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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Mapping 20 Years of Urban Expansion in 45 Urban Areas of Sub-Saharan Africa

Remote Sensing ◽

10.3390/rs13030525 ◽

2021 ◽

Vol 13 (3) ◽

pp. 525

Author(s):

Yann Forget ◽

Michal Shimoni ◽

Marius Gilbert ◽

Catherine Linard

Keyword(s):

Case Studies ◽

Urban Areas ◽

Urban Expansion ◽

Local Population ◽

Sub Saharan Africa ◽

Annual Growth Rate ◽

Urban Dynamics ◽

Mapping Approach ◽

Sub Saharan ◽

The Impact

By 2050, half of the net increase in the world’s population is expected to reside in sub-Saharan Africa (SSA), driving high urbanization rates and drastic land cover changes. However, the data-scarce environment of SSA limits our understanding of the urban dynamics in the region. In this context, Earth Observation (EO) is an opportunity to gather accurate and up-to-date spatial information on urban extents. During the last decade, the adoption of open-access policies by major EO programs (CBERS, Landsat, Sentinel) has allowed the production of several global high resolution (10–30 m) maps of human settlements. However, mapping accuracies in SSA are usually lower, limited by the lack of reference datasets to support the training and the validation of the classification models. Here we propose a mapping approach based on multi-sensor satellite imagery (Landsat, Sentinel-1, Envisat, ERS) and volunteered geographic information (OpenStreetMap) to solve the challenges of urban remote sensing in SSA. The proposed mapping approach is assessed in 17 case studies for an average F1-score of 0.93, and applied in 45 urban areas of SSA to produce a dataset of urban expansion from 1995 to 2015. Across the case studies, built-up areas averaged a compound annual growth rate of 5.5% between 1995 and 2015. The comparison with local population dynamics reveals the heterogeneity of urban dynamics in SSA. Overall, population densities in built-up areas are decreasing. However, the impact of population growth on urban expansion differs depending on the size of the urban area and its income class.

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Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017

Remote Sensing ◽

10.3390/rs12162615 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2615

Author(s):

Jie Zhang ◽

Le Yu ◽

Xuecao Li ◽

Chenchen Zhang ◽

Tiezhu Shi ◽

...

Keyword(s):

Growth Rate ◽

Hong Kong ◽

Urban Areas ◽

Urban Expansion ◽

Driving Factors ◽

Annual Growth ◽

Annual Growth Rate ◽

Spatial And Temporal Scales ◽

Urban Dynamics ◽

Bay Area

The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the largest bay areas in the world. However, the spatiotemporal characteristics and driving mechanisms of urban expansions in this region are poorly understood. Here we used the annual remote sensing images, Geographic Information System (GIS) techniques, and geographical detector method to characterize the spatiotemporal patterns of urban expansion in the GBA and investigate their driving factors during 1986–2017 on regional and city scales. The results showed that: the GBA experienced an unprecedented urban expansion over the past 32 years. The total urban area expanded from 652.74 km2 to 8137.09 km2 from 1986 to 2017 (approximately 13 times). The annual growth rate during 1986–2017 was 8.20% and the annual growth rate from 1986 to 1990 was the highest (16.89%). Guangzhou, Foshan, Dongguan, and Shenzhen experienced the highest urban expansion rate, with the annual increase of urban areas in 51.51, 45.54, 36.76, and 23.26 km2 y−1, respectively, during 1986–2017. Gross Domestic Product (GDP), income, road length, and population were the most important driving factors of the urban expansions in the GBA. We also found the driving factors of the urban expansions varied with spatial and temporal scales, suggesting the general understanding from the regional level may not reveal detailed urban dynamics. Detailed urban management and planning policies should be made considering the spatial and internal heterogeneity. These findings can enhance the comprehensive understanding of this bay area and help policymakers to promote sustainable development in the future.

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Rank-size Distribution of Cities and Municipalities in Bangladesh

Sustainability ◽

10.3390/su12114643 ◽

2020 ◽

Vol 12 (11) ◽

pp. 4643

Author(s):

Pankaj Bajracharya ◽

Salima Sultana

Keyword(s):

Size Distribution ◽

Urban Areas ◽

Urban Expansion ◽

Population Data ◽

Google Earth ◽

Zipf’S Law ◽

Zipf's Law ◽

Spatial Inequalities ◽

Planning Framework ◽

Shed Light

This paper examines and updates the rank-size distribution of cities and municipalities in Bangladesh between 1990 and 2019 based on two criteria: (1) built-up urban areas; and (2) population. The distribution of built-up urban areas and population are compared to provide a robust theoretical underpinning of Zipf’s law for future urban developmental planning framework. The data on built-up urban areas is extracted from land cover classification using Google Earth Engine and the population data is obtained from the decennial censuses. The comparison of the conformity to Zipf’s law indicated contradictory results. While a greater proportion of the population has been increasingly concentrated in the smaller and midsized cities over the last three decades, built-up urban areas, on the other hand, have been mostly clustered in two largest cities— Dhaka and Chittagong—accounting for 50 to nearly 60 percent of the total built-up urban areas. These results shed light on the magnitude of continued spatial inequalities in urban development amongst cities and municipalities in Bangladesh despite there being an overall increase of evenness in the distribution of population over time. These results imply an unsustainable rate of urban expansion in Bangladesh and reinforce the need for the exploration of policies and regulations targeted at guiding the rate and direction of evenness in urban expansion.

