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

2020 ◽  
Vol 12 (10) ◽  
pp. 1634 ◽  
Author(s):  
Raha Hakimdavar ◽  
Alfred Hubbard ◽  
Frederick Policelli ◽  
Amy Pickens ◽  
Matthew Hansen ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Surface Water ◽  
Spatial Resolution ◽  
The Philippines ◽  
Earth Observation ◽  
Landsat 8 ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
Data Products ◽  
Over Time

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.

scholarly journals Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space

2019 ◽  
Vol 11 (3) ◽  
pp. 266 ◽  
Author(s):  
Giles Foody ◽  
Feng Ling ◽  
Doreen Boyd ◽  
Xiaodong Li ◽  
Jessica Wardlaw
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Spatial Resolution ◽  
Earth Observation ◽  
Sustainable Development Goal ◽  
Learning Methods ◽  
Development Goal ◽  
Machine Learning Methods ◽  
Brick Kilns ◽  
Fine Spatial Resolution

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.

scholarly journals Informing action for United Nations SDG target 8.7 and interdependent SDGs: Examining modern slavery from space

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Doreen S. Boyd ◽  
Bertrand Perrat ◽  
Xiaodong Li ◽  
Bethany Jackson ◽  
Todd Landman ◽  
...  
Keyword(s):  
Climate Change ◽  
Sustainable Development ◽  
South Asian ◽  
Sustainable Development Goals ◽  
Earth Observation ◽  
Spatiotemporal Patterns ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
Modern Slavery ◽  
Development Goals

AbstractThis article provides an example of the ways in which remote sensing, Earth observation, and machine learning can be deployed to provide the most up to date quantitative portrait of the South Asian ‘Brick Belt’, with a view to understanding the extent of the prevalence of modern slavery and exploitative labour. This analysis represents the first of its kind in estimating the spatiotemporal patterns in the Bull’s Trench Kilns across the Brick Belt, as well as its connections with various UN Sustainable Development Goals (SDGs). With a principal focus on Sustainable Development Goal Target 8.7 regarding the effective measures to end modern slavery by 2030, the article provides additional evidence on the intersections that exist between SDG 8.7 and those relating to urbanisation (SDG 11, 12), environmental degradation and pollution (SDG 3, 14, 15), and climate change (SDG 13). Our findings are then used to make a series of pragmatic suggestions for mitigating the most extreme SDG risks associated with brick production in ways that can improve human lives and human freedom.

scholarly journals Monitoring changes in South Africa’s surface water extent for reporting Sustainable Development Goal sub-indicator 6.6.1.a

2021 ◽  
Vol 117 (5/6) ◽  
Author(s):  
Heidi van Deventer
Keyword(s):  
Sustainable Development ◽  
Surface Water ◽  
South African ◽  
Aquatic Ecosystems ◽  
The South ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
The Sustainable Development ◽  
Progress Reporting ◽  
Global Statistics

For the first progress reporting on the Sustainable Development Goal sub-indicator 6.6.1a in 2020, the South African and global statistics related to wetlands were compared. Firstly, in terms of the total wetland extent, the South African National Wetland Map version 5 (NWM5) represented 87% more inland, surface aquatic ecosystems than the Global Surface Water (GSW) product. More than half of the lacustrine systems and none of the palustrine and arid systems in NWM5 are represented in the GSW layer. Secondly, in terms of changes in the extent of wetlands, both the global and South African statistics showed a decreasing trend in the spatial extent of surface aquatic ecosystems in South Africa. These trends should be further investigated against systematic assessments of decadal drought periods. The hydroperiod information (permanent, seasonal and ephemeral inundation periods) of the GSW products show that South African lacustrine wetlands do not have a single dominant class (≥70% of the extent of a polygon) of inundation, but consist of a mosaic of these classes.

scholarly journals Analysing Slavery through Satellite Technology: How Remote Sensing Could Revolutionise Data Collection to Help End Modern Slavery

2018 ◽  
Vol 4 (2) ◽  
pp. 169-199
Author(s):  
Bethany Jackson ◽  
Kevin Bales ◽  
Sarah Owen ◽  
Jessica Wardlaw ◽  
Doreen Boyd
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Human Rights ◽  
Earth Observation ◽  
Shrimp Farming ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
Satellite Technology ◽  
Brick Kilns ◽  
Modern Slavery

An estimated 40.3 million people are enslaved globally across a range of industries. Whilst these industries are known, their scale can hinder the fight against slavery. Some industries using slave labour are visible in satellite imagery, including mining, brick kilns, fishing and shrimp farming. Satellite data can provide supplementary details for large scales which cannot be easily gathered on the ground. This paper reviews previous uses of remote sensing in the humanitarian and human rights sectors and demonstrates how Earth Observation as a methodology can be applied to help achieve the United Nations Sustainable Development Goal target 8.7.

scholarly journals News media attention in Climate Action: latent topics and open access

2021 ◽  
Author(s):  
Tahereh Dehdarirad ◽  
Kalle Karlsson
Keyword(s):  
Sustainable Development ◽  
Regression Analysis ◽  
Open Access ◽  
News Media ◽  
Latent Dirichlet Allocation ◽  
Topic Modelling ◽  
Sustainable Development Goal ◽  
Data Set ◽  
Development Goal ◽  
Climate Action

AbstractIn this study we investigated whether open access could assist the broader dissemination of scientific research in Climate Action (Sustainable Development Goal 13) via news outlets. We did this by comparing (i) the share of open and non-open access documents in different Climate Action topics, and their news counts, and (ii) the mean of news counts for open access and non-open access documents. The data set of this study comprised 70,206 articles and reviews in Sustainable Development Goal 13, published during 2014–2018, retrieved from SciVal. The number of news mentions for each document was obtained from Altmetrics Details Page API using their DOIs, whereas the open access statuses were obtained using Unpaywall.org. The analysis in this paper was done using a combination of (Latent Dirichlet allocation) topic modelling, descriptive statistics, and regression analysis. The covariates included in the regression analysis were features related to authors, country, journal, institution, funding, readability, news source category and topic. Using topic modelling, we identified 10 topics, with topics 4 (meteorology) [21%], 5 (adaption, mitigation, and legislation) [18%] and 8 (ecosystems and biodiversity) [14%] accounting for 53% of the research in Sustainable Development Goal 13. Additionally, the results of regression analysis showed that while keeping all the variables constant in the model, open access papers in Climate Action had a news count advantage (8.8%) in comparison to non-open access papers. Our findings also showed that while a higher share of open access documents in topics such as topic 9 (Human vulnerability to risks) might not assist with its broader dissemination, in some others such as topic 5 (adaption, mitigation, and legislation), even a lower share of open access documents might accelerate its broad communication via news outlets.

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