High-resolution mapping of 33 years of material stock and population growth in Germany using Earth Observation data

Global societal material stock in buildings and infrastructure have accumulated rapidly within the last decades, along with population growth. Recently, an approach for nation-wide mapping of material stock at 10 m spatial resolution, using freely available and globally consistent Earth Observation (EO) imagery, has been introduced as an alternative to cost-intensive cadastral data or broad-scale but thematically limited nighttime light-based mapping. This study assessed the potential of EO data archives to create spatially explicit time series data of material stock dynamics and their relation to population in Germany, at a spatial resolution of 30 m. We used Landsat imagery with a change-aftereffect-trend analysis to derive yearly masks of land surface change from 1985 onward. Those served as an input to an annual reverse calculation of six material stock types and building volume-based annual gridded population, based on maps for 2018. Material stocks and population in Germany grew by 13% and 4%, respectively, showing highly variable spatial patterns. We found a minimum building stock of ca. 180 t/cap across all municipalities and growth processes characterized by sprawl. A rapid growth of stocks per capita occurred in East Germany after the reunification in 1990, with increased building activity but population decline. Possible over- or underestimations of stock growth cannot be ruled out due to methodological assumptions, requiring further research.Peer Reviewe

High-resolution data and maps of material stock, population, and employment in Austria from 1985 to 2018

The article processing charge was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491192747 and the Open Access Publication Fund of Humboldt-Universität zu Berlin.High-resolution maps of material stocks in buildings and infrastructures are of key importance for studies of societal resource use (social metabolism, circular economy, secondary resource potentials) as well as for transport studies and land system science. So far, such maps were only available for specific years but not in time series. Even for single years, data covering entire countries with high resolution, or using remote-sensing data are rare. Instead, they often have local extent (e.g., [1]), are lower resolution (e.g., [2]), or are based on other geospatial data (e.g., [3]). We here present data on the material stocks in three types of buildings (commercial and industrial, single- and multifamily houses) and three types of infrastructures (roads, railways, other infrastructures) for a 33-year time series for Austria at a spatial resolution of 30 m. The article also presents data on population and employment in Austria for the same time period, at the same spatial resolution. Data were derived with the same method applied in a recent study for Germany [4].Peer Reviewe

The global wildland–urban interface

The wildland–urban interface (WUI) is where buildings and wildland vegetation meet or intermingle1,2. It is where human–environmental conflicts and risks can be concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation and the spread of zoonotic diseases3. However, a global analysis of the WUI has been lacking. Here, we present a global map of the 2020 WUI at 10 m resolution using a globally consistent and validated approach based on remote sensing-derived datasets of building area4 and wildland vegetation5. We show that the WUI is a global phenomenon, identify many previously undocumented WUI hotspots and highlight the wide range of population density, land cover types and biomass levels in different parts of the global WUI. The WUI covers only 4.7% of the land surface but is home to nearly half its population (3.5 billion). The WUI is especially widespread in Europe (15% of the land area) and the temperate broadleaf and mixed forests biome (18%). Of all people living near 2003–2020 wildfires (0.4 billion), two thirds have their home in the WUI, most of them in Africa (150 million). Given that wildfire activity is predicted to increase because of climate change in many regions6, there is a need to understand housing growth and vegetation patterns as drivers of WUI change

The sodium iodide symporter (NIS) as theranostic gene: its emerging role in new imaging modalities and non-viral gene therapy

Cloning of the sodium iodide symporter (NIS) in 1996 has provided an opportunity to use NIS as a powerful theranostic transgene. Novel gene therapy strategies rely on image-guided selective NIS gene transfer in non-thyroidal tumors followed by application of therapeutic radionuclides. This review highlights the remarkable progress during the last two decades in the development of the NIS gene therapy concept using selective non-viral gene delivery vehicles including synthetic polyplexes and genetically engineered mesenchymal stem cells. In addition, NIS is a sensitive reporter gene and can be monitored by high resolution PET imaging using the radiotracers sodium [ 124 I]iodide ([ 124 I]NaI) or [ 18 F]tetrafluoroborate ([ 18 F]TFB). We performed a small preclinical PET imaging study comparing sodium [ 124 I]iodide and in-house synthesized [ 18 F]TFB in an orthotopic NIS-expressing glioblastoma model. The results demonstrated an improved image quality using [ 18 F]TFB. Building upon these results, we will be able to expand the NIS gene therapy approach using non-viral gene delivery vehicles to target orthotopic tumor models with low volume disease, such as glioblastoma

