EARTH OBSERVATION FOR SDG
Compendium of Earth Observation
contributions to the SDG Targets
and Indicators
May 2020

EARTH OBSERVATION FOR SDG
Compendium of Earth Observation
contributions to the SDG Targets and Indicators
May 2020

ESA Contract No. 4000123494/18/I-NB

Lead authors

Brian O’Connor, Katherine Moul, Barbara Pollini, Xavier de Lamo and Will Simonson(UNEP-WCMC)

Contributing authors

Hilary Allison (UNEP-WCMC), Franziska Albrecht (GeoVille), Radoslaw Marcin
Guzinski (DHI GRAS), Henrik Larsen (UNEP-DHI), Jacqueline McGlade (UCL) andMarc Paganini (ESA, CEOS SDG AHT)

Acknowledgements

Many reviewers have contributed to this compendium and helped to improve thequality and content. Their generous contribution is listed below.
Introductory text: Chu Ishida (JAXA, GEO EO4SDG initiative),
Monica Miguel-Lago (EARSC), Marie Haldorson (Statistics
Sweden, IAEG-SDGs WGGI), Argyro Kavvada (NASA, GEO
EO4SDG initiative), Alex Held (CSIRO, CEOS SDG AHT),
Flora Kerblat (CSIRO, CEOS SDG AHT)
Target 1.5: 	Anestis Trypitsidis and Haris Kontoes(National Observatory of Athens)
Target 6.3: 	Steve Greb (University of Wisconsin-Madison Madison, GEO
AquaWatch), Benjamin Koetz (ESA), Kerstin Stelzer (Brockmann
Consult), Mark Matthews (Cyanolakes)
Target 6.4: 	Annemarie Klasse and Steven Wonink (ELEAF), Jippe Hoogeveen,
Riccardo Biancalan and Livia Peiser (FAO), Benjamin Koetz (ESA)
Target 6.6: 	Ake Rosenqvist (Global Mangrove Watch), Lisa Robelo (CGIAR),
Michael Riffler (Geoville), Jean-Francois Pekel (EC JRC)
Target 7.1: Miguel Roman (NASA), Paul Stackhouse (NASA)
Target 11.1: Richard Sliuzas (ITC), Tomas Soukup (GISAT)
Target 11.2: Sharon Gomez and Amelie Broszeit (GAF AG)
Target 11.3: 	Thomas Esch and Felix Bachofer (DLR),
Christian Tøttrup (DHI GRAS)
Target 11.5: Sharon Gomez (GAF AG)
Target 11.6: Claus Zehner (ESA)
Target 11.7: 	Stefan Kleeschulte (space4environment), Mirko Gregor(space4environment), Tomas Soukup (GISAT), Diana Rocío Galindo
González (IAEG-SDGs WGGI, Colombia)
Target 14.1: 	Emily Smail (NOAA), Gordon Campbell (ESA), Tiit Kutser (University
Tartu), Giulio Ceriola (Planetek), Sami Djavidnia (EMSA), Mads
Christensen (DHI GRAS)
Target 14.3: 	Peter Land (PML), Roberto Sabia (ESA), Shuba Sathyendranath(PML), Mads Christensen (DHI GRAS)
Target 15.1: 	Christophe Sannier (SIRS), Inge Jonckheere (FAO),
Frank Martin Seifert (ESA)
Target 15.2: Frank Martin Seifert (ESA)
Target 15.3: Neil Sims (CSIRO,), Alex Zvoleff (CI)
Target 15.4: 	Davnah Payne and Juerg Krauer (University of Bern),
Carolina Adler (GEO-GNOME, MRI)This Compendium is a contribution to the work of the CEOS Ad-Hoc Team on SDGs
(CEOS SDG AHT) and the GEO EO4SDG initiative. Special thanks go to the leads of the
CEOS SDG AHT and GEO EO4SDG for their valuable guidance while compiling this report.

PREFACE

Preface
The 2030 Agenda for Sustainable Development and its 17 Sustainable Development
Goals (SDG) adopted by world leaders at the United Nations Sustainable Development
Summit on 25 September 2015, provide a global framework for action on sustainable
development for the next two decades. In support of the measurement andmonitoring of progress towards the 17 SDGs and the 169 associated targets, the UN
has established a Global Indicator Framework, designed around 232 SDG Indicators.
In order to progress towards the achievement of the SDG goals and targets, thisframework needs to be assessed using evidence, drawn from accurate and robust data,on a continuous basis.
In July 2015, this need was already recognised by the UN’s Third International
Conference for Financing for Development which produced a comprehensive
framework—the Addis Ababa Action Agenda (AAAA). The agenda highlighted morethan 100 actions to be taken to finance the sustainable development agenda andexplicitly recognizes the need to fund “science, technology, innovation and capacity
building,” as well as “data, monitoring and follow-up” [RD-1]. Therefore, monitoringand evaluation, based on accurate and robust data, must not only be at the centre of
the Global Indicator Framework, but of the Agenda 2030 for Sustainable Developmentin its entirety. Indeed, data and evidence are the foundation of development policies
and effective program implementation, as recognised by the Independent Expert
Advisory Group on a Data Revolution for Sustainable Development (IEAG) [RD-2]
in 2014. National Statistical Offices (NSOs) should generate a sustainable flow ofhigh quality, timely, authoritative and accessible data for on-going monitoring of
progress towards Agenda 2030. In response, the Global Partnership for Sustainable
Development Data (GPSDD) was launched in 2015 in Addis Ababa and New York. It
is an open, independent, multi-stakeholder network harnessing the data revolution
for sustainable development. This message was further promulgated when theCape Town Global Action Plan for Sustainable Development Data was launched inearly 2017 and subsequently adopted by the United Nations Statistical Commission
at its 48th Session [RD-3]. The Plan identified key actions in six strategic areasthat could strengthen national statistical systems to respond to statistical needs toachieve the 2030 Agenda and beyond. The strategic areas were coordination andstrategic leadership, innovation and modernization, strengthening of basic statisticalactivities, dissemination and use of sustainable development data, multi-stakeholder
partnerships and statistical capacity building.
Although this compendium recognises the contribution of all types of accurate andtimely data to further sustainable development, it focuses particularly on locationbased or geospatial data, obtained by satellite-based Earth Observation (EO).
However, integrated geospatial economic, social and environmental datasets are
scarce for many countries, presenting an unrealised opportunity for space technology
to play a key role in the global indicator framework. The compendium shows how
to take advantage of this opportunity and highlights how geospatial informationand EO, the collection of satellite and in-situ information about Earth’s physical,
chemical and biological systems, by tracking and assessing their evolution throughtime, has the potential to complement traditional sources of socio-economic data forboth indicators and targets. Although EO is an all-encompassing term for a rangeof EO instruments, both on the ground and in the air, this compendium focusses on
geospatial information obtained by EO satellites. Satellite-based EO has the advantage
of coverage of the most remote areas of the world, across political boundaries andoceans, with regular and repeat observations, and continuously over decades for mostof free and open data. EO can improve and complement conventional statistical datacollection, as well as provide new types of environmental information. For example,
censuses and household surveys are costly to administer over large territories andtherefore have limitations such as sparse sampling regimes and poor coverage inremote areas. EO can remotely map land cover over time from local to regional

iii

KEY RECOMMENDATIONS

scales, from which economic use of the land can be inferred, supporting census andhousehold surveys and filling information gaps.
This report shows how EO technologies can and should fit into national statisticalsystems for monitoring the progress indicators as well as setting the targets of the
SDG framework. In addition, earth observation has the potential to demonstrate how
new and relevant indicators for the SDG framework could be developed. It illustrates
how observations can directly or indirectly support indicators, and how countries can
set and plan their SDG targets using EO-based support tools. Importantly it shows
how earth observation can support evidence-based decision making in support ofsustainable development policies. There is clearly huge potential for involving thewide range of current and emerging Earth Observation products in target setting andindicator monitoring. For instance, consistent, comparable and readily available timeseries of relevant earth observations will go a long way to supporting countries inmeeting their monitoring and reporting commitments. Access to earth observations
in a ready-to-use format would enable countries to build capacity in developing
methodologies where EO is a sustainable source of primary observation. If this needwere met it would greatly enhance our ability to keep global sustainable development
under proper review and take well informed policy decisions to achieve some goals.

Key Recommendations
This compendium underlines the important contribution that satellite-based EO canmake to the indicator framework of Agenda 2030. It shows that up to 34 indicators
can be either directly (17 indicators) or indirectly (17 indicators) informed with EO data
across 29 targets and 11 goals. Not only are there potential technical improvements
afforded by EO in the indicator methodologies but there are also multiple benefits
of EO for supporting the indicator framework in the long term. Data continuity has
improved greatly with the advent of the Sentinel satellite constellations from theEuropean Copernicus Programme, with Sentinel-1A launched in 2014, and the USGS/NASA Landsat 8 continuity mission launched in 2013. These are publicly funded
EO missions which are supported by open and free data policies, democratisingaccess to EO data for all. The technical infrastructure needed to process and extract
meaningful information from EO data has matured with the advent of advanced
cloud computing and parallel processing systems. Inter-agency coordination has also
improved thanks to the efforts of the Group on Earth Observations (GEO) and theCommittee on Earth Observation Satellites (CEOS) to listen to the needs of countries,
coupled with capacity building efforts specifically targeting developing countries.
However, ongoing efforts are needed to build on this momentum and to ensure
continual progress in developing the potential of EO to inform Agenda 2030, in
particular that we adhere to the principle of the SDGs “that no one be left behind”.
Below are some key recommendations, drawn from evidence presented throughout
this compendium, which outline how the global EO and SDG community should
proceed in bringing the full potential of EO to Agenda 2030.

1. 
Communicate and demonstrate the potential of satellite
Earth Observation so that it can be fully exploited in indicatormethodologies
EO brings new and exciting possibilities for the methodologies of 34 of the
SDG indicators, as well as in national target setting for a least 29 SDG targets.
Yet this potential has not been clearly communicated to date, in particular tocountries and statisticians who need the data the most. There is an everincreasing number of civilian space agency funded and commercial EO satellites

iv

KEY RECOMMENDATIONS

in operation which has raised the expectation that many environmental,
economic and social change can be measured from space. This expectation
should now be fulfilled with concrete action. A first step is to communicate
the success stories and the barriers which have been overcome and nolonger restrict EO data use worldwide. Previous limitations in satellite sensor
performance in certain geographical settings, e.g. in areas prone to cloud cover,have been overcome by launching satellites in constellation, increasing theprobability of clear observations. The time scales of EO data availability havealso increased, e.g. for indicators requiring historical baselines there are now
continual, multi-decadal EO data records, with Landsat time series spanningfrom the late 1970s to the present. Moreover, since then systematic global
coverage by multiple missions has expanded to reach almost all locations on
Earth, as is the case today. The main limitation now is not if EO data exists
but where it can be stored, accessed and in a format ready to be used. Finally,
accuracy of EO data is improving, both at the point of image acquisition, andin derived products such as land cover, atmospheric constituents and ocean
colour – just like statistical data, the accuracies of these derived datasetsneed to be clearly documented by both communities (EO and statistics) tomeet the demands of statistical rigour, demanded by the SDG indicators.

2.Information flows need to be improved to inform SDG related
decision making
The flow of environmental information from EO to geospatial datasets toindicators and into the hands of policy makers in decision making fora should
be streamlined within countries. Custodian agencies are UN bodies (andsometimes other international organisations), who are responsible for compilingand verifying country data. Although custodians have issued methodological
guidelines at the country level, information must flow seamlessly if EO (aswell as other forms of geospatial data) is to be integrated into national
systems and processes. Potential bottlenecks, e.g. where information is
exchanged between government ministries or from private to public sectors
within countries, should be identified and appropriate strategies designed to
improve data flow, e.g. using spatial data infrastructures. Furthermore, theInter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) members
and Inter Governmental Organisations such as the UN should explore thedemand side of requests for EO, e.g. as stated by policy makers, rather thanthe current emphasis on the supply side. It is important therefore to improvethe cooperation between governments and agencies within countries, and toincrease their awareness of EO. This could be achieved by focusing efforts
on providing policy makers with the relevant information needed for them
to become “EO literate,” so that they can see how gaps in the knowledge
landscape can potentially be filled with EO data.

3.Partnerships between National Statistical Offices (NSOs) andgeospatial experts need to be strengthened
There should be stronger collaboration between NSOs and EO experts, toenable the potential of EO to be fully realised within the national statisticalsystems. In particular, if EO data are to be merged with traditional sources ofstatistical data such as census and other big statistical data collection exercises.
New and old ways of thinking need to be combined for a more complete
integration of EO with statistics, as part of the end-to-end information flow
to decision makers. In many countries the statistical agencies, geospatial
and remote sensing experts are to be found in different parts of government.
Creating partnerships amongst the different agencies and departments isa first step to building an effective collaboration. When working together,v

KEY RECOMMENDATIONS

identifying and managing technical language differences (accuracy, validity,
variables can refer to different things) should also be properly addressed.
Finally, strong partnerships are especially beneficial to the communities when
they are bolstered with training plans and capacity building efforts.

4. Commercial image providers should be encouraged to workwith countries on the provision of very high resolution earth
observations
Commercial satellite operators generally provide access to the very highresolution satellite imagery needed for certain applications in SDG reporting.
Some of the SDG indicators, especially those related to small scale
environmental phenomena such as urban greening, waste water pollution andtreatment, climate-smart agriculture in small-holder farming or disaster risk
mapping necessitate measurements at very high spatial resolutions (VHR).
Therefore, low to middle income countries in particular should be financially
supported to access VHR imagery for SDG monitoring and reporting wherecosts for national or even sub-national coverage might be prohibitive. The
Small Island Developing States (SIDS) are also one of the most impacted byenvironmental changes and would definitely benefit of accessing VHR imagery
to help monitor their coastlines evolution, water quality or impacts of disasters.
Data access agreements should be brokered between the commercial sector
and companies in countries with the ministries responsible for SDG reporting.
This could be a potential solution to greater access to such data. Ultimately
the demand for VHR imagery should be passed onto space agencies or their
third party providers if low to middle income countries are to be supported in
the long term in meeting their SDG reporting needs.

viCOMPENDIUM OF EO CONTRIBUTIONSTO THE SDG TARGETS AND INDICATORSCONTENTS

Prefaceiii

Key Recommendations

iv

Introduction

4

Target audience4

The 2030 Agenda for Sustainable Development

4

Scope and purpose of the compendium

6

The Global SDG Indicator Framework

7

The High Level Political Forum (HLPF)

7The Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs)

7

IAEG-SDGs Working Group on Geospatial Information (WGGI)

8

Custodian Agencies

9

National Statistical Offices and line ministries

9

The importance of Earth Observationin the indicator framework

10

Advantages and opportunities of Earth Observation

10

Earth Observation processing infrastructure

10

Challenges and limitations of Earth Observation

11

GEO initiatives related to the SDGs

12

Previous assessments of EO contribution to the SDGs

13

Key EO datasets for the SDG Global Indicator Framework

14

Methodology for analysis of EO for the SDGs

16

Structure of Factsheets17

How can EO be used to help countries achieve the target?

17

The potential of EO to support the SDG indicators

17

Short methodological guidelines illustrated with EO best practice examples

17

EO best practices and national experiences

18

Definition of terms

18

Spatial Resolution

18

Spectral resolution

18

Temporal frequency (resolution)

18

Reference documents

19

Online Resources

20

2

CONTENTS

Compendium of EO contribution to the SDG
targets and indicators

22

Target 1.1 and Target 1.2

24

Target 1.4

28

Target 1.5

30

Target 2.3

34

Target 2.4

37

Target 3.3

41

Target 3.9

44

Target 4.a48

Target 6.1

51

Target 6.3

54

Target 6.4

59

Target 6.6

67

Target 7.1

76

Target 9.1

82

Target 9.4

85

Target 11.1

89

Target 11.2

94

Target 11.3

97

Target 11.5

103

Target 11.6

106

Target 11.7

109

Target 13.1

114

Target 14.1

119

Target 14.3

125Target 14.4

128

Target 15.1

131

Target 15.2

137

Target 15.3

142

Target 15.4

149

Annex: List of Acronyms and Abbreviations

155

3

INTRODUCTION

Introduction
Target audience
This report is intended as a resource for four communities:
first and foremost are countries, consisting of multidisciplinary actors involved in indicator reporting at thenational level and monitoring progress towards theSDGs; second are the custodian agencies responsible forindicator methodological development who can realise
the opportunities for Earth Observation (EO) in indicatormethodologies; third are policy makers interested inevidence that supports progressive, data-driven policy
goals; fourth are EO specialists in order to identify how
their work can contribute to the realisation of Agenda
2030. We hope that the compendium will encourage an
ongoing commitment from all stakeholders to realize thefull potential of the invaluable set of EO tools, techniquesand examples presented in this report. As a result, we hopeindicator custodians and countries will take every available
opportunity and creative step to enhance monitoring ofsustainable development at the national, regional andglobal level.

The 2030 Agenda for Sustainable
Development
The United Nations (UN) system has a long history of
seeking to promote and address sustainable development
across society and to establish governance frameworks
to achieve it:• 1972: 
UN Conference on the Human Environment(Stockholm), the first major conference on
environmental sustainability marking a political
turning point;• 1992: UN Conference on Environment and Development(The Earth Summit, Rio de Janeiro) that included
Agenda 21 calling for global action in all areas ofsustainable development spanning social, economicand environmental issues, and the initiation of thethree Rio Conventions on sustainable development(UNFCCC – the UN Framework Convention on
Climate Change; CBD – Convention on Biological
Diversity; and UNCCD – the UN Convention to
Combat Desertification);• 2000: The Millennium Declaration that sought to reducepoverty and set out targets for the year 2015
– known ultimately as the eight Millennium
Development Goals (MDGs);• 2002: 
The World Summit on Sustainable Development(WSSD) also known as ‘Rio+10’ ( Johannesburg);• 2012: The UN Conference on Sustainable Development(‘Rio+20’, Rio de Janeiro) that resulted in ‘The
Future We Want’ political outcome document,
containing practical measures for implementationof sustainable development principles and a path
to development of Sustainable Development
Goals (SDGs).
Out of the consensus achieved at the UN Conference on
Sustainable Development in Rio 2012 arose the 2030
Agenda for Sustainable Development [RD-4]. The Agenda
was agreed and adopted by the United Nations General
Assembly in September 2015 where Heads of States andGovernments recognised 17 Sustainable Development
Goals and their 169 targets, as a framework for the 2030
Agenda. Although the eight MDGs were realistic and easy
to communicate, their scope was limited to issues thatmostly concerned developing countries - child mortality,
extreme poverty and universal access to education.
Building on the MDGs, the SDGs embraced the three
dimensions of sustainable development (environmental/
biosphere, social/society and economy, as illustrated in
Figure 1), made them applicable to both developing anddeveloped nations and identified concrete actions forpeople, planet, prosperity, peace and partnership. They
also emphasise integration, coherence, indivisibility–
and an underpinning philosophy of “we will leave noone behind”. Overall the Implementation of the 2030
Agenda requires a more holistic, coherent and integrated
approach at national, regional and global levels. It is the
responsibility of governments to set and meet nationaltargets that collectively will achieve the global ambition.
National policies to implement the 2030 Agenda need toaddress inter-linkages within the social, economic andenvironmental dimensions of sustainable development.
This presents unprecedented challenges as well as
opportunities for joined-up, cross-sectorial thinking.
An overview of the 17 Goals is provided below (Box1). The UN System has established a range of formal
processes for achieving the sustainable development
goals and monitoring progress towards the SDG Targets,with a particular focus on supporting the least developed
countries. Further details on the individual SDGs and the
169 targets can be found at the SDG knowledge platform[URL-1].
Each government is also requested to define their own
targets, guided by the global level of ambition, but taking
into account their national circumstances and specificities.
The SDG process is supposed to be country-owned andcountry-led, which implies that countries are leading
both on the delivery and the follow-up of the SDGs; inaddition countries are encouraged to use the frameworkof globally agreed indicators to report on national
progress. Currently there are 231 agreed global indicators
covering the targets. This represents a huge monitoringburden on countries and will require a significant

Compendium of Earth Observation contributions to the SDG Targets and Indicators

4

INTRODUCTION

Figure 1: Illustration of the 17 Sustainable Development Goals across the three spheres of sustainable development: biosphere, society and economy.
Source: (Azote Images for Stockholm Resilience Centre)

level of capacity and resources, especially since not allindicators yet have fully established methodologies oravailable data at the global level. However, countries
are free to choose which indicators to report on, andshould use existing national statistics where possible, inorder to compile indicators which measure the progressagainst the goals and targets. The SDG framework is
a data-driven global agenda, and data is at the coreof the SDGs. This indicates a transformative change
in the way countries deal with development policies.
Primarily, it sees a step change in how data are used to
inform sustainable development polices and that data arerecognised as of central importance in tracking progress
through indicators. In addition to statistical data already inuse by countries for national use, new and complementarysources of data are needed. Geospatial, or location-based
information, is one of these data sources as recognised by
the United Nations Agenda 2030 [RD-4]. Satellite-based
Earth Observation, as a tool to acquire this geospatial
information, is therefore a key technology which must be

taken full advantage of. Countries are also encouraged toalign SDG reporting with the demands of other Multilateral
Environmental Agreements (MEAs), especially those of
the three Rio Conventions and frameworks such as the
Sendai Framework for Disaster Risk Reduction [URL-2] andthe New Urban Agenda [URL-3]. In theory, this alignment
should allow countries to focus resources and increase
reporting efficiency.
The ability to disaggregate SDG indicators where relevant,into thematic areas related to age, gender, economic status,
and income is a key tenet of the “leave no one behind”
philosophy so as to better understand the circumstances ofmultiple groups within society including women, the elderly,
children, those on low incomes and so on. Indicators should
also be disaggregated spatially at the sub-national level,
e.g. by administrative or functional units such as urban
and rural areas. This concept of spatial disaggregation is
an important consideration for the suitability of EO-based
approaches in indicator methodologies.

Compendium of Earth Observation contributions to the SDG Targets and Indicators

5

INTRODUCTION

Scope and purpose of the compendium

Goal 1. End poverty in all its forms everywhere.
Goal 2. 	End hunger, achieve food security and improved
nutrition and promote sustainable agriculture.
Goal 3. 	Ensure healthy lives and promote well-being
for all at all ages.
Goal 4. 	
Ensure inclusive and equitable quality
education and promote lifelong learning
opportunities for all.
Goal 5. 	
Achieve gender equality and empower all
women and girls.
Goal 6. 	
Ensureavailability
andsustainable
management of water and sanitation for all.
Goal 7. 	
Ensure access to affordable, reliable,
sustainable and modern energy for all.
Goal 8. 	Promote sustained, inclusive and sustainableeconomic growth, full and productive
employment and decent work for all.
Goal 9. 	Build resilient infrastructure, promote inclusive
and sustainable industrialization and foster
innovation.
Goal 10. Reduce inequality within and among countries.
Goal 11. 	Make cities and human settlements inclusive,
safe, resilient and sustainable.
Goal 12. 	Ensuresustainableproduction patterns.

consumption

andGoal 13. 	Take urgent action to combat climate change
and its impacts.
Goal 14. 	
Conserve and sustainably use the oceans,seas and marine resources for sustainable
development.
Goal 15. 	Protect, restore and promote sustainable useof terrestrial ecosystems, sustainably manage
forests, combat desertification, and halt andreverse land degradation and halt biodiversity
loss.
Goal 16. 	Promote peaceful and inclusive societies forsustainable development, provide access tojustice for all and build effective, accountable
and inclusive institutions at all levels.
Goal 17. 	Strengthen the means of implementation andrevitalize the global partnership for sustainable
development.

Box 1: The 17 Sustainable Development Goals (SDGs) of the2030 Agenda

The UN 2030 Agenda and its Sustainable Development
Goals (SDGs) has stated its commitment to “leave no one
behind”. Countries will need to align their national policies
with the SDGs to be able to achieve the goals, as well as
setup mechanisms to measure progress and report on the
SDG indicators against the Targets.
In this spirit, this compendium is intended as a resource forall countries (National Statistical Offices and line ministries,
which are national focal points for SDG monitoring andreporting) to realise the potential of Earth Observation (EO)
technology to meet the informational needs of SDG targets
and indicators and to gain an appreciation for how EO can beused to produce high quality, repeatable indicators to assess
progress towards targets. The technical detail and broadtreatment of the indicator framework within this compendium
will also be valuable to the UN Custodian Agencies, the UN
Statistical Commission and its Inter-Agency and Expert Groupon SDG Indicators (IAEG-SDGs), the UN Global Working Groupon Big Data including a team on Geo-spatial information andsatellite imagery [URL-4], and the UN Committee of Experts
on Global Geospatial Information Management (UN-GGIM,
established in July 2011) so that efforts can be aligned andduplication avoided between these various initiatives. The EO
community should also find it useful, especially those who areunaware of the opportunities for EO in the SDGs.
The success of the SDGs themselves relies on access toknowledge and technological development. The SDG targets
9.5 (“Enhance scientific research, upgrade the technologicalcapabilities of industrial sectors in all countries...”, 9.B (“Support
domestic technology development, research and innovationin developing countries…” and 9.C (“Significantly increase
access to information and communications technology…inleast developed countries by 2020”) all mention innovationin support of sustainable development, especially SDG 9.C.
This compendium therefore also makes a major contribution
to Goal 17 which seeks to strengthen global partnerships andcapacity building activities in support of the SDGs.
Finally, the compendium is seen as a catalyst to stimulatedialogue among national governments, the UN system, theEO community, civil society, the private sector and other actors
in order to mainstream EO in the SDG monitoring framework.
The compendium is complementary to the collaborative effortsof the Group on Earth Observations (GEO) and the Committee
on Earth Observation Satellites (CEOS) to promote the use of
EO for the SDGs. It can also be a useful resource to support
other SDG stakeholders, e.g. the GEO Earth Observation for
Sustainable Development Goals (EO4SDG) initiatives, theInter Agency and Expert Group on SDG indicators (IAEGSDGs) and its Working Group on Geospatial Information(WGGI), the Global Partnership for Sustainable Development
Data (GPSDD), National Line Ministries, National Mapping andCadastral Authorities (NMCAs) and others beyond the sciencepolicy sphere (researchers and NGOs).

Compendium of Earth Observation contributions to the SDG Targets and Indicators

6

THE GLOBAL SDG INDICATOR FRAMEWORKThe Global SDG Indicator
Framework
To keep track of progress towards the 17 Sustainable
Development Goals and their associated 169 targets,
a Global Indicator Framework of 232 SDG indicators1 ,was officially adopted by the UN Statistical Commission
at its 48th session in March 2017 following an open andtransparent process involving stakeholders at all levels, andis reviewed annually. The framework collectively provides amanagement tool for countries to implement development
strategies and report on progress toward the SDG Targets.
The UN Statistical Commission (UNSC), the highest
authority of the UN statistical system and a functional
commission of the UN Economic and Social Council (UNECOSOC), has the mandate of overlooking the development
and implementation of the Global Indicator Framework for
the SDGs. The other major actors in the organisation of the
global SDG indicator framework are shown in figure 2.

The High Level Political Forum (HLPF)
Progress towards the Sustainable Development Goals is
discussed by the UN’s High Level Political Forum whichmeets annually under the auspices of the UN Economic
and Social Council (ECOSOC), and every four years under the
auspices of United Nations General Assembly (UNGA). The
Forum has a leadership and review function for the 2030
Agenda, inviting countries to present Voluntary National
Reviews (VNR) of progress towards implementation andpresenting thematic reviews of specific goals and crosscutting themes. An annual review of progress is publishedas the Sustainable Development Goals Report [RD-5].
The 2018 meeting of the HLPF took place 9-18 July
2018, at UN Headquarters in New York [RD-6]. At thisoccasion, a side-event on EO (“From up there to down
1 This number of indicators will change as new indicators
are added and others removed during annual reviews by the
IAEG-SDGs and based on ad hoc requests by indicator custodians. Following the 2020 comprehensive review, the number ofindicators of the Global Indicator Framework has been reduced
to 231 indicators.

here - big space data and the SDGs”) was organised
by Australia and showcased country examples to raise
the awareness and promote the potential in using EOsatellite-data in the process. The 2020 session of the
HLPF, under the auspices of UN General Assembly, looked
retrospectively and prospectively at the totality of theagenda for the ensuing four-year cycle, informed by the
Global Sustainable Development Report. Head of Statesand governments called for a decade of actions to deliver
the SDGs by 2030 and announced actions to advance theagenda. The SDG Summit resulted in the adoption of apolitical declaration: “Gearing up for a decade of action
and delivery for sustainable development”.

The Inter-agency and Expert Group on SDG
Indicators (IAEG-SDGs)
In March 2015 at its 46th session, UNSC created an InterAgency and Expert Group on SDG Indicators (IAEG-SDGs),
made up of representatives of National Statistical Offices(NSOs), to develop and implement the Global Indicator
Framework for the monitoring of the Goals and Targets ofthe 2030 Agenda.
Indicators have been classified by the IAEG-SDGs into
three tiers based on globally accepted methodologies
and availability of data, as described in table 1[URL-5]. The classifications are reviewed annually based onchanges in methodologies, data availability and progressin the development of indicators (as documented inwork plans) [URL-6]. The classification in Table 1 reflects
the decision made during the 51st session of the UN
Statistical Commission in March 2020, following the 2020
comprehensive review of the Global Indicator Framework.
The updated tier classification contains 115 Tier I indicators,
95 Tier II indicators and 19 indicators with a tier level
between I and II, pending a data availability report. Asof the 51st meeting of the UN Statistical Commission(UNSC) in March 2020, the global indicator framework does
not contain any Tier III indicators anymore. In addition
to these, there are 2 indicators that have multiple tiers(different components of the indicator are classified into
different tiers).

UN Statistical Commission (UNSC)
IAEG-SDGs

Inter-agency and Expert Group on
SDG Indicators

National Statistical Offices

National Line Ministries

Custodian Agencies

Working Group on GEO-spatial Information (WGGI)Working Group on Inter-linkages of SDGs

WHO
UNICEF

Working Group on SDMX

FAO
UNISCR
WB

UNSD
UNEP

UNCCD

OECD

UN
HabitatFigure 2: Organisation of the Global SDG Indicator Framework
Compendium of Earth Observation contributions to the SDG Targets and Indicators

7

THE GLOBAL SDG INDICATOR FRAMEWORK

Tier Level

Tier classification

No. of indicators

I

Conceptually clear, have established methodologies,
standards are available and data are regularly produced by countries (at least 50 per cent of countries
and of the population in every region where the
Indicator is relevant).

115

II

Conceptually clear, have established methodologies,
standards are available but data are not regularly
produced by countries95
19 indicators with a tiering (between I and II) pending a data
availability review.
2 indicators will multiple tiers (different components of theindicator classified in tier levels I or II)

III

There are no established methodologies and standards or methodology/standards are being developed/tested

02

Table 1: Updated Tier classification based on the 2020 comprehensive review of the Global Indicator Framework and approved at the 51st sessionof the UN Statistical Commission (March 2020).

The tier classification of many indicators is still expected tochange as methodologies are developed and data availability
increases. Two comprehensive reviews of the Global
Indicator Framework are planned by the IAEG-SDGs tosupport the global monitoring of the 2030 Agenda without
increasing the number of indicators. These reviews include
the replacement, deletion, refinement or adjustment of
indicators, and in a few selected cases, additional indicators.
Additional indicators may be considered only in exceptional
cases when a crucial aspect of a target is not being
monitored or to address a critical or emerging new issue
that is not monitored by the existing indicators. A deletion
will be considered when the methodological development ofa tier III indicator has stalled or not produced the expected
results. The 2020 comprehensive review was conductedby the IAEG-SDGs and resulted in a revision of the Global
Indicator Framework into 231 indicators classified either inTier I or in Tier II.
The implementation of the global indicator frameworkpresents a considerable challenge even for the mostadvanced countries. As acknowledged by the UN General
Assembly, existing statistical systems must be employed
where possible while also leveraging new sources of
information and observations, where needed, to supplement
existing ones. At its 3rd meeting in Mexico (30 March-1 April2016), the IAEG-SDGs established three working groups
to give guidance to countries on how to implement themonitoring framework by examining different data sources.
The expert groups were on (i) Statistical Data and Metadata
Exchange (SDMX), (ii) Geo-spatial information (WGGI), and(iii) Interlinkages of SDG Statistics, to allow for more closely
integrated analyses in monitoring. The work of the WGGI is
the most relevant for this compendium and will be examined.

IAEG-SDGs Working Group on Geospatial
Information (WGGI)
Although the development of the Global Indicator
Framework has primarily been based on statistical data,
it is recognized that geospatial information (includingEO) provides new and consistent data sources that can
support and inform official statistics and consequently
the indicators for the SDGs. The mandate of the WGGI
is to review the Global Indicator Framework through a“geographic location” lens, to identify existing geospatial
data gaps and methodological issues, and to assess how
geospatial information and EO can contribute to the Global
Indicator Framework. The UN Committee of Experts on
Global Geospatial Information Management (UN GGIM).
which leads the global agenda for the management ofgeospatial information to address key global challenges,
provides the secretariat for the WGGI through the Global
Geospatial Information Management (GGIM) Section of the
UN Statistics Division (UNSD).
The WGGI is therefore building on progress made by the
UN-GGIM, the UN GGIM Expert Group on the Integrationof Statistical and Geospatial Information (EG-ISGI), theGroup on Earth Observations (GEO), the UN Global Working
Group on Big Data (an outcome of the 45th Meeting of
the UN Statistical Commission) and other various relatedgroups, in the development of geospatial information and
EO data inputs into the global indicator framework.
The WGGI - currently led by NSO representatives from
Mexico and Ireland - has recently updated its terms ofreference and membership to strengthen the coordination
between the statistical and the geospatial information

2 As of the 51st UNSC (March 2020), the global indicator
framework does not contain any Tier III indicators

Compendium of Earth Observation contributions to the SDG Targets and Indicators

8THE GLOBAL SDG INDICATOR FRAMEWORK

communities. This includes a stronger connection to related
activities, especially the work of custodian agencies, anda wide consultation on the status of geospatial datacollection by countries. The WGGI workplan for the years
2020 and 2021, includes the development of a SDG
geospatial roadmap with a story-telling mechanism tobetter communicate the value of geospatial information andEarth Observations for the SDG indicators, the interlinkages
with expert groups such as the EO4SDG initiative of theGroup on Earth Observations (GEO), the showcase of bestpractice examples that will support Member States toimprove their application of geospatial information andEarth observations, and the development of guidance andrecommendation regarding the use of proven toolkits,
including SDG EO toolkits. The Compendium represents an
input to the work of the WGGI, through the contribution
of GEO EO4SDG.

Custodian Agencies
Recognising the scale of the challenge in ensuring
appropriate methodologies, in data availability and inconsistent and comparable reporting by countries, the UN
has appointed specialised Agencies to play a coordinatingrole as Custodians of SDG Indicators relevant to their area ofexpertise. Custodian agencies are the United Nations bodies(and in some cases, other international organizations) thatare responsible for compiling international data series,
metadata on SDG indicators, and for providing the data,
along with regional and global aggregates, to the United
Nations Statistics Division (UNSD). These agencies havealso the mandate to compile monitoring guidelines formeasuring and reporting on the indicators, to support
countries on their implementation and strengthening
national statistical capacities, and to collect national data
for the global reporting mechanism. To do so, custodians
are expected to coordinate closely with countries andtheir national statistical systems, regional bodies andinternational stakeholders on indicators’ development.
Each SDG indicator has one nominated custodian, or cocustodians as the case of the UN Environment Programme(UNEP) for the indicator 6.6.1 on waterrelated ecosystems
and further partner agencies. Custodian agencies provided
information on their data collection processes and also
nominated a focal point for each of the indicator which
can be useful when questions or issues arise about
definitions, methods or data.
These agencies may publish the country data in their own
databases and use it for thematic reporting. The country
data need to be internationally comparable. Therefore,
the custodians advocate for strict standards for countries
to follow in the indicator methodologies. According to

the 2030 Agenda, SDG reporting is the responsibility ofcountries and conducted in compliance with the UN’s
Fundamental Principles of Official Statistics. These
principles are considered a basic framework that national
statistical offices and other statistical organizations must
follow in recognizing official statistics as a public good.
To this end, the agencies are also responsible for developing
international standards and recommending methodologies
for monitoring. Noting that if they can “recommend”
methodologies for standardization purpose, it is up tocountries in the end to follow those methods or use others
if more relevant to their national specificities. Measurementof some of the indicators is entirely achievable for many
countries today, whilst tracking other indicators will require
further improvements in underlying data and statistics, aswell as corresponding availability of robust methodologies
for National Statistical Offices (NSOs) and the relevant
ministries to follow.
Another central responsibility of the custodian agencies is
to strengthen national monitoring and reporting capacity.
When country data are missing, or collected using a different
methodology or inconsistently reported by different sources,
custodian agencies need to do estimates or adjust the data
together with the specific countries. All final data submitted
to UNSD for the global reporting on SDG indicators need to
be validated and approved by countries.

National Statistical Offices and line ministries
The responsibility for the collection of national data is
country-specific and typically consists of an oversightauthority – the National Statistical Offices (NSOs) – and line
ministries responsible for their respective SDG Indicators.
In order to assist NSOs with this effort, especially incountries low on capacity, the Cape Town Global Action
Plan for Sustainable Development Data was conceived by
the UNSC to recognise that the implementation of the SDGs
requires coordinated action and considerable resources
to process vast and disparate data sources [RD-4]. The
action plan calls for enhanced capacity building in countries
facing high data challenges, modernized national statisticalsystems, and encourages NSOs to embrace open data
initiatives while integrating new data sources (including EOdata) into statistical production programmes. The Global
Action Plan proposes to leverage the efforts of the NSOs tomodernize their national statistical systems, and also theefforts of international organizations and partnerships suchas those of the EO community to promote EO in support ofSDG monitoring.

Compendium of Earth Observation contributions to the SDG Targets and Indicators

9THE IMPORTANCE OF EARTH OBSERVATION IN THE INDICATOR FRAMEWORKThe importance of Earth
Observation in the indicator
framework
Advantages and opportunities of Earth
Observation
Of the various forms of earth observations, including
those from space-borne, airborne and in-situ platforms,
satellite-based EO is the primary source of additional andcomplementary observations for national statistics at theappropriate scale for SDG reporting. Other forms of EO
are also important at the national and subnational levels,
especially for indicator disaggregation at finer spatial
scales.
There is huge potential for satellite EO to support the aimof the 2030 Agenda in “leaving no one behind” as, by
nature, space-borne observations are borderless, impartial
and inclusive of all. It is also a crucial data source for manyof the indicators describing the environmental aspectsof the planet and as a spatial disaggregation method ofother geospatial statistics. Designed for planetary-scale
coverage, satellite EO has some key characteristics whichmake it an indispensable source of data for a select few
SDG indicators and a supporting source of data for manyothers. Satellite EO provides:
1. A synoptic view of the Earth’s surface: polar-orbiting,sun synchronous EO sensors observe wide swathesof the Earth in one pass, acquiring and storing large
amounts of Earth surface imagery under constantconditions of solar illumination. Geostationary satellites
observe hemispherical-scale patterns and can produce
imagery up to every 15 minutes.
2.Regular and repeatable observations: polar-orbiting EOsatellites orbit the Earth several times per day allowing
consistent and systematic surface observations of theentire Earth surface. The continuity of observations in
the long term is now guaranteed through dedicated,
operational missions such as those of the Copernicus
programme.
3. Multi-annual time series of observations: since the1970s the average operational lifetime of an EO mission
has almost tripled to today’s average mission lifetime of
8.6 years [RD-7], enabling more stable and continuous
observations from the same sensor over several years
or more.
4. Cost-effectiveness for monitoring remote andinaccessible areas: EO satellites are designed to observe
any location on the Earth’s surface at some time in theirorbit, albeit with some constraints around polar regions,permitting observation of areas otherwise inaccessible
for ground based surveys.

In addition to these technical benefits, access to EO data
is increasingly democratised and open with free andopen data policies proliferating as well as the processing
infrastructure to process them. The Copernicus programmeof the European Commission has made data from its
Sentinel satellite programme free for all – data continuity
is an essential element of the Copernicus program. The
United States Geological Survey (USGS) also liberated thevast archive of Landsat data in 2012, dating from 1978
onwards, making it free for all to use. These progressive
initiatives set a precedent for future EO programmes. Other
geospatial data and statistics are increasingly free andopen, in line with the GEO principles of sharing of data,
information, knowledge, products and services. One of themain outcomes of GEO since its inception (2005) is actuallyto have stimulated the open data policy as it has gonefrom the exception to the global norm.

Earth Observation processing infrastructure
Coupled with this Big Data revolution, the EO dataprocessing and analytics infrastructures has also advanced
considerably. The widespread use of data architectures andservers on the internet, enabling “cloud computing”, hasbeen a key element of this revolution. High Performance
Computing (HPC) is another. HPC allows advanced
processing infrastructure to run efficiently, reliably andquickly meaning that larger, more complex datasets,
especially from new data sources, i.e. Big Data, can beprocessed more efficiently than before. The advent of cloudcomputing and HPC has led to several advances in EO dataprocessing. Some of the main enabling infrastructures aredescribed below – the Earth Observations Data Cubes(e.g. the Open Data Cube promoted by CEOS or the Euro
Data Cube supported by ESA), the European Commission’s
Data and Information Access Services (DIAS) and the
ESA Thematic Exploitation Platforms (TEPs). In addition,
Trends.Earth, a customised platform for SDG monitoring
reporting with EO data, is briefly presented.
Data cubes organise satellite imagery into stacks ofconsistent, calibrated, geographic ‘tiles’, underpinned by arelational database ready for rapid manipulation in a highpowered, computing environment. Data cubes can serve the
EO data and information needs (in terms of data discovery,
access, processing and analysis) of the large communityof SDG stakeholders, addressing both global and national
data processing needs. Some of the most widespread data
cubes for processing and analysing large spatiotemporalgeospatial datasets are listed here:
Data Cube [URL-8]• CEEOS
System Data Cube (ESDC) [URL-9]• DarthAustralia Open Data Cube (ODC) [URL-10]• Euro igitalDataEarth
Cube [URL-11]• Google EarthEngine [URL-12]• Open Data Cube
[URL-13]•Compendium of Earth Observation contributions to the SDG Targets and Indicators

10

THE IMPORTANCE OF EARTH OBSERVATION IN THE INDICATOR FRAMEWORKThe European Copernicus Programme has deployed fivecloud-based platforms known as the DIAS which are
funded by the European Commission [URL-14]. The DIAS
facilitate access to Copernicus data and services, including
the Sentinel data, as well as to on-line EO data processing
and analytic tools. The Amazon Web Services (AWS) arealso providing access to Sentinel-2 imagery, through theSentinel Hub [URL-15], thereby significantly reducing theuser burden of downloading, archiving and processing
petabytes of data.
The Thematic Exploitation Platforms (TEPs) were createdby ESA to help users access an interconnected, virtual work
environment, providing access to EO data and the tools,
processors, and processing infrastructure required to workwith them, through one coherent interface. Instead of theuser downloading and working locally on EO and non-EO
data, the TEP allows the user to access these data on thecloud. Currently there are TEPs addressing the following
applications:[URL-16]• Coastal
Forestry• Hydrology[URL-17]• Geohazards[URL-18]• Polar [URL-20][URL-19]• Urban [URL-21]• Food security [URL-22]•
Trends.Earth [URL-23], an EO-based tool, developed byConservation International in corporation with NASA and
Lund University, under funding of the Global Environment
Facility, is a customised platform for SDG monitoring
reporting with EO data, currently equipped with tools forreporting on SDG 15.3.1 on land degradation. It integrates
national level data with globally available EO datasets tocalculate the proportion of degraded land. It is based onstandardised methods that have been compiled in a Good
Practice Guidance by the UNCCD and partners including
CSIRO [RD-8], while also providing the flexibility forcustomisation to local conditions. The tool uses data fromthree sub-indicators –land cover, vegetation productivity
and soil organic carbon - to estimate the degraded landarea and is able to produce spatially explicit outputs aswell as tabular results.

Challenges and limitations of Earth
Observation
It is all too easy to view EO data and processing tools as
solutions with endless possibility, that will solve manyof the SDG data challenges but there are limitations to
EO data and the human and technical capacity to processthem. Here the main technical and capacity limitations
are discussed in relation to optical and radar-based
methods of data acquisition.

Optical satellites, i.e. those that passively record reflectedsolar radiation from the Earth’s surface, are affected bycloud coverage as reflected light cannot pass throughcloud. Equally they cannot operate at night. Therefore,
optimal viewing conditions for optical satellite are cloudfree days – infrequent in moist and humid climates such as
those in the Tropics. The availability of dense time series,
combined with the capacity for pixel-based processing,
due to high geometrical accuracies (within pixel), allows
extraction of useful information, especially in cloudy regions
such as in the tropics. Optical satellites are also subject
to trade-offs between spatial and temporal resolution,
between revisit time and geographical coverage. This
necessitates, for example, that optical satellites which
achieve very high resolutions, e.g. <1m, cover less area
and acquire imagery of smaller dimension than a medium
resolution satellite which could image larger areas in one
pass. The emergence of satellite constellations (including
constellation of micro satellites) allows high revisiting
even at high spatial resolutions. Trade-off also applies
to revisit time as satellites that acquire imagery in lower
detail can generally revisit the same point on the Earth’s
surface more regularly, even once or twice per day for somesatellite systems.
Satellites carrying radar, i.e. those that actively emit a highfrequency signal, can operate at night and through cloud
cover as they are not dependent on sunlight as a sourceof illumination to image the Earth’s surface. The ability toimage the same area repeatedly regardless of cloud coveror time of day gives radar a significant advantage overoptical imagery. The limitation in this is that, being an active
instrument with power limitations, radar cannot remotelysense the whole surface of the globe as done by the optical
instruments. All radar systems have a duty cycle (a fraction of
the time when the radar is transmitting). Although radar is a
powerful tool for remote sensing in tropical and ice-covered
regions, it is also subject to the same trade-offs in temporaland spatial resolution as described above. In addition it has
the unique and undesirable property of producing speckle inimagery which results from the nature of the radar signal
interacting with surface objects, and is therefore not as
intuitive as optical imagery. Earth surfaces do not appear
as the human eye might see them. Radar imagery needs
extra pre-processing and expert interpretation to be usedeffectively. Some big progresses have also been made byradar. For example the Sentinel 1 mission allows imaging of
the whole land surface of the globe every 12 days, and every
observation is a ‘good’ observation, since it is not affectedby clouds and by sun illumination.
In addition to the technical characteristics of EO dataacquisition, there are limitations associated with integrationof EO into national statistics. The challenge of synthesisingmultiple data sources will be a key one for custodians and
NSOs alike as will the design of methodologies which canharmonise EO with statistical data according to the rigorous
standards demanded by official national statistics. As EO

Compendium of Earth Observation contributions to the SDG Targets and Indicators

11

THE IMPORTANCE OF EARTH OBSERVATION IN THE INDICATOR FRAMEWORK

data is a highly specialised field, the technical capacityand financial resources to stock, process and manipulate
EO data can be an issue for countries. Space agencies,
with specialists in these realms, are increasingly launching
initiatives to make EO data less complex and accessible
for users. At an international level these efforts are being
coordinated by the Committee on Earth Observation
Satellites (CEOS) which includes promotion of Analysis
Ready Data (ARD) standards and exploration of open data
platforms such as the Open Data Cube.
Other limitations include the difficulties for some countries
to access and process the large amount of EO data
available on their countries and the need to have somelarge computing infrastructures to process and analyse
the data. Cloud-based solutions can be used to overcome
this problem, as these allow remote access to the data
and the processing capacities. However due to sovereignty
reasons some governments are reluctant to use cloudbased platform and lose control of their national data, andhence prefer using their local computing platforms which
are confronted to big data challenges. Another obstacle isthe lack of technical expertise in some countries, hence
the need for capacity building. Many EO Massive Open
Online courses (MOOCs) have been developed to build
these capacities, and some organisations (CEOS or national
scientific organisations) are also dedicated to provide
technical training and technology transfer to help countries
use EO data and run these platforms.

GEO initiatives related to the SDGs
There is more open access to EO data and cloud computing
power than ever before, such that EO products and thecomputational tools to process them, can now readily help
countries to monitor change in many land, freshwaterand ocean surface processes. However, with a plethoraof satellite sensors and downstream products, it ischallenging for users to identify those which meet theirreporting needs. The Group on Earth Observations (GEO)
is a key player to ensure that such observations are easily
accessible and fit for purpose.
The Group on Earth Observations (GEO) is anintergovernmental partnership that aims to improve theavailability, access and use of Earth observations for asustainable planet. GEO promotes open, coordinatedand sustained data sharing and infrastructure for better
research, policy making, decisions and action across many
disciplines. Formed in 2004 at the third Earth Observation
Summit by a resolution from almost 60 countries, GEO
now consists of 105 governments, 126 Participating
Organizations, and vast EO resources in the Global Earth
Observation System of Systems (GEOSS) Platform. GEOSS
is a central part of GEO’s Mission, consisting of coordinated,
independent EO data, information and processing systems
that serve free data to a myriad of public and private sector
users, underpinned by open data sharing policies andpractices. In addition to building observation systems, GEO
supports policy making through its strategic engagementswith the 2030 Agenda on Sustainable Development, theParis Agreement for Climate, and the Sendai Frameworkfor Disaster Risk Reduction. Very recently, the NextGEOSS
Platform has recently been published to address more
specifically the SDG process [URL-24]. GEO plays an
instrumental role in promoting and showcasing the valueof EO in support of the SDGs and launched the EO4SDG
initiative in 2016 [URL-25]. The GEO EO4SDG initiative
is run in close partnership with the UN agencies and the
NSOs and has four primary lines of actions: (i) nationalpilot projects integrating EO with national statistical data;(ii) capacity building around the methodologies needed
to apply EO data; (iii) identification and development ofdata and information products to advance understanding
and access to suitable EO resources and (iv) outreach andengagement.
In addition to the GEO EO4SDG initiative, a number of GEO
flagships, initiatives, foundational tasks and community
activities are designed to bring scientific observations
to users who need them in different sectors such as
biodiversity, forestry, agriculture or urban. A number of
GEO activities and how they support the SDG indicator
framework are briefly introduced below:
GEO Biodiversity Observation Network (GEO BON)
[URL-26] is aimed at users in the biodiversity community.
GEO BON initiates and coordinates efforts to design andimplement interoperable national and regional biodiversity
monitoring networks and is spearheading the emerging
Essential Biodiversity Variables – a set of variables required
to report on and monitor biodiversity change. Theseobservation networks support indicators that concern life
on land (15.1.1, 15.1.2), in freshwater (6.6.1) and oceans(14.4.1).
AquaWatch [URL-27], the GEO Water Quality Initiative,
aims to develop and build the global capacity and utilityof Earth Observation-derived water quality data, products
and information to support water resources managementand decision making. AquaWatch is aiming to produce a
global monitoring system for water quality by 2025 called
the Water Quality Information Service which will be adirect contribution to indicator 6.3.2 (water quality), 6.1.1
and 6.1.12 (sanitation).
GEO Global Agricultural Monitoring (GEOGLAM)[URL-28] is an initiative that supports the AgriculturalMarket Information System (AMIS) and provides monthly
crop monitoring and early price warnings, food supply
stress (2.4.1,2c.1) and sustainable use of resources such as
water (6.4.2, 6.5.1). In addition the GEOGLAM initiative has
supported the development of the world’s first Rangelands
and Pasture Productivity (RAPP) Map [URL-29]. The RAPP
Map is the spatial data platform for GEOGLAM’s RAPP
activities.

