Exploring the use of remote sensing tools and geospatial technologies applied to the multidimensional food security problem
DOI:
https://doi.org/10.15359/ru.36-1.48Keywords:
Food Security, remote sensing, geospatial technologies, risk mappingAbstract
[Objective] The aim of this study was to analyze which role remote sensing technologies can play to study multidimensional factors influencing the Food and Nutrition Security (FNS), in Córdoba Argentina. [Methodology] The study area includes the city of Córdoba, Argentina. Epidemiological data on the prevalence of underweight, overweight, and obesity (malnutrition) in 2013 were obtained from 23 primary health care centers in the city. The environmental conditions of the surroundings of the health centers were explored within a radius of 1000m. SPOT 5 images were classified using spectral and spatial features and we show how a non-supervised classification can give information to describe the social dimension and economic access to food. In addition, a multivariate stepwise linear regression was performed to examine the relation between the prevalence of malnutrition and the environmental and spatial variables, derived from the SPOT image, proposed. [Results] The results of the unsupervised image classification show the difference in the spectral-spatial pattern of neighborhoods showing how a simple satellite image classification can become a useful discrimination tool. Multiple regression analyses with adjusted R2 of 0.70 and 0.6435 respectively are obtained for undernutrition, and overweight, and obesity. On the basis of the obtained models, continuous maps of prevalence are built. [Conclusions] The method proposed in this work can discriminate socially different areas related to FNS. It is innovative and necessary to take advantage of remote sensing tools and geospatial technologies, in our region, applied to FNS.
References
Andreo, V., Neteler, M., Rocchini, D., Provensal, C., Levis, S., Porcasi, X., ... & Polop, J. (2014). Estimating Hantavirus risk in southern Argentina: a GIS-based approach combining human cases and host distribution. Viruses, 6(1), 201-222. https://doi.org/10.3390/v6010201
Andreo, V., Provensal, C., Scavuzzo, M., Lamfri, M., & Polop, J. (2009). Environmental factors and population fluctuations of Akodon azarae (Muridae: Sigmodontinae) in central Argentina. Austral Ecology, 34(2), 132-142. https://doi.org/10.1111/j.1442-9993.2008.01889.x
Arboleda, S., & Peterson, A. T. (2009). Mapping environmental dimensions of dengue fever transmission risk in the Aburrá Valley, Colombia. International journal of environmental research and public health, 6(12), 3040-3055. https://doi.org/10.3390/ijerph6123040
Arganaraz, J., Lighezzolo, A., Clemoveki, K., Bridera, D., Scavuzzo, J., & Bellis, L. (2018). Operational meteo fire risk system based on space information for Chaco Serrano. IEEE Latin America Transactions, 16(3), 975-980. https://doi.org/10.1109/TLA.2018.8358681
Ashe, L. M., & Sonnino, R. (2013). At the crossroads: new paradigms of food security, public health nutrition and school food. Public health nutrition, 16(6), 1020-1027. https://doi.org/10.1017/S1368980012004326
Bianchi, E. & Szpak, C. (2016). Seguridad Alimentaria y el Derecho a la Alimentación Adecuada. Revista brasileira de estudos jurídicos, 11(2), 37-45.
Britos, S. (2008). Hambre, seguridad alimentaria, obesidad y políticas públicas en la Argentina reciente. Revista Observatorio Social, (19).
Brown, M. E. (2008). Famine early warning systems and remote sensing data. Springer Science & Business Media.
Charreire, H., Mackenbach, J. D., Ouasti, M., Lakerveld, J., Compernolle, S., Ben-Rebah, M., ... & Oppert, J. M. (2014). Using remote sensing to define environmental characteristics related to physical activity and dietary behaviours: a systematic review (the SPOTLIGHT project). Health & place, 25, 1-9. https://doi.org/10.1016/j.healthplace.2013.09.017
Chen, J. (2007). Rapid urbanization in China: A real challenge to soil protection and food security. Catena, 69(1), 1-15. https://doi.org/10.1016/j.catena.2006.04.019
da Costa Almeida, A., Barros, P. L. C., Monteiro, J. H. A., & da Rocha, B. R. P. (2009). Estimation of aboveground forest biomass in Amazonia with neural networks and remote sensing. IEEE Latin America Transactions, 7(1), 27-32. https://doi.org/10.1109/TLA.2009.5173462
Defagó, M. D., Perovic, N. R., Aguinaldo, C. A., & Actis, A. B. (2009). Desarrollo de un programa informático para estudios nutricionales. Revista Panamericana de Salud Pública, 25, 362-366. https://doi.org/10.1590/S1020-49892009000400011
Elgart, J. F., Pfirter, G., Gonzalez, L., Caporale, J. E., Cormillot, A., Chiappe, M. L., & Gagliardino, J. J. (2010). Obesidad en Argentina: epidemiología, morbimortalidad e impacto económico. Revista Argentina de Salud Pública, 1.
