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Space for Sustainability: Using Data from Earth Observation to Support Sustainable Development Indicators

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 31596

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Special Issue Editors

Prof. Dr. Stephen Morse

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Guest Editor

Chair in Systems Analysis for Sustainability in the Centre for Environment and Sustainability, University of Surrey, Guildford, Surrey GU2 7XH, UK
Interests: sustainability assessment; sustainability indicators; natural resource management; earth observation; participatory techniques
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Richard Murphy

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Co-Guest Editor

Centre for Environment and Sustainability, University of Surrey, Guildford GU2 7XH, UK
Interests: bioenergy and biomaterials; life cycle assessment
Special Issues, Collections and Topics in MDPI journals

Ms. Ana Andries

E-Mail Website
Co-Guest Editor

Centre for Environment and Sustainability (CES), University of Surrey, Guildford, Surrey GU2 7XH, UK
Interests: Earth Observation; Satellite Systems; Geographical Information Systems; Indicators

Special Issue Information

Dear Colleagues,

Global progress towards living sustainably is now urgent. Actions for sustainability are typically informed through the use of indicator-based frameworks encompassing diverse attributes of the environmental, social and economic dimensions of ‘sustainability’. Reporting on such indicators is embedded in frameworks such as the United Nations Sustainable Development Goals (SDGs) with the primary responsibilities for reporting carried by national and local governments. Additionally, many businesses and public bodies (e.g. universities, health services) are increasingly under internal and external pressure to similarly report via these sustainability indicators, especially as part of the SDGs, and such reporting is of increasing interest to investors and the financial services sectors from a risk and assurance perspective. However, the use of these indicator-based frameworks face many challenges. One of the most significant of these is the challenge of acquiring sufficient, timely and good quality data to populate these indicators via ‘conventional’ methods (e.g. surveys at the local, national or corporate level) as this is often expensive and time consuming. Many developing regions, in particular, suffer from a lack of resources or established systems for such data collection and, indeed, it is also proving to be challenging for more developed economies.

One approach to address this issue of data provision for indicators of Sustainable Development (SD) is the use of Earth Observation (EO). EO-based data, geospatial information and ‘big data’ can support the population of sustainability indicators at all scales, and the integration of these sources is a step forward in advancing the well-being of our societies. While EO derived data have been used for many years to assess important issues such as deforestation and changes in land use, their use to address more socio-economic issues (e.g. inequality, poverty, corruption, health care) within SD remains limited. Nonetheless, EO tools and technologies are developing rapidly with an expanding range of capabilities, resolutions, frequency, data power, accuracy etc., and this is anticipated to continue into the foreseeable future.

This Special Issue in the journal Sustainability aims to present the current ‘state of play’ with regard to the use of EO data for SD indicators, and given the rapid progress in the field it provides a timely and welcome milestone in the journey. The editors welcome contributions that explore progress to date and how this informs potential for future use of EO derived data for many aspects of SD and that provide cutting-edge examples of where EO can provide insights, particularly for a number of the socio-economic dimensions of SD that have proved to be challenging to assess. This could include, but is not limited to, the following applications:

Applications of EO in tracking the state of socio-economic issues at sub-national, national and regional levels;
Integration of EO with survey data;
Filling traditional data gaps using EO data;
Assessing inequalities, poverty, food insecurity, water scarcity using EO data

Papers submitted should include an uncertainty assessment of the approach and ideally a cost-effectiveness analysis that might highlight the usefulness of using EO data.

We very much look forward to your submissions.

Prof. Dr. Stephen Morse
Prof. Dr. Richard Murphy
Ms. Ana Andries
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Earth Observation
Sustainable Development
Indicators
Data Management and Use
Socio-Economic
Big Data

Published Papers (8 papers)

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Research

33 pages, 2608 KiB

Open AccessArticle

Are Global Environmental Uncertainties Inevitable? Measuring Desertification for the SDGs

by Alan Grainger

Sustainability 2022, 14(7), 4063; https://0-doi-org.brum.beds.ac.uk/10.3390/su14074063 - 29 Mar 2022

