A Multi-Dimensional Characterization of Settlements with Earth Observation Data

• Main Author: Schug, Franz
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01-01-2021
• Subjects: Aerospace engineering; Buildings; Civil engineering; Climate Change; Consumption; Earth; Economics; Emissions; Energy; Engineering; Environmental economics; Infrastructure; International agreements; International relations; Mapping; Metabolism; Morphology; Per capita; Population density; Population growth; Radiation; Remote sensing; Remote sensing systems; Satellites; Sensors; Society; Sustainability; Time series; Urban areas; Urban planning; Urbanization; Vegetation
• ISBN: 9798209784579

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 longlived 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, such as shelter, food, or mobility. Settlement expansion, and the provision of these services has direct and indirect environmental effects.In the future, 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 and smarter response to challenges of global (climate) change. These challenges are also addressed in international frameworks and agreements. Earth Observation is a valuable tool to systematically map settlement structure and derived parameters.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, using Germany as an example.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. Large area multi-class sub-pixel land cover mapping is facilitated using synthetic training data from intra-annual spectral-temporal metrics. Building height can be accurately predicted using optical and radar texture data, making use of shadow and roughness effects. Texture data, providing environmental context, can also be used to reliably map building type information.The second part quantifies key parameters of the socio-economic metabolism, i.e., population and material stock, using previously generated datasets on settlement structure at 10 m resolution. Population mapping largely benefits from the integration of building height, and spatially explicit material stock mapping, unprecedented at this resolution at a national scale, and was only enabled by the encompassing character of previous structure datasets.The third part uses the Landsat data archive to quantify spatial-temporal patterns and dynamics of population and material stock development in Germany since 1985. While a Change-Aftereffect-Trend analysis is well suited to map change processes, challenges remain in the detection of stock demolition and replacement. Strong patterns of development occur along the urban-rural gradient and between former East and West Germany.Findings demonstrate that freely available and globally consistent decameter resolution Earth Observation data and machine learning techniques have great potential to improve the spatially explicit high-resolution understanding of socio-economic variables based on multidimensional settlement mapping in a seamless workflow. This contributes to addressing future challenges of settlement transformation and resource management.