Espinosa, M., Weinberg, D., Rotela, C. H., Polop, F., Abril, M., & Scavuzzo, C. M. (2016). Temporal dynamics and spatial patterns of Aedes aegypti breeding sites, in the context of a dengue control program in Tartagal (Salta province, Argentina). PLoS neglected tropical diseases, 10(5), e0004621. https://doi.org/10.1371/journal.pntd.0004621
FAO. (2016). Cambio climático y seguridad alimentaria y nutricional América Latina y el Caribe. Food andAgricultural Organization. http://www.fao.org/fileadmin/user_upload/rlc/docs/Cambioclimatico.pdf.
Hall-Beyer, M. (2017). Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales. International Journal of Remote Sensing, 38(5), 1312-1338. https://doi.org/10.1080/01431161.2016.1278314
Helldén, U., & Eklundh, L. (1988). National Drought Impact Monitoring-A NOAA NDVI and precipitation data study of Ethiopia. Lund Studies in Geography, Ser. C. General, Mathematical and Regional Geography, 15.
Hielkema, J. U., & Snijders, F. L. (1994). Operational use of environmental satellite remote sensing and satellite communications technology for global food security and locust control by FAO: The ARTEMIS and DIANA systems. Acta Astronáutica, 32(9), 603-616. https://doi.org/10.1016/0094-5765(94)90071-X
Illner, A. K., Freisling, H., Boeing, H., Huybrechts, I., Crispim, S. P., & Slimani, N. (2012). Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. International journal of epidemiology, 41(4), 1187-1203. https://doi.org/10.1093/ije/dys105
Jerrett, M., Gale, S., & Kontgis, C. (2010). Spatial modeling in environmental and public health research. International journal of environmental research and public health, 7(4), 1302-1329. https://doi.org/10.3390/ijerph7041302
Johnson, G. E., Achutuni, V. R., Thiruvengadachari, S., & Kogan, F. (1993). The role of NOAA satellite data in drought early warning and monitoring: selected case studies. In Drought assessment, management, and planning: Theory and case studies (pp. 31-47). Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3224-8_3
Johnson, K. B., Jacob, A., & Brown, M. E. (2013). Forest cover associated with improved child health and nutrition: evidence from the Malawi Demographic and Health Survey and satellite data. Global Health: Science and Practice, 1(2), 237-248. https://doi.org/10.9745/GHSP-D-13-00055
Justice, C. O., Townshend, J. R. G., Holben, B. N., & Tucker, E. C. (1985). Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing, 6(8), 1271-1318. https://doi.org/10.1080/01431168508948281
Kemeling, I. (2001). Mapping Urban and Peri-Urban Agricultural Areas in Ouagadougou, Burkina Faso. Centre for Geo-Information, Wageningen, The Netherlands.
Knight, L. (2011). World Disaster Report 2011: Focus on hunger and malnutrition. In World Disaster Report 2011: Focus on hunger and malnutrition. International Federation of Red Cross and Red Crescent Societies (IFRC).
Kogan, F., Goldberg, M., Schott, T., & Guo, W. (2015). Suomi NPP/VIIRS: improving drought watch, crop loss prediction, and food security. International Journal of Remote Sensing, 36(21), 5373-5383. https://doi.org/10.1080/01431161.2015.1095370
Lee, T. E., Miller, S. D., Turk, F. J., Schueler, C., Julian, R., Deyo, S., Dills, P., & Wang, S. (2006). The NPOESS VIIRS day/night visible sensor. Bulletin of the American Meteorological Society, 87(2), 191-200. https://doi.org/10.1175/BAMS-87-2-191
Linetzky, B., Morello, P., Virgolini, M., & Ferrante, D. (2011). Resultados de la primera encuesta nacional de salud escolar: Argentina, 2007. Archivos argentinos de pediatría, 109(2), 111-116.
López-Carr, D., Mwenda, K. M., Pricope, N. G., Kyriakidis, P. C., Jankowska, M. M., Weeks, J., ... & Michaelsen, J. (2015). A spatial analysis of climate-related child malnutrition in the Lake Victoria Basin. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 2564-2567). IEEE. https://doi.org/10.1109/IGARSS.2015.7326335
Minamiguchi, N. (2005). The application of geospatial and disaster information for food insecurity and agricultural drought monitoring and assessment by the FAO GIEWS and Asia FIVIMS. In Workshop on Reducing Food Insecurity Associated with Natural Disasters in Asia and the Pacific (Vol. 27, p. 28).
Parra-Henao, G. (2010). Geographic information systems and remote sensing. Applications in vector-borne diseases. CES Medicina, 24(2), 75-89.