Cited by 1 | Viewed by 1745

Abstract

Continuing uncertainty about the present magnitudes of global environmental change phenomena limits scientific understanding of human impacts on Planet Earth, and the quality of scientific advice to policy makers on how to tackle these phenomena. Yet why global environmental uncertainties are so great, why they persist, how their magnitudes differ from one phenomenon to another, and whether they can be reduced is poorly understood. To address these questions, a new tool, the Uncertainty Assessment Framework (UAF), is proposed that builds on previous research by dividing sources of environmental uncertainty into categories linked to features inherent in phenomena, and insufficient capacity to conceptualize and measure phenomena. Applying the UAF shows that, based on its scale, complexity, areal variability and turnover time, desertification is one of the most inherently uncertain global environmental change phenomena. Present uncertainty about desertification is also very high and persistent: the Uncertainty Score of a time series of five estimates of the global extent of desertification shows limited change and has a mean of 6.8, on a scale from 0 to 8, based on the presence of four conceptualization uncertainties (terminological difficulties, underspecification, understructuralization and using proxies) and four measurement uncertainties (random errors, systemic errors, scalar deficiencies and using subjective judgment). This suggests that realization of the Land Degradation Neutrality (LDN) Target 15.3 of the UN Sustainable Development Goal (SDG) 15 (“Life on Land”) will be difficult to monitor in dry areas. None of the estimates in the time series has an Uncertainty Score of 2 when, according to the UAF, evaluation by statistical methods alone would be appropriate. This supports claims that statistical methods have limitations for evaluating very uncertain phenomena. Global environmental uncertainties could be reduced by devising better rules for constructing global environmental information which integrate conceptualization and measurement. A set of seven rules derived from the UAF is applied here to show how to measure desertification, demonstrating that uncertainty about it is not inevitable. Recent review articles have advocated using ‘big data’ to fill national data gaps in monitoring LDN and other SDG 15 targets, but an evaluation of a sample of three exemplar studies using the UAF still gives a mean Uncertainty Score of 4.7, so this approach will not be straightforward. Full article

(This article belongs to the Special Issue Space for Sustainability: Using Data from Earth Observation to Support Sustainable Development Indicators)

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22 pages, 4958 KiB

Open AccessArticle

Assessing Urban Vulnerability to Flooding: A Framework to Measure Resilience Using Remote Sensing Approaches

by Mercio Cerbaro, Stephen Morse, Richard Murphy, Sarah Middlemiss and Dimitrios Michelakis

Sustainability 2022, 14(4), 2276; https://0-doi-org.brum.beds.ac.uk/10.3390/su14042276 - 17 Feb 2022

Cited by 2 | Viewed by 2888

Abstract

Assessing and measuring urban vulnerability resilience is a challenging task if the right type of information is not readily available. In this context, remote sensing and Earth Observation (EO) approaches can help to monitor damages and local conditions before and after extreme weather events, such as flooding. Recently, the increasing availability of Google Street View (GSV) coverage offers additional potential ways to assess the vulnerability and resilience to such events. GSV is available at no cost, is easy to use, and is available for an increasing number of locations. This exploratory research focuses on the use of GSV and EO data to assess exposure, sensitivity, and adaptation to flooding in urban areas in the cities of Belem and Rio Branco in the Amazon region of Brazil. We present a Visual Indicator Framework for Resilience (VIFOR) to measure 45 indicators for these characteristics in 1 km² sample areas in poor and richer districts in the two cities. The aim was to assess critically the extent to which GSV-derived information could be reliable in measuring the proposed indicators and how this new methodology could be used to measure vulnerability and resilience where official census data and statistics are not readily available. Our results show that variation in vulnerability and resilience between the rich and poor areas in both cities could be demonstrated through calibration of the chosen indicators using GSV-derived data, suggesting that this is a useful, complementary and cost-effective addition to census data and/or recent high resolution EO data. Furthermore, the GSV-linked approach used here may assist users who lack the technical skills to process raw EO data into usable information. The ready availability of insights on the vulnerability and resilience of diverse urban areas by straightforward remote sensing methods such as those developed here with GSV can provide valuable evidence for decisions on critical infrastructure investments in areas with low capacity to cope with flooding. Full article

(This article belongs to the Special Issue Space for Sustainability: Using Data from Earth Observation to Support Sustainable Development Indicators)

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21 pages, 3856 KiB

Open AccessArticle

Assessing Education from Space: Using Satellite Earth Observation to Quantify Overcrowding in Primary Schools in Rural Areas of Nigeria

by Ana Andries, Stephen Morse, Richard J. Murphy, Jim Lynch and Emma R. Woolliams

Sustainability 2022, 14(3), 1408; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031408 - 26 Jan 2022

