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Mountains socio-ecological system are both complex and vulnerable. Europe’s mountain socio-ecological system have evolved over millennia and continue to adapt to the pressure of a changing climate and to changing societal demands. Scientist and practitioners have now at disposal a number of datasets to better understand socio-ecological dynamics on Europe’s mountain, including the European Settlement Map (ESM), a 2x2 m resolution building map generated for 39 European countries including Turkey (Sabo et al 2021). The map is produced based on very high spatial resolution satellite imagery form a mix of sensors acquired through the European Coperncius program and a combination of ancillary datasets including land use. The 2 x 2 m2 spatial resolution map is well suited to study and quantify the presence of buildings in small settlements typical of mountain areas. The settlement map distinguishes between residential and nonresidential buildings. It covers the epoch 2013 and 2018 and thus allows quantifying the change in the building stock between the two epochs. The datasets is particularly suited to assess exposure- and change in exposure - to natural hazards, to assess accessibility and cost of transport. It can be used as a spatial infrastructure to model societal impact on protected areas and on ecosystem services. The map will be combined with population data from censuses to provide insights on depopulation in the more marginal mountain areas of Europe as well as to assess the growth of buildings and infrastructure in the municipality with higher rate of development. The datasets will be available as open source, and feedback from the mountain community of researcher and practitioners will be welcome to understand the interest for this information and the need for future map updates.

Changing climate and human demographics in the world's mountains will have increasingly profound environmental and societal consequences across all elevations. Quantifying current human populations in and near mountains is crucial to ensure that any interventions in these complex social-ecological systems are appropriately resourced, and that valuable ecosystems are effectively protected. However, comprehensive and reproducible analyses on this subject are lacking. Here, we develop and implement an open workflow to quantify the sensitivity of mountain population estimates over recent decades, both globally and for several sets of relevant reporting regions, to alternative input dataset combinations. Relationships between mean population density and several potential environmental covariates are also explored across elevational bands within individual mountain regions (i.e. "sub-mountain range scale"). Globally, mountain population estimates vary greatly-from 0.344 billion (5% of the corresponding global total) to 2.289 billion (31%) in 2015. A more detailed analysis using one of the population datasets (GHS-POP) revealed that in ∼35% of mountain sub-regions, population increased at least twofold over the 40-year period 1975-2015. The urban proportion of the total mountain population in 2015 ranged from 6% to 39%, depending on the combination of population and urban extent datasets used. At sub-mountain range scale, population density was found to be more strongly associated with climatic than with topographic and protected-area variables, and these relationships appear to have strengthened slightly over time. Such insights may contribute to improved predictions of future mountain population distributions under scenarios of future climatic and demographic change. Overall, our work emphasizes that irrespective of data choices, substantial human populations are likely to be directly affected by-and themselves affect-mountainous environmental and ecological change. It thereby further underlines the urgency with which the multitudinous challenges concerning the interactions between mountain climate and human societies under change must be tackled.

The application of last-generation spatial data modelling, integrating Earth Observation, population, economic and other spatially explicit data, enables insights into the sustainability of the global urbanisation processes with unprecedented detail, consistency, and international comparability. In this study, the land use efficiency indicator, as developed in the Sustainable Development Goals, is assessed globally for the first time at the level of Functional Urban Areas (FUAs). Each FUA includes the city and its commuting zone as inferred from statistical modelling of available spatial data. FUAs represent the economic area of influence of each urban centre. Hence, the analysis of land consumption within their boundary has significance in the fields of spatial planning and policy analyses as well as many other research areas. We utilize the boundaries of more than 9,000 FUAs to estimate the land use efficiency between 1990 and 2015, by using population and built-up area data extracted from the Global Human Settlement Layer. This analysis shows how, in the observed period, FUAs in low-income countries of the Global South evolved with rates of population growth surpassing the ones of land consumption. However, in almost all regions of the globe, more than half of the FUAs improved their land use efficiency in recent years (2000-2015) with respect to the previous decade (1990-2000). Our study concludes that the spatial expansion of urban areas within FUA boundaries is reducing compactness of settlements, and that settlements located within FUAs do not display higher land use efficiency than those outside FUAs.

