Special Issue "EO Solutions to Support Countries Implementing the SDGs"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 March 2020).

Special Issue Editors

Dr. Zoltán Vekerdy
E-Mail Website
Guest Editor
1 Department of Water Resources, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7500 AA Enschede, The Netherlands
2 Department of Water Management and Agro-meteorology, Faculty of Agriculture and Environmental Sciences, Szent István University, Páter Károly u. 1., 2100 Gödöllő, Hungary
Interests: Earth Observation of hydrological cycle and agriculture; water management; agro-hydrological modelling
Special Issues and Collections in MDPI journals
Mr. Marc Paganini
E-Mail Website1 Website2
Guest Editor
1European Space Agency (ESA), Directorate of Earth Observation Programmes, Science, Applications and Climate Department, ESRIN, Largo Galileo Galilei 1, Frascati 00044, Italy
2CEOS Ad-Hoc Team on SDGs
Interests: Earth observations science and applications; sustainable development; environmental governance; terrestrial ecosystem structure and processes; biodiversity; land degradation; water and wetlands; urban mapping; multi-temporal and multi-source remote sensing; EO exploitation platforms
Dr. Argyro Kavvada
E-Mail Website
Guest Editor
National Aeronautics and Space Administration, Booz Allen Hamilton, Washington, DC, USA
Interests: sustainable development; Earth science; remote sensing; climate change; Sustainable Development Goals; integration of Earth Observation and GIScience; spatiotemporal analytics; science policy
Ms. Andiswa Mlisa
E-Mail Website
Guest Editor
South African National Space Agency (SANSA), Earth Observation Programme, Enterprise Building, Mark Shuttleworth Street, Innovation Hub, Pretoria 0001, South Africa
Interests: Earth observations; capacity building; water resource management; sustainable development; open data platforms; science policy and diplomacy
Dr. Christoph Aubrecht
E-Mail Website1 Website2
Guest Editor
1European Space Agency (ESA-ESRIN), Directorate of Earth Observation Programmes, Frascati, Italy
2World Bank, Earth Observation for Sustainable Development Partnership, Washington, DC, USA
Interests: spatio-temporal analytics; integration of Earth Observation and GIScience; sustainable development; urban mapping; population distribution modeling; disaster risk management; exposure analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The international community recently engaged in an ambitious universal agenda on sustainable development with the aim to end poverty, promote prosperity and people’s well-being while protecting the environment. The 2030 Agenda on Sustainable Development ratified by the UN General Assembly in September 2015 is a new transformative and integrated development agenda that will drive the global agenda on sustainable development till 2030 and beyond. In total 17 Sustainable Development Goals (SDGs) and 169 targets were adopted by the world leaders, which later got translated into 232 indicators that collectively provide a management tool for countries to implement development strategies and report on progress toward the SDG targets. The 2030 Agenda for Sustainable Development clearly stresses the importance of Geospatial Information and Earth Observations (EO) to monitor progress and achieve the SDG targets. Effective monitoring of the SDG indicators and reporting of the progresses towards the SDG targets require the use of multiple types of data that go well beyond the traditional socio-economic data that countries have been exploiting to assess their development policies. Hence, it is considered of crucial importance to integrate data coming from technologies new to this domain, such as EO, in order to produce high-quality and timely information, with more detail, at higher frequencies, and with the ability to disaggregate development indicators. EO, together with modern data processing and analytics, offer unprecedented opportunities to make a quantum leap in the capacities of countries to efficiently track all facets of sustainable development.

Amongst all the SDG targets, those related to a sustainable use of natural resources are of particular importance since pressures on our planet’s environment and finite resources are expected to increase further in the future to support continued economic growth or increased food production and consumption patterns. Recent advances in EO research, both on methodological development and technological solutions, offer promising prospects for helping countries set up informed and evidence-based development policies for an optimum management of terrestrial, coastal and marine resources.

This special issue aims at presenting and showcasing the latest advances in EO solutions for supporting countries in better achieving their SDG targets, monitoring progress and reporting on the SDG global indicator framework. The papers of this special issue will aim at presenting the state of research, with practical cases having a potential for national implementation. Emphasis is put on those SDG targets and indicators that are related to the sustainable use of natural resources. Examples of existing/ongoing national implementation are also welcome.