Cited by 3 | Viewed by 3285

Abstract

Nigeria is a country with a rapidly growing youthful population and the availability of good quality education for all is a key priority in the sustainable development of the country. An important element of this is the need to improve access to high-quality primary education in rural areas. A key indicator for progress on this is the provision of adequate classroom space for the more than 20 million learners in Nigerian public schools because overpopulated classrooms are known to have a strong negative impact on the performance of both pupils and their teachers. However, it can be challenging to rapidly monitor this indicator for the over 60 thousand primary schools, especially in rural areas. In this research, we used satellite Earth Observation (EO) and Nigerian government data to determine the size of available teaching spaces and evaluate the degree of overcrowding in a sample of 1900 randomly selected rural primary schools across 19 Nigerian states spanning all regions of the country. Our analysis shows that 81.4% of the schools examined were overcrowded according to the minimum standard threshold for school size of at least 1.2 m² of classroom space per pupil defined by the Federal Government of Nigeria. Such overcrowding can be expected to have a negative impact on educational performance, on achieving universal basic education and UN Sustainable Development Goal (SDG) 4 (Quality Education), and it can lead to poverty. While measuring floor area can be performed manually on site, collecting, and reporting such data for the number of rural primary schools in a large and populous country such as Nigeria is a serious, time-consuming administrative task with considerable potential for errors and data gaps. Satellite EO data are readily available including for remote areas, are reproducible and are easy to update over time. This paper provides a proof-of-concept example of how such EO data can contribute to addressing this socio-economic dimension of the SDGs framework. Full article

(This article belongs to the Special Issue Space for Sustainability: Using Data from Earth Observation to Support Sustainable Development Indicators)

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28 pages, 1583 KiB

Open AccessArticle

Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future

by Ana Andries, Stephen Morse, Richard J. Murphy, Jim Lynch and Emma R. Woolliams

Sustainability 2022, 14(3), 1191; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031191 - 21 Jan 2022

Cited by 15 | Viewed by 7110

Abstract

The Sustainable Development Goals (SDG) framework aims to end poverty, improve health and education, reduce inequality, design sustainable cities, support economic growth, tackle climate change and leave no one behind. To monitor and report the progress on the 231 unique SDGs indicators in all signatory countries, data play a key role. Here, we reviewed the data challenges and costs associated with obtaining traditional data and satellite data (particularly for developing countries), emphasizing the benefits of using satellite data, alongside their portal and platforms in data access. We then assessed, under the maturity matrix framework (MMF 2.0), the current potential of satellite data applications on the SDG indicators that were classified into the sustainability pillars. Despite the SDG framework having more focus on socio-economic aspects of sustainability, there has been a rapidly growing literature in the last few years giving practical examples in using earth observation (EO) to monitor both environmental and socio-economic SDG indicators; there is a potential to populate 108 indicators by using EO data. EO also has a wider potential to support the SDGs beyond the existing indicators. Full article

(This article belongs to the Special Issue Space for Sustainability: Using Data from Earth Observation to Support Sustainable Development Indicators)

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28 pages, 1837 KiB

Open AccessArticle

Can Current Earth Observation Technologies Provide Useful Information on Soil Organic Carbon Stocks for Environmental Land Management Policy?

by Ana Andries, Stephen Morse, Richard J. Murphy, Jim Lynch, Bernardo Mota and Emma R. Woolliams

Sustainability 2021, 13(21), 12074; https://0-doi-org.brum.beds.ac.uk/10.3390/su132112074 - 01 Nov 2021

Cited by 7 | Viewed by 2499

Abstract

Earth Observation (EO) techniques could offer a more cost-effective and rapid approach for reliable monitoring, reporting, and verification (MRV) of soil organic carbon (SOC). Here, we analyse the available published literature to assess whether it may be possible to estimate SOC using data from sensors mounted on satellites and airborne systems. This is complemented with research using a series of semi-structured interviews with experts in soil health and policy areas to understand the level of accuracy that is acceptable for MRV approaches for SOC. We also perform a cost-accuracy analysis of the approaches, including the use of EO techniques, for SOC assessment in the context of the new UK Environmental Land Management scheme. We summarise the state-of-the-art EO techniques for SOC assessment and identify 3 themes and 25 key suggestions and concerns for the MRV of SOC from the expert interviews. Notably, over three-quarters of the respondents considered that a ‘validation accuracy’ of 90% or better would be required from EO-based techniques to be acceptable as an effective system for the monitoring and reporting of SOC stocks. The cost-accuracy analysis revealed that a combination of EO technology and in situ sampling has the potential to offer a reliable, cost-effective approach to estimating SOC at a local scale (4 ha), although several challenges remain. We conclude by proposing an MRV framework for SOC that collates and integrates seven criteria for multiple data sources at the appropriate scales. Full article

