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Remote Sensing Measurements for Monitoring Achievement of the Sustainable Development Goals (SDGs)

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 35152

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Special Issue Editors

Dr. Ran Goldblatt

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Guest Editor

New Light Technologies Inc., Washington, DC, USA
Interests: remote sensing; image classification; economic development; disaster management; night-time lights; built-up land cover
Special Issues, Collections and Topics in MDPI journals

Mr. Nicholas Jones

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Guest Editor

Global Facility for Disaster Reduction and Recovery/World Bank, Washington, DC, USA

Dr. Nicholas Clinton

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Guest Editor

Google Earth Engine, Mountain View, CA, USA

Mr. Trevor Monroe

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Guest Editor

Big Data Innovation, Development Economics Data Group, The World Bank, Washington, DC, USA

Special Issue Information

Dear Colleagues,

The 2030 Agenda for Sustainable Development reflects a unique global consensus and commitment of countries to action to end poverty and hunger, protect the planet, foster peaceful, just and inclusive societies and ensure that all people can enjoy prosperous and fulfilling lives and that economic, social and technological progress will occur in harmony with nature.

The increasing availability of satellite data has transformed how we use remote sensing analytics to understand, monitor and achieve the 2030 Sustainable Development Goals. As satellite data becomes ever more accessible and frequent, it is now possible not only to better understand how Earth is changing, but also to utilize these insights to improve decision making, guide policy, deliver services, and promote better-informed governance. Satellites capture many of the physical, economic and social characteristics of Earth, providing a unique asset for developing countries, where reliable socio-economic and demographic data is often not consistently available. Analysis of satellite data was once relegated to researchers with access to costly data or to “super computers”. Today, the increased availability of “free” satellite data, combined with powerful cloud computing and open source analytical tools have democratized data innovation, enabling local governments and agencies to use satellite data to improve sector diagnostics, development indicators, program monitoring and service delivery. As petabytes of geo data are being collected, novel methods are developed to convert these data into meaningful information about the nature and pace of change on Earth, for example, the formation of urban landscapes and human settlements, the creation of transportation networks that connect cities or the conversion of natural forests into productive agricultural land. New possibilities emerge for harnessing this data for a better understanding about our changing world.

The purpose of this Special Issue is to stimulate progress in the remote sensing research domain related to measurements of developing countries achievement and monitoring of the SDGs. The issue will bring together novel methods and studies dedicated to remotely sensed measurement techniques of the progress towards achieving the SDG goals and robust methods and tools that improve the timeliness, coverage, and quality of SDG related data.

Dr. Ran Goldblatt
Mr. Nicholas Jones
Dr. Nicholas Clinton
Mr. Trevor Monroe
Guest Editors

Manuscript Submission Information

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

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. 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 2700 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 Goals (SDGs)
developing countries
United Nations

Published Papers (8 papers)

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Research

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28 pages, 5071 KiB

Open AccessArticle

Time Series of Land Cover Mappings Can Allow the Evaluation of Grassland Protection Actions Estimated by Sustainable Development Goal 15.1.2 Indicator: The Case of Murgia Alta Protected Area

by Cristina Tarantino, Mariella Aquilino, Rocco Labadessa and Maria Adamo

Remote Sens. 2023, 15(2), 505; https://doi.org/10.3390/rs15020505 - 14 Jan 2023

Cited by 1 | Viewed by 1434

Abstract

Protected areas, or national parks, are established to preserve natural ecosystems; their effectiveness on the territory needs to be evaluated. We propose considering a time series of the SDG 15.1.2 indicator, “Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type”, to quantify the presence over time of grassland ecosystem in Murgia Alta (southern Italy), within the Natura 2000 and national park boundaries. Time series of remote sensing imagery, freely available, were considered for extracting, by Support Vector Machine classifiers, a time series of grassland cover mappings from 1990 to 2021. This latter was, then, used for computing a time series of the SDG 15.1.2 indicator. A high reduction (about 15,000 ha) of grassland presence from 1990 to 2004, the foundation years of the national park, followed by the increasing stability up to nowadays, was evaluated. Furthermore, grassland presence was evaluated in a 5-km buffer area, surrounding Natura 2000 boundary, revealing a continuous loss from 1990 up to now (about 500 ha) in the absence of protection actions. This study represents the first long-term analysis for the grassland ecosystem in Murgia Alta and the first effort to analyze a time series of the SDG 15.1.2 indicator. The findings can provide inputs to governments in monitoring the effectiveness of protection actions. Full article

