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

Abstract

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.

Estimating CO2 emissions for 108 000 European cities

“…The lack of even basic data characterizing buildings by height, area, age or material makes it impossible to differentiate buildings as varied as a terrace house block, separated house, mall or hospital. Some novel approaches for characterizing building stocks have recently been proposed (Haberl et al, 2021;Milojevic-Dupont et al, 2020;Peled and Fishman, 2021) which could be used. Developing more accurate town-level models of building emissions may require different modeling approaches, such as utilizing data from national building cadaster registries or from advanced remote sensing datasets such as from synthetic aperture radar satellite constellations, airborne lidar sensors, and machine learning used with mobile airborne or ground cameras.…”

Material flows and GHG emissions from housing stock evolution in US counties, 2020–60

“…Developments in material stock and flow modeling have brought about increasing spatial resolution, particularly in studies that combine MFAs with geographic information system (GIS) data (Haberl et al 2021;Yang et al 2020). Although the spatial resolution of this housing stock model is lower than GIS-based studies, the geographical unit of US counties is still useful for comparing local-scale material in-and outflows and the potential for local material reuse without the need for long-distance transportation.…”

Remote Sensing Analysis of the Changes in Vegetated Coastland: The Case of Southeastern Europe

“…Optical Sentinel time series can be used to derive metrics capturing the variability over time for various parts of the electromagnetic spectrum i.e., visible light, near and shortwave infrared (Haberl et al 2021) for urban areas. Night-time lights from Earth-observing (EO) satellites and OpenStreetMap may help classify buildings and small flat infrastructure (Haberl et al, 2021) e.g., roads from trees and green areas in urban areas. Conditional Random Field (CRF) approach may be used for urban objects like building (Niemeyer et al 2012), Table 1.…”