Global Human Settlement Analysis for Disaster Risk Reduction

Pesaresi, Martino; Ehrlich, Daniele; Stefano, Ferri; Florczyk, Aneta J.; Freire, Sérgio; Haag, F.; Halkia, Matina; Julea, Andreea; Kemper, Thomas; Soille, Pierre

doi:10.5194/isprsarchives-xl-7-w3-837-2015

Cited by 35 publications

(34 citation statements)

References 21 publications

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“…Several application areas and a wide user community can benefit from the deployment of the SML classifier to the classification of Sentinel-2 imagery as has been demonstrated by the different GHSL applications: population spatial modeling [35], global exposure mapping [36], identification of informal human settlements [37], mapping and monitoring of refugees and internally displaced people [38,39], disaster risk reduction [2] and as a support to damage and needs assessment [40].…”

Section: Discussionmentioning

confidence: 99%

“…The need for accurate, reliable, and timely estimates of land cover of the world at sufficiently detailed spatial and thematic resolutions is underscored by national and international programs and the global change research community [1]. In particular, the understanding of the global human settlements and urban expansion are critical for a large number of issues including housing and urban development, poverty reduction, sustainable development, climate change, biodiversity conservation, ecosystem services provision and disaster management [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas

Pesaresi¹,

Corbane²,

Julea³

et al. 2016

Remote Sensing

168

110

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Abstract:Monitoring of the human-induced changes and the availability of reliable and methodologically consistent urban area maps are essential to support sustainable urban development on a global scale. The Global Human Settlement Layer (GHSL) is a project funded by the European Commission, Joint Research Centre, which aims at providing scientific methods and systems for reliable and automatic mapping of built-up areas from remote sensing data. In the frame of the GHSL, the opportunities offered by the recent availability of Sentinel-2 data are being explored using a novel image classification method, called Symbolic Machine Learning (SML), for detailed urban land cover mapping. In this paper, a preliminary test was implemented with the purpose of: (i) assessing the applicability of the SML classifier on Sentinel-2 imagery; (ii) evaluating the complementarity of Sentinel-1 and Sentinel-2; and (iii) understanding the added-value of Sentinel-2 with respect to Landsat for improving global high-resolution human settlement mapping. The overall objective is to explore areas of improvement, including the possibility of synergistic use of the different sensors. The results showed that noticeable improvement of the quality of the classification could be gained from the increased spatial detail and from the thematic contents of Sentinel-2 compared to the Landsat derived product as well as from the complementarity between Sentinel-1 and Sentinel-2 images.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas

Pesaresi¹,

Corbane²,

Julea³

et al. 2016

Remote Sensing

168

110

View full text Add to dashboard Cite

show abstract

“…The GHSL concept was introduced by the European Commission (EC), Joint Research Centre (JRC) during the years 2008-2011, in the frame of the program named "Information Support for Effective and Rapid External Action", developing new image information mining technologies that are in support of geo-spatial information analysis for global security and stability [20,21]. At that time, the application areas setting the requirements for the GHSL were framed inside the post-natural-disaster and post-conflict damage, needs and reconstruction assessment, including refugee camps and temporary, rapidly-changing human settlement monitoring [21][22][23]. In this frame, the notion of "built-up area" was introduced after a critical revision of the available satellite-derived land-use/land-cover information categories.…”

Section: The Global Human Settlement Layer Principlesmentioning

confidence: 99%

Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1

Melchiorri

Pesaresi

Florczyk

et al. 2019

IJGI

115

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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.

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“…We first measure built-up intensity, street intersection nodal density, job-housing balance, and job accessibility at a high-resolution (along a 1 km by 1 km grid), using census, satellite, and GIS data. Built-up intensity is measured using the Global Human Settlement Built-Up Area Product [68], a Landsat-derived dataset that characterizes the spatial distribution of building footprints. Street intersection nodal density is calculated from the US Census Bureau's 2014 Tigerline road files [69] by summing the nodal degree of all street intersections in each grid cell.…”

Section: Operationalizing the Stand Conceptmentioning

confidence: 99%

Characterizing and measuring urban landscapes for sustainability

Stokes

Seto

2019

Environ. Res. Lett.

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Urban areas are key to sustainability, and understanding heterogeneity in urban landscapes is important for linking development patterns to ecological, economic, and social health. Here, we characterize the urban landscape for the purpose of revealing structural variations that affect sustainability. We develop a new language and classification schema for breaking down urban areas into sub-metropolitan land units that, unlike administrative boundaries, are based on objective measures of the built and natural environment and are comparable across and within urban areas. These units capture structural differences that population density does not. The classification schema offers a process-based characterization of urban landscapes-one where 'urban' is defined by the human and biophysical interactions mediated by the urban environment and complements existing land classification systems, like those based on land use and land cover. As an example, the schema is applied here to understand transportation behaviors-a particular urban process with wide-ranging implications for urban sustainability. Using GIS, satellite, and census spatial data, we apply the classification schema in 909 US urban areas, systematically clustering development with similar structural attributes linked to transportation behaviors. In this way, an urban area is divided into a collection of smaller landscapes, larger than individual households and smaller than census tracts, that are distinct in how they function. The study shows that characterizing the urban landscape in this way can distinguish between neighborhoods with different travel behaviors. Extensions of the schema can be used to monitor and manage urban systems towards sustainability, targeting spatial planning strategies to the micro-geographies where they would be most relevant.

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Global Human Settlement Analysis for Disaster Risk Reduction

Cited by 35 publications

References 21 publications

Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas

Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas

Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1

Characterizing and measuring urban landscapes for sustainability

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