Compendium of Earth Observation contributions to the SDG Targets and Indicators

12

THE IMPORTANCE OF EARTH OBSERVATION IN THE INDICATOR FRAMEWORKGEO Blue Planet [URL-30] plays a coordinating role in oceanand coastal observations. It is an umbrella initiative for manyother programmes responsible for gathering observations inocean and coastal waters. It improves engagement withusers across a variety of disciplines to improve the qualityof services delivered to them and to ensure inter-operabilityof marine observational data. Through its activities, the Blue
Planet initiative raises awareness of the societal benefitsof ocean observation for both the public and policy sectors.
The activities of Blue Planet are of particular relevance to
the SDG indicators on coastal marine pollution (14.1.1) andocean acidification (14.3.1).

and information on mountains. It specifically aims to halt
and manage the drivers of change in mountain ecosystems
that result in negative consequences for the health ofthese ecosystems such as land use and climate change bybring the best available data for decision making impacting
mountain communities. Some of its data-oriented tasks
involve more precise delineation of mountain ecosystems
and harnessing the best available spatial data tocharacterise mountain ecosystems, including EO-derived
land cover change data. Therefore, it is of particular
relevance for 15.4.1 (mountain green cover) and 15.4.2(mountain biodiversity).

Global Forest Observation Initiative (GFOI) [URL-31]
was a concept that arose from the Forest Carbon Tracking
Project undertaken as part of the 2009-2011 GEO Work
Plan whose start-up phase commenced in 2012. The GFOI
is primarily tasked with ensuring a sustained supply ofobservations for national forest monitoring systems andassisting countries in making best use of them. In addition,
it helps countries in starting up national forest observation
systems according to GEO principles of openness andcommon standards. The GFOI interfaces with the majorinternational forest assessments and assist countries toreport to the Global Forest Resource Assessments of theFood and Agricultural Organisation (FAO) and the national
Green House Gas (GHG) inventories reported to the UNFCCC
using IPCC methods. For the SDGs, the GFOI is mainly
concerned with indicator 15.1.1 (Forest areas).

The GEO Land Degradation Neutrality (GEO LDN) Initiative[URL-35] was launched in 2018 to enhance national
capacities to map and measure the extent of degradedlands and report on SDG Indicator 15.3.1 (“proportion ofland that is degraded over total land area”) and ultimately
to achieve Land Degradation Neutrality (LDN; SDG Target
15.3) under the auspices of the UNCCD. GEO is well-placed
to assist the UNCCD (the custodian agency for Target 15.3
and Indicator 15.3.1) and its contracting parties with therapid provision and deployment of EO datasets, in-country
capacity building and training, along with guidance on theuse and development of EO tools and platforms.

The GEO-Wetlands [URL-32] initiative aims to realise
the possibility of a Global Wetlands Observation System(GWOS) on behalf of the Ramsar Convention. Being a GEO
Initiative it follows the same basic principles of opennessand data sharing. This will be achieved through a wetlandcommunity geo-portal. GEO-Wetlands is already building acommunity of wetland observation practitioners, spanning a
range of actors and has pilot projects, e.g. Global Mangrove
Watch, with a view to building the GWOS. GEO Wetlands
will play a major role in coordinating data collection forindicator 6.6.1(water-related ecosystems).
The GEO Human Planet [URL-33] initiative is focused on
developing a global baseline map of human settlement andpopulation density, as well as testing methods for regular
updates of the baseline for post-2015 indicators such as
those of the SDGs, the UNFCCC and the Sendai Framework.
The core partnership consists of several universities andagencies with expertise in human settlement mapping. EO
plays a key role in the Human Planet mapping approach,both for baseline studies and mapped updates, as it
captures the physical infrastructures of human settlements.
For SDGs, the Human Planet initiative is a key partnershipfor indicator 11.3.1 (land consumption).
The GEO Global Network for Observation andInformation in Mountain Environments (GEO-GNOME)[URL-34] initiative addresses the paucity of observations

Previous assessments of EO contribution to
the SDGs
Recognising the fundamental role for satellite EO in therealisation of the Global Indicator Framework, CEOS has
identified the SDGs as a top priority and established in
October 2016 the CEOS Ad-Hoc Team on SDGs (AHT SDG)
[URL-36] dedicated to better coordination of the world’s
space agencies in support of the provision of satellite data
for the 2030 Agenda. The AHT SDG team aligns with the
SDG agenda through GEO, UN agencies and at national level
through NSOs and ministries. The team is also dedicated tofulfil its unique role in providing information about satellite
data to the SDG community, and informing space agencies
about specific data requirements to help measure someindicators.
There have been many studies and reports made available
on the contribution of EO to the SDG indicator frameworkand to the Agenda 2030 more broadly. Here, three of themost relevant assessments are briefly described – that ofthe IAEG-SDGs WGGI; GEO with the support of JapaneseSpace Agency (JAXA); CEOS with the support of theEuropean Space Agency (ESA).
In 2016-2017, the IAEG-SDGs WGGI convened two
meetings, one in Mexico City [RD-9] and one in Kunming,
China [RD-10], and identified a short-list of 24 SDG
indicators which could benefit from geospatial data,
categorized into two types (direct contribution or indirect
but significant support). They suggested that there are
15 indicators where geospatial information together, with

Compendium of Earth Observation contributions to the SDG Targets and Indicators

13

THE IMPORTANCE OF EARTH OBSERVATION IN THE INDICATOR FRAMEWORK

statistical data, can contribute directly to the productionof indicators and 9 indicators where geospatial data cansignificantly support the production of indicators.
In 2017, GEO, in collaboration with JAXA and CEOS,
produced a booklet entitled “Earth Observations in supportof the 2030 Agenda for Sustainable Development” on thepotential role that EO data can play in relation to the 17
SDGs [RD-11]. This booklet mainly featured case studies
showcasing EO for specific indicators as well as a very
brief assessment of SDG Targets and Indicators that canbe supported by EO. Their conclusion was that almost ahalf (71) of the targets could be supported and about one
eighth (29) of the indicators.
In 2018 CEOS, supported by ESA, produced a handbook on“Satellite Earth Observations in Support of the Sustainable
Development Goals” [RD-12]. CEOS analysed which targets
and indicators could be supported by EO, the distribution of
the custodian agencies for the indicators supported by EO
and the tier status of the indicators supported by EO, whileraising awareness of the importance of EO for achieving the
SDG goals and targets. This highlighted the opportunities
and challenges to mainstream EO in the SDG processes, andprovided different perspectives from various stakeholders
regarding the integration of EO in the SDG framework, while
showcasing the value of earth observation with practicalexamples. The analysis done by GEO on the relevance of
EO for the SDG targets and indicators was re-used andbuilt-upon –an independent analysis was not carried out.
A number of statistics were added on the custodians andtier levels. The analysis concluded that 73 targets and
29 indicators in total could be supported by EO (45 %of tier 1 indicators and 24% of tier 2) and that the FAO
was the custodian whose indicators could most benefit
from EO, followed by UNEP and UN-Habitat. The reportdemonstrated the importance of EO for goals 6, 11, 14 and
15 in particular as their respective targets and indicators
require information on land cover, land productivity, above
ground biomass, water extent or quality characteristics, aswell as air quality and pollution.

Key EO datasets for the SDG Global Indicator
Framework
Custodian agencies have the mandate to mobilize resources,
including data, in support of national efforts to monitor
and report on the SDG indicators. The data mobilization
includes the provision of global or regional datasets thatcan complement national data when not adequately
available.
This compendium only addresses key, critical and freely
accessible EO datasets at the time of writing and, as thisis a rapidly evolving environment, precludes an exhaustive
review of all possible global EO datasets of potential use
for SDG reporting.

It is impossible to do an exhaustive review of all potential
global EO-based tools (i.e. tools that allow access to global
datasets) that could assist NSOs and indicator custodianswith delivering and implementing EO-based methodologies.
Below are a sample of key global products that covermajor thematic areas of the biosphere and society suchas land cover, vegetation productivity, forests, wetlands,
surface water, human settlements and which can support
the methodological development and measurement of anumber of SDG indicators.
Global Forest Watch (GFW) [URL-37] is a platform,
delivered by the World Resources Institute (WRI), whichhouses geospatial datasets related to forest cover, condition
and use. It is the key visualisation tool for the Global Forest
Change product produced by the University of Maryland incollaboration with Google Earth Engine. The Global Forest
Change product depicts a global tree cover baseline for theyear 2000 and shows losses of tree cover on an annual
basis up to 2017. Gains in tree cover are also visible but
not resolved to annual time steps. This platform serves
datasets of key importance for reporting on indicator 15.1.1(forest area).
Global Mangrove Watch (GMW) [URL-38], developed
under the JAXA’s Kyoto & Carbon initiative, provides
geospatial information about mangrove extent and changes
to the Ramsar Convention, national wetland practitioners,
decision makers and NGOs. The global mangrove extent
baseline map for the year 2010 is displayed on WRI’s
Global Forest Watch portal. Data are available for baselinechanges from 1996 and 1997 (JERS-1), 2007 to 20010(ALOS) and from 2015 (ALOS-2).This provides invaluable
data for indicators 6.6.1, and 15.1.1., 15.1.2 as well as
indicators under target 13.1 on disaster risk reduction,
recognizing that mangroves have a key role to play inmitigating climate–related hazards such as sea level rise.
The Global Urban Footprint (GUF) [URL-39] / World
Settlement Footprint (WSF) [URL-21], produced by the
DLR’s Earth Observation Centre, provides global mapping of
built-up areas, as man-made built structures with a verticalcomponent. The GUF 2012 provides a global map of built
areas for the reference year 2012, based on TerraSAR-X
and TanDEM-X imagery, from the Airbus Defense and Space
company. It is currently available to the scientific researchcommunity, at ~12 m spatial resolution near the equator,
with increasing spatial resolution towards the poles. A
75m resolution data is also available for public use. The
WSF 2015 is a new, globally consistent map of the world’s
human settlements at an unprecedented spatial resolutionof 10 metres, generated from the joint processing of
optical and radar imagery. WSF 2015 has been derived
from Landsat 8 and Sentinel 1. A WSF evolution which
will allow to map the urban development patterns overthe last 30 years, based on the whole Landsat archive, isunder preparation in cooperation with Google Earth Engine.
The WSF 2019, to be released by the end of 2020, will beCompendium of Earth Observation contributions to the SDG Targets and Indicators

14

THE IMPORTANCE OF EARTH OBSERVATION IN THE INDICATOR FRAMEWORK

the first global dataset of human settlements based on
Sentinel 1 and 2. Both GUF and WSF datasets are available
through the ESA’s Urban Thematic Exploitation Platform(U-TEP). These global data sets are important for reportingon indicator 11.3.1.
The Global Human Settlement Layer (GHSL) [URL-40],
produced by the European Commission’s Joint ResearchCentre (JRC), provides global coverage of the humansettlement at 30m spatial resolution for four time steps
from 1975 through to the year 2015. It consists of spatial
datasets of built-up areas, population density and humansettlement. The degree of area built-up is measured as
the proportion of building footprint area within the total
area of the pixel. Although built-up area is expressed at
30m resolution, when combined with census data, thepopulation metric layers are produced at 1km resolution.
The Global Surface Water Explorer [URL-41] / Surface
Water Viewer [URL-42], produced by the European
Commission’s Joint Research Centre (JRC) and UN
Environment respectively, provide global coverage of theworld’s surface water resources including their temporal
dynamics over the last 32 years. The 30m resolution waterproducts within these tools are based on the Landsat
archive from 1984 to 2015 and could have a wide range
of uses in the SDG framework, supporting applications
including water resource management, climate modelling,
biodiversity conservation and food security. Indicator 6.6.1,
in particular, could benefit from the information containedin the Global Surface Water Explorer within the Explorer
and the UN’s Surface Water viewer.
The ESA Climate Change Initiative (CCI) Land Cover[URL-43] is a project that has produced long-term,
consistent, global land cover time series from 300m spatial
resolution satellite imagery based on the UN Land CoverClassification System (LCCS). The CCI project has alsodelivered a free user tool to allow users to customise theland cover product for the purpose of climate modelling.
The ESA CCI land cover products are potentially key inputs
to the indicator 15.3.1.

The International Soil Reference and Information
Centre (ISRIC) [URL-45] has produced a global maps ofsoil property and class called SoilGrids at 1 km / 250 m
spatial resolutions.
The Global RAPP (Rangelands and Pasture Productivity)
Map [URL-29] is an online geospatial tool that provides
EO-derived information about the state and conditionof global rangelands. It gives time-series data on thevegetation and environmental conditions, allowing nationaland regional tracking of the resources which sustains
livestock production. It has been developed in Australia,
and is currently hosted by Data61 with the assistance ofIT resources and services from the National Computational
Infrastructure (NCI), and the AusCover facility. RAPP Map
is managed and supported by the Commonwealth Scientificand Industrial Research Organisation (CSIRO) and throughfunding from the Australian Government’s National
Landcare Programme.
In summary, there are global EO-derived data sets which areof direct relevance for some of the SDG indicators, yet there
are some caveats to their use. In terms of computational
resources, the global datasets can only be produced throughautomatic processing using enabling digital infrastructuresuch as cloud computing which necessitate high internet
bandwidth, access costs, computing infrastructure andexpertise. In terms of accuracy, the global accuracy reportedby the dataset producer might not be reached at country
level, simply because of the need to favour global coverageat the expense of local precision. Therefore most of thesedatasets should be validated at the country level in orderto estimate their accuracy for national scale uses, and thisis why GEO global initiatives like GEOGLAM have a networkof in-country validation sites. With these caveats in mind,
global EO datasets, produced by major global collaborative
initiatives, under the auspices of GEO, are an importantsource of information to complement national data andmay represent the only data source for data poor countries.

The Global Soil Organic Carbon map (GSOCMap)[URL-44], utilises remote sensing imagery as prediction
factors for global soil organic carbon mapping as traditionalsoil mapping involves a sampling based approach. This
process of integrating remote sensing imagery in digital
soil mapping of the world also paves the way for soilorganic carbon (SOC) maps. The Global Soil Partnership(GSP), of the FAO, and its Intergovernmental TechnicalPanel on Soils (ITPS) has launched the GSOCMap, in
support of the Sustainable Development Goal Indicator
15.3.1. The map, compiled from national SOC maps,
can be used to assess soil condition, identify degradedareas, set restoration targets, explore SOC sequestration
potentials and support the greenhouse gas emission
reporting required by the UNFCCC.

Compendium of Earth Observation contributions to the SDG Targets and Indicators

15

METHODOLOGY FOR ANALYSIS OF EO FOR THE SDGS

Methodology for analysis of EO for
the SDGs
The analysis of EO potential was undertaken based on
the tier status and methodologies as of in February 2019.
The presentation of results in this Compendium has beensubsequently updated to reflect changes arising fromthe 2020 comprehensive review of the Global Indicator
Framework. Due to broad scope of all 232 indicators3 ,it was necessary to first conduct a broad screening of all
indicators and select the subset of indicators that would
be analysed in depth and presented in this compendium.
This initial screening exercise was necessary to identify only
those indicators where EO could reasonably contribute to thedevelopment or implementation of their methodology. In
order to do this systematically, a ”traffic light” system of red,amber, green colours was applied across the full indicator
suite to flag EO relevance, where:
Green: SDG Indicators for which Earth Observations
have or have not been currently identified as a source of
information but would make a definite contribution to their
methodological development with relative ease
Amber: SDG Indicators for which Earth Observations has
not been currently identified as a source of information
but where there is potential to do so with further
methodological development
Red: Earth Observation currently has no contribution to themethodology, i.e. all other SDG Indicators
The amber and green indicators resulted from a two –phaseanalysis: (i) an initial screening exercise based on an internal
review of the full indicator suite with respect to EO potentialand (ii) an in-depth analysis, according to the readiness and3 After the 2020 comprehensive review of the Global Indicator
Framework, the number of indicators has been reduced from 232
to 231 indicators.

adequacy criteria, as described and presented in a seriesof “factsheets”, one per indicator. This in-depth analysis
followed a similar logic of a traffic colour system but thistime applied across eight criteria per indicator which led
to an overall rating of EO relevance for the indicator. The
overall colour rating was deduced from a review of all thecolours across the eight criteria and an informed judgementon the use of EO for the indicator. A brief justification is
given to support the final colour assessment. The eight
criteria are listed below (table 2), covering the readiness of
EO for the indicator (1-4) and the adequacy for the indicatormethodology (5-8). The criteria for their colour rating are
explained at the end of each factsheet for reference purposes.
Readiness criteria:
1. Maturity of EO technologies: How tried and tested are the
EO technologies and are they globally applicable?
2. 
Status of EO in indicator guidelines: Is EO explicitly
mentioned in the indicator methodology?
3. Technical capacity required: What is the level of expertise
required to use EO at the country level?
4. 
Availability of global EO data: Are global or regional
datasets publicly available and free to use?
Adequacy criteria:
5. Compliance with reporting calendar: Does the EO
methodology align with the proposed reporting calendar?
6. Sensitivity to change: Are the EO data sensitive to change
in the parameter of interest?
7. 
Scalability (spatial): Are the EO data geographicallyscalable or can they only be retrieved at limited scale?
8. 
Substitutability of gaps: Is EO the only source of
information for the methodology or are there alternative
data sources which could fill gaps in the EO data record?

Readiness

Criteria

Supporting
Comments

Adequacy

Maturity of EO
technologies

Statusof EO inindicator
guidelines

Technical
capacityrequired

Availability of
global EO data

Compliancewithreportingcalendar4

Sensitivity tochangeIs itscaleable(spatial)?

Is there asubstitute forgaps in the
EO record?

Tried andtested andglobally
applicable

Frequently
mentioned

Little capacity– easy to use
and implement
in country

Widely available andin public domain

Yes

Dynamic
variables

Good

Yes, stableapproaches
found

Demonstrated
on a limited
scale

Suggestedas part ofother spatial
approaches

Medium levelof expertiseneeded, not all
countries will
implement

Publicly available
but only for limited
regions

PartlyStatic variables- Patchy-that may be
relevant but not
useful for changemonitoring

Some but notconsistent

ExperimentalNotmentioned
or implied

High levelof expertise,
demandingNot publicly available No
anywhere but maybe
on request

Insensitive
to change inparameter ofinterest

EO is the only
support for the
methodology

Poor orlimitedTable 2: Explanation of criteria used to assign the RAG colours for each of the criterion used to assess overall contribution of EO to the indicator

Compendium of Earth Observation contributions to the SDG Targets and Indicators

16
4 Baseline year and reporting interval as mentioned in metadata

METHODOLOGY FOR ANALYSIS OF EO FOR THE SDGS

Once the final colour rating was assigned and justified by
an explanatory sentence in the comments column (either
amber or green, as red indicators would already have been
eliminated by the screening exercise), the factsheets were
populated with more detailed information gleaned from thepeer-reviewed and grey literature, while supplemented byexamples taken from expert contributions. The factsheet
itself is carefully structured, mirroring the sub-sectionsof the metadata guidelines produced by the indicator
custodians, for ease of interpretation and transferability
to indicator methodologies on the SDG indicator Metadata
repository [URL-27]. The factsheets were finally reviewed
by the same experts for consistency and content.
It is accepted that the colours assigned and information
collated could be subject to debate and further dialogue
throughout the SDG and EO community. Therefore, futureversions of this compendium are envisioned as the maturityof EO technologies develops and the demands of the SDG
indicator framework continue to evolve while taking on
board expert opinion in future iterations of the analysis.

Structure of Factsheets
The core body of this document is the compendium of EO
contribution to the SDG targets and indicators, consistingof 29 factsheets, one per target, covering 34 indicators
where EO has a potential or definite contribution to theindicator methodology. When a target has more than one
indicator, each indicator is addressed separately in a uniquefactsheet.
These factsheets are intended to be stand-alone sothat they can be used a resource material for NSOs, line
ministries, etc. The factsheets are structured consistently
and are composed of following three main sections and insome cases sub-sections:
How can EO be used to help countries achieve thetarget?
This is intended to be a broad assessment of how EO
can help countries achieve the target, in isolation fromthe indicators. This section draws out the contributionof EO to (i) setting national targets as part of theirvoluntary commitments to Agenda 2030 and (ii) planning/
implementation of the targets. This latter part also
involves proposing new EO-based indicators that could be
useful for countries.
The potential of EO to support the SDG indicators
The next section, in the form of a table, is indicator specific.
It contains a transparent and objective assessment of how
EO can support the indicators of the target, as describedabove in the methodology section. Non-EO based indicators
are listed in the table for reference only. For a supporting
guide to NSOs on using EO to support official statistics, see

the Satellite Imagery and Geospatial Data Task Team reportof the United Nations Task Team on Satellite Imagery andGeospatial Data [RD-13].
Short methodological guidelines illustrated with EO
best practice examples
This section summarises (i) where EO can contribute to themethodology as currently written by the indicator custodianand (ii) also proposes changes to the methodology forbetter EO uptake (as explained below under computation
method). As green indicators were considered to be
more open to the possibility of EO integration into their
methodologies, this section is elaborated in more detailthan for amber indicators.
The section is filled out for tier 1 and tier 2 indicators
only, following a logical order, mirroring the sub-sectionsof the Methodology in the metadata guideline structure
provided by the UN Department of Economic and Social
Affairs. Tier 3 indicators (as they were at the start of theanalysis), in the absence of a published methodology, were
not analysed at this level of detail.
Computation method
This sub-section reviews the computation method on how
the indicator is to be computed and either (i) provides asummary of the EO methods proposed by the custodian, (ii)
proposes a methodology where EO is not mentioned but
has a potential or definite contribution or (iii) proposes amethodology for a better use of EO than what is indicatedin the metadata file.
Disaggregation
This sub-section describes opportunities for spatial
disaggregation of the indicators using EO data, and/or
thematic disaggregation where non-EO data are available
for this purpose.
Treatment of missing values
This sub-section summarises the possibility of missing
values in the availability of EO source data and proposes
substitutes for the EO data record.
Regional aggregatesThis sub-section suggests how the EO-based method
can mitigate the problem of regional aggregations whencountries use different definitions or different methods.
Sources of discrepancies
This sub-section documents discrepancies in indicatorvalues related to accuracies of EO methods. Discrepancies
arising from the integration of EO with non-EO data arealso discussed where applicable.
Limitations
This sub-section briefly lists some restrictions andconstraints on the use of an EO-based approach in theindicator computation.

Compendium of Earth Observation contributions to the SDG Targets and Indicators

17

METHODOLOGY FOR ANALYSIS OF EO FOR THE SDGS

Data sources
This sub-section covers the main datasets, tools, platforms
and services related to the EO method described. It covers
the following main resources:- Source satellite data: EO legacy data and present data(with long term perspectives, distinguishing between
free and commercial)- Global/regional datasets: Derived EO datasets, some ofwhich have been described above- Software, tools and platforms: Free of charge software
toolboxes, which can be used to implement, in part orin full, the EO methods (e.g. software tools to handle
EO data cubes). On-line computing infrastructures whichfacilitate the production and/or access to EO datasets for
public and private users-
Operational or commercial services: Existing service
centres where the EO data (global, downstream
datasets) are delivered on an operational basis, either
freely or with a service fee. These are merely examplesof service providers but there are many more private andpublic service providers of EO data tools and products
which could be of use for SDG reporting than could belisted here.
Reference List
This sub-section lists a few critical, selected references
for further reading on the topic.
Key messages for countries
This sub-section is intended as a brief summary, in theform of short bullet points, of the analysis with some takehome messages for countries on how EO can assist themin indicator reporting.
EO best practices and national experiences
This section describes EO best practices and national
experiences for specific indicators, collated, where
possible and available, from EO experts and national
focal points through solicited requests. This section isintended to bring the theory and concepts explained tolife and make the EO methodologies proposed relevant
for countries. We document examples only for thoseindicators where we could find appropriate examples, thisdoes not preclude that EO is being actively used to support
indicator reporting and methodological development inother countries which are not documented here.

Definition of terms
Terms used throughout this compendium have beenstandardised for ease of interpretation. Term definitions
are described below.
Spatial Resolution
Refers to the size of the area covered by a pixel in asatellite image. In optical and thermal remote sensing,each pixel in an image corresponds to a patch on the
Earth’s surface. It is also known as ‘ground resolution’and is usually expressed in meters.
For the purpose of this analysis, the following categoriesof spatial resolution are considered:High Resolution ≤ 5m
• VeryHigh
Resolution ≤ 30m• Medium
Resolution: ≤100m• Low Resolution>100m•
Spectral resolution
Refers to the wavelength intervals in which a satellite
sensor receives electromagnetic radiation. It describes
the ability of a sensor to define narrow wavelengthintervals. The finer the spectral resolution, the narrower
the wavelength range for a particular channel or band.
The following categories are generally used describing
the requirement for spectral resolution:– 1 band (i.e. black and white image)• Panchromatic
Multispectral–
to ±15 bands• Hyperspectral –4hundredsof bands•
Temporal frequency (resolution)
This is the required interval between two successive
instances of a satellite observation in the same area
and often expressed on an hourly, daily, weekly, monthly,
yearly basis.

Compendium of Earth Observation contributions to the SDG Targets and Indicators

18

METHODOLOGY FOR ANALYSIS OF EO FOR THE SDGS

Reference documents[RD-1]	Addis Ababa Action Agenda, Third International Conference on Financing forDevelopment, 13 to 16 July 2015, Addis Ababa (2015)
http://bit.ly/AAAAFundDev[RD-2]	A World That Counts, Mobilising the data revolution for sustainable development(2014)
http://www.undatarevolution.org/wp-content/uploads/2014/11/A-World-ThatCounts.pdf[RD-3]	Cape Town Global Action Plan for Sustainable Development Data (2017)https://unstats.un.org/sdgs/hlg/Cape_Town_Global_Action_Plan_for_Sustainable_
Development_Data.pdf[RD-4]	Transforming our world: the 2030 Agenda for Sustainable Development, UN
Resolution 70/1, 21 October 2015
https://undocs.org/A/RES/70/1[RD-5]	2019 Sustainable Development Goals Reporthttps://unstats.un.org/sdgs/report/2019[RD-6]	UN High Level Political Forum meeting New York. July 2018https://sustainabledevelopment.un.org/hlpf/2018[RD-7]	Belward & Skøien (2015) Who launched what, when and why; trends in global
land-cover observation capacity from civilian earth observation satellites. ISPRS
Journal of Photogrammetry and Remote Sensing. 103, 115-128.
https://doi.org/10.1016/j.isprsjprs.2014.03.009[RD-8]	UNCCD Good Practice Guidance, SDG Indicator 15.3.1
https://www.unccd.int/sites/default/files/relevant-links/2017-10/Good%20
Practice%20Guidance_SDG%20Indicator%2015.3.1_Version%201.0.pdf[RD-9]	1st Expert Group Meeting of the IAEG-SDGs Working Group on Geospatial
Information, 12-14 December 2016, Mexico
http://ggim.un.org/meetings/2016-1st_Mtg_IAEG-SDG-Mexico[RD-10]	3rd Expert Group Meeting of the IAEG-SDGs Working Group on Geospatial
Information, 8-10 May 2017, Kunming, Yunnan, China
http://ggim.un.org/meetings/2017-3rd_Mtg_IAEG-SDG-China[RD-11]	GEO/CEOS joint report on “Earth Observations in support of the 2030 Agenda for
Sustainable Development”
https://www.earthobservations.org/documents/publications/201703_geo_eo_
for_2030_agenda.pdf[RD-12]	ESA Satellite Earth Observations in Support of the Sustainable Development
Goals
http://eohandbook.com/sdg/files/CEOS_EOHB_2018_SDG.pdf[RD-13]	UN Statistics Satellite Imagery and Geospatial Data Task Team report (2017)https://unstats.un.org/bigdata/task-teams/earth-observation

19

TITLE

Online Resources[URL-1]	The UN Sustainable Development Knowledge Platform
https://sustainabledevelopment.un.org/sdgs[URL-2]	Sendai Framework for Disaster Risk Reduction
https://www.unisdr.org/we/inform/publications/43291[URL-3]	New Urban Agenda
http://habitat3.org/the-new-urban-agenda/[URL-4]	UN Global Working Group on Big Data team on Geo-spatial information andsatellite imagery
https://unstats.un.org/bigdata/task-teams/earth-observation[URL-5]	The Inter-agency and Expert Group on SDG Indicators. Tier classification.
https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/[URL-6]	The Inter-agency and Expert Group on SDG Indicators
https://unstats.un.org/sdgs/iaeg-sdgs/[URL-7]	UN Fundamental Principles of Official Statistics
https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx[URL-8]	CEOS Data Cube | https://www.opendatacube.org/ceos[URL-9]	Earth System Data Cube (ESDC) | http://earthsystemdatacube.net[URL-10] D igital Earth Australia ODC | http://www.ga.gov.au/dea/odc[URL-11] E uro Data Cube
https://eurodatacube.com[URL-12] Google Earth Engine | https://earthengine.google.com/[URL-13] Open Data Cube | https://www.opendatacube.org/[URL-14] D
 IAS, European Copernicus Programme
https://www.copernicus.eu/en/access-data/dias[URL-15] Sentinel Hub | https://www.sentinel-hub.com[URL-16] Coastal Thematic Exploitation Platform | https://www.coastal-tep.eu[URL-17] Forestry Thematic Exploitation Platform | https://f-tep.com[URL-18] Hydrology Thematic Exploitation Platform | https://hydrology-tep.eu[URL-19] Geohazards Thematic Exploitation Platform | https://geohazards-tep.eu[URL-20] Polar Thematic Exploitation Platform | https://portal.polartep.io[URL-21] Urban Thematic Exploitation Platform | https://urban-tep.eu[URL-22] Food Security Thematic Exploitation Platform | https://foodsecurity-tep.net[URL-23] Trends.Earth |http://trends.earth

20

TITLE[URL-24] NextGEOSS | https://nextgeoss.eu[URL-25] EO4SDG initiative | http://eo4sdg.org[URL-26] GEO BON | https://geobon.org[URL-27] AquaWatch | https://www.geoaquawatch.org[URL-28] GEOGLAM | http://geoglam.org[URL-29] GEOGLAM RAPP | https://www.geo-rapp.org[URL-30] GEO Blue Planet | https://geoblueplanet.org[URL-31] Global Forest Observation Initiative (GFOI) | http://www.fao.org/gfoi[URL-32] GEO-Wetlands | https://geowetlands.org[URL-33] GEOHuman Planet Initiative (HPI)
https://www.earthobservations.org/documents/gwp20_22/HUMAN-PLANET.pdf[URL-34] G EO Global Network for Observation and Information in Mountain Environments
https://mountainresearchinitiative.org/activities/projects/geo-mountains[URL-35] G EO Land Degradation Neutrality
https://www.earthobservations.org/documents/gwp20_22/GEO-LDN.pdf[URL-36] CEOS Ad-Hoc Team on SDGs | www.ceos.org/sdg[URL-37] Global Forest Watch | https://www.globalforestwatch.org[URL-38] Global Mangrove Watch | https://www.globalmangrovewatch.org[URL-39] G lobal Urban Footprint
https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-9628/16557_read-40454[URL-40] Global Human Settlement Layer | https://ghsl.jrc.ec.europa.eu[URL-41] Global Surface Water Explorer | https://global-surface-water.appspot.com[URL-42] Freshwater Ecosystems Explorer | https://www.sdg661.app[URL-43] E SA Climate Change Initiative Land Coverhttps://climate.esa.int/en/projects/land-cover[URL-44] G lobal Soil Organic Carbon map |http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/global-soilorganic-carbon-map-gsocmap[URL-45] International Soil Reference and Information Centre
https://www.isric.org/explore/soilgrids[URL-46] SDG Indicators Metadata Repository | https://unstats.un.org/sdgs/metadata

Compendium of Earth Observation contributions to the SDG Targets and Indicators

21

TITLECOMPENDIUM OF EO CONTRIBUTION TO
THE SDG TARGETS AND INDICATORS

Compendium of Earth Observation contributions to the SDG Targets and Indicators

22

GOAL 1COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target: 1.1
By 2030, eradicate extreme poverty for all people
everywhere, currently measured as people living on lessthan $1.25 a day

Target: 1.2
By 2030, reduce at least by half the proportion of men,
women and children of all ages living in poverty in all its
dimensions according to national definitions
How can EO be used to help countries achieve thetargets?
Targets 1.1 and 1.2 have been pooled together in thisfactsheet, due to their similarities.
Earth Observation (EO) data can be used to track and target
poverty, and aid the allocation of scarce resources which
can help improve human livelihoods. EO can be used tomap spatial distribution of socioeconomic deprivations, aswell as providing information that may indicate areas at riskof poverty (e.g. contribute towards famine early warning
systems) (NASA, 2018). EO data can be used to forecast
weather, monitor fires, determine populations at risk fromflooding/landslides, analyse climate change and map land
cover change (e.g. deforestation and degradation). These
factors can all help identify areas currently at risk frompoverty, and in the future.
Satellite images can also be used to estimate economic
activity (e.g. through monitoring night lights) and mapping
houses (e.g. slums), which can be identified throughsatellite images using physical parameters, clusteringof structures with or without a road network, irregularand haphazardly grouped temporary, poorly-constructedor semi-permanent households (Montana et al., 2016).
Further, these datasets can be combined with in-fieldsurvey data (from socioeconomic household surveys, social
media, mobile phone networks) (Leidig & Teeuw, 2015),
and often by additionally using machine learning algorithms(Jean et al., 2016), can estimate consumption expenditure
and asset wealth of the region analysed. Such approaches
can assist efforts to track and target poverty.
Current Indicator(s)1.1.1 Proportion of population below the international
poverty line, by sex, age, employment status andgeographical location (urban/rural)
1.2.1 Proportion of population living below the national
poverty line, by sex and age
1.2.2 Proportion of men, women and children of all ages
living in poverty in all its dimensions according tonational definitions

TARGET 1.1 | TARGET 1.2

Short methodological guidelines illustrated
with EO best practice examples
Indicator 1.1.1
Computation method
EO is not currently discussed in the methodological
guidelines for these indicators, but there is scope for it
to be used alongside the current methods. The guidelines
set out a poverty standard, so that it can be consistently
compared across different countries – the extreme poverty
line is set at $1.90 per day in 2011 (World Bank, 2015).
Once this is calculated, the working poverty rate can becalculated as = (employed persons living on less than
$1.90 a day/total employment) x 100.
The level of poverty in an area is estimated throughsocioeconomic household surveys, which are based on census
data. ‘Big Data’ has the potential to help understand poverty
trends, e.g. through electronic money schemes. Most studies
are limited to using single source data, such as mobile phonedata or environmental data from satellite imagery, rather
than amalgamating different sources together. However,new studies have combined these sources to create a moreaccurate representation of poverty.
EO data collects information on metrics such as nighttime lights, vegetation cover, meteorological conditions(e.g. flood or drought events), proximity to services (e.g.
schools, hospitals), density of infrastructure (e.g. roads,
railway, waterways). These data can be used alongsidesurvey data to estimate the level of economic activity,
food scarcity, households at risk of extreme events suchas floods or droughts, and the services and infrastructure
available. These data are useful, but lack information about
population structure and other socioeconomic data thatare directly (e.g. household income) or indirectly (e.g. childmortality) relating to poverty.
In many rural areas, e.g. sub-Saharan Africa, internet
usage is low, although mobile phones are widely used
and Call Data Records can capture how, when, where and,with whom individuals communicate. Further, electronicmoney schemes like M-Pesa provide information on the
consumption patterns. These can capture spatial andtemporal patterns of human interaction.
Limitations
In terms of in-situ data, the main limitation is the cost of
the environmental surveys, as well as the time required toundertake them. Open Street Map is crowdsourced data
which are used to map infrastructure, housing and buildings– and provide useful plans of unmapped areas; however
the level of completeness varies. There is also selection
bias in mobile phone ownership, some countries only haveone provider, and mobile phone usage may not incorporate

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

24

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

some demographic subgroups like children and the ultrapoor. For EO data, there is varying spatial granularity at
which the different datasets are available which requires
an aggregation mechanism to merge them. Clouds canobscure data, especially in tropical and subtropical regions.
Very high resolution data can be prohibitively expensive,and high expertise is required to process and analyse it.
When using EO data, it is often aggregated together
from different time series, to combat issues of data gaps
through cloud cover etc. However, data availability fromother sources, such as access to mobile phone data, canoften be an issue – e.g. access to call data records haveto be obtained through individual telecommunicationcompanies. In addition, EO data is complemented throughthe use of model fitting, e.g. through Gaussian Process
regression (Pokhriyal & Jacques, 2017), and therefore there
are challenges with variance and uncertainty.

TARGET 1.1 | TARGET 1.2

Key messages for countries on EO contribution to thecomputation method
O data collects information on metrics such as• Enight
time lights, vegetation cover, meteorologicalconditions etc.
hese data can be used alongside other socioeconomic•Tdatathat relate to poverty.
can be inferred from a combination of these• PEOoverty
and non-EO datasets and mapped out spatially bygeographic location but further statistics are required
to disaggregate by gender and age.

Data sources
Data category

Data sources

WebsiteSource satellite data

WorldView, GeoEye, QuickBird, IKONOS
satellite imagery

https://www.maxar.com

Landsat satellites

https://earthexplorer.usgs.gov

The Global Urban Footprint (GUF) / World
Settlement Footprint (WSF)

https://urban-tep.eu

The Global Human Settlement Layer(GHSL)

https://ghsl.jrc.ec.europa.eu/data.php

The GEO Human Planet initiative

https://ghsl.jrc.ec.europa.eu/HPI.php

The Urban Thematic Exploitation Platform(U-TEP)

https://urban-tep.eu

Global/regional
datasets

Software, tools andplatforms

Reference List
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D.
B. & Ermon, S. (2016) Combining satellite imagery
and machine learning to predict poverty. Science, 353(6301) 790-794.
Leidig, M. & Teeuw, R. M. (2015) Quantifying and
Mapping Global Data Poverty. PLoS ONE, 10 (11),e0142076.
Montana, L., Lance, P. M., Mankoff, C., Speizer, I. S.
& Guilkey, D. (2016) Using satellite data to delineate
slum and non-slum sample domains for an urban
population survey in Uttar Pradesh, India. Spat
Demogr. 4 (1), 1 -16.

NASA (2018) US Uses Landsat satellite data to fighthunger, poverty. Landsat Science: Nasa. [Online]
Availableat:https://landsat.gsfc.nasa.gov/u-suses-landsat-satellite-data-to-fight-hunger-poverty/[Accessed 28th June 2018]
Pokhriyal, N. & Jacques, D. C. (2017) Combining data
sources for poverty mapping. Proceedings of the
National Academy of Sciences. 114 (46) E9783-E9792.
World Bank (2015) FAQs: Global poverty line update.
The World Bank. [Online] Available at http://www.
worldbank.org/en/topic/poverty/brief/global-povertyline-faq [Accessed 23rd July 2018]

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

25

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 1.1 | TARGET 1.2

Indicator 1.2.1

Limitations

Computation method

National poverty estimates use a different methodology tointernational poverty. National poverty rates are defined atcountry-specific poverty lines in local currencies, which are
different in real terms across countries, and different fromthe $1.90 a day international poverty line.

EO is not currently discussed in the methodological
guidelines for these indicators. However, there is potential
for Earth Observation (EO) data to be used to track andtarget poverty, and aid the allocation of scarce resources
which can help improve human livelihoods.
Currently, the formula for calculating the proportion of
total, urban and rural population living below the national
poverty line, or headcount index is:
N

P0=

1
N

I(yi < z)=iNp

Key messages for countries on EO contribution to thecomputation method
here currently are no EO products that can help establish• Tpoverty
specifically by sex and age, however there ispotential for EO to be used to track and target poverty,
e.g. by mapping the spatial extent of informal settlements(see indicator 11.1.1).

N=1
Income data is gathered through nationally representative
household surveys. National poverty rates use a country
specific poverty line, which is reflective of the country’s
economic and social circumstances. This can be adjusted
to reflect different regions within a country – i.e. costs ofliving are generally higher in urban areas than rural.

Reference List
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D.
B. & Ermon, S. (2016) Combining satellite imagery
and machine learning to predict poverty. Science, 353(6301) 790-794.
Leidig, M. & Teeuw, R. M. (2015) Quantifying and
Mapping Global Data Poverty. PLoS ONE, 10 (11),e0142076.

NASA (2018) US Uses Landsat satellite data to fighthunger, poverty. Landsat Science: Nasa. [Online]
Available at:https://landsat.gsfc.nasa.gov/u-s-uses-landsat-satellitedata-to-fight-hunger-poverty/ [Accessed 28th June
2018]

Montana, L., Lance, P. M., Mankoff, C., Speizer, I. S.
& Guilkey, D. (2016) Using satellite data to delineate
slum and non-slum sample domains for an urban
population survey in Uttar Pradesh, India. Spat
Demogr. 4 (1), 1 -16.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

26

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

1.1.1
Proportion of
population below theinternational poverty
line, by sex, age,
employment status
and geographicallocation (urban/rural)

Custodian agency

TARGET 1.1 | TARGET 1.2

1.2.1
Proportion of population
living below the national
poverty line, by sex andageWorld Bank; ILO

1.2.2
Proportion of men,
women and childrenof all ages living
in poverty in all its
dimensions according
to national definitions

World Bank

Tier

I

I

II

Status of step-by-step methodology
document on the metadata repository

Published

Published

Published

EO is only partiallyrelevant, due to lackof information inindicator guidelines
and high technicalcapacity.

EO is also only partiallyrelevant, for similarreasons as the above,in addition to thelimited EO data products
available. There
currently are no EO
products that canhelp establish poverty
specifically by sex andage.

Not supported by EO.
EO cannot be used tosupport any evidenceofnon-material poverty(e.g. education,knowledge, culture,lack of voice etc.).

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity required
Availability of global EO
data
Compliance with Reporting
calendarRobustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute forgaps in the EO record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

27

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 1.4
By 2030, ensure that all men and women, in particular
the poor and the vulnerable, have equal rights to economic
resources, as well as access to basic services, ownershipand control over land and other forms of property,
inheritance, natural resources, appropriate new technology
and financial services, including microfinance.
How can EO be used to help countries achieve the target?
Improving human well-being and access to services, EO
data can help develop various proxy indicators of humanwell-being, for example access to services such as electric
power, as well as patterns of human interaction
Remote sensing and call data records can help monitor
access to basic services. Call Data Records (CDR) provide
information on where, when, how and with whom someone
made a mobile phone call – mainly used for billing purposes,
but also provide spatial information on patterns of humaninteraction. Mobile phone usage and movement can indicate
household access to financial resources and services, forexample, via electronic money schemes such as M-Pesa.
Remote sensing can capture information on biophysicalproperties such as rainfall, temperature and vegetation coveras well to variables such as infrastructure (e.g. railway, main
roads, waterways), distance to water sources and power
plants, electricity use, agriculture productivity and distanceto roads and urban areas (which reflects access to markets
and information). The state of the road network (e.g. if it’sa dirt track or an impervious road) can be derived throughOpen Street Map, but can be combined with remote sensing
data to provide a more detailed picture.
Current Indicator(s)Indicator 1.4.1 Proportion of population living in householdswith access to basic service.
Indicator 1.4.2 P roportion of total adult population withsecure tenure rights to land, with legally
recognized documentation and who perceive
their rights to land as secure, by sex and by
type of tenure.

Short methodological guidelines illustrated
with EO best practice examples
Indicator 1.4.1
Indicator 1.4.1 was a Tier III indicator at the time of theanalysis, and therefore had no published metadata. However,it has been considered within this analysis, as it is perceived
that Earth Observation (EO) has clear potential to improvemonitoring.

TARGET 1.4

Data will be collected through routine national surveys, service
providers, directly from country/local government data or
websites, joint surveys with national agencies and international
entities. This will be complemented through EO data andremote sensing techniques. For example, relative welfare canbe measured through travel time to market towns, percentage
of a village covered with woodland, percentage of a village
covered with winter crop. Satellite data can be used to measureelectrification, through measuring night-time luminosity (Ghoshet al., 2013). The level of technology use can be an indicator ofaccess to services – for example number of households withinternet access or mobile phone usage (Steele et al., 2016).
The work plan divides basic services into three categories: basic
infrastructure services, social services, quality of life services.
However, this list is not exhaustive. There is a lack of consistentdefinition on what constitutes a basic service, and this is likely
to be interpreted differently, depending on who is collecting,reporting and using the data, respectively.
There are limitations to the proxies used to describe these
services, e.g. in some rural villages, rural electrification is often
not accurately measured due to intermittent power supply.
Additionally remote sensing and call data records (e.g. where,
when, how and with whom someone made a mobile phonecall) mainly used for billing purposes, but also provide spatial
information on patterns of human interaction. These are often
generated at different spatial scales, which can be difficult toreconcile.
It is proposed that data will be collected through national
surveys, service providers, directly from country/local
government data or websites, joint surveys with national
agencies and international entities. National statistic systems
will be a key source of data: selected national statistical agencies
will be consulted on methodological development and piloting
in a limited set of countries.
As the methodology for indicator 1.4.1 was not yet finalised
at the time of the analysis (still a Tier III), the EO-based
methodology is not discussed in the same detail as Tier I and
II indicators.
Data sources and tools
There are a number of global EO-based tools that are available
that could assist NSOs and indicator custodians with deliveringand implementing EO-based methodologies in relation topoverty. For example: The Global Urban Footprint (GUF) /World Settlement Footprint, The Global Human Settlement
Layer (GHSL), The GEO Human Planet initiative.
Key messages for countries on EO contribution to thecomputation method
ata will be collected through surveys, and there is• Dscopefor this to be complemented with EO data, suchas night-time luminosity – which will give an indicationof access to services.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

28

TARGET 1.4

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Reference List
Ghosh, T., Anderson, S. J., Elvidge, C. D., Sutton, P. C.
(2013) Using nighttime satellite imagery as a proxymeasure of human well-being. Sustainability. 5,4988–5019]
Pokhriyal, N. & Jacques, D. C. (2017) Combining
disparate data sources for improved poverty prediction
and mapping. Proceedings of the National Academy of
Sciences. 46 (114), E9783-E9792.

Indicators

Steele, J. E., Sundsøy, P. R., Pezzulo, C., Alegana, V. A.,
Bird, T. J., Blumenstock, J., Bjelland, J., Engø-Monsen,
K., de Montjoye, Y-A., Iqbal, A. M., Hadiuzzaman,
K. N., Lu, X., Wetter, E., Tatem, A. J. & Bengtsson,
L. (2017) Mapping poverty using mobile phone andsatellite data. Journal of the Royal Society Interface.
14 (127).

1.4.1
Proportion of population living inhouseholds with access to basic
service.

Custodian agency

1.4.2
Proportion of total adult
population with secure
tenure rights to land,with legally recognizeddocumentation and who
perceive their rights to landas secure, by sex and by
type of tenure.
UN-Habitat
World Bank

UN-Habitat

Tier

I

II

Status of step-by-step methodology document on
the metadata repository

Unpublished(Tier III at the time of theanalysis)

Unpublished(Tier III at the time of theanalysis)

EO data and GIS techniques
can be applied to measure thedistance to some basic services(e.g. road network, waterways,
main cities etc.).

Not supported by EO.

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the
EO record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

29

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 1.5
By 2030, build the resilience of the poor and those invulnerable situations and reduce their exposure andvulnerability to climate-related extreme events and othereconomic, social and environmental shocks and disasters
How can EO be used to help countries achieve the target?
The frequency and severity of natural disasters have been
increasing in the last decades. Research has revealed
that it is generally the poor who tend to suffer worst
from disasters. The fact that climate change is expected
to increase the frequency and intensity of these events
threaten to derail international efforts to eradicate poverty.
The importance of EO in disaster management andassessment has gained increasing significance over the past
years. One of the ways EO can contribute to build resilienceof vulnerable populations is through disaster-risk reduction.
EO datasets and methods can contribute to disasterrisk management and reduction by providing relevant
information to the full cycle of disaster and environmental
shock management: mitigation, preparedness, warning andresponse.
EO has proven successful for a wide range of disastertypes, particularly for flooding, extreme drought events,
earthquakes, landslides and volcanic eruptions. In fact, EO
data is providing a reliable data basis for deriving useful
information such as the extent of damaged area along withthe land-use types as well as the population affected. Thiscan be done through hazard mapping and risk modelling,real time monitoring, producing input data for feeding
early warning systems, or for producing maps to support
disaster response actions. For a comprehensive review
on how EO can contribute to disaster-risk management,
see ESA (2015). EO data lies also at the heart of climate
modelling, which represent key tools to inform actions
aiming to reduce vulnerability to climate change.
Current Indicator(s):1.5.1: 
Number of deaths, missing persons and directly
affected persons attributed to disasters per 100,000
population
1.5.2: Direct economic loss attributed to disasters in relation
to global gross domestic product (GDP)
1.5.3: 
Number of countries that adopt and implement
national disaster risk reduction strategies in line with
the Sendai Framework for Disaster Risk Reduction
2015-2030
1.5.4: 
Proportion of local governments that adopt andimplement local disaster risk reduction strategies inline with national disaster risk reduction strategies

TARGET 1.5

Short methodological guidelines illustrated
with EO best practice examples
Indicator 1.5.2
Computation method
Although Target 1.5 consists of four indicators, we areconcentrating our analysis on 1.5.2 as it is the only indicator
to which EO has a relevant contribution. The indicatormeasures the ratio of direct economic loss, defined as themonetary value of total or partial destruction of physicalassets existing in the affected area, attributed to disasters
in relation to the Gross Domestic Product (GDP).
EO datasets and methods can be used to gather useful
information on the economic losses attributed to disasters
and hence, the computation of indicator 1.5.2. Direct
economic loss due to disasters can be estimated from thequantification of damaged areas (such as flooded surfaces,
affected transport infrastructures, burnt areas, landslide
scars, etc.) using optical imagery and radar data at different
spatial resolutions. However very high spatial resolutions
are particularly useful for mapping hotspots of damage suchas in urban areas where high to medium spatial resolution
imagery might underestimate the damaged area.
In detail, the indicator is a simple summation of fiveeconomic loss indicators divided by GDP, which include:(1) agricultural assets (based on crops, livestock, fisheries,
apiculture, aquaculture and forest sectors as well as
associated facilities and infrastructure); (2) other productive
assets (disaggregated by economic sector, including
services, according to standard international classifications);(3) housing (disaggregated according to damaged anddestroyed dwellings); (4) critical infrastructure; and (5)cultural heritage. EO datasets and methods, combinedwith land cover maps and other thematic maps can help
estimate such losses, especially for agriculture (FAO, 2017).
Treatment of missing values
Inconsistent and the lack of detailed data is a key issue.
Systematic collection and cataloguing is required to makeinformation robust enough for SDG reporting. This would
require ground observations of damage to complement
the damage mapped in EO imagery. Airplane and drone
monitoring technologies could also replace missing EO
derived estimates of damage if deployed on time.
Sources of discrepancies
Standardization of measurement approaches between
countries is also a challenge but is being addressed(through Sendai and SDG processes).

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

30

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 1.5

Key messages for countries on EO contribution to thecomputation method

Limitations
The indicator is based on many sub-indicators from multiple
sectors and data sources and is therefore data-intensive.
The area of damaged infrastructure reported using EO stillneeds to be converted to economic loss. Values of global
GDP will need to be derived elsewhere. Different EO data
will be needed to assess the impacts depending on thenature of the disaster.
Apart from the challenge on collecting such datasets,financial and technical capacity at the country level
represent a challenge, too. Acquiring imagery at the timeof the disaster can also be challenging and could require
specific tasking of satellite assets. This approach is limited
by resources and budgets and is largely in the domain ofcommercial image providers.

t is likely that an EO-based method for the indicator• Iwould
have to be customised for the nature of thedisaster (fire, floods, etc.).
urrent EO based assets for disaster monitoring mostly• Cfocus
on post disaster recovery efforts but for thisindicator would need to be enhanced to evaluate physicaldamage on infrastructures and productive lands.