A multi-dimensional characterization of settlements with Earth Observation data

Einhergehend mit schnellem Bevölkerungs- und Wirtschaftswachstum erlebt die Welt innerhalb der letzten Jahrzehnte eine schnelle Akkumulation langlebiger Ressourcen in Gebäuden und Infrastruktur, auch gesellschaftlicher Materialbestand genannt. Im 21. Jahrhundert wird die Fortsetzung dieser Entwicklung zur großen Herausforderung für den sozioökonomischen Stoffwechsel der Erde und zum Erreichen biophysikalischer Grenzen führen. Siedlungen sind von besonderem Interesse, da Menschen dort Nachfrage nach Leistungen wie Nahrung oder Mobilität generieren und mit ihnen interagieren. Zukünftig wird neben einer globalen Entwicklungsperspektive auf Materialbestände und Bevölkerung auch ein räumlich explizites, hochauflösendes Verständnis lokaler Muster und Prozesse von Relevanz für eine datenbasierte Antwort auf Herausforderungen des globalen Wandels sein. Diese Arbeit präsentiert einen Workflow zur Kartierung und Quantifizierung von Materialbeständen und Bevölkerungsverteilung und -dynamik mittels hochaufgelöster mehrdimensionaler Siedlungskartierung mit Multisensor-Erdbeobachtungsdaten auf nationaler Ebene. Der erste Abschnitt demonstriert das Potenzial der Verwendung von Sentinel-1 und -2 Zeitreihendaten mit Methoden des maschinellen Lernens für die Kartierung von Siedlungsstrukturen, d.h. Subpixel-Landbedeckung, Gebäudehöhe und Gebäudetyp. Der zweite Abschnitt quantifiziert Schlüsselparameter des sozioökonomischen Metabolismus, d. h. Bevölkerung und Materialbestand, anhand zuvor generierter Datensätze zur Siedlungsstruktur. Der dritte Abschnitt nutzt das Landsat-Datenarchiv und Zeitreihenanalyse, um räumliche Muster und Dynamiken von Bevölkerung und Materialbeständen in Deutschland seit 1985 zu quantifizieren. Frei verfügbare und global konsistente Erdbeobachtungsdaten und Techniken des maschinellen Lernens haben großes Potenzial, das räumlich explizite hochaufgelöste Verständnis sozioökologischer Variablen basierend auf mehrdimensionaler Siedlungskartierung zu verbessern.During the recent decades of the Anthropocene, the world has experienced rapid growth of population and economic activity. This went along with a considerable accumulation of long-lived resources, for example in buildings and infrastructure, i.e., societal material stock. In the 21st century, a continuation of this development will be a major challenge to the Earth’s socio-economic metabolism, as some limitations of the Earth’s biophysical basis might be reached. Settlements are of particular interest, because they are the places where people generate demand for, and interact with services. Both an overarching perspective on the global long-term development of material stock and population as well as a spatially explicit, high-resolution understanding of local patterns and processes will be of particular relevance for a more data-informed response to challenges of global change. This dissertation presents a workflow to map and quantify material stocks and population distribution and dynamics by means of multi-dimensional settlement mapping with decameter resolution multi-source Earth Observation data on a national scale. The first part demonstrates the potential of using Sentinel-1 and -2 time series imagery with machine learning regression and classification for settlement structure mapping, including sub-pixel land cover, building height and building type mapping. The second part quantifies key parameters of the socio-economic metabolism, i.e., population and material stock, using previously generated datasets on settlement structure. The third part uses the Landsat data archive and Change-Aftereffect-Trend analysis to quantify spatial-temporal patterns and dynamics of population and material stock development in Germany since 1985. Findings demonstrate that freely available and globally consistent Earth Observation data and machine learning techniques have great potential to improve the spatially explicit high-resolution understanding of socio-metabolic variables based on multi-dimensional settlement mapping in a seamless workflow

Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates.

Gridded population data is widely used to map fine scale population patterns and dynamics to understand associated human-environmental processes for global change research, disaster risk assessment and other domains. This study mapped gridded population across Germany using weighting layers from building density, building height (both from previous studies) and building type datasets, all created from freely available, temporally and globally consistent Copernicus Sentinel-1 and Sentinel-2 data. We first produced and validated a nation-wide dataset of predominant residential and non-residential building types. We then examined the impact of different weighting layers from density, type and height on top-down dasymetric mapping quality across scales. We finally performed a nation-wide bottom-up population estimate based on the three datasets. We found that integrating building types into dasymetric mapping is helpful at fine scale, as population is not redistributed to non-residential areas. Building density improved the overall quality of population estimates at all scales compared to using a binary building layer. Most importantly, we found that the combined use of density and height, i.e. volume, considerably increased mapping quality in general and with regard to regional discrepancy by largely eliminating systematic underestimation in dense agglomerations and overestimation in rural areas. We also found that building density, type and volume, together with living floor area per capita, are suitable to produce accurate large-area bottom-up population estimates