The International Journal of Life Cycle Assessment, 26 (2), ISSN:0948-3349, ISSN:1614-7502

Highlights • This paper introduces two essential societal variables (ESV) – global built-up area and global population density. • The two ESVs are the building blocks for quantifying the human societal system as they measure the human presence on Earth. • The two ESVs complement the essential climate variables in modelling climate impact on societies. • The two ESVs are already used in disaster early warning systems, within disaster risk models, in system of indicators and in crisis management. • The two ESVs are also tested for use in system of indicators used to measure progress towards the 2030 Development Agenda., Scientists use Essential Climate Variables to understand and model the Earth’s climate. Complementary to the Climate Variables this paper introduces global built-up area and population density, referred to as Essential Societal Variables, that can be used to model human activities and the impact of climate induced hazards on society. Climate impact scenarios inform policy makers on current and future risk and on the cost for mitigation and adaptation measures. The global built-up area and global population densities are generated from Earth observation image archives and from national population census data in the framework of the Global Human Settlement Layer (GHSL) project. The layers are produced with fine granularity for four epochs: 1975, 1990, 2000 and 2015, and will be updated on a regular basis with open satellite imagery. The paper discusses the relevance of global built-up area and population density for a number of policy areas, in particular to understand regional and global urbanization processes and for use in operational crisis management and risk assessment. The paper also provides examples of global statistics on exposure to natural hazards based on the two ESVs and their use in policy making. Finally, the paper discusses the potential of using population and built-up area for developing indicators to monitor the progress in Agenda 2030 including the Sustainable Development Goals (SDGs).

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.

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.

One of the key objectives of the new EU civil protection mechanism is an enhanced understanding of risks the EU is facing. Developing a European perspective may create significant opportunities of successfully combining resources for the common objective of preventing and mitigating shared risks. Risk assessments and mapping represent the first step in these preventive efforts. The EU is facing an increasing number of natural disasters. Among them earthquakes are the second deadliest after extreme temperatures. A better-shared understanding of where seismic risk lies in the EU is useful to identify which regions are most at risk and where more detailed seismic risk assessments are needed. In that scope, seismic risk assessment models at a pan-European level have a great potential in obtaining an overview of the expected human and economic losses using a homogeneous quantitative approach and harmonized datasets. This study strives to demonstrate the feasibility of performing a seismic risk assessment at a pan-European level with an open access methodology and using open datasets available across the EU. It also aims at highlighting the challenges and needs in datasets and the information gaps for a consistent assessment of seismic risk at the pan-European level. Results are expressed as expected casualties and economic losses for a return period of 475-year. The study constitutes a “proof of concept” that can complement the information provided by Member States in their National Risk Assessments. Its main contribution lies in pooling open-access data from different sources in a homogeneous format, which could serve as baseline data for performing more in depth risk assessments in Europe.

International audience; Demographic data is a fundamental component of disaster risk models. Fine scale population distribution information is needed for the assessment of casualties, determination of shelter needs and proper implementation of evacuation plans in pre- and/or post-disaster phases at the city scale, i.e. earthquake scenario modelling and rapid emergency response. This paper describes the techniques that are used to map the population distribution. Methods to map population density are described focusing on different downscaling techniques and the contribution of ancillary data. An application for the Larger Urban Zone (LUZ) of Vienna is illustrated. The case study disaggregates the residential population from a local and a country level census at the level of single building blocks. The downscaling is based on a clasymetric approach using an urban land use map as ancillary information. The model was applied after testing two different methods: a limiting variable and a fixed-ratio method. The latter was applied on the entire study area. The results of the proposed methodology can be used to perform population vulnerability analysis for night time scenarios. The enhanced spatial detail influences the accuracy of the information on human exposure when the population map is used in risk assessment models.

The Global Human Settlement Layer (GHSL) is supported by the European Commission, Joint Research Center (JRC) in the frame of his institutional research activities. Scope of GHSL is developing, testing and applying the technologies and analysis methods integrated in the JRC Global Human Settlement analysis platform for applications in support to global disaster risk reduction initiatives (DRR) and regional analysis in the frame of the European Cohesion policy. GHSL analysis platform uses geo-spatial data, primarily remotely sensed and population. GHSL also cooperates with the Group on Earth Observation on SB-04-Global Urban Observation and Information, and various international partners andWorld Bank and United Nations agencies. Some preliminary results integrating global human settlement information extracted from Landsat data records of the last 40 years and population data are presented.