Data sources
Data category

Global/regional
datasets

Software, tools andplatforms

Operational orcommercial services

Data sources

WebsiteThe Global Urban Footprint (GUF) / World
Settlement Footprint (WSF)

https://urban-tep.eu

The Global Human Settlement Layer(GHSL)

https://ghsl.jrc.ec.europa.eu/data.php

Emergency Events Database (EM-DAT)

https://www.emdat.be

Disasters and conflicts:
UNEP Data Explorer:

http://geodata.grid.unep.ch/results.php

The GEO Human Planet initiative

https://ghsl.jrc.ec.europa.eu/HPI.php

Global Gridded Geographically Based
Economic Data (G-Econ), v4 (1990, 1995,
2000, 2005)

http://sedac.ciesin.columbia.edu/data/set/spatialecon-gecon-v4/docs

European Flood Awareness System

https://www.efas.eu

European Forest Fire Information System

http://effis.jrc.ec.europa.eu

The UN-SPIDER programme

http://www.un-spider.org

Reference List
European Space Agency (ESA) (2015). Satellite Earth
Observations in Support of Disaster Risk Reduction.
Special 2015 WCDRR Edition.
Food and Agriculture Organization of the United
Nations (FAO) (2017) The Impact of disasters andcrises on agriculture and Food Security.

UNISDR (2017) Technical Guidance for Monitoring andReporting on Progress in Achieving the Global Targetsof the Sendai Framework for Disaster Risk Reduction.
For indicator 1.5.2, see Target C, pages 36-91 (55pages). https://www.preventionweb.net/files/54970_
techguidancefdigitalhr.pdf

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

31

TARGET 1.5

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

1.5.1
Number ofdeaths, missing
persons anddirectly affected
persons
attributed
to disasters
per 100,000
population

1.5.2
Direct economic
loss attributed
to disasters
in relation to
global grossdomestic
product (GDP)

Custodian agency

1.5.3
Number ofcountries that
adopt and
implement
national
disaster risk
reduction
strategies
in line with
the Sendai
Frameworkfor Disaster
Risk Reduction
2015-2030

1.5.4
Proportionof localgovernmentsthat adopt andimplement
local disasterrisk reduction
strategies
in line withnational disasterrisk reduction
strategies

UNISDR

Tier

II

II

II

II

Status of step-by-step methodology
document on the metadata repository

Published

Published

Published

PublishedNot
supported by EO

Not supported
Damage to
by EO
infrastructure
and productive
land uses canbe directly
mapped fromEO. However,
GDP impact can
only be inferredfrom such
visible damage.

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity required
Availability of global EO
data
Compliance with Reporting
calendarRobustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute forgaps in the EO record?

Overall EO relevanceComments to support criteria

Not supported
by EO

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

32

GOAL 2COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 2.3
By 2030, double the agricultural productivity and incomesof small-scale food producers, in particular women,
indigenous peoples, family farmers, pastoralists andfishers, including through secure and equal access to land,
other productive resources and inputs, knowledge, financial
services, markets and opportunities for value addition andnon-farm employment.
How can EO be used to help countries achieve the target?
Smallholder farmers play a key role in global foodproduction, particularly in developing countries. Is
estimated that small-scale farming systems provide up to
80 percent of the food supply in Asian and sub-SaharanAfrica. These systems usually host the majority of poor andhungry people worldwide. Therefore, increasing agricultural
productivity in these systems would be key to achieve foodsecurity.
Even though EO has been proven potentially useful
to contribute to the management of farming, pastoraland forestry systems at regional scales, particularly bygenerating data to feed crop simulation models and early
warning systems, it still has limitations to provide the typeof fine scale data needed to feed models operating at thefarm scale. This is mainly due to the need for high spatial
and temporal resolution and repeat monitoring on demand,
which satellites cannot yet guarantee (Jin et al. 2018,
Kasampalis et al. 2018). The Global Ecosystem Dynamics
Investigation Lidar (GEDI), to be launched in 2018, is
expected to produce promising data to fill this gap, at least
for forestry systems.
Further efforts are needed in order to implement ways to
put the information derived from crop simulation models
and early warning systems in the hands of small-scale foodproducers, as required by this target. Recent pilot cases
suggest that information derived from EO, such as weather
forecasts, can be made accessible to small-scale foodproducers even in isolated areas in a way that can inform
crop management decisions, such as the time to plant andcrop variety selection (UNDP, 2016).
As this indicator was classified as Tier III at the time of
the analysis, no internationally established methodology or
standards were yet available.
Current Indicator(s):There are two agreed indicators for this target:
2.3.1: Volume of production per labour unit by classes offarming/pastoral/forestry enterprise size.

TARGET 2.3

Short methodological guidelines illustrated
with EO best practice examples
Indicator 2.3.1
Computation method
The computational method for indicator 2.3.1 was not
yet finalised and published at the time of the analysis.
However, results from discussions so far suggest that themethodology will entail 3 different steps:1) Identification of the target population (“small-scale foodproducers”),
2) Computation of the “volume of production per labour
unit by classes of farming/pastoral/forestry enterprise
size”
3) 
Calculation of “average income of small-scale foodproducers, by sex and indigenous status”.
Much of the discussion during the development of thisindicator has focused on an adoption of an international
definition for “small-scale food producer”. The custodian
agency of this indicator, the FAO, proposes to define thisconcept based on the physical size of the food producer,
as expressed by the amount of operated land and numberof livestock heads in production, and the economic size ofthe food producer, as expressed by its revenues. Labour
productivity is proposed to be computed as the ratio of thevalue of agricultural/livestock/fisheries/forestry production
and the labour input (number of labour days per small
scale farmer).
EO could help support the calculation indicator 2.3.1
by estimating some of the parameters needed for the
calculation of these indicator. For example, EO could
support the estimation of areas under cultivation, which isone of the required parameters to define “small-scale foodproducers” (GEOSS 2009), in addition to economic size. EO
can also support the estimation of agricultural and forestry
yields with reasonable accuracy.
Limitations
In addition of farm area and agricultural production, themethodology under discussion also requires the estimationof parameters such as the economic size (expressed throughthe gross monetary value of agricultural production) of thelabour input (expressed as the number of labour days andnumber of small-scale food produced), which cannot becomputed by EO.

2.3.2: Average income of small-scale food producers, by sex
and indigenous status

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

34

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 2.3

Key messages for countries on EO contribution to thecomputation method
O is very well suited to support the increase of• Eagricultural
productivity through the estimation of crop
yields, assessing nutritional and water requirements,
and weed control. However, it still has limitations toprovide the type of fine scale data needed to feed models
operating at the farm scale.
O can also support the calculation of some of the• Eparameters
needed for the computation of this indicator,
particularly for countries limited agricultural census data.
Data sources
Data category

Data sources

WebsiteGlobal/regional
datasets

Copernicus Dry matter productivity product https://land.copernicus.eu/global/products/dmp

Reference List
GEOSS (2009). Best practices for crop area estimation
with Remote Sensing. Edited by Gallego J. Craig M.,
Michaelsen J., Bossyns B., Fritz S. Ispra, June 5-6,
2008

Kasampalis, D.A., Alexandridis, T.K., Deva, C., Challinor,
A., Moshou, D. and Zalidis, G. (2018) Contribution of
Remote Sensing on Crop Models: A Review. Journal of
Imaging, 4 (4). 52. ISSN 2313-433.

Jin, X. Kumar, L. Li, Z. Feng, H. Xu, X. Yang, G. Wang, J.
(2018) A review of data assimilation of remote sensing
and crop models. European Journal of Agronomy Vol
92: 141-152

United Nations Development Programme (2016)
Climate Information and Early Warning Systems
Communications Toolkit. UNDP Programme on Climate
Information for Resilient Development in Africa.
Available at: https://reliefweb.int/sites/reliefweb.int/
files/resources/communications-toolkit-v3.pdf

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

35

TARGET 2.3

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

2.3.1
Volume of production per
labour unit by classes offarming/pastoral/forestry
enterprise size.

2.3.2
Average income of smallscale food producers, by sex
and indigenous status

FAO

Custodian agency
Tier

II

II

Status of step-by-step methodology document on
the metadata repository

Unpublished(Tier III at the time of theanalysis)

Unpublished(Tier III at the time of theanalysis)

As the metadata is not yet
finalised, not all criteria could
be assessed. However, basedon criteria assessed, EO may
support the computationof some of the parameters
required to calculate theindicator

Not supported by EO.

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the
EO record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

36

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 2.4
By 2030, ensure sustainable food production systems andimplement resilient agricultural practices that increase
productivity and production, that help maintain ecosystems,
that strengthen capacity for adaptation to climate change,
extreme weather, drought, flooding and other disasters andthat progressively improve land and soil quality
How can EO be used to help countries achieve the target?
Continuing population and consumption growth is likely toincrease the global demand for food in the next decades.
The achievement of food security will require profoundchanges in the global food and agriculture system. At thesame time, unsustainable agriculture expansion has creatednumerous environmental problems, such as soil erosion,water pollution as well as greenhouse gases emissions.
This target aims to contribute to this goal by increasing
the economic, social and environmental sustainability ofagricultural practices, including through enhancing theresilience to climate change and extreme whether events.
EO methods can play an important role in increasing
agricultural productivity as well as minimising theenvironmental impact of the agricultural sector. Some of
the ways EO has proven successful to contribute to increase
the sustainability of agricultural production include:(1) yield estimation, (2) vegetation vigour and drought
stress monitoring, (3) assessment of crop phenologicaldevelopment, (4) crop acreage estimation and croplandmapping and (5) mapping of disturbances and land use/land cover (LULC) changes (Atzberger, 2014). EO datasets
can also be used by countries to inform spatial land use
planning and minimize the potential environmental impactof crop expansion through optimizing the allocation of lands(Laurence et al. 2014). In addition to crops, satellite remotesensing techniques can also be applied for rangeland
monitoring and management (Ali et al. 2016). EO data andmethods can be useful for assessing the future exposure toclimate change as well as to extreme weather events, as
explained in the 1.5.2 indicator factsheet.
Current Indicator(s)2.4.1. Proportion of agricultural area under productive andsustainable agriculture.

TARGET 2.4

Short methodological guidelines illustrated
with EO best practice examples
Indicator 2.4.1
Computation method
The computational method for indicator 2.4.1 is underdiscussion and has not yet been finalised or officiallypublished but is in work plan format. The suggested
computation method is as follows:
SDG 2.4.1 = Area under productive and sustainableagriculture / Agricultural land area where:
The denominator, Agricultural land area = arable land
+ permanent crops + permanent meadows and pastures
The numerator, Area under productive and sustainableagriculture, captures the three dimensions of sustainableproduction: environmental, economic and social.
Much of the discussion during the development of
this indicator has focused on a clarification of terms,
particularly for the concept of ‘sustainable agriculture’.
Progress to date suggests that the intention isto design a threshold-based aggregate indicatorwhich, through different sub-indicators, captures the
economic, environmental and social main dimensions ofsustainability. The estimation of proportion of agriculturalarea under sustainable agriculture, according to the workplan, will be based on data most likely collected throughagricultural surveys and household surveys organizedby the national statistical agencies, with support fromthe custodian agency (FAO) or other international
agencies to ensure methodological harmonization. The
opportunity for EO in supporting this indicator is mainly
to benchmark the sustainability of farming practices.
For example, it is unlikely that farmers would be able
to assess the environmental impact of their farming
practices on issues like fertilizer pollution or pesticide
impact without a tool that can monitor impact in thewider landscape. An EO-based monitoring system canmeasure the impact of agriculture on the environment.
Regarding the computational method, EO technologies
could particularly be useful to inform the agriculturalland area. Agricultural land area is a common category in
EO-derived land cover/land use maps. Satellite remotesensing has been used for several decades to support
land cover mapping. It is the most cost-effective means
for gathering spatially explicit information over large
areas with high revisit frequency in a consistent andsystematic manner.
EO could also be a tool to contribute to several
sub-indicators of the environmental dimension ofCompendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

37

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 2.3

the indicator, such as farm output value per hectare,prevalence of soil degradation, variation in water
availability and use of biodiversity-friendly practices.
In addition, EO can provide support for cost-effective
collection of agricultural and rural data by optimizing
sampling designs and support to field surveys.

scale agriculture is practiced with commensurably large
field sizes, VHR EO data might not be a necessity forcomputing this indicator.

However, the current work plan specifies that thisindicator will be assessed at the farm level. This is likely
to require high to very high resolution EO-based data inorder to produce meaningful results.

for this indicator is in development but• Titheis methodology
likely that EO will play a role in the computational

Limitations

Key messages for countries on EO contribution to thecomputation method

method, particularly for supplementing farm survey dataf this indicator is calculated at the farm level, high to• Iveryhigh resolution EO is likely to be needed (dependingon national context of field sizes).

In some cases, some of the definitions and assumptions of
the EO-product might not match that of the sub-indicator,
potentially leading to misleading numbers being reported.
It could also be the case that validation has not been
performed for a certain product for the conditions present
in the country. Most freely available EO data might not be
precise enough for field level monitoring. However fieldsize can vary by country and in countries where industrial

O could also be used to quantify the amount of• Eagriculturalarea, using land-use/land-cover products.
O also has potential in assessing the impact of• Eunsustainable
farming and for monitoring the robustnessof the indicator in monitoring the level of sustainabilityof agriculture with regards to wider landscape condition.

Data sources
Data category

Data sources

Global/regional
datasets

Copernicus Dry matter productivity product https://land.copernicus.eu/global/products/dmp

Software, tools andplatforms

WebsiteGEO Global Agricultural Monitoring(GEOGLAM)

https://cropmonitor.org/index.php/data-and-tools/
cmet

Sentinel 2 for agriculture monitoring

http://www.esa-sen2agri.org

Sentinels for Common Agriculture Policy

http://esa-sen4cap.org

Reference List
Ali, I., Cawkwell, F., Dwyer, E., Barrett, B. & Green, S.
(2016) Satellite remote sensing of grasslands: fromobservation to management, Journal of Plant Ecology,
9 (6): 649–671
Atzberger, C. (2014) Advances in Remote Sensing ofAgriculture: Context Description, Existing Operational
Monitoring Systems and Major Information Needs.
Remote Sensing (5): 949-981

FAO (2018) Methodological note, November
2018:http://www.fao.org/3/CA2639EN/ca2639en.pdf
Laurance, W.F., Sayer, J., & Cassman, K.G. (2014)
Agricultural expansion and its impacts on tropicalnature. Trends in Ecology & Evolution, 29 (2): 107-116

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

38

TARGET 2.3

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

2.4.1
Proportion of agricultural area under productive
and sustainable agriculture.

FAO

Custodian agency
Tier

II

Status of step-by-step methodology document on the metadata
repository

Methodological note available from November
2018

Maturity of EO technologies
Relevance of EO for
the indicator criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness of proposed
methodology Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the EO
record?

Overall EO relevanceComments to support criteria

Household surveys have been identified as
the main data collection instrument for thisindicator. EO will have a supporting role, with afocus on environmental parameters that affectlarger areas than the individual farm, or forparameters where it is unlikely that the farmers
can evaluate the effects locallyCompendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

39

GOAL 3

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 3.3
By 2030, end the epidemics of AIDS, tuberculosis, malaria
and neglected tropical diseases and combat hepatitis,
water-borne diseases and other communicable diseases
How can EO be used to help countries achieve the target?
This target aims to end three of the world’s major
epidemics, which combined claim millions of lives per year
(Murray et al., 2014). This makes this target one of the
SDG targets with the greatest potential impact in terms of
reducing mortality.
The utility of EO methods for improving the understanding,prevention, and control of vector-borne diseases has been
extensively demonstrated, particularly for malaria (Gebreslasie,2015). EO data has been used, for instance, to derive
environmental data to feed malaria risk models, identificationof potential vector habitats and to inform the developmentof early warning systems. Some of the EO-derived data thathave been applied to malaria epidemiology include: land cover,land and sea surface temperature vegetation indices such as
NDVI and enhanced vegetation index, precipitation and actual
evapotranspiration. Results of the models developed with EOderived data can be useful to identify locations where the riskof disease is highest and direct resources to the population
most in need. EO-derived data can also be applied to develop
risk models for other tropical diseases such as dengue or
schistosomiasis, among others.
Current Indicator(s)There are five indicators for this target:
3.3.1 Number of new HIV infections per 1,000 uninfected
population, by sex, age and key populations
3.3.2 Tuberculosis incidence per 100,000 population
3.3.3 Malaria incidence per 1,000 population
3.3.4 Hepatitis B incidence per 100,000 population
3.3.5 Number of people requiring interventions against
neglected tropical diseases

TARGET 3.3

Short methodological guidelines illustrated
with EO best practice examples
Indicator 3.3.3
Computation method
Incidence of malaria is defined at the country level as
the number of new cases of malaria per 1,000 people
at risk each year. The number of new cases of malaria isestimated from the number of malaria cases reported by
the Ministry of Health of each country, using a series ofstatistical methods.
For some high-transmission countries where the qualityof case reporting is considered insufficient for the abovementioned method, estimates of the number of malaria
cases are derived from the Malaria Atlas (https://map.ox.ac.
uk/making-maps/) a geospatial model which is partly feedby EO-derived data, such as land surface temperature andvegetation indices (Bhatt et al., 2015)
Treatment of missing values
For missing values of the parameters a distribution based ona mixture of the distribution of the available values is used.
When no reported data is available the number of cases isinterpolated taking into account the population growth. EO
datasets and methods could also represent an alternative
way to fill the gaps in areas with missing values.
Limitations
While EO has great potential to contribute to reducing
the impact of malaria and other vector-borne diseases,
to be effective, it must be adopted as part of a holistic,
multi-disciplinary approach, as it will necessarily involve
coordination among different levels of government, healthfacilities, scientists and local population.
Key messages for countries on EO contribution to thecomputation method:
he agreed methodology to compute this indicator is• Tbasedon a series of statistical methods, EO-derived
methods are only envisaged to be used when thequality of case reporting in high incidence countries is
considered insufficient.
data, combined with geospatial modelling,• EcanO derived
also be useful to fill the gaps in areas with missing
values elsewhere.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

41

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 3.3

Data sources
Data category

Data sources

WebsiteGlobal/regional
datasets

Malaria Atlas Project

https://map.ox.ac.uk/making-maps

Reference List
Bhatt, S., Weiss, D. J., Cameron, E., Bisanzio, D.,
Mappin, B., Dalrymple, U., … Gething, P. W. (2015). The
effect of malaria control on Plasmodium falciparumin Africa between 2000 and 2015. Nature, 526, 207.
Retrieved from http://dx.doi.org/10.1038/nature15535
Gebreslasie, M. T. (2015). A review of spatial
technologies with applications for malaria transmission
modelling and control in Africa. Geospatial Health,10(2), 239–247. https://doi.org/10.4081/gh.2015.328

Murray, C. J. L., Ortblad, K. F., Guinovart, C., Lim, S.
S., Wolock, T. M., Roberts, D. A., … Vos, T. (2014).
Global, regional, and national incidence and mortality
for HIV, tuberculosis, and malaria during 1990–2013:a systematic analysis for the Global Burden of Disease
Study 2013. Lancet, 384(9947), 1005–1070. https://doi.org/10.1016/S0140-6736(14)60844-8

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

42

TARGET 3.3

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

3.3.1
Number of new
HIV infections
per 1,000
uninfected
population, by
sex, age andkey populations

3.3.2
Tuberculosis
incidenceper 100,000
population

UNAIDS

Custodian agency

3.3.3
Malaria incidence
per 1,000
population

WHO

3.3.4
Hepatitis B
incidenceper 100,000
population

WHO

3.3.5
Number ofpeople requiring
interventions
against neglected
tropical diseases

WHO

WHO

Tier

I

I

I

I

I

Status of step-by-stepmethodology document on themetadata repository

Published

Published

Published

Published

Published

Not supported
by EO.

Not supported
by EO.

Not supported by
Althoughcurrent indicator
EO.
methodology only
envisages the useof EO derived datawhen the qualityof case reporting
is consideredinsufficient,
the potentialof geospatialmodelling toprovide accurate
estimations ofMalaria prevalence
is high.

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in
indicator guidelines
Technical capacity
required
Availability of
global EO data
Compliance with
Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity tochangeIs it scalable(spatial)?
Is there asubstitute for gaps
in the EO record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

Not supported
by EO.

43

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 3.9
By 2030, substantially reduce the number of deaths andillnesses from hazardous chemicals and air, water and soil
pollution and contamination
How can EO be used to help countries achieve the target?
EO data can be used to monitor the level of air quality data
(e.g. PM2.5, CO2, CO, NOx, SO2) (van Donkelaar et al., 2010)
water quality data (E.g. Chlorophyll-a, turbidity) (Mohamed,2015), as well as soil pollution data (e.g. concentrationof hydrocarbons) (Karkush et al., 2014). These can becorrelated with census data on human health and mortality
to monitor progress towards reducing the number of deaths
and illness from hazardous chemicals and air, water andsoil and contamination. In order to reduce the number ofdeaths by creating public awareness, a wider forecasting
system based on modelling EO data for the likely risk ofdanger from hazardous chemicals and air, water and soil
pollution and contamination is needed.
Indicator(s)TARGET 3.9

can be modelled using the two-way nested TM5 Global
Chemical Transport Model (Anderson et al., 2012).
Evans et al. 2013 used satellite data to derive PM2.5
concentration estimates, and then used previously
developed concentration- response functions to calculate
the relative risks of associations between PM2.5 and fourcases of mortality: all causes, cardiopulmonary disease,lung cancer, and ischemic heart disease.
Data from Sentinel-5P has recently been made available– allowing O3, CO and NO2 data to be recorded. SO2 data
was made available in September 2018. The Sentinel5P spacecraft uses a TROPOMI instrument which uses
passive remote sensing techniques to measure at the
Top Of Atmosphere (TOA) the solar radiation reflectedby and radiated from the earth (ESAa, 2018). It is thefirst Copernicus satellite dedicated to monitoring theatmosphere. The TROPOMI instrument can provide highly
detailed and accurate data about the atmosphere with aresolution up to 7 x 3.5km – detecting air pollution overindividual cities (ESAb, 2018).

3.9.2 Mortality rate attributed to unsafe water, unsafe
sanitation and lack of hygiene (exposure to unsafe
Water, Sanitation and Hygiene for All (WASH)
services)

Aerosol dispersion varies spatially and temporally,
therefore can display both local and regional patterns
depending on their source. These characteristics limit theability of fixed site ground-based PM monitors to capture
large scale, regional and global PM distributions. Satellites
can however detect this spatial variability both within theupper atmosphere and over broad regions. The choice ofmodel parameters influenced the baseline estimates.

3.9.3 Mortality rate attributed to unintentional poisoning.

Limitations

Short methodological guidelines illustrated
with EO best practice examples

Additionally, there are errors associated with satellite
measurements. There are often a lack of PM2.5 sensors
in rural regions. PM2.5 calculations are often basedon stimulations, which have measures of uncertainty(Anenberg et al., 2010).

3.9.1 Mortality rate attributed to household and ambient
air pollution

Indicator 3.9.1
Computation method

Key messages for countries on EO contribution to thecomputation method

EO is not currently discussed in the methodological
guidelines for these indicators, but there is scope for it to
be used alongside the current methods.

discussed in the methodological• Eguidelines
O isn’t currently
for these indicators

Household census data on health and mortality (e.g.
respiratory disease) can be used alongside pollution data.
Satellite based observations can be used to estimate
a number of pollutants. Anderson et al. (2012) usedannual ground level PM2.5 concentrations by combining
aerosol vertical profiles obtained from the global chemicaltransport model GEOS-Chem with total column aerosol
depth obtained from 2 spectroradiometers (MODIS and
MISR). Concentrations were averaged over a number ofyears. Similarly, NO2 concentrations can be estimated by
combining GEOS-Chem NO2 profiles with tropospheric NO2columns obtained from the satellite Aura. O3 concentrations

number of satellites and instruments can be used to• Agather
data on airborne pollutants, principally in theupper atmosphere, for example: Satellite Aura, Sentinel5P and Spectroradiometers MODIS and MISR.
can be used to highlight the risk of dangerous• EairO data
pollution, when the level of pollutants exceeds abackground normal
owever, health information gathered through household• Hcensus
data is necessary to measure air pollution at thehousehold level

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

44

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 3.9

Data sources
Data category

Data sources

WebsiteSatellite Aura
Sentinel-5P
Source satellite data
MODIS

https://search.earthdata.nasa.gov
https://scihub.copernicus.eu

MISR
Software, Tools andPlatforms

Water Observation and Information
System (WOIS)

http://www.tiger.esa.int/page_eoservices_wois.php

AquaWatchhttps://www.geoaquawatch.org/

Reference List
Anderson, H. R., Butland, B. K., van Donkelaar, A.,
Brauer M., Strachan, D. P., Clayton, T., van Dingenen, R.,
Amann, M., Brunekreef, B., Cohen, A., Dentener, F., Lai,
C., Lamsal, L. N. & Martin, R. V. (2012) Satellite-based
estimates of ambient air pollution and global variations
in childhood asthma prevalence. Environmental Health
Perspectives. 120 (9), 1333-1339.
Anenberg, S. C., Horowitz, L. W., Tong, D. Q., West,J. J. (2010) An estimate of the global burden ofanthropogenic ozone and fine particulate on premature
human mortality using atmospheric modelling.
Environmental Health Perspectives. 118, 1189–1195.
ESAa (2018) Sentinel-5P TROPOMI User Guide. ESA.
[Online] Available at: https://sentinels.copernicus.
eu/web/sentinel/user-guides/sentinel-5p-tropomi[Accessed 24th July 2018]
ESAb (2018) Copernicus Sentinel-5P releases first
data. ESA. [Online] Available at: https://m.esa.int/
Our_Activities/Observing_the_Earth/Copernicus/
Sentinel-5P/Copernicus_Sentinel-5P_releases_first_
data [Accessed 24th July 2018]

Evans, J., van Donkelaar, A., Martin, R. V., Burnett,
R., Rainham, D. G., Birkett, N. J. & Krewski, D. (2013)
Estimates of global mortality attributable to particulate
air pollution using satellite imagery. Environmental
Research. 120, 33-42.
Karkush, M. O., Ziboon, A. R. T. & Hussien, H. M. (2014)
Studying the effects on soil properties using remotesensing. Journal of Engineering. 20 (6), 78-90.
Mohamed, M. F. (2015) Satellite data and real time
stations to improve water quality of Lake Manzalah.
Water Science. 29 (1), 68-76.
van Donkelaar, A., Martin, R. V., Brauer, M., Kahn,
R., Levy, R., Verduzco, C. & Villeneuve, P. J. (2010)
Global estimates of ambient fine particulate matter
concentrations from satellite-based aerosol opticaldepth: development and application. Environmental
Health Perspectives. 118 (6), 847-855.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

45

TARGET 3.9

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

3.9.1
Mortality rate attributed
to household andambient air pollution

3.9.2
Mortality rate
attributed to unsafe
water, unsafe
sanitation and lackof hygiene (exposure
to unsafe Water,
Sanitation and Hygienefor All (WASH)
services)

Custodian agency

3.9.3
Mortality rate
attributed tounintentional
poisoning.

WHO

Tier

I

I

I

Status of step-by-step methodology
document on the metadata repository

Published

Published

Published

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity required
Availability of global EO
data
Compliance with Reporting
calendarRobustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute forgaps in the EO record?

Overall EO relevanceComments to support criteria

An overall amber status: Not
supported by EO
technology is available
to use EO, and acrossdifferent scales highlighting sensitivity
to change. However,there is currently little
integration, and thetechnical capacity
required to process andanalyse is high.

Not supported by EO

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

46

GOAL 4

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 4.a
Build and upgrade education facilities that are child,disability and gender sensitive and provide safe, nonviolent, inclusive and effective learning environments for all
How can EO be used to help countries achieve the target?
EO data could be used to monitor aspects of education
facilities, such as access to electricity and basic drinking
water. However, it has limited capacity in measuring
general effective learning environments. The target
addresses the need to both build new and upgrade theexisting education facilities. Hence, there is a gap for EO tocontribute by identifying sites for development of education
facilities, based on criteria such as proximity to humansettlement and roads and upgrade damaged or defective
buildings and other visible infrastructure. EO can therefore
be used in urban and rural settings and as a planning toolfor education facilities and help countries achieve theirtarget. This will necessitate commercially produced very
high resolution (VHR) imagery and may be a drawback for
implementation of EO unless sufficiently financed.
Current Indicator(s)4.a.1 Proportion of schools with access to (a) electricity; (b)the Internet for pedagogical purposes; (c) computers
for pedagogical purposes; (d) adapted infrastructure
and materials for students with disabilities; (e)basic drinking water; (f) single-sex basic sanitation
facilities; and (g) basic handwashing facilities (as per
the WASH indicator definitions)Short methodological guidelines illustrated
with EO best practice examples
Indicator 4.a.1

TARGET 4.A

EO could be integrated into calculations of the access tobasic drinking water, which are mainly evaluated throughsurvey data. However, one project in Lima, Peru has started
to use EO to develop innovative solutions and planning
tools for drinking water supply. It uses satellite-based
remote sensing and water balance modelling alongside
strategic decision-making tools and concepts for integrated
water supply and wastewater disposal.
In addition, satellite data could be used to aid Demographicand Health Surveys (DHS). These surveys are nationallyproduced household survey data, which are collected
in 90 countries worldwide, and record information on
demographic, social, economic and health-related outcomes.
The DHS monitors the time it would take to reach a sourceof drinking water in minutes – this can also be applied forschools. This can be complemented with satellite imagery
for information about surface waterbody extent and water
quality to estimate access to basic drinking water atschools.
Moreover, census data can also be used alongsidesatellite data. Currently census data provides information
on electricity access, and satellite data can be used tocomplement this data by measuring electrification throughmeasuring night-time luminosity (Min et al., 2013). Nightlight data can be easily downloaded from NOAA (National
Oceanic and Atmospheric Administration). Many analyses
only use the ‘stable lights’ datasets, which only includes
locations with persistent lighting – excluding ephemeral
events such as fires.
Limitations
Measuring night time luminosity through satellites will beless reliable in areas with constrained power supplies, andfor detecting non-electrified villages.
Key messages for countries on EO contribution to thecomputation method

Computation method
EO is not currently discussed in the methodological
guidelines for the indicator, but there is scope for it to
be used alongside the current methods. The indicator is
calculated as –PSn,f = Sn,fused to measure the percentage of• Eschools
O isn’t bycurrently
level of education with access to the given
facility or service
is scope for EO to be used to complement survey• Tdataheresuchas DHS and census surveys.

Where, PSn,f = percentage of schools at level n of
education with access to facility f; Sn,f = schools at level nof education with access to facility f; Sn = total number ofschools at level n of education.
Data sources
Data category

Data sources

WebsiteGlobal/regional
datasets

The Global Surface Water Explorer

https://global-surface-water.appspot.com

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

48

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 4.A

Reference List
Min, B., Gaba, K. M., Sarr, O. F. & Agalassou, A. (2013) Detection of Rural Electrification in Africa Using DMSP-OLS
Night Lights Imagery. International Journal of Remote Sensing. 34 (22): 8118–8141.

Indicators

4.A.1
Proportion of schools with access to (a)electricity; (b) the Internet for pedagogical
purposes; (c) computers for pedagogical
purposes; (d) adapted infrastructure andmaterials for students with disabilities; (e) basic
drinking water; (f) single-sex basic sanitation
facilities; and (g) basic handwashing facilities(as per the WASH indicator definitions)Custodian agency

UNESCO-UIS

Tier

II

Status of step-by-step methodology
document on the metadata repository

Published

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity required
Availability of global EO
data
Compliance with Reporting
calendarRobustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute forgaps in the EO record?

Overall EO relevanceComments to support criteria

This is amber as there is currently limited
integration and use of EO with this indicator.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

49

GOAL 6

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 6.1
By 2030, achieve universal and equitable access to safe
and affordable drinking water for all

TARGET 6.1

Short methodological guidelines illustrated
with EO best practice examples
Indicatior 6.1.1

How can EO be used to help countries achieve the target?

Computation method

One of the most essential uses of water is for domestic
consumption within households. This purpose is captured
in target 6.1, which seeks to guarantee safe and affordable
drinking water for drinking and hygiene purposes for all.
Households are an important share of total water use andtherefore represent a significant sector in achievementof target 6.4 on water use efficiency. “Safe” water isconsidered to be free of contaminants and is determined by
the quality of untreated water prior to human consumption.
The necessity of the target, for a supply of high water
quality in the first place, is threatened by conversion of
wetlands, forests and woodlands to agriculture aroundpopulated areas and water catchments. This target istherefore linked to the protection of the water catchment
which is the focus of target 6.6 and the quality of water intarget 6.3. The use of land cover change data, particularly
in models to estimate the impact on water supplies in theevent of widespread deforestation and land conversion, isa powerful way to show how drinking water becomes bothunsafe and unaffordable in the event of a water supply
being compromised by the conversion of natural ecosystems
which regulate it. As EO-based observations are primary
inputs to such models, countries can be supported towards
achievement of target 6.1 by implementing such models
and to plan for better baseline water quality throughregulation of land use in water catchments.

The current indicator methodology is based on household
surveys and censuses. Although the notion of safe
management of the water supply cannot be directly
evaluated from EO, it can be inferred from the change
in water quality before and after treatment, the former
coming form EO-derived water quality maps and thelatter from in-situ sampling.

Current Indicator(s)6.1.1 Proportion of population using safely managed
drinking water services

State-of-the-art hyperspectral remote sensing-derived
information on water quality is generating an increasing
choice of products relevant to this indicator. These
range from more accurate estimates of turbidity andtransparency measures, chlorophyll, suspended matter andcoloured dissolved organic matter concentration, to moresophisticated products such as particle size distributions,
phytoplankton functional types or distinguishing sourcesof suspended and coloured dissolved matter, estimatingwater depth and mapping types of heterogeneous
substrates (Giardino et al., 2018). As indicator 6.6.1
specifically requires comparison of water quality againstinternational standards (faecal and chemical) fromadministrative reporting or regulatory bodies, these EOderived parameters will need to be compliant with suchstandards. Another EO-based approach is through theprovision of land cover and land use change layers forhydrological modelling and water quality assessment, e.g.
to estimate nitrate leaching in groundwater (Abbaspour
et al., 2015).
EO can also help to compute the level of access to basic
services for each country, separately in urban and rural
areas, though the provision of human settlement data,
even at very high spatial resolutions, as discussed forindicator 11.1.1. Computing the proximity of these humansettlements to a “safe” water supply would be a core partof an EO-based methodology.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

51

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Limitations
EO has limited relevance for evaluating the types of basic
drinking water sources, whether piped or sourced from awell, or to evaluate the level of management and serviceof the water supply.
Key messages for countries on EO contribution to thecomputation method
he EO methods that can support this indicator mostly• Trelate
to assessment of water quality and land cover /land use around water catchments

TARGET 6.1

hile water quality can be used to assess the status• W
of water supplies before treatment, in-situ samplingis required during and after treatment to assess the“safety” of water compared to international standards
required for this indicator
he planning for achievement of the target 6.1 can be• Tsupported
by models of land use and hydrology which
can use EO–derived information as inputs such as highresolution land cover. This is important to plan for water
catchment management, ensuring that water supplies
are as clean as possible before being supplied fordomestic consumption

Data sources
Data category

Data sources

WebsiteGlobWetland II

http://www.globwetland.org/index.php

The Global Human Settlement Layer(GHSL)

https://ghsl.jrc.ec.europa.eu/data.php

The Global Surface Water Explorer

https://global-surface-water.appspot.com

Software, tools andplatforms

The Satellite-based Wetlands Observation
Service (SWOS)

http://portal.swos-service.eu/mapviewer/detail/1.html

Operational orcommercial services

Copernicus Global Land Service

https://land.copernicus.eu/global/

Global/regional
datasets

Reference List
Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S.,
Srinivasan, R., Yang, H., & Kløve, B. (2015). A
continental-scale hydrology and water quality
model for Europe: Calibration and uncertainty of ahigh-resolution large-scale SWAT model. Journal of
Hydrology, 524, 733–752.
https://doi.org/10.1016/j.jhydrol.2015.03.027

Pesaresi, M., Blaes, X., Ehrlich, D., Ferri, S., Gueguen,
L., Halkia, M., … Zanchetta, L. (2013). A Global
Human Settlement Layer From Optical HR/VHR RS
Data: Concept and First Results. IEEE Journal of
Selected Topics in Applied Earth Observations andRemote Sensing, 6(5), 2102–2131.
https://doi.org/10.1109/JSTARS.2013.2271445

Giardino, C., Brando, V. E., Gege, P., Pinnel, N.,
Hochberg, E., Knaeps, E., … Dekker, A. (2018). Imaging
Spectrometry of Inland and Coastal Waters: State of
the Art, Achievements and Perspectives. Surveys inGeophysics.
https://doi.org/10.1007/s10712-018-9476-0

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

52

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

TARGET 6.1

6.1.1
Proportion of population using safely
managed drinking water services

Custodian agency

WHO; UNICEF

Tier

II

Status of step-by-step methodology
document on the metadata repository

Published

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity required
Availability of global EO
data
Compliance with Reporting
calendarRobustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute forgaps in the EO record?

Overall EO relevanceComments to support criteria

The proposed methodology is only
partially supported because EO can
support monitoring and modelling ofwater quality prior to treatment thus
in quantifying “safe” levels of water
quality. However quantifying proportionof population with access to safe water
beyond the remit of EO.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

53

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 6.3
By 2030, improve water quality by reducing pollution,
eliminating dumping and minimizing release of hazardous
chemicals and materials, halving the proportion of untreatedwastewater and substantially increasing recycling and safe
reuse globally
How can EO be used to help countries achieve the target?
Target 6.3 aims to improve ambient water quality,
important for protecting both ecosystem health andhuman health, by eliminating, minimizing and significantly
reducing different streams of pollution into water bodies.
The main sources of water pollution include wastewaterfrom households, commercial establishments andindustries (point sources), as well as run-off from urban
and agricultural land (non-point sources). Water quality
can be measured in different ways and EO methods ofwater quality detection differ from those based on insitu assessment. Therefore EO support to this target will
be limited by what can be detected based on current
sensor technology. EO is important in ongoing, routine
water quality monitoring of large water bodies, ideallyin combination with in situ sampling. Yet it is feasible,
although challenging, to use EO as a monitoring tool for
illegal contamination of water supplies. The release ofcertain hazardous chemicals and materials, for example,
can alter the opacity, turbidity and colour of lakes, rivers
or other water bodies, which can be sensed from amultispectral or hyperspectral sensor. Armed with thisEO-derived information on sudden declines in water
quality, e.g. due to dumping of hazardous materials, water
management authorities could track down polluters.
However, the spatial resolution of the Sentinel 3 Ocean
and Land Colour Imager (OLCI) is likely to be too coarse toaccurately detect such events, while the Sentinel 2 Multi
Spectral Imager (MSI), although better suited in terms ofspatial resolution, is too sparse in terms of revisit time
(5 days). A combination of commercial and free sensors(OLCI, S-2, L8, SPOT, RapidEye, IKONOS) could be an
option for targeted, local efforts at detection of pollution
events. EO can also support countries with the target byassessment of the risk of eutrophication of a country’s
water bodies by monitoring ambient nutrient pollution instanding waters.
Current Indicator(s):6.3.1 Proportion of wastewater safely treated
6.3.2 Proportion of bodies of water with good ambient
water quality
Potential new indicator(s) based on EO:
The number of cyanobacteria blooms in water supplies
The number of pollution events (including thermal) causing
illegal contamination of water supplies

TARGET 6.3

Short methodological guidelines illustrated
with EO best practice examples
Indicator 6.3.1
Computation method
Indicator 6.3.1 is calculated as the amount of waste
treated (off-site and on-site) divided by the total amountproduced. The breakdown of treated wastewater into
untreated versus treated can be calculated based oncompliance records, related to national standards. Unlessverified otherwise, through audited compliance records, thewaste generated will be considered untreated. However it
may be possible where there are no compliance records touse EO-derived nutrient concentrations present in standing
waterbodies as an indicator of the amount of untreated /poorly treated wastewater entering these systems.
Parameters retrieved with medium to high confidence:(chl-a),• Chlorophyll-aColoreddissolved
matter (CDOM),• Secchi disk depth organic(SDD),• Turbidity,• Total suspended matter (TSM)•
Parameters retrieved with reduced resolution:ater temperature (WT): SST can be retrieved with
• W
quite good accuracy (and confidence) for the coarse•

resolution (large water bodies). Higher resolution data
can provide relative temperature patterns (e.g. Landsat).
Sea surface salinity (SSS): passive radar restricted to
low spatial resolution (50km), which is not suitable forfreshwater.

Limitations
The calculation of indicator 6.3.1 using an EO-based
approach is limited because not all hazardous chemicals
and materials, pollutants (such as litter) and untreatedwastewater can be detected by multispectral or
hyperspectral sensors. These errors of omission would
need to be quantified before attempting an EO-based
approach to this indicator. In coastal areas, Synthetic
Aperture Radar (SAR) has been shown to be able to detectsurface runoff plumes of biogenic substances associatedwith storm events (Svejkovsky & Jones, 2001), although
the effectiveness of this method is dependent on weather
conditions and sea surface state. However, its application inother aquatic environments, e.g. lakes, remains unproven.
Key messages for countries on EO contribution to thecomputation method:
approaches to water quality monitoring are• EinO-based
varying levels of maturity with up to 7 physical andbiological parameters currently retrievable from low toCompendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

54

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

high confidence. Those parameters which do not haveany optical (e.g. pathogens, salinity) can only be detected
via proxy parameters (if at all) and are therefore retrieved
with the lowest confidence and might contain the largest
errors and need local adjustment, especially in opticallycomplex waters.
he safe treatment of wastewater is more challenging to• Tevaluate
from EO since it requires a prior knowledge ofthe status of the wastewater prior to treatment in orderto deduce if the treatment has been “safe” according
to acceptable standards. In addition water quality
parameters, such as the presence of pathogens, are notretrievable from EO

TARGET 6.3

tandards of treatment will vary internationally, and• Sestablishinga wastewater quality baseline from EO
alone is not possible, and will require compliance records
obtained in-situ
ater bodies include rivers, lakes and groundwater.
• W
Assessing the ambient water quality of ground waterand very small rivers or lakes (e.g. 1km surface area) is
not possible using EO
ndicator 6.3.2 and 6.3.1 are interlinked because inadequate• Iwastewater
treatment leads to degradation in quality of
the waters receiving the wastewater effluents.

Data sources
Data category

Data sources

WebsiteGlobal/regional
datasets

The Global Surface Water Explorer

https://global-surface-water.appspot.com

Software, tools andplatforms

Bio-physical parameters can be derived
from Sentinel 2 and 3 using processors
provided in the SNAP toolbox

http://step.esa.int/main/

Copernicus Global Land Service

https://land.copernicus.eu/global/

CyanoLakes

http://www.cyanolakes.com/monitoring

EOMAP

https://www.eomap.com/

Brockmann Consult

https://web.brockmann-consult.de

Operational orcommercial services

Reference List
Svejkovsky, J. & Jones, B. (2001) ‘Satellite imagery detects coastal stormwater and sewage runoff’, Eos, 82(50),pp. 624–625. doi: 10.1029/01EO00357.

Indicator 6.3.2

Parameters retrieved with medium to high confidence:

Computation method

hlorophyll-a (chl-a),• CColoreddissolved organic matter (CDOM),• Secchi disk
• Turbidity, depth (SDD),• Total suspended matter (TSM)•

Indicator 6.3.2 is computed as the proportion of all water
bodies (river, lake and groundwater bodies) classified
as having good quality, expressed as a percentage. The
methodology considers a body of water to have good quality
if at least 80% of all monitoring data from all monitoring
stations within the water body are in compliance withthe respective targets. Mapping the global extent anddynamics of water bodies from space has been well proven(Pekel et al., 2016). The mapping of water quality has been
demonstrated in many different applications and services.
EO can sense the water constituents that have an influenceon the colour of the water and thus is providing a subsetof parameters that are measured in-situ and used forindicator 6.3.2.

Parameters retrieved with reduced resolution:ater temperature (WT): SST can be retrieved with quite• Wgood accuracy (and confidence) for the coarse resolution•(large water bodies). Higher resolution data can provide
relative temperature patterns (e.g. Landsat).
Sea surface salinity (SSS): passive radar restricted to
low spatial resolution (50km), which is not suitable forfreshwater.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

55

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Treatment of missing values
Indicator 6.3.2: Missing values within water bodies could
possibly be filled by spatiotemporal interpolation, e.g.
using Data Interpolating Empirical Orthogonal Functions(DINEOF), an optimised gap filling procedure. However thisrisks introducing large uncertainties because bio-physicalproperties of water can vary over small distances. Anotherapproach would be to use a time series of observations(e.g. yearly) and to calculate temporal statistics (e.g. mean,
25th and 75th percentiles, median). This would reduce thelikelihood of a missed observation significantly.

TARGET 6.3

nutrient levels and dissolved minerals, etc. While EO cansense a limited number of these bio-physical components itcannot monitor chemical ones, e.g. heavy metals, nor water
quality as a whole and (ii) spatial resolution of remotesensing instruments in the aquatic domain is generallycoarse (>300m) meaning that groundwater bodies orsmall lakes or rivers are either undetectable or averagedout over large areas. Therefore EO is a useful source of
supplementary information for in situ physicochemical
water quality monitoring programmes but will never
be a replacement for them. Seven such water quality
parameters are directly measurable from EO, albeit withlarge uncertainties (Gholizadeh et al., 2016).

Sources of discrepancies
Possible discrepancies will exist between national andinternational water quality datasets, derived from EO, if the
input data are different. In addition compliance with water
quality standards post-treatment will vary from country
to country as do guidelines for acceptable standards ofnutrient pollution.

Key messages for countries on EO contribution to thecomputation method:
approaches to water quality monitoring are• EinO-based
varying levels of maturity with up to 7 physical andbiological parameters currently retrievable from low tohigh confidence. Those parameters which do not haveany optical (e.g. pathogens, salinity) can only be detected
via proxy parameters (if at all) and are therefore retrieved
with the lowest confidence and might contain the largest
errors and need local adjustment, especially in opticallycomplex waters.

Limitations
There is no information about ground water (or any subsurface water) quality from EO for indicator 6.3.2. Thereis also a limitation in the comparison of EO-derived water
quality products and the monitoring data collected forselected parameters at monitoring locations as required by
the indicator for assessing “good” quality. This is because
EO does not measure all water quality parameters but
only bio-physical parameters, therefore it is not possible
to comment on the overall water quality exclusively using
EO data. For example, mapping ambient water quality ofwater bodies is a much more challenging prospect for two
main reasons: (i) water quality is a combination of many
biological, such as sedimentation, pathogens or algae,
and physicochemical components, such as heavy metals,

ater bodies include rivers, lakes and groundwater.
• W
Assessing the ambient water quality of ground water andvery small rivers or lakes is not possible using EO. With
Sentinel-3 water bodies less than 1 km2 is not feasible
to map, while Sentinel-2 can monitor water bodies down
to approximately 150 by 150 meters (but limited by thespectral and temporal resolution).
ndicator 6.3.2 and 6.3.1 are interlinked because• Iinadequate
wastewater treatment leads to degradation inquality of the waters receiving the wastewater effluents.

Data sources
Data category

Data sources

WebsiteGlobal/regional
datasets

The Global Surface Water Explorer

https://global-surface-water.appspot.com

Software, tools andplatforms

Bio-physical parameters can be derived
from Sentinel 2 and 3 using processors
provided in the SNAP toolbox

http://step.esa.int/main/

Copernicus Global Land Service

https://land.copernicus.eu/global/

CyanoLakes

http://www.cyanolakes.com/monitoring/

EOMAP

https://www.eomap.com/

Brockmann Consult

https://web.brockmann-consult.de

DHI GRAS

https://www.dhi-gras.com/solutions/water-quality/

Operational orcommercial services

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

56

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 6.3

Reference List
Gholizadeh, M. H., Melesse, A. M. & Reddi, L. (2016) ‘A
Comprehensive Review on Water Quality Parameters
Estimation Using Remote Sensing Techniques’, Sensors(Basel, Switzerland). Edited by H. Tømmervik. MDPI,
16(8), p. 1298. doi: 10.3390/s16081298.

Pekel, J.-F. et al. (2016) ‘High-resolution mappingof global surface water and its long-term changes’,
Nature, 540(7633), pp. 418–422. doi: 10.1038/
nature20584.

Matthews, M. W. et al. (2018) The Integration ofEarth Observation into the National Eutrophication
Monitoring Programme.

Two show cases for utilizing EO for waste water detection(indicator 6.3.1)
Target 6.3 aims for improved water
quality, reduced pollution, eliminateddumping and minimized release ofhazardous chemicals and materials
as well as halving the proportionof untreated wastewater andsubstantially increasing recycling
and safe reuse globally by 20130.
The indicator 6.3.1 “Proportion ofwastewater safely treated” addresses
households as well as industry. The
support that Earth Observation (EO)
might give here is not in covering
the treatment of wastewater itself.
However, we would like to illustratewith two show cases what EO data
can provide for the subject of waste
water.

coastal engineering work in coastal
waters have a large impact on local
and regional water quality andsedimentology. Depending on thesource, dredging material can becontaminated with heavy metals andother organic or inorganic substances.
Different water masses are identified
by their colour and interpreted

The first shows the detection of heatplumes at the outflow of cooling waterfrom power plants into rivers. Landsat
thermal data have been used to showtemperature anomalies. Under certain
conditions, the anomalies are clearlyvisible (showcase 1), while otherimages do not show any differencesof cooling water and river water.
Influencing factors are differences intemperature of the river water and thecooling water, the performance of thepower plant, direction and strength of(tidal) currents and wind speed.

Figure 3: Examples of Landsat images showing anomalies in temperature caused be cooling waterfrom power plants (left and right: Landsat-7, middle: Landsat-8). Shown are power plants at theElbe River (left and middle) and the Weser River in Germany. (Brockmann Consult service; image
data: Landsat ©USGS)

The second show case illustrates themonitoring of dredging activities incoastal waters (showcase 2). Humanactivities due to dredging and otheraccordingly. The phenomena are ofcomparably small spatial scale andcan be mapped with spatial highresolution images such as Landsat-8
or Sentinel-2. The revisit time of thesatellites limits the potential to run
an operational monitoring scheme but
can complement other methods.

Figure 4: Sentinel-2 observation of dumping activities. Above: True colour image, below: derived
parameter: absorption (humic substances), differentiating between dumped water and coastal
waters. (Brockmann Consult service; image

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

57

TARGET 6.3

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSSouth Africa: The Earth Observation National Eutrophication Monitoring
Programme (indicator 6.3.2)
The “Integration of Earth Observation
into the National Eutrophication
Monitoring Programme” (EONEMP)
Project (funded by the Water
Research Commission, ZAR 3 Million,
2015-2018) used earth observation
satellite remote sensing to monitor
cyanobacteria blooms (fig.1) andnutrient pollution (eutrophication,
fig.2) in South Africa’s large andmedium-sized (>900 m) fresh
waterbodies (Matthews et al., 2018).
Using EO, it determined the status andseverity of nutrient enrichment andthe health risks from cyanobacteria
for reporting to Parliament on the
blooms, for both the Table
previous
decadenutrient pollution levels and risks
39 Waterbodies worst affected by eutrophication as calculated by the area of eutrophic and hypertrophic
waters for 2016/17.
(2002 to 2012) andfor the year
from cyanobacteria in South Africa’s
Area (km ) Name
Area (km )2016/17. A prototype onlineName
near
water
supply reservoirs.
The project
Gariep302.4 Kalkfontein
32.7
real-time monitoring service website
demonstrates
how
EO
can
produceVaal
206.4 Erfenis
29.5
134.7 Theewaterskloof
25.3 records for
was developed (fig.3) through Bloemhof
whichinvaluable
environmental
Vanderkloof
69.9 Allemanskraal
24.3
chlorophyll-a and associated Darlington
datareportingon and analysing
pollution of
34.4 Grootdraai
22.0
were delivered to, and integrated
standing waters by shedding light on
When considering the spatial distribution of eutrophication (Figure 69) the majority of
into, the Water Management
Systemthe impacts from poorly or untreatedhypertrophic waterbodies were situated on the Highveld, in the Free State, Gauteng anddatabase of the national
governmental
wastewater which is known to be a
North-West provinces. The majority of oligotrophic waterbodies were situated on the eastern
Department of Waterhalfand
Sanitation
significant
problemin TheSouth
Africa’sof SouthAfrica, in the Kwa-Zulu
Natal and Mpumalanga
provinces.
spatial distribution
median chl-a values(Figure 70)
had similar patterns, with a grouping of high-chl-a dams
to supplement in situof monitoring
andinfrastructure.
north of Gauteng, the main drainage basin from the Gauteng metropole, and in the Vaal andfill information gaps.
The satellite
Orange catchments to the south. The spatial pattern of median chl-a from cyanobacteria
information was ultimately
usedblooms was very similar, suggesting a strong correlation between trophic status and the22

occurrence of cyanobacteria blooms (Figure 70).