Peña Zamalloa, G. R. (2021). Clasificación espacial del suelo urbano por el valor especulativo del suelo e imágenes MSI satelitales usando K-MEANS, Huancayo, Perú. Urbano (Concepción), 24(44), 70-83. https://doi.org/10.22320/07183607.2021.24.44.06
Pereira, G., Foster, S., Martin, K., Christian, H., Boruff, B. J., Knuiman, M., & Giles-Corti, B. (2012). The association between neighborhood greenness and cardiovascular disease: an observational study. BMC public health, 12(1), 1-9. https://doi.org/10.1186/1471-2458-12-466
Polop, F., Provensal, M. C., Lamfri, M., Scavuzzo, M., Calderón, G., & Polop, J. (2008). Environmental variables in the incidence of the Argentine Hemorrhagic Fever (AHR). Ecological Research, 23, 217-225. https://doi.org/10.1007/s11284-007-0371-2
Popkin, B. M., & Gordon-Larsen, P. (2004). The nutrition transition: worldwide obesity dynamics and their determinants. International journal of obesity, 28(3), S2-S9. https://doi.org/10.1038/sj.ijo.0802804
Porcasi, X., Catalá, S. S., Hrellac, H., Scavuzzo, M. C., & Gorla, D. E. (2006). Infestation of rural houses by Triatoma infestans (Hemiptera: Reduviidae) in southern area of Gran Chaco in Argentina. Journal of medical entomology, 43(5), 1060-1067. https://doi.org/10.1093/jmedent/43.5.1060
Puissant, A., Hirsch, J., & Weber, C. (2005). The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery. International Journal of Remote Sensing, 26(4), 733-745. https://doi.org/10.1080/01431160512331316838
Ra, P. K., Nathawat, M. S., & Onagh, M. (2012). Application of multiple linear regression model through GIS and remote sensing for malaria mapping in Varanasi District, INDIA. Health Science Journal, 6(4), 731.
Ribeiro, H. M. C., da Costa Almeida, A., Rocha, B. R. P. D., & Krusche, A. V. (2008). Water quality monitoring in large reservoirs using remote sensing and neural networks. IEEE Latin America Transactions, 6(5), 419-423. https://doi.org/10.1109/TLA.2008.4839111
Rotela, C. H., Spinsanti, L. I., Lamfri, M. A., Contigiani, M. S., Almirón, W. R., & Scavuzzo, C. M. (2011). Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotely sensed data. Geospatial health, 6(1), 85-94. https://doi.org/10.4081/gh.2011.160
Salomón, O. D., Orellano, P. W., Lamfri, M., Scavuzzo, M., Dri, L., Farace, M. I., & Quintana, D. O. (2006). Phlebotominae spatial distribution asssociated with a focus of tegumentary leishmaniasis in Las Lomitas, Formosa, Argentina, 2002. Memórias do Instituto Oswaldo Cruz, 101, 295-299. https://doi.org/10.1590/S0074-02762006000300013
Salvia, A., Tuñón, I, Musante, B. (2012) La inseguridad alimentaria en la Argentina: Hogares urbanos, año 2011. Universidad Católica Argentina. http://www.uca.edu.ar/uca/common/grupo68/files/Informe_Inseguridad_Alimentaria___doc_de_trabajo_.pdf.
Sepulcre Canto, G., Waldner, F., Radoux, J., Valero, S., Inglada, J., Hagolle, O., ... & Defourny, P. (2014). How to think global: Exploring different alternatives for global cropland classification in the framework of the project Sentinel 2 Agriculture. In The 4th International Symposium on Recent Advances in Quantitative Remote Sensing: RAQRS'IV.
Smith, M. D., Kassa, W., & Winters, P. (2017). Assessing food insecurity in Latin America and the Caribbean using FAO’s food insecurity experience scale. Food policy, 71, 48-61. https://doi.org/10.1016/j.foodpol.2017.07.005
Sousa, W. R. N., Couto, M. S., Castro, A. F., & Silva, M. P. S. (2013). Evaluation of desertification processes in ouricuri-pe through trend estimates of times series. IEEE Latin America Transactions, 11(1), 602-606. https://doi.org/10.1109/TLA.2013.6502869
Traoré, M., Thompson, B., & Thomas, G. (2012). Sustainable nutrition security. Restoring the bridge between agriculture and health. Food and Agriculture Organisation of the United Nations, Rome, Italy.
Valero, S., Morin, D., Inglada, J., Sepulcre, G., Arias, M., Hagolle, O., ... & Defourny, P. (2015). Processing Sentinel-2 image time series for developing a real-time cropland mask. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 2731-2734). IEEE. https://doi.org/10.1109/IGARSS.2015.7326378
Villeneuve, P. J., Goldberg, M. S., Burnett, R. T., van Donkelaar, A., Chen, H., & Martin, R. V. (2011). Associations between cigarette smoking, obesity, sociodemographic characteristics and remote-sensing-derived estimates of ambient PM2. 5: results from a Canadian population-based survey. Occupational and environmental medicine, 68(12), 920-927. https://doi.org/10.1136/oem.2010.062521
Xie, M., Jean, N., Burke, M., Lobell, D., & Ermon, S. (2016, March). Transfer learning from deep features for remote sensing and poverty mapping. In Thirtieth AAAI Conference on Artificial Intelligence.
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