Cloud services with big data provide a solution for monitoring and tracking sustainable development goals

Abstract

To achieve the Sustainable Development Goals (SDGs), high-quality data are needed to inform the formulation of policies and investment decisions, to monitor progress towards the SDGs and to evaluate the impacts of policies. However, the data landscape is changing. With emerging big data and cloud-based services, there are new opportunities for data collection, influencing both official data collection processes and the operation of the programmes they monitor. This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation (EO) data products for monitoring and tracking the SDGs. This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs, particularly for low-income countries. The paper also discusses the challenges of using cloud services and big data for SDG monitoring. Validation and quality control of public EO data is very important; otherwise, the user will be unable to assess the quality of the data or use it with confidence.

Keywords

Big data / Cloud services / SDGs / Monitoring

Declaration Competing of Interest

The authors declare no conflict of interest.

Acknowledgements

This study was funded by the National Key Research and Development Program of China (Grant No. 2016YFA0600304) and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA19030201).

References

[1] Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Niño, F., Weiss, M., Samain, O., Roujean, J.L., 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sens. Environ. 110 (3), 275-286.

[2] Biggeri, M., Clark, D., Ferrannini, A., Mauro, V., 2019. Tracking the SDGs in an ‘integrated’ manner: A proposal for a new index to capture synergies and trade-offs between and within goals. World Dev. 122, 628-647.

[3] Blumenstock, J., Cadamuro, G., On, R., 2015. Predicting poverty and wealth from mobile phone metadata. Science 350 (6264), 1073-1076.

[4] Buontempo, C., Hutjes, R., Beavis, P., Berckmans, J., Cagnazzo, C., Vamborg, F., Thepaut, J., Bergeron, C., Almond, S., Amici, A., 2019. Fostering the development of climate services through Copernicus Climate Change Service (C3S) for agriculture applications. Weather Climate Extremes, 100226.

[5] Campbell, J., 2019. We Lack Data for 68% of SDG Indicators. https://un-spbf.org/editorial/we-lack-data-for-68-of-sdg-indicators-closing-data-gaps-essential-to-achieving-sdgs/ (accessed 19 Mar 2020).

[6] Castell, N., Dauge, F.R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., Bartonova, A., 2017. Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates. Environ. Int. 99, 293-302.

[7] China Meteorological Administration. 2019. Data sharing from more than 20,000 hydrological and meteorological stations helps flood prevention in the Yangtze River. http://www.cma.gov.cn/2011xzt/2019zt/20190927/2019092706/201909/t20190930_536784.html (accessed 19 Mar 2020).

[8] Chinese Academy of Science. 2019. Big Earth Data in Support of the Sustainable Development Goals. https://www.fmprc.gov.cn/mfa_eng/topics_665678/2030kcxfzyc/P020190924800116340503.pdf (accessed 19 Mar 2020).

[9] Corbane, C., Pesaresi, M., Kemper, T., Politis, P., Florczyk, A.J., Syrris, V., Melchiorri, M., Sabo, F., Soille, P., 2019. Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data 3, 140-169.

[10] Dong, J., Xiao, X., Menarguez, M.A., Zhang, G., Qin, Y., Thau, D., Biradar, C., Moore III, B., 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ. 185, 142-154.

[11] European Space Agency. 2019. EO4SD Agriculture and Rural Development cluster. https://www.eo4idi.eu/sites/default/files/eo4sd_agri_finalreport_en_2019-final.pdf (accessed 19 Mar 2020).

[12] FAO. 2019. Investing in data for the SDGs: Why good numbers matter. http://www.fao.org/partnerships/resource-partners/news/news-article/en/c/1200471/ (accessed 19 Mar 2020).

[13] Fritz, S., McCallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F., Kraxner, F., Obersteiner, M., 2009. Geo-Wiki. Org: The use of crowdsourcing to improve global land cover. Remote Sens. 1, 345-354.

[14] Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., Duerauer, M., Albrecht, F., Schill, C., Perger, C., 2015. Mapping global cropland and field size. Global Change Biol. 21 (5), 1980-1992.

[15] Giuliani, G., Mazzetti, P., Santoro, M., Nativi, S., Lehmann, A., 2020. Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation. Int. J. Appl. Earth Observ. Geoinform. 88, 102068.

[16] Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., 2013. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM + data. Int. J. Remote Sens. 34 (7), 2607-2654.

[17] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18-27.

[18] Guo, H., 2017. Big Earth data: A new frontier in Earth and information sciences. Big Earth Data 1 (1-2), 4-20.

[19] Guo, H., Liu, Z., Jiang, H., Wang, C., Liu, J., Liang, D., 2016. Big Earth Data: a new challenge and opportunity for Digital Earth’s development. Int. J. Digital Earth 10 (1), 1-12.

[20] Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S., Tyukavina, A., Thau, D., Stehman, S., Goetz, S., Loveland, T.R., 2013. High-resolution global maps of 21st-century forest cover change. Science 342 (6160), 850-853.

