Monitoring water-related ecosystems with Earth observation data in support of Sustainable Development Goal (SDG) 6 reporting
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 demonst...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | https://hdl.handle.net/1721.1/125741 |
| _version_ | 1826212752139485184 |
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| author | Hakimdavar, Raha Hubbard, Alfred Policelli, Frederick Pickens, Amy Hansen, Matthew Fatoyinbo, Temilola Lagomasino, David Pahlevan, Nima Unninayar, Sushel Kavvada, Argyro Carroll, Mark Smith, Brandon Hurwitz, Margaret Wood, Danielle Renee Schollaert Uz, Stephanie |
| author2 | Program in Media Arts and Sciences (Massachusetts Institute of Technology) |
| author_facet | Program in Media Arts and Sciences (Massachusetts Institute of Technology) Hakimdavar, Raha Hubbard, Alfred Policelli, Frederick Pickens, Amy Hansen, Matthew Fatoyinbo, Temilola Lagomasino, David Pahlevan, Nima Unninayar, Sushel Kavvada, Argyro Carroll, Mark Smith, Brandon Hurwitz, Margaret Wood, Danielle Renee Schollaert Uz, Stephanie |
| author_sort | Hakimdavar, Raha |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:37:39Z |
| format | Article |
| id | mit-1721.1/125741 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:37:39Z |
| publishDate | 2020 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1257412022-10-02T03:01:17Z Monitoring water-related ecosystems with Earth observation data in support of Sustainable Development Goal (SDG) 6 reporting Hakimdavar, Raha Hubbard, Alfred Policelli, Frederick Pickens, Amy Hansen, Matthew Fatoyinbo, Temilola Lagomasino, David Pahlevan, Nima Unninayar, Sushel Kavvada, Argyro Carroll, Mark Smith, Brandon Hurwitz, Margaret Wood, Danielle Renee Schollaert Uz, Stephanie Program in Media Arts and Sciences (Massachusetts Institute of Technology) 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. 2020-06-09T14:47:45Z 2020-06-09T14:47:45Z 2020-05 2020-03 2020-05-28T14:08:07Z Article http://purl.org/eprint/type/JournalArticle 2072-4292 https://hdl.handle.net/1721.1/125741 Hakimdavar, Raha, et al., "Monitoring water-related ecosystems with Earth observation data in support of Sustainable Development Goal (SDG) 6 reporting." Remote Sensing 12, 10 (May 2020): no. 1634 doi 10.3390/rs12101634 ©2020 Author(s) 10.3390/rs12101634 Remote Sensing Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Hakimdavar, Raha Hubbard, Alfred Policelli, Frederick Pickens, Amy Hansen, Matthew Fatoyinbo, Temilola Lagomasino, David Pahlevan, Nima Unninayar, Sushel Kavvada, Argyro Carroll, Mark Smith, Brandon Hurwitz, Margaret Wood, Danielle Renee Schollaert Uz, Stephanie Monitoring water-related ecosystems with Earth observation data in support of Sustainable Development Goal (SDG) 6 reporting |
| title | Monitoring water-related ecosystems with Earth observation data in support of Sustainable Development Goal (SDG) 6 reporting |
| title_full | Monitoring water-related ecosystems with Earth observation data in support of Sustainable Development Goal (SDG) 6 reporting |
| title_fullStr | Monitoring water-related ecosystems with Earth observation data in support of Sustainable Development Goal (SDG) 6 reporting |
| title_full_unstemmed | Monitoring water-related ecosystems with Earth observation data in support of Sustainable Development Goal (SDG) 6 reporting |
| title_short | Monitoring water-related ecosystems with Earth observation data in support of Sustainable Development Goal (SDG) 6 reporting |
| title_sort | monitoring water related ecosystems with earth observation data in support of sustainable development goal sdg 6 reporting |
| url | https://hdl.handle.net/1721.1/125741 |
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