Three-dimensional building and mobility infrastructure of the CONUS

<p>Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks.</p><p>This dataset features intermediate mapping results for estimating material stocks in the CONUS (see related identifiers) on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), Microsoft building footprints, NLCD Impervious data, and crowd-sourced geodata (OSM). These data may also be useful on their own.</p><p><strong>Provided layers @10m resolution</strong><br>- Building height<br>- Building type<br>- Building area<br>- Impervious fraction<br>- street, and rail area<br>- Building and street climate zones<br>- County zones<br>- State masks<br>- EQUI7 correction factors</p><p><strong>Spatial extent</strong><br>This dataset covers the whole CONUS. </p><p><strong>Temporal extent</strong><br>The maps are representative for ca. 2018.</p><p><strong>Data format</strong><br>The data are organized in 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p><p><strong>Further information</strong><br>For further information, please see the main publication.<br>A web-visualization of the resulting dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/us-stocks/">here</a>.<br>Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p><p><strong>Publication</strong><br>D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. <i>Nature Communications</i> <strong>14</strong>, 8014. <a href="https://doi.org/10.1038/s41467-023-43755-5">https://doi.org/10.1038/s41467-023-43755-5</a></p><p><strong>Funding</strong><br>This research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p><p><strong>Acknowledgments</strong><br>We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.</p&gt

Land cover fraction map of Austria at 10m spatial resolution based on Sentinel-1 and Sentinel-2 spectral temporal metrics

The increasing impact of humans on land and ongoing global population growth requires an improved understanding of land cover (LC) processes in general and of those related to settlements in particular. The heterogeneity of settlements and landscapes as well as the importance of not only mapping, but also characterizing anthropogenic and landscape structures suggests using a sub-pixel mapping approach for analysing related LC from space. This map has been created using a regression-based unmixing approach for mapping built-up surfaces and infrastructure, woody vegetation and non-woody vegetation for all of Austria at 10 m spatial resolution. Spectral-temporal metrics from all Sentinel-1 and Sentinel-2 observation in 2018 have been used to create synthetically mixed training data for regression. An elevation threshold of 1350m has been applied above which built-up surfaces and infrastructures were masked out. The mapping workflow has been established in the corresponding publication. This dataset is an enhanced dataset that uses an alternative set of spectral-temporal metrics for land cover modeling, including: - 25th, 50th and 75th quantile of Sentinel-2 reflectance - Average Sentinel-1 VH polarized backscatter - 90th quantile and standard deviation of Sentinel-2 Tasseled Cap Greenness This enhanced set makes use of Sentinel-1 imagery, which reduces confusion of built-up features and seasonal soil-covered surfaces. Sentinel-2 Tasseled Cap Greenness is a more robust indicator for vegetation in temperate regions than the NDVI, which was used in the corresponding publication. The file is of GeoTiff format and contains three bands: Band 1 - Fraction of built-up surfaces and infrastructure Band 2 - Fraction of woody vegetation Band 3 - Fraction of non-woody vegetation For further information, please see the publication or contact Franz Schug ([email protected]). Sentinel-1 data was kindly provided by TU Vienna (https://www.geo.tuwien.ac.at/) through EODC (https://www.eodc.eu/). This research has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 741950)

Land cover fraction map of Germany at 10m spatial resolution based on Sentinel-1 and Sentinel-2 spectral temporal metrics

The increasing impact of humans on land and ongoing global population growth requires an improved understanding of land cover (LC) processes in general and of those related to settlements in particular. The heterogeneity of settlements and landscapes as well as the importance of not only mapping, but also characterizing anthropogenic and landscape structures suggests using a sub-pixel mapping approach for analysing related LC from space. This map has been created using a regression-based unmixing approach for mapping built-up surfaces and infrastructure, woody vegetation and non-woody vegetation for all of Germany at 10 m spatial resolution. Spectral-temporal metrics from all Sentinel-1 and Sentinel-2 observation in 2018 have been used to create synthetically mixed training data for regression. An elevation threshold of 1350m has been applied above which built-up surfaces and infrastructures were masked out. The mapping workflow has been established in the corresponding publication. This dataset is an enhanced dataset that uses an alternative set of spectral-temporal metrics for land cover modeling, including: - 25th, 50th and 75th quantile of Sentinel-2 reflectance - Average Sentinel-1 VH polarized backscatter - 90th quantile and standard deviation of Sentinel-2 Tasseled Cap Greenness This enhanced set makes use of Sentinel-1 imagery, which reduces confusion of built-up features and seasonal soil-covered surfaces. Sentinel-2 Tasseled Cap Greenness is a more robust indicator for vegetation in temperate regions than the NDVI, which was used in the corresponding publication. The file is of GeoTiff format and contains three bands: Band 1 - Fraction of built-up surfaces and infrastructure Band 2 - Fraction of woody vegetation Band 3 - Fraction of non-woody vegetation For further information, please see the publication or contact Franz Schug ([email protected]). Sentinel-1 data was kindly provided by TU Vienna (https://www.geo.tuwien.ac.at/) through EODC (https://www.eodc.eu/). This research has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 741950)