Cyanobacteria risk level

Cyanobacteria risk level(2016/17)

Trophic status
Hypertrophic
Eutrophic
Mesotrophic
Oligotrophic

Very highHigh
MediumLow

Trophic status(2016/17)

100

100

0

0

100 200 km

100 200 km
Figure 69 Trophic status for 102 waterbodies during 2016/17.

Figure 66 Cyanobacteria risk level for 2016/17 estimated by OLCI.
Figure5: The cyanobacteria risk level derived from Sentinel-3 OLCI
forfor 102 South African waterbodies.
3.5.22016/17
Eutrophication

The trophic status of the 102 waterbodies during 2016/17 were as follows (Figure 67 andFigure 68):

Figure 6: The nutrient pollution level (trophic status) derived from
Sentinel-3 OLCI for 2016/17 for 102 South African waterbodies
174

l 35% of the total area was hypertrophic
l 27% of the total area was eutrophic
l 26% of the total area was oligotrophic
l 40 of the 102 waterbodies were on average hypertrophic
l seven of the 102 waterbodies were on average eutrophic
l 41 of the 102 waterbodies were on average oligotrophic
Roughly two-thirds of the total area of the 102 waterbodies were either eutrophic or
hypertrophic (chl-a greater than 30 mg m-3). Roughly one quarter of the total area wasoligotrophic, meaning it had low algal and cyanobacteria biomass. The five waterbodies worst
affected by eutrophication as measured by the median chl-a value during 2016/17 (in order)
were Roodeplaat, Floriskraal, Kalkfontein, Klipvoor and Krugersdrift Dams. Those with thelargest area of eutrophic and hypertrophic water (Table 39) were Gariep, Vaal, Bloemhof,
Vanderkloof and Darlington Dams.

Figure 7: The online monitoring service
developed by CyanoLakes during the EONEMP
project for monitoring nutrient pollution andcyanobacteria health risks in waterbodies innear real-time.

172

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

58

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 6.4
By 2030, substantially increase water-use efficiency
across all sectors and ensure sustainable withdrawals
and supply of freshwater to address water scarcity andsubstantially reduce the number of people suffering fromwater scarcity
How can EO be used to help countries achieve the target?
Target 6.4 addresses water scarcity, aiming to ensure
there is sufficient water for the population, the economy
and the environment by increasing water-use efficiency
across all sectors of society. Finding a balance between
demands for water from environmental requirements andhuman demand is essential to maintaining ecosystem
health and resilience. An imbalance due to unsustainablelevels of demand can result in water stress withnegative effects on economic development, increasing
competition and potential conflict among users. Thisrequires effective supply and demand management
policies and an increase in water-use efficiency.
EO has an obvious – if yet unrecognised – contribution
to the monitoring of the target in quantifying surface
water changes over time, water consumed by key wateruser sector such as agriculture, as well as soil moisture
deficits. Therefore EO can help countries achieve
water use efficiency gain targets by identify areas ofcurrent and future surface water deficits, e.g. throughhydrological models, based on EO parameters such as
evapotranspiration, soil moisture and surface water,and by modelling supply and demand across sectors
based on land use change. In agricultural areas, EO canmonitor how effectively water uptake by vegetationis translated into crop yield, using a metric that isreferred to as agriculture water productivity (yield/m³
of water consumed). This can ultimately help countries
to plan for water deficits in advance of stresses suchas climate extremes or when demand is excessive. The
number of people suffering from or potentially affectedby such water deficits could then be calculated basedon demographic statistics A range of options exists forcoping with water scarcity that address the supply side or
the demand side or a combination of the two, dependingon the bio-physical and socio-economic context.
Current Indicator(s)6.4.1 Change in water-use efficiency over time
6.4.2 Level of water stress: freshwater withdrawal as aproportion of available freshwater resources
Potential new indicator(s) based on EO:
Proportion of the population suffering from water scarcity
Water productivity

TARGET 6.4

Short methodological guidelines illustrated
with EO best practice examples
Indicator 6.4.1
Computation method
Indicator 6.4.1 defines water use efficiency as thevolume of water used divided by the value added of agiven major sector. Traditionally the sectors considered
are agriculture; forestry; fishing and MIMEC (mining andquarrying; manufacturing; electricity, gas, steam and air
conditioning supply; construction) and services (i.e. thepublic distribution network). Efficiency is defined as thechange in the ratio of the value added (by the sector) to thevolume of water use, over time. The sectoral approach isdesigned to identify economic sectors where value added
is small relative to water use at the national scale andtherefore where gains in water use efficiency can be madeover time. Data on volumes of used and distributed water
are collected at country level from the municipal supply
utilities records and reported in questionnaires. Services
value added is obtained from national statistics, deflated
to the baseline year. There is currently no mention of EO in
the indicator guidelines.
EO-based methods of measuring water use efficiency forthis indicator include the concepts of water productivity
for agriculture and rain use efficiency for environmental
applications. Both concepts are based on productionof vegetation in relation to water usage. Net Primary
Production (NPP) is the total carbon fixed by photosynthesis
during a period and is the rate of organic biomass growth oraccumulation by plants. EO NPP models can be divided intomodels based on empiric relationships with the Normalised
Difference Vegetation Index, radar backscatter or anothersimple satellite-derived parameter, and in physical models
using the absorbed photosynthetically active radiation(fPAR). Methods based on EO derived fPAR have beensuccessfully used to measure biomass production acrossscales, climates, and ecosystems.
Timeseries of optical and radar EO images can be very
useful for separating the landscape into land use and land
cover classes focused specifically on water use. Examples
of such classes are irrigated and non-irrigated agriculture
or individual crop types. Such maps can be produced athigh enough accuracy (up to 10 m when using open data)
to capture individual fields or linear landscape features
such as riparian zones.
EO is increasingly being used to compute actual
evapotranspiration, thereby allowing for a direct estimationof the amount of water consumed in each spatial unit (pixel).
When that information is associated to land use maps
(mostly EO-based), it becomes possible to calculate water
consumption by land use class (e.g. irrigated agriculture,
rainfed agriculture, water bodies, different crops etc.)

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

59

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSThis concept can be applied to water use efficiency in the
agricultural sector, e.g. where EO-based approaches havebeen used to estimate crop water productivity based onwater uptake (Bastiaanssen and Steduto 2017). Recently,
FAO, IHE-Delft, IWMI, and other partners have been
joining forces to develop an open-access database thatmonitors water productivity in Africa and the Near East
(WaPOR) using satellite derived information (FAO, 2018).
Other water using sectors, like industry, energy, or
domestic use, have different impacts on water resources(like deteriorating water quality, or altering its temporalavailability) that are not captured through evapotranspiration
and are not easily detectable through EO.
Sources of discrepancies
When using EO for the total freshwater withdrawn (TWW)
component (explained further in the section on 6.4.2), there
would not be discrepancies expected as optical image inputs(Leaf Area Index, the crop height, vegetation indices, surface
albedo, and land surface temperature) are usually standard andare required for most ET models which also use thermal data.
Limitations
For TWW the spatial resolution of satellite thermal images
is not commensurate with the size of small agricultural
fields. Since the pixels contain broad mixtures anddensities of crops, the land surface temperature signals are
mixed and the evapotranspiration retrievals are difficult tointerpret. For example, the pixel size ranges from 100 m forthe thermal sensor on board Landsat 8 to 375m for VIIRS
to 1000 m for MODIS-AQUA, MODIS-TERRA and Sentinel-3(Allen et al. 2011). This might not pose a problem fornational reporting on the TWW component of indicator
6.4.2, but might pose a challenge for disaggregation byhydrological units (river basins, aquifers), with the caveatthat if a thermal-based model is being used, low spatial
resolutions, e.g. the 1km of the Sentinel-3 SLSTR, might
be just adequate for river basins but is not enough forindividual fields. Various techniques exist for improving the
spatial resolution of thermal data using high-resolution
optical observations. However, caution should be exercisedwhen using such fused data for detailed field-scale analysis.
For the total renewable freshwater resources (TRWR),
only wetlands reservoirs and similar water bodies arequantifiable from EO but it’s not clear how they are counted
in the internal renewable water resource.
There also imitations on the accuracy of model inputs
derived from EO such as land use and meteorological datasuch as rainfall (Karimi & Bastiaanssen 2015).
Finally, as the indicator also depends on the economic
value of the output, and hence on fluctuations in prices, themonetary value component cannot be estimated from EO.

TARGET 6.4

Key messages for countries on EO contribution to thecomputation method:
These indicators are based on quantifying water•consumption across different sectors - domestic,
industrial, services and agriculture and comparing it
to a baseline or background water storage in order toascertain water use efficiency
both experimental and robust applications of• TEOherein are
the computational methodologies. For example
the estimation of ground water storage from microwave
remote sensing is in its infancy compared to surface water
extent and depth mapping based on optical sensors andscanning radiometry
EO technologies to quantify water use efficiency•of vegetation, both natural and agricultural, byassessing the vegetation productivity – NPP, biomass
production – versus the water consumption – actual
evapotranspiration- have matured in the scientificcommunity and found their way to commercial and open
applications.
herefore it is easier to quantify water use over time for• Tcertain
sectors, e.g. agriculture, than others, e.g. domestic
use and for evaluating surface parts of the freshwater
budget, e.g. reservoirs and lakes
Methods applied to rain water use efficiency in•meteorology are now employed for water use efficiency inagriculture and are based on evapotranspiration models
where a range of EO parameters are usedpatial resolution and accuracy of retrieval will vary for• Scertain
optical and thermal properties which should beconsidered for national circumstances
O technology offer the opportunity to change not• Eonly
the way but also what we measure. For example
traditional methodologies only capture water extracted
for irrigation, while satellite earth observation can alsomeasure water usage by natural vegetation, forest
plantations, and rainfed agriculture.
he use of EO for calculation of the indicators in the• Tagricultural
sector has a number of advantages comparedto the currently available dataset (Graveland et al., 2016):
gricultural water use derived from remote sensing,• Awhichis actual ET, can be made for each country••

relatively easily;A number of AET data sources are already publicly
available;
Historical archives make it possible to assess thetrend in water use efficiency, even when no priorinformation has been collected;

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

60

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

can be consistently implemented• Tinheeachmethodology
country, making cross-country comparison for

TARGET 6.4

t has a high level of spatial and temporal resolution,• Iwhichwill enable more targeted policies to improve

AET possible; andagricultural water use efficiency.

Data sources
Data category

Global/regional
datasets

Operational orcommercial services

Data sources

WebsiteESA CCI Land Coverhttps://climate.esa.int/en/projects/land-cover

Global Map of Irrigation Areas (GMIA)
of FAO

http://www.fao.org/nr/water/aquastat/irrigationmap/
index10.stm

Global Irrigated Area Map (GIAM) of
IWMI

http://waterdata.iwmi.org/Applications/GIAM2000

The FAO portal to monitor Water
Productivity through Open access of
https://wapor.apps.fao.org
Remotely sensed derived data (WaPOR)
Dry matter productivity (yield) and water bodies map from Copernicus Land
Services

https://land.copernicus.eu/global

IrriSAThttps://irrisat-cloud.appspot.com

FruitLook (for South Africa)

https://www.fruitlook.co.za

Reference List
Allen, Richard G, Luis S Pereira, Terry A Howell, andMarvin E Jensen. 2011. “Evapotranspiration Information
Reporting: I. Factors Governing Measurement Accuracy.”
Agricultural Water Management 98(6): 899–920.
http://www.sciencedirect.com/science/article/pii/
S0378377411000023.
Bastiaanssen, Wim G M, and Pasquale Steduto. 2017.
“The Water Productivity Score (WPS) at Global andRegional Level: Methodology and First Results from
Remote Sensing Measurements of Wheat, Rice andMaize.” Science of The Total Environment 575: 595–
611.
http://www.sciencedirect.com/science/article/pii/
S0048969716319490.
Calera, Alfonso et al. 2017. “Remote Sensing for Crop
Water Management: From ET Modelling to Services for
the End Users.” Sensors (Switzerland) 17(5): 1–25.
Chen, J L et al. 2016. “Long-Term Groundwater Storage
Change in Victoria, Australia from Satellite Gravity andin Situ Observations.” Global and Planetary Change 139:
56–65. http://www.sciencedirect.com/science/article/pii/
S0921818116300108.

Basin.” Agriculture, Ecosystems & Environment 200:
126–42. http://www.sciencedirect.com/science/article/pii/
S0167880914004861.
FAO 2018. WaPOR Database Methodology: Level 1.
Remote Sensing for Water Productivity Technical Report:
Methodology Series. Rome, FAO. 72 pages. Licence: CC BYNC-SA 3.0 IGO. https://wapor.apps.fao.org.
Ferrant, Sylvain et al. 2017. “Detection of Irrigated Crops
from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal
Groundwater Use in South India.” Remote Sensing 9(11).
Graveland et al. (2016) Sustainable Development
Goals for water - SDG 6.4 - Three step approach formonitoring. CBS, Den Haag. https://www.cbs.nl/engb/background/2016/51/sdgs-for-water-three-stepapproach-for-monitoring
He, Bin et al. 2017. “A New Indicator of Ecosystem Water
Use Efficiency Based on Surface Soil Moisture Retrieved
from Remote Sensing.” Ecological Indicators 75: 10–16.
http://www.sciencedirect.com/science/article/pii/
S1470160X16307105.

Dardel, Cécile et al. 2014. “Rain-Use-Efficiency: What It
Tells Us about the Conflicting Sahel Greening and Sahelian
Paradox.” Remote Sensing 6(4): 3446–74.

Holmes, T R H et al. 2018. “Microwave Implementationof Two-Source Energy Balance Approach for Estimating
Evapotranspiration.” Hydrology and Earth System Sciences22(2): 1351–69. https://www.hydrol-earth-syst-sci.
net/22/1351/2018/.

van Eekelen, M W et al. 2015. “A Novel Approach to
Estimate Direct and Indirect Water Withdrawals from
Satellite Measurements: A Case Study from the Incomati

Le Houerou, H. N. 1984. “Rain Use Efficiency: A Unifying
Concept in Arid-Land Ecology.” Journal of Arid Environments
7(3): 213–47.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

61

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 6.4

Huilin, Gao. 2015. “Satellite Remote Sensing of Large Lakes
and Reservoirs: From Elevation and Area to Storage.” Wiley
Interdisciplinary Reviews: Water 2(2): 147–57. https://doi.
org/10.1002/wat2.1065.

Ozdogan, Mutlu, Yang Yang, George Allez, and Chelsea
Cervantes. 2010. “Remote Sensing of Irrigated Agriculture:
Opportunities and Challenges.” Remote Sensing 2(9):2274–2304.

Karimi, P. & Bastiaanssen, W. G. M (2015) “Spatial
Evapotranspiration, Rainfall and Land Use Data in Water
Accounting &ndash; Part 1: Review of the Accuracy of
the Remote Sensing Data.” Hydrology and Earth System
Sciences 19(1): 507–32. https://www.hydrol-earth-systsci.net/19/507/2015/.

Zhang, Li et al. 2015. “Evaluation of Water Use Efficiency
Derived from MODIS Products against Eddy Variance
Measurements in China.” Remote Sensing 7(9): 11183–
201.

EO for improving water-use efficiency in the agricultural sector in South
Africa for indicator 6.4.1
FruitLook – supporting farmers to increase water use efficiency
Like in many other countries, South
Africa is facing a growing demand
for water while at the same time
water availability is under threat fromclimate change. Recently South Africa
experienced one of the worst droughts
in centuries with a strong impact onurban and agricultural communities.
In Western Cape Province, the fruit
and wine industry represents almosta third of the province’s exports but
is also a large consumer of irrigationwater. Optimizing production while
minimizing the ecological impact of
the sector will have both economicand environmental benefits and calls
for innovative solutions.

generally optimize use of resources.
And with success: the water use
efficiency of FruitLook users has
already increased by between 10%
and 30%.
Earth Observation technologies help
farmers to address the challenge of
producing more with less input, in
response to a growing population,

climate change, increased competition
for water, and rising input costs. The
PiMapping® technology behind theFruitLook application is applicable on
any land surface, and the concept canbe expanded to other countries andcrops to assist farmers all over theworld in attaining more sustainable,
efficient and resilient water resource
management.

FruitLook (http://www.FruitLook.co.za)
is an online platform for fruit andgrape farmers powered by satellite
Earth Observation data, developedby the Dutch company eLEAF andsupported by the Department ofAgriculture: Western Cape. It provides
farmers with weekly updates on
their crop’s water and growth status.
FruitLook has been available since2010 and services an area of 9 million
ha, including 200,000 ha of fruit crops.
FruitLook helps farmers to makebetter management decisions which
are reflected in more efficient andproductive water usage (Figure 8).
Farmers use FruitLook to monitor
crop development, detect and locate
growth problems, evaluate andimprove water management, andFigure 8: Fruitlook helps farmers to achieve water use efficiency in the agricultural
sector using EO technology

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

62

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 6.4

Open access water productivity data for Africa and the Near East forindicator 6.4.1
Achieving food security in the future
while using water resources in asustainable manner will be a major
challenge in the face of climatechange. Agriculture is a key wateruser and is one of the key sectors forcomputing indicator 6.4.1. In orderto monitor the performance of water
use in agriculture, FAO developed
a publicly accessible near real time
database using EO data.

factors, provides evidence onrisks and their impact, advocatesmanagement with water accounting,defines strategies and prioritization,and plans interventions more
effectively. Moreover, WaPOR is a
powerful instrument to help in thecomputation of indicator 6.4.1 andmore broadly to achieve and measureprogress towards SDG6.

The FAO portal to monitor Water
Productivity through open accessof remotely sensed derived data
(WaPOR) can be used to report on
agricultural water productivity overAfrica and the Near East from 2009
to present. It provides open access(through its portal and dedicatedApplication Programming Interfaces)
to various spatial data layers
related to land and water use. It
allows for direct data queries, timeseries analyses, area statistics anddata download of key variables toestimate land and water productivity
gaps in irrigated and rainfed
agriculture, monitor trends of water
use in irrigated areas and assess theinfluence of droughts on agricultural
production.
Land productivity is assessed in terms
of production (biomass production or
yield) in kg/ha while water used for
agricultural production is expressedin actual evapotranspiration. When
combined these datasets result in
the water productivity: production
per volume of water in kg/m3. Data
is available at three different spatial
resolutions (Figure 9) and generated
by the FRAME consortium members,eLEAF and VITO, with financial
support from the Dutch Ministry ofForeign Affairs.

Figure 9: Water Productivity through open access of remotely sensed derived data (WaPOR) is a
new tool developed by the FAO to report on agricultural water productivity over Africa and the
Near East from 2009 to present

WaPOR can help countries to improvethe understanding of trends inwater productivity and contributing

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

63

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicator 6.4.2
Computation method
Indicator 6.4.2 is computed as the total freshwater
withdrawn (TWW) divided by the difference between the
total renewable freshwater resources (TRWR) and theenvironmental water requirements (Env.), multiplied by 100.
To compute this indicator by sector (as is required), data
for each of the relevant sectors (agriculture, municipalities
and industry) are needed. Like 6.4.1, there is currently no
mention of EO in the indicator guidelines.
However, EO data can be used to quantify both the TWW
and TRWR components of the indicator. For TWW, mostwater withdrawals are for agricultural irrigation. The
amount of water used by irrigation can be estimated/modelled as incremental evapotranspiration, using EO dataon actual evapotranspiration (see computational method
for 6.4.1) in irrigated agriculture as compared to thesurrounding rainfed land, and rainfall.
It is important to realise EO data provides a new way tomeasure water use, which goes beyond the traditional
ways of measuring water withdrawals by sectors. Throughactual evapotranspiration modelling it is now possible todetermine water usage by not only irrigated agriculture, butalso by rainfed agriculture, forest, and natural vegetation.
Secondly the TRWR is expressed as the sum of internal andexternal renewable water resources. Internal renewable
water resources are defined as the long-term averageannual flow of rivers and recharge of groundwater for agiven country generated from endogenous precipitation.
Precipitation is another parameter that can largely benefit
from improved EO-based techniques. External renewable
water resources refer to the flows of water entering thecountry. Groundwater has been inferred from EO throughgravimetric measurements, however this is not yet a robust
approach (Chen et al., 2016) and also limited in terms ofdetail. Arguably, reservoirs are a component of the internal
renewable water resource. Water reservoirs and theirevolution over time can be mapped from EO, providing dataon water surface area, water surface height and volume.
The available time period and update frequency depends
on the satellite sensors used, but using only Landsat
data one can have monthly updates at a global scale forreservoirs larger than one hectare. Optical sensors such
as Landsat and SPOT can easily identify reservoirs of size
down to one hectare. In cloudy areas SAR observations
can fill the gaps or provide the complete reservoir map
with comparable levels of accuracy to optical data, except
when wind and other weather conditions distort the water
surface (Huilin, 2015).
In summary, EO can contribute to quantifying the TWW
and TRWR components of the indicator – in the formercase only for the agricultural sector (Ferrant et al., 2017),
and in the latter case only for reservoirs and other open

TARGET 6.4

water bodies. Moreover, EO can be very useful for the
spatial disaggregation of the indicator at sub-national/
basin level
Another way of estimating the level of water stress islooking at the transpiration deficit, which is based on theratio of actual water consumption and the potential water
consumption.
Treatment of missing values
For indicator 6.4.2, An EO-based approach will contain missing
values due to cloud as the approaches proposed are based on
optical or thermal data. Gaps in observed Evapotranspiration(ET) are usually interpolated using meteorological data. Also
work is being done on all-weather ET estimation using
microwave temperature (Holmes et al., 2018).
Key messages for countries on EO contribution to thecomputation method:(6.4.1 and 6.4.2) are based on quantifying• Twaterhese indicators
consumption across different sectors - domestic,
industrial, services and agriculture and comparing it to abaseline or withdrawal (water stress)
are both experimental and robust applications of• TEOherein thecomputational methodologies. For example theestimation of ground water storage from microwave remotesensing is in its infancy compared to surface water extent
and depth mapping based on optical sensors and scanningradiometry
to quantify water use efficiency of• Evegetation,
O technologies
both natural and agricultural, by assessing thevegetation productivity – NPP, biomass production – versus
the water consumption – actual evapotranspiration - havematured in the scientific community and found their way tocommercial and open applications.
it is easier to quantify water use over time for• Tcertain
hereforesectors,
e.g. agriculture, than others, e.g. domestic
use and for evaluating surface parts of the freshwater
budget, e.g. reservoirs and lakes, than underground, e.g.
aquifers
applied to rain water use efficiency in meteorology• Mare ethods
now employed for water use efficiency in agriculture
and are based on evapotranspiration models where a rangeof EO parameters are used
and accuracy of retrieval will vary for• Scertainpatial resolution
optical and thermal properties which should beconsidered for national circumstances
offer the opportunity to change not only the• EwayO technology
but also what we measure. For example traditional
methodologies only capture water extracted for irrigation,
while satellite earth observation can also measure water

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

64

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

usage by natural vegetation, forest plantations, and rainfed
agriculture.

•The use of EO for calculation of the indicators in the
agricultural sector has a number of advantages comparedto the currently available dataset (Graveland et al., 2016):

water use derived from remote sensing, which• Aisgriculturalactual ET, can be made for each country relatively easily;• A number of AET data sources are already publicly available;

TARGET 6.4

make it possible to assess the trend in• Hwater istoricalusearchives
efficiency, even when no prior information has
been collected;
The methodology can be consistently implemented in eachcountry, making cross-country comparison for AET possible;
andIt has a high level of spatial and temporal resolution, which
will enable more targeted policies to improve agricultural
water use efficiency.

••Data sources
Data category

Data sources

WebsiteESA CCI Land Coverhttps://climate.esa.int/en/projects/land-cover

Global Map of Irrigation Areas (GMIA) of FAO

http://www.fao.org/nr/water/aquastat/irrigationmap/
index10.stm

Global Irrigated Area Map (GIAM) of IWMI http://waterdata.iwmi.org/Applications/GIAM2000
Global/regional
datasets

Operational orcommercial services

The FAO portal to monitor Water
Productivity through Open access of
https://wapor.apps.fao.org
Remotely sensed derived data (WaPOR)
Dry matter productivity (yield) and water bodies map from Copernicus Land
Services

https://land.copernicus.eu/global

IrriSAThttps://irrisat-cloud.appspot.com

FruitLook (for South Africa)

https://www.fruitlook.co.za

Reference List
Allen, Richard G, Luis S Pereira, Terry A Howell, andMarvin E Jensen. 2011. “Evapotranspiration Information
Reporting: I. Factors Governing Measurement
Accuracy.” Agricultural Water Management 98(6):899–920.
http://www.sciencedirect.com/science/
article/pii/S0378377411000023.
Bastiaanssen, Wim G M, and Pasquale Steduto. 2017.
“The Water Productivity Score (WPS) at Global andRegional Level: Methodology and First Results from
Remote Sensing Measurements of Wheat, Rice andMaize.” Science of The Total Environment 575: 595–
611. http://www.sciencedirect.com/science/article/pii/
S0048969716319490.
Calera, Alfonso et al. 2017. “Remote Sensing for Crop
Water Management: From ET Modelling to Services
for the End Users.” Sensors (Switzerland) 17(5): 1–25.
Chen, J L et al. 2016. “Long-Term Groundwater Storage
Change in Victoria, Australia from Satellite Gravity andin Situ Observations.” Global and Planetary Change
139: 56–65. http://www.sciencedirect.com/science/
article/pii/S0921818116300108.

Dardel, Cécile et al. 2014. “Rain-Use-Efficiency: What
It Tells Us about the Conflicting Sahel Greening andSahelian Paradox.” Remote Sensing 6(4): 3446–74.
van Eekelen, M W et al. 2015. “A Novel Approach to
Estimate Direct and Indirect Water Withdrawals from
Satellite Measurements: A Case Study from the Incomati
Basin.” Agriculture, Ecosystems & Environment 200:
126–42. http://www.sciencedirect.com/science/article/
pii/S0167880914004861.
FAO 2018. WaPOR Database Methodology: Level 1.
Remote Sensing for Water Productivity Technical Report:
Methodology Series. Rome, FAO. 72 pages. Licence: CC
BY-NC-SA 3.0 IGO.
https://wapor.apps.fao.org.
Ferrant, Sylvain et al. 2017. “Detection of IrrigatedCrops from Sentinel-1 and Sentinel-2 Data to Estimate
Seasonal Groundwater Use in South India.” Remote
Sensing 9(11).

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

65

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Graveland et al. (2016) Sustainable Development
Goals for water - SDG 6.4 - Three step approach formonitoring. CBS, Den Haag. https://www.cbs.nl/engb/background/2016/51/sdgs-for-water-three-stepapproach-for-monitoring
He, Bin et al. 2017. “A New Indicator of Ecosystem
Water Use Efficiency Based on Surface Soil Moisture
Retrieved from Remote Sensing.” Ecological Indicators
75: 10–16. http://www.sciencedirect.com/science/
article/pii/S1470160X16307105.
Holmes, T R H et al. 2018. “Microwave Implementationof Two-Source Energy Balance Approach for Estimating
Evapotranspiration.” Hydrology and Earth System
Sciences 22(2): 1351–69.
https://www.hydrol-earth-syst-sci.net/22/1351/2018/.
Le Houerou, H. N. 1984. “Rain Use Efficiency: A
Unifying Concept in Arid-Land Ecology.” Journal of Arid
Environments 7(3): 213–47.

Indicators

TARGET 6.4

Huilin, Gao. 2015. “Satellite Remote Sensing of Large
Lakes and Reservoirs: From Elevation and Area to
Storage.” Wiley Interdisciplinary Reviews: Water
2(2): 147–57. https://doi.org/10.1002/wat2.1065.
Karimi, P. & Bastiaanssen, W. G. M (2015) “Spatial
Evapotranspiration, Rainfall and Land Use Data in
Water Accounting &ndash; Part 1: Review of the
Accuracy of the Remote Sensing Data.” Hydrology
and Earth System Sciences 19(1): 507–32. https://www.hydrol-earth-syst-sci.net/19/507/2015/.
Ozdogan, Mutlu, Yang Yang, George Allez, and Chelsea
Cervantes. 2010. “Remote Sensing of IrrigatedAgriculture: Opportunities and Challenges.” Remote
Sensing 2(9): 2274–2304.
Zhang, Li et al. 2015. “Evaluation of Water Use
Efficiency Derived from MODIS Products against Eddy
Variance Measurements in China.” Remote Sensing
7(9): 11183–201.

6.4.1
Change in water-use
efficiency over time

6.4.1
Level of water stress: freshwater
withdrawal as a proportion of available
freshwater resources

Custodian agency

FAO

Tier

I

I

Status of step-by-step methodology
document on the metadata repository

Published

Published

EO can support
estimations of water use
efficiency in agriculture interms of yield gained perunit of water added.

EO-based mapping of terrestrial ecosystems
that can monitor freshwater resources, e.g.
wetland extent. Most of water withdrawals
are for agricultural irrigation, whose extent
can be estimated based on EO data.

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity required
Availability of global EO data

Robustness ofproposedmethodology
Criteria

Compliance with Reporting
calendar
Sensitivity to change
Is it scalable (spatial)?
Is there a substitute forgaps in the EO record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

66

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 6.6
By 2020, protect and restore water-related ecosystems,
including mountains, forests, wetlands, rivers, aquifers andlakes
How can EO be used to help countries achieve the target?
For the purposes of this target, water-related ecosystems
are grouped into five categories: 1) vegetated wetlands, 2)rivers and estuaries, 3) lakes, 4) aquifers, and 5) artificial
waterbodies. Water-related ecosystems contain andmaintain the global stock of freshwater, from which water
related services flow to society. They are characterisedby high biodiversity and because they are carbon-rich,are important for climate change mitigation. In termsof services they provide micro climate regulation, e.g.
minimising the negative impacts of urban heat islands.
They capture and store water and maintain water quality
since they can decompose and/or absorb water pollutants.
In addition they are important for fisheries and provision of
construction materials. Therefore this target promotes thesustainable management of water catchment ecosystems
such as wetlands, rivers, lakes, reservoirs and groundwater,
as well as water-related forests and mountains, which
are crucial for provision of these services. The ecosystem
based approach is important for flood regulation, public
water supply and access to clean drinking water. In thisrespect, target 6.6 is the starting point for other waterrelated targets as it aims to protect water at source. As
water-related ecosystems are often highly complex andvery diverse, management is challenging and monitoringis expensive and time consuming. Therefore EO provides
a standardised monitoring approach which can capture
the multiple dimensions of change from hydrological tobiophysical processes. However as this target is focused on
the watershed EO at high spatial resolution, e.g. Landsat
and Sentinel-1/-2 (10-30m) should be acquired. For
example, high resolution land cover change can be used
to track changes in water-related ecosystems, to assess
the success of catchment-wide restoration efforts and theeffectiveness of protection measures or to identify threats
to sensitive habitat. Other EO products such as the extent
of water bodies and their temporal dynamics, as well as
digital terrain models, are inputs to models that assess theavailability of surface and ground water.
Current Indicator(s)6.6.1 Change in the extent of water-related ecosystems
over time
Potential new indicators based on EO:
Proportion of land restored to wetland

TARGET 6.6

Short methodological guidelines illustrated
with EO best practice examples
Indicator 6.6.1
Computation method
The Indicator 6.6.1 is composed of 5 sub-indicators:
Sub-Indicator 1 – spatial extent dynamics of water-related
ecosystems
Sub-Indicator 2 – water quality of lakes and artificial water
bodiesSub-Indicator 3 – quantity of water (discharge) in rivers
and estuaries
Sub-Indicator 4 – water quality imported from SDG
indicator 6.3.2
Sub-Indicator 5 – quantity of groundwater within aquifers
As this indicator is composed of sub-indicators, for global
reporting on percentage change of Indicator 6.6.1, each
sub-indicator must be aggregated up to form a single scorefor each country. Scores of each sub-indicator should also
be kept.
Currently the sub-indicator 1 (spatial extent dynamics)
methodology does employ EO but is based on a two-tierapproach – a global approach to map the four major global
datasets on the spatial extent of water related ecosystems:
spatial extent of lakes, rivers, and estuaries; spatial extent
of artificial waterbodies; spatial extent of vegetatedwetlands.; and (in the tropics/sub-tropics) spatial extent ofmangroves. The second tier consists of a national approachto validate these global layers. Once validated, the global
datasets are used to calculate percentage change of spatial
extent over time, using a 2001-2005 baseline period.
Subsequent five year averages are compared to thisbaseline.
Three EO-derived products are important for calculating
sub-indicator 1 - land use and land cover (LULC), open
water and the effective wetland area (in order to demarcate
the boundaries of infrequently flooded wetlands as wellas the permanently flooded). The Global Surface Water
Explorer (GSWE) is now the official product for calculating
indicator 6.6.1 (Pekel et al., 2016). The GSWE documents
the spatiotemporal dynamics of the world’s open waterfrom the long time series of the Landsat satellite series,
spanning the 1984-2015 era. The GSWE contains uniquedata on water seasonality (documenting the intra-annual
persistence) and water recurrence (documenting the interannual variability of surface water presence). However
ecological definitions of wetland are complex to map froman EO point of view and usually require accurate in situ
information and other ancillary data. For example, theCopernicus high resolution layer “combined Water andWetness product” scheme contains only four classes: (1)permanent water, (2) temporary water, (3) permanent
wetness and (4) temporary wetness. In addition to theCompendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

67

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

physical properties of the water surface, it is necessary to
map the different types of wetland ecosystems using a
dedicated LULC product. The computational method suggests
mapping 6 types of vegetated Wetlands –swamps, fens,
peatlands, marshes, paddies, and mangroves which aligns
with the Ramsar Convention on Wetlands definition of
wetlands (Robelo et al., 2018).
Although useful for the above applications, optical data hasserious limitations in detecting vegetated wetlands, wherelong wavelength-band (L-band) Synthetic Aperture Radar(SAR) has the unique capability to detect standing water(inundation extent) under a closed forest canopy, e.g. inmangroves (Bunting et al., 2018). Mapping of inundation
extent and dynamics in forested wetlands by L-band SAR
has been demonstrated over semi-continental scales e.g. in
the Amazon and Congo river basins (Chapman et al., 2015).
In order to capture the full extent of vegetated wetlands,a multi-annual time series of L-band radar (or in the caseof open water, optical imagery) should be constructed thenre-evaluated through a moving time window (e.g. 20152017, 2016-2018, 2017-2019, etc.) in order to avoid missing“dormant” wetlands which do not recur on an annual basis.
In order to conduct a wetland inventory with EO, it isnecessary to delineate a zone for wetland potential. Potential
wetland is derived from a number of EO-derived parameters
for topography, hydrology, climate and soil characteristics(e.g. soil moisture). This layer includes all open areas(without a dense vegetation cover) that are permanently
or temporarily flooded and could also include ecosystems
that are converted wetlands, like agricultural lands or urbanzones (e.g. former wetlands that are converted by humanactivities). Areas with high potential wetland value could beconsidered as the functional area of the effective wetlands,
however this still needs to be demonstrated.
For sub-Indicator 2, the EO methods are focused on
chlorophyll a (Chl) and total suspended solids (TSS) withinlakes and artificial water bodies globally, as these parameters
are retrievable with higher confidence from EO. Historically,
water quality products were derived from Envisat MERIS (nolonger operational), MODIS and VIIRS data but Sentinel-3
Ocean and Land Colour Imager (OLCI) provides similarcapacity for quantitative measures of chlorophyll a, suspended
sediments and coloured dissolved organic matter (CDOM)(ESA, 2017). Sentinel 2 and Landsat 8 can also be used
to retrieve water quality parameters at higher resolutions.
However, these sensors are limited by the spectral bands
available - they don’t have all the spectral bands available to
make an accurate retrieval. This is in comparison to MERIS,
Sentinel-3 OLCI, MODIS and VIIRS, which are set at 300m
spatial resolution. EO-derived water quality parameters canbe used as indicators of eutrophication, physical disturbanceand contamination in the water body.

TARGET 6.6

inundation directly by classification of satellite imagery and/or water level estimation by satellite altimetry. A study
combining both satellite altimetry and MODIS imagery
was able to estimate river discharges at the basin scale
for rivers greater than 800m wide (Sichangi et al., 2016).
Streamflow modelling involves a range of EO data including
snow cover and evapotranspiration estimates to understandthe potential capacity of stream flow given a range of terrain
and meteorological parameters. River discharge can also bemodelled with a hydrodynamic model calibrated with all
available in-situ observations and water-surface elevation
observations from radar altimetry. Then the calibrated model
can be used to predict discharge, as has been done for the
Ogooué River in Africa using multi-mission EO data (Kittel
et al., 2018). Although the sub-Indicator 3 methodology
proposes in-situ measurements by gauging stations ordischarge meters, the EO-based approaches described above,
based on direct observations and modelling, have potential
for areas were no in situ data exists.
As water is such a complex medium to observe fromspace, the choice of satellite imagery is key to the successof EO in measuring the sub-indicators. Therefore thecomplementarity of SAR and optical measurements should
be considered as both have advantageous properties
for water detection. In the optical domain Landsat andSentinel-2 provide adequate spectral range and resolution
to characterise surface water and its dynamics (especially
Sentinel -2 with a more regular revisit time than Landsat
8). Sentinel-1 is also relevant to detect surface water
dynamics. It provides high temporal resolution (six days
for Sentinel-1A and -1B at the Equator), high spatial
resolution, cloud-penetrating qualities, illumination
independence and wide swath allowing for a much-needed
operational change detection system (Muro et al., 2016).
Other comparable radar datasets are the (L-band) Synthetic
Aperture Radar (SAR) satellite series operated by JAXA:JERS-1 SAR (1992-1998), ALOS PALSAR (2006-2011) and
ALOS-2 PALSAR-2 (2014-present) which are provided
by JAXA as annual 25m global mosaics. The sensitivityof the long wavelength L-band to vegetation structure isespecially useful in detecting densely vegetated wetlands
as show in boreal zones (Whitcomb et al., 2009) and formapping of inundation extent and dynamics in tropical
wetlands (Rosenqvist and Birkett, 2002; Hess et al., 2003).
All these data are cost-free. For water quality, the main
source of data is the OLCI on board the Sentinel-3 satellite
and Terra/Aqua MODIS, and the Visible Infrared Imaging
Radiometer Suite (VIIRS) weather monitoring system. For
freely available global satellite precipitation data sets, seetable 1 in Tan et al. (2014). Finally, radar altimetry has thepotential to determine changes (through the measurementsof water levels) in water volumes both in lakes and rivers.
Treatment of missing values

For sub-Indicator 3, there are two EO based approaches tostreamflow measurement by EO – direct estimation andmodelling (Tan et al., 2014). Estimation consists of mapping

Gaps in EO time series can be filled by alternative climatic
proxies such as inundation regime recorded by flood gauges

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

68

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

or precipitation levels from meteorological stations. However
point measurements are sparse and might represent local
precipitation events. Gap filling should always be used withcaution and – should be based on evidential reasoning
approaches.
Sources of discrepancies
Discrepancies could be introduced for all sub-indicators
where EO data are merged over multi-annual baselines, e.g.
where there are five year baseline reference periods. Within
this period it is feasible that a country would merge satellite
imagery with different spatial and temporal characteristics,
especially in order to fill gaps due to cloud and other areas
without adequate EO coverage. It should also be noted thatsatellite coverage and technology has evolved over time. We
therefore cannot be as accurate for the baseline 2001-2005,
as we will be for 2016-2020.
Limitations
For sub-indicator 1, the application of EO to mapping wetland
extent in areas of dense vegetation cover is limited. Thisis because optical sensors and short wavelength (C-band,
X-band) radar sensors are constrained to measuring the
top of the vegetation canopy. Long wavelength radar
sensors (L-band) can penetrate the canopy to the water
surface in most conditions, although data availability canbe a limitation outside the tropics/sub-tropics where mostL-band SAR time-series data have been collected to date.
This poses challenge because of the reporting frequency of
the indicator on a 5 year basis without any overlap.
In addition, SAR alone would not allow to accurately map
open water, e.g. the occurrence of the water. Even if all
the observations are considered valid, for water it’s notnecessarily the case. For example, the presence of surface
waves on a lake can strongly increase radar backscattering– expressed as a distortion of the imagery. This would be asignificant source of omission error and therefore a source ofunderestimation of the water occurrence. The 2001–2015
dataset GSWE dataset includes freshwater and saltwater
rivers, lakes and estuaries greater than 30 m2, however
this will be reduced to at least 20 m2 for Sentinel-1 andSentinel-2 (possibly even 10 m2, at the native resolution of
Sentinel-2’s visible and near infrared bands). Yet, the vast
majority of rivers, however, are not captured as they are too
narrow to detect or are blocked by the vegetation canopy.
For sub-indicator 2, the main limitation is that the global
EO-based approach for water quality proposed is limited toparameters measurable by satellite remote sensing. These
parameters are therefore biophysical in nature, properties
that alter the water colour. However there are many water
quality factors such as heavy metals and pathogens thathave no impact on water colour and are not measurablefrom EO. In order to overcome this limitation, the morecomprehensive water quality indicator (6.3.2), that must
adhere to standards, is imported as sub-Indicator 4.

TARGET 6.6

For sub-indicator 3, precipitation products, for example fromthe Tropical Rainfall Measuring Mission radar data, comprisehigh spatial variability problems that lead to uncertainties
in model estimates (Strauch et al., 2012). TRMM Is also
limited with respect to the spatial coverage. However, there
is a multitude of other satellite based precipitation datasets
available, e.g. GPCP, CMAP, etc.
Key messages for countries on EO contribution to thecomputation method:
6.6.1 has 5 sub-indicators but the first two• Iarendicator
the most appropriate for an EO-based methodology(with the exception of sub-indicator 4 on water quality
imported from indicator 6.3.2)
Sub-indicator 1 requires extent of water related•
ecosystems information in 3 categories: open and natural
surface waters, artificial water bodies and vegetatedwetlands (this is further divided into different vegetatedwetland types)
ptical EO can distinguish open surface water with
• Ohigher
accuracy than vegetated wetland but is challenged
to separate artificial (e.g. reservoir) from natural water
bodies (e.g. glacial lake). In order to separate thesecategories, the methodology would need ancillary data,
e.g. on water infrastructure or land use
wetlands present complicated surfaces for• VEOegetatedsensors as the water underneath the canopy is notpossible to detect by optical methods especially wherewater is seasonal and is only sometimes present. SAR
presents a more useful tool, especially L-band SAR, which
can in most cases penetrate vegetation canopies to thewater surface underneath, e.g. in forested wetlands. An
optimised wetland mapping strategy should consider
both optical and radar sources of EO imagery.
or sub-indicator 2, EO-based methods are mature for• Ftotal
suspended solids and chlorophyll concentrations,which can be used to report on some aspects of water
quality of lakes and artificial water bodies but it’s not acomplete assessment of water quality. Sentinel 2 andLandsat 8 will be particularly applicable, as these havea higher spatial resolution, but with a lower accuracy inwater quality retrieval.
or sub-indicator 2, all ocean colour scanner like MODIS,• FVIIRS,MERIS have the potential to measure water qualityof inland water bodies with variable results. However,the OLCI is designed for water quality monitoring in bothinland and ocean settings. The main limitation for OLCI,
as with pervious sensors such as MERIS, is the spatial
resolution (300m). Issues associated with strong hazeover large water bodies and sun glint have also beenreported (ESA, 2019).

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

69

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 6.6

Data sources
Data category

Source satellite data

Data sources

WebsiteLandsathttps://earthexplorer.usgs.gov

Sentinel data (1,2 and 3) from theCopernicus Open Access Hub

https://scihub.copernicus.eu

L-Band SAR satellite series operated
by JAXA: JERS-1 SAR (1992-1998),
ALOS PALSAR (2006-2011) and ALOS-2
PALSAR-2 (2014-present)

https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm

Terra/Aqua MODIS

https://search.earthdata.nasa.gov

Visible Infrared Imaging Radiometer
Suite (VIIRS) weather monitoring
systemhttps://earthdata.nasa.gov/earth-observation-data/near-real-time/download-nrt-data/viirs-nrt

The Global Surface Water Explorer

https://global-surface-water.appspot.com

FLO1K, a consistent streamflow dataset
at a resolution of 30 arc seconds
https://www.nature.com/articles/sdata201852(~1 km) and global coverage

Global/regional
datasets

Software, tools andplatforms

CMAP (CPC Merged Analysis of Precipitation) refers to a collection of precipihttps://climatedataguide.ucar.edu/climate-data/tation data sets, though the 2.5° x 2.5°
cmap-cpc-merged-analysis-precipitation
global monthly version is probably themost widely used.
GCP (Global Precipitation Climatology
Project)

https://precip.gsfc.nasa.gov

GMW (Global Mangrove Watch), consistent dataset of mangrove extent from2010 (and from Feb 2019, also from1996, 2007-2009, 2015-2017)https://www.globalmangrovewatch.org

The Satellite-based Wetlands Observation Service (SWOS), developed by a
Horizon 2020 consortium led by Jena
Optronik, Germany which has tools andworkflows for the following wetland
products:• LULC, long/short term changes• Surface Water Dynamics• Wetland inventory and delineation• Water Quality• Land Surface Temperature
GlobWetland Africa – a joint product of the Ramsar Convention and
ESA - provides a toolbox rather than adataset, although maps are produced
for selected pilot sites in Africa. It provides workflows for wetland inventory,
wetland habitat mapping, inundation
regimes, Water quality, hydrologicalmodelling and mangroves

http://portal.swos-service.eu

http://globwetland-africa.org

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

70

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 6.6

Reference List
Bauer-Gottwein, P. et al. (2015) ‘Operational riverdischarge forecasting in poorly gauged basins: The
Kavango River basin case study’, Hydrology and Earth
System Sciences, 19(3), pp. 1469–1485. doi: 10.5194/
hess-19-1469-2015.
Bunting, P. et al. (2018) ‘The Global Mangrove
Watch-A New 2010 Global Baseline of Mangrove
Extent’, Remote Sensing, 10, p. 1669. doi: 10.3390/
rs10101669.
Chapman, B. et al. (2015) ‘Mapping regional inundation
with spaceborne L-Band SAR’, Remote Sensing, 7(5),pp. 5440–5470. doi: 10.3390/rs70505440.
ESA (2019) ESA (2019) Water Resource Management.
Earth Observation for Sustainable Development(eo4sd).
http://eo4sd-water.net/portfolio/product/water-quality-monitoring. (Accessed: 13 March 2019).
ESA (2017) Sentinel-3 helps water-quality monitoring,Sentinel online. Available at: https://sentinels.
copernicus.eu/web/sentinel/news/-/article/sentinel3-helps-water-quality-monitoring (Accessed: 23 July2018).
Hess, L. L. et al. (2003) ‘Dual-season mapping of
wetland inundation and vegetation for the central
Amazon basin’, Remote Sensing of Environment, 87(4),pp. 404–428. doi: 10.1016/j.rse.2003.04.001.
Kittel, C. M. M., Nielsen, K., Tøttrup, C., & BauerGottwein, P. (2018). Informing a hydrological model of
the Ogooué with multi-mission remote sensing data.
Hydrol. Earth Syst. Sci., 22(2), 1453–1472.
https://doi.org/10.5194/hess-22-1453-2018

Pekel, J.-F. et al. (2016) ‘High-resolution mappingof global surface water and its long-term changes’,
Nature, 540(7633), pp. 418–422. doi: 10.1038/
nature20584.
Rebelo, L.-M. et al. (2018). The use of Earth Observation
for wetland inventory, assessment and monitoring:
An information source for the Ramsar Convention
on Wetlands. Ramsar Technical Report No.10. Gland,
Switzerland: Ramsar Convention Secretariat.
Rosenqvist, Å. and Birkett, C. M. (2002) ‘Evaluationof JERS-1 SAR mosaics for hydrological applicationsin the Congo river basin’, International Journal of
Remote Sensing. Taylor & Francis, 23(7), pp. 1283–
1302. doi: 10.1080/01431160110092902.
Strauch, M. et al. (2012) ‘Using precipitation data
ensemble for uncertainty analysis in SWAT streamflow
simulation’, Journal of Hydrology, 414–415, pp. 413–
424. doi:
https://doi.org/10.1016/j.jhydrol.2011.11.014.
Tan, M. L. et al. (2014) ‘Streamflow modelling by
remote sensing: A contribution to digital Earth’, IOP
Conference Series: Earth and Environmental Science,18(1). doi: 10.1088/1755-1315/18/1/012060.
Whitcomb, J. et al. (2009) ‘Mapping vegetatedwetlands of Alaska using L-band radar satellite
imagery’, Canadian Journal of Remote Sensing. Taylor& Francis, 35(1), pp. 54–72. doi: 10.5589/m08-080.

Muro, J. et al. (2016) ‘Short-term change detection
in wetlands using Sentinel-1 time series’, Remote
Sensing, 8(10), pp. 1–14. doi: 10.3390/rs8100795.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

71

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 6.6

Global Mangrove Watch – an example of EO for SDG 6.6.1

A time-series of maps of the global
mangrove extent has been generated
within the framework of the Global
Mangrove Watch (GMW) project(Bunting et al. 2018), based on 25
meter resolution global satellite
mosaic data from the Japanese radar
satellites (JERS-1, ALOS and ALOS2), combined with optical (Landsat)
satellite data. As of November 2018,
maps for seven annual epochs havebeen produced: 1996, 2007, 2008,2009, 2010, 2015 and 2016 (andwith 2017 foreseen to be completed
in late 2018). By comparing maps
from different years in the timeseries, the corresponding change
maps can be derived. In addition toreporting on SDG 6.6.1 (sub-indicator1), mangrove extent maps can beused for reporting on the Nationally
Determined Contributions under theParis Agreement and the UN Reducing
Emissions from Deforestation andforest Degradation scheme (REDD+)
under the UN Framework Convention
on Climate Change (UNFCCC).
The Global Mangrove Watch maps
provide an effective means for periodic
mapping and monitoring over national
to regional and global scales, in a
uniform manner as the same type ofdata and classification algorithms areused over all areas and over several
temporal epochs. Using these data
enable a more consistent comparisonof extent between different countries
and regions as well as analysis ofchanges over time from a definedbaseline, in comparison to the useof data obtained from different
sources. It should, however, notbe expected that global datasets
can achieve the same high level ofaccuracy everywhere as a local scale
map derived through ground surveys;
global mapping exercise using

consistent data and methods which
typically necessitates a trade-off interms of accuracy at the local scale.
Yet, for countries with incomplete
or out-dated geospatial information
about their mangrove resources, theGMW maps can provide importantfirst baseline maps, and support fieldsurvey planning by indicating areas ofpotential past or recent changes.

Figure 10: An example of GMW data for the Kahan River Delta, North Kalimantan, Indonesia. [Left] Multi-temporal radar image composite (1996
JERS-1 SAR and 2016 ALOS-2 PALSAR-2); [Right] Global Mangrove Watch extent and change map; Red – mangrove loss 1996-2007; Orange – loss
2007-2016; Green – mangrove cover in 2016. (Satellite image copyright JAXA/METI).

