High-resolution mapping of protected agriculture in Mexico, through remote sensing data cloud geoprocessing

References

• Agüera, F., Aguilar, M.A., & Aguilar, F.J. (2006). Detecting greenhouse changes from QuickBird imagery on the Mediterranean coast. International Journal of Remote Sensing, 27(21), 4751–4767. doi:10.1080/01431160600702681
   Web of Science ®Google Scholar
• Aguilar, M.A., Nemmaoui, A., Novelli, A., Aguilar, F.J., & Lorca, A.G. (2016). Object-based greenhouse mapping using very high-resolution satellite data and Landsat 8 time series. Remote Sensing, 8(6), 10–12. doi:10.3390/rs8060513
   Web of Science ®Google Scholar
• Arnold, G.L., Luckenbach, M.W., & Unger, M.A. (2004). Runoff from tomato cultivation in the estuarine environment: Biological effects of farm management practices. Journal of Experimental Marine Biology and Ecology, 298(2), 323–346. doi:10.1016/S0022-0981(03)00366-6
   Web of Science ®Google Scholar
• Bastida Tapia, A. (2017). Evolución y Situación Actual de la Agricultura Protegida en México. In Memorias, sexto congreso internacional de investigación de ciencias básicas y agronómicas (pp. 281–294). Texcoco, Edo. De México: Universidad Autónoma Chapingo.
   Google Scholar
• Camacho Olmedo, M.T., Paegelow, M., Mas, J.-F., & Escobar, F. (Editors). (2018). Geomatic approaches for modeling land change scenarios (pp. 525). New York: Series: Lecture Notes in Geoinformation and Cartography, Springer. Print ISBN 978-3-319-60800-6, Online ISBN 978-3-319-60801-3.
   Google Scholar
• Card, D. (1982). Using know map category marginal frequencies to improve estimates of thematic map accuracy. Photogrammetry Engineering and Remote Sensing, 48(3), 431–439.
   Web of Science ®Google Scholar
• Comision Nacional de Vivienda (CONAVI). (2017). Mapas con los perimetros de contención urbana (PCU) de las localidades urbanas del Sistema Urbano Nacional. Gobierno de México. Retrieved from https://datos.gob.mx/busca/dataset/mapas-con-los-perimetros-de-contencion-urbana-pcu-de-las-localidades-urbanas/resource/f9c81c39-744c-44b0-bdf5-abe43f65737a
   Google Scholar
• Demchak, K. (2009). Small fruit production in high tunnels. HortTechnology, 19(1), 44–49. doi:10.21273/HORTSCI.19.1.44
   Web of Science ®Google Scholar
• European Space Agency. (2015). SENTINEL-2 user handbook. Retrieved from https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi
   Google Scholar
• Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., … Alsdorf, D. (2007). The shuttle radar topography mission. Reviews of Geophysics, 45(RG2004). doi:10.1029/2005RG000183.1.INTRODUCTION
   Google Scholar
• Gitelson, A.A., Kaufman, Y.J., & Merzlyak, M.N. (1982). Using known map category marginal frequencies to improve estimates of thematic map accuracy. Remote Sensing of Environment, 58(3), 431–439. doi:10.1016/S0034-4257(96)00072-7
   Google Scholar
• Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., … Toulmin, C. (2010). Food security: The challenge of feeding 9 billion people. Science, 327(February), 812–819. doi:10.1126/science.1185383
   PubMedGoogle Scholar
• Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. doi:10.1016/j.rse.2017.06.031
   Web of Science ®Google Scholar
• Instituto Mexicano de Tecnologia del Agua (IMAT). (2015). Estudio y Desarrollo De Tecnología Modular Para Una Agricultura Protegida Sustentable. Jiutepec: Morelos.