A method for analyzing the urbanization process from multitemporal SPOT 5 panchromatic images is presented. The analysis is performed by unsupervised change detection between built-up presence indicators extracted separately from the scenes. The obtained index has an effective resolution which is coarser than the Ground Sample Distance, thus allowing a good spatial match between the indicators. Then, a local Mixed Information change indicator is employed in order to capture the non-linear temporal behaviors. A region-based approach is developed in synergy with the local Mixed Information in order to process consistently large scenes which may be affected by regional acquisition distortions. Experiments are conducted on SPOT 5 scenes acquired in 2003 and 2008 which cover a suburban area (45×45 km2) of the city of Tangshan in China. The urbanization detector is validated by visual interpretation, giving an equal omission/commission probability of 10%. A comparison to linear change indicators highlights an improvement of 15% of the equal omission/commission probability.

A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.

The paper focuses on automatic extraction building stock information for quantifying physical exposure and its vulnerability from High and Very High Resolution (VHR) optical satellite imagery. We use two case studies. In Sana’a (Yemen) we use automatic techniques to extract the building stock as well as building height that is used to characterize its vulnerability. In Port-au-Prince (Haiti) – in the area affected by the 2010 earthquake -we map the building stock based on a pre-disaster imagery – and we show the added value of area based information when added to point based damage assessment from visual change interpretation of post disaster aerial images. The paper shows that VHR imagery can be used to locate and quantify the building stock and its height. This paper also shows that damages measured from changes detected from pre-and post-disaster imagery can in principle map and provide vulnerability information related to the structural fragility of the building stock., JRC.G.2-Global security and crisis management

This work addresses the use of remote sensing imagery to quantify the built environment and its spatial and temporal changes. It identifies building footprint map, building location map and built-up area map as information products that can be used to quantify physical exposure, one of the variables required in disaster risk assessments. The paper also reviews urban land use maps and urban classes in land cover maps as potential source for deriving exposure information. The paper focuses on the latest generation of satellite-borne remote sensing imaging systems that deliver high-resolution optical imagery able to resolve buildings and other three-dimensional man-made constructions. This work also reviews the semantics, the spatial unit used to define physical exposure, image processing procedures and change techniques.

The work presented here tests an automatic procedure able to recognize the presence of built-up areas in the satellite images with the output nominal scale of 1:50,000. The input data is a set of 54 Ikonos and Quick Bird scenes considered as representative of the variety of human settlement patterns in large cities at global level. The methodology for automatic image information extraction is based on calculation of anisotropic rotation-invariant textural grey-level co-occurrence measures, also called PANTEX methodology. The total area analyzed covers 35,000 km2. The data under test shows high variety in latitude, season, sun elevation and sun azimuth at the time of image data collection. The output of the automatic image information retrieval is evaluated by comparison with a collection of reference information visually interpreted from the same satellite data input. Two complementary evaluation strategies are presented here: i) interactive selection of one threshold level in the textural measurement and then unsupervised application of the same threshold level to all the datasets under test, and ii) per-scene optimization of the threshold based on the available reference samples. This work briefly summarizes the nature of the errors and implications for global settlement classification.

Settlement maps derived by Earth Observation data represent a critical dataset for building stock quantification. The accuracy of the settlement maps varies across the different spatial scales and across the space according to specific spatial patterns. The aim of this paper is to assess the accuracy of the settlement map at different scales, and to analyze the relationships between spatial allocation of error and built-up distribution patterns. The paper identifies two general trends. First that the building stock overestimation error increases with increasing values of spatial scattering. Second that at coarser scales the relation between building area overestimation and spatial scattering became stronger. The results have important implications when settlement maps are used to estimate the building stock.

Every year earthquakes cause substantial economic losses and loss of life in many parts of the world. Earth observations may provide valuable information on spatial damage distribution and through that increase the efficiency of rapid response actions, finally resulting in reduction of earthquake induced losses. However, a methodology for quantitative assessment of the potential value of information in such a context has not yet been developed. That knowledge would be of high importance for planning and development of Earth observation systems on both regional and international levels when aiming at sustainable development goals. In this paper we suggest a stochastic modeling approach to assess the value of information for earthquake rapid response. Our analysis is focused on a rather short time interval after an earthquake occurrence (order of several hours) when there is a maximum need for urgent help to the earthquake victims. We use rescue efficiency as a performance measure of earthquake response actions; this efficiency is described in terms of timely arrival of rescue teams at places, where their help is mostly needed. We quantify the benefit of using spatial damage distribution information in terms of cost reduction/rescue efficiency gains. We intentionally conduct our analysis at a methodological level and do not go deeper into case studies to keep general conclusions traceable. The modeling exercise we present in the paper is a first step towards a more detailed and integrated approach that could contribute to better systematic understanding of earthquake response actions and ultimately improve their efficiency.