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Using building types and demographic data to improve our understanding and use of urban sprawl simulation

Proceedings of the ICA ◽

10.5194/ica-proc-2-28-2019 ◽

2019 ◽

Vol 2 ◽

pp. 1-8

Author(s):

Mojtaba Eslahi ◽

Rani El Meouche ◽

Anne Ruas

Keyword(s):

Cellular Automata ◽

Urban Growth ◽

Urban Areas ◽

Geographical Information Systems ◽

Dynamic Models ◽

Urban Expansion ◽

Demographic Data ◽

Population Data ◽

Geographical Information ◽

Sleuth Model

Abstract. Many studies, using various modeling approaches and simulation tools have been made in the field of urban growth. A multitude of models, with common or specific features, has been developed to reconstruct the spatial occupation and changes in land use. However, today most of urban growth techniques just use the historical geographic data such as urban, road and excluded maps to simulate the prospective urban maps. In this paper, adding buildings and population data as urban fabric factors, we define different urban growth simulation scenarios. Each simulation corresponds to policies that are more or less restrictive of space considering what these territories can accommodate as a type of building and as a global population.Among the urban growth modeling techniques, dynamic models, those based on Cellular Automata (CA) are the most common for their applications in urban areas. CA can be integrated with Geographical Information Systems (GIS) to have a high spatial resolution model with computational efficiency. The SLEUTH model is one of the cellular automata models, which match the dynamic simulation of urban expansion and could be adapted to morphological model of the urban configuration and fabric.Using the SLEUTH model, this paper provides different simulations that correspond to different land priorities and constraints. We used common data (such as topographic, buildings and demography data) to improve the realism of each simulation and their adequacy with the real world. The findings allow having different images of the city of tomorrow to choose and reflect on urban policies.

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Spatio-Temporal Evaluation of Urban Growth of Zuru Metropolis, Nigeria

Nigerian Journal of Environmental Sciences and Technology - March 2018 ◽

10.36263/nijest.2021.02.0293 ◽

2021 ◽

Vol 5 (2) ◽

pp. 456-467

Author(s):

Y.A. Maleeks ◽

A.O. Aliyu ◽

A. Bala ◽

A.U. Isiaka ◽

K.Z. Atta

Keyword(s):

Population Growth ◽

Urban Area ◽

Urban Growth ◽

Water Body ◽

Anthropogenic Activities ◽

Landsat Imagery ◽

Urban Expansion ◽

Population Increase ◽

Urban Dynamics ◽

Spatio Temporal

The pattern of development in a city is mostly governed by urban dynamics, with population increase being the primary driving force. Built-up cover is the most important predictor of urban expansion. Zuru metropolis in Kebbi State has witnessed remarkable developmental activities caused by human influences such as buildings, road constructions, and population growth for over decades. Urban growth was ascertained for a period of 30 years through the analysis of Landsat imagery of 1988, 1998, 2008 and 2018. The datasets were classified into five (5) land covers, namely, built-up, water body, rocky surface, vegetation, and others. Quantitative assessment of the urban growth was ascertained by computing post-classification LC dynamics and Land Consumption Rate/Land Absorption Coefficient (LCR/LAC). The results showed that the built-up cover (urban area) conspicuously increased with area of 693.35 ha, 728.74 ha, 5210.5 ha and 6845.75 ha respectively for the period of study (1988 – 2018). The increment in built-up area was indicative of population growth from 1988 to 2018. The study revealed that between 1988 to 2018 showed that built-up increased by 11.78%, while rocky surface and water body have shrunk by 16.44% and 0.02% respectively, which can be attributed to anthropogenic activities in which rocky surface and waterbody have been transformed into built-up cover. It further revealed that the urban area experienced crowdedness in the years 2008 and 2018 respectively due to high LCR values of 2.71% compared to LCR values of 0.0714% and 0.0558% in 1988 and 1998. Land transformation into urban area and spread of the population to the outskirts of the study area was prominent between 1998 and 2008 due to high LAC value of 0.0998. The study concluded that there was transformation of rocky surface and waterbody into urban area, which was caused by population growth, human and agricultural activities in Zuru metropolis.