   Google Scholar
• Instituto Nacional de Estadística y Geografía (INEGI). (2001). Conjunto de datos vectoriales Fisiográficos. Continuo Nacional serie I. Provincias fisiográficas. Gobierno de México. Retrieved from https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825267575
   Google Scholar
• Instituto Nacional de Estadística y Geografía (INEGI). (2012). Conjunto de datos vectoriales de uso de suelo y vegetación escala 1:250000, serie V. Gobierno de México. Retrieved from http://www.conabio.gob.mx/informacion/metadata/gis/usv250s5ugw.xml?_httpcache=yes&_xsl=/db/metadata/xsl/fgdc_html.xsl&_indent=no
   Google Scholar
• Jambeck, J.R., Geyer, R., Wilcox, C., Siegler, T.R., Perryman, M., Andrady, A., … Law, K.L. (2015). Plastic waste inputs from land into the ocean. Marine Pollution, 347(6223), 768–771. doi:10.1126/science.1260879
   Google Scholar
• Jensen, M.H., & Malter, A.J. (1995). Protected agriculture: A global review. Washington, D.C.: World Bank Publications. Retrieved from https://books.google.es/books?hl=es‎&id=F1eghGKD6bwC&oi=fnd&pg=PR7&dq=advantages+of+protected+agriculture&ots=upA-BvlbiE&sig=FoAYR9l8gv55y425zljbR_XWnRI#v=onepage&q&f=false
   Google Scholar
• Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1–15. doi:10.3390/rs10101509
   Web of Science ®Google Scholar
• Lamont, W.J.J. (2009). Overview of the use of high tunnels worldwide. HortTechnology, 19(1), 25–29. doi:10.21273/HORTSCI.19.1.25
   Web of Science ®Google Scholar
• Levin, N., Lugassi, R., Ramon, U., Braun, O., & Ben-Dor, E. (2007). Remote sensing as a tool for monitoring plasticulture in agricultural landscapes. International Journal of Remote Sensing, 28(1), 183–202. doi:10.1080/01431160600658156
   Web of Science ®Google Scholar
• Lu, L., Di, L., & Ye, Y. (2014). A decision-tree classifier for extracting transparent plastic-mulched Landcover from Landsat-5 TM images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4548–4558. doi:10.1109/JSTARS.2014.2327226
   Web of Science ®Google Scholar
• Mann, G.S., Mcdonald, R., Mohri, M., Silberman, N., & Walker, D. (2009). Efficient large-scale distributed training of conditional maximum entropy models. Advances in Neural Information Processing Systems, 22, 1231–1239.
   Google Scholar
• Mas, J.-F., Kolb, M., Paegelow, M., Camacho Olmedo, M.T., & Houet, T. (2014). Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software, 51(1), 94–111. doi:10.1016/j.envsoft.2013.09.010
   Google Scholar
• McFeeters, S.K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. doi:10.1080/01431169608948714
   Web of Science ®Google Scholar
• Meneses, J., Corrales, C.M., & Valencia, M. (2007). Síntesis y caracterización de un polímero biodegradable a partir del almidón de Yuca. Revista EIA, 8, 57–67.
   Google Scholar
• Mills, S., Weiss, S., & Liang, C. (2013). VIIRS day/night band (DNB) stray light characterization and correction. Proc. SPIE, 8866. doi: 10.1117/12.2023107
   Google Scholar
• Novelli, A., Aguilar, M.A., Nemmaoui, A., Aguilar, F.J., & Tarantino, E. (2016). Performance evaluation of object-based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain). International Journal of Applied Earth Observation and Geoinformation, 52, 403–411. doi:10.1016/j.jag.2016.07.011
   Web of Science ®Google Scholar
• Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., & Wulder, M.A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. doi:10.1016/j.rse.2014.02.015
   Web of Science ®Google Scholar
• Olofsson, P., Foody, G.M., Stehman, S.V., & Woodcock, C.E. (2013). Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, 129, 122–131. doi:10.1016/j.rse.2012.10.031
   Web of Science ®Google Scholar
• Ortho Books. (2001). All about greenhouses. Meredith Books, (Ed.), (first edit). Des Moines, Iowa: Meredith Publishing Group. Retrieved from https://books.google.com.co/books?id=OsMq-bzvWyYC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false
   Google Scholar
• Phillips, S.J., Anderson, R.P., & Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259. doi:10.1016/j.ecolmodel.2005.03.026
   Web of Science ®Google Scholar
• Picuno, P., Tortora, A., & Capobianco, R.L. (2011). Analysis of plasticulture landscapes in Southern Italy through remote sensing and solid modelling techniques. Landscape and Urban Planning, 100(1–2), 45–56. doi:10.1016/j.landurbplan.2010.11.008
   Web of Science ®Google Scholar
• Rouse, W., Haas, H., & Deering, W. (1974). Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Third ERTS Symposium, NASA SP-35 (December). Washington, DC.
