Slavery from Space: Demonstrating the role for satellite remote sensing to inform evidence-based action related to UN SDG number 8

Boyd, Doreen S.; Jackson, Bethany; Wardlaw, Jessica; Foody, Giles M.; Marsh, Stuart; Bales, Kevin

doi:10.1016/j.isprsjprs.2018.02.012

Cited by 79 publications

(61 citation statements)

References 27 publications

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“…Interestingly, the findings of the MMF suggest that many socio‐economic indicators may also be amenable to population via EO and this is an area that has received much less attention in the literature. There are published examples of utilising EO derived data for socio‐economic dimensions of sustainable development such as poverty (Ghosh et al, ; Jean et al, ), electricity consumption (Doll & Pachauri, ), human rights (Li et al, ), child labour and slavery (Boyd et al, ), corruption (Hodler & Raschky, ), and the incidence of breast cancer (Rybnikova & Portnov, ), but EO needs to achieve greater prominence with regard to its potential for supporting the SDGs that span both natural, social, and economic dimensions of sustainable development. Moreover, terrestrial applications of EO satellite data can respond in near real‐time to humanitarian and peace‐keeping operations (Corbane, Kemper, Pesaresi, Louvrier, & Freire, ) and natural disasters (e.g., flood hazard; Kerle & Oppenheimer, ).…”

Section: Discussionmentioning

confidence: 99%

Translation of Earth observation data into sustainable development indicators: An analytical framework

Andries

Morse

Murphy

et al. 2018

Sustainable Development

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In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators that are based on the three pillars of sustainable development: economic, social, and environmental. Substantial challenges remain in obtaining data of the required quality, especially in developing countries, given the often limited resources available. One promising and innovative way of addressing this issue of data availability is to use Earth observation (EO). This paper presents the results of research to develop a novel analytical framework for assessing the potential of EO approaches to populate the SDG indicators. We present a Maturity Matrix Framework and apply it to all of the 232 SDG indicators. The results demonstrate that although the applicability of EO-derived data do vary between the Sustainable Development Goal indicators, overall, EO has an important contribution to make towards populating a wide diversity of the Sustainable Development Goals indicators.

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Section: Discussionmentioning

confidence: 99%

Translation of Earth observation data into sustainable development indicators: An analytical framework

Andries

Morse

Murphy

et al. 2018

Sustainable Development

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“…Consequently, this group of volunteers annotated broadly the same set of images reducing the potential for problems such as optimistic bias in their labelling that could occur by skipping the complex to label cases and focusing on only the easier images. The focus on a relatively small group of volunteers is also in keeping with suggestions in the literature [15,25] as well as a means of balancing the competing pressures of seeking multiple annotations but wishing to label many cases [29].…”

Section: Datamentioning

confidence: 98%

Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data

Foody

See

Fritz

et al. 2018

IJGI

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Simple consensus methods are often used in crowdsourcing studies to label cases when data are provided by multiple contributors. A basic majority vote rule is often used. This approach weights the contributions from each contributor equally but the contributors may vary in the accuracy with which they can label cases. Here, the potential to increase the accuracy of crowdsourced data on land cover identified from satellite remote sensor images through the use of weighted voting strategies is explored. Critically, the information used to weight contributions based on the accuracy with which a contributor labels cases of a class and the relative abundance of class are inferred entirely from the contributed data only via a latent class analysis. The results show that consensus approaches do yield a classification that is more accurate than that achieved by any individual contributor. Here, the most accurate individual could classify the data with an accuracy of 73.91% while a basic consensus label derived from the data provided by all seven volunteers contributing data was 76.58%. More importantly, the results show that weighting contributions can lead to a statistically significant increase in the overall accuracy to 80.60% by ignoring the contributions from the volunteer adjudged to be the least accurate in labelling.

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“…But globally, areas where populations are most dependent on forests and their ecosystem services for subsistence and equitable sustainable development are also areas where modern slavery persists—often in activities perpetuating deforestation and similarly destructive practices that threaten biodiversity conservation (Bales, ; Food and Agriculture Organization [FAO], ; Brown et al, ). Thus, this modern slavery‐environmental degradation nexus may add to anthropogenic pressures on forests, compromising their ability to support the attainment of the afore‐mentioned SDGs—as noted in other sectors, including brick making (Boyd et al, ), farming, and fishing (Brown et al, ).…”

Section: Introductionmentioning

confidence: 99%

Understanding the co‐occurrence of tree loss and modern slavery to improve efficacy of conservation actions and policies

Jackson

Sparks

Brown

et al. 2020

Conservat Sci and Prac

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Locations where populations are most reliant on forests and their ecosystem services for subsistence and development are also areas where modern slavery persists. These issues are noted within the Sustainable Development Goals (SDGs), both target 15.2 and 8.7 respectively. Often activities using slavery perpetuate deforestation, bolstering a slavery-environment nexus; which has been examined by comparing modern slavery estimates against environmental protection levels. This study assesses the relationship between tree loss and modern slavery focusing on four countries: Brazil, Ghana, Indonesia, and Mozambique. Previously mapped levels of tree loss and predicted future levels of loss have been compared against modern slavery estimates from the Global Slavery Index 2016 and illegal logging analyses to determine an estimate of the risk for slavery related tree loss. These results provide an insight in to the cooccurrence between modern slavery and tree loss due to a number of activities that are highlighted, including mining, illegal logging, and agricultural practices. The co-occurrence is both complex, and yet, beyond coincidental. Implications for both national and global policy are noted assessing the benefits that could be achieved by limiting tree loss and ending modern slavery; of benefit to both the conservation and antislavery communities.

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Slavery from Space: Demonstrating the role for satellite remote sensing to inform evidence-based action related to UN SDG number 8

Cited by 79 publications

References 27 publications

Translation of Earth observation data into sustainable development indicators: An analytical framework

Translation of Earth observation data into sustainable development indicators: An analytical framework

Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data

Understanding the co‐occurrence of tree loss and modern slavery to improve efficacy of conservation actions and policies

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