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Quantifying trends and predictors of decline in eastern grey kangaroo (Macropus giganteus) populations in a rapidly urbanising landscape

Pacific Conservation Biology ◽

10.1071/pc17034 ◽

2018 ◽

Vol 24 (1) ◽

pp. 63 ◽

Cited By ~ 5

Author(s):

Elizabeth A. Brunton ◽

Sanjeev K. Srivastava ◽

David S. Schoeman ◽

Scott Burnett

Keyword(s):

Population Growth ◽

Urban Areas ◽

Human Population ◽

Urban Expansion ◽

Natural Habitat ◽

Urban Landscapes ◽

Human Population Growth ◽

Terrestrial Mammals ◽

Grey Kangaroo ◽

The Impact

Human population growth and the resultant expansion of urban landscapes are drivers of biodiversity loss globally. Impacts of urbanisation on wildlife are not well understood, although the importance of preserving biodiversity in urban areas is widely recognised. The eastern grey kangaroo (Macropus giganteus), a common species of large macropod, can be found in high densities in many urban landscapes across Australia. South East Queensland is a subtropical region of Australia that has experienced high rates of urban expansion. Human population growth in the region has resulted in widespread changes to the landscape and much of the eastern grey kangaroo’s natural habitat has been modified. Declines in kangaroo populations have been anecdotally reported; however, the impact of urbanisation on kangaroo populations has not been quantified. This study used a modelling approach, collecting data from the community, and private and government organisations to: (1) map the current distribution of eastern grey kangaroos; (2) quantify trends in kangaroo abundance; and (3) identify anthropogenic drivers of changes in kangaroo abundance in the region. Of the kangaroo populations identified, 42% were reported to have undergone an overall decline in abundance since 2000. Higher human population growth rate and smaller area remaining under natural land use were predictors of kangaroo population declines. Further kangaroo declines can be anticipated in the region, particularly in areas with projected human population growth rates over 80% for the next decade. This study emphasises the importance of integrated urban development over large spatial extents to mitigate impacts of urbanisation on terrestrial mammals.

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Using Earth Observation for Monitoring SDG 11.3.1-Ratio of Land Consumption Rate to Population Growth Rate in Mainland China

Remote Sensing ◽

10.3390/rs12030357 ◽

2020 ◽

Vol 12 (3) ◽

pp. 357 ◽

Cited By ~ 4

Author(s):

Yunchen Wang ◽

Chunlin Huang ◽

Yaya Feng ◽

Minyan Zhao ◽

Juan Gu

Keyword(s):

Growth Rate ◽

Sustainable Development ◽

Population Growth ◽

Urban Areas ◽

Population Growth Rate ◽

Consumption Rate ◽

Urban Expansion ◽

Mainland China ◽

Earth Observation ◽

Land Consumption

Urban sustainable development has attracted widespread attention worldwide as it is closely linked with human survival. However, the growth of urban areas is frequently disproportionate in relation to population growth in developing countries; this discrepancy cannot be monitored solely using statistics. In this study, we integrated earth observation (EO) and statistical data monitoring the Sustainable Development Goals (SDG) 11.3.1: “The ratio of land consumption rate to the population growth rate (LCRPGR)”. Using the EO data (including China’s Land-Use/Cover Datasets (CLUDs) and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data) and census, we extracted the percentage of built-up area, disaggregated the population using the geographically weighted regression (GWR) model, and depicted the spatial heterogeneity and dynamic tendency of urban expansion and population growth by a 1 km × 1 km grid at city and national levels in mainland China from 1990 to 2010. Then, the built-up area and population density datasets were compared with other products and statistics using the relative error and standard deviation in our research area. Major findings are as follows: (1) more than 95% of cities experienced growth in urban built-up areas, especially in the megacities with populations of 5–10 million; (2) the number of grids with a declined proportion of the population ranged from 47% in 1990–2000 to 54% in 2000–2010; (3) China’s LCRPGR value increased from 1.69 in 1990–2000 to 1.78 in 2000–2010, and the land consumption rate was 1.8 times higher than the population growth rate from 1990 to 2010; and (4) the number of cities experiencing uncoordinated development (i.e., where urban expansion is not synchronized with population growth) increased from 93 (27%) in 1990–2000 to 186 (54%) in 2000–2010. Using EO has the potential for monitoring the official SDGs on large and fine scales; the processes provide an example of the localization of SDG 11.3.1 in China.