   Google Scholar
• Sanders, D., Granberry, G., & Cook, W.P. (1996). Plasticulture for commercial vegetables. North Carolina State University: NC Cooperative Extension Service. Extension Document, ( AG-489). Retrieved from https://content.ces.ncsu.edu/plasticulture-for-commercial-vegetables#section_heading_5149
   Google Scholar
• Servicio de Información Agroalimentaria y Pesquera (SIAP). (2015). Superficie cubierta y número de instalaciones de agricultura protegida (2015). Dirección de Soluciones Geoespaciales. Retrieved from http://infosiap.siap.gob.mx/gobmx/datosAbiertos.php
   Google Scholar
• Shimabukuro, Y.E., Santos, J., Rudorff, B.F.T., Arai, E., Duarte, V., & Lima, A. (2011). Detección operacional de deforestación y de áreas quemadas en tiempo casi real por medio de imágenes del sensor MODIS. In Aplicaciones de sensor MODIS para el monitoreo del territorio (pp. 123–143). México: CIGA-INECC. Retrieved from http://www.ciga.unam.mx/publicaciones/images/abook_file/aplicacionesMODIS.pdf
   Google Scholar
• Swain, P.H. (1973). Pattern recognition: A basis for remote sensing data analysis. NASA Technical Report, LARS-11157 (January). Retrieved from https://ntrs.nasa.gov/search.jsp?R=19730008457
   Google Scholar
• Tilman, D., Balzer, C., Hill, J., & Befort, B.L. (2011). Global food demand and the sustainable intensification of agriculture. PNAS, 108(50), 20260–20264. doi:10.1073/pnas.1116437108
   PubMed Web of Science ®Google Scholar
• Tolón Becerra, A., & Lastra Bravo, X. (2010). La agricultura intensiva del poniente almeriense. Diagnóstico e instrumentos de gestión ambiental. M+A. Revista Electrónica De Medioambiente, 8, 1.
   Google Scholar
• Vázquez, N., Pardo, A., Suso, M.L., & Quemada, M. (2006). Drainage and nitrate leaching under processing tomato growth with drip irrigation and plastic mulching. Agriculture, Ecosystems and Environment, 112(4), 313–323. doi:10.1016/j.agee.2005.07.009
   Web of Science ®Google Scholar
• Wan, Y., & El-Swaify, S.A. (1999). Runoff and soil erosion as affected by plastic mulch in a Hawaiian pineapple field. Soil and Tillage Research, 52(1–2), 29–35. doi:10.1016/S0167-1987(99)00055-0
   Web of Science ®Google Scholar
• Wu, C.F., Deng, J.S., Wang, K., Ma, L.G., & Tahmassebi, A.R.S. (2016). Object-based classification approach for greenhouse mapping using Landsat-8 imagery. International Journal of Agricultural and Biological Engineering, 9(1), 79–88. doi:10.3965/j.ijabe.20160901.1414
   Web of Science ®Google Scholar
• Yang, D., Chen, J., Zhou, Y., Chen, X., Chen, X., & Cao, X. (2017). Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 47–60. doi:10.1016/j.isprsjprs.2017.03.002
   Web of Science ®Google Scholar
• Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. doi:10.1080/01431160304987
   Web of Science ®Google Scholar