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The Potential Contribution of Earth Observation Data Cubes for the Production of Information for Sustainable Development in Emerging Countries
Corresponding Author(s) : Andrea Ramírez Santiago
Geomatics and Environmental Engineering,
Vol. 16 No. 3 (2022): Geomatics and Environmental Engineering
Abstract
One of the great challenges of achieving the shared vision of the 2030 Agenda for Sustainable Development is having high-quality, timely, comparable, and accessible data that allows to measure and report progress on the Sustainable Development Goals (SDG). Hence, in many countries, geospatial information (including Earth observation) and algorithms implemented in cloud computing platforms have become important tools to monitor indicators of the SDG thanks to their broad accessibility and global coverage. However, emerging countries still face barriers to the implementation of technologies to manage the large amounts of EO data. This article aims to show the advantages of satellite-based EO in the measurement of SDG indicators, as well as challenges emerging countries face in the use of these technological tools. It addresses why the open-source tool Open Data Cube (ODC) should be seen as a response to the said challenges. Finally, there is a description regarding the experience of Mexico with the use and application of this tool for the measurement of SDG indicators, from the development and implementation of the Mexican Geospatial Data Cube (MGDC) to the results obtained from its application in the support for the measurement of SDG indicators 6.6.1 Change in the extent of water-related ecosystems over time and 15.1.1 Forest area as a proportion of total land area.
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- Marcovecchio I., Thinyane M., Estevez E., Fillottrani P.: Capability Maturity Models as a Means to Standardize Sustainable Development Goals Indicators Data Production. Journal of ICT Standardization, vol. 6(3), 2018, pp. 216–244. https://doi.org/10.13052/jicts2245-800X.633.
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- Thinyane M.: Small data and sustainable development–Individuals at the center of data-driven societies. [in:] 2017 ITU Kaleidoscope: Challenges for a Data-Driven Society (ITU K), IEEE, 2017, pp. 1–8. https://doi.org/10.23919/ITU-WT.2017.8246991.
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- Anderson K., Ryan B., Sonntag W., Kavvada A., Friedl L.: Earth observation in service of the 2030 Agenda for Sustainable Development. Geo-spatial Information Science, vol. 20(2), 2017, pp. 77–96. https://doi.org/10.1080/10095020.2017.1333230.
- Paganini M., Petiteville I., Ward S., Dyke G., Steventon M., Harry J., Kerblat F.: Satellite earth observations in support of the sustainable development goals. The CEOS Earth Observation Handbook Special 2018 Edition. European Space Agency, 2018. http://eohandbook.com/sdg/files/CEOS_EOHB_2018_SDG.pdf [access: 19.04.2022].
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- Arnold S., Chen J., Eggers O.: Global and Complementary (Non-authoritative) Geospatial Data for SDGs: Role and Utilisation. 2019. https://ggim.un.org/documents/Report_Global_and_Complementary_Geospatial_Data_for_SDGs.pdf [access: 19.04.2022].
- Allen C., Metternicht G., Wiedmann T.: Prioritising SDG targets: assessing baselines, gaps and interlinkages. Sustainability Science, vol. 14(2), 2019, pp. 421–438. https://doi.org/10.1007/s11625-018-0596-8.
- Filchev L., Pashova L., Kolev V., Frye S.: Challenges and solutions for utilizing Earth Observations in the “Big Data” era. Paper presented at the BigSkyEarth conference: AstroGeoInformatics, Tenerife, Spain, December 17–19, 2018. https://doi.org/10.5281/zenodo.2391937.
- Rosa W. (ed.): A New Era in Global Health: Nursing and the United Nations 2030 Agenda for Sustainable Development. Springer Publishing Company, 2017.
- Li W., El-Askary H., Lakshmi V., Piechota T., Struppa D.: Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries. Remote Sensing, vol. 12(9), 2020, 1391. https://doi.org/10.3390/rs12091391.
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- Dhu T., Giuliani G., Juárez J., Kavvada A., Killough B., Merodio P., Ramage S.: National Open Data Cubes and Their Contribution to Country-level Development Policies and Practices. Data, vol. 4(4), 2019, 144. https://doi.org/10.3390/data4040144.
- Giuliani G., Masó J., Mazzetti P., Nativi S., Zabala A.: Paving the Way to Increased Interoperability of Earth Observations Data cubes. Data, vol. 4, 2019, 113. https://doi.org/10.3390/data4030113.