[21] He, B., Wu, J., Lü, A., Cui, X., Zhou, L., Liu, M., Zhao, L., 2013. Quantitative assessment and spatial characteristic analysis of agricultural drought risk in China. Nat. Hazards 66 (2), 155-166.

[22] IEAG, 2014. A World that Counts-Mobilising the Data Revolution for Sustainable Development. The United Nations Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development.

[23] Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S., 2016. Combining satellite imagery and machine learning to predict poverty. Science 353 (6301), 790-794.

[24] Kalia, A.C., Frei, M., Lege, T., 2017. A Copernicus downstream-service for the nationwide monitoring of surface displacements in Germany. Remote Sens. Environ. 202, 234-249.

[25] Kennedy, R.E., Yang, Z., Cohen, W.B., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr —Temporal segmentation algorithms. Remote Sens. Environ. 114 (12), 2897-2910.

[26] Killian, S., Lannon, J., Murray, L., Avram, G., Giralt, M., Oriordan, S., 2019. Social Media for Social Good: Student engagement for the SDGs. Int. J. Manage. Educ. 17 (3), 100307.

[27] Leeper, R.D., Rennie, J., Palecki, M.A., 2015. Observational Perspectives from US Climate reference network (USCRN) and cooperative observer program (COOP) network: Temperature and precipitation comparison. J. Atmos. Oceanic. Technol. 32 (4), 703-721.

[28] Liu, Q., Reichle, R.H., Bindlish, R., Cosh, M.H., Crow, W.T., de Jeu, R., De Lannoy, G.J., Huffman, G.J., Jackson, T.J., 2018. The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. J. Hydrometeorol. 12 (5), 750-765.

[29] Long, T., Zhang, Z., He, G., Jiao, W., Tang, C., Wu, B., Zhang, X., Wang, G., Yin, R., 2019. 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine. Remote Sens. 11 (5), 489.

[30] Mondal, P., Mcdermid, S.S., Qadir, A., 2020. A reporting framework for Sustainable Development Goal 15: Multi-scale monitoring of forest degradation using MODIS, Landsat and Sentinel data. Remote Sens. Environ. 237, 111592.

[31] Nabil, M., Zhang, M., Bofana, J., Wu, B., Stein, A., Dong, T., Zeng, H., Shang, J., 2020. Assessing factors impacting the spatial discrepancy of remote sensing based cropland products: A case study in Africa. Int. J. Appl. Earth Observ. Geoinform. 85, 102010.

[32] Pal, M., 2005. Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26 (1), 217-222.

[33] Pekel, J.F., Cottam, A., Gorelick, N., Belward, A.S., 2016. High-resolution mapping of global surface water and its long-term changes. Nature 540 (7633), 418-422.

[34] Ramm, F., Topf, J., Chilton, S., 2010. OpenStreetMap: Using, and Contributing to, the Free World Map. UIT Cambridge, Cambridge.

[35] Rollason, E., Bracken, L.J., Hardy, R.J., Large, A.R.G., 2018. The importance of volunteered geographic information for the validation of flood inundation models. J. Hydrol. 562, 267-280.

[36] Rosenqvist, A., Shimada, M., Suzuki, S., Ohgushi, F., Tadono, T., Watanabe, M., Tsuzuku, K., Watanabe, T., Kamijo, S., Aoki, E., 2014. Operational performance of the ALOS global systematic acquisition strategy and observation plans for ALOS-2 PALSAR-2. Remote Sens. Environ. 155, 3-12.

[37] Sauer, J.R., Link, W.A., Fallon, J.E., Pardieck, K.L., Ziolkowski Jr, D.J., 2013. The North American breeding bird survey 1966-2011: summary analysis and species accounts. N. Am. Fauna 79, 1-32.

[38] Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., Nickless, D., 2004. Terra and Aqua MODIS products available from NASA GES DAAC. Adv. Space Res. 34 (4), 710-714.

[39] Schmidt-Traub, G., De la Mothe Karoubi, E., Espey, J., 2015. Indicators and a monitoring framework for the Sustainable Development Goals: Launching a data revolution for the SDGs. Sustainable Development Solutions Network.

[40] Schmidt-Traub, G., Kroll, C., Teksoz, K., Durand-Delacre, D., Sachs, J.D., 2017. National baselines for the Sustainable Development Goals assessed in the SDG Index and Dashboards. Nat. Geosci. 10 (8), 547-555.

[41] Shah, M. 2019. Alibaba Cloud Ranked Third Globally. https://www.techfunnel.com/information-technology/alibaba-cloud-ranked-third-globally/ (accessed 19 Mar 2020).

[42] Shao, Y., Di, L., Bai, Y., Guo, B., Gong, J., 2012. Geoprocessing on the Amazon cloud computing platform —AWS. In. 2012 first international conference on agro-geoinformatics (agro-geoinformatics). IEEE, pp. 286-291.

