Preface

Earth observation (EO) data are remote sensing image data obtained by air-borne or space-borne sensors, which has the characters of distribution and heterogeneity. Because these data are owned by different space agencies. With Petabyte-scale data volumes of Earth Observation (EO) obtained, the EO data has been one of the most important part of big data. The characters of distribution and heterogeneity of big EO data storage and processing systems owned by different space agencies brings challenges to integrate and use these data. To meet the big EO data challenges, the new generation Spatial data infrastructure is needed to provide the solution and implementation technique to big EO data storage, access, process and analysis. The concept and architecture of Next-generation national EO Spatial Data Infrastructure (EO-SDI) is proposed in the paper. Key techniques are introduced to solve the complexity of distributed and heterogeneous EO data integration and on-demand processing. An interoperable national EO data system of systems is developed to integrate distributed EO archives of eight EO data centers.

<p>Earth Observation (EO) data availability is drastically increasing thanks to the Copernicus Sentinel missions. In 2014 Sentinel data volumes were approximately 200 TB (one operational mission) while in 2019 these volumes rose to 12 PB (nine operational missions) and will increase further with the planned launch of new Sentinel satellites. Dealing with this big data evolution has become an additional challenge in the development of downstream services next to algorithm development, product quality control, and data dissemination techniques.</p><p>The H2020 project ‘Data Cube Service for Copernicus (DCS4COP)’ addresses the downstream challenges of big data integrating Copernicus services in a data cube system. A data cube is typically a four-dimensions object, with a parameter dimension and three shared dimensions (time, latitude, longitude). The traditional geographical map data is transformed into a data cube based with user-defined spatial and temporal resolutions using tools such as mathematical operations, sub-setting, resampling, or gap filling to obtain a set of consistent parameters.</p><p>This work describes how different EO datasets are integrated in a data cube system to monitor the water quality in the Belgian Continental Shelf (BCS) for a period from 2017 to 2019. The EO data sources are divided in four groups: 1) high resolution data with low temporal coverage (i.e. Sentinel-2), 2) medium resolution data with daily coverage (i.e. Sentinel-3), 3) low resolution geostationary data with high coverage frequency (i.e. MSG-SEVIRI), and 4) merged EO data with different spatial and temporal information acquired from CMEMS. Each EO dataset from group 1 to 3 has its own thematic processor that is responsible for the acquisition of Level 1 data, the application of atmospheric corrections and a first quality control (QC) resulting in a Level 2 quality-controlled remote sensing reflectance (Rrs) product. The Level 2 Rrs is the main product used to generate other ocean colour products such as chlorophyll-a and suspended particulate matter. Each product generated from the Rrs passed by a second QC related to its characteristic and improvements (when applied) and organized in a common data format and structure to facilitate the direct integration into a product and sensor specific. At the end of the process, these products are defined as quality-controlled analysis ready data (ARD) and are ingested in the data cube system enabling fast and easy access to these big data volumes of multi-scale water quality products for further analysis (i.e. downstream service). The data cube system grants a fast and easy straightforward access converting netCDF data to Zarr and placing it on the server. In Zarr datasets, the object is divided into chunks and compressed while the metadata are stored in light weight .json files. Zarr works well on both local filesystems and cloud-based object stores which makes it possible to use through a variety of tools such as an interactive data viewer or jupyter notebooks.</p>

Notes on Contributors

Young soil researchers represent a generation that will respond to future soil challenges and implement connected policy developments. They will deal with emerging challenges and opportunities for soil protection, such as climate change, land use change, scenario analysis, big data, modelling integration, one health aspects, and soil living laboratories. In this context, the EU Soil Observatory (EUSO) provides an inclusive forum for participatory research in the EU and beyond. In 2021, at the first gathering of the EUSO Stakeholders Forum, young researchers were actively engaged through the establishment of the EU Young Soil Researchers Forum. This resulted in interesting presentations, discussions, and research outputs, which have been brought together in this virtual thematicissue. Similar support for young soil researchers is expected at the 2024 EUSO Stakeholders Forum.

