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Faculty of Mathematics and Natural Sciences - Earth Observation Lab

Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society (MAT_STOCKS)

Sustainability transformations imply fundamental changes in the societal use of biophysical resources. Current socioeconomic metabolism research traces flows of energy, materials or substances to capture resource use, but socio-metabolic research has not yet incorporated material stocks or related services. The ERC Advanced Grant project “Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society (MAT_STOCKS)” addresses this gap. It will create a comprehensive material stocks and services database as well as maps of material stocks from remote-sensing data.

MAT_STOCKS thereby aims at identifying barriers and leverage points for future sustainability transformations and the SDGs. The Geomatics Lab at HU Berlin contributes the remote sensing and geoinformation component to MAT_STOCKS. The project focuses on quantifying anthropogenic material stocks from remote sensing data (mostly Sentinel-1/2; night time lights; potentially future GEDI data) and from globally available databases (e.g. OpenStreetMap, population databases). In the remote sensing related research, we will create wall-to-wall maps with a focus on anthropogenic land cover as proxies for material stocks. We target ca. 12 national-scale, wall-to-wall studies, ranging from small (e.g. Austria and UK) to large countries (e.g. India, China and USA).

Processing will be performed in a high performance computing environment to allow handling the massive data processing required to create such novel information. Human-made infrastructures and the degree of imperviousness will be mapped from time series of opti¬cal S2 data and S1 SAR data using machine learning or – where available – extracted from existing da¬ta sets (e.g. European Copernicus High Resolution Layers, SEDAC databases) – or from both. The methodology will be developed for selected test areas and then deployed for all national cases. Results are wall-to-wall, fine-scale layers of built-up area. Spectral-temporal metrics from combined optical and SAR data and fractional cover maps will build the basis for the subsequent infrastructure functional types mapping. Functional types mapping will be performed based on training data from available national databases and freely available datasets. As spectral and spatial features related to building infrastructures vary widely with socio-cultural contexts, results will be regionally fine-tuned. The mapped material stocks will be aggregated to typologies that allow estimating material stocks in national to global material and energy flow analyses, e.g. based on a Self-Organizing Maps approach.



ERC Advanced Grant (grant no. 741950)

Project members

Partner institution(s)

Institute for Social Ecology, Vienna, Austria