(This article belongs to the Special Issue Remote Sensing Measurements for Monitoring Achievement of the Sustainable Development Goals (SDGs))

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19 pages, 4455 KiB

Open AccessArticle

Regional Monitoring of Fall Armyworm (FAW) Using Early Warning Systems

by Ma. Luisa Buchaillot, Jill Cairns, Esnath Hamadziripi, Kenneth Wilson, David Hughes, John Chelal, Peter McCloskey, Annalyse Kehs, Nicholas Clinton, José Luis Araus and Shawn C. Kefauver

Remote Sens. 2022, 14(19), 5003; https://doi.org/10.3390/rs14195003 - 08 Oct 2022

Cited by 3 | Viewed by 3296

Abstract

The second United Nations Sustainable Development Goal (SDG2), zero hunger, aims to improve the productivity, food security, nutrition, and sustainability of small-scale farmers. The fall armyworm (FAW, Spodoptera frugiperda) has been devasting to smallholder farmer food security since it spread to sub-Saharan Africa in 2016, who have suffered massive crop losses, particularly maize, an important staple for basic sustenance. Since the FAW mainly devours green leaf biomass during the maize vegetative growth stage, the implementation of remote sensing technologies offers opportunities for monitoring the FAW. Here, we developed and tested a Sentinel 2 a+b satellite-based monitoring algorithm based on optimized first-derivative NDVI time series analysis using Google Earth Engine. For validation, we first employed the FAO Fall Armyworm Monitoring and Early Warning System (FAMEWS) mobile app data from Kenya, and then subsequently conducted field validation campaigns in Zimbabwe, Kenya, and Tanzania. Additionally, we directly observed loss of green biomass during maize vegetative growth stages caused by the FAW, confirming the observed signals of loss of the leaf area index (LAI) and the total green biomass (via the NDVI). Preliminary analyses suggested that satellite monitoring of small-scale farmer fields at the regional level may be possible with an NDVI first-derivative time series anomaly analysis using ESA Sentinel 2 a+b (R² = 0.81). Commercial nanosatellite constellations, such as PlanetScope, were also explored, which may offer benefits from greater spatial resolution and return interval frequency. Due to other confounding factors, such as clouds, intercropping, weeds, abiotic stresses, or even other biotic pests (e.g., locusts), validation results were mixed. Still, maize biomass anomaly detection for monitoring the FAW using satellite data could help confirm the presence of the FAW with the help of expanded field-based monitoring through the FAO FAMEWS app. Full article

(This article belongs to the Special Issue Remote Sensing Measurements for Monitoring Achievement of the Sustainable Development Goals (SDGs))

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16 pages, 3458 KiB

Open AccessArticle

From Illegal Waste Dumps to Beneficial Resources Using Drone Technology and Advanced Data Analysis Tools: A Feasibility Study

by Adi Mager and Vered Blass

Remote Sens. 2022, 14(16), 3923; https://doi.org/10.3390/rs14163923 - 12 Aug 2022