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

72

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 6.6

EO-based Wetland Inventory of Uganda supporting the monitoring of
SDG indicator 6.6.1
A consistent mapping and monitoringof global wetland ecosystems
is essential for tracking wetlandchanges and trends in support of
the Sustainable Development Goals
indicator 6.6.1, on the extent of waterrelated ecosystems. Although EO data
are ideal for large-scale inventoryingof wetlands, the tremendous diversityof wetland ecosystems makes remotedetection particular challenging andno global dataset on the distributionof wetlands is yet available. Thus,
within the framework of several
projects (Copernicus Pan-European
2015 High-Resolution Layer on
Water and Wetness, ESA project
GlobWetland Africa, ESA EO4SD
Water Resource Management), a
methodology has been developed
to detect and monitor wetlands in ahighly automated manner to support
the sustainable management of
wetland ecosystems. This approach
has also been applied to Uganda,
for which the Global Partnershipfor Sustainable Development Data
(GPSDD) funded a project to compile
an EO-based wetland inventory andto develop a monitoring and reporting
platform for SDG indicator 6.6.1.
One of the major challenges when
mapping wetlands by EO-based
techniques is due to their diversity andhigh temporal variability compared toother land cover types. To properly
address these challenges, a multitemporal and multi-sensor approaches
is most valuable and provides thebest-possible information. Therefore,a hybrid sensor approach making useof both radar and optical imagery
has been developed which leads toa more robust wetland delineationcompared to traditional (either
optical or radar) approaches with
optical imagery being more sensitive
to the vegetation cover and radar
imagery to soil moisture content.
The methodology does not detectwetlands in the ecological sense,
but rather identifies the physicalproperties of water and wet soils. The
main output is the Water and Wetness(wet soil) Presence Index which canbe translated into categorical classes
showing areas which are permanently
or seasonally flooded as well as areas
permanently or seasonally waterlogged vegetated/bare areas in
Uganda (cf. Figure 11).

Figure 11: Wetland inventory of Uganda based on Sentinel-1 radar and Sentinel-2 optical imagery using data from the years 2016 and 2017 in supportof SDG Indicator 6.6.1 on the extent of water-related ecosystems.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

73

TARGET 6.6

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

6.6.1
Change inthe extent ofwater-related
ecosystems
over time

Custodian agency

Sub-Indicator 1: Sub-Indicatorspatial extent
2: waterdynamics
quality (lakes/
artificial water
bodies)Sub-Indicator
3: waterquantity

Sub-Indicator
4: waterqualityimported from
SDG indicator
6.3.2

Sub-Indicator
5: groundwater
quantity inaquifers

UN Environment; Secretariat of the Ramsar Convention on Wetlands

Tier

I

Status of step-by-stepmethodology documenton the metadata
repositoryTwo
methodologies
published byco-custodians

n/a

n/a

n/a

n/a

n/a

A variety of
EO methods
are available
to map water
bodies andsurrounding
land covers.
However there
are no EO
establishedmethods forvegetatedwetland areas.

A variety of
EO methods
are available
to monitor
water quality
at different
spatialresolutions andwith different
accuracies.

A challenge for
EO to estimate
discharge, but
methods usinghydrologicalmodelshave beensuccessful in alimited numberof studies

EO data canbe used tofind hotspots(e.g. eutrophic
lakes) wherethese WQ
parameters
should be fieldsampled.

Currently notrobust enoughto consider EO
as a tool

Maturityof EO
technologies
Relevanceof EO for theindicator
criteria

Status of EO
in indicator
guidelines
Technical
capacityrequired
Availabilityof global EO
data
Compliancewith
ReportingcalendarRobustnessof proposed
methodology Criteria

Sensitivity tochangeIs it scalable(spatial)?
Is there asubstitutefor gapsin the EO
record?

Overall EO relevanceComments to support
criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

74

GOAL 7COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 7.1
By 2030, ensure universal access to affordable, reliable andmodern energy services

TARGET 7.1

Short methodological guidelines illustrated
with EO best practice examples
Indicator 7.1.1

How can EO be used to help countries achieve the target?

Computation method

Approximately 1 billion people still have no access toelectricity, 50% of which live in sub-Saharan Africa.
Access to energy is essential for achieving manysustainable development goals, from poverty reduction,
improved health care, gender equity, and education tocombating climate change. Energy accounts for around
60% of total global greenhouse gas emissions and whileenergy sources are transitioning from coal, to natural gas,
to renewables, global energy use is still primarily basedon fossil fuels. In this context, Target 7.1 can be achieved
and monitored with the integration of EO data. Applicable
on a larger scale, remote sensing data is able to giveinformation that by field surveys alone would be moretime consuming and often difficult to gain because of theaccessibility of many locations. The regular collection of
EO ensures the long-term availability of data to monitor
the status of remote and rural settlements. A useful data
source is nigh-time luminosity data, which can collectdaily variations at sufficiently low light levels to detect
artificial lights at night across remote and rural areas.
Night time lights products have been used for a vast rangeof purposes for more than 50 years, including to map
the distribution of economic activity, poverty levels andto generate CO2 emission maps. In particular, the NASA’s
Black Marble product suite can be used to monitor innear-real time areas not reached by centralized electricity
services, and then inform the development of investment
and implementation plans for electrical infrastructures
aiming to protect and/or increase energy access by thehighest number of people in a country.

Data for access to electricity are collected entirely fromhousehold surveys, such as Demographic and Health
Surveys (DHS), Living Standards Measurement Surveys(LSMS), Multi-Indicator Cluster Surveys (MICS), the World
Health Survey (WHS), and other nationally developedand implemented surveys, including those by various
government agencies (for example, ministries of energy andutilities).

EO-based technologies for monitoring access to different
types of energy resources varies depending on the detailsof the information needed. For instance, the type ofenergy source (e.g. solar panels, diesel power generators,
nationwide electrical networks) can only be acquired using
high resolution EO data in combination with sophisticatedstatistical techniques, but various use cases (e.g., from
Zambia to Pakistan) show the high potential that EO haveto monitor several aspects of this target.
Current indicators:
7.1.1 Proportion of population with access to electricity
7.1.2 Proportion of population with primary reliance on
clean fuels and technology

Household surveys even if more precise and capable toprovide a more comprehensive set of information, have thedisadvantages of being more expensive, time-consuming
and often unable to gain data from remote locations, noteasily accessible. This results in an uncomplete view ofthe energy access of a country. EO using solar irradiance,
meteorological, urban build up, and night time lights
can enable systematic monitoring of electricity access,complementing nationwide household surveys. Global andfreely available data also at high spatial resolution and at awide radiometric detection range, can be utilised to report
the proportion of population with access to electricity.
Night luminosity data are particularly helpful to detect
artificial lights at night across remote and rural areas. The
NASA’s Black Marble product suite, available at 500 m
resolution since January 2012 (Román et al., 2018) andproduced with data captured by the Suomi –NPP and NOAA20 VIIRS Day/Night Band sensors, allows to disaggregate at
sufficient spatial and temporal granularity the daily patterns
in night time lights, to monitor abrupt, seasonal, and gradualchanges associated with electrification and other humanactivities, such as conflict, rapid electrification, and cultural
holidays. In particular, recent studies have combined Black
Marble nighttime lights with EO-derived human settlement
data to estimate access to electricity at very spatial (Zheng
et al., 2018) and high temporal resolution.
Disaggregation
Using nighttime lights, spatial disaggregation is now
possible at the neighbourhood level. The use of additional
spectral channels, machine learning algorithms, andhigh resolution satellite imagery would further augment
disaggregation down to the parcel level.
Treatment of missing values
Missing values can be obtained using household surveys or
through interpolation using known data.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

76

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Limitations
The energy access to villages located inside forests are
more difficult to detect using night lights imagery. In thiscase a different approach, such as household surveys,
might be more appropriate. The use of high resolution
EO and more advanced techniques based on SAR data
can be used when possible (Esch et al., 2013, 2017). The
difficulty in discriminating the use of generators to produce
electricity from other sources can overestimate the accessto sustainable energy.
Erratic power supply in rural areas can potentially bias the
final results, because of the fluctuations in the nighttime
luminosity between satellite orbits. Traditionally, thisproblem has been overcome by using image-based
composites of a high number of images from monthly andannual averages of moon- and cloud-free radiances. This
has limited the use of nighttime lights maps to annualintervals, or roughly half of available acquisitions during
the lunar cycle (Elvidge et al., 2017). Recent studies
have uncovered additional sources of measurement error,
particularly when image compositing methods are used.
This includes biases resulting from aerosol contamination,seasonal vegetation cycles, and snow effects (Levin &Zhang, 2017). These findings point to an increasing needby the EO community to ensure that night time lights data
sources are of sufficient quality and traceability to address
Target 7.1 objectives.
Key messages for countries on EO contribution to thecomputation method
here are several challenges surrounding energy access,• TReisser
et al. (2018) have identified the four key ones:

TARGET 7.1

by the countries’ need of expanding energy access;the social and environmental impact (habitat loss,
displacement of people, etc.) caused by renewables,in particular hydropower; the negative relationshipbetween population growth and energy access,where population is exploding (e.g. Africa and India)
the energy access is lower than where populationis stagnant (e.g. Japan, Europe); and finally how toaddress the economic and livelihood needs resulting
from greater demand for modern energy services.
availability and on a large spatial scale• TofheEOregular
data provides the opportunity to monitor
access to electricity in a systematic way and informthe implementation of electrical infrastructuresdevelopment plans.
ighttime lights imagery have been widely used to• Nmap
human presence and as a proxy measure ofhuman well-being and can be used to monitor potential
presence of electrical power.
Nighttime lights have been used to track the•effectiveness of rural electrification projects in West
Africa (e.g. Ivory Coast) and in disaster-afflictedcommunities (e.g. Puerto Rico after Hurricane Maria)
where electricity restoration often relies on selfreporting from local utilities.
the proportion of population with access• Qtouantifying
electricity is more difficult as it requires humansettlement data from which distance to the electrical
grid can be calculated. However human settlement
data can also be mapped from EO and global data are
available (e.g. The Global Human Settlement Layer).

the potential expansion of the use of fossil fuels driven

Data sources
Data category

Global/regional
datasets

Data sources

WebsiteNASA’s Black Marble night-time lights
product suite (VNP46)

https://earthobservatory.nasa.gov/Features/NightLights/page3.php

NOAA VIIRS Night Lights Annual Composites https://www.ngdc.noaa.gov/eog/viirs.html
POWER

http://power.larc.nasa.gov

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

77

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Reference List

Further reading and resources

Elvidge, C. D. et al. (2017) ‘VIIRS night-timelights’, International Journal of Remote Sensing.
Taylor & Francis, 38(21), pp. 5860–5879. doi:
10.1080/01431161.2017.1342050.

Best practice resources:

Esch, T. et al. (2013) Urban footprint processor-Fully
automated processing chain generating settlementmasks from global data of the TanDEM-X mission.
IEEE Geosci Remote Sens Lett 10(6):1617–1621.
Esch, T. et al. (2017) Breaking new ground in mapping
human settlements from space -The Global Urban
Footprint. arXiv Prepr arXiv170604862.
Levin, N. and Zhang, Q. (2017) A global analysis
of factors controlling VIIRS nighttime light levels
from densely populated areas. Remote Sens Environ
190:366–382.
Reisser, W. J. and Reisser, C. (2018) Energy Resources:
From Science to Society. Oxford University Press.
Román, M. O. et al. (2018) ‘NASA’s Black Marble
nighttime lights product suite’, Remote Sensing ofEnvironment, 210 (November 2017), pp. 113–143. doi:
10.1016/j.rse.2018.03.017.
Zheng, Q. et al. (2018) “A new source of multi-spectral
high spatial resolution night-time light imagery—JL13B.” Remote Sensing of Environment 215: 300-312.

TARGET 7.1

Eckman, R. S. and P.W. Stackhouse, Jr., (2012) CEOS
Contributions to Informing Energy Management andPolicy Decision Making Using Space-Based Earth
Observations. Applied Energy, 90, pp. 206-210
Gelaro, R., et al. (2017) The Modern-Era Retrospective
Analysis for Research and Applications, Version 2(MERRA-2). J. Climate, 30, 5419–5454,
https://doi.org/10.1175/JCLI-D-16-0758.1
Stackhouse, Jr., P.W. et al. (2011) The NASA/GEWEX
Surface Radiation Budget Release 3.0: 24.5-Year
Dataset. GEWEX News, 21, No. 1, February, 10-12.
Stackhouse, Jr., P. W. et al. (2012) Usage of NASA’s
Near Real-time Solar and Meteorological Data for
Monitoring Building Energy Systems Using RETScreen
International’s Performance Analysis Module.
Proceedings of the World Renewable Energy Forum2012 (American Solar Energy Society), May 13-17,
Denver, Colorado
Supervised object detection methodologies for thedetection of solar panels:
Malof, J. M. et al. (2016) Automatic Detection of Solar
Photovoltaic Arrays in High Resolution Aerial Imagery,
arXiv preprint arXiv: 1607.06029v1, 183, pp. 229–
240. doi:10.1016/j.apenergy.2016.08.191.
Machine learning algorithms to detect High Voltage
towers that have been tested in Pakistan, Nigeria andZambia:Development Seed (2018) Mapping the electric grid. A
report developed for World Bank. Available at:
http://devseed.com/ml-grid-docs/

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

78

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 7.1

Expansion of reliance on clean fuels and technology by coupling EO withdecision support tools
Besides monitoring, EO is a criticalcomponent
towardenabling
communities to adopt and increase
reliance upon clean fuels andtechnology. This is accomplishedby coupling EO with financial andengineering decision support toolsthat enables the planning, design andfeasible of clean energy technologies.
One example is the coupling between
the solar and meteorological data
sets produced by NASA’s Predictionof Worldwide Energy Resource(POWER) and the RETScreen clean
energy management software system(https://www.nrcan.gc.ca/energy/
software-tools/7465).
RETScreen Expert, the advanced
premium version of the software,
helps to make sound decisions
on clean energy. Developed by
the Government of Canada, thesoftware is used by more than

600,000 energy professionals anddecision-makers globally, including
energy engineers, facility managers,
researchers, instructors, architects,
financial planners and policy analysts.
RETScreen is also used for teaching
and research in over 1,000 universities
and colleges around the world. The
tools supports project developmentin 36 different languages, allowing
for global partnerships to aid in the
adoption of clean energy technologies.
The tool also enables measurement
and verification of the actual andongoing energy performance of awide range of buildings, factories
and power generation facilities. The
global applicability of the RETScreen
Expert tool is enabled thanks to theongoing partnership with the NASA
POWER project that provides global
climatological solar energy information
coupled with surface meteorologicalparameters up to a few days of real-time (Eckman and Stackhouse, 2012;
Stackhouse et al., 2012). The solar
and meteorological parameters arederived from NASA research projects
using a combination of remote sensing
analysis (Stackhouse et al., 2011)
and the NASA’s global atmospheric
data assimilation products from theModern-era Retrospective Analysis for
Research and Applications (MERRA-2;
Gelaro et al., 2017). Thus, projectplanners and officials evaluate thefeasibility of clean energy projects andtheir subsequent performance even inareas where surface measurementsof those quantities are not readily
available.

Figure 12: RETScreen Expert and NASA partnership couples Earth Observations with a decision support tool aimed at enabling clean energy projects
worldwide

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

79

TARGET 7.1

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

7.1.1
Proportion of population withaccess to electricity

Custodian agency

7.1.2
Proportion of population withprimary reliance on clean fuels
and technology

World Bank

WHO

Tier

I

I

Status of step-by-step methodology
document on the metadata repository

Published

Published

Observations of night-time lights
have been widely used to map
human presence and availabilityof electricity at global and regional
scales. There are fewer examplesof the use of this method atnational and sub-national level,
but several studies show a highpotential for its application tomonitor this indicator.

Not supported by EO

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity required
Availability of global EO data

Robustness ofproposedmethodology
Criteria

Compliance with Reporting
calendar
Sensitivity to change
Is it scalable (spatial)?
Is there a substitute forgaps in the EO record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

80

GOAL 9

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 9.1
Develop quality, reliable, sustainable and resilient infrastructure,including regional and trans-border infrastructure, to support
economic development and human well-being, with a focus
on affordable and equitable access for all
How can EO be used to help countries achieve the target?
Infrastructures that are sustainable, durable and disasterresilient, provide the basic and essential structures
to a country to effectively function, but also allow to
generate employment and wealth and drive economic
development. This target is strongly linked to othersustainable development goals, including zero hunger, nopoverty, good health and well-being, as well as quality
education. Rural development processes, including accessto markets, education and health services, cannot succeed
without a reliable access to roads year-round. EO data caninform the production of efficient and effective plans forinfrastructures development and management. The global
coverage of remote sensing images allows the identificationof areas currently lacking infrastructures for transportationor energy access. These data coupled with information
on topography, land cover, precipitation patterns, climatechange scenarios, can support the development of climate
resilient infrastructures. EO data has been widely used to
extract infrastructures such as urban areas, roads and dams
using data at different spatial resolution (e.g. rural roads canbe detected just with high resolution images) and different
techniques (e.g. supervised and unsupervised classification,neural networks, and mathematical morphology). Researchis also currently focused on using high resolution or radar
data to monitor the status of infrastructures, particularly
in areas prone to natural disasters, such as flooding, butalso in areas affected by conflicts. The need to plan forregional and trans-border infrastructure is also well served
by EO since it is technology that crosses borders and is
not limited by a single country’s or region’s national datacollection systems. In theory, open access EO data should
help countries collaborate on shared infrastructure projects.
Current indicators
9.1.1 Proportion of the rural population who live within
2 km of an all-season road
9.1.2 Passenger and freight volumes, by mode of transport

TARGET 9.1

Short methodological guidelines illustrated
with EO best practice examples
Indicator 9.1.1
Computation method
A final methodology to measure rural access is currently
under development by the World Bank with support fromthe Research for Community Access Partnership (ReCAP)
funded by the Department for International Development(DFID) of the United Kingdom. This new method has been
anticipated to use spatial data, obtained directly fromcountry database/website, Joint survey/compilation withnational agency and international entity, satellite images,
remote sensing. Georeferenced information on roadconditions, will be collected through consultations with line
ministries and Road Agencies.
All season roads defined as “roads motorable all year
round by the prevailing means of rural transport (often apick-up or a truck which does not have four-wheel drive),with some predictable interruption of short duration during
inclement weather (e.g. heavy rainfall) allowed” (Roberts
et al., 2006) can be identified from satellite images basedon features such as geometry, photometry, topology,function and texture, or using a road model (Wang et al.,2016; Ahmad & Deore, 2016). Several factors affect theeffective extraction of roads from EO data, such as sensor
type, spectral and spatial resolution, weather, cloud cover,variation and ground characteristics.
Manual digitalisation and automatic or semi-automaticclassification can be used for the extraction of roads, but
often is preferable to utilise a combination of methods.
Even though manually digitising an image can be time
consuming in many areas with several linear features,manual editing is required for the elimination of segmentsthat are not roads (Brandão and Souza, 2006).
The methods that have been used to extract roads from RS
images are classification based methods (supervised andunsupervised), knowledge based methods, mathematicalmorphology methods usually combined with image
segmentation techniques, active contour models anddynamic programming and grouping (Wang et al., 2016).
These techniques consider different features, such as
spectral information of the pixels, edge detection, length
and width of the feature, texture, etc.; all of them haveboth advantages and disadvantages and require different
technical skills. A combination of methods, including
digitisation should be used, and ground truth activities
should complement the analyses.
In order to produce more accurate road products, highspatial resolution satellite images, such as Spot 6/7(Transport & ICT, 2016), should be used. Landsat images
have also been successfully utilised to extract roads using

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

82

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

bands 3 (Red) and 5 (Shortwave Infrared - SWIR), in adense forest environment, where these bands appear witha brighter intensity, contrasting with areas of dense forest.
The linear features with brighter intensity can thus bedigitised (Brandão & Souza, 2006).
The resulting road map using any of the approaches
above and a settlement layer, such as the Global Human
Settlement Layer and the Global Urban Footprint, can beused to calculate the distance from the nearest road.

TARGET 9.1

Limitations
In areas with high could cover radar images are required.
Technical capacity, expensive high resolution images andtime-intensity when the detection is country-wide, areother main limitations.
Key messages for countries on EO contribution to thecomputation method

Treatment of missing values

O data has been widely and successfully used to extract• Edifferent
types of infrastructures including roads;

Missing values can be treated using higher resolution
images and field surveys or composites.

Plans for the development of climate resilient•
infrastructures can be generated using the locationof roads and spatial data on topography, land cover,precipitation patterns, and climate change scenarios;

Sources of discrepancies
Discrepancy can be determined by misclassification of roads
and omission errors, based on the methods and satellite
images used.

nfrastructures located in areas prone to natural disasters• Icanbe efficiently monitored using EO imagery.

Data sources
Data category

Source Satellite Data

Global/regional
datasets

Data sources

WebsiteLandsathttps://earthexplorer.usgs.gov

Sentinel data (1,2 and 3) from theCopernicus Open Access Hub

https://scihub.copernicus.eu

Pleiadeshttps://www.intelligence-airbusds.com

WorldView, GeoEye, QuickBird, IKONOS

https://www.maxar.com

OpenStreetMap

https://www.openstreetmap.org

Reference List
Ahmad, N. and Deore, P. J. (2016) ‘Road detection
from satellite images’, International Journal of
Recent Trends in Engineering and Research, 2(4),pp. 299–304.
Brandão, A. O. and Souza, C. M. (2006) ‘Mapping unofficial
roads with Landsat images: A new tool to improvethe monitoring of the Brazilian Amazon rainforest’,International Journal of Remote Sensing, 27(1), pp.
177–189. doi: 10.1080/01431160500353841.
Hoppe, E., et al. (2016) ‘Transportation Infrastructure
Monitoring Using Satellite Remote Sensing’. In
Materials and Infrastructures 1 (Eds J. Torrenti and F.
La Torre). doi:10.1002/9781119318583.ch14

Transport and ICT (2016) ‘Measuring Rural Access:Using New Technologies’. Washington DC: World
Bank, License: Creative Commons Attribution CC BY 3.0
Wang, W., et al. (2016) ‘A review of road extraction
from remote sensing images’. Journal of Traffic
and Transportation Engineering (English Edition).
doi:10.1016/j.jtte.2016.05.005

Further reading and resources
Roberts, P., et al. (2006) ‘Rural Access Index: A Key
Development Indicator’. Transport Papers TP-10. The
World Bank Group, Washington, DC.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

83

TARGET 9.1

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

9.1.1
Proportion of the rural population
who live within 2 km of an allseason road

9.1.2
Passenger and freight volumes,
by mode of transport

World Bank

ICAO

Custodian agency
Tier

II

Status of step-by-step methodology
document on the metadata repository

Unpublished(Tier III at the time of theanalysis)I
Published

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity required
Availability of global EO data

Robustness ofproposedmethodology
Criteria

Compliance with Reporting
calendar
Sensitivity to change
Is it scalable (spatial)?
Is there a substitute forgaps in the EO record?

Overall EO relevanceComments to support criteria

EO imagery has been widely used
to extract the location and insome cases the status of roads,
using different techniques (e.g.
supervised and unsupervisedclassification, neural networks, andmathematical morphology).

Not supported by EO

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

84

TARGET 9.4

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 9.4
By 2030, upgrade infrastructure and retrofit industries
to make them sustainable, with increased resource-use
efficiency and greater adoption of clean and environmentallysound technologies and industrial processes, with allcountries taking action in accordance with their respective
capabilities
How can EO be used to help countries achieve the target?
EO data can be used to measure pollutants that arise
from industries and infrastructure. Satellite data can also
be used to locate potential pollutant hotspots throughanalysis of global emissions, and monitoring pollution
plumes. Satellites also collect weather and climate data,
which can aid decision-making in relation to clean energy
installations. For example, satellites can be used toforecast surface wind field data to guide operations of windturbines, and predict their energy input into power grids.
It can also be used to map photovoltaic solar electricity
potential, based on solar irradiance climatology – which
can be used to aid solar energy installation.
Current indicator(s)9.4.1 CO2 emission per unit of value added

Short methodological guidelines illustrated
with EO best practice examples
Indicator 9.4.1
Computation method
EO is not currently discussed in the methodological
guidelines for the indicator, but there is wide availabilityof EO products, which would be able to complement thecurrent methods. The indicator is based on the following
equation:
CO² emission frommanufacturing(kg)
CO² emission per unitof value added = (Manufacturing value
added (constant 2010 USD)

Gases Observing Satellite (GOSAT) and the orbiting
Carbon Observatory-2 (OCO-2) have been used to measureatmospheric column-averaged concentrations of the key
greenhouse gases CO2 and CH4. These can be used tocomplement the current methods, through use in flux
inversions to provide estimates of natural fluxes of CO2.
In general methods for monitoring ambient pollutant
levels are, however, better established than monitoring
emission levels. Several strategies are being developed touse atmospheric CO2 data for estimating fossil fuel CO2
emissions (Hardwick & Graven, 2016), including targetingstrong emitters such as large power plants and megacities(Velazco et al., 2011), and measuring other gases (e.g.
CO and NOx) that help distinguish fossil fuel-derived CO2
from natural emissions (Reuter et al., 2014). GHGSat, acompany that undertakes global emissions monitoring, hasused its demonstration satellite, Claire, to detect methane
emissions, to monitor targeted sites – this could be used
more widely.
However currently, there is limited capacity of using EO
data to measure CO2 emissions per unit of value added(the ratio between CO2 emissions from fuel combustion
and the value added of associated economic activities).
However, if the methodology of using satellites to measure
CO2 emissions are developed further, there is potential for
it to be combined with business data to create a unit ofvalue added.
One development which will aid this is the data from
Sentinel-5P, which has recently been made available –allowing O3, CO, SO2 and NO2 data to be recorded. The
Sentinel-5P spacecraft uses a TROPOMI instrument uses
passive remote sensing techniques to measure at the Top
Of Atmosphere (TOA) the solar radiation reflected by andradiated from the earth. It is the first Copernicus satellite
dedicated to monitoring the atmosphere. The TROPOMI
instrument can provide highly detailed and accurate data

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

85

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 9.4

about the atmosphere with a resolution up to 7 x 3.5km –detecting air pollution over individual cities.

Key messages for countries on EO contribution to thecomputation method

A project by the University of Denmark, looks to improve theassessment of wind energy resources, through combining
EO methodologies. It will use Copernicus Global and panEuropean products to describe vegetation properties, in
combination with digital elevation models to derive thesurface drag force, to lead to more accurate wind modelling
for turbine placement, which will aid emission reduction.

is not currently used to monitor CO2 emission• EperO data
unit of value added, but there are many products andmethodologies available to do so.

Limitations
The ability to distinguish manmade CO2 emissions
from natural sources using EO data is currently not wellestablished.

Data sources
Data category
Global/regional
datasets

Data sources

WebsiteGases Observing Satellite (GOSAT)

http://www.gosat.nies.go.jp/en/index.html

Carbon Observatory-2 (OCO-2)

https://ocov2.jpl.nasa.gov

Sentinel 5-P

https://sentinel.esa.int/web/sentinel/missions/sentinel-5p/data-products

Reference List
Hardwick, S. & Graven, H. (2016) Satellite
observations to support monitoring of greenhousegas emissions. Grantham Institute Briefing Paper No.
16 [Online] Available at: https://www.imperial.ac.uk/
media/imperial-college/grantham-institute/public/
publications/briefing-papers/Satellite-observationsto-support-monitoring-of-greenhouse-gas-emissionsGrantham-BP-16.pdf [Accessed 27th June 2018]
Reuter, M., M. Buchwitz, A. Hilboll, A. Richter, O.
Schneising, M. Hilker, J. Heymann, H. Bovensmann& J. P. Burrows (2014) Decreasing emissions of NOx
relative to CO2 in East Asia inferred from satellite
observations, Nature Geoscience, 7, 792-795.
Velazco, V. A., M. Buchwitz, H. Bovensmann, M. Reuter,
O. Schneising, J. Heymann, T. Krings, K. Gerilowski &J. P. Burrows (2011) Towards space based verificationof CO2 emissions from strong localized sources: fossil
fuel power plant emissions as seen by a CarbonSat
constellation, Atmos. Meas. Tech., 4, 2809–2822.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

86

TARGET 9.4

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

9.4.1
CO2 emission per unit of value added

Custodian agency

IEA; UNIDO

Tier

I

Status of step-by-step methodology document on themetadata repository

Published

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the EO
record?

Overall EO relevanceComments to support criteria

Overall amber relevance, due to high technical capacity
required, but the wide availability, especially as a resultof the recent release of Sentinel-5P.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

87

GOAL 11

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 11.1
By 2030, ensure access for all to adequate, safe andaffordable housing and basic services and upgrade slums

TARGET 11.1

Short methodological guidelines illustrated
with EO best practice examples
Indicator 11.1.1

How can EO be used to help countries achieve the target?

Computation method“Currently, an estimated 1.6 billion people live in inadequate
housing globally, of which 1 billion live in slums andinformal settlements”. The rapid urban growth of recent
decades has led to an increase of slums and informal
settlements, as well as air pollution and inadequate
basic services and infrastructure. The lack of proper urban
planning and management can bring tenure insecurity,
increase poverty, pollution, health risks, as well as a higher
vulnerability and exposure to natural and technologicalhazards. To make urban spaces more inclusive, safe,
resilient and sustainable the development, monitoring andmanagement of better forms of urban plans are urgently
needed. Many local governments ignore the extent ofslums. “Slums disappear not through being removed,
but by being transformed”, and in order to carry out thistransformation the extent, the physical characteristics aswell as the dynamics of slums, such as their densificationand expansion, need to be understood and monitored. EO
can help both understanding and monitoring slums, but
can also link their morphology with socio-economic data,
as well as help to identify hazardous areas where manyof these settlements are located. An increased numberof studies in the last 15 years have been published on
the use of EO to understand geography and dynamics ofslums, thanks to the availability of very-high-resolution(VHR) data and the advances in the methodologies toanalyse them. The use of EO can support monitoring ofslums and informal settlements growth, thanks to theirfrequent coverage of large areas, for which it would be
difficult to regularly undertake on the ground household
surveys. By knowing the dynamics and the extent of slums,
sustainable urban plans and slum improvement polices canbe developed and monitored, including the improvementof the building structures, access to water, electricity andother basic needs. VHR images are also increasingly usedby slum communities and NGOs as a basis for mappingand enumeration, who then use the data to negotiate forrecognition and their right to the city and its services.

The current computation method to report on this indicator,
used two components the Slum/Informal Settlement
households (SISH = 100 * [N. of people living in SISH
households/city population]) and the Inadequate housing
households (IHH = 100 * [N. of people living in IHH/city
population]).

Current indicators
11.1.1 
Proportion of urban population living in slums,
informal settlements or inadequate housing.

The data for slums and informal settlements are derived
from census and national household surveys, including
Demographic and Health Surveys (DHS) and Multiple
Indicator Cluster Surveys (MICS). The data for the
inadequate housing component can be computed throughincome and household surveys that capture housing
expenditures.
Census and national household surveys, can compile
several types of information at once, but have three main
disadvantages: a long temporal gap between surveys,
often 10 years; the time needed to check and analyse raw
data; and the underestimation of the population living inslums/informal settlements, because of the un-transparent
and often subjective set of criteria used to categorize them
(Kit et al., 2013).
Two main initiatives have produced a database of built-up
areas, the Global Human Settlement Layer (GHSL) (Esch etal., 2012) and the Global Urban Footprint (Pesaresi et al.,2013), but none of them have been able to fully accountfor slums, because of their different morphology fromplanned built-up areas and the resolution of the currently
used images for GHSL which does not allow the distinction
between slum and non-slum areas.
The use of EO data can help in standardizing the criteria
used to identify slums/informal settlements by setting clear
physical characteristics identifiable with remote sensing.
The Generic Slum Ontology (GSO), for example, consistsof a list of slum characteristics at three spatial levels:
object level (building characteristics, access network),slum settlement level (density and shape) and the slum
environment (location and neighbourhood characteristics)(Kohli et al., 2012; Kuffer et al., 2016). The GSO by being
adapted to local conditions, can account for specificcharacteristics within the same city or among cities. Several
techniques have been used to detect slums and informal
settlements and thanks to the increased availability of highand very-high-resolution (HR & VHR) imagery, with spatial
resolutions between less than 1 m to 5 m produced by
sensors such as Ikonos, QuickBird, WorldView, the detailed
spatial analyses, required to detect slums and informal
settlements, are now feasible. In general for building

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

89

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.1

objects the spatial resolution needed is 2 m, for footpaths
1–2 m, and for minor roads 5 m. If detailed building object
information are required than a resolution of 0.5 m isnecessary. For very dense slum areas such as those foundin India, the highest resolutions are required if individual
buildings are to be identified and mapped.

For areas where VHR satellite images cannot be acquired
due to cloud cover, radar could be used as well asairborne/UAV data. In-situ data derived from household
surveys can be used for areas where data are missing. An
increasing number of NGOs are doing this (e.g. Know yourCity programme of Slum/Shack Dwellers International)

Three main approaches can be used to map slums andinformal settlements from EO imagery:
Proxies. Various proxies capable of identifying slums
have been tested (Kuffer et al., 2016), also using highresolution imagery (e.g., Landsat, Sentinel 2 and 3, Terra
ASTER). Vegetation cover, for example, has been provento be negatively correlated to slum areas, sparser
vegetation being associated to slum-like areas (Stoler
et al., 2012). Depending on the context specific proxies
can be identified.

Sources of discrepancies-bject-Based Image (OBIA or GEOBIA).
This- Otechniqueaccounts for spatial, spectral and contextual
characteristics of the slum surface (building
characteristics and density, shape, access networks,
location, etc.). It works well for the extraction of
objects (e.g. roofs and roads) on settlement level. It
is recommended that a combination of ontologicalindicators adapted to local levels, and converted into
object-based parameters, such as texture, spectral
range, and geometry are used to identify slums from
VHR satellite images (Kohli et al., 2016).
achine learning techniques (e.g. Random Forest,- MNeural
Networks, Support Vector Machines). These
techniques, based on the use of a large and rich set oftraining data, are successful in extracting slum areasat the city scale. Building up a sufficiently large anddiverse training set is critical for global slum mappingwith EO.
Location and extent of slum areas can be mapped quickly
using any of these three approaches or a combination ofthem. The proportion of population living in the slum areas
identified can then be calculated using municipal data likeelection wards. In some contexts, where multi-storey
slum buildings exist (e.g. India, Egypt, Brazil, Colombia)
stereo images may be useful to estimate building volume
as well as density, in order to allow better estimates of
population to be made.
Disaggregation
A temporal disaggregation using EO data is possible,
since for normal circumstances daily images are notrequired, and in the case of fast growing cities, annual
or bi-annual images are enough. This also allows theuse of VHR images, which are characterized by a low
temporal frequency. A thematic disaggregation can also
be performed using income levels.
Treatment of missing values

The criteria to identify slums, if not objective, cangenerate discrepancies and the extent of slums can beunderestimated. Delineating the boundary of the slums
can also be challenging and thus generate discrepancies(Kohli et al., 2016). Informal/slum areas receive amultiplicity of names in many languages, dialects
and slangs all over the world and their definition (UNHABITAT, 2015) includes far more than morphological andcontextual aspects as for example cultural, legal, security,level of deprivation ones – some of them are not directly
observable using EO techniques.
Limitations
EO data have technical challenges for characterizing
slums such as mixed pixels or the obliqueness of images
introducing shadowing effects, even with VHR images,
in situations where slums and very tall buildings areclosely packed (e.g. Mumbai, Cairo). Choosing the rightspecifications for image acquisition can thus be very
important. Also, the cost and processing of VHR data canoften be prohibitive, but slum mapping should not be astandalone application, it should be combined with otherapplications, so that the cost/benefit ratio improves.
In areas with persistent cloud cover the use of VHR islimited and the use of other data such as radar has tobe preferred.
Open source tools and platforms
Key messages for countries on EO contribution to thecomputation method
O can be used to regularly monitor the extent• Eanddensification of slums without waiting for the
implementation of census or national household
surveys;onitoring slums through EO, helps understanding their• Mpattern,density, location and distribution, enabling thedevelopment of sustainable, disaster risk resilient andinclusive urban plans.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

90

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.1

Data sources
Data category

Source satellite data

Data sources

WebsiteLandsathttps://earthexplorer.usgs.gov

Sentinelhttps://scihub.copernicus.eu

Terra ASTER

https://terra.nasa.gov/data/aster-data

WorldView, GeoEye, QuickBird,
IKONOShttps://www.maxar.com

Pleiadeshttps://www.intelligence-airbusds.com

Reference List

Further reading and resources

Esch, T. et al. (2013) ‘Urban Footprint Processor-Fully
Automated Processing Chain Generating Settlement
Masks From Global Data of the TanDEM-X Mission’,IEEE Geoscience and Remote Sensing Letters, 10(6), pp.
1617–1621. doi: 10.1109/LGRS.2013.2272953.

Cobbett, W. How cities can get rid of slums by supporting
them. Informal City Dialogues, 22 April 2013.

Kit, O. et al. (2013) ‘Defining the bull’s eye: Satellite
imagery-assisted slum population assessment inHyderabad, India’, Urban Geography, 34(3), pp. 413–
424. doi: 10.1080/02723638.2013.778665.
Kohli, D. et al. (2016) ‘Urban slum detection using texture
and spatial metrics derived from satellite imagery’,Journal of Spatial Science. Taylor & Francis, 61(2), pp.
405–426. doi: 10.1080/14498596.2016.1138247.
Kohli, D. et al. (2012) ‘An ontology of slums for imagebased classification’, Computers, Environment and Urban
Systems. Elsevier Ltd, 36(2), pp. 154–163. doi: 10.1016/j.
compenvurbsys.2011.11.001.
Kuffer, M. et al.(2016) ‘Slums from space-15 years ofslum mapping using remote sensing’, Remote Sensing,8(6). doi: 10.3390/rs8060455.

Know your City programme of Slum/Shack Dwellers
International (http://sdinet.org/,
http://knowyourcity.info/
Kohli, D. et al. (2016) Uncertainty analysis for image
interpretations of urban slums. Computers, Environment
and Urban Systems. 60, pp 37-49.
UN-Habitat. (2015) ‘Informal Settlements’. UN-Habitat:
New York, NY, USA; pp. 1–8.
Stoler, J. et al. (2012) ‘Assessing the utility of satellite
imagery with differing spatial resolutions for deriving
proxy measures of slum presence in Accra, Ghana’. GISci.
Remote Sensing, 49, 31–52.
Wurm, M. et al. (2017) ‘Exploitation of Textural andMorphological Image Features in Sentinel-2A Data for
Slum Mapping’. In Proceedings of the Urban Remote
Sensing Event, Dubai, UAE, 6–8 March 2017.

Pesaresi, M. et al. (2013) ‘A global human settlement
layer from optical HR/VHR RS data: Concept and first
results’. IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, 6, 2102–2131.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

91

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.1

Predicting informal settlement dwellers’ deprivations from space: a pilot
study in Dhaka for planning for target 11.1
One of the most pressing development
challenges is how to respond to theunmet demand for basic infrastructureservices, like adequate housing, cleanwater, and sanitation, for the 1 billion
people living in informal settlements.
One of the main difficulties whenlooking at approaches to support
informalsettlement
upgradinginitiatives is the lack of adequate
spatial data.
With an eye towards using
innovative data analytics to support
the formulation of interventions
and policies to upgrade informal
settlements, in 2017, World Bank
Group Water Global Practice launched,under the “Water Supply andSanitation in Rapid Urbanization”
umbrella, a pilot study in Dhaka(Bangladesh). A city particularly
challenged due to congestions, poor

infrastructures and regular flooding.
The main objective was to create ananalytical tool to support decisionmaking leading to improved pro-poor
policy interventions. The project was
conducted through a collaboration
betweenthe
WASH
Poverty
Diagnostic team in Bangladesh, theremote sensing service company
GISAT, a member of an EO4SD Urban
consortium working for the European
Space Agency (ESA), and researchers
from the University of Massachusetts
Boston. As part of this project, anovel, predictive model combining
spatial characterization analysis,
with statistical modelling to identify
anddelineate
informal/informal
settlement areas and characterize
informal settlement deprivation, wasdevised and tested. Two sources ofdata were combined: VHR EO data
and analytics of informal areas/

informal settlements for the whole
Dhaka Metropolitan area, and an indepth household survey conducted
in 2016. Multiple variables derived
from EO data were found to bestatistically significantly associatedwith measures of deprivation. For
example, distance to Central BusinessDistricts, arterial roads, averagedwelling size, percentage of informal,
local primary, secondary and tertiary
streets in informal settlements, were
found to increase the relative riskof overall deprivation. The resultsfrom this analysis, because of theirpotential ability to predict futurescenarios, will be able to support
the development of inclusive policies
and targeted planning interventions
to help populations living in informal
settlements.

Figure 13: VHR EO data and analytics of informal areas/slums for the Dhaka Metropolitan area

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

92

TARGET 11.1

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

9.4.1
Proportion of urban population living in slums, informal
settlements or inadequate housing.

Custodian agency

UN-Habitat

Tier

I

Status of step-by-step methodology document on themetadata repository

Published

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the EO
record?

Overall EO relevanceComments to support criteria

The increased number of studies and advancementof technology allows the use of EO to map slums andinformal settlements, but due to broad definition andcomplex structure, it still requires more research toidentify their physical characteristics in order to develop
more robust proxies and generalised slum models.
Many studies and methods are developed and tested forspecific contexts. Upscaling to global level is the nextchallenge.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

93

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 11.2
By 2030, provide access to safe, affordable, accessible
and sustainable transport systems for all, improving roadsafety, notably by expanding public transport, with special
attention to the needs of those in vulnerable situations,
women, children, persons with disabilities and older
persons.
How can EO be used to help countries achieve the target?
EO data can inform the production of efficient and effectiveplans for road infrastructures and shipping routes (although,the global coverage of remote sensing images allows theidentification of areas currently lacking infrastructures fortransportation. This data can be combined with census data
to provide more detailed information on public transportthat cannot be measured through EO (e.g. railways andsubways), as well as data on vulnerable people. EO data
has been widely used to extract infrastructures such asurban areas, roads and dams using data at different spatial
resolution (e.g. rural roads can be detected just with highresolution images) and different techniques (e.g. supervisedand unsupervised classification, neural networks, andmathematical morphology). Research is also currently
focused on using high resolution or radar data to monitor
the status of infrastructures, particularly in areas prone
to natural disasters, such as flooding, but also in areas
affected by conflicts (Roberts et al., 2006). The need toplan for regional and trans-border infrastructure is also wellserved by EO since it is technology that crosses borders
and is not limited by a single country’s or region’s national
data collection systems. In theory, open access EO data
should help countries collaborate on shared infrastructure
projects.
Current indicator:
11.2.1 Proportion of population that has convenient accessto public transport, by sex, age and persons withdisabilities

TARGET 11.2

Short methodological guidelines illustrated
with EO best practice examples
Indicator 11.2.1
Computation method
The indicator methodology currently uses earth observationin relation to urban planning, however there is scope forit to be integrated further, especially in relation to road
identification.
All season roads can be identified from satellite images
based on features such as geometry, photometry, topology,function and texture, or using a road model (Wang et al.,2016; Ahmad and Deore, 2016). A combination of methods,
including digitisation should be used, and ground truth
activities should complement the analyses.
In order to produce more accurate road products, very
high spatial resolution satellite images, such as Spot 6/7(Transport and ICT, 2016), WorldView, GeoEye or Pleiades(spatial resolution = 0.6 metre). An important ancillarydataset that can be used in conjunction with the VHR data
is OpenStreetMap.
Limitations
While EO can identify access to public transport, there
currently isn’t any capacity to identify this specifically bysex, age and persons with disabilities.
Harmonised global/local data on urban transport systems
do not exist, nor are they comparable at the global level.
It is also recognised that there are various forms of public
transport in the member countries that are not fully definedor captured.
Manual digitalisation and automatic or semi-automaticclassification can be used for the extraction of roads, but
often is preferable to utilise a combination of methods.
Even though manually digitising an image can be time
consuming in many areas with several linear features,manual editing is required for the elimination of segmentsthat are not roads.
Open source tools and platforms
There are a number of global EO-based tools that areavailable that could assist NSOs and indicator custodianswith delivering and implementing EO-based methodologies.
For example, the GEO Human Planet initiative, the Global
Urban Footprint/World Settlement Footprint, and the
Global Human Settlement Layer. OpenStreetMap, is a useful
resource, however the accuracy of this data is unknown.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

94

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.2

Key messages for countries on EO contribution to thecomputation method
is currently used to monitor the indicator in relation• EtoO urban
planning
O can be further used to identify different roads and• Eshipping
routesData sources
Data category

Source satellite data

Global/regional
datasets
Software, tools andplatforms

Data sources

WebsiteLandsathttps://earthexplorer.usgs.gov/

Sentinel data (1,2 and 3) fromthe Copernicus Open Access Hub

https://scihub.copernicus.eu/

Pleiadeshttps://www.intelligence-airbusds.com

WorldView, GeoEye, QuickBird,
IKONOS

ttps://www.maxar.com

The Global Urban Footprint (GUF)
https://urban-tep.eu/ World Settlement Footprint
The Global Human Settlement
Layer (GHSL)

https://ghsl.jrc.ec.europa.eu/data.php

The GEO Human Planet initiative https://ghsl.jrc.ec.europa.eu/HPI.php
OpenStreetMap

https://www.openstreetmap.org

Reference List
Ahmad, N. and Deore, P. J. (2016) ‘Road detection fromsatellite images’, International Journal of Recent Trends
in Engineering and Research, 2(4), pp. 299–304.

Transport and ICT (2016) ‘Measuring Rural Access: Using
New Technologies’. Washington DC: World Bank, License:
Creative Commons Attribution CC BY 3.0

Roberts, P., et al. (2006) ‘Rural Access Index: A Key
Development Indicator’. Transport Papers TP-10. The
World Bank Group, Washington, DC.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

95

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

TARGET 11.2

11.2.1
Proportion of population that has convenient access to public
transport, by sex, age and persons with disabilities

Custodian agency

UN-Habitat

Tier

I

Status of step-by-step methodology document on
the metadata repository

Published

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in
the EO record?

Overall EO relevanceComments to support criteria

There is an overall amber relevance. Urban accessibility canbe measured through percentage of street space in cities
and number of intersections/km2 from analysis of earth
observations and/or city maps.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

96

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 11.3
By 2030, enhance inclusive and sustainable urbanization andcapacity for participatory, integrated and sustainable humansettlement planning and management in all countries
How can EO be used to help countries achieve the target?
Urban areas are rapidly expanding to accommodate thegrowing number of people moving to cities. “From 2000
to 2015, in all regions of the world, the expansion ofurban land outpaced the growth of urban populations”.
An uncontrolled urban sprawl can lead to the increase ofcarbon emissions, poverty, health and safety risks, social
inequalities, and vulnerability to natural disasters. The
achievement of this target will ensure that land is usedefficiently, allowing sustainability and inclusiveness, but it
also provides the foundation for attaining other sustainable
development goals related to health, food security, energy,
safety and poverty. Urban growth needs to be monitored
and managed to ensure the sustainable use of land. The
improvement of EO technology and the availability of hightemporal and spatial resolution images, as well as theadvancement in the methodologies proposed to identify
built up areas from satellite images, have created a good
opportunity to plan and monitor urban development. EO
has been used for the direct monitoring and dynamic
simulation of urban expansion since the ‘60s and newmodels and methods are continually being proposed andtested. Remote sensing data can support the generationof country specific urban expansion models and inform thedevelopment of sustainable urban plans. In particular, they
can inform the development of urban plans that include theincrease of green spaces where these are lacking, identify
where these spaces have the capacity to mitigate natural
hazard such as floods, and therefore should be prioritise forprotection, which are the most vulnerable areas to disaster,
as well as to enhance infrastructures such as roads oraccess to energy, in poorer areas of the city, to include its
inclusiveness. The effectiveness of these plans can then be
regularly monitored and adapted through EO.
Current indicators:
11.3.1 Ratio of land consumption rate to population growthrate
11.3.2 Proportion of cities with a direct participation structureof civil society in urban planning and managementthat operate regularly and democratically

TARGET 11.3

Short methodological guidelines illustrated
with EO best practice examples
Indicator 11.3.1
Computation method
The current computation method includes the estimateof two components, the population growth rate and theland consumption rate, which indicates the progressive
expansion of a city. The land consumption rate can becalculated using the total areal extent of built-up areas fora past year, and the current year, divided by the numberof years between the two measurement periods. Similar
approach is used to derive the population growth rate,
and the combination of the two rates will provide the final
indicator “Ratio of land consumption rate to population
growth rate”. Satellite images are indicated as the sourceof data to extract the extent of built up areas.
Countries can report on the expansion of built up areas,
by using image acquisitions of multiple dates. Satellite
sensors derive information about the earth surface with arange of spatial resolutions ranging from a low resolution(e.g. MODIS, MERIS, Sentinel-3), medium resolution (e.g.
Sentinel-1/-2, Landsat, ASTER, Radarsat) and high to very
high resolution (e.g. TerraSAR-X, COSMO Sky-Med, QuickBird,
WorldView, GeoEye, Pléiades) including aerial images andimages derived from drone campaigns. The remote sensing
sensors acquire information also in different wavelengthsof the electromagnetic spectrum. Three main types can
be classified in optical sensors (e.g. Sentinel-2, Landsat,
WorldView, etc.), microwave (SAR) sensors (e.g. Sentinel-1,
TerraSAR-X, Radarsat) and thermal sensors (ASTER,
Sentinel-3). Some of the above mentioned satellite missions
provide images in various spatial resolutions and spectral
ranges. Medium resolution sensors are suitable to monitor
large areas up to the entire globe. EO-based thematic
baseline layers such as the Global Human Settlement Layer
and the Global Urban Footprint (GUF) / World Settlement
Footprint (WSF) are derived of sensors with this resolution.
The Sentinel missions with their systematic and long-term
operational service guarantee represent a unique, but yet
to be fully realised, opportunity for advancing global urban
mapping. High and very high resolution sensors are suitablefor city and sub-city scale applications.
Night time lights (NTL) derived of optical sensors canprovide also information on settlement areas. NTL datafrom the Defense Meteorological Satellite Program’s
Operational Line-Scan System (DMSP-OLS) can also be
used to measure artificial illumination and extract builtup areas, for example by combining vegetation indicesand night-time light urban areas (Goldblatt et al., 2018).
Night time lights data are a valuable source in particularwhen the goal is to gain information on poverty and energy
consumption, and when analysing climate/heat related
aspects of built-up areas. A useful products that combine

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

97

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

population statistics and nightlights is the Global Rural
Urban Mapping Project (GRUMP). It provides a series ofmulti-temporal grids (1990-1995-2000) at approximately
1 km spatial resolution, but can under-estimate countries
where the illumination footprint is not as strong as indeveloped countries (Melchiorri et al., 2018).
Built-up areas can be characterized by thermal sensors by ahigher land surface temperature than that of suburban, withlower density, and rural areas due to the heat emissions
from the residences, traffics, industries and manual labour inthe built-up area. The thermal infrared band of EO data, such
as Landsat TM, ETM+, OLI, TIRS, can be used to extract theland surface temperature. Boundaries are then drawn aroundareas with higher temperature (Wang et al., 2018).
A huge variety of methods to delineate urban areas using
remote sensing data is documented in the scientific
literature. Many approaches combine multiple workflows.
Most methods use the spectral and structural properties ofbuilt-up areas to classify urban and non-urban areas. Two
widely used methods are described below:•

Machine learning techniques.
The machine learning techniques that are commonlyused to detect built-up areas are supervised classification
approaches such as Support Vector Machines, Random
Forest or decision tree technology (Corbane et al., 2017;
Esch et al., 2013). These methods basically link the image
data to a defined collection of semantic layers based on
training data sets. Training data can be any representative
data that allows to approximate the built-up areas class.
The resulting classification models can then be used forderiving human settlements information from satellite
imagery – e.g., Sentinel-1 and Landsat data (Esch etal., 2018; Corbane et al., 2017). Deep learning methods
emerged also in this domain in the last years.