- Giuliani G., Chatenoux B., De Bono A., Rodila D., Richard J.P., Allenbach K., Dao H., Peduzzi P.: Building an Earth Observations Data Cube: Lessons Learned from the Swiss Data Cube (SDC) on Generating Analysis Ready Data (ARD). Big Earth Data, vol. 1(1–2), 2017, pp. 100–117. https://doi.org/10.1080/20964471.2017.1398903.
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- Juárez Carrillo O.J., Merodio Gómez P., Ponce Medina M.D.S., Ornelas de Anda J.L., Corona Iruegas A.A.: Cubo de datos geoespaciales para el uso de las imágenes satelitales en la generación de información geográfica y estadística. Realidad, Datos y Espacio Revista Internacional de Estadística y Geografía, vol. 11(3), 2020, pp. 124–139.
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- INEGI: Producción y publicación de la Geomediana Nacional a Partir de imágenes del Cubo de Datos Geoespaciales de México: documento metodológico. Instituto Nacional de Estadística y Geografía, México 2020. https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos/nueva_estruc/702825198763.pdf [access: 19.04.2022].
- INEGI: Producción del Índice de Clasificaciones de Agua Superficial desde el Espacio (ICASE) Landsat: documento metodológico. Instituto Nacional de Estadística y Geografía, México 2021. https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos/nueva_estruc/889463903642.pdf [access: 19.04.2022].
- UN ECOSOC: In-depth review of satellite imagery / earth observation technology in official statistics: Prepared by Canada and Mexico. Economic Commission for Europe Conference of European Statisticians, 67th plenary session, Geneva, 26–28 June 2019, United Nations Economic and Social Council, 2019. https://unece.org/DAM/stats/documents/ece/ces/2019/ECE_CES_2019_16-1906490E.pdf [access: 19.04.2022].
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- UN STATS: SDG Indicators Metadata repository [Indicator 6.6.1: Change in the extent of water-related ecosystems over time]. United Nations Statistics Division. https://unstats.un.org/sdgs/metadata/files/Metadata-06-06-01a.pdf [access: 1.03.2021].
- UN Environment and UN Water: Measuring change in the extent of water-related ecosystems over time: Sustainable Development Goal Monitoring Methodology Indicator 6.6.1. 2020. https://files.habitatseven.com/unwater/SDG-Monitoring-Methodology-for-Indicator-6.6.1.pdf [access: 1.03.2021].
- UN Water: Indicator 6.6.1 – Progress on Water-related Ecosystems. https://sdg6data.org/indicator/6.6.1 [access: 5.03.2021].
- INEGI: National Water Bodies Map. Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/temas/hidrologia/ [access: 1.03.2021].
- Mueller N., Lewis A., Roberts D., Ring S., Melrose R., Sixsmith J., Lymburner L. et al.: Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, vol. 174, 2016, pp. 341–352. https://doi.org/10.1016/j.rse.2015.11.003 [access: 15.01.2022].
- UN STATS: SDG Indicators Metadata repository [Indicator 15.1.1: Forest area as a proportion of total land area]. United Nations Statistics Division. https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-01.pdf [access: 1.03.2021].
- UN FAO: Global Forest Resources Assessments. Food and Agriculture Organization of the United Nations. http://www.fao.org/forest-resources-assessment/past-assessments/en/ [access: 15.03.2021].
- UN FAO: Global Forest Resources Assessments 2015. Food and Agriculture Organization of the United Nations. http://www.fao.org/forest-resources-assessment/past-assessments/fra-2015/en/ [access: 15.03.2021].
- Roberts D., Dunn B., Mueller N.: Open Data Cube Products Using High-Dimensional Statistics of Time Series. [in:] IGARSS 2018: IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2018, pp. 8647–8650. https://doi.org/10.1109/igarss.2018.8518312.
References
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Kavvada A., Metternicht G., Kerblat F., Mudau N., Haldorson M., Laldaparsad S., Friedl L. et al.: Towards delivering on the sustainable development goals using earth observations. Remote Sensing of Environment, vol. 247, 2020, 111930. https://doi.org/10.1016/j.rse.2020.111930.
Thinyane M.: Small data and sustainable development–Individuals at the center of data-driven societies. [in:] 2017 ITU Kaleidoscope: Challenges for a Data-Driven Society (ITU K), IEEE, 2017, pp. 1–8. https://doi.org/10.23919/ITU-WT.2017.8246991.
UN DESA: 2019 Voluntary National Reviews Synthesis Report. United Nations Department of Economic and Social Affairs, New York 2019. https://sdghelpdesk.unescap.org/sites/default/files/2019-11/252302019_VNR_Synthesis_Report_DESA.pdf [access: 19.04.2022].