Cited by 5 | Viewed by 3217

Abstract

In a resource-constrained world, there is ongoing concern over the exploitation and potential future shortage of Earth’s natural resources. In this paper, we present the results of two pilot studies in which we used drone technology with spatial mapping tools and environmental and economic analysis to map illegal waste sites. Besides the technical feasibility, we aimed at understanding the benefits, costs, and tradeoffs of extracting the materials stocked therein, transforming illegal waste sites into valuable resources. The innovation of our work is reflected in the integration of existing technologies for aerial mapping and economic\environmental assessment methodologies for promoting a local circular economy. The pilot results suggest that it is feasible to identify valuable materials left on the ground in the form of unattended, illegally disposed waste. Our initial national estimates for the illegal waste cleanup based on the pilot results suggest that the treatment cost in Israel can be reduced by 58 million USD and even reach zero, with the potential to generate up to 82.8 million USD profits. Finally, we link our results to the Sustainable Development Goals framework and suggest how mapping and implementing the recycling potential can promote achieving some of the goals. Our work provides missing data that the state, local authorities, contractors, and companies that monitor and manage waste and recycled raw materials may find useful. Full article

(This article belongs to the Special Issue Remote Sensing Measurements for Monitoring Achievement of the Sustainable Development Goals (SDGs))

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21 pages, 7765 KiB

Open AccessArticle

Ecological Safety Assessment and Analysis of Regional Spatiotemporal Differences Based on Earth Observation Satellite Data in Support of SDGs: The Case of the Huaihe River Basin

by Shan Sang, Taixia Wu, Shudong Wang, Yingying Yang, Yiyao Liu, Mengyao Li and Yuting Zhao

Remote Sens. 2021, 13(19), 3942; https://doi.org/10.3390/rs13193942 - 01 Oct 2021

Cited by 11 | Viewed by 2017

Abstract

Terrestrial ecosystems provide a variety of benefits for human life and production, and are a key link for achieving sustainable development goals (SDGs). The basin ecosystem is one type of terrestrial ecosystem. Ecological security (ES) assessments are an important component of the overall strategy to achieve regional sustainable development. The Huaihe River Basin (HRB) has the common characteristics of most basins, such as high population density, a rapidly developing economy, and many environmental problems. This study constructed an ES evaluation system by applying a pressure-state-response framework as an assessment method for the sustainable development of basins. Taking the HRB as an example, this study determined the ES status of the region from 2001 to 2019 and analyzed crucial factors for any variation observed by combining remote sensing and climate data, relevant policies, and spatial information technology. The results highlight the importance of reserves and the negative impact of urban expansion on ES. Additionally, the enactment of policies had a positive impact on ES, whereas precipitation had a negative effect on ES in most areas of the HRB. Based on these results, the government should strengthen the protection of forests, grasslands, and wetlands and improve water conservation facilities. This study provides guidance for the subsequent economic development, environmental protection, and the achievements of SDG 15 in the HRB. Full article

(This article belongs to the Special Issue Remote Sensing Measurements for Monitoring Achievement of the Sustainable Development Goals (SDGs))

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18 pages, 3292 KiB

Open AccessArticle

Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals

by Zinhle Mashaba-Munghemezulu, George Johannes Chirima and Cilence Munghemezulu

Remote Sens. 2021, 13(9), 1666; https://doi.org/10.3390/rs13091666 - 24 Apr 2021

Cited by 9 | Viewed by 3326

Abstract

Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs. Full article

(This article belongs to the Special Issue Remote Sensing Measurements for Monitoring Achievement of the Sustainable Development Goals (SDGs))

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18 pages, 1905 KiB

Open AccessArticle

Research Gap Analysis of Remote Sensing Application in Fisheries: Prospects for Achieving the Sustainable Development Goals