•

Geographic object-based image analysis (GEOBIA).
This technique typically uses very high resolution satellite
imagery (<5m), usually between 2 and 0.5m (Herold et al.,2012; Wezyk et al., 2016; Molenaar, 2001). The objects tobe analysed, in this case built-up areas, can be obtained
from image segmentation and then classification of thesegments. Image segments are representative clustersof neighbouring pixel with similar spectral properties.
This approach requires a high processing effort and it isrealistically applicable just at local to regional scale.

TARGET 11.3

Disaggregation
EO-based products can be used to disaggregate typesof built-up structure (residential, industrial, etc.).
Disaggregation is often used to distribute population data,
as well as socio-economic data, such as income level.
Using the data from indicator 11.1.1 residential areas canbe differentiated from slums/informal settlements.
Treatment of missing values
At a local scale missing values can be obtained using groundtruth surveys or very high resolution EO data. Temporalinterpolation is also feasible as long as a reasonable timeseries is available.
Sources of discrepancies
One of the main source of discrepancy derives from thedefinition of built up area, this can be avoided by using thesame baseline datasets. Other sources of discrepancy are
the underlying imagery (sensor), the classification method,
the training data and the quality assurance procedure. If
the NTL approach is used, discrepancies can be generated
from the use of different thresholds for the pixel value atwhich the category urban development is assigned.
Limitations
Missing data in areas with high cloud cover prevent the
mapping of entire sections of built-up areas leading to
the underestimation of the real extent of urban areas. The
calculation of rate of change in land consumption is limitedby the number of acquisitions the EO satellite can obtainof built up area, which has to be consistent at the national
scale, requiring longer compositing periods, several years
in the case, for example, of tropical regions. The use ofSAR data and of improved methods (e.g. TimeScan) can
overcome some of these issues. At a local scale, missing
data can be obtained using ground truth surveys or very
high resolution EO data.
Key messages for countries on EO contribution to thecomputation method
O are a valuable source of data, to monitor urban• Egrowthand have been widely used to map and model
the expansion of cities;
O can support the development of urban plans that use• Eefficiently
and sustainably the available land;he combination of several types of EO data and• Tproducts,
such as topography, climate change scenarios,
land cover and precipitation patterns, can inform thedevelopment of natural disaster resilient urban plans.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

98

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.3

Data sources
Data category

Source satellite data

Global/regional
datasets

Data sources

WebsiteLandsathttps://earthexplorer.usgs.gov/

Sentinel data (1,2 and 3) fromthe Copernicus Open Access Hub

https://scihub.copernicus.eu/

MODIS

https://search.earthdata.nasa.gov

The Global Urban Footprint(GUF) / World Settlement Footprint (WSF)

https://urban-tep.eu

NASA’s Black Marble nighttime
lights product suite (VNP46)

https://earthobservatory.nasa.gov/Features/NightLights/
page3.php
https://www.ngdc.noaa.gov/eog/viirs.html

NOAA. N.d. “Version 4 DMSP-OLS Nighttime Lights Time
Series.”https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html‘GHS built-up grid, derived fromLandsat, multitemporal (1975,
1990, 2000, 2014)’. European
Commission, Joint ResearchCentre (JRC) [Dataset]

http://data.europa.eu/89h/jrc-ghsl-ghs_built_ldsmt_globe_r2015b‘GHS Settlement grid following the REGIO model 2014 inapplication to GHSL Landsat andhttp://data.europa.eu/89h/jrc-ghsl-ghs_smod_pop_globe_
CIESIN GPW v4-multitemporalr2016a(1975-1990-2000-2015)’. European Commission, Joint ResearchCentre (JRC) [Dataset]

Software, tools andplatforms

Trends.Earth Urban Mapper from
Conservation International

https://geflanddegradation.users.earthengine.app/view/
trendsearth-urban-mapper

Global Rural-Urban Mappingfrom the Centre for International Earth Science Information
Network (CIESIN), Columbia
University.

http://sedac.ciesin.columbia.edu/data/set/grump-v1-urbanextents

Urban Thematic Exploitation
Platform (U-TEP)

https://urban-tep.eu

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

99

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.3

Reference List
Esch, T., Marconcini, M., Felbier, A., Roth, A., Heldens, W.,
Huber, M., Schwinger, M., Taubenböck, H., Müller, A., Dech,
S. (2013) Urban Footprint Processor – Fully AutomatedProcessing Chain Generating Settlement Masks from
Global Data of the TanDEM-X Mission. IEEE Geoscienceand Remote Sensing Letters, Vol. 10, No. 6, November
2013.
Esch, T., Bachofer, F., Heldens, W., Hirner, A., Marconcini,M., Palacios-Lopez, D., Roth, A., Üreyen, S., Zeidler, J., Dech,
S., Gorelick, N. (2018): Where we live – A summary of
the achievements and planned evolution of the Global
Urban Footprint. Remote Sensing 2018, 10(6), 895, 18 pp.;https://doi.org/10.3390/rs10060895.

ESA (2018) ‘Satellite Earth Observations in Support of the
Sustainable Development Goals’. Special 2018 Edition
Esch, T., Heldens, W., Hirner, A., Keil, M., Marconcini, M.,
Roth, A., Zeidler, J., Dech, S., Strano, E. (2017) Breaking
new ground in mapping human settlements fromspace – The Global Urban Footprint. ISPRS Journal of
Photogrammetry and Remote Sensing, 134, pp. 30–42.
https://doi.org/10.1016/j.isprsjprs.2017.10.012
GEO/CEOS (2017) Earth Observations in support of the2030 Agenda for Sustainable Development. Available
at:https://www.earthobservations.org/documents/
publications/201703_geo_eo_for_2030_agenda.pdf(Accessed: 25 April 2018).

Corbane, C., Pesaresi, M., Politis, P., Syrris, V., Florczyk, A.J.,
Soille, P., Maffenini, L., Burger, A., Vasilev, V., Rodriguez,
D., Sabo, F., Dijkstra, L., Kemper, T. (2017). ‘Big earth data
analytics on Sentinel-1 and Landsat imagery in support
to global human settlements mapping’. Big Earth Data 1,
118–144. doi:10.1080/20964471.2017.1397899

Goldblatt, R. et al. (2018) ‘Using Landsat and nighttime
lights for supervised pixel-based image classificationof urban land cover’, Remote Sensing of Environment.
Elsevier, 205(November 2017), pp. 253–275. doi:
10.1016/j.rse.2017.11.026.

Herold, H. et al. (2012) ‘A GEOBIA approach to map
interpretation multitemporal building footprint building
retrieval for high resolution monitoring of spatial urban
dynamics’, Proceedings of the 4th. GEOBIA, 4, pp. 252–256.

Li, X. and Zhou, Y. (2017). ‘Urban mapping using DMSP/OLS stable night-time light: a review’. International
Journal of Remote Sensing, 38, 6030–6046. doi:10.1080/
01431161.2016.1274451

Melchiorri, M. et al. (2018) ‘Unveiling 25 years of planetaryurbanization with remote sensing: Perspectives from theglobal human settlement layer’, Remote Sensing, 10(5),pp. 1–19. doi: 10.3390/rs10050768.

Lindner, M. et al. (2011) ‘An object-based multisensoral
approach for the derivation of urban land use structuresin the city of Rostock, Germany’, p. 81811F. doi:
10.1117/12.898134.

Molenaar M. (2001) ‘Hierarchical Object-Based Image
Analysis of High-Resolution Imagery for Urban Land Use
Classification’. In: IEEE/ISPRS Joint Workshop on Remote
Sensing and Data Fusion over Urban Area, 35–39. Rome,
University of Rome La Sapienza: IEEE. DOI: 10.1109/
DFUA.2001.985721.

Jensen, J.R. and Toll, D.L. (1982) ‘Detecting residential landuse development at the urban fringe’. PhotogrammetricEngineering & Remote Sensing, 48, 629–643.

Wang, L. et al. (2018) ‘Urban built-up area boundary
extraction and spatiotemporal characteristics based onland surface temperature retrieval’, Remote Sensing, 10(3).
doi: 10.3390/rs10030473.
Wężyk, P. et al. (2016) ‘Using Geobia and Data Fusion
Approach for Land use and Land Cover Mapping’,Quaestiones Geographicae, 35(1). doi: 10.1515/
quageo-2016-0009.

Further reading and resources
Balk, D.L. et al. (2006) ‘Determining Global Population
Distribution: Methods, Applications and Data’. Advances inParasitology, 62, 119–156.

Mertes, C.M., Schneider, A., Sulla-Menashe, D., Tatem,
A.J., Tan, B. (2015). ‘Detecting change in urban areas atcontinental scales with MODIS data’. Remote Sensing ofEnvironment 158, 331–347. doi:10.1016/j.rse.2014.09.023
Trianni, G. et al. (2015) ‘Scaling up to national/regional
urban extent mapping using Landsat data’. IEEE J. Sel.
Top. Appl. Earth Obs. Remote Sens., 8, 3710–3719
Zhang, Q. et al. (2013) ‘The vegetation adjusted NTL
urban index: a new approach to reduce saturation andincrease variation in nighttime luminosity’. Remote Sens.
Environ., 129, 32–41
Zhang, Y. et al. (2015) ‘Research on the contribution ofurban land surface moisture to the alleviation effect ofurban land surface heat based on Landsat 8 data’. Remote
Sensing, 7, 10737–10762.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

100

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.3

A new dataset and online platform to plan for inclusive and sustainableurbanisation for SDG target 11.3
Satellites enable a spatially andtemporally continuous observation ofEarth’s surface. Recently, the German
Aerospace Centre (DLR) and the 6team have succeeded in deploying a
newly developed method to map thegrowth of human settlement extent
at 30m spatial resolution on a yearlybasis over three decades from 1985
to 2015. The generation of this World
Settlement Footprint (WSF) Evolution
dataset is based on a multi-temporal
analysis of more than six million
satellite images of the Landsat
mission.
To facilitate an effective and joint
exploration of data collections related
to the built environment – e.g. the
new WSF Evolution that will beaccessible on a free and open basis
in 2019 – a consortium funded by theEuropean Space Agency (ESA) and led
by DLR has set-up the Urban Thematic
Exploitation Platform (U-TEP). U-TEP
represents a web-based, open andcollaborative virtual environment
providingservicesforhighperformance data access, processing,analysis and visualization. At thesame time this enabling technology
aims at supporting the development
and sharing of technical solutions,thematic data and knowledge related
to sustainable cities and communities
in general.
One key use scenario of U-TEP’s
analytics and visualization toolbox

includes the implementation of aservice for the worldwide on-demandcalculation of SDG 11.3.1 “Ration ofland consumption rate to population
growth rate” based on the WSF
Evolution and the statistics of theWorld Bank Open Data Catalogue(https://data.worldbank.org).

Figure 14: WSF Evolution of Bangkok region, Thailand. Red corresponds to the urban extent in1985 and yellow-green-blue indicates the growth to 2015. Credit: DLR

Figure 15: Calculation of indicator “Ratio of land consumption rate to population growth rate” (SDG 11.3.1) with the Urban Thematic Exploitation
Platform. The bar chart and table show the Population Change normalized by Settlement Area Change(www.urban-tep.eu). Credit: DLR/Gisat

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

101

TARGET 11.3

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

11.3.1
Ratio of land consumption
rate to population growthrate

Custodian agency

11.3.2
Proportion of cities with adirect participation structureof civil society in urban
planning and managementthat operate regularly anddemocratically

UN-Habitat

Tier

II

Status of step-by-step methodology document on
the metadata repository

Published

UnknownII
Unpublished(Tier III at the time of theanalysis)Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the
EO record?

Overall EO relevanceComments to support criteria

EO has been used to monitor Not supported by EO
urban growth since the 60s.
Several techniques havebeen tested and new ones
are under development,
but technical expertiseare required to implement
these methods. Systematicand comparative accuracy
assessment of thematic
products (derived from EOdata) and related methods
are required.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

102

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 11.5
By 2030, significantly reduce the number of deaths and thenumber of people affected and substantially decrease thedirect economic losses relative to global gross domestic
product caused by disasters, including water-related
disasters, with a focus on protecting the poor and peoplein vulnerable situations
How can EO be used to help countries achieve the target?
This target seeks to reduce the adverse effects of natural
disasters. It recognises the growing impact of natural
disasters around the world and the heightened risk,
especially to vulnerable populations, e.g. in unplanned
urban settlements without adequate protection or planning.
It targets both slow-onset, climate related disasters suchas sea level rise and extreme weather events. Human loss
is the main focus of the target, both in terms of mortality
and injury, and negative economic impacts of disasters.
This target also relates to targets 1.3, 1.5, 3.6, 3.9, 15.3,
12.1 and 14.2 as well as related targets in the Sendai
Framework for Disaster Risk Reduction 2015-2030. EO canplay a role in both planning and achievement of this target.
For planning purposes, EO can map both the areas thatare vulnerable to disasters, e.g. coastal, low-lying areas or
areas of deforested, steep slope, susceptible to landslides
as well as to map vulnerable populations, e.g. throughinformal urban settlement mapping. EO also has a role toplay in planning for natural disasters by the provision ofearly warnings systems where flooding, fires and landslides
pose a risk to people and their material goods. In order toachieve the target EO can be used to assess the overlap or
proximity between vulnerable population and areas prone
to disaster and the extent of change in this overlap area inorder to ascertain if the human related loss is increasingor decreasing over time. Although not EO-derived, globally
gridded GDP data can contribute to the achievement of thistarget by mapping the geographic location of the poor (seeindicators 1.2.2 and 1.2.1).
Current Indicator(s)11.5.1 
Number of deaths, missing persons and persons
affected by disaster per 100,000 people
11.5.2 
Direct economic loss in relation to global GDP,damage to critical infrastructure and number of
disruptions to basic services, attributed to disasters

TARGET 11.5

Short methodological guidelines illustrated
with EO best practice examples
Indicator 11.5.1
Computation method
Indicator 11.5.1 relies on a long-established method ofquantifying natural disaster loss - the national disaster loss
database compiled at a national scale and reported to theUnited Nations Office for Disaster Risk Reduction (UNISDR).
This approach involves registering physical damage value(housing unit loss, infrastructure loss etc.), which needs
conversion to monetary value. The converted global,
monetary value is divided by global GDP calculated fromthe World Bank Development Indicators. Spatial gridded
GDP data are available globally but these are derived fromstatistical data, not EO (Nordhaus, 2006).
Although the computational method does not employ EO
data currently, mapping and quantifying the economic valueof physical damage to housing and roads is a developing
area for EO, particularly on disaster preparedness/warningand response/monitoring (Bello & Aina, 2014). The EO
methods are designed to relay information on sites ofdamage to authorities as quickly as possible to minimise
disruption and loss of life. Such information, compiled overtime, could be fed into a national disaster loss database.
An important EO based Emergency and Disaster Mapping
Service hitherto not used for the assessment of Indicatorsof Target 11.5, is the European Commission supported
Copernicus Emergency Management Service (EMS) which“uses satellite imagery and other geospatial data to provide
free of charge mapping service in cases of natural disasters,
human-made emergency situations and humanitariancrises throughout the world,”(http://emergency.copernicus.
eu/mapping/ems/emergency-management-servicemapping). The programme provides a “Rapid mapping”
service to assess extent of damage shortly after an event
as well as a “Risk & Recovery Mapping” Service.
The power of EO to monitor and map damage from natural
disasters has been demonstrated notably for: assessing
shoreline damage following tsunamis (Bello & Aina, 2014);
building damage following earthquakes using very highresolution optical imagery at spatial resolution of 1m(Chesnel, Binet, & Wald, 2007); and flood-related damage
to roads using very high resolution optical imagery, a digital
elevation model and SAR for flooded area detection (Frey& Butenuth, 2009). Fusion of optical and SAR data andmore detailed use of radiometric elements of the opticalimagery is encouraged for detecting manmade objects andtheir destruction, particularly for mapping usable roads
post-flooding (Butenuth et al., 2011). With the adventof the Sentinel suite of EO data and the Copernicus EMS
programme, the impact of a natural disaster on a population
can be assessed using the EMS products in conjunction
with geo-spatial population density data.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

103

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSAll the above examples require vector-based maps to
support the analysis in a GIS environment, e.g. building
footprints and road networks, comparing them before
and after disaster, to evaluate loss. However, those layers
could potentially be derived from the imagery used to map
disaster areas.

TARGET 11.5

Key messages for countries on EO contribution to thecomputation method
indicator follows a well-established methodology based• Tonhisrelated
indicators, e.g. for the Sendai Framework, fromnational disaster loss databases.
n EO-based method for the indicator would have to be• Acustomised
for the nature of the disaster, whether long-

Limitations
The area of damaged infrastructure reported using EO stillneeds to be converted to economic loss. Values of global
GDP will need to be derived elsewhere. More generally, thetype of EO data used varies with the nature of the disaster,i.e. whether slow onset or from extreme weather or events.
Slow onset events require an EO-based monitoring system
which is commensurate with the rate of change and thechange in disaster risk level. The use of EO to monitor
sudden events is limited to post-disaster recovery efforts,
e.g. in surveying infrastructure damaged and therefore
needs careful selection and combination with other typesof spatial data such as the pre-disaster road network.

term onset or sudden, and whether in coastal, inland or
mountainous settings.
urrent EO based assets for disaster monitoring mostly• Cfocus
on post disaster recovery efforts but for this indicator
would need to be enhanced to evaluate physical damage
and the proportion of the population affected by the disaster
The EO based methods for infrastructural damage•assessment are more advanced than those for assessing
human impact (mortality, injury, relocation etc.)
owever, combining advances made in EO-based human• Hsettlement
mapping with the ability to evaluate physicaldamage in disaster zones could pave the way for the use of
EO in the indicator computation

Data sources
Data category
Global/regional
datasets

Data sources

WebsiteThe Global Urban Footprint(GUF) / World Settlement Footprint (WSF)

change hyperlink to

The Global Human Settlement
Layer (GHSL)

https://ghsl.jrc.ec.europa.eu/data.php

The GEO Human Planet initiative https://ghsl.jrc.ec.europa.eu/HPI.php
Software, tools andplatforms

Operational orcommercial services

Global Gridded Geographically
Based Economic Data (G-Econ),v4 (1990, 1995, 2000, 2005)

http://sedac.ciesin.columbia.edu/data/set/spatialecon-gecon-v4/docs

The UN-SPIDER programme

http://www.un-spider.org/

Copernicus Emergency Managehttp://emergency.copernicus.eu/mapping/ems/emergenment Service (EMS)
cy-management-service-mapping
The GEO Human Planet initiative

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

104

TARGET 11.5

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Reference List
Bello, O. M., & Aina, Y. A. (2014). Satellite Remote Sensing
as a Tool in Disaster Management and Sustainable
Development: Towards a Synergistic Approach. Procedia- Social and Behavioral Sciences, 120, 365–373.
https://doi.org/10.1016/j.sbspro.2014.02.114

Chesnel, A.-L., Binet, R., & Wald, L. (2007). Quantitative
assessment of building damage in Urban area using very
High Resolution images. 2007 Urban Remote Sensing
Joint Event, URS.
https://doi.org/10.1109/URS.2007.371865

Butenuth, M., Frey, D., Nielsen, A. A., & Skriver, H. (2011).
Infrastructure assessment for disaster management
using multi-sensor and multi-temporal remote sensing
imagery. International Journal of Remote Sensing,
32(23), 8575–8594.
https://doi.org/10.1080/01431161.2010.542204

Frey, D., & Butenuth, M. (2009). Multi-Sensorial Imagery
for Near-Realtime Disaster Management, XXXVIII, 3–4.
Nordhaus, W. D. (2006). Geography and macroeconomics:
New data and new findings. Proceedings of the National
Academy of Sciences of the United States of America,
103(10), 3510 LP-3517. Retrieved from http://www.
pnas.org/content/103/10/3510.abstract

Indicators

11.5.1
Number of deaths, missing
persons and persons affectedby disaster per 100,000
peopleCustodian agency

11.5.2
Direct economic loss
in relation to global
GDP, damage to criticalinfrastructure and numberof disruptions to basic
services, attributed todisasters

UNISDR

Tier

II

Status of step-by-step methodology document on
the metadata repository

Published

II
Published

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the
EO record?

Overall EO relevanceNot supported by EO
Comments to support criteria

Damage to criticalinfrastructure and disruption
to services can be directly
mapped from EO while GDP
impact can only be inferredfrom such visible damage.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

105

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 11.6
By 2030, reduce the adverse per capita environmental
impact of cities, including by paying special attention to air
quality and municipal and other waste management
How can EO be used to help countries achieve the target?
Minimising the per capita environmental impact of cities
is challenged by traffic congestion, lack of funds to provide
basic services, a shortage of adequate housing, declining
infrastructure and rising air pollution within cities. Thistarget covers aspects of waste generated by cities andaims to reduce the amount of solid waste generated andair polluted, while encouraging better waste management.
Therefore EO can be used in three major aspects of thistarget – the spatial mapping of cities and the sources ofpollution, the identification and treatment of waste in andaround cities and in planning better waste management forper capita pollution reduction.
Firstly, satellite observations of human settlement are
increasingly more sophisticated allowing the impact of
cities to be assessed based on their spatial extent anddensity. EO-derived maps of cities allow the size, shape andother metrics of urban setting (e.g. urban population) to bemonitored from which likely environmental impact could
be inferred. Within cities there are identifiable sourcesof aerosol emissions such as power plants and various
industrial processes. These sources generate significant
amounts of particulates, e.g. fine particulate matter(PM2.5), which can have adverse effects on human health.
Remote sensing of dry PM2.5 mass concentration near the
ground is now feasible. In addition to particulates, tracegases that affect air quality are now routinely monitored
over large urban areas.
In addition to air pollution, solid waste management canbe supported in cities by using EO as a tool to evaluate theimpact of different phases of the waste cycle. In particular,very high resolution EO has been shown to be effectivein the detection of illegal waste disposal sites throughvisual image interpretation and classification as well as themonitoring of the spread of municipal landfill sites using
multi-temporal thermal Landsat imagery.
Current Indicator(s):11.6.1 Proportion of urban solid waste regularly collected
and with adequate final discharge out of total urban
solid waste generated, by cities
11.6.2 Annual mean levels of fine particulate matter (e.g.
PM2.5 and PM10) in cities
Potential new indicator(s) based on EO:
The number of illegal waste sites identified and eradicatedin cities

TARGET 11.6

Short methodological guidelines illustrated
with EO best practice examples
Indicator 11.6.2
Computation method
For indicator 11.6.2 the annual urban mean concentrationof PM2.5 is estimated by modelling satellite remotesensing data with ground measurements from the 2016
WHO ambient (outdoor) air quality database, which serves
for calibration of the satellite data (WHO, 2016). EOderived aerosol optical depth data are typically retrieved
at low spatial resolutions, e.g. Aerosol Optical Depth
product from VIIRS at 750m resolution (NOAA STAR,
2018). Derived products such as particle size describe
the nature of the aerosol and can be used to estimate
the size of the particulate matter (e.g. <2.5µg). Annual
mean concentrations of particulates over cities are then
combined with the corresponding number of inhabitants
to derive the population-weighted exposure to particulatematter in cities.
Treatment of missing values
Missing values are currently excluded from the regionaland global averages. However, there is potential to use
models (e.g. the Copernicus Atmosphere Monitoring Service
outputs from the ECMWF) to fill data gaps.
Sources of discrepancies
Pixel-based satellite measurements and cell-based
chemical transport modelling are fundamentally different
approaches and vary in their ability to capture different
microscale features that may be reflected in the groundmeasurements. All three sources of data will be subject toerror, which may not be consistent with each other. The
variance in spatial resolution between ground monitors(point locations) and estimates from satellite and chemicaltransport models (grid cells) has led to the use of spatiallyvarying coefficient models, which are often referred to as
downscaling models (Shaddick et al., 2018).
Limitations
Ground station measurements of air particulates are needed
to validate the EO-derived measurements, therefore thisindicator remains partly measurable with EO.
Key messages for countries on EO contribution to thecomputation method
ir quality is a relatively well established area of EO• Aapplication,in particular in the detection of aerosols fromfossil fuel combustion – both industrial and domestic
sources in cities

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

106

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.6

he indicator methodology already integrates EO withhe estimate of per capita impact requires the total• Tground• Tnumber
data to estimate levels of air pollution over cities
of city inhabitants to be accurately estimatedbut as the method relies on ground data for validationand ground level air pollution levels, it is only partly
measurable with EO

so that the indicator can be correctly computed. Suchinformation should be readily available at the national
level.

Data sources
Data category

Source satellite data

Data sources

WebsiteGOSAT-2

http://global.jaxa.jp/activity/pr/brochure/files/sat38.pdf

Sentinel- 5P

https://sentinel.esa.int/web/sentinel/missions/sentinel-5p/
data-products

MODIS Aerosol Producthttps://modis.gsfc.nasa.gov/data/dataprod/mod04.php

Sentinel-3 Aerosol Optical Depth https://sentinel.esa.int/web/sentinel/technical-guides/sentiProductnel-3-olci/level-2/aerosol-optical-thickness
Operational orcommercial services

The Copernicus atmospheremonitoring service

https://www.ecmwf.int/en/about/what-we-do/environmental-services/copernicus-atmosphere-monitoring-service

Reference List
NOAA STAR. (2018). STAR Product Catalog. Retrieved
July 23, 2018, from https://www.star.nesdis.noaa.gov/
portfolio/productListings.php

WHO. (2016). Ambient air pollution: a global assessmentof exposure and burden of disease, WHO Geneva.
Geneva.

Shaddick, G., Thomas, M. L., Green, A., Brauer, M., vanDonkelaar, A., Burnett, R., … Prüss-Ustün, A. (2018). Data
integration model for air quality: a hierarchical approachto the global estimation of exposures to ambient air
pollution. Journal of the Royal Statistical Society. Series
C: Applied Statistics, 67(1), 231–253.
https://doi.org/10.1111/rssc.12227

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

107

TARGET 11.6

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

11.6.1
Proportion of urban solid
waste regularly collected andwith adequate final dischargeout of total urban solid waste
generated, by cities

Custodian agency

11.6.2
Annual mean levels of fineparticulate matter (e.g.
PM2.5 and PM10) in cities

UN-Habitat; WHO

Tier

II

Status of step-by-step methodology document on
the metadata repository

Published

I
Published

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the
EO record?

Overall EO relevanceNot supported by EO
Comments to support criteria

EO based methods for fineparticulate matter detection
over cities are routine andoperational

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

108

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 11.7
By 2030, provide universal access to safe, inclusive andaccessible, green and public spaces, in particular for womenand children, older persons and persons with disabilities
How can EO be used to help countries achieve the target?
Green and public spaces in cities are important for humanwell-being and economic development and should besustainable spaces for all to use equally. Increasingly
there are inadequate, poorly designed, or privatized public
spaces in cities that generate exclusion and marginalization
for inhabitants, especially those who are vulnerable. Thistarget addresses the drastic reduction in the quality ofgreen and public space in cities and seeks to make themsafe and inclusive for all regardless of gender, age or levelof mobility. Public space is made of streets and green andopen spaces in public use. EO can help countries to achieve
this target because it is a useful tool to establish theextent of urban areas as well as to do an inventory of open
space in cities, especially green open space. The challenge
in using EO to complete the target will be in discerning
what private and public space is as they will have the same
spatial characteristics from an EO point of view. Furthermore
the notion of access is complicated as it implies freedom of
movement and this not a practically measurable quantity
from EO. Therefore while certain aspects of the target canbe planned for using EO, much will depend on other insitu data sources or local ancillary data such as cadastral
records, land use or basic topography.
Current Indicator(s)11.7.1 Average share of the built-up area of cities that isopen space for public use for all, by sex, age andpersons with disabilities
11.7.2 Proportion of persons victim of physical or sexual
harassment, by sex, age, disability status and placeof occurrence, in the previous 12 months

TARGET 11.7

Short methodological guidelines illustrated
with EO best practice examples
Indicator 11.7.1
Computation method
The computational method for indicator 11.7.1 was still
under discussion and not yet finalised and published at thetime of the analysis. Much of the discussion has focused ona clarification of terms, e.g. of ‘built-up area’. Neverthelessthe method for the estimation of area of public space,according to the work plan, will consist of three steps: a)spatial analysis to delimit the built-up area of the city; b)estimation of the total open public space and; c) estimationof the total area allocated to streets. EO is mentioned in
the methodology as a potential source of data in step 1 tomap built up area (urban delimitation), in combination withthe methodology “National Sample of Cities” proposed by
UN-Habitat1 . Land use maps can be used to identify the
public space within the built up area. Estimation of theland allocated to street is based on an in situ sampling
regime or, as proxy, using of open datasets (e.g. OSM)
when available and EO is not foreseen as a method here.
Limitations
The main limitation expected will be in the acquisition of
optical imagery at the required spatial resolution to delineate
open space within ‘locales’. There are some small green
open spaces (local parks) that cannot be distinguished dueto the limited spatial resolution of free and open satellite
images. The possibility of small urban space detection
increases with VHR imagery. However such imagery israrely free and would put the burden on city authorities topurchase expensive imagery. The variance in definition ofopen space between cities (and countries) will also be achallenge for an EO-based approach.
Key messages for countries on EO contribution to thecomputation method
methodology is in development but it is likely that
• TEOhiswill
play a role in the computational method. The
spatial resolution of images is a key factor to use themin 11.7.1 calculation, the higher the resolution, the betterthe classifications can be to differentiate green, built-up
and street areas.
need to make a choice between the delimitation• Countriesof built up area from EO imagery by themselves or to use
existing EO products

1 https://unhabitat.org/national-sample-of-cities/

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

109

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.7

ery high resolution EO imagery to delineate the built-up
O will not be used to quantify the amount of open,• Varea• Epublic
and small green areas will obligate countries to pay
space within the built up area and the estimation
for commercially acquired imagery. Although at the edgeof applicability in an urban context with 10m resolution,
Sentinel-2 images are free of charge. A world-wide dataarchive is currently being built up.
ountries can use ready-to-use products, such as the EO• Cderived
GHSL or GUF listed above, in which case it is notnecessary to acquire any new satellite images.

of area covered by streets which is more feasible using
local land use and urban datasets
eadily available and global EO products are already• Rbeingused for built up area mapping, e.g. as part of
the GEO Human Planet initiative, but the definition of
built up area has not yet been finalised in the indicatormethodology and it may differ from the definition usedin these data products

Data sources
Data category
Global/regional
datasets

Software, tools andplatforms
Operational orcommercial services

Data sources

WebsiteThe Global Urban Footprint (GUF)/ World Settlement Footprint
https://urban-tep.eu(WSF)
The Global Human Settlement
Layer (GHSL)

https://ghsl.jrc.ec.europa.eu/data.php

Night time lights data

https://sos.noaa.gov/datasets/nighttime-lights/

Urban Thematic Exploitation
Platform

https://urban-tep.eu

Copernicus Services: Urban Atlas

https://land.copernicus.eu/local/urban-atlas

Copernicus High Resolution
Layershttps://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness

Reference List
Esch, T., Marconcini, M., Felbier, A., Roth, A., Heldens, W.,
Huber, M., … Dech, S. (2013). Urban Footprint ProcessorFully Automated Processing Chain Generating Settlement
Masks From Global Data of the TanDEM-X Mission. IEEE
Geoscience and Remote Sensing Letters, 10(6), 1617–
1621. https://doi.org/10.1109/LGRS.2013.2272953

Pesaresi, M., Blaes, X., Ehrlich, D., Ferri, S., Gueguen,
L., Halkia, M., … Zanchetta, L. (2013). A Global Human
Settlement Layer From Optical HR/VHR RS Data: Concept
and First Results. IEEE Journal of Selected Topics inApplied Earth Observations and Remote Sensing, 6(5),2102–2131.
https://doi.org/10.1109/JSTARS.2013.2271445

Lee, H. (2018). Quantifying public spaces for better
quality of urban assets. World Bank Group Sustainable
Cities Blog, 10/24/2018, https://blogs.worldbank.org/
sustainablecities/quantifying-public-spaces-betterquality-urban-assets.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

110

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 11.7

Planning for target 11.7: Earth Observation for Transforming Cities
through Public Spaces
VHR satellite imagery is being used
in a framework to examine howurban green and open areas (GOA)
can transform urban environments bypromoting inclusive green growth andenhance liveability in megacities. This
framework has been developed by
the World Bank Group UrbanScapesTeam which has initiated an Advisory
Services and Analytics (ASA) activity
to support ongoing investment
in operations on the ground. The
framework is being piloted in Karachi(Pakistan) and Dhaka (Bangladesh,fig.1). Overall cooperation under this
ASA aims to:(i) Develop an enhanced diagnostic on
the nature of public spaces, and theopportunities and challenges;(ii) Provide a concrete body ofevidence on public spaces-related

policies and programs, for the
Bank operations to assist cities
with strategic advice and inputs topublic spaces;(iii) Gain a better understanding of
the state and problems of urban
public spaces, focusing on selected
cities, and to identify future
investments and implementation
strategy;(iv) 
Provide a platform for related
knowledge exchange and policy
dialoguewithpractitioners,
academics, and clients.
As part of this program, theUrbanScapes multidisciplinary teamcooperated with GISAT – a remotesensing company, on developing aseries of analytical projects focusedon characterizing land use andidentification of public municipal
assets (e.g. public spaces) using highresolution satellite imagery, and tobetter define intervention areas during
project preparation. The EO part ofthe project is supported by the EO4SD(Earth Observation for Sustainable
Development) ESA initiative andbuilds on previous successful ESAWorld Bank collaborations. These
joint projects aim to achieve a stepchange in the use of satellite-based
environmental information in theWorld Bank’s regional and global
programs. Although the project isbeing piloted at two locations in the
preparation phase, there is potential
for streamlining the approach to
future World Bank UrbanScapes
projects (Lee, 2018).

Figure 16: Very high resolution satellite
imagery has been used in cooperation
between the World Bank and ESA EO4SD
Urban project to promote urban green growth.
An example map of Green and Open (GOA)
areas is shown here for one of the pilots Dhaka, Bangladesh.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

111

TARGET 11.7

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

11.7.1
Average share of the built-up
area of cities that is open
space for public use for all,by sex, age and persons withdisabilities

11.7.2
Proportion of persons
victim of physical or sexual
harassment, by sex, age,disability status andplace of occurrence, in theprevious 12 months

UN-Habitat

UNODC

Custodian agency
Tier

II

II

Status of step-by-step methodology document on
the metadata repository

Work plan only

Not published(Tier III at the time of theanalysis)Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the
EO record?

Overall EO relevanceComments to support criteria

EO is useful to map open andgreen spaces within urban
areas. EO is very limited
in mapping socioeconomic
variables associated with thisindicator.

Not supported by EO

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

112

GOAL 13COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 13.1
Strengthen resilience and adaptive capacity to climaterelated hazards and natural disasters in all countries

TARGET 13.1

Short methodological guidelines illustrated
with EO best practice examples
Indicator 13.1.1

How can EO be used to help countries achieve the target?

Computation method

This target is complementary to the global targets of the
Sendai Framework for Disaster Risk Reduction, specificallytargets A and B. The definition of hazard, according tothe open-ended intergovernmental expert working groupof the UNISDR (United Nations Office for Disaster Risk
Reduction (UNISDR) and United Nations General Assembly(UNGA), 2016), is “a process, phenomenon or humanactivity that may cause loss of life, injury or other healthimpacts, property damage, social and economic disruption
or environmental degradation”. Hazards may be natural,
anthropogenic or socio-natural in origin.

The relevant global indicators for the Sendai Framework will
be used to report for this indicator.

EO can be used by countries in two ways, both in planningfor the target, though a more robust system of identifying,monitoring and preparing for climate related hazards andnatural disasters and in achieving the target, throughimproved resilience to disasters through ecosystem-based
adaptation strategies. For the former, EO is a powerfulmonitoring technology to track natural, anthropogenic
or socio-natural hazards on the land or sea surface.
Populations in the path of disasters can be prepared andalert to disasters before they occur if EO is used effectively
in an early warning system, e.g. in tracking hurricanes
approaching coastlines, tracking wildfires near humansettlements or in detecting terrain movements prior tovolcanic eruptions. For the latter, strengthening resilienceand adaptive capacity to disasters requires longer termplanning. Ecosystem-based approaches to climate change
adaptation are included in many disaster risk reduction
strategies as they provide a natural buffer to hazards
while providing other ecosystem services to surrounding
communities. EO is useful as a national planning tool
for the target in ecosystem-based adaptation to natural
disasters because it can map the extent of such ecosystems
as well as potential areas for increasing the extent andcomposition of these ecosystems to strengthen resilience.
For example coastal ecosystems such as mangroves arereadily mapped through EO and can be conserved andresorted to strengthen resilience to coastal hazards suchas storm surges and wave damage.
Current Indicator(s):13.1.1 
Number of deaths, missing persons and directly
affected persons attributed to disasters per 100,000
population
13.1.2 
Number of countries that adopt and implement
national disaster risk reduction strategies in linewith the Sendai Framework for Disaster Risk
13.1.3 
Proportion of local governments that adopt andimplement local disaster risk reduction strategies inline with national disaster risk reduction strategies

Indicator 13.1.1. is calculated as a summation of related
indicators (deaths, missing people, and affected people)
from national disaster loss databases, divided by the total
population of the area concerned, derived from relevant
global or national databases (e.g. World Bank or UN Statistics
information). Affected people, whether directly or indirectly,
are defined as those that “experience short-term or longterm consequences to their lives, livelihoods or health andto their economic, physical, social, cultural and environmental
assets. In addition, people who are missing or dead may
be considered as directly affected” (United Nations Office forDisaster Risk Reduction (UNISDR) and United Nations General
Assembly (UNGA), 2016).
Although EO is not mentioned as a potential source of data for
the indicator, an EO-based approach could be used to estimate
the extent of area impacted by the disaster from which theimpact to “physical or economic assets” could be inferred,
according to the UNISDR definition above. Census data would
be required for the area’s population while counting in-situ
the number of deaths, missing persons and directly affected
persons. To be consistent with the indicator, a conclusion on
the number of affected persons would have to account for
those indirectly and directly affected by the disaster. For this
distinction, the UNISDR proposes the estimation of “directly
affected” as a proxy for the number of affected. Directly
affected are those “People who have suffered injury, illness
or other health effects; who were evacuated, displaced,
relocated or have suffered direct damage to their livelihoods,
economic, physical, social, cultural and environmental assets”.
This could be feasibly estimated by overlaying a population
density map on a disaster zone map derived from high to very
high resolution EO data, perhaps stratifying zones into areas
with the highest to the lowest impact based on variables
for proximity to the disaster, vegetation cover, slope andtopographic factors etc. Clearly, the type of disaster would
determine the parameterisation of such a model.
There are also limits to the type of disaster that could
be monitored with EO but the main applications would
be in earthquakes, in particular using SAR interferometry
for ground motion detection, e.g. from Sentinel-1 (ESA,
2015, 2018b), COSMOS-Skymed or TerraSAR-X data;
Sentinel-1 for floods (Twele et al., 2016) and tropical
cyclones/storm surges (Guo, 2010). Newer applications
have shown the potential of EO for monitoring dangerous
heat waves (or urban heat islands) , e.g. from MODIS Land
Surface Temperature (Clinton & Gong, 2013) and dangerous
emissions from natural sources such as wildfires (Zielinski
et al., 2016) and volcanoes (Prata et al., 2015). Other

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

114

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

disasters that could be directly monitored with EO are
wildfires (ESA, 2018a), industrial fires, drought (Sinergise,2018), ice (sea ice and icebergs), landslides (Casagli et al.,2016), oil spills and volcanoes (Rothery, 1992; CEOS, 2015).
Limitations
Use of multi-sensor/satellite information will introduce
errors and potentially gaps where certain sources of imagery
don’t exist. There are limitations on the use of remotelysensed EO data for landslide studies including the high costof VHR imagery for detailed geomorphologic mapping andthe size of ground deformations in relation to the capacityof radar interferometry to detect motion. The use of polarand geostationary orbiting weather satellites for volcano
monitoring (ash clouds, toxic emissions, heat etc.) is limited
by cloud cover (meteorological cloud as well as volcanic
cloud), reduced capability at night and limited ability to detect
small-scale events (meteorological satellites have very coarse
resolutions >1km) (Rothery, 1992). In addition the polar
regions have less coverage than the rest of the planet.

TARGET 13.1

Key messages for countries on EO contribution to thecomputation method:
here are a wide variety of disaster impacts which EO can• Tmonitor,
especially those that produce material damage,such as earthquakes and hurricanes. Longer-term, slower
onsets disasters such as urban heat island impacts anddrought can be monitored remotely but their impact is lesseasily quantified.
his indicator requires a quantification of the number of• Tdirectly
affected people as well as deaths and missing
persons. For these accurate head counts are required whichcannot be readily by EO methods.
EO can be used to map the extent and intensity• Hofowever
impact of the affected area and theoretically population
density maps could be overlaid to infer the numbers of
those directly affected by a disaster while missing person
would remain unquantifiable.
ven with an EO-based method, for accurate reporting of• Ethis
indicator, ground based methods would still be needed
to count the numbers of persons in the different categories
within and around the disaster zone

Data sources
Data category

Data sources

WebsiteSoftware, tools andplatforms

The ESA Thematic Exploitation
Platform (TEP) on Geohazards

https://geohazards-tep.eu

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

115

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 13.1

Reference List
Casagli, N. et al. (2016) ‘Landslide mapping andmonitoring by using radar and optical remote sensing:
Examples from the EC-FP7 project SAFER’, Remote
Sensing Applications: Society and Environment. Elsevier,4, pp. 92–108. doi: 10.1016/j.rsase.2016.07.001.
CEOS (2015) CEOS Earth Observations Handbook on
satellite Earth Observations in support of Disaster Risk
Reduction. Available at: http://www.eohandbook.com/
eohb2015/ (Accessed: 25 April 2018).
Clinton, N. and Gong, P. (2013) ‘MODIS detected surface
urban heat islands and sinks: Global locations andcontrols’, Remote Sensing of Environment, 134, pp. 294–
304. doi: https://doi.org/10.1016/j.rse.2013.03.008.
ESA (2015) Nepal Earthquake on the Radar, Copernicus.
Availableat:
http://www.esa.int/Our_Activities/
Observing_the_Earth/Copernicus/Sentinel-1/Nepal_
earthquake_on_the_radar (Accessed: 23 July 2018).
ESA (2018a) Forest Fires, Sentinel online. Available at:https://sentinels.copernicus.eu/web/sentinel/thematicareas/emergency-management/forest-fires (Accessed:
23 July 2018).

Sinergise (2018) Drought monitoring, Sentinel Hub.
Available at: https://www.sentinel-hub.com/explore/
industries-and-showcases/use-cases-drought-monitoring(Accessed: 23 July 2018).
Thomas, N. et al. (2017) ‘Distribution and drivers ofglobal mangrove forest change, 1996–2010’, PLOS ONE.
Public Library of Science, 12(6), p. e0179302. Available
at: https://doi.org/10.1371/journal.pone.0179302.
Twele, A. et al. (2016) ‘Sentinel-1-based flood mapping:a fully automated processing chain’, International
Journal of Remote Sensing. Taylor & Francis, 37(13), pp.
2990–3004. doi: 10.1080/01431161.2016.1192304.
UN Environment Programme (2016) Ecosystem-based
approaches to disaster risk reduction and climate change
adaptation. Available at: https://europa.eu/capacity4dev/
unep/blog/ecosystem-based-approaches-disaster-riskreduction-and-climate-change-adaptation (Accessed: 26
June 2018).

ESA (2018b) Sentinel-1 provides support in Taiwan’s
earthquake, Earth Online. Available at: https://earth.esa.
int/web/guest/content/-/article/sentinel-1-providessupport-in-taiwan-s-earthquake (Accessed: 23 July 2018).

United Nations Office for Disaster Risk Reduction(UNISDR) and United Nations General Assembly (UNGA)(2016) Report of the open-ended intergovernmental
expert working group on indicators and terminology
relating to disaster risk reduction, UNGA Seventy-firstsession, Agenda item 19 (c), Sustainable development:
disaster risk reduction. doi: https://www.preventionweb.
net/files/50683_oiewgreportenglish.pdf

Guo, H. (2010) ‘Understanding global natural disasters
and the role of earth observation’, International Journalof Digital Earth. Taylor & Francis, 3(3), pp. 221–230. doi:
10.1080/17538947.2010.499662.

Zielinski, T. et al. (2016) ‘Impact of wild forest fires
in Eastern Europe on aerosol composition and particle
optical properties’, Oceanologia, 58(1), pp. 13–24. doi:
https://doi.org/10.1016/j.oceano.2015.07.005.

Prata, F. et al. (2015) ‘Remote sensing of gas emissions
from volcanoes’, in Monitoring Volcanoes in the North
Pacific: Observations from Space. Berlin, Heidelberg:
Springer Berlin Heidelberg, pp. 145–186. doi:
10.1007/978-3-540-68750-4_6.

Further Reading and Resources:
Space-based information for disaster management andemergency response
http://www.un-spider.org/

Rothery, D. A. (1992) ‘Monitoring and warning of volcanic
eruptions by remote sensing’, in McCall, G. J. H., Laming,
D. J. C., and Scott, S. C. (eds) Geohazards: Natural andman-made. Dordrecht: Springer Netherlands, pp. 25–32.
doi: 10.1007/978-94-009-0381-4_4.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

116

TARGET 13.1

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

13.1.113.1.213.1.3

Number of deaths,
missing persons
and directly affected
persons attributed
to disasters per
100,000 population

Number ofcountries that
adopt and
implement national
disaster risk
reduction strategies
in line with the
Sendai Frameworkfor Disaster Risk

Proportion of local
governmentsthat adopt andimplement local
disaster risk
reduction strategies
in line with national
disaster risk
reduction strategies

Custodian agency

UNISDR

Tier

II

II

II

Status of step-by-step methodology documenton the metadata repository

Published

Published

Not published(Tier III at the timeof the analysis)

Number of directly
affected people could
be inferred from
EO-based maps ofthe extent of affectedarea in combination
with population data

Not supported by
EO

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in
the EO record?

Overall EO relevanceComments to support criteria

Not supported by EO

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

117

GOAL 14

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 14.1
By 2025, prevent and significantly reduce marine pollutionof all kinds, in particular from land-based activities,
including marine debris and nutrient pollution
How can EO be used to help countries achieve the target?
This target addresses the need to reduce marine pollution
by recognising the land-based sources that emit pollutants
such as nutrients and plastic debris. It is therefore aninterconnected target that seeks to join land and sea
based approaches to pollution reduction and prevention.
In setting a deadline for achievement of this target by2025, the community must act quickly towards global
pollution reduction. EO is useful in relation to this target
because it has both land, sea and coastal coverage thereby
enabling integrated monitoring, e.g. of land based debris
which accumulates on shorelines before being transported
seaward. Equally, EO can monitor the location and extent
of inland waterways, including their water quality as
mentioned in relation to SDG 6, enabling the transport ofland based, water-dissolved pollutants such as excessive
nutrients to be monitored. At sea, the detection of surface,coarse marine debris is an experimental area for EO but
with increasing sophistication this technique could yield
results in being able to map large debris fields and plot
their movement for subsequent intervention and clean up.
Evaluation of coastal eutrophication status, anomalies andtrends is a challenging but evolving application of EO andcontributes to the land-based pollution reduction aspect of
this target.
Current Indicator(s)14.1.1 Index of Coastal Eutrophication and floating plastic
debris density

Short methodological guidelines illustrated
with EO best practice examples
Indicator 14.1.1
Computation method
Due to the complexity of this indicator, the methodology
will be divided into multiple primary and secondary subindicators. The data for the sub-indicators will be based on
two levels of data:
1. Global level data derived from satellite
observations, and
2. National and regional in situ data.

TARGET 14.1

The identification and development of secondary subindicators is currently in progress. Information about two
potential sub-indicators is outlined below.
Sub-Indicator 1: Index of Coastal Eutrophication
Provisional sub-indicator 1.1: Surface water chlorophylla as a proxy for phytoplankton biomass (chlorophyll atrends, anomalies and annual maximum)
Sub-Indicator 2: Floating plastic debris density
Provisional sub-indicator 2.1: Beach litter
14.1.1 Provisional sub-indicator 1.1: Surface Chlorophyll-aconcentration as an indicator of phytoplankton biomass
Chlorophyll-a is a pigment contained in plants, algae
and phytoplankton that provides a proxy indicator for
eutrophication. It can be measured by EO using ocean
colour radiometry sensors. Data on Chlorophyll-a isfreely accessible on a number of data portals, databases and satellite missions (see data sources) (ESAa,
2018). These data sources can be used to identify
satellite Chlorophyll-a data for the national waters underconsideration but the use of the satellite Chlorophyll-a forreporting on this indicator is restricted because current
methods are designed for open ocean applications. As aresult they produce spurious results in optically complex
coastal (and inland) waters (see limitations section).
Chlorophyll-a trends should be monitored due toseasonal changes in phytoplankton growth, rainfall andoceanographic processes that impact surface nutrient
concentrations such as upwelling and stratification.
To use Chlorophyll-a as a proxy for eutrophication, national
Chlorophyll-a eutrophication threshold levels have to be
defined (i.e. the Chlorophyll-a levels in μg l-1 at which
eutrophication occurs in national waters). However, due
to the uncertainties inherent in EO retrievals, it would be
more realistic for this to be a percent change over timeof Chlorophyll-a concentrations rather than a specific ug/lvalue. These can be calculated using historical data,
modelling outputs and expert judgement. Historical data
for satellite-remote sensing derived Chlorophyll-a levels
is available (see data sources).
To determine Chlorophyll-a concentrations, the satellite
data should be analysed using appropriate algorithms forthe prediction of apparent optical properties of coastal
waters, as shown in a study by Zheng & DiGiacomo(2017). Initial in-situ measurements are required to
ground truth these algorithms. A number of software
packages, online toolboxes and web portals are available
to support the processing and analysis of satellite data
(see data sources). Chlorophyll-a concentrations willneed to be compared to time series data to identify
trends. Chlorophyll-a anomaly data can also serve asa proxy for harmful algal bloom occurrence/potential
eutrophication. This can be done for representative

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

119

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

water bodies which are then monitored and statisticalindicators are generated per water body, as per theapproaches of the EU’S Water Framework and Marine
Strategy Framework Directives.
14.1.1 Sub-indicator 2: floating plastic debris density,and provisional sub-indicator 2: beach litter
Floating plastic in coastal areas could be observed using
optical data (e.g. Sentinel-2 and Landsat 7/8), with the
caveat that the resolutions will be max 10m (for Sentinel-2)
and 30m (for Landsat), indicating that only large (>10m)
floating plastic and/or beach litter can be observed andeven still with large uncertainties. Therefore it is currently
impossible to implement a robust monitoring strategy formarine plastic debris using direct EO measurements.
However one potential application which is currently being
explored is the identification of distinct spectral signatureof plastic picked up from Sentinel-2 SLSTR and Sentinel-3
OLCI data. In addition the Research & Development
domain is working on a new generation of multi to hyper
spectral near infrared (NIR) & short wave infrared (SWIR)
sensors which are able to pick up the spectral signatureof micro plastic in the sea. These two latter activities,
together with detailed observation of the ocean dynamics
in boundary habitats such as frontal zones (known to be
hot spots of surface polymer aggregation), could eventuallyenable the monitoring of the floating plastic debris
density indicator. However these technologies are not yet
operational. As an alternative solution, efforts should focus
on a robust characterization of the cycle of marine plastic,
with EO being used to measure parameters at key points inthe cycle where it is most suited. For example, one of themain entry points for plastic waste is rivers. However thisrequires an assumption of the percentage of total wastethat is plastic. EO data could then inform models of riverdischarge, as suggested for indicator 6.6.1 (sub-indicator3), form which plastic volume supplied to oceans could be
inferred.
Chlorophyll-a data can be obtained from a number of opticalsatellite missions, including:Sentinel-2 MSI1 	EU Copernicus satellite, supporting land
and monitoring studies, including themonitoring of vegetation, soil and water
cover, as well as observation of inland
waterways and coastal areas.