The problems of data leakage and unreliable information transfer in the management process make sustainability management an inevitable need for future development. Globally, there is increasing attention paid to blockchain technology and particularly its application in addressing sustainable management issues, both from academia and industry. Aiming to deepen the understanding of how blockchain technology could deal with sustainable management issues across different disciplines, this paper investigates the latest research on the application of blockchain technology in sustainable management published from 2017 to 2021. It is found that there is a drastic surge of publications in the recent 2 years. The analysis focuses on authors’ origins, the collaboration network of the keywords, countries, and research topics covered. The application of blockchain technology in five key sectors of sustainable management, encompassing energy management, construction management, supply chain management, environmental management, and e-government management, is selected for further analysis detail. Also, a general framework for applying blockchain technology is proposed for broadening its use and dealing with sustainable management issues. The findings show that the identified 108 publications are distributed in 75 different journals, and scholars from China, the UK, and the USA have been working closely in BT-based sustainable management research. Blockchain technology is just emerging in sustainable management, and there is a great potential for applying blockchain technology to improve sustainable management performance and, more importantly, to provide solutions to quite a few long-lasting problems in these sectors. Opportunities for future research are also presented and discussed.

Prelims

Lead exposure is a worldwide health risk despite substantial declines in blood lead levels following the leaded gasoline phase-out. For the first time, to our knowledge, we aimed to estimate the global burden and cost of intelligence quotient (IQ) loss and cardiovascular disease mortality from lead exposure. In this modelling study, we used country blood lead level estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. We estimated IQ loss (presented as estimated loss in IQ points with 95% CIs) in the global population of children younger than 5 years using the blood lead level-IQ loss function from an international pooled analysis. We estimated the cost of IQ loss, which was calculated only for the proportion of children expected to enter the labour force, as the present value of loss in lifetime income from the IQ loss (presented as cost in US dollars and percentage of gross domestic product with a range). We estimated cardiovascular deaths (with 95% CIs) due to lead exposure among people aged 25 years or older using a health impact model that captures the effect of lead exposure on cardiovascular disease mortality that is mediated through mechanisms other than hypertension. Finally, we used values of statistical life to estimate the welfare cost of premature mortality (presented as cost in US dollars and percentage of GDP). All estimates were calculated by World Bank income classification and region (for low-income and middle-income countries [LMICs] only) for 2019. We estimated that children younger than 5 years lost 765 million (95% CI 443-1098) IQ points and that 5 545 000 (2 305 000-8 271 000) adults died from cardiovascular disease in 2019 due to lead exposure. 729 million of the IQ points lost (95·3% of the total global IQ loss) and 5 004 000 (90·2% of total) cardiovascular disease deaths due to lead exposure occurred in LMICs. IQ loss in LMICs was nearly 80% higher than a previous estimate. Cardiovascular disease deaths were six times higher than the GBD 2019 estimate. The global cost of lead exposure was US$6·0 trillion (range 2·6-9·0) in 2019, which was equivalent to 6·9% (3·1-10·4) of the global gross domestic product. 77% (range 70-78) of the cost was the welfare cost of cardiovascular disease mortality, and 23% (22-30) was the present value of future income losses from IQ loss. Our findings suggest that global lead exposure has health and economic costs at par with PM2·5 air pollution. However, much work remains to improve the quality of blood lead level measurement data, especially in LMICs. The Korea Green Growth Trust Fund and the World Bank's Pollution Management and Environmental Health Program.

