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This review analyses peer-reviewed scientific publications and policy documents that use built-up density, population density and settlement typology spatial grids from the Global Human Settlement Layer (GHSL) project to quantify human presence and processes for sustainability. Such open and free grids provide detailed time series spanning 1975–2015 developed with consistent approaches. Improving our knowledge of cities and settlements by measuring their size extent, as well as the societal processes occurring within settlements, is key to understanding their impact on the local, regional and global environment for addressing global sustainability and the integrity of planet Earth. The reviewed papers are grouped around five main topics: Quantifying human presence; assessing settlement growth over time; estimating societal impact, assessing natural hazard risk and impact, and generating indicators for international framework agreements and policy documents. This review calls for continuing to refine and expand the work on societal variables that, when combined with essential variables including those for climate, biodiversity and ocean, can improve our understanding of the societal impact on the biosphere and help to monitor progress towards local, regional and planetary sustainability.
Daniele Ehrlich; Sergio Freire; Michele Melchiorri; Thomas Kemper. Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications. Sustainability 2021, 13, 7851 .
AMA StyleDaniele Ehrlich, Sergio Freire, Michele Melchiorri, Thomas Kemper. Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications. Sustainability. 2021; 13 (14):7851.
Chicago/Turabian StyleDaniele Ehrlich; Sergio Freire; Michele Melchiorri; Thomas Kemper. 2021. "Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications." Sustainability 13, no. 14: 7851.
Between 1970 and 2015 urban population almost doubled worldwide with the fastest growth taking place in developing regions. To aid the understanding of how urbanisation has influenced anthropogenic CO2 and air pollutant emissions across all world regions, we make use of the latest developments of the Emissions Database for Global Atmospheric Research. In this study, we systematically analyse over 5 decades of emissions from different types of human settlements (from urban centres to rural areas) for different sectors in all countries. Our analysis shows that by 2015, urban centres were the source of a third of global anthropogenic greenhouse gases and most of the air pollutant emissions. The high levels of both population and emissions in urban centres therefore call for focused urban mitigation efforts. Moreover, despite the overall increase in urban emissions, megacities with more than 10 million inhabitants in high-income countries have been reducing their emissions, while emissions in developing regions are still growing. We further discuss per capita emissions to compare different types of urban centres at the global level.
Monica Crippa; Diego Guizzardi; Enrico Pisoni; Efisio Solazzo; Antoine Guion; Marilena Muntean; Aneta Florczyk; Marcello Schiavina; Michele Melchiorri; Andres Fuentes Hutfilter. Global anthropogenic emissions in urban areas: patterns, trends, and challenges. Environmental Research Letters 2021, 16, 074033 .
AMA StyleMonica Crippa, Diego Guizzardi, Enrico Pisoni, Efisio Solazzo, Antoine Guion, Marilena Muntean, Aneta Florczyk, Marcello Schiavina, Michele Melchiorri, Andres Fuentes Hutfilter. Global anthropogenic emissions in urban areas: patterns, trends, and challenges. Environmental Research Letters. 2021; 16 (7):074033.
Chicago/Turabian StyleMonica Crippa; Diego Guizzardi; Enrico Pisoni; Efisio Solazzo; Antoine Guion; Marilena Muntean; Aneta Florczyk; Marcello Schiavina; Michele Melchiorri; Andres Fuentes Hutfilter. 2021. "Global anthropogenic emissions in urban areas: patterns, trends, and challenges." Environmental Research Letters 16, no. 7: 074033.
This study assesses the global mountain population, population change over the 1975–2015 time-range, and urbanisation for 2015. The work uses the World Conservation Monitoring Centre (WCMC) definition of mountain areas combined with that of mountain range outlines generated by the Global Mountain Biodiversity Assessment (GMBA). We estimated population change from the Global Human Settlement Layer Population spatial grids, a set of population density layers used to measure human presence and urbanisation on planet Earth. We show that the global mountain population has increased from over 550 million in 1975 to over 1050 million in 2015. The population is concentrated in mountain ranges at low latitudes. The most populated mountain ranges are also the most urbanised and those that grow most. Urbanisation in mountains (66%) is lower than that of lowlands (78%). However, 34% of the population in mountains live in cities, 31% in towns and semi-dense areas, and 35% in rural areas. The urbanisation rate varies considerably across ranges. The assessments of population total, population trends, and urbanisation may be used to address the issue “not to leave mountain people behind” in the sustainable development process and to understand trajectories of change.
Daniele Ehrlich; Michele Melchiorri; Claudia Capitani. Population Trends and Urbanisation in Mountain Ranges of the World. Land 2021, 10, 255 .