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.

This study uses high-resolution (HR) satellite imagery to quantify the stock of buildings, referred herein as building stock. The risk assessment requires information on the natural hazards and on the element at risk, that is the building stock in this article. This study combines (1) texture-based image processing to map built-up areas, (2) statistical sampling that allows locating the building samples and (3) photo-interpretation to encoding building footprints. Statistical inference is then used to quantify the building stock per class of building size. Legaspi in the Philippines is used as a case study. The results show that texture-based computer algorithms provide accurate area estimations of the built-up, that the detail of HR imagery allows the mapping of single buildings using photo-interpretation, and that a systematic sampling approach that uses building encoding and built-up maps can be used to quantify the building stock.

Conflicts may be directly responsible for the modification of features in the landscape by causing damage to built-up areas or to the environment. Landscape features may also be indirectly affected by conflict as the result of changes in the way of life of inhabitants and their use of natural resource. Conflict-induced changes in landuse features may thus be associated with changes in population vulnerability. This study focuses on the environmental indicators for population vulnerability, an important parameter contributing to risk assessment during and after conflict. These environmental indicators are first identified using field data and are then derived from satellite data. The satellite-derived indicators are used as model input to create a risk map for two areas in Northern Iraq that were targeted during the Anfal Campaigns in 1987 and 1988: Jafati Valley and the southern region of Dahuk. The satellite-driven model is further applied to three dates for the same study areas: 1987, 1989 and 2000–2001. The output describes the risk level within the region for each of the dates studied, and the changes which occurred in Northern Iraq as the result of the Anfal Campaigns. Results show that spatial-based hazard risk assessment is possible using environmental indicators derived from Earth Observation data. For conflict-driven changes in the Jafati Valley study area, there is an apparent change in human activity, manifested as a conversion from agricultural land to grassland, the harvesting of rural mountainous woodland and the net disappearance of built-up areas. For this study area affected by conflict, 86% of the regions where these land cover changes occur were labelled as being at risk according to the model output. In the second study area, 63% of the changes in land cover occur in the regions labelled as being most vulnerable. Further research on this second study site shows that the area was affected by climatic and economic factors rather than conflict.

The paper discusses the potential of very high resolution (VHR) satellite imagery for post-earthquake damage assessment in comparison with the role of aerial photographs. Post-disaster optical and radar satellite data are assessed for their ability to resolve collapsed buildings, destroyed transportation infrastructure, and specific land cover changes. Optical VHR imagery has shown to be effective in quantifying building stock and for assessing damage at the building level. High-resolution synthetic aperture radar (SAR) imagery requires further research to identify optimum information extraction procedures for rapid assessment of affected buildings. Based on current technical and operational capabilities increasing efforts should be devoted to the generation of spatial datasets for disaster preparedness.

The objective of this literature review is to understand where Graphical Information Systems (GIS) can be useful to address security issues and how it has been used until now. While the geographic drivers of territorial conflicts have been extensively described by a number of political studies, the quantitative analysis of these drivers is quite new. This study traces an evolution from conceptual research to quantitative development. It then discusses the advantages and challenges of applying new geographic techniques to analyze spatial drivers of conflict. We identify the main spatial components in conflict and security, the existing types of information/data and the quantitative methods used. We describe the spatial component of security by looking at: (i) the main sociopolitical concepts linked to territory, (ii) the kind of geographic concepts linked to territory, (iii) measures used to describe such geographic concepts; and (iv) the issues raised in any attempt to integrate geographic concepts into a GIS. We conclude that GIS tools can be useful in the analysis of civil disputes, particularly where subnational level data exists. This paper shows that spatial processing tools in GIS allow us to represent some spatial components and to address new issues such as the fuzzy complexity of border permeability.

It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature-based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p

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.