(This article belongs to the Special Issue Space for Sustainability: Using Data from Earth Observation to Support Sustainable Development Indicators)

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34 pages, 19926 KiB

Open AccessArticle

Earth Observation for Monitoring, Reporting, and Verification within Environmental Land Management Policy

by Ana Andries, Richard J. Murphy, Stephen Morse and Jim Lynch

Sustainability 2021, 13(16), 9105; https://0-doi-org.brum.beds.ac.uk/10.3390/su13169105 - 14 Aug 2021

Cited by 6 | Viewed by 4009

Abstract

The main aim of the new agricultural scheme, Environmental Land Management, in England is to reward landowners based on their provision of ‘public goods’ while achieving the goals of the 25 Year Environment Plan and commitment to net zero emission by 2050. Earth Observation (EO) satellites appear to offer an unprecedented opportunity in the process of monitoring, reporting, and verification (MRV) of this scheme. In this study, we worked with ecologists to determine the habitat–species relationships for five wildlife species in the Surrey Hills ‘Area of Outstanding Natural Beauty’ (AONB), and this information was used to examine the extent to which EO satellite imagery, particularly very high resolution (VHR) imagery, could be used for habitat assessment, via visual interpretation and automated methods. We show that EO satellite products at 10 m resolution and other geospatial datasets enabled the identification and location of broadly suitable habitat for these species and the use of VHR imagery (at 1–4 m spatial resolution) allowed valuable insights for remote assessment of habitat qualities and quantity. Hence, at a fine scale, we obtained additional habitats such as scrub, hedges, field margins, woodland and tree characteristics, and agricultural practices that offer an effective source of information for sustainable land management. The opportunities and limitations of this study are discussed, and we conclude that there is considerable scope for it to offer valuable information for land management decision-making and as support and evidence for MRV for incentive schemes. Full article

(This article belongs to the Special Issue Space for Sustainability: Using Data from Earth Observation to Support Sustainable Development Indicators)

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21 pages, 4193 KiB

Open AccessArticle

Implications for Tracking SDG Indicator Metrics with Gridded Population Data

by Cascade Tuholske, Andrea E. Gaughan, Alessandro Sorichetta, Alex de Sherbinin, Agathe Bucherie, Carolynne Hultquist, Forrest Stevens, Andrew Kruczkiewicz, Charles Huyck and Greg Yetman

Sustainability 2021, 13(13), 7329; https://0-doi-org.brum.beds.ac.uk/10.3390/su13137329 - 30 Jun 2021

Cited by 15 | Viewed by 4483

Abstract

Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products. Full article

(This article belongs to the Special Issue Space for Sustainability: Using Data from Earth Observation to Support Sustainable Development Indicators)

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17 pages, 888 KiB

Open AccessArticle

Challenges in Using Earth Observation (EO) Data to Support Environmental Management in Brazil

by Mercio Cerbaro, Stephen Morse, Richard Murphy, Jim Lynch and Geoffrey Griffiths

Sustainability 2020, 12(24), 10411; https://0-doi-org.brum.beds.ac.uk/10.3390/su122410411 - 12 Dec 2020

Cited by 3 | Viewed by 2854

Abstract

This paper presents the results of research designed to explore the challenges involved in the use of Earth Observation (EO) data to support environmental management Brazil. While much has been written about the technology and applications of EO, the perspective of end-users of EO data and their needs has been under-explored in the literature. A total of 53 key informants in Brasilia and the cities of Rio Branco and Cuiaba were interviewed regarding their current use and experience of EO data and the expressed challenges that they face. The research builds upon a conceptual model which illustrates the main steps and limitations in the flow of EO data and information for use in the management of land use and land cover (LULC) in Brazil. The current paper analyzes and ranks, by relative importance, the factors that users identify as limiting their use of EO. The most important limiting factor for the end-user was the lack of personnel, followed by political and economic context, data management, innovation, infrastructure and IT, technical capacity to use and process EO data, bureaucracy, limitations associated with access to high-resolution data, and access to ready-to-use product. In general, users expect to access a ready-to-use product, transformed from the raw EO data into usable information. Related to this is the question of whether this processing is best done within an organization or sourced from outside. Our results suggest that, despite the potential of EO data for informing environmental management in Brazil, its use remains constrained by its lack of suitably trained personnel and financial resources, as well as the poor communication between institutions. Full article

(This article belongs to the Special Issue Space for Sustainability: Using Data from Earth Observation to Support Sustainable Development Indicators)

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