. Models developed on the basis of indirect measurements may then lead to an underconstrained problem for which the output cannot be reliably validated. To address such a problem in the tick hazard use case, we propose a novel oracle approach. The goal of the oracle is to generate test scenarios that can be used to test the validity and robustness of our tick hazard model. We report on preliminary results that support the potential of both the model and the oracle approach.

Change detection is a well-established process of detaining spatial and temporal changes of entities between two or more timesteps. Current advancements in digital map processing offer vast new sources of multitemporal geodata. As the temporal aspect gains complexity, the dismantling of detected changes on a pixel-based scale becomes a costly undertaking. In efforts to establish and preserve the evolution of detected changes in long time series, this paper presents a method that allows the decomposition of pixel evolution vectors into three dimensions of change, described as directed change, change variability, and change magnitude. The three dimensions of change compile to complex change analytics per individual pixels and offer a multi-faceted analysis of landscape changes on an ordinal scale. Finally, the integration of class confidence from learned uncertainty estimates illustrates the avenue to include uncertainty into the here presented change analytics, and the three dimensions of change are visualized in complex change maps

Digital map processing techniques have enabled new computational methods to extract geographic features from scanned map sources. These are mainly historical maps and are rich in spatiotemporal information which can be derived by descriptive metrics. River channel width is such a typical ecological metric used to calculate properties such as water discharge rates and, generally, often applied in hydrological and ecological monitoring. Measures of river channel width in previous ecological work have successfully measured width based on in situ measurements or remote sensing efforts, but poorly captured the variability in river width due to simplifications of the river network or due to sparse availability of in situ measurements. As river width can greatly vary and change over space and time, capturing its variability with modern geospatial processing techniques is a key interest in interdisciplinary fields studying spatiotemporal changes of riverine properties. This article proposes HydroWidth, a small‐scale approach to measure river width continuously along a channel, capable of capturing the variability in width in simple as well as complex river structures without the use of river masks. The small‐scale approach is compared to two conventional vector‐based discrete river width measurement techniques to test the robustness of the methods in terms of measuring river width and its variability. Furthermore, this article evaluates the adaptability of HydroWidth by performing three experiments measuring river width variability at rivers of different structure, length, and complexity. The methods are applied on rivers of historical map sources, allowing a novel perspective of applying algorithms measuring river width on non‐airborne data sources. Lastly, the computational performance results of the conventional and HydroWidth methods at each experimental site are presented and discussed.