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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.

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.

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.

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.

What Is Special about Global Urban Remote Sensing? Qihao Weng Global Urban Observation: Needs and Requirements Global Urban Observation and Information: GEO's Effort to Address the Impacts of Human Settlements, Qihao Weng, Thomas Esch, Paolo Gamba, Dale Quattrochi, and George Xian EO Data Processing and Interpretation for Human Settlement Characterization: A Really Global Challenge, Paolo Gamba, Gianni Lisini, Gianni Cristian Iannelli, Inmaculada Dopido, and Antonio Plaza Urban Observing Sensors, Qihao Weng, Paolo Gamba, Giorgos Mountrakis, Martino Pesaresi, Linlin Lu, Thomas Kemper, Johannes Heinzel, George Xian, Huiran Jin, Hiroyuki Miyazaki, Bing Xu, Salman Quresh, Iphigenia Keramitsoglou, Yifang Ban, Thomas Esch, Achim Roth, and Christopher D. Elvidge Global Urban Footprint: Data Sets and Products Mapping Global Human Settlements Pattern Using SAR Data Acquired by the TanDEM-X Mission, Thomas Esch, Mattia Marconcini, Andreas Felbier, Achim Roth, and Hannes Taubenbock National Trends in Satellite-Observed Lighting: 1992-2012, Christopher D. Elvidge, Feng-Chi Hsu, Kimberly E. Baugh, and Tilottama Ghosh Development of a Global Built-Up Area Map Using ASTER Satellite Images and Existing GIS Data, Hiroyuki Miyazaki, Xiaowei Shao, Koki Iwao, and Ryosuke Shibasaki Building of a Global Human Settlement Layer from Fine-Scale Remotely Sensed Data, Martino Pesaresi, Vasileios Syrris, Daniele Ehrlich, Matina Halkia, Thomas Kemper, and Pierre Soille Urban Observation, Monitoring, Forecasting, and Assessment Initiatives Spatial Dynamics and Patterns of Urbanization: The Example of Chinese Megacities Using Multitemporal EO Data, Hannes Taubenbock, Thomas Esch, Michael Wiesner, Andreas Felbier, Mattia Marconcini, Achim Roth, and Stefan Dech Mapping and Monitoring of Refugees and Internally Displaced People Using EO Data, Thomas Kemper and Johannes Heinzel Assessment of Fine-Scale Built-Up Area Mapping in China, Linlin Lu, Huadong Guo, Martino Pesaresi, Daniele Ehrlich, and Stefano Ferri Climatological and Geographical Impacts on the Global Pandemic of Influenza A (H1N1) 2009, Bing Xu, Zhenyu Jin, Zhiben Jiang, Jianping Guo, Michael Timberlake, and Xiulian Ma Investigations of the Diurnal Thermal Behavior of Athens, Greece, by Statistical Downscaling of Land Surface Temperature Images and Pattern Analysis, Iphigenia Keramitsoglou Innovative Concepts and Techniques in Urban Remote Sensing Integrated Urban Sensing in the Twenty-First Century, Gunther Sagl and Thomas Blaschke Object-Based Image Analysis for Urban Studies, Vivek Dey, Bahram Salehi, Yun Zhang, and Ming Zhong Defining Robustness Measures for OBIA Framework: A Case Study for Detecting Informal Settlements, Peter Hofmann Automated Techniques for Change Detection Using Combined Edge Segment Texture Analysis, GIS, and 3D Information, Manfred Ehlers, Natalia Sofina, Yevgeniya Filippovska, and Martin Kada Fusion of SAR and Optical Data for Urban Land Cover Mapping and Change Detection, Yifang Ban, Osama Yousif, and Hongtao Hu Index

This paper describes a framework for an image processing procedure for operational agricultural crop area estimation. This operational framework has been conceived within the development of an Advanced Agricultural Information System (AAIS) for the “Regione del Veneto “ (RdV ‐ Veneto Region) in northeastern Italy. The objective of this program is to develop the ability to generating timely and accurate area estimates and production information for four major agricultural crops: soybeans, sugar beets, corn, and small grains. AAIS uses state of the art methods in remote sensing and geographic information systems (GIS) technology and integrates a variety of data types including satellite imagery. This paper describes the methodology developed for image and ancillary data processing for the production of crop area statistics. Using a combination of standard unsupervised classification and GIS operations that incorporate knowledge about the agricultural system, a “sequential masking” classification pr...

The Regione del Veneto (Italy) is cooperating with the University of California, Santa Barbara and other researchers in Italy and the U.S.A. to develop a system of econometric crop production modeling. Five crops are to be included in this project: small grains (wheat and barley), corn, sugar beets, soybeans, orchards and vineyards. A critical part of the crop yield modeling process is the identification of crops using multispectral satellite data. This paper explores two strategies to improve crop classification accuracies: (1) use of ancillary data stored in digital format and (2) use of multitemporal data. Ancillary information stored on digital files were used in this research to remove (mask) non‐agricultural areas from satellite image data. Comparison between the classification of masked and unmasked images showed that improvement ranged from 3% to 26% depending on crop type. The multidate classification was performed by compiling an image of transformed spectral bands and three TM‐5 bands....

This paper tackles the need of enhanced population data for disaster management and aid delivery studies in developing countries. It analyses the usefulness of a set of spatial data layers, including medium resolution satellite imagery, for population density estimations in rural Zimbabwe. The exercise conducted on a 185 × 185km area at a grid cell size of 150m allowed us to develop a methodology that can be extended to the whole of Zimbabwe.

As an important urban agglomeration of China, the Jing-Jin-Tang area has experi- enced intense urbanization since the 1980s. This study explores the spatiotemporal dynamics of urban areas in this region using multitemporal Landsat images. An enhanced built-up (BU) index method was applied to extract BU areas with an overall accuracy ranging from 75% to 91.35%. Seven spatial metrics were used to discern urban growth patterns at city and county levels. The results indicate that all cities witnessed a rapid growth of BU areas with different spatial patterns. Beijing has been aggregating since the 1990s and a large homogeneous urban patch has formed. The construction and development of metropolitan Beijing and Tianjin started in the early 1980s and became almost fully developed by the end of 1990. Tangshan, like many medium-sized cities in China, is still enduring a development process with an accelerating pace. The metropolitan areas of Beijing and Tianjin have been greatly developed with BU densities exceeding 90% since 2000, compared with Tangshan's 55% in 2010. These results provide spatial information on the evolution of urban extent in the period of 1990s to 2010s in this region. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original pub- lication, including its DOI. (DOI: 10.1117/1.JRS.8.084592) The Beijing-Tianjin-Tangshan (a.k.a. Jing-Jin-Tang) urban agglomeration is mainly com- posed of three adjacent cities: Beijing, Tianjin, and Tangshan in northern China. It is located at the heart of the Bohai Economic Rim, and plays a vital role in the nation's economic growth. 3 This area has experienced intense urbanization since the national economic reform of the late 1980s. The rapid growth of built-up (BU) areas has brought great pressure to local environment and has led to serious environmental problems such as water shortage, air/water pollution, and vegetation degradation, especially in overpopulated areas. Air pollution results in significant neg- ative health impacts on the large population in cities like Beijing. 4 Dumping solid waste on the

In the aftermath of man-made crises, refugees (or internally displaced persons) cluster in relatively safe areas that rapidly become refugee camps. The maintenance of these camps is one of the chal...