AMA StyleDaniele Ehrlich, Michele Melchiorri, Claudia Capitani. Population Trends and Urbanisation in Mountain Ranges of the World. Land. 2021; 10 (3):255.
Chicago/Turabian StyleDaniele Ehrlich; Michele Melchiorri; Claudia Capitani. 2021. "Population Trends and Urbanisation in Mountain Ranges of the World." Land 10, no. 3: 255.
Sustainable Development Goal (SDG) 11 aspires to “Make cities and human settlements inclusive, safe, resilient and sustainable”, and the introduction of an explicit urban goal testifies to the importance of urbanisation. The understanding of the process of urbanisation and the capacity to monitor the SDGs require a wealth of open, reliable, locally yet globally comparable data, and a fully-fledged data revolution. In this framework, the European Commission–Joint Research Centre has developed a suite of (open and free) data and tools named Global Human Settlement Layer (GHSL) which maps the human presence on Earth (built-up areas, population distribution and settlement typologies) between 1975 and 2015. The GHSL supplies information on the progressive expansion of built-up areas on Earth and population dynamics in human settlements, with both sources of information serving as baseline data to quantify land use efficiency (LUE), listed as a Tier II indicator for SDG 11 (11.3.1). In this paper, we present the profile of the LUE across several territorial scales between 1990 and 2015, highlighting diverse development trajectories and the land take efficiency of different human settlements. Our results show that (i) the GHSL framework allows us to estimate LUE for the entire planet at several territorial scales, opening the opportunity of lifting the LUE indicator from its Tier II classification; (ii) the current formulation of the LUE is substantially subject to path dependency; and (iii) it requires additional spatially-explicit metrics for its interpretation. We propose the Achieved Population Density in Expansion Areas and the Marginal Land Consumption per New Inhabitant metrics for this purpose. The study is planetary and multi-temporal in coverage, demonstrating the value of well-designed, open and free, fine-scale geospatial information on human settlements in supporting policy and monitoring progress made towards meeting the SDGs.
Marcello Schiavina; Michele Melchiorri; Christina Corbane; Aneta Florczyk; Sergio Freire; Martino Pesaresi; Thomas Kemper. Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data. Sustainability 2019, 11, 5674 .
AMA StyleMarcello Schiavina, Michele Melchiorri, Christina Corbane, Aneta Florczyk, Sergio Freire, Martino Pesaresi, Thomas Kemper. Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data. Sustainability. 2019; 11 (20):5674.
Chicago/Turabian StyleMarcello Schiavina; Michele Melchiorri; Christina Corbane; Aneta Florczyk; Sergio Freire; Martino Pesaresi; Thomas Kemper. 2019. "Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data." Sustainability 11, no. 20: 5674.
This paper presents the analysis of Earth Observation data records collected between 1975 and 2014 for assessing the extent and temporal evolution of the built-up surface in the frame of the Global Human Settlement Layer project. The scale of the information produced by the study enables the assessment of the whole continuum of human settlements from rural hamlets to megacities. The study applies enhanced processing methods as compared to the first production of the GHSL baseline data. The major improvements include the use of a more refined learning set on built-up areas derived from Sentinel-1 data which allowed testing the added-value of incremental learning in big data analytics. Herein, the new features of the GHSL built-up grids and the methods are described and compared with the previous ones using a reference set of building footprints for 277 areas of interest. The results show a gradual improvement in the accuracy measures with a gain of 3.6% in the balanced accuracy, between the first production of the GHSL baseline and the latest GHSL multitemporal built-up grids. A validation of the multitemporal component is also conducted at the global scale establishing the reliability of the built-up layer across time.
Christina Corbane; Martino Pesaresi; Thomas Kemper; Panagiotis Politis; Aneta J. Florczyk; Vasileios Syrris; Michele Melchiorri; Filip Sabo; Pierre Soille. Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data 2019, 3, 140 -169.
AMA StyleChristina Corbane, Martino Pesaresi, Thomas Kemper, Panagiotis Politis, Aneta J. Florczyk, Vasileios Syrris, Michele Melchiorri, Filip Sabo, Pierre Soille. Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data. 2019; 3 (2):140-169.
Chicago/Turabian StyleChristina Corbane; Martino Pesaresi; Thomas Kemper; Panagiotis Politis; Aneta J. Florczyk; Vasileios Syrris; Michele Melchiorri; Filip Sabo; Pierre Soille. 2019. "Automated global delineation of human settlements from 40 years of Landsat satellite data archives." Big Earth Data 3, no. 2: 140-169.