The increasing spatial, temporal and spectral resolutions of EO data offer an invaluable opportunity for better informing development policies and quantifying various SDG indicators. However, those EO advances pose several challenges related to the acquisition, processing, integration, analysis and understanding of the data which need to be tackled by the scientific community in order to ensure operational applicability.

Instead of theoretical desktop studies, we seek articles about:

  • Robust EO methods for supporting countries in setting development policies (including national targets), monitoring progress toward SDG targets, and reporting on SDG indicators;
  • Multi-source data integration methods (e.g., active/passive remote sensing, optical/microwave, EO/in situ, socio-economic);
  • EO algorithms and workflows for quantifying SDG indicators related to natural resources;
  • Modelling and data assimilation methods for SDG monitoring;
  • Innovative and dedicated EO tools (e.g., on-line platforms, data cubes, open source software, teaching material, etc.).
  • EO solutions to address SDG interlinkages, trade-offs and complementariness (one EO solution – many SDG targets).

These articles shall address, but are not limited to, SDG 2 (zero hunger), SDG 6 (clean water and sanitation), SDG 11 (sustainable cities and communities), SDG 13 (climate action); SDG 14 (life below water) and SDG 15 (life on land).

Where relevant, the articles should tackle the aspects of accuracy, validation, standardisation, limitations and transferability for an easy and seamless integration in national processes and systems.

All articles shall have a practical demonstration in a country, preferably with the involvement of national governmental or scientific authorities.

We hope that this special issue will deliver scientific and practical solutions that can be exploited by countries in the setting and implementation of their SDG related actions.

Dr. Zoltán Vekerdy
Mr. Marc Paganini
Dr. Argyro Kavvada
Ms. Andiswa Mlisa
Dr. Christoph Aubrecht
Guest Editors

Deadline for Full Paper Submission: 15 May 2019

Manuscript Submission Information

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

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

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

Keywords

  • Sustainable Development
  • SDGs
  • Country level
  • Natural resources
  • Land degradation
  • Land use / land cover, Urban, Agriculture
  • Water
  • Wetlands
  • Rangelands
  • Forestry
  • Biodiversity
  • Costal areas
  • Oceans
  • Ecosystems
  • Methods
  • Indicators
  • Targets
  • Tools
  • Platforms
  • Monitoring and Reporting
  • Zero hunger
  • Clean water and sanitation
  • Sustainable cities and communities
  • Climate action
  • Life below water
  • Life on land

Published Papers (9 papers)

