Ana Andries is a Postdoctoral Researcher at the Centre for Environment and Sustainability at the University of Surrey. She is also co-editor of Sustainability, a leading environmental open access journal. Her current research focuses on exploring the EO technologies to enhance the sustainable use of biomass for energy and her previous research (PhD) was to assess the potential of EO satellite data to support the SDG indicators.
Ana gained a bachelor's degree in Environment Geography and two master's degrees in Sustainable Development and Geographical Information Systems, respectively, and spent several years working as GIS Quality Control for Rural Payment Agency (RPA), office of DEFRA, inside European Union projects.
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.
In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals (SDGs) at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators which are based on the three pillars of sustainable development: economic, social and environmental. Substantial challenges remain in obtaining data of the required quality, especially in developing countries, given the often limited resources available. One promising and innovative way of addressing this issue of data availability is to use Earth Observation (EO). This paper presents the results of research to develop a novel analytical framework for assessing the potential of EO approaches to populate the SDG indicators. We present a Maturity Matrix Framework (MMF) and apply it to all of the 232 SDG indicators. The results demonstrate that while the applicability of EO-derived data does vary between the SDG indicators, overall, EO has an important contribution to make towards populating a wide diversity of the SDG indicators.
In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals (SDGs) at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators which are based on the three pillars of sustainable development: economic, social and environmental. Substantial challenges remain in obtaining data of the required quality, especially in developing countries, given the limited resources involved. One promising and innovative way of addressing this issue of data availability is to use Earth Observation (EO). This paper presents the results of research to analyse and optimise the potential of EO approaches to populate the SDG indicators and targets. We present a matrix of EO technologies with respect to the full set of current SDG indicators which shows the potential for direct or proxy calibrations across the span of the social, economic and environmental SDG indicators. We have focussed particularly on those SDG indicators covering the social and economic dimensions of sustainable development as these are relatively unexplored from an EO context. Results suggest that EO can make an important contribution towards populating the SDG indicators, but there is a spectrum from at one end the sole use of EO to the other end where the EO derived data have to be used in concert with data collected via non-EO means (surveys etc.). Complicating factors also include the lack of driving force and pressure indicators in the SDG framework and the use of ‘proxy’ indicators not part of the SDG framework but more amenable to EO-derived assessment. The next phase of the research will involve the presenting of these ideas to experts in the EO and indicator arenas for their assessment.
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.
In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals (SDGs) at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators based on the three pillars of sustainable development: economic, social, and environmental. However, substantial challenges remain in obtaining data of the required quality and quantity to populate these indicators efficiently. One promising and innovative way of addressing this issue is to use Earth observation (EO). The research reported here updates our original work to develop a Maturity Matrix Framework (MMF) for assessing the suitability of EO-derived data for populating the SDG indicators, with a special focus on those indicators covering the more social and economic dimensions of sustainable development, as these have been under-explored in terms of the contribution that can be made by EO. The advanced MMF 2.0 framework set out in this paper is based on a wide consultation with EO and indicator experts (semi-structured interviews with 38 respondents). This paper provides detail of the evolved structure of MMF 2.0 and illustrates its use for one of the SDG indicators (Indicator 11.1.1). The revised MMF is then applied to published work covering the full suite of SDG indicators and demonstrates that EO can make an important contribution to providing data relevant to a substantial number of the SDG indicators.