Monitoring of urban sprawl and densification processes in western Germany in the light of SDG indicator 11.3.1 based on an automated retrospective classification approach

  • By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning.

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Metadaten
Author:Gohar GhazaryanGND, Andreas RienowORCiDGND, Carsten OldenburgGND, Frank ThonfeldORCiDGND, Birte TrampnauGND, Sarah StickselGND, Carsten JürgensORCiDGND
URN:urn:nbn:de:hbz:294-82429
DOI:https://doi.org/10.3390/rs13091694
Parent Title (English):Remote sensing
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Date of Publication (online):2021/07/28
Date of first Publication:2021/04/27
Publishing Institution:Ruhr-Universität Bochum, Universitätsbibliothek
Tag:Landsat time series; SDG 11.3.1; change detection; impervious surface; population change
Volume:13
Issue:9, Article 1694
First Page:1694-1
Last Page:1694-17
Institutes/Facilities:Geographisches Institut
Geographisches Institut, Arbeitsgruppe Geomatik
open_access (DINI-Set):open_access
faculties:Fakultät für Geowissenschaften
Licence (English):License LogoCreative Commons - CC BY 4.0 - Attribution 4.0 International