[43] Steele, J.E., Sundsøy, P.R., Pezzulo, C., Alegana, V.A., Bird, T.J., Blumenstock, J., Bjelland, J., Engø-Monsen, K., de Montjoye, Y.-A., Iqbal, A.M., Hadiuzzaman, K.N., Lu, X., Wetter, E., Tatem, A.J., Bengtsson, L., 2017. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690.

[44] Stehman, S.V., Fonte, C.C., Foody, G.M., See, L., 2018. Using volunteered geographic information (VGI) in design-based statistical inference for area estimation and accuracy assessment of land cover. Remote Sens. Environ. 212, 47-59.

[45] Sustainable Development Solutions Network, 2015. Data for development: A needs assessment for SDG monitoring and statistical capacity development.

[46] Tian, F., Wu, B., Zeng, H., Zhang, X., Xu, J., 2019. Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform. Remote Sens. 11 (6), 629.

[47] Tulloch, D., 2014. Crowdsourcing geographic knowledge: volunteered geographic information (VGI) in theory and practice. Int. J. Geog. Inf. Sci. 28 (4), 847-849.

[48] United Nations, 2015. Transforming our world: The 2030 agenda for sustainable development.

[49] United Nations, 2019. The Sustainable Development Goals Report 2019 (accessed 19 Mar 2020).

[50] Van Achteren, T., Dierckx, W., Sterckx, S., Livens, S., Saint, G., 2012. Proba-V, a SPOT-VGT Successor Mission, Product Definition and Specifications. In: 32nd EARSEL Symposium Proceedings-Advances in Geosciences, Mykonos Island.

[51] Vaz, A.S., Goncalves, J., Pereira, P., Santarem, F., Vicente, J.R., Honrado, J., 2019. Earth observation and social media: Evaluating the spatiotemporal contribution of non-native trees to cultural ecosystem services. Remote Sens. Environ. 230, 111193.

[52] Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S.D., Tegmark, M., Nerini, F.F., 2020. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 11 (1), 233.

[53] Whitcraft, A.K., Beckerreshef, I., Justice, C.O., Gifford, L., Kavvada, A., Jarvis, I., 2019. No pixel left behind: Toward integrating Earth Observations for agriculture into the United Nations Sustainable Development Goals framework. Remote Sens. Environ. 235, 111470.

[54] Wu, B., Gommes, R., Zhang, M., Zeng, H., Yan, N., Zou, W., Zheng, Y., Zhang, N., Chang, S., Xing, Q., 2015. Global crop monitoring: a satellite-based hierarchical approach. Remote Sens. 7 (4), 3907-3933.

[55] Wu, B., Meng, J., Li, Q., Yan, N., Du, X., Zhang, M., 2014. Remote sensing-based global crop monitoring: experiences with China’s CropWatch system. Int. J. Digital Earth 7 (2), 113-137.

[56] Wu, B.Q., Jinkai, Zeng, Yuan, 2017. Land Cover Atlas of the People’s Republic of China (1: 1 000 000). SinoMaps Press, Beijing.

[57] Wu, H., Zhang, L., Zhang, X., 2019. Cloud Data and Computing Services Allow Regional Environmental Assessment: A Case Study of Macquarie-Castlereagh Basin, Australia. Chin. Geogr. Sci. 29 (3), 394-404.

[58] Xiong, J., Thenkabail, P.S., Gumma, M.K., Teluguntla, P., Poehnelt, J., Congalton, R.G., Yadav, K., Thau, D., 2017. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS J. Photogramm. Remote Sens. 126, 225-244.

[59] Xu, Z., Chau, S.N., Chen, X., Zhang, J., Li, Y., Dietz, T., Wang, J., Winkler, J.A., Fan, F., Huang, B., 2020. Assessing progress towards sustainable development over space and time. Nature 577 (7788), 74-78.

[60] Zhang, M. 2017a. GVG for Android. https://play.google.com/store/apps/details?id = com. sysapk.gvg (accessed 19 Mar 2020).

[61] Zhang, M. 2017b. GVG for IOS. https://itunes.apple.com/us/app/apple-store/id1244686128?mt = 8 (accessed 19 Mar 2020).

[62] Zhang, L., Li, X., Yuan, Q., Liu, Y., 2014. Object-based approach to national land cover mapping using HJ satellite imagery. J. Appl. Remote Sens. 8, 083686.

[63] Zhang, X., Wu, B., Ponce-Campos, G., Zhang, M., Chang, S., Tian, F., 2018. Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images. Remote Sens. 10 (8), 1200.

[64] Zhao, X., Liang, S., Liu, S., Yuan, W., Xiao, Z., Liu, Q., Cheng, J., Zhang, X., Tang, H., Zhang, X., 2013. The Global Land Surface Satellite (GLASS) remote sensing data processing system and products. Remote Sens. 5 (5), 2436-2450.