Satellite instruments monitor the Earth’s surface day and night, and, as a result, the size of Earth observation (EO) data is dramatically increasing. Machine Learning (ML) techniques are employed routinely to analyze and process these big EO data, and one well-known ML technique is a Support Vector Machine (SVM). An SVM poses a quadratic programming problem, and quantum computers including quantum annealers (QA) as well as gate-based quantum computers promise to solve an SVM more efficiently than a conventional computer; training the SVM by employing a quantum computer/conventional computer represents a quantum SVM (qSVM)/classical SVM (cSVM) application. However, quantum computers cannot tackle many practical EO problems by using a qSVM due to their very low number of input qubits. Hence, we assembled a coreset (“core of a dataset”) of given EO data for training a weighted SVM on a small quantum computer, a D-Wave quantum annealer with around 5000 input quantum bits. The coreset is a small, representative weighted subset of an original dataset, and its performance can be analyzed by using the proposed weighted SVM on a small quantum computer in contrast to the original dataset. As practical data, we use synthetic data, Iris data, a Hyperspectral Image (HSI) of Indian Pine, and a Polarimetric Synthetic Aperture Radar (PolSAR) image of San Francisco. We measured the closeness between an original dataset and its coreset by employing a Kullback–Leibler (KL) divergence test, and, in addition, we trained a weighted SVM on our coreset data by using both a D-Wave quantum annealer (D-Wave QA) and a conventional computer. Our findings show that the coreset approximates the original dataset with very small KL divergence (smaller is better), and the weighted qSVM even outperforms the weighted cSVM on the coresets for a few instances of our experiments. As a side result (or a by-product result), we also present our KL divergence findings for demonstrating the closeness between our original data (i.e., our synthetic data, Iris data, hyperspectral image, and PolSAR image) and the assembled coreset.

\n The use and sustainable development of cropland soils requires the continuous monitoring and promotion of good practices that support soil quality and the provision of its several functions. As the soil quality and functioning can be affected by several factors and interventions, resulting in changes at the temporal and spatial scales, Earth observation (EO) systems become an sound alternative for monitoring soils due to ability in providing data in a timely manner, covering large geographical areas, and revisiting the same place in Earth in short periods of time. Furthermore, as the availability of detailed information about cropland soils is still a challenge in most countries, and recent literature has been supporting the proposition that collections of EO data are a valuable source for environmental studies, this study aimed at exploring the collection of satellite images for mapping and monitoring cropland soils over large geographical areas. For this, we developed the routines for processing big EO data within a high-performance cloud-based platform. With the combination of extracted features from EO data, legacy soil datasets, and machine learning algorithms, we performed the medium-resolution mapping of cropland soils over the geographical extents of Europe and Brazil. We demonstrated in this study that the collection of Landsat images is a valuable source for extracting spectral features useful for mapping and monitoring cropland soils. The bare soil composite based on the median of 37 years of Landsat imagery allowed the prediction of clay and calcium carbonates with moderate performance in Europe. In addition to that, using the Google Earth Engine, we developed and made publicly available a package to calculate terrain attributes customized to different spatial resolutions, which can be scaled up to the global extent. This package was particularly important for preparing additional information for mapping the cropland soils in Brazil. The spectral and terrain features extracted from EO data allowed the calibration of prediction models of clay, sand, soil organic carbon (SOC) content, and SOC stock with satisfactory accuracy across the Brazilian cropland soils. With the resulting maps, we were able to estimate the total SOC stock and identify some aspects related to the distribution of soil attributes regarding the main agricultural regions. Therefore, this study supports the proposition that EO data is a valuable source for extracting environmental features for mapping and monitoring cropland soils at finer resolutions, assisting the evaluation of soil spatial distribution and the historical agriculture expansion in Europe and Brazil.\n

1. Global Ideologies and Urban Landscapes: Introduction Manfred B. Steger (RMIT University, Professor of Global Studies) and Anne McNevin (RMIT University, Research Fellow) 2. After Neoliberalization? Neil Brenner (New York University, Professor of Sociology and Metropolitan Studies), Jamie Peck (University of British Columbia, Canada Research Chair in Urban and Regional Political Economy) and Nik Theodore (University of Illinois, Chicago, Associate Professor and Director of the Center for Urban Economic Development) 3. Provoking 'globalist Sydney': neoliberal summits and spatial reappropriation James Goodman (University of Technology Sydney, Associate Professor) 4. Toronto's Distillery District: Consumption and Nostalgia in a Post-Industrial Landscape Margaret Kohn (University of Toronto, Associate Professor) 5. Delhi: Global mobilities, identity and the postmodern consumption of place Chris Hudson (RMIT University, Senior Lecturer) 6. Materializing the Metaphors of Global Cities: Singapore and Silicon Valley Terrell Carver (University of Bristol, Professor of Political Theory) 7. Gaming Space: Casinopolitan Globalism from Las Vegas to Macau Timothy W. Luke (Virginia Polytechnic Institute and State University, Distinguished Professor of Political Science) 8. Border Policing and Sovereign Terrain: The Spatial Framing of Unwanted Migration in Australia and Melbourne Anne McNevin (RMIT University, Research Fellow) 9. Hong Kong and Berlin: Alternative Scopic Regimes Michael J Shapiro (University of Hawaii, Professor of Political Science) 10. An Emergent Landscape of Inequality in Southeast Asia: Cementing Socio-Spatial Inequalities in Viet Nam James H. Spencer (University of Hawaii at Manoa, Associate Professor)