Andries A., Morse S., Murphy R.J., Lynch J., Woolliams E.R.: Seeing Sustainability from Space: Using Earth Observation Data to Populate the UN Sustainable Development Goal Indicators. Sustainability, vol. 11(18), 2019, 5062. https://doi.org/10.3390/su11185062.
Anderson K., Ryan B., Sonntag W., Kavvada A., Friedl L.: Earth observation in service of the 2030 Agenda for Sustainable Development. Geo-spatial Information Science, vol. 20(2), 2017, pp. 77–96. https://doi.org/10.1080/10095020.2017.1333230.
Paganini M., Petiteville I., Ward S., Dyke G., Steventon M., Harry J., Kerblat F.: Satellite earth observations in support of the sustainable development goals. The CEOS Earth Observation Handbook Special 2018 Edition. European Space Agency, 2018. http://eohandbook.com/sdg/files/CEOS_EOHB_2018_SDG.pdf [access: 19.04.2022].
GEO and UN-GGIM: Earth Observations and Geospatial Information: Supporting Official Statistics in Monitoring and Achieving the 2030 Agenda. Group on Earth Observations, United Nations Committee of Experts on Global Geospatial Information Management, 2017. https://earthobservations.org/documents/publications/201704_geo_unggim_4pager.pdf [access: 19.04.2022].
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Giuliani G., Chatenoux B., De Bono A., Rodila D., Richard J.P., Allenbach K., Dao H., Peduzzi P.: Building an Earth Observations Data Cube: Lessons Learned from the Swiss Data Cube (SDC) on Generating Analysis Ready Data (ARD). Big Earth Data, vol. 1(1–2), 2017, pp. 100–117. https://doi.org/10.1080/20964471.2017.1398903.
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Asmaryan S., Muradyan V., Tepanosyan G., Hovsepyan A., Saghatelyan A., Astsatryan H., Grigoryan H. et al.: Paving the Way towards an Armenian Data Cube. Data, vol. 4(3), 2019, 117. https://doi.org/10.3390/data4030117.
Juárez Carrillo O.J., Merodio Gómez P., Ponce Medina M.D.S., Ornelas de Anda J.L., Corona Iruegas A.A.: Cubo de datos geoespaciales para el uso de las imágenes satelitales en la generación de información geográfica y estadística. Realidad, Datos y Espacio Revista Internacional de Estadística y Geografía, vol. 11(3), 2020, pp. 124–139.
INEGI: Land Use and Vegetation. Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/temas/usosuelo/#Descargas [access: 19.04.2022].
INEGI: Uso del suelo y vegetación: Metodología. Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/contenidos/temas/mapas/usosuelo/metadatos/metodologia.pdf [access: 1.03.2021].
Lewis A., Oliver S., Lymburner L., Evans B., Wyborn L., Mueller N., Raevksi G. et al.: The Australian geoscience data cube – foundations and lessons learned. Remote Sensing of Environment, vol. 202, 2017, pp. 276–292. https://doi.org/10.1016/j.rse.2017.03.015.
Dhu T., Dunn B., Lewis B., Lymburner L., Mueller N., Telfer E., Lewis A. et al.: Digital Earth Australia – unlocking new value from earth observation data. Big Earth Data, vol. 1(1–2), 2017, pp. 64–74. https://doi.org/10.1080/20964471.2017.1402490.
INEGI: Producción y publicación de la Geomediana Nacional a Partir de imágenes del Cubo de Datos Geoespaciales de México: documento metodológico. Instituto Nacional de Estadística y Geografía, México 2020. https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos/nueva_estruc/702825198763.pdf [access: 19.04.2022].
INEGI: Producción del Índice de Clasificaciones de Agua Superficial desde el Espacio (ICASE) Landsat: documento metodológico. Instituto Nacional de Estadística y Geografía, México 2021. https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos/nueva_estruc/889463903642.pdf [access: 19.04.2022].
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UN STATS: SDG Indicators Metadata repository [Indicator 15.1.1: Forest area as a proportion of total land area]. United Nations Statistics Division. https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-01.pdf [access: 1.03.2021].
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Roberts D., Dunn B., Mueller N.: Open Data Cube Products Using High-Dimensional Statistics of Time Series. [in:] IGARSS 2018: IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2018, pp. 8647–8650. https://doi.org/10.1109/igarss.2018.8518312.