Unveiling patterns in human dominated landscapes through mapping the mass of US built structures.

Built structures increasingly dominate the Earth's landscapes; their surging mass is currently overtaking global biomass. We here assess built structures in the conterminous US by quantifying the mass of 14 stock-building materials in eight building types and nine types of mobility infrastructures. Our high-resolution maps reveal that built structures have become 2.6 times heavier than all plant biomass across the country and that most inhabited areas are mass-dominated by buildings or infrastructure. We analyze determinants of the material intensity and show that densely built settlements have substantially lower per-capita material stocks, while highest intensities are found in sparsely populated regions due to ubiquitous infrastructures. Out-migration aggravates already high intensities in rural areas as people leave while built structures remain - highlighting that quantifying the distribution of built-up mass at high resolution is an essential contribution to understanding the biophysical basis of societies, and to inform strategies to design more resource-efficient settlements and a sustainable circular economy.

Rising wildfire risk to houses in the United States, especially in grasslands and shrublands.

Wildfire risks to homes are increasing, especially in the wildland-urban interface (WUI), where wildland vegetation and houses are in close proximity. Notably, we found that more houses are exposed to and destroyed by grassland and shrubland fires than by forest fires in the United States. Destruction was more likely in forest fires, but they burned less WUI. The number of houses within wildfire perimeters has doubled since the 1990s because of both housing growth (47% of additionally exposed houses) and more burned area (53%). Most exposed houses were in the WUI, which grew substantially during the 2010s (2.6 million new WUI houses), albeit not as rapidly as before. Any WUI growth increases wildfire risk to houses though, and more fires increase the risk to existing WUI houses.

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.

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

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].

National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series.

Urban areas and their vertical characteristics have a manifold and far-reaching impact on our environment. However, openly accessible information at high spatial resolution is still missing at large for complete countries or regions. In this study, we combined Sentinel-1A/B and Sentinel-2A/B time series to map building heights for entire Germany on a 10 m grid resolving built-up structures in rural and urban contexts. We utilized information from the spectral/polarization, temporal and spatial dimensions by combining band-wise temporal aggregation statistics with morphological metrics. We trained machine learning regression models with highly accurate building height information from several 3D building models. The novelty of this method lies in the very fine resolution yet large spatial extent to which it can be applied, as well as in the use of building shadows in optical imagery. Results indicate that both radar-only and optical-only models can be used to predict building height, but the synergistic combination of both data sources leads to superior results. When testing the model against independent datasets, very consistent performance was achieved (frequency-weighted RMSE of 2.9 m to 3.5 m), which suggests that the prediction of the most frequently occurring buildings was robust. The average building height varies considerably across Germany with lower buildings in Eastern and South-Eastern Germany and taller ones along the highly urbanized areas in Western Germany. We emphasize the straightforward applicability of this approach on the national scale. It mostly relies on freely available satellite imagery and open source software, which potentially permit frequent update cycles and cost-effective mapping that may be relevant for a plethora of different applications, e.g. physical analysis of structural features or mapping society's resource usage.

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.

High-Resolution Maps of Material Stocks in Buildings and Infrastructures in Austria and Germany.

The dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Two main types of data are currently used to map stocks, night-time lights (NTL) from Earth-observing (EO) satellites and cadastral information. We present an alternative approach for broad-scale material stock mapping based on freely available high-resolution EO imagery and OpenStreetMap data. Maps of built-up surface area, building height, and building types were derived from optical Sentinel-2 and radar Sentinel-1 satellite data to map patterns of material stocks for Austria and Germany. Using material intensity factors, we calculated the mass of different types of buildings and infrastructures, distinguishing eight types of materials, at 10 m spatial resolution. The total mass of buildings and infrastructures in 2018 amounted to ∼5 Gt in Austria and ∼38 Gt in Germany (AT: ∼540 t/cap, DE: ∼450 t/cap). Cross-checks with independent data sources at various scales suggested that the method may yield more complete results than other data sources but could not rule out possible overestimations. The method yields thematic differentiations not possible with NTL, avoids the use of costly cadastral data, and is suitable for mapping larger areas and tracing trends over time.

Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data.

The increasing impact of humans on land and ongoing global population growth requires an improved understanding of land cover (LC) and land use (LU) processes related to settlements. The heterogeneity of built-up areas and infrastructures as well as the importance of not only mapping, but also characterizing anthropogenic structures suggests using a sub-pixel mapping approach for analysing related LC from space. We implement a regression-based unmixing approach for mapping built-up surfaces and infrastructure, woody vegetation and non-woody vegetation for all of Germany and Austria at 10 m resolution to demonstrate the potential of sub-pixel mapping. We map LC fractions for one point in time, using all available Sentinel-2 data from 2017 and 2018 (<70% cloud cover). We combine the concept of synthetically mixed training data with statistical aggregations from spectral-temporal metrics (STM) derived from Sentinel-2 reflectance time series. We specifically examine how STM can be used for creating synthetically mixed training data. STM are known to facilitate large area mapping by being largely independent of image acquisition dates and inherently incorporate phenological information. Vegetation is an important part of settlements and time series information supports its mapping. Synthetically mixed training data facilitates a streamlined training by using pure reference spectra to generate artificial mixtures as input to regression modelling of LC fractions in mixed pixels. We here show how combining both offers great potential for wall-to-wall LC fraction mapping. We further investigate the positive effect of STM on map results by comparing the performance of different subsets of STM combinations. Our results indicate that many STM combinations containing spectral variability and vegetation indices provide suitable input to creating synthetic training data for regression-based fraction mapping. Results for built-up surfaces and infrastructure (MAE 0.13/RMSE 0.18 at 20 m resolution), woody vegetation (0.18, 0.22) and non-woody vegetation (0.14, 0.19) are highly consistent across Germany and Austria. Only a few surface types were not accurately predicted in our nation-wide mapping. Further research is required to optimize mapping of temporally invariant bare soil and rock surfaces that show spectral similarity to built-up surfaces and infrastructure. The proposed methodology combines benefits of both regression-based modelling with synthetically mixed training data and STM, and thus facilitates mapping of LC fractions on a national scale and at high resolution. Such information will allow to better characterize settlements and identifying processes such as densification that are best represented by continuous LC mapping.