In the past 50 years, there have been two major changes that are of methodological and consequential importance to the McHargian land-use suitability analysis (LUSA): increasing evidence of non-stationarity of global and regional ecological conditions and increasing availability of high-resolution spatial–temporal earth observation data. For 50 years, the McHargian LUSA has been an important analysis tool for designers and planners for both regional conservation planning and development. McHarg’s LUSA is a decision support tool that reduces the dimensions of spatial–temporal data. This makes the technique relevant beyond decision support to spatial identification and prediction of areas of socio-ecological opportunity, risk, and priority. In this article, I use a set of recent studies relating to agricultural LUSA to reveal relationships between the traditional McHargian LUSA and related spatial–temporal research methods that are adapting to more data and non-stationary ecological conditions. Using a classification based on descriptive, predictive, and prescriptive research activities, I organize these related methods and illustrate how linkages between research activities can be used to assimilate more kinds of spatial “big data,” address non-stationarity in socio-ecological systems, and suggest ways to enhance decision-making and collaboration between planners and other sciences.

When predicting the health impacts of PM2.5 from future air quality changes, it is crucial to consider both air quality improvements and population aging. This study divided future emission scenarios into a base and control scenario to project air quality from 2015 to 2030 and assess health outcomes. The GUIDE model, an Integrated Assessment Model (IAM), was used to estimate future emissions, while the CMAQ (Chemical Transport Model) and BenMAP (Health Impact Model) evaluated health impacts resulting from changes in air quality in Korea. The study focused on the impact of population aging on future health outcomes. Both scenarios showed improved PM2.5 concentrations, with the control scenario showing more substantial improvements due to stronger policy measures. When applying current age patterns, health impacts decreased as PM2.5 concentrations decreased. However, when considering future population aging, health impacts increased despite improved air quality. The results excluding aging show that the number of premature deaths due to cardiovascular disease and all other causes caused by PM2.5 is 18,413 in the base year, while in the future control scenario, the number decreases to 11,729. In contrast, when aging is taken into account, the number of premature deaths increases to 23,037. This finding suggests that, although PM2.5 concentrations are expected to decline, the increasing proportion of elderly individuals will exacerbate health risks. Therefore, accounting for aging population trends is essential when studying the health impacts of future air quality changes.

Contributors

Focus on Authors

Environmental sustainability is nowadays a major global issue that requires efficient and effective responses from governments. Essential variables (EV) have emerged in different scientific communities as a means to characterize and follow environmental changes through a set of measurements required to support policy evidence. To help track these changes, our planet has been under continuous observation from satellites since 1972. Currently, petabytes of satellite Earth observation (EO) data are freely available. However, the full information potential of EO data has not been yet realized because many big data challenges and complexity barriers hinder their effective use. Consequently, facilitating the production of EVs using the wealth of satellite EO data can be beneficial for environmental monitoring systems. In response to this issue, a comprehensive list of EVs that can take advantage of consistent time-series satellite data has been derived. In addition, a set of use-cases, using an Earth Observation Data Cube (EODC) to process large volumes of satellite data, have been implemented to demonstrate the practical applicability of EODC to produce EVs. The proposed approach has been successfully tested showing that EODC can facilitate the production of EVs at different scales and benefiting from the spatial and temporal dimension of satellite EO data for enhanced environmental monitoring.