Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting
| Autor: | Alfred Hubbard, A. H. Pickens, Temilola Fatoyinbo, Sushel Unninayar, Raha Hakimdavar, Argyro Kavvada, Mark L. Carroll, Brandon I Smith, Matthew C. Hansen, Nima Pahlevan, Danielle Wood, David Lagomasino, Stephanie Schollaert Uz, M. M. Hurwitz, Frederick Policelli |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Earth observation
Resource (biology) 010504 meteorology & atmospheric sciences Science 0211 other engineering and technologies Context (language use) 02 engineering and technology Shuttle Radar Topography Mission computer.software_genre 01 natural sciences water quality water-related ecosystems Indicator 6.6.1 021101 geological & geomatics engineering 0105 earth and related environmental sciences surface water extent business.industry mangroves Environmental resource management Sustainable Development Goal 6 General Earth and Planetary Sciences Environmental science Moderate-resolution imaging spectroradiometer Water quality Small Island Developing States business computer Data integration |
| Zdroj: | Remote Sensing, Vol 12, Iss 1634, p 1634 (2020) Remote Sensing Volume 12 Issue 10 Pages: 1634 |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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