Sérgio Freire

Multi-level geospatial modeling of human exposure patterns and vulnerability indicators

In the context of disaster risk management and in particular for improving preparedness and mitigation of potential impacts, information on socioeconomic characteristics including aspects of situation-specific human exposure and vulnerability is considered vital. This paper provides an overview on available multi-level geospatial information and modeling approaches from global to local scales that could serve as inventory for people involved in disaster-related areas. Concepts and applications related to the human exposure and social vulnerability domains are addressed by illustrating the varying dimensions and contextual implications. Datasets and methods are highlighted that can be applied to assess earthquake-related population exposure, ranging from global and continental-scale population grids (with a focus on recent developments for Europe) to high-resolution functional urban system models and space–time variation aspects. In a further step, the paper elaborates on the integration of social structure on regional scale and the development of aggregative social and economic vulnerability indicators which would eventually enable the differentiation of situation-specific risk patterns. The presented studies cover social vulnerability mapping for selected US federal states in the New Madrid seismic zone as well as the advancement of social vulnerability analysis through integration of additional economic features in the index construction by means of a case study for Turkey’s provinces.

Advancing tsunami risk assessment by improving spatio-temporal population exposure and evacuation modeling

Tsunamis are among the most destructive and lethal of coastal hazards. These are time-specific events, and despite directly affecting a narrow strip of coastline, a single occurrence can have devastating effects and cause massive loss of life, especially in urbanized coastal areas. In this work, in order to consider the time dependence of population exposure to tsunami threat, the variation of spatio-temporal population distribution in the daily cycle is mapped and analyzed in the Lisbon Metropolitan Area. High-resolution daytime and nighttime population distribution maps are developed using ‘intelligent dasymetric mapping,’ that is, applying areal interpolation to combine best-available census data and statistics with land use and land cover data. Workplace information and mobility statistics are considered for mapping daytime distribution. In combination with a tsunami hazard map, information on infrastructure, land use and terrain slope, the modeled population distribution is used to assess people’s evacuation speed, applying a geospatial evacuation modeling approach to the city of Lisbon. The detailed dynamic population exposure assessment allows producing both daytime and nighttime evacuation time maps, which provide valuable input for evacuation planning and management. Results show that a significant amount of population is at risk, and its numbers increase dramatically from nighttime to daytime, especially in the zones of high tsunami flooding susceptibility. Also, full evacuation can be problematic in the daytime period, even if initiated immediately after a major tsunami-triggering earthquake. The presented approach greatly improves tsunami risk assessment and can benefit all phases of the disaster management process.

Combining GHSL and GPW to improve global population mapping

Available global population grids suffer from limitations that constrain their usability. The Global Human Settlement Layer (GHSL) may benefit population disaggregation and mapping. We test the integration of the GHSL built-up grid and the Gridded Population of the World (GPW) in order to refine the mapping of population distribution in Syria, for the year 2000, greatly improving depiction of population distribution and density. Preliminary results indicate that GHSL is a good proxy for population disaggregation.

VGDI – Advancing the Concept: Volunteered Geo‐Dynamic Information and its Benefits for Population Dynamics Modeling

The concept of Volunteered Geographic Information (VGI) has progressed from being an exotic prospect to making a profound impact on GIScience and geography in general, as initially anticipated. However, while massive and manifold data is continuously produced voluntarily and applications are built for information and knowledge extraction, the initially introduced concept of VGI lacks certain methodological perspectives in this regard which have not been fully elaborated. In this article we highlight and discuss an important gap in this concept, i.e. the lack of formal acknowledgment of temporal aspects. By coining the proposed advanced framework ‘Volunteered Geo-Dynamic Information’ (VGDI), we attempt to lay the ground for full conceptual and applied spatio-temporal integration. To illustrate that integrative approach of VGDI and its benefits, we describe the potential impact on the field of dynamic population distribution modeling. While traditional approaches in that domain rely on survey-based data and statistics as well as static geographic information, the use of VGDI enables a dynamic setup. Foursquare venue and user check-in data are presented for a test site in Lisbon, Portugal. Two core modules of spatio-temporal population assessment are thereby addressed, namely time use profiling and target zone characterization, motivated by the potential integration in existing population dynamics frameworks such as the DynaPop model.

Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer

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.

Remote sensing derived continental high resolution built-up and population geoinformation for crisis management

Detailed geoinformation on settlements are required for disaster risk analysis and crisis management, yet are only readily available for specific areas, varying widely in quality and characteristics. Remote sensing can contribute to fill this gap. The Global Human Settlement Layer (GHSL) project provides detailed data on the distribution and densities of built-up for continent-wide expanses, in a consistent way. This paper illustrates the application of GHSL-derived geoinformation in the assessment of physical and human exposure to floods at the local level. Results show that GHSL refines on existing information and contributes to improve analyses of exposure and risk of natural disasters.

Remote sensing derived datasets supporting disaster alert systems on multiscales via web services

This paper shows how remote sensing derived information about the human and physical exposure can be used to support disaster alert systems. The human and physical exposures are calculated from LandScan and GHSL datasets, respectively. In this approach, a multiscale alert system relies on Web services that offer information about the amount of population and infrastructure affected by a given hazard. Four natural disaster scenarios have been selected to cover different spatial scales and disaster types.

Testing the Contribution of WorldView-2 Improved Spectral Resolution for Extracting Vegetation Cover in Urban Environments

Abstract Detailed spatial data concerning land cover constitutes valuable information not only for urban design and planning but also to support informed decisions toward urban sustainable development. Obtaining such information from satellite imagery, with quality compatible with cartographic and thematic standards, is still a challenging task. Until recently, very high-resolution (VHR) satellites acquired data with high spatial resolution (1 m or less) but with only four spectral bands, typically in the visible and infrared regions. WorldView-2 (WV-2) offers improved spectral capability by acquiring data in an additional set of spectral bands. Using Lisbon as the case study, this research tests the contribution of the new bands of WV-2 sensor for extracting vegetation information in urban landscapes with different levels of plant heterogeneity. The methodology is based on an automated feature extraction procedure, followed by an object-based accuracy assessment. Results show that including the new WV-2 bands coastal and yellow (bands 1 and 8), along with the standard four-band set, increases the overall accuracy by 1–6%. However, the magnitude of improvement depends on the homogeneity of plants species present in the site.

Improving Flood Risk Management in the City of Lisbon: Developing a Detailed and Updated Map of Imperviousness Using Satellite Imagery

The spatial distribution and extent of pervious and impervious areas in the city are important variables for planning, mitigating, preparing and responding to potential urban flooding events. Remote sensing constitutes a valuable data source to derive land cover information required for flood risk assessment. The present paper describes the generation of a Land Cover Map for the city of Lisbon, Portugal. The data source is an IKONOS-2 pansharp image, from 2008, with a spatial resolution of 1 m, and a normalized Digital Surface Model (nDSM) from 2006. The methodology was based on the extraction of features of interest, namely: vegetation, soil and impervious surfaces. It is demonstrated that using a methodology based on Very-High Resolution (VHR) images, quick updating of detailed land cover information is possible and can be used to support decisions in a crisis situation where official maps are generally outdated.

Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time

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.