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Research

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Open AccessArticle
Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries
Remote Sens. 2020, 12(9), 1391; https://doi.org/10.3390/rs12091391 - 28 Apr 2020
Cited by 4 | Viewed by 1587
Abstract
In September 2015, the members of United Nations adopted the 2030 Agenda for Sustainable Development with universal applicability of 17 Sustainable Development Goals (SDGs) and 169 targets. The SDGs are consequential for the development of the countries in the Nile watershed, which are [...] Read more.
In September 2015, the members of United Nations adopted the 2030 Agenda for Sustainable Development with universal applicability of 17 Sustainable Development Goals (SDGs) and 169 targets. The SDGs are consequential for the development of the countries in the Nile watershed, which are affected by water scarcity and experiencing rapid urbanization associated with population growth. Earth Observation (EO) has become an important tool to monitor the progress and implementation of specific SDG targets through its wide accessibility and global coverage. In addition, the advancement of algorithms and tools deployed in cloud computing platforms provide an equal opportunity to use EO for developing countries with limited technological capacity. This study applies EO and cloud computing in support of the SDG 6 “clean water and sanitation” and SDG 11 “sustainable cities and communities” in the seven Nile watershed countries through investigations of EO data related to indicators of water stress (Indicator 6.4.2) and urbanization and living conditions (Indicators 11.3.1 and 11.1.1), respectively. Multiple approaches including harmonic, time series and correlational analysis are used to assess and evaluate these indicators. In addition, a contemporary deep-learning classifier, fully convolution neural networks (FCNN), was trained to classify the percentage of impervious surface areas. The results show the spatial and temporal water recharge pattern among different regions in the Nile watershed, as well as the urbanization in selected cities of the region. It is noted that the classifier trained from the developed countries (i.e., the United States) is effective in identifying modern communities yet limited in monitoring rural and slum regions. Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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Open AccessArticle
Earth Observation for the Implementation of Sustainable Development Goal 11 Indicators at Local Scale: Monitoring of the Migrant Population Distribution
Remote Sens. 2020, 12(6), 950; https://doi.org/10.3390/rs12060950 - 15 Mar 2020
Cited by 3 | Viewed by 1686
Abstract
This study focused on implementation of the Sustainable Development Goal (SDG) 11 indicators, at local scale, useful in monitoring urban social resilience. For this purpose, the study focused on updating the distribution map of the migrant population regularly residing in Bari and a [...] Read more.
This study focused on implementation of the Sustainable Development Goal (SDG) 11 indicators, at local scale, useful in monitoring urban social resilience. For this purpose, the study focused on updating the distribution map of the migrant population regularly residing in Bari and a neighboring town in Southern Italy. The area is exposed to increasing migration fluxes. The method implemented was based on the integration of Sentinel-2 imagery and updated census information dated 1 January 2019. The study explored a vector-based variant of the dasymetric mapping approach previously used by the Joint Research Center (JRC) within the Data for Integration initiative (D4I). The dasymetric variant implemented can disaggregate data from census areas into a uniform spatial grid by preserving the information complexity of each output grid cell and ensure lower computational costs. The spatial distribution map of regular migrant population obtained, along with other updated ancillary data, were used to quantify, at local level, SDG 11 indicators. In particular, the map of regular migrant population living in inadequate housing (SDG 11.1.1) and the ratio of land consumption rate to regular migrant population growth rate (SDG 11.3.1) were implemented as specific categories of SDG 11 in 2018. At the local level, the regular migrant population density map and the SDG 11 indicator values were provided for each 100 × 100 m cell of an output grid. Obtained for 2018, the spatial distribution map revealed in Bari a high increase of regular migrant population in the same two zones of the city already evidenced in 2011. These zones are located in central parts of the city characterized by urban decay and abandoned buildings. In all remaining city zones, only a slight generalized increase was evidenced. Thus, these findings stress the need for adequate policies to reduce the ongoing process of residential urban segregation. The total of disaggregated values of migrant population evidenced an increase of 44.5% in regular migrant population. The indicators obtained could support urban planners and decision makers not only in the increasing migration pressure management, but also in the local level monitoring of Agenda 2030 progress related to SDG 11. Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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Open AccessEditor’s ChoiceArticle
Potential of Night-Time Lights to Measure Regional Inequality
Remote Sens. 2020, 12(1), 33; https://doi.org/10.3390/rs12010033 - 20 Dec 2019
Cited by 4 | Viewed by 1611
Abstract
Night-time lights satellite images provide a new opportunity to measure regional inequality in real-time by developing the Night Light Development Index (NLDI). The NLDI was extracted using the Gini coefficient approach based on population and night light spatial distribution in Romania. Night-time light [...] Read more.
Night-time lights satellite images provide a new opportunity to measure regional inequality in real-time by developing the Night Light Development Index (NLDI). The NLDI was extracted using the Gini coefficient approach based on population and night light spatial distribution in Romania. Night-time light data were calculated using a grid with a 0.15 km2 area, based on Defense Meteorological Satellite Program (DMSP) /Operational Linescan System (OLS satellite imagery for the 1992–2013 period and based on the National Polar-orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) satellite imagery for the 2014–2018 period. Two population density grids were created at the level of equal cells (0.15 km2) using ArcGIS and PostgreSQL software, and census data from 1992 and 2011. Subsequently, based on this data and using the Gini index approach, the Night Light Development Index (NLDI) was calculated within the MATLAB software. The NLDI was obtained for 42 administrative counties (nomenclature of territorial units for statistics level 3 (NUTS-3 units)) for the 1992–2018 period. The statistical relationship between the NLDI and the socio-economic, demographic, and geographic variables highlighted a strong indirect relationship with local tax income and gross domestic product (GDP) per capita. The polynomial model proved to be better in estimating income based on the NLDI and R2 coefficients showed a significant improvement in total variation explained compared to the linear regression model. The NLDI calculated on the basis of night-time lights satellite images proved to be a good proxy for measuring regional inequalities. Therefore, it can play a crucial role in monitoring the progress made in the implementation of Sustainable Development Goal 10 (reduced inequalities). Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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Open AccessArticle
Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa
Remote Sens. 2019, 11(24), 2928; https://doi.org/10.3390/rs11242928 - 06 Dec 2019
Cited by 12 | Viewed by 1535
Abstract
Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and [...] Read more.
Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827–2807 km2 for Senegal and 245–1271 km2 for The Gambia with one outlier for each country. We further report “Places of Agreement” (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making. Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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Open AccessArticle
Combining Earth Observations, Cloud Computing, and Expert Knowledge to Inform National Level Degradation Assessments in Support of the 2030 Development Agenda
Remote Sens. 2019, 11(24), 2918; https://doi.org/10.3390/rs11242918 - 06 Dec 2019
Cited by 1 | Viewed by 1306
Abstract
Monitoring progress towards the 2030 Development Agenda requires the combination of traditional and new data sources in innovative workflows to maximize the generation of relevant information. We present the results of a participatory and data-driven land degradation assessment process at a national scale, [...] Read more.
Monitoring progress towards the 2030 Development Agenda requires the combination of traditional and new data sources in innovative workflows to maximize the generation of relevant information. We present the results of a participatory and data-driven land degradation assessment process at a national scale, which includes use of earth observation (EO) data, cloud computing, and expert knowledge for Argentina. Six different primary productivity trend maps were produced from a time series of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset (2000–2018), including the most widely used trajectory approach and five alternative methods, which include information on the timing and magnitude of the changes. To identify the land productivity trend map which best represented ground conditions, an online application was developed, allowing 190 experts to choose the most representative result for their region of expertise nationwide. Additionally, the ability to detect decreases in land productivity of each method was assessed in 43,614 plots where deforestation had been recorded. The widely used trajectory indicator was the one selected by most experts as better reflecting changes in land condition. When comparing indicators’ performance to identify deforestation-driven reductions in productivity, the Step-Wise Approach Trend Index (SWATI), which integrates short- and long-term trends, was the one which performed the best. On average, decreases of land productivity indicate that 20% of the Argentine territory has experienced degradation processes between 2000 and 2018. The participatory data generation and verification workflow developed and tested here represents an innovative low cost, simple, and fast way to validate maps of vegetation trends and other EO-derived indicators, supporting the monitoring of progress towards land degradation neutrality by 2030. Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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Open AccessFeature PaperArticle
Evaluation of Earth Observation Solutions for Namibia’s SDG Monitoring System
Remote Sens. 2019, 11(13), 1612; https://doi.org/10.3390/rs11131612 - 07 Jul 2019
Cited by 4 | Viewed by 1944
Abstract
In recent years, with more open data platforms and tools available to store and process satellite imagery, Earth Observation data have become widely accessible and usable especially for countries previously not in the possession of tasking rights to satellites and the needed processing [...] Read more.
In recent years, with more open data platforms and tools available to store and process satellite imagery, Earth Observation data have become widely accessible and usable especially for countries previously not in the possession of tasking rights to satellites and the needed processing capacity. Due to its ideal scanning and acquisition conditions for low cloud coverage imagery, Namibia aims to make use of this new development and integrate Earth Observation data into its national monitoring system of sustainable development goals (SDG). The purpose of this study is to assess the potential of open source tools and global datasets to estimate the national SDG indicators on Change of water-related ecosystems (6.6.1), Rural population with access to roads (9.1.1), Forest coverage (15.1.1) and Land degradation (15.3.1). The results are set into perspective of existing information in each particular sector. The study shows that, in the absence of in-situ measurements or data collected through surveys, the Earth Observation-based results represent a high potential to supplement the national statistics for Namibia or to serve as primary data sources once validated through ground-truthing. Furthermore, examples are given for the limitations of the assessed Earth Observation solutions in the context of Namibia. Hence, the study also serves as valuable input for discussions on a consensus on national definitions and standards by all stakeholders responsible for releasing official statistics. Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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Open AccessArticle
Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space
Remote Sens. 2019, 11(3), 266; https://doi.org/10.3390/rs11030266 - 29 Jan 2019
Cited by 18 | Viewed by 4501
Abstract
A large proportion of the workforce in the brick kilns of the Brick Belt of Asia are modern-day slaves. Work to liberate slaves and contribute to UN Sustainable Development Goal 8.7 would benefit from maps showing the location of brick kilns. Previous work [...] Read more.
A large proportion of the workforce in the brick kilns of the Brick Belt of Asia are modern-day slaves. Work to liberate slaves and contribute to UN Sustainable Development Goal 8.7 would benefit from maps showing the location of brick kilns. Previous work has shown that brick kilns can be accurately identified and located visually from fine spatial resolution remote-sensing images. Furthermore, via crowdsourcing, it would be possible to map very large areas. However, concerns over the ability to maintain a motivated crowd to allow accurate mapping over time together with the development of advanced machine learning methods suggest considerable potential for rapid, accurate and repeatable automated mapping of brick kilns. This potential is explored here using fine spatial resolution images of a region of Rajasthan, India. A contemporary deep-learning classifier founded on region-based convolution neural networks (R-CNN), the Faster R-CNN, was trained to classify brick kilns. This approach mapped all of the brick kilns within the study area correctly, with a producer’s accuracy of 100%, but at the cost of substantial over-estimation of kiln numbers. Applying a second classifier to the outputs substantially reduced the over-estimation. This second classifier could be visual classification, which, as it focused on a relatively small number of sites, should be feasible to acquire, or an additional automated classifier. The result of applying a CNN classifier to the outputs of the original classification was a map with an overall accuracy of 94.94% with both low omission and commission error that should help direct anti-slavery activity on the ground. These results indicate that contemporary Earth observation resources and machine learning methods may be successfully applied to help address slavery from space. Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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Review