by Kuo-Wei Yen and Chia-Hsiang Chen

Remote Sens. 2021, 13(5), 1013; https://doi.org/10.3390/rs13051013 - 08 Mar 2021

Cited by 8 | Viewed by 4073

Abstract

Remote sensing (RS) technology, which can facilitate the sustainable management and development of fisheries, is easily accessible and exhibits high performance. It only requires the collection of sufficient information, establishment of databases and input of human and capital resources for analysis. However, many countries are unable to effectively ensure the sustainable development of marine fisheries due to technological limitations. The main challenge is the gap in the conditions for sustainable development between developed and developing countries. Therefore, this study applied the Web of Science database and geographic information systems to analyze the gaps in fisheries science in various countries over the past 10 years. Most studies have been conducted in the offshore marine areas of the northeastern United States of America. In addition, all research hotspots were located in the Northern Hemisphere, indicating a lack of relevant studies from the Southern Hemisphere. This study also found that research hotspots of satellite RS applications in fisheries were mainly conducted in (1) the northeastern sea area in the United States, (2) the high seas area of the North Atlantic Ocean, (3) the surrounding sea areas of France, Spain and Portugal, (4) the surrounding areas of the Indian Ocean and (5) the East China Sea, Yellow Sea and Bohai Bay sea areas to the north of Taiwan. A comparison of publications examining the three major oceans indicated that the Atlantic Ocean was the most extensively studied in terms of RS applications in fisheries, followed by the Indian Ocean, while the Pacific Ocean was less studied than the aforementioned two regions. In addition, all research hotspots were located in the Northern Hemisphere, indicating a lack of relevant studies from the Southern Hemisphere. The Atlantic Ocean and the Indian Ocean have been the subjects of many local in-depth studies; in the Pacific Ocean, the coastal areas have been abundantly investigated, while offshore local areas have only been sporadically addressed. Collaboration and partnership constitute an efficient approach for transferring skills and technology across countries. For the achievement of the sustainable development goals (SDGs) by 2030, research networks can be expanded to mitigate the research gaps and improve the sustainability of marine fisheries resources. Full article

(This article belongs to the Special Issue Remote Sensing Measurements for Monitoring Achievement of the Sustainable Development Goals (SDGs))

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25 pages, 10979 KiB

Open AccessArticle

Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting

by Raha Hakimdavar, Alfred Hubbard, Frederick Policelli, Amy Pickens, Matthew Hansen, Temilola Fatoyinbo, David Lagomasino, Nima Pahlevan, Sushel Unninayar, Argyro Kavvada, Mark Carroll, Brandon Smith, Margaret Hurwitz, Danielle Wood and Stephanie Schollaert Uz

Remote Sens. 2020, 12(10), 1634; https://doi.org/10.3390/rs12101634 - 20 May 2020

Cited by 42 | Viewed by 9039

Abstract

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6 reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped over time for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1 reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future. Full article

(This article belongs to the Special Issue Remote Sensing Measurements for Monitoring Achievement of the Sustainable Development Goals (SDGs))

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16 pages, 1425 KiB

Open AccessTechnical Note

Nightlights and Subnational Economic Activity: Estimating Departmental GDP in Paraguay

by Gordon Carlos McCord and Mario Rodriguez-Heredia

Remote Sens. 2022, 14(5), 1150; https://doi.org/10.3390/rs14051150 - 25 Feb 2022

Cited by 11 | Viewed by 4066

Abstract

Subnational measures of economic activity are crucial for analyzing inequalities that persist across subnational regions and for tracking progress towards sustainable development within a country. Eighteen of the Sustainable Development Goals (SDG) indicators require having estimates of Gross Domestic Product (GDP), making subnational GDP estimates crucial for local SDG monitoring. However, many countries do not produce official subnational GDP estimates. Using Paraguay as an example, we show how nightlights imagery from the Visible Infrared Imaging Radiometer Suite’s Day-Night Band (VIIRS-DNB) and data from neighboring countries can be used to produce subnational GDP estimates. We first estimate the relationship between VIIRS and economic activity in South American countries at the first subnational administrative level, employing various econometric models. Results suggest that nightlights are strongly predictive of subnational GDP variation in South American countries with available data. We assess various models’ goodness-of-fit using both cross-validation against other countries’ subnational GDP data and comparing predictions against an input–output accounting of Paraguay’s subnational GDP. Finally, we use the preferred model to produce a time series of department-level GDP in Paraguay. Full article

(This article belongs to the Special Issue Remote Sensing Measurements for Monitoring Achievement of the Sustainable Development Goals (SDGs))

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