TARGET 14.1

Meteorological Satellites). Data available
from 2016. Global coverage, max
spatial resolution 300m, orbit cycle 27
days. Currently 1 satellite but shortly
become a constellation of 2. The twoin-orbit SENTINEL-3 satellites enablea short revisit time of less than twodays for OLCI (Ocean and Land Colour
Instrument).
ASTER-Terra	NASA satellite carrying the Advanced
Spaceborne Thermal Emission and
Reflection Radiometer (ASTER). Global
coverage. Data available form 1999.
MODIS-Aqua	NASA satellite carrying a Moderate
Resolution Imaging Spectroradiometer(MODIS). Global coverage, max spatial
resolution 300m. Data available from2002.
Landsat 8	NASA satellite. Launched in Feb 2013.
Carrying the Thermal Infrared Sensor(TIRS) with 100m spatial resolution,and the Operational Land Imager (OLI)
with 30m spatial resolution.
Archive satellite data sources:
SeaWiFS	
Sea-Viewing Wide Field-of-View Sensor. NASAsatellite, no longer operational. Global coverage,
spatial resolution 1.1km, historical data are
available covering the period 1997-2010.
CZCS	Coastal Zone Color Scanner. NASA satellite, nolonger operational. Global coverage, max spatial
resolution 800m, historical data available covering
period 1978-1986.
Treatment of missing values
Missing values (e.g. due to cloud cover) can be estimatedusing models (hydrodynamic or mathematical gap filling
procedures). In the absence of modelling, statisticalmeasures could be taken, e.g. of monthly and seasonal
average of the water body. This would ensure issues suchas missing pixels are effectively corrected for and enable
the compilation of year on year relative changes
Sources of discrepancies

Sentinel-3 OLCI	EU Copernicus satellite(s), launchedby ESA (European Space Agency) andoperated by EUMETSAT (European
Organisation for the Exploitation of
1 Some combination of S2 and S3 is needed – S2 channels are
not optimized for Chlorophyll-a retrieval but S2/S3 fusion can enable extension of S3 measurements into areas requiring higher
resolution data such as estuaries and lagoons

For 14.1.1 (sub-indicator 1), remote sensing sensors detect
Chlorophyll-a based on its absorption signal, meaning thata constant value between absorption and concentrations(called specific absorption coefficient) is assumed. Howevereach phytoplankton species are present in different
oceans and seas, resulting in distinctly different bloom
seasons in many waterbodies. This requires different
specific absorption coefficients, depending on the coastal

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

120

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

setting. This modification of the coefficients for different
groups of phytoplankton species could introduce unwanted
discrepancies when comparing Chlorophyll-a retrievals at
the global level.
Limitations
For 14.1.1 (sub-indicator 1), the availability of ocean colour
data from optical satellite imagery is dependent on theabsence of clouds. However this is less of a problem thanfor other surface parameters as algal bloom growth isslower in cloudy conditions due to the lower light intensity.
Nevertheless, a merged multi-sensor satellite remotesensing Chlorophyll-a dataset is available, covering theperiod 1997 to now. The sensors included in this merged
product are: SeaWIFS, MERIS, MODIS, VIIRS and OLCI-A.
Where data gaps exist, these can be filled by modelling or
estimates based on data aggregates.
The achievable accuracy of the estimation of chlorophyllfrom optical satellite imagery depends greatly from the typeof waters considered. In a most common classification, sea
waters are classified as Case 1 and Case 2 waters (Morel& Bélanger, 2006). Case 1 waters belongs mostly to open
sea areas or to oligotrophic coastal areas, in such areas theaccuracy that can be achieved by EO data is very high. Case
2 waters are sediments dominated waters: for such waters
the challenge is to separate the contribution to the measured
signal of the water turbidity and of the atmosphere, andwithin the former to distinguish the component related tothe chlorophyll (phytoplankton). Different algorithms exist
which rely on different approaches to measure chlorophyll
for Case 2 waters (for example, Moses et al., 2009) and in
most cases while it is difficult to measure absolute valuesof chlorophyll with high accuracy, it is possible to find/evaluate trends and anomalies. For both Case 1 and Case2 algorithms, the availability of reliable in situ measures/
sampling to be used for calibration and validation isessential to achieve a high accuracy in the chlorophyllmeasurement.
Without measurement of further eutrophication parameters,
it is not possible to determine whether Chlorophyll-aconcentrations are linked to an (anthropogenic) increase innutrients. Therefore, where possible, Chlorophyll-a should
be supplemented by monitoring further eutrophication

TARGET 14.1

parameters, by means of traditional at sea sampling/measurements and/or by using of models which are able tomerge different information (also satellite ones) to predict
with good accuracy values of parameters like nitrates or
dissolved oxygen. In addition, for many coastal areas itis necessary to understand the local ocean conditions(currents, temperature etc.) to understand what is driving
Chlorophyll-a concentration variations.
For 14.1.1, (sub-indicator 2), there are serious limitations towhat is possible to directly measure from EO. Therefore, as
the bulk of marine plastic debris at sea originates on land,
EO methods will focus on quantifying the land contributionof plastic debris, e.g. through river outflow.
Key messages for countries on EO contribution to thecomputation method
his indicator is composed of two sub-indicators, on• Tcoastal
eutrophication and floating plastic debris, forwhich provisional sub-indicators have been agreed until
the sub-indicator methodologies have been finalised, due
for roll-out by 2021.
he agreed indicator for 14.1 is the Index of Coastal• TEutrophication(ICEP); the ICEP methodology is currently
being developed; it is based on concentrations and ratiosof nitrogen, phosphorous and silica, which currently
cannot be directly measured by remote sensing.
he provisional sub-indicator 1 methodology, based on• Tchlorophyll-a,does mention EO as a possible source ofdata. Furthermore, eutrophication can also be monitored
remotely through images of harmful algal blooms andcoloured dissolved organic matter.
n coastal and inland waters chlorophyll-algorithms and• Imethods
are all experimental and site specific. Thereis no global and universally applicable EO method for
Chlorophyll-a in the coastal zone.
any countries do not have validation data for satellite• Mchlorophyll
and absolute values of Chlorophyll-a (ug/l)will not be accurate. Hence a % change measure isrecommended to report on the indicator.

Data sources
Data category

Source satellite data

Data sources

WebsiteLandsat 8

https://earthexplorer.usgs.gov/

Sentinel data (1,2 and 3) fromthe Copernicus Open Access Hub

https://scihub.copernicus.eu/

ASTER-Terra

http://asterweb.jpl.nasa.gov/

MODIS-Aqua & Terra

https://oceancolor.gsfc.nasa.gov/

SeaWiFShttps://oceancolor.gsfc.nasa.gov/data/seawifs/

CZCS

https://oceancolor.gsfc.nasa.gov/data/czcs/

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

121

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Data category

Data sources

TARGET 14.1

WebsiteSeaDas (National Aeronautics andhttps://seadas.gsfc.nasa.gov/
Space Administration)

Software, tools andplatforms

Coastal Thematic Exploitation
Platform

https://www.coastal-tep.eu/portal

Sentinel Application Platform(SNAP)

http://step.esa.int/main/toolboxes/snap/

Ocean Virtual Laboratory

https://ovl-project.oceandatalab.com/home

Global Marine Information
Systemhttps://ec.europa.eu/jrc/en/scientific-tool/global-marine-information-system

NOAA (National Oceanic andAtmospheric Administration)
CoastWatch/OceanWatch

https://coastwatch.noaa.gov/cw_html/index.html

NASA (National Aeronautics andSpace Administration) OceanColor https://oceancolor.gsfc.nasa.gov/data/overview/Web

Operational orcommercial services

ChloroGIN data portal

www.chlorogin.org/index.php

GlobColour

http://www.globcolour.info

Copernicus Marine Environment
Monitoring Service (CMEMS)

http://marine.copernicus.eu

NASA Ocean Color

https://oceancolor.gsfc.nasa.gov/

Reference List
Beman, J. M., Arrigo, K. R. & Matson, P. A. (2005)
Agricultural runoff fuels large phytoplankton blooms invulnerable areas of the ocean, Nature. Nature PublishingGroup, 434(7030), pp. 211–214.

Morel, A., & Bélanger, S. (2006). Improved detection of
turbid waters from ocean colour sensors information.
Remote Sensing of Environment, 102(3-4), 237-249

ESAa (2018) Maritime Monitoring. ESA. [Online]
Available at: https://sentinel.esa.int/web/sentinel/userguides/sentinel-3-olci/applications/maritime-monitoring[Accessed 24th July 2018]

Moses, W. J., Gitelson, A. A., Berdnikov, S., & Povazhnyy,
V. (2009). Estimation of Chlorophyll-a concentration incase II waters using MODIS and MERIS data—successes
and challenges. Environmental Research Letters, 4(4),045005.)

European Environment Agency (2002) Remote sensing’s
contribution to evaluating eutrophication in marine
and coastal waters. Technical report 79. www.vliz.be/
imisdocs/publications/119746.pdf

OSPAR (2012) JAMP Eutrophication Monitoring
Guidelines: Chlorophyll a in Water. OSPAR Commission.
http://mcc.jrc.ec.europa.eu/documents/OSPAR/
Eutrophication_MonitoringGuidelinesch_%20a.pdf

Maximenko, N., Chao, Y. & Moller, D. (2016) Developinga remote sensing system to track marine debris. Eos 97.
https://doi.org/10.1029/2016EO061605

Zheng, G. & DiGiacomo, P.M. (2017) Remote sensingof Chlorophyll-a in coastal waters based on the lightabsorption coefficient of phytoplankton. Remote Sensing
of Environment 201: 331-341.
https://doi.org/10.1016/j.rse.2017.09.008

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

122

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 14.1

Reporting on coastal eutrophication in the Italian coastal zone using EO,
in support of indicator 14.1.1
In Italy the Ministry for the protectionof Environment and Sea (MATTM) is
responsible to provide law directives
for the SDG 14.1. For indicator 14.1.1,
related to coastal eutrophication,
the adopted benchmark values
are those indicated by the Italian
implementation of the European
Marine Strategy Framework Directive(MSFD) through the legislative decree
D.lgs 190/2010.
In particular the ministerial decree
D.M. of 17/10/2014 specifies theenvironmental targets to determine
the Good Environmental State (GES)
and the following ministerial decree
D.M. of 11/2/2015, identifies the
specific indicators to be measured
to evaluate coastal eutrophication.
In this way chlorophyll and nitrates

concentrations have been adopted as
key parameters for 14.1.1
Current practice is based on insitu measurements and samplings
performed regularly (usually monthly
or bi-monthly) by local environmental
authorities. Even if some pilots
services have been implemented
for evaluating the use of Earth
Observation data for the MSFD (inparticular chlorophyll and turbidity),
currently it is not routinely employedin the measurement of chlorophyll
due to these limitations: poor
spatial resolution of existing satellite
missions dedicated to measurementof ocean colour (ranging from 300m
to few kilometres) and the lack of an
extensive validation over the Italian
territory. A promising improvement

is currently being provided byhydrological and ecosystem models
that merges different kinds ofsatellite data and (when) available
in situ measurements to scale down
the former and enrich the waterparameters that can be evaluated.
Some implementations based onmodels can be found in the Copernicus
MarineEnvironment
Monitoring
Service (see data sources), which
provides measurements of nitrates
and chlorophyll. Such products
have been used by ISPRA (Superior
Institute for the Environmental
Protection and Research) to obtain
qualitative synergic views over the
Italian coasts (see Figure 17), butthey are not used as official sources
mainly due to their coarse spatial
resolution.

Figure 17: Geometric means of Nitrates concentration (left) and Chlorophyll-a concentration (right) for the years 2004-2009 obtained using products
from the Copernicus Marine Environment Monitoring Service (former MyOcean) over the Italian coasts.
Credit: The Italian position with respect to the 17 SDGs http://www.minambiente.it/sites/default/files/archivio/allegati/sviluppo_sostenibile/
posizionamento_Italia_SDGs_3.1_16012017_2.pdf

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

123

TARGET 14.1

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

14.1.1

Sub-Indicator 1: Provisional
Index of Coastal SubEutrophication
Indicator
1.1:
Surfacewater
chlorophyll

Custodian agency

Sub-Indicator 2:
Floating plastic
debris density

Provisional SubIndicator 2.1: Beachlitter

UN Environment

Tier

II

II

Status of step-by-stepmethodology document on themetadata repository

Unpublished(Tier III at
the time of
the analysis)

Unpublished(Tier III at
the time of theanalysis)

n/a

II

n/a

n/a

Unpublished(Tier III at
the time of theanalysis)n/aMaturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO
in indicator
guidelines

n/aTechnical capacity
required
Availability of
global EO data
Compliance with
Reporting calendar

Robustness ofproposedmethodology
Criteria

n/a

n/a

n/a

Sensitivity tochangeIs it scalable(spatial)?
Is there asubstitute forgaps in the EO
record?

Overall EO relevanceSee primary
EO is applicable
sub-indicators to this suband provisional indicatorsecondary subindicators forassessment
Comments to support criteria

EO is mentioned
as a potential
data sourcein the work
plan for thissub-indicator;
however, the EO
methodology
for measuring
floating
plastic debris
density is still
experimental.

This is being
considered as a subindicator that is usedprovisionally while
the methodology for
floating plastic debris
density is underdevelopment.
EO methodology formeasuring beach litteris still experimental.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

124

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 14.3
Minimize and address the impacts of ocean acidification,
including through enhanced scientific cooperation at alllevelsHow can EO be used to help countries achieve the target?
As the ocean’s biology and biochemistry is largely undersampled, this target presents a significant challenge forcountries hence the stated need to enhance scientificcooperation at all levels. Nevertheless, this enhanced
scientific cooperation should involve the remote sensing
community, at least at the target level. For instance, EO
can support countries in planning for and setting targets on
minimising ocean acidification, as part of a wider climatechange monitoring/management strategy. EO could help
countries with significant marine areas to identify areas
at risk from acidification and estimate their extent, e.g. ofwaters with aragonite close to its saturation level, below
which organisms find it more difficult to form and retain
their shells. EO can also be used as a diagnostic tool, e.g. to
map the impacts of ocean acidification on coral reefs. The
utility of the satellite measurements comes in obtaining a
synoptic view where few or no in situ measurements of the
carbonate system exist. Although EO is limited to the ocean
surface layer, these observations are important because
the change in carbonate chemistry due to atmospheric CO2
occurs in the ocean surface first.
Current Indicator(s)14.3.1 
Average marine acidity (pH) measured at agreed
suite of representative sampling station

Short methodological guidelines illustrated
with EO best practice examples
Indicator 14.3.1
Computation method
Indicator 14.3.1 adopts the International Panel on Climate
Change (IPCC) Workshop on Impacts of Ocean Acidification(OA) on Marine Biology and Ecosystems definition of OA as“a reduction in the pH of the ocean over an extended period,
typically decades or longer, which is caused primarily by
uptake of carbon dioxide from the atmosphere, but can also
be caused by other chemical additions or subtractions fromthe ocean.” The indicator computational method is based on
an in-situ sampling strategy in order to calculate the mean(monthly or annual) surface seawater pH and aragonite
saturation state, based on ocean acidification observations.
These observations must include: two parameters of the
carbonate system (Dissolved Inorganic Carbon, total pH,
pCO2, and total alkalinity), in situ seawater temperature,salinity, as well as relevant metadata, to be measured
at an agreed suite of representative sampling stations.

TARGET 14.3

An agreed suite of representative sampling stations aresites that: 1) have a measurement frequency adequate todescribe variability and trends in carbonate chemistry todeliver critical information on the exposure of and impacts
on marine systems to ocean acidification, and 2) provide
data of sufficient quality and with comprehensive metadata
information to enable integration with data from othersites in the country.
Although EO is not mentioned in the work plan, satellites
such as NASA’s Aquarius (no longer operational), NASA’s
salinity sensor SMAP, and ESA’s SMOS can measure ocean
salinity globally. Coupled with EO-derived sea surface
temperature (SST) measurements and surface chlorophyll-a,
pH can be estimated using the empirical relationshipderived from in-situ data. As EO based estimation ofocean acidity is reliant upon in-situ data observations, EO
based methods cannot replace the need for in-situ datacollection, however it could be used to derive spatially
explicit datasets. Furthermore the utility of the EO-based
method could be useful for identifying regions at risk from
OA and the study of complex systems challenging for insitu monitoring such as river plumes and upwelling areas.
The Copernicus Marine Environment Monitoring service(CMEMS) is launching a new product on Ocean Health inApril 2019, which includes Ocean Acidification. The pH
estimates in this product relies on in situ data from buoys,
EO data on SST and salinity, auxiliary data on nitrate anddissolved silica and combines this within a modelling
environment. The exact details of the product and accuracy
assessments are not currently available.
Limitations
The EO algorithms in their current state have quite large
uncertainties, unless at relatively coarse spatial resolutions,rendering them unsuitable for detecting fine-scale changes
in pH expected from OA. The current proposed methodology
requires that sample station observations must include:
two parameters of the carbonate system (Dissolved
Inorganic Carbon, total pH, pCO2, total alkalinity) and in situ
seawater temperature and salinity. However only direct EObased observations of salinity and sea surface temperatureare currently feasible. The indicator methodology would
have to be adapted to incorporate these observations
globally while still relying on carbonate parameters fromsampling stations. As salinity and temperature can be
sampled relatively simply in-situ the use of EO-derived
salinity and temperature from space is really in broad scaleocean acidification mapping, e.g. over a square kilometre or
more. The utility of the satellite measurements therefore
comes in obtaining a synoptic view where few or no in situ
measurements exist (as mentioned in the introduction fortarget 14.3).

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

125

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 14.3

Key messages for countries on EO contribution to thecomputation method:•

This indicator methodology uses an ocean in-situ(buoys and ships) sampling approach for key parametersof importance for ocean pH in order to determine average
pH from which anomalous deviations could indicate anacidification risk
he methodology does not currently mention EO as a
• Tdata
source but models of global ocean acidification
risk, inferred from EO derived salinity and sea surface
temperature measurements (and possibly chlorophyll-a).
These together with empirical algorithms to estimate
carbonate system parameters, have been used to map
OA risk, albeit at coarse spatial resolution (>1km) andwith large uncertainties

O is therefore not yet ready for the precise monitoring• Erequired
for the indicator but as EO based methods
improve, there is a possibility that it could therefore
become integrated into future iterations of themethodology, especially if enhanced scientific cooperation(between modellers, remote sensing experts and ocean
scientists) is achieved in line with target 14.3
he upcoming product from CMEMS on Ocean Acidification• Twillallow easy access to updated data based partly
on EO inputs. The accuracy of the product for national
reporting should be evaluated before being used for SDG
indicator reporting.

Data sources
Data category

Source satellite data

Operational products

Data sources

WebsiteESA Soil Moisture and Ocean
Salinity (SMOS)

https://smos-diss.eo.esa.int/oads/accessNASA Aquarius (no longer operational but a source of historicaldata):

https://aquarius.nasa.gov/data.html

NASA Soil Moisture Active Passive (SMAP)

Meissner, T. and F. J. Wentz, (2016) Remote Sensing Systems
SMAP Ocean Surface Salinities Level 3, Version 2.0 validated release. Remote Sensing Systems, Santa Rosa, CA, USA.
Available online at www.remss.com/missions/smap, doi:
10.5067/SMP20-3SMCS

Copernicus Marine Environment
Monitoring Service - Ocean
Health Monitoring Indicator

http://marine.copernicus.eu/science-learning/ocean-monitoring-indicators/catalogue

Reference List
Land, Peter E et al. 2015. “Salinity from Space Unlocks
Satellite-Based Assessment of Ocean Acidification.”
Environmental Science & Technology 49(4): 1987–94.
https://doi.org/10.1021/es504849s.

Salisbury, Joseph et al. 2015. “How Can Present andFuture Satellite Missions Support Scientific Studies
That Address Ocean Acidification?” Oceanography 25(2):108–21. https://tos.org/oceanography/article/how-canpresent-and-future-satellite-missions-support-scientificstudies-th.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

126

TARGET 14.3

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

14.3.1
Average marine acidity (pH) measured at agreed suite ofrepresentative sampling station

Custodian agency

IOC-UNESCO

Tier

II

Status of step-by-step methodology document on
the metadata repository

Unpublished(Tier III at the time of the analysis)

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines

n/aTechnical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the
EO record?

Overall EO relevanceComments to support criteria

EO algorithms for direct estimation of OA/pH have quite large
uncertainties rendering them unsuitable for operational use,
but EO products on salinity and SST are mature and are usedin the CMEMS OA product.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

127

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 14.4
By 2020, effectively regulate harvesting and end
overfishing, illegal, unreported and unregulated fishingand destructive fishing practices and implement sciencebased management plans, in order to restore fish stocks
in the shortest time feasible, at least to levels that can
produce maximum sustainable yield as determined by their
biological characteristics
How can EO be used to help countries achieve the target?
This target is aimed at regulation through consistentreporting of overfishing as well as restoration of alreadydepleted fish stocks. The most conservative estimatessuggest that illegal, unreported and unregulated (IUU)
fishing on the high seas, affecting species such as tunas
and sharks, is worth US$1.25 billion annually (Global
Ocean Commission, 2014). Current measures taken
for implementing, monitoring and enforcing plans forfish stock conservation at the national level are mostly
inadequate. Satellite remote sensing has the potential to
improve plans for monitoring and management of fisheries
in a number of ways (Stuart et al. 2011a). For example
satellite data on ocean parameters such as temperature,salinity, phytoplankton and chlorophyll-a concentrations
can help identify ocean areas where fish tend to aggregate(e.g. thermal fronts) and to estimate primary production.
Studies have shown that satellite remote sensing ofprimary production in the ocean could be used to support
fish stock assessments, for example, using ocean colour
images to infer primary production and estimate global
fish biomass. This would be particularly useful for thistarget given that the fish stock assessments demand highlevels of technically capacity and data, which is currently
lacking at country level. However, the uptake of satellite
remote sensing in fisheries management has so far been
limited. This is due to a number of reasons, including the
spatial and temporal inadequacy of available ocean colour
data for fisheries management purposes, lack of technicalcapacity to analyse remote sensing data sets, and thelimited accuracy of ocean colour algorithms for coastal
areas, where most fishing activities take place (Stuart etal. 2011b, Wilson 2011).
Current Indicator(s)14.4.1 
Proportion of fish stocks within biologicallysustainable levels

TARGET 14.4

Short methodological guidelines illustrated
with EO best practice examples
Indicator 14.4.1
Computation method
The indicator measures the sustainability of fisheryresources based on stock assessments (fish catch statistics,
fishing effort, biological information, population dynamics
models. The methodology is very well established and FAO
has maintained and reported on this indicator since 1974.
Yet; satellite remote sensing data can support fish stock
assessments over vast areas of the ocean both within andoutside national waters through estimates of ocean primary
productivity. Non-EO data such as tracking fishing vessel
movements could be used to strengthen the estimate ofover fishing in areas of high primary productivity.
Limitations
The accuracy of ocean colour algorithms for coastal areas islimited, where most fishing activities take place.
Key messages for countries on EO contribution to thecomputation method
nregulated and unlicensed fishing is causing extreme• Upressure
on global fish stocks as well as lost revenue
for countries
his indicator reports on the proportion of fish stocks• Twithinbiologically sustainable levels• It is using a tried and tested statistical methodology
O can however help improve estimates of fish socks• Eespeciallyin open ocean, based on the levels of ocean
primary productivity inferred form measurements ofocean colour
reas of high primary productivity generally support• Ahigher
fish stocks than area of lower productivity
on-EO data such as illegal fishing vessel movement• Ntracking
can be used to complement EO data and in order
to identify where fish stocks are being unsustainably
depleted, e.g. by overlaying on areas of high primary
productivity or on zones where fisheries are protected

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

128

TARGET 14.4

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

14.4.1
Proportion of fish stocks within biologically sustainable levels

Custodian agency

FAO

Tier

I

Status of step-by-step methodology document on
the metadata repository

Unpublished(Tier III at the time of the analysis)

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the
EO record?

Overall EO relevanceComments to support criteria

Ocean colour remote sensing technologies are wellestablished from which fish stocks can be inferred. Further
work needed to estimate the sustainability of stocks fromremote sensing.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

129

GOAL 15

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 15.1
By 2020, ensure the conservation, restoration andsustainable use of terrestrial and inland freshwater
ecosystems and their services, in particular forests,
wetlands, mountains and drylands, in line with obligations
under international agreements.
How can EO be used to help countries achieve the target?
This target aims to ensure sustainable management andmonitoring, and use of terrestrial ecosystems including
freshwater ecosystems and their restoration. It is anambitious target which considers the interrelatednessof life in different elements of terrestrial ecosystems mountains, wetlands, arid lands and forests and that thehealth of one part impacts the other.
EO can play multiple roles in achieving the target as it is a
crucial part of the monitoring strategies for conservation,
restoration and sustainable use of terrestrial ecosystems.
The availability of multi-decadal time series datasets ofthe (global) land surface from multiple satellite sensors
means that there are fewer remote sensing data gaps
and greater ability to monitor long term changes overgreater areas. Multi-scale land-cover information can be
retrieved over whole countries which, complemented withfield data, can support integrated land use plans, including
evaluation of the services provided by terrestrial andfreshwater ecosystems. Remote sensing data coupled withmodelling tools can support the identification of priority
areas for ecosystem services provision that needs specificmanagement activities. It can also be used to monitor
the effectiveness of restoration activities planned for thesesites or to assess their status over time. Optical or radar
sensors, or a combination of the two, can detect not justforest cover area, but also other attributes as wetlands,
lakes and to estimate their biophysical parameters, as wellas surface and volume measures.
Existing indicators primarily report on the extent to whichareas are conserved (15.1.2) or sustainably used (15.1.1),
therefore, there is a gap for an indicator on the restorationof these ecosystems. For example, indicator 15.1.1 only
includes forest areas – drylands and mountains are
considered under indicators 15.3.1, 15.4.1 and 15.4.2.
Similarly, freshwater ecosystems are monitored using
indicator 6.6.1 – so are not considered under target 15.1.

TARGET 15.1

EO has a lot of potential here as it can effectively monitor
land cover change over time with high accuracy both atvery high to high spatial resolution. The identification ofterrestrial and inland freshwater ecosystems which havebeen modified by humans paves the way for a tool that
could identify areas for habitat restoration. Moreover, FAO
is custodian of these SDGs and will work closely withother partners.
Current Indicator(s):15.1.1 Forest area as a proportion of total land area
15.1.2 
Proportion of important sites for terrestrial andfreshwater biodiversity that are covered by protectedareas, by ecosystem type
Potential new indicator(s) based on EO:
Proportion of terrestrial and freshwater ecosystems
restored

Short methodological guidelines illustrated
with EO best practice examples
Indicator 15.1.1
Computation method
This indicator is calculated from national data on forest that
FAO has been collecting since 1946, as part of the Global
Forest Resources Assessment (FRA). Each country provides
this data to FAO following a standard format, which
includes raw data files. Based on this national data, forest
statistics are produced and archived in the FAO Statistical
Database (FAOSTAT). This data is complemented withdata from the global Remote Sensing survey conducted by
FAO in conjunction with the European Commission’s Joint
Research Centre (JRC) for 1990, 2000 and 2010. However,in the 2020 FRA the online reporting platform provides
correspondents with access to global remote sensing
products via Collect Earth (Bey et al., 2016).
EO, mostly in combination with ground observations, arewidely used to produce forest cover maps at the nationaland sub-national level, and thus to determine the proportionof land covered by forests. Freely available Landsat satellite
images at 30m resolutions and Sentinels missions’ data

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

131

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

from the European Copernicus programme can be processedto generate national, regional or global forest cover maps,
e.g. as has been done for the Global Forest Change product(Hansen et al., 2013). In particular Sentinel-2 mission can
provide high-resolution imaging (from 10 to 60m resolution)
for land monitoring, including imagery of vegetation, soil
and water cover, inland waterways and coastal areas.
Different land cover classification techniques can be used,classifiers such as Random Forest, Gaussian Mixture Modelclassifier, Support Vector Machines or K-Nearest neighbours,
have been widely used and tested (Noi & Kappas, 2017).
Validation using ground truth data is a critical final stepthat ensures the accuracy of the final map (Rwanga &Ndambuki, 2017).
Countries can also opt to generate the statistics on theirnational forests based on a sample based approach.
Therefore, they can use freely available tools such as theCollect Earth developed by FAO’s Open Foris initiative (FAO
Forestry Department), which enables to collect land cover/land use at point locations using Google Earth imager in auser-friendly and easy way.
Global products, such as the Global Forest Change
developed by independent laboratories such as the
University of Maryland’s Global Land Analysis and Discoverlaboratory (GLAD) (Hansen et al., 2013), and by initiatives
such as the Global Forest Watch, which builds on GLAD
data, can also be used and aggregated at the country level
and complemented with field surveys if necessary. These
products should be validated at the country level in order
to be used as data for national use. The combination of
EO based products with reference data not only reduces
bias, but also provides an estimate of uncertainty for theindicator in the form of a confidence interval.
Disaggregation
Disaggregation can be by spatial unit, such as administrative
districts, or by forest type, but only by broad categories suchas deciduous, coniferous and mixed forests for example.
This is because the seasonality of the vegetation withinthese forest types is relatively easy to discriminate withtime series of satellite imagery. Spatial disaggregation
requires sufficient reference data and class disaggregationis heavily dependent on spectral and phenologicalbehaviours of different forest types. It is also importantthat all countries use the same definition of forest (suchas the FAO definition) – this will allow easier comparison.
Treatment of missing values

TARGET 15.1

Regional aggregates
Regional aggregation is possible, by disaggregation of a
global forest product for example, but when the forestclassification is performed at the national scale, the useof different types of EO as well as different techniques
can hinder the possibility of meaningfully aggregating thecountry products.
Sources of discrepancies
Different definitions of forests across countries can generate
discrepancies, as well as the use of different products, atdifferent resolution across countries. The quality of input
imagery and training data as well as classification methods
are also a large source of discrepancy.
Limitations
The definition of forest used (FAO, FRA or other) as well as
the difficulties in discriminating among land cover types, inparticular in dry areas, is a limitation in using EO. Moreover,
if the goal is to map land use, field data or other auxiliary
data are required. Using optical EO data in cloudy regions
is challenging and it might require the use of radar data.
The technical capacity necessary to process images can bea limitation in many countries.
Key messages for countries on EO contribution to thecomputation method:
O are widely used to report on forest cover extent• Eandstrong methodologies are established to accurately
classify forest/non forest areas.
forest systems are more difficult to map using optical• DEOrydata,
because can be easily confused with grasslands.
The use of radar and/or VHR data is preferable for thisforest type. Radar data should also be used in regions
characterised by persistent cloud cover.
of online platforms and free global data,• Te.g.he availability
through Collect Earth, allows countries to easily
report on forest cover extent, but this should always
be combined with ground observations to validate andimprove upon forest cover estimates derived from global
EO products

Missing values because of cloud cover can be treatedusing different EO sensor types such as radar (e.g. a newalgorithms under development using Sentinel-1), using
ground observations or image composites.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

132

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 15.1

Data sources
Data category
Source satellite data
Global/regional
datasets

Software, tools andplatforms

Data sources

WebsiteLandsat data

https://earthexplorer.usgs.gov/

Sentinel from the Copernicus Open Access Hub

https://scihub.copernicus.eu/

Global Forest Watch

https://www.globalforestwatch.org/

University of Maryland’s Global Land Analysis
and Discovery laboratory

https://glad.umd.edu/

Open Foris, of the FAO, a collection of free andopen source tools for EO-based environmental
monitoring

http://www.openforis.org

SEPAL: System for Earth Observation Data
Access, Processing and Analysis for Land
Monitoring

https://sepal.io

Forestry TEP

https://f-tep.com

Reference List
Ban, Y., Gong, P. and Giri, C. (2015) ‘Global land cover
mapping using Earth observation satellite data:
Recent progresses and challenges’, ISPRS Journal of
Photogrammetry and …, 103, pp. 1–6. doi: 10.1016/j.
isprsjprs.2015.01.001.

Townshend, J. R. et al. (2012) ‘Global characterizationand monitoring of forest cover using Landsat data:
Opportunities and challenges’, International Journal of
Digital Earth. Taylor & Francis, 5(5), pp. 373–397. doi:
10.1080/17538947.2012.713190.

Bey, A.; Sánchez-Paus Díaz, A.; Maniatis, D.; Marchi, G.;
Mollicone, D.; Ricci, S.; Bastin, J.-F.; Moore, R.; Federici,S.; Rezende, M.; Patriarca, C.; Turia, R.; Gamoga, G.; Abe,H.; Kaidong, E.; Miceli, G. (2016) Collect Earth: Land Useand Land Cover Assessment through Augmented VisualInterpretation. Remote Sensing, 8, 807.

Further Reading and Resources

Hansen, M. C., et al. (2013) ‘High-Resolution Global
Maps of 21st-Century Forest Cover Change’ Science
342 (15 November): 850–53. Data available online from: http://earthenginepartners.appspot.com/
science-2013-global-forest.

GFOI (2016) ‘Integration of remote-sensing and groundbased observations for estimation of emissions andremovals of greenhouse gases in forests: Methods andGuidance from the Global Forest Observation Initiative’.
Edition 2.0, Food and Agriculture Organization, Rome.

Mitchard, E. (2016) ‘A Review of Earth Observation
Methods for Detecting and Measuring Forest Change in
the Tropics’. Ecometrica, Edingburgh, UK

Hestir, E.L. et al. (2015) ‘Measuring freshwater aquatic
ecosystems: The need for a hyperspectral global mapping
satellite mission’. Remote Sensing of Environment. 167,
181-195.

Noi, P.T. and Kappas, M. (2017) ‘Comparison of Random
Forest, k-Nearest Neighbor, and Support Vector Machine
Classifiers for Land Cover Classification Using Sentinel-2
Imagery’. Sensors. 18, 18.
Rose et al. (2014) ‘Ten ways remote sensing cancontribute to conservation’. Conserv. Biol. 29, 350–359.
Rwanga, S.S. and Ndambuki, J.M. (2017) ‘Accuracy
Assessment of Land Use/Land Cover Classification Using
Remote Sensing and GIS’. International Journal of
Geosciences, 8, 611-622.
Sass, G. Z. et al. (2012) Defining protected areaboundaries based on vascular-plant species richness
using hydrological information derived from archived
satellite imagery. Biol. Conserv. 147, 143–152.

FAO and SDGs (2018) Keeping an eye on SDG 15 –Working with countries to measure indicators for forestsand mountains. Available to download at:
http://www.fao.org/3/a-i7334e.pdf

IUCN (2016) A Global Standard for the Identification of
Key Biodiversity Areas, Version 1.0. First edition. Gland,
Switzerland: IUCN.
McRoberts, R.E. (2011) ‘Satellite image-based maps:
Scientific inference or pretty pictures?’ Remote Sensing
of Environment, 115(2), 715-724.
UNEP-WCMC (2017). World Database on Protected Areas
User Manual 1.5. UNEP-WCMC: Cambridge, UK. Available
at: http://wcmc.io/WDPA_Manual

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

133

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 15.1

Forest cover mapping in Gabon to calculate forest area (indicator 15.1.1)

Forest cover maps were producedin collaboration with the Gabonese
Agency for Space Studies andObservations (AGEOS) for 1990, 2000,
2010 and 2015 for the whole country.
The maps were constructed using a
combination of a semi-automatedclassification procedure and manualenhancements to ensure the highest
possible level of accuracy. A two-stage
area frame sampling approach was
adopted to collect reference data forassessing the accuracy of the forest
cover maps and to produce forest
cover and forest cover change area
estimates. A total of 665 2x2 km
segments or primary sample units(PSUs) were visually interpreted
by a team of photo-interpreters
independently from the production
team and produced a reference data

set representing about 1% of thestudy area. Paired observations wereextracted from the forest cover map
and the reference data for a random
selection of 50 pixels or secondary
sample units (SSUs) for each PSU.
Overall map accuracies greater than
0.95 were achieved. PSUs and SSUs
outputs were used to produce forest
cover and forest cover change area
estimates using both direct expansion
and model-assisted regression (MAR)estimators. All area estimates weresimilar, but the variances of the MAR
forest cover area estimates were
smaller by factors as great as 50 thandirect expansion estimates. In 2010,
88.30 ± 0.26% of Gabon is coveredby forest. In addition despite large
overall map accuracies, deforestation
estimates obtained from the maps

alone can be misleading as indicatedby the finding that the adjusted
estimates of net change were twice
the non-adjusted map estimates (forperiods 1990-2000 and 1990-2010).
The results confirmed the expected
generally low level of deforestation
for Gabon. However, net deforestation
appears to have almost stopped in
the last 10 years, which could belinked to the implementation of forest
concession management plans from2000 onward. Deforestation increased
again from 2010 to 2015 largely due
to the development of large project asshown below:

Figure 18: Extent of forest in Gabon in 2015 and main causes of deforestation.
Credit: Christophe SannierCompendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

134

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 15.1

Indicator 15.1.2

Limitations

Computation method

The singular limitation of using EO data to calculate indicator
15.1.2 is the inability of automated remote-sensed data tocapture spatially defined areas that reflect human decisionmaking rather than physical/biological features. EO data
could conceivably aid in the delineation of KBAs and PAs,
but not in their identification.

This indicator is calculated from data derived from a spatial
overlap between digital polygons for protected areas fromthe World Database on Protected Areas (IUCN & UNEPWCMC 2017) and digital polygons for terrestrial andfreshwater Key Biodiversity Areas (KBA) from the World
Database of KBA, including Important Bird and Biodiversity
Areas, Alliance for Zero Extinction sites, and other KBA. The
indicator tracks the mean percentage [%] of each KBA
that is covered by protected areas in order to better reflect
trends in protected area coverage for countries or regionswith few or no KBA.
As discussed above, EO are widely used to derive
information on biophysical aspects of terrestrial and inland
freshwater ecosystems and their services. Protected Areas
and KBAs are often correlated with biophysical aspects,
e.g. by following a mountain ridge or a river but theirboundaries are not defined by such biophysical aspects(Sass et al 2012). Instead, both PAs and KBAs have theirboundaries defined through a diverse series of potential
mechanisms, for example participatory mapping involving
local communities. EO does have the potential in helping
define KBA boundaries, for example through using ‘extent ofsuitable habitat’ to define proportion of a global populationof a species (criteria A1, B1, B2) or extent of a threatened
habitat (criteria A2). These criteria are explained in the KBA
standard document (IUCN, 2016).

Key messages for countries on EO contribution to thecomputation method:ndicator 15.1.2 is based on a well-established• Imethodologythat uses the mean percentage [%] ofeach Key Biodiversity Area that is covered by protectedareas in order to better reflect trends in protected area
coverage for countries or regions with few or no Key
Biodiversity Areas (KBA)
O data can be successfully used to (i) separate• Eterrestrialfrom freshwater ecosystems and map
their ecosystem types and (ii) delineate the edges ofprotected areas, e.g. based on biophysical features
such as lake shores, as well as to assess certain KBA
criteria such as extent of suitable habitat
O will not be able to replace certain aspects of the• Eindicator
computational such as participatory mappingby communities in proximity to KBAs

Data sources
Data category

Data sources

WebsiteGlobal/regional
datasets

World Database on Protected Areas

https://www.protectedplanet.net/

World Database on Key Biodiversity Areas

http://www.keybiodiversityareas.org/home

Reference List
Ban, Y., Gong, P. and Giri, C. (2015) ‘Global land cover
mapping using Earth observation satellite data:
Recent progresses and challenges’, ISPRS Journal of
Photogrammetry and …, 103, pp. 1–6. doi: 10.1016/j.
isprsjprs.2015.01.001.
Hansen, M. C., et al. (2013) ‘High-Resolution Global Mapsof 21st-Century Forest Cover Change’ Science 342 (15November): 850–53. Data available on-line from: http://
earthenginepartners.appspot.com/science-2013-globalforest.
Mitchard, E. (2016) ‘A Review of Earth Observation
Methods for Detecting and Measuring Forest Change in
the Tropics’. Ecometrica, Edingburgh, UK

Noi, P.T. and Kappas, M. (2017) ‘Comparison of Random
Forest, k-Nearest Neighbor, and Support Vector Machine
Classifiers for Land Cover Classification Using Sentinel-2
Imagery’. Sensors. 18, 18.
Rose et al. (2014) ‘Ten ways remote sensing cancontribute to conservation’. Conserv. Biol. 29, 350–359.
Rwanga, S.S. and Ndambuki, J.M. (2017) ‘Accuracy
Assessment of Land Use/Land Cover Classification Using
Remote Sensing and GIS’. International Journal of
Geosciences, 8, 611-622.
Sass, G. Z. et al. (2012) Defining protected areaboundaries based on vascular-plant species richness
using hydrological information derived from archived
satellite imagery. Biol. Conserv. 147, 143–152.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

135

TARGET 15.1

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Reference List
Townshend, J. R. et al. (2012) ‘Global characterizationand monitoring of forest cover using Landsat data:
Opportunities and challenges’, International Journal of
Digital Earth. Taylor & Francis, 5(5), pp. 373–397. doi:
10.1080/17538947.2012.713190.

Hestir, E.L. et al. (2015) ‘Measuring freshwater aquatic
ecosystems: The need for a hyperspectral global mapping
satellite mission’. Remote Sensing of Environment. 167,
181-195.

Further Reading and Resources

IUCN (2016) A Global Standard for the Identification of
Key Biodiversity Areas, Version 1.0. First edition. Gland,
Switzerland: IUCN.

FAO and SDGs (2018) Keeping an eye on SDG 15 –Working with countries to measure indicators for forestsand mountains. Available to download at:
http://www.fao.org/3/a-i7334e.pdf

McRoberts, R.E. (2011) ‘Satellite image-based maps:
Scientific inference or pretty pictures?’ Remote Sensing
of Environment, 115(2), 715-724.

GFOI (2016) ‘Integration of remote-sensing and groundbased observations for estimation of emissions andremovals of greenhouse gases in forests: Methods andGuidance from the Global Forest Observation Initiative’.
Edition 2.0, Food and Agriculture Organization, Rome.
Indicators

UNEP-WCMC (2017). World Database on Protected Areas
User Manual 1.5. UNEP-WCMC: Cambridge, UK. Available
at: http://wcmc.io/WDPA_Manual

15.1.1
Forest area as a proportion of total land area

Custodian agency

FAO

15.1.2
Proportion of important sites
for terrestrial and freshwater
biodiversity that are covered byprotected areas, by ecosystem type

UNEP-WCMC; UNEP; IUCN

Tier

I

I

Status of step-by-step methodology
document on the metadata repository

Published

Published

Reliable methods are established and widely used
to extrapolate forest cover data from EO. One of
the strengths of using EO is the possibility ofdetecting change, unless the area of interest is
located in cloudy regions. The combination of EO
based products with sampled reference data can
produce reliable change estimates.

EO can separate terrestrial from
freshwater ecosystem types(e.g. wetland, mangrove, forest,
grassland etc.). However theindicator uses management as wellas biophysical factors to identify
important sites for biodiversity.

Maturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator
guidelines
Technical capacity
required
Availability of global EO data

Robustness ofproposedmethodology
Criteria

Compliance with Reporting
calendar
Sensitivity to change
Is it scalable (spatial)?
Is there a substitute forgaps in the EO record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

136

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 15.2
By 2020, promote the implementation of sustainable
management of all types of forests, halt deforestation,
restore degraded forests and substantially increaseafforestation and reforestation globally
How can EO be used to help countries achieve the target?
Forests are a key terrestrial ecosystem, providing various
ecosystem services, including food, biodiversity, protection
from soil erosion, climate change mitigation. This ecosystem
is rapidly disappearing, “thirteen million hectares of forests
are being lost every year while the persistent degradationof drylands has led to the desertification of 3.6 billion
hectares”, and this indicates the need for effective strategies
to reduce deforestation and implement sustainable forest
and land management practices. Together with target 15.1,
this target ensures that forests are efficiently managed, anda sustainable balance between conservation and the use ofnatural resources is achieved. EO can be used to assess thechange in forest extent and quality (e.g. degradation), butalso to plan for the effective implementation of activities
aiming to achieve the sustainable management of forest.
Satellite images and subsequent analyses can help to identify
sites where to implement reforestation and afforestation
activities, as well as areas that are at higher risk of
deforestation because of past forest clearing for agriculture
or because of the proximity to infrastructures such as roads,
and their protection should be prioritised. Different types of
EO sensor systems are available (optical, radar and LiDAR)
and can be used to map forest change based on type offorest, climatic conditions, technical capacity available in the
country. Mapping forest degradation and biomass change is
generally more challenging than monitoring forest extent,
but new promising methods are being developed and tested.
Current indicator(s)15.2.1 Progress towards sustainable forest management

Short methodological guidelines illustrated
with EO best practice examples
Indicator 15.2.1
Computation method
Countries already report directly to the FAO on forest area,
biomass stock, forest area within protected areas, forest
area under a management plan and forest area under
an independently verified forest management scheme.
FAO then applies the compound interest formula to makeestimates of the forest area net change rate, as well as the
proportion of forest area within protected area and undera management plan. Countries report on forest cover using
a combination of remote sensing data and national forest
inventory.

TARGET 15.2

Sub-indicator a: Forest area net change rate
Sub-indicator b: Above-ground biomass stock in forest
Sub-indicator c: Proportion of forest area located within
legally established protect areas
Sub-indicator d: Proportion of forest area under a long term
forest management plan
Sub-indicator e:Forest area under an independently verified
forest management certification scheme
Sub-Indicator a – Forest area net change rate: EO are
widely used to monitor forest extent. Freely available
Landsat images (30m resolution) and Sentinel-2 images(10 – 20m resolution) can be used to map changes offorest at the national and sub-national scale. This approach
can be easily used for monitoring deforestation in medium/low cloud regions like boreal forests. However, for regionswith high cloud cover such as the Amazon forest, theCongo Basin, and Malaysia/Indonesia, or where thebiophysical environment is complex, forest monitoringis more challenging (Mitchard, 2016). Combinations ofLandsat and Sentinel-2 can be used to increase theirindividual temporal resolution and further combinations
with Sentinel-1 in areas where cloud cover is persistent,
but in many cases Radar can be the best option. Forest
area mapping approaches are reliant on 2D classificationof SAR backscatter or automated change analysis applied
to calibrated time-series. Estimates of forest height andbiomass are possible using L-band data. P-band SAR
penetrates vegetation canopies to a greater depth, butthere are no currently operational P-band SARs. However,the European Space Agency P-band BIOMASS mission isscheduled for launch around 2021. A lot of radar datasets
are commercial datasets, such as ALOS PALSAR (12.5 m)
and TerraSAR-X (6 m). Therefore, data availability can be anissue in using Radar to monitor deforestation.
As described in Mitchard (2016) at least three approaches
can be used to estimate deforestation, comparing onetime layers, time series analyses and machine learningalgorithms. The first approach, which is the most widelyused, compares classified forest layers (e.g. forest/noforest) at different points in time. The accuracy of the input
layers are critical for this method to be valid. The time series
analyses compare a series of observations of the sameforest parameter for example the Normalised Difference
Vegetation Index (NDVI) using a specific algorithm. Thesetime-series should include more than one observation/
year in order to avoid errors due to seasonality or cloud
cover. Algorithms, such as pixel-based Break detection
For Additive Seasonal Trends (BFAST) monitor (Mitchell etal., 2017), try to distinguish either a sudden break-point(for example due to a deforestation/degradation event)or a long-term trend (for example forest regrowth/forest
degradation), from the natural variation that can be duejust to seasonality (Mitchard, 2016). Machine learningalgorithm, for example artificial neural networks, basedon back propagation training algorithm, have also been
used, mostly when more than one vegetation parameter

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

137

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSor spectral band, are used to detect forest change. The
initial computational development of the algorithm canbe expensive, but once the specific algorithm has beendeveloped can be run easily. The model developed is valid
just for the type of forests for which it has been trained,
for different contexts new models needs to be generated.
Global and regional deforestation datasets are also
currently freely available and can be used as baselines ordownscaled at the national level. The coverage of thesedatasets goes from global to regional or at the country level(GLAD; Hansen et al., 2013 and 2016; Camara et al., 2013;
PRODES 2016); their spatial resolution from 30m to 500
m and their temporal resolution can be annual, monthly or
bi-weekly. Considering the reporting frequency of 5 years
the use of the global products downscaled at the country
level can be used and provide a more systematic reportingof this indicator. Initiatives such as Open Foris by FAO andGlobal Forest Watch provide tools that can easily be usedby countries to map their forest. In particular within Open
Foris the tool Collect Earth enables country to collect data
through Google Earth and assess their forest cover extent
and to monitor its changes. The Global Forest Observation
Initiative provides methodological advice on the joint useof remotely sensed and ground-based data to countries forforest monitoring and Green House Gas reporting (GFOI
MGD 2.0, 2016).
Sub-Indicator b – Above-ground brown biomass stock inforest: Above-ground brown biomass, i.e. woody components
such as the tree trunk and branches, requires the use of fieldplots and EO data. Traditional SAR and optical observations
can be used to extract biomass values, but ground truth
training data is required (Laurin et al., 2018).
Artificial neuronal network (ANN) techniques have beenused to integrate remotely-sensed satellite data and fieldinventory data and thus estimate forest biomass. Somestudies show that vegetation indices such as normalized
difference vegetation index (NDVI), shortwave infrared(SWIR) band reflectance, transformed normalized difference
vegetation index (TNDVI), soil adjusted vegetation index(SAVI), principal component analysis (PCA) and difference
vegetation index (DVI) are suitable to predict forest
biomass, by using optical EO data, but field plots are stillnecessary to calibrate the models at the country level(Nandy et al., 2017).
The biomass change is more difficult to detect using
current EO technology. LiDAR and SAR tomography are theonly suitable techniques, but are both rather expensive
and need high technical expertise. Global data on biomass
are also available and can potentially be downscaled at
the national level and be used as baseline. The main
datasets are Avitabile et al. (2016) at 1 km resolution and
GlobBiomass (Santoro et al., 2018) developed using Radar
data at 100m resolution. The Global Forest Observation
Initiative (GFOI) has also developed a set of methods andguidelines for estimating carbon stocks using EO data such

TARGET 15.2

as Landsat and Sentinel, and ground based observations,which can support countries.
Recently launched (ICESat-2, SAOCOM) and upcoming
missions (GEDI, MOLI, NISAR, ALOS-4, BIOMASS) fromvarious space agencies have biomass estimation as primaryor secondary objective. It is expected that they will have astrong impact on the accuracy of biomass estimation fromspace.
Sub-Indicator c – Proportion of forest area locatedwithin legally established protect areas, Sub-Indicatord – Proportion of forest area under a long term forest
management plan, Sub-Indicator e – Forest area under
an independently verified forest management certification
scheme: the proportion of forest located within these forest
management categories can be extracted from the forest
datasets used to detect changes in forest extent, as longas updated shapefiles of the boundaries of these forest
management areas of interest are available.
Disaggregation
Spatial disaggregation can be carried out to downscale thedata at the protected area level and other forest management
types scale. Temporal disaggregation can also be performed
thanks to the regular production of EO data.
Treatment of missing values
The combination of different types of EO data, such as theuse of radar in cloudy areas, and at different resolutions(e.g. EO at less than 10m resolution) can be used to treat
missing values. Regional and global products can also be
used to fill gaps.
Regional aggregates
If using global products the regional aggregations is
possible, but in case different deforestation detection
methods are used the aggregation can be more difficult.
Sources of discrepancies
Discrepancies related to forest changes can arise basedon the techniques used to detect deforestation and to thedefinition of forest used. The regional aggregation can also
produce discrepancies if different forest change detection
methods are used. National definitions of forest sometimes
contradict the biophysical view of EO sensors that justobserve tree cover. Furthermore the use of the tree cover,e.g. for industrial logging, is not easily discerned from EO
data. The discrepancies between EO-derived forest extent
and national statistics have been well documented (Tropek
et al., 2014). Discrepancies related to forest biomass
are associated with uncertainties in retrieval but also
the prevailing environmental conditions which can vary
between acquisitions even for the same forest area. SAR
sensitivity to biomass also varies with frequency, with C-

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

138

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 15.2

and X-band tending to saturate at low biomass levels while
L and P bands are more sensitive to medium and highbiomass respectively. Data fusion approaches may help
overcome sensor specific limitations such as saturation,
operating modes and temporal gaps.

are experimental space LIDAR missions such as GEDI
which had just been launched from the International Space
Station (ISS) by NASA. The technical capacity required toestimate biomass from EO and ground-observation, canrepresent a limitation for many countries.