The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of resident people. Both of the factors are observed in the long-term temporal domain and per unit area, in order to support the analysis of the trends and indicators for monitoring the implementation of the 2030 Development Agenda and the related thematic agreements. The GHSL uses various input data, including global, multi-temporal archives of high-resolution satellite imagery, census data, and volunteered geographic information. In this paper, we present a global estimate for the Land Use Efficiency (LUE) indicator—SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015. In addition, we analyze the characteristics of the GHSL information to demonstrate how the original frameworks of data (gridded GHSL data) and tools (GHSL tools suite), developed from Earth Observation and integrated with census information, could support Sustainable Development Goals monitoring. In particular, we demonstrate the potential of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for Sustainable Development Goal 11. The results of our research demonstrate that there is potential to raise SDG 11.3.1 from a Tier II classification (manifesting unavailability of data) to a Tier I, as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.
Michele Melchiorri; Martino Pesaresi; Aneta J. Florczyk; Christina Corbane; Thomas Kemper. Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1. ISPRS International Journal of Geo-Information 2019, 8, 96 .
AMA StyleMichele Melchiorri, Martino Pesaresi, Aneta J. Florczyk, Christina Corbane, Thomas Kemper. Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1. ISPRS International Journal of Geo-Information. 2019; 8 (2):96.
Chicago/Turabian StyleMichele Melchiorri; Martino Pesaresi; Aneta J. Florczyk; Christina Corbane; Thomas Kemper. 2019. "Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1." ISPRS International Journal of Geo-Information 8, no. 2: 96.
Geo-information on settlements from Earth Observation offers a base for objective and scalable monitoring of the evolution of cities and settlements, including their location, extent and other attributes. In this work, we deploy the best available global knowledge on the presence of human settlements and built-up structures derived from Earth Observation to advance the understanding of the human presence on Earth. We start from a concept of Generalised Settlement Area to identify the Earth surface within which any built-up structure is present. We further characterise the resulted map by using an agreement map among the state of the art of remote sensing products mapping built-up areas or other strictly related semantic abstractions as urban areas or artificial surfaces. The agreement map is formed by a grid of 1 km2, where each cell is classified according to the number of EO-derived products reporting any positive occurrence of the abstractions related to the presence of built-up structures. The paper describes the characteristics of the Generalised Settlement Area, the differences in the agreement map across geographic regions of the world, and outlines the implications for potential users of the EO-derived products used in this study.
A. J. Florczyk; M. Melchiorri; J. Zeidler; C. Corbane; M. Schiavina; S. Freire; F. Sabo; P. Politis; T. Esch; M. Pesaresi. The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas. International Journal of Digital Earth 2019, 13, 45 -60.
AMA StyleA. J. Florczyk, M. Melchiorri, J. Zeidler, C. Corbane, M. Schiavina, S. Freire, F. Sabo, P. Politis, T. Esch, M. Pesaresi. The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas. International Journal of Digital Earth. 2019; 13 (1):45-60.
Chicago/Turabian StyleA. J. Florczyk; M. Melchiorri; J. Zeidler; C. Corbane; M. Schiavina; S. Freire; F. Sabo; P. Politis; T. Esch; M. Pesaresi. 2019. "The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas." International Journal of Digital Earth 13, no. 1: 45-60.
The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on the planet based mainly on two quantitative factors: i) the spatial distribution (density) of built-up structures and ii) the spatial distribution (density) of resident people. Both factors are observed in the long-term temporal domain and per uniform surface units in order to support trends and indicators for monitoring the implementation of international framework agreements. The GHSL uses various input data including global, multi-temporal archives of fine-scale satellite imagery, census data, and volunteered geographic information. In this paper, we present the characteristics of GHSL information to demonstrate how original frameworks of data and tools rooted on Earth Observation could support Sustainable Development Goals monitoring. In particular, we demonstrate the reach of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for the Sustainable Development Goal 11. Our experiments produce a global estimate for the Land Use Efficiency indicator (SDG 11.3.1) for 10,000 urban centers, calculating the ratio of land consumption to population growth rate that took place between 1990 and 2015. The results of our research demonstrate that there is a potential to lift SDG 11.3.1 from a tier 2 as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.
Michele Melchiorri; Martino Pesaresi; Aneta J. Florczyk; Christina Corbane; Thomas Kemper. Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator –SDG 11.3.1. 2018, 1 .
AMA StyleMichele Melchiorri, Martino Pesaresi, Aneta J. Florczyk, Christina Corbane, Thomas Kemper. Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator –SDG 11.3.1. . 2018; ():1.
Chicago/Turabian StyleMichele Melchiorri; Martino Pesaresi; Aneta J. Florczyk; Christina Corbane; Thomas Kemper. 2018. "Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator –SDG 11.3.1." , no. : 1.