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Open AccessReview
Open Earth Observations for Sustainable Urban Development
Remote Sens. 2020, 12(10), 1646; https://doi.org/10.3390/rs12101646 - 21 May 2020
Cited by 5 | Viewed by 1359
Abstract
Our cities are the frontier where the battle to achieve the global sustainable development agenda over the next decade would be won or lost. This requires an evidence-based approach to local decision-making and resource allocation, which can only be possible if current gaps [...] Read more.
Our cities are the frontier where the battle to achieve the global sustainable development agenda over the next decade would be won or lost. This requires an evidence-based approach to local decision-making and resource allocation, which can only be possible if current gaps in urban data are bridged. Earth observation (EO) offers opportunities to provide timely, spatially disaggregated information that supports this need. Spatially disaggregated information, which is also demanded by cities for forward planning and land management, has not received much attention largely due to three reasons: (i) the cost of generating this data through traditional methods remains high; (ii) the technical capacity in geospatial sciences in many countries is low due to a shortage of skilled professionals who can find and/or process available data; and (iii) the inertia against disturbing routine workflows and adopting new practices that are not imposed through legal requirements at the country level. In support of overcoming the first two challenges, this paper discusses the importance of EO data in the urban context, how it is already being used by some city leaders for decision making, and what other applications it offers in the realm of urban sustainability monitoring. It also illustrates how the EO community, via the Group on Earth Observations (GEO) and its members, is working to make this data more easily accessible and lower barriers of use by policymakers and urban practitioners that are interested in implementing and tracking sustainable development in their jurisdictions. The paper concludes by shining a light on the challenges that remain to be overcome for better adoption of EO data for urban decision making through better communication between the two groups, to enable a more effective alignment of the produced data with the users’ needs. Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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Other

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Open AccessPerspective
Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements
Remote Sens. 2019, 11(20), 2443; https://doi.org/10.3390/rs11202443 - 21 Oct 2019
Cited by 15 | Viewed by 5611
Abstract
Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this [...] Read more.
Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements. Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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