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

Abstract

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.

Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

“…Due to the short imaging times of these satellites, Sentinel 2 Multi Spectral Imager (MSI) satellite multispectral imagery acquired from the Copernicus Open Access Hub (https: //scihub.copernicus.eu/dhus/#/home, accessed on 10 March 2021) provided by European Space Agency (ESA), covers the study area in 2015 and 2020 only (Table 1). Landsat and Sentinel satellite images are very useful in this study because they have similar spectral ranges and are considered one of the most reliable, effective, and publicly available sources for LULC detection and quantification over the years due to their spatial resolution, spectral resolution, synopticity, and the frequency of real-time data acquisition [25]. Ancillary data, such as aerial photographs, were used to identify different areas undergoing LULC changes to validate our results; while demographic data concerning, inter alia, the number of population according to the division into urban units that make up Opole's districts, taking into account the enlarged city boundaries in 2017 to include the areas of neighbouring municipalities, were obtained from the population register resources of the Opole Municipality and the offices of the attached commune offices (Table 1).…”

“…Urbanisation processes are not uniform worldwide [21]; significant population growth due to natural increase, migration towards urban areas and transformation of rural areas into urban centres make urbanisation processes in developing countries more dynamic, which is unfavourable and worrying for sustainable urban development [22]. Other challenges Geosciences 2021, 11, 312 2 of 22 currently concern cities in most regions of Europe, where urban expansion has entered a phase of intensive suburbanisation, manifested by the dynamic development of urban areas in the peripheral urban districts and suburban zones, mainly at the expense of agricultural land and forests, despite the aging and shrinking population and depopulation of city centres [23][24][25], and which could already be observed in previous decades in wealthy countries, especially in North America [26]. In suburban areas, as a result of population influx and increased construction activity, urban sprawl is observed, being one of the forms of urban development, where residential areas are highly dispersed in space and distances to workplaces and major public facilities are relatively distant [27,28].…”

“…On the other hand, the periodicity of their update lasting several years and their relatively low spatial resolution is a certain limitation of their use in the studies of local LULC changes in urban areas [60]. Since geospatial data on LULC changes are not compiled at the local level as regularly and accurately as socio-demographic data, it has become increasingly common to use remote sensing data from publicly available Landsat and Sentinel satellite imagery to quantify urban expansion and model optimal sustainable urbanisation scenarios, based on which an automated retrospective classification of LULC using spectral information is performed [17,25,[61][62][63][64][65][66].…”

Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts

“…In urban remote sensing, many approaches exist to map cities and urban areas (e.g., [2][3][4][5][6][7]), attaining high spatial precision and accuracy. For many applications, the recognition of built-up structures is of major interest (e.g., [8,9]).…”

Contribution of Earth Observation and Geospatial Information for Urban Planning of Historic Cities’ Centres: The Case Study of Nicosia, Cyprus

“…A 2014 review regarding how remotely sensed data have been used in the past to understand the impact of urbanisation on global environmental change is presented in [14]. Urban growth-related imperviousness in Western Germany is presented by [15] following a classification strategy of archive multi-temporal Landsat data. Various spectral indices extracted from more than 90 Landsat images have been studied in regards to the phenomenon of the anthropogenic heat island intensity [16], while spatial urban dynamics at an intercontinental scale are presented in [17].…”