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Integrated Mapping of Spatial Urban Dynamics—A European-Chinese Exploration. Part 1—Methodology for Automatic Land Cover Classification Tailored towards Spatial Allocation of Ecosystem Services Features

Remote Sensing ◽

10.3390/rs13091744 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1744

Author(s):

Ellen Banzhaf ◽

Wanben Wu ◽

Xiangyu Luo ◽

Julius Knopp

Keyword(s):

Ecosystem Services ◽

Land Cover ◽

Urban Expansion ◽

Sample Selection ◽

Mapping Method ◽

Google Earth ◽

Classification Model ◽

Observation Data ◽

Urban Dynamics ◽

Earth Observation Data

Urbanisation processes inherently influence land cover (LC) and have dramatic impacts on the amount, distribution and quality of vegetation cover. The latter are the source of ecosystem services (ES) on which humans depend. However, the temporal and thematical dimensions are not documented in a comparable manner across Europe and China. Three cities in China and three cities in Europe were selected as case study areas to gain a picture of spatial urban dynamics at intercontinental scale. First, we analysed available global and continental thematic LC products as a data pool for sample selection and referencing our own mapping model. With the help of the Google Earth Engine (GEE) platform and earth observation data, an automatic LC mapping method tailored for more detailed ES features was proposed. To do so, differentiated LC categories were quantified. In order to obtain a balance between efficiency and high classification accuracy, we developed an optimal classification model by evaluating the importance of a large number of spectral, texture-based indices and topographical information. The overall classification accuracies range between 73% and 95% for different time slots and cities. To capture ES related LC categories in great detail, deciduous and coniferous forests, cropland, grassland and bare land were effectively identified. To understand inner urban options for potential new ES, dense and dispersed built-up areas were differentiated with good results. In addition, this study focuses on the differences in the characteristics of urban expansion witnessed in China and Europe. Our results reveal that urbanisation has been more intense in the three Chinese cities than in the three European cities, with an 84% increase in the entire built-up area over the last two decades. However, our results also show the results of China’s ecological restoration policies, with a total of 963 km2 of new green and blue LC created in the last two decades. We proved that our automatic mapping can be effectively applied to future studies, and the monitoring results will be useful for consecutive ES analyses aimed at achieving more environmentally friendly cities.

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Cities on the Coast and Patterns of Movement between Population Growth and Diffusion

Entropy ◽

10.3390/e23081041 ◽

2021 ◽

Vol 23 (8) ◽

pp. 1041

Author(s):

Dmitry V. Kovalevsky ◽

Dimitri Volchenkov ◽

Jürgen Scheffran

Keyword(s):

Climate Change ◽

Population Growth ◽

Urban Areas ◽

Climate Adaptation ◽

Maximum Entropy Principle ◽

Coastal Hazards ◽

Coastal Zones ◽

Demographic Development ◽

Climate Services ◽

Urban Dynamics

Sea level rise and high-impact coastal hazards due to on-going and projected climate change dramatically affect many coastal urban areas worldwide, including those with the highest urbanization growth rates. To develop tailored coastal climate services that can inform decision makers on climate adaptation in coastal cities, a better understanding and modeling of multifaceted urban dynamics is important. We develop a coastal urban model family, where the population growth and urbanization rates are modeled in the framework of diffusion over the half-bounded and bounded domains, and apply the maximum entropy principle to the latter case. Population density distributions are derived analytically whenever possible. Steady-state wave solutions balancing the width of inhabited coastal zones, with the skewed distributions maximizing population entropy, might be responsible for the coastward migrations outstripping the demographic development of the hinterland. With appropriate modifications of boundary conditions, the developed family of diffusion models can describe coastal urban dynamics affected by climate change.

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Dynamics and forecasting of population growth and urban expansion in Srinagar City – A Geospatial Approach

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-709-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 709-716 ◽

Cited By ~ 6

Author(s):

M. Farooq ◽

M. Muslim

Keyword(s):

Population Growth ◽

Urban Sprawl ◽

Urban Growth ◽

Urban Areas ◽

Geographical Information Systems ◽

Urban Expansion ◽

Geographical Information ◽

Spatial And Temporal Variability ◽

Jammu And Kashmir ◽

Srinagar City

The urban areas of developing countries are densely populated and need the use of sophisticated monitoring systems, such as remote sensing and geographical information systems (GIS). The urban sprawl of a city is best understood by studying the dynamics of LULC change which can be easily generated by using sequential satellite images, required for the prediction of urban growth. Multivariate statistical techniques and regression models have been used to establish the relationship between the urban growth and its causative factors and for forecast of the population growth and urban expansion. In Srinagar city, one of the fastest growing metropolitan cities situated in Jammu and Kashmir State of India, sprawl is taking its toll on the natural resources at an alarming pace. The present study was carried over a period of 40 years (1971–2011), to understand the dynamics of spatial and temporal variability of urban sprawl. The results reveal that built-up area has increased by 585.08 % while as the population has increased by 214.75 %. The forecast showed an increase of 246.84 km2 in built-up area which exceeds the overall carrying capacity of the city. The most common conversions were also evaluated.

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