Limitations

Key messages for countries on EO contribution to thecomputation method

The main limitations are related to the persistent cloud
cover in many areas when using optical EO data forforest area and change mapping, the complexity of someecosystems, seasonality and the technical capacity required
to perform the EO data analyses often unavailable. Errorsof commission or omission appear in EO-derived forest
cover change products based on the techniques used todetect deforestation. While SAR is a powerful alternative
it is limited by less frequent observation relative to opticalsensors and fewer source of freely available data.
Biomass does not change naturally as quickly as otherforest parameters and is often driven by subtle processesof degradation not easily detectable using optical sensors.
Although the use of LiDAR could overcome these issues,
airborne LiDAR is currently not sufficiently affordable togovernments to acquire multi-year and wall-to-wall, otherthan for local projects. Although not yet spaceborne, there

he sustainable management of forest can be monitored• Tandplanned through EO thanks to well establishedmethodologies and the availability of global datasets andopen source platforms
onitoring forest biomass stock and their changes• MthroughEO is challenging, but new technologies andmethods are becoming available to users with therequired technical capacity to work with radar data forexample. Consensus methods which combine optical,
SAR, Interferometric SAR (InSAR) and LiDAR tend to be
the most effective at overcoming the limitation of eachof these EO technologies used in isolation.
round observations are still needed to complement EO• Gdata
to monitor forest cover change and biomass stocks

Data sources
Data category
Source satellite
data

Global/regional
datasets (forest
biomass)

Global/regional
datasets (forest
cover andchange)Data sources

WebsiteLandsathttps://earthexplorer.usgs.gov/

Sentinel data (1,2 and 3) from the Copernicus Open
Access Hub

https://scihub.copernicus.eu/

Pan-tropical biomass map

https://www.wur.nl/en/Research-Results/Chairgroups/Environmental-Sciences/Laboratory-ofGeo-information-Science-and-Remote-Sensing/Research/Integrated-land-monitoring/Forest_
Biomass.htm

GlobBiomass (global terrestrial biomass map for2010)

http://globbiomass.org/products/globalmapping

University of Maryland’s Global Land Analysis
and Discovery laboratory generates near real time
deforestation alerts

https://glad.umd.edu

Global Forest Watch (global change 2000-2017)https://www.globalforestwatch.org/

PRODES (Amazon change 2004 to 2018)

http://www.obt.inpe.br/prodes/index.php

Global Forest Observation Initiative (GFOI) Methods
and Guidance Documentation aimed at REDD+
http://www.gfoi.org/methods-guidance/
Measurement, Reporting, and Verification
Software, tools
and platforms

Open Foris, of the FAO, a collection of free and open
source tools for EO-based environmental monitoring

http://www.openforis.org

SEPAL: System for Earth Observation Data Access,Processing and Analysis for Land Monitoring

https://sepal.io

Forestry TEP

https://f-tep.com

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

139

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 15.2

Reference List
Laurin Vaglio, G. et al. (2018) ‘Above-ground biomass
prediction by Sentinel-1 multitemporal data in central
Italy with integration of ALOS2 and Sentinel-2 data’.
Journal of Applied Remote Sensing, 12(1)Mitchell, A. L., Rosenqvist, A. and Mora, B. (2017)‘Current remote sensing approaches to monitoring forest
degradation in support of countries measurement,
reporting and verification (MRV) systems for REDD+’,
Carbon Balance and Management. Springer International
Publishing, 12(1). doi: 10.1186/s13021-017-0078-9.
Mitchard, E. (2016) ‘A Review of Earth Observation
Methods for Detecting and Measuring Forest Change in
the Tropics’. Ecometrica, Edingburgh, UK
Nandy, S. et al. (2017) ‘Neural network-basedmodelling for forest biomass assessment’, Carbon
Management. Taylor & Francis, 8(4), pp. 305–317. doi:
10.1080/17583004.2017.1357402.

Further reading and resources
GEO/CEOS (2017) Earth Observations in support of the2030 Agenda for Sustainable Development. Available
at:https://www.earthobservations.org/documents/
publications/201703_geo_eo_for_2030_agenda.pdf(Accessed: 25 April 2018).
Hammer, D., et al. (2014) ‘Alerts of forest disturbancefrom MODIS imagery’. International Journal of Applied
Earth Observation and Geoinformation, 33: 1-9
Heino, M. et al. (2015) ‘Forest loss in protected areas andintact forest landscapes: A global analysis’, PLoS ONE,10(10), pp. 1–21. doi: 10.1371/journal.pone.0138918.

Ho Tong Minh, D. et al. (2016) ‘SAR tomography for the
retrieval of forest biomass and height: Cross-validation
at two tropical forest sites in French Guiana’, Remote
Sensing of Environment. Elsevier, 175, pp. 138–147. doi:
10.1016/J.RSE.2015.12.037.
Koju, U., et al. (2017) ‘Exploring multi-scale forest
above ground biomass estimation with optical remotesensing imageries’. IOP Conference Series: Earth andEnvironmental Science, 57, 012011
Leisher, C. et al. (2013) ‘Land and forest degradation
inside protected areas in latin america’, Diversity, 5(4),pp. 779–795. doi: 10.3390/d5040779.
Milodowski, D. T., Mitchard, E. T. A. and Williams, M.
(2017) ‘Forest loss maps from regional satellite monitoring
systematically underestimate deforestation in two rapidlychanging parts of the Amazon’, Environmental Research
Letters, 12(9). doi: 10.1088/1748-9326/aa7e1e.
Tropek, R. t al. (2014). Comment on ‘Highresolution global maps of 21st-century forest coverchange’. Science (New York, N.Y.), 344(6187), 981.
https://doi.org/10.1126/science.1248753
Shimabukuro, Y.E. et al. (2012) ‘The Brazilian Amazon
Monitoring Program: PRODES and DETER Projects’. In
Global Forest Monitoring from Earth Observation, ed. F
Achard, MC Hansen: CRC Press
Townshend, J. R. et al. (2012) ‘Global characterizationand monitoring of forest cover using Landsat data:
Opportunities and challenges’, International Journal of
Digital Earth. Taylor & Francis, 5(5), pp. 373–397. doi:
10.1080/17538947.2012.713190

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

140

TARGET 15.2

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

15.2.1

Sub-Indicator a:
Forest area netchange rate

Sub-Indicator b:
Above-groundbiomass stockin forest

Custodian agency

Sub-Indicator
c: Forest area
within legally
establishedprotect areas

Sub-Indicator
d: forest
area under amanagement
plan

Sub-Indicator
e: Forest
area undermanagement
certification
scheme

FAO

Tier

I

Status of step-by-stepmethodology document on themetadata repository

Published

n/a

n/a

n/a

n/a

n/a

EO are widelyused to map
forest cover,but in several
context themisinterpretationof forest as
non-forest or
the detection of
degraded forest
can be challenging
and requires
higher technicalcapacity.

Changes inbiomass can bedetected using
EO tool such as
LiDAR and SAR
tomography, but
the technology
is not fully
mature, often
expensiveand requires
a high level ofexpertise.

Forest area canbe derived fromEO. However,boundaries ofprotected areas
are required,
which cannot
be derived from
EO data.

Only possible
in combination
with in-situ
data.

Only possible
in combination
with in-situ
data.

Maturity of EO
technologies

Relevance of EO
for the indicator
criteria

Status of EO
in indicator
guidelines
Technical
capacityrequired
Availability of
global EO data
Compliancewith Reporting
calendarRobustness ofproposedmethodology
Criteria

Sensitivity tochangeIs it scalable(spatial)?
Is there asubstitute forgaps in the
EO record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

141

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 15.3
By 2030, combat desertification, restore degraded land andsoil, including land affected by desertification, drought andfloods, and strive to achieve a land degradation-neutral world
How can EO be used to help countries achieve the target?
Land degradation, defined as “a state whereby the amountand quality of land resources necessary to support ecosystem
functions and services and enhance food security remain
stable or increase within specified temporal and spatial
scales and ecosystems” (decision 3/COP.12, UNCCD, 2015a)
is negatively impacting the well-being of billions of people(IPBES, 2018). Already at this point, there have been anumber of global initiatives aiming to halt land degradation
and restore degraded land. The global community’s efforts
to halt desertification, maintain and restore land and soil
productivity, and to mitigate the effects of drought are
spearheaded by the United Nations Convention to Combat
Desertification (UNCCD) which was adopted in Paris on 17
June 1994. As the dynamics of land, climate and biodiversity
are intimately connected, the UNCCD collaborates closelywith the other two Rio Conventions; the Convention on
Biological Diversity (CBD) and the United Nations Framework
Convention on Climate Change (UNFCCC). The vision of the
UNCCD aligns in particular with the CBD’s Aichi Biodiversity
Target 15, which aim to restore at least 15% of degradedecosystems; the Bonn Challenge (2011) and its regional
initiatives to restore more than 150 million hectares; andmost recently, the UN Sustainable Development Goals (SDGs)(Sims et al. 2017). Target 15.3 is strongly aligned with theland degradation neutrality (LDN) target setting process of
the UNCCD which is supporting interested countries (now)through a dedicated target setting programme (TSP),
including the definition of national baselines, targets andassociated measures to achieve LDN by 2030. While 120
countries are in the process of setting voluntary targets,
80 countries already have. As part of the TSP, the UNCCDTARGET 15.3

has selected data partners to assist countries with data fortarget setting in the absence of national data. These include
the ISRIC soil grids, the JRC Land productivity dynamics data
layer and the ESA-CCI land cover. All of these global datasets
are reliant on EO data as inputs thereby directly contributing
to countries in the LDN Target Setting Program.
Current Indicator(s)15.3.1: Proportion of land that is degraded over total landarea, with three sub-indicators capturing trends in land
cover, land productivity, and carbon stocks.

Short methodological guidelines illustrated
with EO best practice examples
Indicator 15.3.1
Computation method
The indicator falls under official reporting to the UNCCD, andas custodian agency it has already established a universal
methodology for reporting on SDG 15.3.1, documented in
the Good Practice Guidance (GPG, Version 1.0, 2017), which
explicitly considers EO for reporting purposes. However, thepotential for land degradation can already be detected using
EO to estimate other drivers of change, as is used in theconvergence of evidence approach (see discrepancies section).
The current indicator 15.3.1 methodology is a binary degraded/not degraded - quantification based on theassessment of available data for three sub-indicators,
namely Land cover and land cover changes, land
productivity changes and carbon stock changes, to bevalidated and reported by national authorities. The subindicators were adopted by the UNCCD’s governing bodyin 2013 as part of its monitoring and evaluation approach.
They represent proxies that ought to reflect the capacity of
the land to deliver ecosystem services and changes have toCompendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

142

TARGET 15.3

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

be assessed and depicted as (i) positive or improving, (ii)
negative or declining, or (iii) stable or unchanging.
Based on the evaluation of the changes, the proportion ofland that is degraded over total land area (%) is calculated
following a “One out all out” (1OAO) principle, definedby the Scientific Conceptual Framework of the LDN (Land
Degradation Neutrality) (see Figure below). That is, if oneof the sub-indicators is negative (or stable when degradedin the baseline or previous monitoring year) for a particular
land unit, the particular area is considered as degraded.
The baseline for assessment is established over the period
2000 to 2015, with the base year being 2015. All changes
are assessed relative to the baseline value with a reportinginterval of 4 years, starting in the year 2018. Countries are
responsible for submitting national reports to UNCCD.
For each of the sub-indicators, countries can access a wide
range of global and generic data sources, including Earthobservation (EO) and geospatial information, while at thesame time ensuring national ownership.
For land cover and land cover change, a range of global
EO-derived products are available at coarse resolution(see Data Sources section), which generally detail the
distribution of vegetation types, water bodies and humanmade infrastructure, and reflect the use of land resources(i.e., soil, water and biodiversity) for agriculture, forestry,human settlements and other purposes. These datasets
differ in the number of classes in which they describe thelandscape, their spatial resolution and duration. National
land cover datasets can more accurately represent the rangeof land cover types that exists in the respective country.
There is an international standard for land cover which
includes the Land Cover Meta Language (LCML), a common
reference structure (statistical standard) for the comparison

and integration of data for any generic land coverclassification system. LCML is also used for defining land
cover and ecosystem functional units used in the SEEA, andclosely linked to the Intergovernmental Panel on Climate
Change (IPCC) classification on land cover/land use. The
IPCC land use change legend suggests six main classes(forest land, grassland, cropland, wetlands, settlements
and other lands) which should be considered as a minimum
set. Changes in landcover are identified as degradation or
not using a transition matrix, in which reporting authorities
have the opportunity to nominate which transitions to
identify as degraded for their country (see below).
Note: This sub-indicator is also expected to be used forreporting on SDG indicators 6.6.1, 11.3.1 and 15.1.1.
A large variety of methods exist to estimate land
productivity and its associated changes from EO data
sources. The sub-indicator refers to the total above-ground
net primary production (NPP) defined as the energy fixedby plants minus their respiration which translates into
the rate of biomass accumulation that delivers ecosystemservices. While the ability to calculate changes in NPP
in specific units such as tonnes/ha is preferred, changes
in a unitless proxy of productivity, such as the NDVI orother vegetation index, can equally be used to identify
degradation through relative changes in productivity overtime. However, calibration and validation of the EO-based
observations of productivity against field data can be achallenging task and requires a solid in situ data basis.
The sub-indicator on carbon stocks, above and belowgroundis currently represented as a provisional proxy by Soil
Organic Carbon (SOC) stocks. In UNCCD decision 22/COP.11,
soil organic carbon (SOC) stock was adopted as the metric
to be used with the understanding that this metric will
be replaced by total terrestrial system carbon stocks,

Original Class

Final Class
IPCC Class

Forest Land

Grassland

CroplandWetlands

Settlements

Other
Land

Forest Land

StableVegetation loss

Deforestation

Inundation

Deforestation

Vegetation
loss

Grassland

Afforestation

StableAgricultural
expansion

Inundation

Urban
expansion

Vegetation
loss

CroplandAfforestation

Withdrawal of
Agriculture

StableInundation

Urban
expansion

Vegetation
loss

Wetlands

Woody
Encroachment

Wetlanddrainage

Wetlanddrainage

StableWetlanddrainage

Wetlanddrainage

Settlements

Afforestation

Vegetation
establishment

Agricultural
expansion

Wetlandestablishment

StableWithdrawalofSettlements

Other Land

Afforestation

Vegetation
establishment

Agricultural
expansion

Wetlandestablishment

Urban
expansion

StableCompendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

143

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

once operational. SOC is an indicator of overall soil quality
associated with nutrient cycling and its aggregate stability
and structure with direct implications for water infiltration,soil biodiversity, vulnerability to erosion, and ultimately
the productivity of vegetation, and in agricultural contexts,
yields. For carbon stocks, IPCC (2006 & 2019) contains
the most relevant definitions and standards related tosoil infrastructure, and data transfer. EO methods can also
be used to estimate aboveground carbon stocks, but SOC
will be used until methods to measure total terrestrial
carbon stock is operational and robust. The methodologicalapproaches range from using default values based on land
cover type which are modified by a range of use factors
similar to those described in the IPCC processes, to highly
detailed country-specific digital soil mapping, calibrated
and validated using process-based models.
Already at this point, Conservation International (CI)
together with Lund University, and the National Aeronautics
and Space Administration (NASA), have developed a platform
for monitoring land change using EO in an innovative
desktop and cloud-based system with the support of the
Global Environment Facility (GEF). The Trends.Earth tool
box draws on a variety of different data sources including
the Normalized Difference Vegetation Index (NDVI), soil
moisture, precipitation, evapotranspiration, land cover, soil
carbon, agro-ecological zones as well as administrative
boundaries. The toolbox is able to estimate the three subindicators for monitoring the achievement of LDN and canbe used by countries to analyse the data as well as toreport to UNCCD.
Treatment of missing values
In case no data or information is available at country level
for any sub-indicator, there exists a wide range of regionaland global EO derived products with an acceptable spatial
resolution to derive estimates for all three sub-indicators(See data sources section). The land area of countries withmissing values (with no default data) are proposed to be
excluded from regional and global aggregation.
Sources of discrepancies
Differences between global and national figures may
arise due to differences in spatial resolution of datasets,
classification approaches (i.e. definition of land cover classes
or difference in classifying methods) and/or contextualization
with other indicators, data and information. In such cases,
the use of regional and global datasets derived from EO canplay a role in clarifying such discrepancies. Alternatively, theconvergence of evidence approach, adopted by the World
Atlas of Desertification of the JRC, could be used to resolve
discrepancies arising from the 1OAO principle. Convergenceof reliable, global evidence of human environment, in theform of global change issues (GCIs) at a location suggests
a potential for land degradation (at least in some form).
The GCIs are a mixture of biophysical and socio-economic

TARGET 15.3

drivers, and were selected because of their availability as
global data and their usefulness as factors associated withland degradation (WAD, 2018).
Limitations
While access to remote sensing imagery has improved
dramatically in recent years, there is still a need for essential
historical time series that is currently only available atcoarse to medium spatial resolution. This resolution can
represent an issue especially in mountainous areas, small
island states and highly fragmented landscapes (GEO,
2017). There is an urgent need to move to high resolution
datasets (GEO, 2017).
Some aspects of land degradation, such as the loss of
biodiversity and ecosystems services are not currently
included in the UNCCD definition of Land Degradation andmay also be not well captured by EO methods.
While land productivity is not difficult to monitor from an
EO points of view, for example by using a vegetation index
time series, it is more difficult to determine if the land
productivity trend observed corresponds to degradation.
For example, bush encroachment or the presence ofinvasive plant species will feature a positive trend in land
productivity but it is seen as land degradation.
Although, global coarse to medium resolution EO datasets
are delivering acceptable results for reporting of the
progress under Agenda 2030, efforts should be focusedon the incorporation of high resolution satellite EO
imagery as a starting point for the further development
and improvement of the SDG reporting system of indicator
15.3.1
For raw data, data sources include:
andsat-5 to 8 archive to derive the baseline period as- Lwellas the base year 2015
S

entinel-2
for monitoring and reporting starting from- the year 2016
and onwards
Sentinel-1
(evaluate
potential for integration)
Existing products (with coverage exceeding national orregional initiatives) include:
Copernicus High resolution Layers (HRL)- Pforan-European
imperviousness, forest, grasslands, water and-wetness as well as small woody features for the year
2015 at 20m resolution
S2 prototype LC 20m map of Africa 2016
Copernicus Dynamic Land Cover map at 100 mresolution produced annually for the entire globe

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

144

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSKey messages for countries on EO contribution to thecomputation method

he other sub-indicators can use EO more readily as• Tmethods
for land cover and land productivity mapping
are well established. However, the limitations arethat these sub-indicators may not be available at theappropriate spatial scale and that land productivity must
be quantified from a degradation perspective

he sub-indicators of indicator 15.3.1 are theoretically• Twellsuited to an EO-based methodology but thestrengths and limitations of the EO approach must
be carefully understood in order to be compliant withthe sub-indicator requirements, e.g. on assessing landdegradation from a land productivity point of view not abiophysical one

TARGET 15.3

lthough indicator 15.3.1 adopts the “One out all out”
• Aprinciple,
a convergence of evidence approach was

The sub-indicator on carbon stocks, above and•belowground is currently the most challenging. It willtake more time to harmonise EO-based methods ofabove ground carbon assessment with other methodsof soil organic carbon quantification before this subindicator is robust enough for EO.

used in the World Atlas of Desertification to map areas
vulnerable to land degradation. Some of the globalchange issues which could indicate degradation at alocation are mapped by EO.

Data sources
Sub-indicator 15.3.1 on Land Cover and Land Cover Change
Data category
Global/regional
datasets

Data sources

WebsiteESA CCI Land Coverhttps://climate.esa.int/en/projects/land-cover

SEEA-MODIS

https://search.earthdata.nasa.gov

Copernicus Global Land Coverhttps://land.copernicus.eu/global/products/lc

World Atlas of Desertification

https://wad.jrc.ec.europa.eu/

Sub-indicator 15.3.1 on Land Productivity
Data category

Global/regional
datasets

Data sources

WebsiteCopernicus Global Land Service products

https://land.copernicus.eu/global/

SEEA-MODIS

https://search.earthdata.nasa.gov

Land Productivity Dynamics

http://publications.jrc.ec.europa.eu/repository/
handle/JRC80541

World Atlas of Desertification

https://wad.jrc.ec.europa.eu/

Sub-indicator 15.3.1 on Carbon Stocks, Above and Belowground
Data category

Global/regional
datasets

Data sources

WebsiteHarmonized World Soil Database (HWSD)

http://webarchive.iiasa.ac.at/Research/LUC/
External-World-soil-database/HTML

SoilGrids

https://soilgrids.org

Global Soil Organic Carbon Map (GSOC)

http://54.229.242.119/GSOCmap/

World Atlas of Desertification

https://wad.jrc.ec.europa.eu/

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

145

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 15.3

Reference List

Further Reading and Resources

Dubovyk, O. (2017) The role of Remote Sensing in landdegradation assessments: opportunities and challenges.
European Journal of Remote Sensing, 50:1, 601-613.

Sims, N.; Green, C.; Newnham, G.; England, J.; Held, A.;
Wulder, M. et al. (2017) Good practice guidance. SDG
Indicator 15.3.1: Proportion of land that is degradedover total land area. Version 1.0. Bonn, Germany: United
Nations Convention to Combat Desertification.

Group on Earth Observations (GEO) (2017) Earthobservations in support of the 2030 Agenda on
Sustainable Development. Available at: http://
earthobservations.org/documents/publications/201703_
geo_eo_for_2030_agenda.pdf
IPBES (2018): Summary for policymakers of theassessment report on land degradation and restorationof the Intergovernmental Science-Policy Platform on
Biodiversity and Ecosystem Services. R. Scholes, L.
Montanarella, A. Brainich, N. Barger, B. ten Brink, M.
Cantele, B. Erasmus, J. Fisher, T. Gardner, T. G. Holland,
F. Kohler, J. S. Kotiaho, G. Von Maltitz, G. Nangendo, R.
Pandit, J. Parrotta, M. D. Potts, S. Prince, M. Sankaran and
L. Willemen (eds.). IPBES secretariat, Bonn, Germany. 44
pages
IPCC (2019). 2019 Refinement to the 2006 IPCC
Guidelines for National Greenhouse Gas Inventorieshttps://www.ipcc.ch/report/2019-refinement-to-the2006-ipcc-guidelines-for-national-greenhouse-gasinventories

Sims, N.C., Barger, N.N., Metternicht, G.I., & England,
J.R. (2020). A land degradation interpretation matrix
for reporting on UN SDG indicator 15.3.1 and landdegradation neutrality. Environmental Science & Policy,
114, 1-6
Tutorials on UNCCD PRAIS reporting: https://knowledge.
unccd.int/knowledge-products-and-pillars/accesscapacity-policy-support-technology-tools/tutorials-unccd
Trends.Earth: a platform from Conservation International
for monitoring land change using earth observations in
an innovative desktop and cloud-based system.
LDN Scientific Conceptual Framework (2017). ScientificConceptual Framework for Land Degradation Neutrality.
A report of the Science-Policy Interface. Available
at:https://www.unccd.int/publications/scientificconceptual-framework-land-degradation-neutralityreport-science-policy

Shoshany, M., Goldshleger, N. and Chudnovsky, A. (2013)
Monitoring of agricultural soil degradation by remotesensing methods: a review. International Journal of
Remote Sensing, 34:17, 6152-6181.
UNCCD (2015). Report of the Conference of the Parties
on its twelfth session, held in Ankara from 12 to 23
October 2015. Part two: Actions taken by the Conferenceof the Parties at its twelfth session. ICCD/COP(12)/20/
Add. Bonn: United Nations Convention to Combat
Desertification.
WAD - World Atlas of Desertification, 2018. Joint
Research Centre (JRC), European Commission.
https://wad.jrc.ec.europa.eu/convergenceofevidence

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

146

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 15.3

Earth as a tool for calculating 15.3.1 in Uganda

Trends.Earth,developedbyConservation
Internationalin
corporation with NASA and Lund
University, under funding of the
Global Environment Facility, is atool to integrate national level datawith globally available EO datasets
to calculate SDG indicator 15.3.1(proportion of degraded land). It isbased on standardised methods,
while also providing the flexibility
for customisation to local conditions.
The tool uses data from three subindicators –land cover, vegetation

productivity and soil organic carbon- to estimate the degraded land area
and is able to produce spatial explicit
information (Figure 19).
For each Sub-indicator, changes
have to be assessed and depicted
as (i) positive or improving, (ii)
negative or declining, or (iii) stable orunchanging. Based on the evaluationof the changes in these three subindicators, the proportion of landthat is degraded over total land area(%) is calculated and reported as a

Figure 19: Overview of the SDG 15.3.1 Sub-Indicators.

binary (i.e. degraded/not degraded)
quantification as required by SDG
target indicator 15.3.1.
The quantification follows the socalled “One out all out” (1OAO)
principle. That is, if one of the subindicators is negative (or stable whendegraded in the baseline or previous
monitoring year) for a particular landunit, then the particular area would beconsidered as degraded. The baseline
is established over the period 2000 to2015, with the base year being 2015.
All changes are assessed relative tothe baseline value with a reportinginterval of 4 years, starting in theyear 2018. The land degradationassessment is illustrated in Figure 20.
The method for calculating SDG
indicator 15.3.1 is extensively
described in the Good PracticeGuidance (GPG) developed by UNCCD.
In collaboration with UNCCD, theTrends. Earth team, as of November
2018, has trained over 400 people– from representatives of national
statistics offices and ministries ofenvironment, to academic and nonprofit users – on how to use thetool for reporting purposes. Accessto simplified summaries of spatial
outputs were greatly appreciated byusers to simplify obtaining the data
they needed directly in the format
required for reporting.
Lessons learned:
A key need that Trends.Earth users
identified is the importance of
support for local and nationallyavailable information to supplement
EO data. Particularly for analyses ofland cover and carbon stocks, country
representatives noted the limitationsof globally available EO data in someareas.

Figure 20: Schematic illustration of Trends.Earth processing and results for Uganda.

Credit: Franziska Albrecht (GeoVille)

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

147

TARGET 15.3

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

15.3.1

Sub-Indicator on
Trends in land cover

Sub-Indicator on
trends in land
productivity

Custodian agency

Sub-Indicator on trends
in carbon stocks, aboveand below ground.

UNCCD

Tier

II

Status of step-by-stepmethodology document on themetadata repository

Published

n/a

n/a

n/a

There is sufficientevidence to suggestthat EO could fully
support countries
in the productionof this indicator
as land cover iswidely available androutinely monitored.

There is sufficientevidence to suggestthat EO could fully
support countries
in the productionof this indicator as
land productivity
proxies are routinely
monitored.

Carbon stock monitoring
using EO data has
advanced but challenges
remain to trend detection
as comparability between
different time periods islimited by changes in EO
and lack of robust timeseriesMaturity of EO
technologies
Relevance of EO
for the indicator
criteria

Status of EO
in indicator
guidelines
Technical
capacity required
Availability of
global EO data
Compliance with
Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity tochangeIs it scalable(spatial)?
Is there asubstitute forgaps in the EO
record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

148

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Target 15.4
By 2030, ensure the conservation of mountain ecosystems,
including their biodiversity, in order to enhance their capacity
to provide benefits that are essential for sustainable
development
How can EO be used to help countries achieve the target?
Mountain ecosystems are hotspots of biodiversity andprovide essential ecosystem services for population living
in and beyond mountain areas. Accordingly, their safeguard
is of primary importance to help achieving certain SGDs.
This is explicitly recognized in SDG 15 through this target,
which is fully dedicated to conserving mountain ecosystems
so that their biodiversity and the services that flow fromthem are not in peril and are sustained in the long term.
EO can help support the conservation and sustainable
management of mountain ecosystems through multiple
ways. The applicability of EO to monitor land use dynamics
and the drivers of land use change, such as expansion ofhuman settlements or crop conversion, as well as theirimplications for biodiversity, has been extensively proven.
EO data, such as Digital Elevation Models (DEMs) or dataon climate and dynamic processes, can also be used tofeed models that assess the supply of and demand for
mountain ecosystem services. These models, in turn, could
be combined with information derived from climate orland use change scenarios to assess how the provision of
ecosystems services could be affected by them, allowing
to identify priority areas to implement adaptation actions.
Current Indicator(s):15.4.1 Coverage by protected areas of important sites for
mountain biodiversity
15.4.2 Mountain Green Cover Index.
Potential new indicator(s) based on EO
Given that mountain ecosystems have been highlighted as
one of the most vulnerable to climate change, the degree ofvulnerability to climate change could also be considered bycountries in order achieve this target. Mountain snow and
ice are the main control parameters of the hydrological cycleof many watersheds. The melting glaciers and decreasein snow volume may therefore affect water availability
for the population living in and outside mountain areas.
An indicator focusing on the proportion of snow and ice
cover in mountain areas could easily be developed using
EO methods and would provide valuable insights on theeffects of climate change on regional water availability.
While indicator 15.4.2 is relevant, it may not be able to fully
capture some of the main drivers of mountain ecosystem

TARGET 15.4

degradation processes such as overgrazing, pollution,
invasive species and fuelwood extraction or timber
extraction, as these processes do not always translate
into land cover/land use conversion. An additional indicator
focusing on ecosystem functioning, such as vegetation
productivity, may be appropriate to complement the current
set of indicators.
Given the importance of human pressures on mountainbiodiversity and ecosystem service use and demand, an EObased socio-economic indicator, such as population density
in mountain areas would possibly be meaningful as well;especially also in view of the link between demography
and land use.

Short methodological guidelines illustrated
with EO best practice examples
Indicator 15.4.1
Computation method
This indicator is calculated from data derived from aspatial overlap between digital polygons of protectedareas included in the World Database on Protected Areas(IUCN & UNEP-WCMC 2017), Key Biodiversity Areas (fromthe World Database of Key Biodiversity Areas, including
Important Bird and Biodiversity Areas, Alliance for
Zero Extinction sites, and other Key Biodiversity Areas;
available through the Integrated Biodiversity Assessment
Tool), and mountains (UNEP-WCMC 2002). The indicator iscomputed as the mean percentage of each mountain Key
Biodiversity Area currently recognised that is covered byprotected areas.
Sources of discrepancies
Protected areas are not static measures, they continuallychange in response to the varying pressures they face.
Despite concerted efforts by UNEP-WCMC in collaboration
with national, regional and international partners theWorld Database on Protected Areas (WDPA) is not always
entirely up-to-date. As such, there can be discrepancies
between national statistics and those calculated by stateparties on national protected area coverage.
Limitations
The indicator does not measure the effectiveness ofprotected areas in reducing biodiversity loss, which
ultimately depends on a range of management andenforcement factors not covered by the indicator.
Regarding important sites, the biggest limitation is thatsite identification to date has focused on specific subsets
of biodiversity, for example birds (for Important Bird andBiodiversity Areas) and highly threatened species (forAlliance for Zero Extinction sites).

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

149

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSAn additional limitation is that PAs don’t protect elevational
gradients uniformly across continents (Elsen et al., 2018)
and not sufficiently either to preserve biodiversity,
especially under climate change.

t is not employing any EO data in the computational• Imethod
but could feasibly use it to (i) identify mountainecosystem types (such as mountain bioclimatic belts) byincorporating a Digital Elevation Models (Körner et al.,2011) and (ii) delineate the edges of protected areas inmountains, e.g. based on biophysical features such as
lake shores, as well as to assess certain KBA criterion
such as extent of suitable habitat.

Key messages for countries on EO contribution to thecomputation method:
Indicator 15.4.1 is based on a well-established•methodology that uses the mean percentage [%] ofeach mountain Key Biodiversity Area that is coveredby protected areas in order to better reflect trends inprotected area coverage for countries or regions with few
or no mountain Key Biodiversity Areas (KBA).

TARGET 15.4

esides topography, direct use of EO for 15.4.1 is not• Bobvious
but their indirect use for the monitoring andassessment of biodiversity and ecosystems (and change
therein) is easy to imagine.

Data sources
Data category

Global/regional
datasets

Data sources

Website

The World Database on Protected areas, as
accessible via Protected Planet

www.protectedplanet.net

The World Database on Key Biodiversity Areas, as
accessible via BirdLife

http://www.keybiodiversityareas.org/home

Global Mountain Explorer

https://rmgsc.cr.usgs.gov/gme

Socio-Economic Data and Application Center:

http://sedac.ciesin.columbia.edu

Reference List
Bey, A.; Sánchez-Paus Díaz, A.; Maniatis, D.; Marchi, G.;
Mollicone, D.; Ricci, S.; Bastin, J.-F.; Moore, R.; Federici,S.; Rezende, M.; Patriarca, C.; Turia, R.; Gamoga, G.; Abe,H.; Kaidong, E.; Miceli, G. (2016) Collect Earth: Land Useand Land Cover Assessment through Augmented VisualInterpretation. Remote Sensing, 8, 807.
Elsen, P. R., Monahan, W. B. and Merenlender, A. M. (2018)‘Global patterns of protection of elevational gradients inmountain ranges’, Proceedings of the National Academyof Sciences, 115(23), p. 6004 LP-6009. Available at:
http://www.pnas.org/content/115/23/6004.abstract.

Kapos, V., J. Rhind, M. Edwards, M.F. Price and Ravilious,
C. (2000): Developing a map of the world’s mountain
forests. In: Forests in Sustainable Mountain Development:
A State-of-Knowledge Report for 2000, M.F. Price and N.
Butt (eds.), CAB International, Wallingford: 4–9
Körner, C., Paulsen, J. and Spehn, E. M. (2011) ‘Adefinition of mountains and their bioclimatic belts for
global comparisons of biodiversity data’, Alpine Botany,
121(2), p. 73. doi: 10.1007/s00035-011-0094-4.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

150

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 15.4

Open access EO data and web tools for watching over the Alps forplanning towards target 15.4
Remotenessandcomplextopographies
represent
major
challenges for the acquisition of dataon the state of and trends in mountainecosystems, their biodiversity, andthe ecosystem resources they provide,

including water and timber. Yet suchdata are critically needed to develop
sustainable pathways towards thelong-term conservation of mountainecosystems and their biodiversity. EO is
therefore a critical component toward

acquiring the biological and physicalparameters to monitor environmental
processes in mountains and support
the proactive management of natural
assets. In the European Alps, theacquisition and delivery of reliable
up-to-date information is achieved
with the Sentinel Alpine Observatory(SAO). The SAO is an initiative of
Eurac Research-Institute for Earth
Observation that gathers more than 10
years of expertise in remote sensingbased environmental monitoring in
the alpine region of South Tyrol, Italy.
Thanks to innovative methodologies
for the exploitation of the new sensor
technology on board the Copernicus
Sentinel satellites, the SAO provides
users of Earth Observation data in
the alpine region with a range of map
products related to key environmental
parameters extracted from Sentinel
data as well as a range of services.
The products comprise:•
Level 2 data (Sentinel-2 RGB,
Sentinel-2 surface reflectance,
Sentinel-1 backscatter),•
Water and cryosphere data(snow,
glacier,permafrost,
evapotranspiration, soil moisture)• Vegetation and land use dynamics
data (grass leaf area index,
Normalized Difference Vegetation
Index, forest cover change)•Natural hazards data (landslide
monitoring)
These products are made available
for unrestricted use to the scientificcommunity through a user-friendly
web GIS platform. Services include acatalogue to publish and access data
at different scales, cloud-computing
services to perform customized
analysis on the data, and web tools
for visualization and interactive
analysis of Sentinel data time series.

Figure 21: Sentinel Alpine Observatory (Source: http://sao.eurac.edu/sao/#mission)Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

151

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicator 15.4.1
Computation method
The Mountain Green Cover Index, or SDG indicator 15.4.2,
measures changes in the area of green vegetation in
mountain areas. Mountain areas are defined according
to UNEP-WCMC (Kapos et al. 2000), which classifies
mountains in 6 different classes based on a combination ofelevation and ruggedness. These classes are:Class 1: elevation > 4,500 metres
Class 2: elevation 3,500 – 4,500 metres
Class 3: elevation 2,500 – 3,500 metres
Class 4: elevation 1,500 – 2,500 metres and slope > 2Class 5: elevation 1,000 –1,500 metres and slope > 5 or
local elevation range (LER 7 kilometre radius) > 300 meters
Class 6: elevation 300 – 1,000 metres and local elevation
range (7 kilometre radius) > 300 meters
Changes in the area of green vegetation are reported as
the change of the proportion of the area covered by 3 IPCC
land cover/land use classes (forest, grassland/shrublandand cropland). This figure is expressed as a percentage ofthe total mountain area.
Remote sensing methods can contribute to the assessmentof changes in land cover in mountain areas. As indicated in
the indicator’s metadata, these changes can be estimatedusing Collect Earth (Bey et al. 2016), based on the visualinterpretation of medium to high resolution multi-temporalsatellite images from DigitalGlobe, SPOT, Sentinel 2, Landsat
and MODIS imagery within Google Earth, Bing Maps andGoogle Earth Engine. Image resolution ranges from 3 cm to
250 meters. Data and images are stored and made globally
available for any year from 2000. The changes in land cover
are assessed using a sample-based approach. Collect Earthallows the user to distribute sampling plots in 3 different
ways: Systematic, Random and Stratified Random. The
result is a grid of sampling plots, in which each plot isclassified according to the dominant land cover by the user.
Disaggregation
This indicator is disaggregated by the mountain classes
defined by Kapos et al. (2000). The disaggregation by
mountain areas can be carried out with the help of EO

TARGET 15.4

derived products such as digital elevation models. A global
map of mountains based on UNEP-WCMC classification was
produced using these products in 2015 by the Mountain
Partnership Secretariat at the Food and Agriculture
Organization of the United Nations.
Treatment of missing values
The visual interpretation of satellite images can giverise to missing values due to cloud cover, especially intropical areas. However, considering that only one valueof land cover class is needed per year for a given area, theproportion of missing values is expected to be very low,
given the high variety of imagery available in Collect Earth.
In cases with persistent cloud cover, radar data could be
potentially used as a substitute.
Sources of discrepancies
Protected areas are not static measures, they continuallychange in response to the varying pressures they face.
Despite concerted efforts by UNEP-WCMC in collaboration
with national, regional and international partners the World
Database on Protected Areas (WDPA) is not always entirely
up-to-date. As such, there can be discrepancies between
national statistics and those calculated by state parties onnational protected area coverage.
Limitations
The sampling density established by FAO (a systematic
grid of 500 000 plots, half hectare each plot, 16 km grid)
may not be high enough to produce a fully representative
dataset for all countries. Some countries may need tocomplement this and collect more data, especially forcountries with highly heterogeneous mountain landscapes.
Key messages for countries on EO contribution to thecomputation method:
15.4.2 is fully based on EO data, but does
• Inotndicator
allow to fully capture some of the main drivers ofmountain ecosystem degradation processes affecting
vegetation such as overgrazing, fuelwood and timber
extraction or forest to cropland conversion, which has
important implications for biodiversity in mountainecosystems of the world.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

152

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORSTARGET 15.4

Data sources
Data category

Global/regional
datasets

Software, tools
and platforms

Data sources

WebsiteShuttle Radar Topography Mission (SRTM) 1 arc-sec
globalhttps://earthdata.nasa.gov/nasa-shuttle-radartopography-mission-srtm-version-3-0-global1-arc-second-data-released-over-asia-andaustralia

ASTER DEM 30 m global

https://asterweb.jpl.nasa.gov/gdem.asp

Precise Global Digital 3D Map "ALOS World 3D"

https://www.eorc.jaxa.jp/ALOS/en/aw3d/
index_e.htm

OpenTopography

https://opentopography.org/

Collect Earth

http://www.openforis.org/tools/collect-earth.html

Global Mountain Explorer

https://rmgsc.cr.usgs.gov/gme/

Reference List
Bey, A.; Sánchez-Paus Díaz, A.; Maniatis, D.; Marchi, G.;
Mollicone, D.; Ricci, S.; Bastin, J.-F.; Moore, R.; Federici,S.; Rezende, M.; Patriarca, C.; Turia, R.; Gamoga, G.; Abe,H.; Kaidong, E.; Miceli, G. (2016) Collect Earth: Land Useand Land Cover Assessment through Augmented VisualInterpretation. Remote Sensing, 8, 807.
Elsen, P. R., Monahan, W. B. and Merenlender, A. M. (2018)‘Global patterns of protection of elevational gradients inmountain ranges’, Proceedings of the National Academyof Sciences, 115(23), p. 6004 LP-6009. Available at:
http://www.pnas.org/content/115/23/6004.abstract.

Kapos, V., J. Rhind, M. Edwards, M.F. Price and Ravilious,
C. (2000): Developing a map of the world’s mountain
forests. In: Forests in Sustainable Mountain Development:
A State-of-Knowledge Report for 2000, M.F. Price and N.
Butt (eds.), CAB International, Wallingford: 4–9
Körner, C., Paulsen, J. and Spehn, E. M. (2011) ‘Adefinition of mountains and their bioclimatic belts for
global comparisons of biodiversity data’, Alpine Botany,
121(2), p. 73. doi: 10.1007/s00035-011-0094-4.

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

153

TARGET 15.4

COMPENDIUM OF EO CONTRIBUTION TO THE SDG TARGETS AND INDICATORS

Indicators

15.4.1Goverage by protected areas ofimportant sites for mountainbiodiversity

15.4.2
Mountain Green Cover Index

UNEP-WCMC; UNEP; IUCN

FAO

Custodian agency
Tier

I

I

Status of step-by-step methodology document on
the metadata repository

Published

Published

EO data could be useful if there
are a set of mountain-specificessential biodiversity variables
that are identified and if EO
data get collected at the rightresolution for these variables,
accounting for the fact thatmountain ranges vary greatly intheir nature, topography, ecology,
etc. between countries.

The methodology toassess this indicator isfully based on EO data. The
methodology proposes theuse of a tool (Collect Earth)that is freely available andfor which training materials
are abundant.

Maturity of EO technologies
Relevance of EO
for the indicator
criteria

Status of EO in indicator guidelines
Technical capacity required
Availability of global EO data
Compliance with Reporting calendar

Robustness ofproposedmethodology
Criteria

Sensitivity to change
Is it scalable (spatial)?
Is there a substitute for gaps in the
EO record?

Overall EO relevanceComments to support criteria

Compendium of guidance on Earth Observation to support the targets and indicators of the Sustainable Development Goals

154

ANNEXLIST OF ACRONYMS AND ABBREVIATIONS
AAAA

Addis Ababa Action Agenda

ET

Evapotranspiration

AET

Actual Evapotranspiration

ETM+

Enhanced Thematic Mapper Plus

AGEOS 	Gabonese Agency for Space Studies andObservations

EVI

Enhanced Vegetation Index

AHT SDG 	Ad-Hoc Team on Sustainable Development
Goals

FAPAR 	Fraction of Absorbed Photosynthetically
Active Radiation
FAO

Food and Agricultural Organisation

FAOSTATFAO Statistical Database

ALOS

Advanced land Observing Satellite

ANN

Artificial Neuronal Network

ASA

Advisory Services and Analytics

FEWS 	RFE Famine Early Warning Systems Rainfall
Estimates

ASAR

Advanced Synthetic Aperture Radar

fCover

ASTER 	Advanced Spaceborne Thermal Emission and
Reflection Radiometer
ATSR

Along Track Scanning Radiometer

AVHRR

Advanced Very High Resolution Radiometer

BFAST 	pixel-based Break Detection for Additive
Seasonal Trends

fraction of Green Vegetation CoverfPAR 	fraction of Photosynthetically Active Radiation
FRA

Forest Resources Assessment

GEDI 	Global Ecosystem Dynamics Investigation
Lidar
GEO

Group on Earth Observations

GEOBIA

Geographic Object-Based Image Analysis

BLI

BirdLife International

CCI

Climate Change Iniative

GEO BON 	Group on Earth Observations Biodiversity
Observation Network

CDOM

Colored dissolved organic matter

G-EconCEOS Committee on Earth Observation Satellites

GEOGLAM 	Group on Earth Observations Global
Agricultural Monitoring

CMEMS 	Copernicus Marine Environment Monitoring
ServiceCSIRO 	Commonwealth Scientific and Industrial
Research Organisation

Geographically based Economic data

GEO-GNOME 	Global Network for Observation andInformation in Mountain Environments
GEOSS

Global Earth Observation System of Systems

CZCS

Coastal Zone Color Scanner

GES

Good Environmental StateDAAC

Distributed Active Archive Centre

GCP

Global Precipitation Climatology Project

DEM

Digital Elevation Model

GFOI

Global Forest Observation Initiative

DG-JRC

Directorate General – Joint Research Council

GFW

Global Forest Watch

DHS

Demographic and Health Surveys

GIS

Geographic Information System

DLR

German Aerospace Centre

GHSL

Global Human Settlement Layer

EDC

Euro Data CubeDLR

German Aerospace Center

GMW

Global Mangrove Watch

GMIA

Global Map of Irrigation Areas

GOCI

Geostationary Ocean Color Imager

GOSAT

Greenhouse Gas Observation Satellite

GPS

Global Positioning System

EG-ISGI 	Expert Group on the Integration of Statisticaand Geospatial Information
EM-DAT

Emergency Events Database

EMS

Copernicus Emergency Management Service

EO

Earth Observations

EO4SDG 	Earth Observation for Sustainable
Development Goals
ESA

European Space Agency

ESDC

Earth System Data CubeGPSDD 	Global Partnership for Sustainable
Development Data
GRACE 	Gravity Recovery and Climate Experiment
missionCompendium of Earth Observation contributions to the SDG Targets and Indicators

155

ANNEXGRUMPGlobal Rural Urban Mapping Project

MEA

GSO

Generic Slum Ontology

GSOC

Global Soil Organic Carbon

MERIS 	Medium Resolution Imaging
Spectroradiometer

GSP

Global Soil Partnership

GUF

Global Urban Footprint

GWOS

Global Wetlands Observation System

HLPF

High Level Political Forum

HPC

High Performance Computing

HWSD

Harmonized World Soil Database

Multilateral Environmental Agreement

MICS

Multiple Indicator Cluster Surveys

MISR

Multi-angle Imaging SpectroRadiometer

MODIS 	Moderate Resolution Imaging Spectrometer
Sensor
MOOC

Massive Open Online Course

MSFD EU

Marine Strategy Framework Directive

IAEG-SDGs 	Inter Agency Expert Group on SDG Indicators

NASA 	National Aeronautics and Space Admin
istration

ICAO

International Civil Aviation Organization

NDVI

Normalized Difference Vegetation Index

ICEP

Index of Coastal EutrophicationNGO

Non-Government Organisation

IEA

International Energy Agency

NIR

Near Infrared

ILO

International Labour Organization

NMCA

National Mapping and Cadastral Authorities

IOC 	Intergovernmental Oceanographic
Commission
IOC-UNESCO 	Intergovernmental Oceanographic
Commission of UNESCO
IOOS

Integrated Ocean Observing System

IPCC

International Panel on Climate Change

IRS

Indian Remote Sensing satellite

ISPRA 	Superior Institute for the Environmental
Protection and Research
ISRIC 	International Soil Reference and Information
CentreITPS

Intergovernmental Technical Panel on Soils

IUCN
NatureInternational Union for Conservation of

JERS

Japanese Earth Resources Satellite

JAXA

Japanese Aerospace Exploration Agency

JRC

Joint Research Centre

KBA

Key Biodiversity Areas

LAI

Leaf Area Index

LCCS

Land Cover Classification System

LDNLand Degradation Neutrality

LiDAR

Light Detection and Ranging

LSMS

Living Standards Measurement Surveys

LULC

Land Use and Land Cover

MAR

Model-assisted Regression

MATTM 	Italian Ministry for the protection ofEnvironment and Sea
MDG

Millennium Development Goal

NOAA 	National Oceanic and Atmospheric Association
NPP

Net Primary Productivity

NSO

National Statistic Office

MSI:

Multi Spectral Imager

OA

Ocean Acidification

OBIA

Object-based Image

OCO

Orbiting Carbon Observatory

OLCI

Ocean and Land Colour Imager

OLI

Operational Land Imager

PALSAR

Phased Array Synthetic Aperture Radar

PAR

Photosynthetically Active Radiation

PRODES 	Programa de Cálculo do Desflorestamento da
Amazônia (Deforestation in Brazil)
PSU

Primary Sample Units

REDD+

 nited Nations Reducing Emissions fromU
Deforestation and forest Degradation
SchemeSAO

Sentinel Alpine Observatory

SAR

Synthetic Aperture Radar

SeaWiFS

Sea-Viewing Wide Field-of-View Sensor

SEPAL

S ystem for Earth Observation Data Access,Processing and Analysis for Land Monitoring

SDD

Secchi Disk Depth

SDMX

Statistical Data and Metadata Exchange

SISH

Slum/Informal Settlement Households

SMAP 	Soil Moisture Active Passive satellite mission
SMOS 	Soil Moisture Ocean Salinity satellite mission

Compendium of Earth Observation contributions to the SDG Targets and Indicators

156

ANNEX

SNAP

Sentinel Application Platform

UNSD

United Nations Statistics Division

SPOT

Systeme Pour l’Observation de la Terre(French satellite)

USGS

United States Geological Survey

UV

Ultraviolet

SSS

Sea Surface Salinity

VCF

Vegetation Continuous Field

SST

Sea Surface Temperature

VCI

Vegetation Condition Index

SSU

Secondary Sample Units

VPI

Vegetation Productivity Index

SDG

Sustainable Development Goal

VHR

Very High Resolution

SWOS

S atellite-based Wetlands Observation
Service

VIIRS

Visible Infrared Imaging Radiometer Suite

TEP

Thematic Exploitation Platforms

VIS

Visible Spectrum

TIRS

Thermal Infrared Sensor

WaPOR

Water Productivity Open-access portal

TOMS

Total Ozone Mapping Spectrometer

WASH

Water, Sanitation and Hygiene for All

TOA

Top Of Atmosphere

WGGIWorking Group on Geospatial Information

TRMM

Tropical Rainfall Measuring Mission

WHO

World Health Organization

TRWR

Total Renewable Freshwater Resources

WOIS

Water Observation and Information

TSM

Total Suspended Matter

WRI

World Resources Institute

TSS

Total Suspended Solids

WSF

World Settlement Footprint

TWW

Total freshwater withdrawn

WSSD

World Summit on Sustainable Development

UAV

Unmanned aerial vehicle

WT

Water Temperature

UN

United Nations

UNAIDS

United Nations Programme on HIV/AIDSUN CBD

Convention on Biological Diversity

UNCCD

UN Convention to Combat Desertification

UN ECOSOC UN Economic and Social Council
UNFCCC 	United Nations Framework Convention on
Climate Change
UNEP

United Nations Environment Programme

UNGA

United Nations General Assembly

UN-GGIM N Committee of Experts on GlobalU
Geospatial Information Management

UNICEF 	United Nations International Children’s
Emergency FundUNIDO 	United Nations Industrial Development
Organization
UNISDR 	United Nations International Strategy for
Disaster Reduction
UNEP-WCMC 	UN Environment World Conservation
Monitoring Centre
UNESCO 	United Nations Educational, Scientific andCultural Organization
UNESCO-UIS UNESCO Institute for Statistics
UNFCCC N Framework Convention on Climate
U
ChangeCompendium of Earth Observation contributions to the SDG Targets and Indicators

157