Exposure is reported to be the biggest determinant of disaster risk, it is continuously growing and by monitoring and understanding its variations over time it is possible to address disaster risk reduction, also at the global level. This work uses Earth observation image archives to derive information on human settlements that are used to quantify exposure to five natural hazards. This paper first summarizes the procedure used within the global human settlement layer (GHSL) project to extract global built-up area from 40 year deep Landsat image archive and the procedure to derive global population density by disaggregating population census data over built-up area. Then it combines the global built-up area and the global population density data with five global hazard maps to produce global layers of built-up area and population exposure to each single hazard for the epochs 1975, 1990, 2000, and 2015 to assess changes in exposure to each hazard over 40 years. Results show that more than 35% of the global population in 2015 was potentially exposed to earthquakes (with a return period of 475 years); one billion people are potentially exposed to floods (with a return period of 100 years). In light of the expansion of settlements over time and the changing nature of meteorological and climatological hazards, a repeated acquisition of human settlement information through remote sensing and other data sources is required to update exposure and risk maps, and to better understand disaster risk and define appropriate disaster risk reduction strategies as well as risk management practices. Regular updates and refined spatial information on human settlements are foreseen in the near future with the Copernicus Sentinel Earth observation constellation that will measure the evolving nature of exposure to hazards. These improvements will contribute to more detailed and data-driven understanding of disaster risk as advocated by the Sendai Framework for Disaster Risk Reduction.
Daniele Ehrlich; Michele Melchiorri; Aneta J. Florczyk; Martino Pesaresi; Thomas Kemper; Christina Corbane; Sergio Freire; Marcello Schiavina; Alice Siragusa. Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time. Remote Sensing 2018, 10, 1378 .
AMA StyleDaniele Ehrlich, Michele Melchiorri, Aneta J. Florczyk, Martino Pesaresi, Thomas Kemper, Christina Corbane, Sergio Freire, Marcello Schiavina, Alice Siragusa. Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time. Remote Sensing. 2018; 10 (9):1378.
Chicago/Turabian StyleDaniele Ehrlich; Michele Melchiorri; Aneta J. Florczyk; Martino Pesaresi; Thomas Kemper; Christina Corbane; Sergio Freire; Marcello Schiavina; Alice Siragusa. 2018. "Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time." Remote Sensing 10, no. 9: 1378.
In the last few decades the magnitude and impacts of planetary urban transformations have become increasingly evident to scientists and policymakers. The ability to understand these processes remained limited in terms of territorial scope and comparative capacity for a long time: data availability and harmonization were among the main constraints. Contemporary technological assets, such as remote sensing and machine learning, allow for analyzing global changes in the settlement process with unprecedented detail. The Global Human Settlement Layer (GHSL) project set out to produce detailed datasets to analyze and monitor the spatial footprint of human settlements and their population, which are key indicators for the global policy commitments of the 2030 Development Agenda. In the GHSL, Earth Observation plays a key role in the detection of built-up areas from the Landsat imagery upon which population distribution is modelled. The combination of remote sensing imagery and population modelling allows for generating globally consistent and detailed information about the spatial distribution of built-up areas and population. The GHSL data facilitate a multi-temporal analysis of human settlements with global coverage. The results presented in this article focus on the patterns of development of built-up areas, population and settlements. The article reports about the present status of global urbanization (2015) and its evolution since 1990 by applying to the GHSL the Degree of Urbanisation definition of the European Commission Directorate General for Regional and Urban Policy (DG-Regio) and the Statistical Office of the European Communities (EUROSTAT). The analysis portrays urbanization dynamics at a regional level and per country income classes to show disparities and inequalities. This study analyzes how the 6.1 billion urban dwellers are distributed worldwide. Results show the degree of global urbanization (which reached 85% in 2015), the more than 100 countries in which urbanization has increased between 1990 and 2015, and the tens of countries in which urbanization is today above the global average and where urbanization grows the fastest. The paper sheds light on the key role of urban areas for development, on the patterns of urban development across the regions of the world and on the role of a new generation of data to advance urbanization theory and reporting.
Michele Melchiorri; Aneta J. Florczyk; Sergio Freire; Marcello Schiavina; Martino Pesaresi; Thomas Kemper. Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer. Remote Sensing 2018, 10, 768 .
AMA StyleMichele Melchiorri, Aneta J. Florczyk, Sergio Freire, Marcello Schiavina, Martino Pesaresi, Thomas Kemper. Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer. Remote Sensing. 2018; 10 (5):768.
Chicago/Turabian StyleMichele Melchiorri; Aneta J. Florczyk; Sergio Freire; Marcello Schiavina; Martino Pesaresi; Thomas Kemper. 2018. "Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer." Remote Sensing 10, no. 5: 768.