Abstract
Changes in the Common Agricultural Policy (CAP) had several consequences on land-use and on the environment. This calls for detailed disaggregated agricultural data with precise geographical references. To tackle such problems data disaggregation processes are needed and a series of studies are being carried out at international level, which still have not taken the utmost advantage of remote sensing technologies by combining them with mathematical programming methods, namely entropy. Therefore, the objective of this article was to provide an approach to disaggregate agricultural data at the local level, taking advantage of the existent up-to-date satellite imagery and an entropy approach for manage different sets of data. The results were compared with other approaches and showed to be coherent, and may be improved further with the inclusion of other information.
Similar content being viewed by others
References
Britz, W. 2008. EU-wide spatial down-scaling of results of regional economic models to analyze environmental impacts. Paper presented In 107th European Association of Agricultural Economists (EAAE) Seminar” Modelling of Agricultural and Rural Development Policies. Sevilla, Spain, pp. 78–87 (January 29th–February 1st, 2008).
Castela, E., Villardón, M.P. 2010. Ecological inference for the characterization of electoral Turnout: the Portuguese case. Spatial and organizational dynamics. Quantitative Methods Applied to Social Sciences. Discussion papers No. 3, pp. 6-25
Chakir, R. 2009. Spatial downscaling of agricultural land-use data: an econometric approach using cross–entropy. Land Economics. 85 (2): 238–251.
Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37 (1): 35–46.
Congedo, L. 2015. Semi-automatic classification plugin documentation release 4.8.0.1. https://semiautomaticclassificationmanual-v4.readthedocs.org/en/latest/. Accessed 15 Feb 2016.
Demirkesen, A.C., F. Evrendilek, S. Berberoglu, and S. Kilic. 2007. Coastal flood risk analysis using Landsat-7 ETM+ imagery and SRTM DEM: A case study of Izmir, Turkey. Environmental Monitoring and Assessment 131 (1): 293–300.
EUROSTAT. 2013. LUCAS 2012 (Land-Use/Cover Area Frame Survey-Technical Reference Document). EUROSTAT. https://ec.europa.eu/eurostat/documents/205002/208012/LUCAS2012_C1-InstructionsRevised_20130110a.pdf.
Fragoso, R., M.B. Martins, and M.R. Lucas. 2008. Generate disaggregated soil allocation data using a minimum cross entropy model. WSEAS Transaction on Environment and Development. 9 (4): 756–766.
Gadiga, B.L., Adesina, F.A., Orimoogunje, I.O.O. 2013. Spatial-temporal analysis of vegetation dynamics in the semi arid belt of Nigeria. Global Journal of Human-Social Science 13(7).
Graciani, S.D., and E.M.L.M. Novo. 2003. Determinação da cobertura de macrófitas aquáticas em reservatórios tropicais. Anais do XI Simpósio Brasileiro de Sensoriamento Remoto 2003: 2509–2516.
Golan, A., G. Judge, and D. Miller. 1996. Maximum entropy econometrics: robust estimation with limited data. NewYork: Wiley.
Good, I. 1963. Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. The Annals of Mathematical Statistics 34 (3): 911–934.
Howitt, R., and A. Reynaud. 2003. Spatial disaggregation of agricultural production data using maximum entropy. European Review of Agricultural Economics 30 (3): 359–387.
INE-Instituto Nacional de Estatística. 2011. Recenseamento geral da agricultura de 2009. Lisbon: INE.
INE-Instituto Nacional de Estatística. 2014. Inquérito à Estrutura das Explorações Agrícolas 2013. Lisbon: INE.
Jaynes, E.T. 1957. Information theory and statistical methods I. Physics Review. 106: 620–630.
Jeon, Y.-J., Choi, J.-G., and Kim, J.-I. 2004. A study on supervised classification of remote sensing satellite image by bayesian algorithm using average fuzzy intracluster distance. In Combinatorial image analysis. Berlin, Heidelberg: Springer, pp. 597–606
Kempen, M., T. Heckelei, W. Britz, A. Leip, R. Koeble, and G. Marchi. 2005. Computation of a European agricultural land-use map-statistical approach and validation. Discussion paper. Bonn: Institute for Food and Resource Economics.
Lu, D., and Q. Weng. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28 (5): 823–870. https://doi.org/10.1080/01431160600746456.
Martins, M.B., R. Fragoso, and A. Xavier. 2011. Spatial disaggregation of agricultural data in Castelo de Vide, Alentejo, Portugal: an approach based on maximum entropy. J.P. Jounal of Biostatistics 5 (1): 1–16.
Martins, N. M. N. 2012. Utilização de imagens de satélite de alta resolução para a extracção de elementos em ambiente urbano, Master dissertation in Geografic engenniring, University of Lisbon.
Rajaraman, A., J. Leskovec, and J. Ullman. 2010. Mining of massive datasets. Cambridge, Stanford: Cambridge University Press, Stanford University.
Shannon, C.E. 1948. A mathematical theory of communication. Bell System Technical Journal 27: 379–423.
StatSoft, Inc. 2013. Electronic Statistics Textbook. Tulsa, OK: StatSoft. http://www.statsoft.com/textbook/.
Tan, J., P. Yang, Z. Liu, W. Wu, L. Zhang, Z. Li, and Z. Li. 2014. Spatio-temporal dynamics of maize cropping system in Northeast China between 1980 and 2010 by using spatial production allocation model. Journal of Geographical Sciences 24 (3): 397–410.
Thu, P.M., and J. Populus. 2007. Status and changes of mangrove forest in Mekong delta: case study in Tra Vinh, Vietnam. Estuarine, Coastal and Shelf Science 71 (1): 98–109.
USGS-United States Geological Survey. 2015. Lansat project factsheet: Landsat—earth observation satellites. http://pubs.usgs.gov/fs/2015/3081/fs20153081.pdf. Accessed 16 March 2016.
You, L., and S. Wood. 2006. An entropy approach to spatial disaggregation of agricultural production. Agricultural Systems 90 (2006): 29–347.
You, L., S. Wood, and U. Wood-Sichra. 2009. Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agricultural Systems 99 (2–3): 126–140.
You, L., S. Wood, U. Wood-Sichra, and W. Wu. 2014. Generating global crop distribution maps: from census to grid. Agricultural Systems 127: 53–60.
Xavier, A., Martins, M. B., Fragoso, R. 2010. Combined disaggregation of agricultural land-uses, livestock numbers and crops’ production: an entropy approach. In Advances in mathematical and computational methods, proceedings of the 12th WSEAS International Conference on Mathematical and computational methods in science and engineering. Faro: WSEAS.
Xavier, A., Martins, M. B., Fragoso. R. 2011. A mininum cross entropy model to generate disaggregated data at the local level. Paper presented at the 122nd European Association of Agricultural Economists (EAAE) Seminar “Evidence-based agricultural and rural policy making: Methodological and empirical challenges of policy evaluation”, Ancona (17–18 February 2011).
Xavier, A., and M.B. Costa Freitas. 2014. Recent dynamics and trends of Portuguese agriculture—a Biplot analysis. New Medit. 13 (4): 67–74.
Xavier, A., Freitas, M. B., Socorro Rosário, M., Fragoso, R. 2015. Disaggregating statistical data at field level: na entropy approach, 53 Congresso da Sociedade Brasileira de Economia, Administração e Sociologia Rural, 26 a 29 de julho de 2015, UFPB João Pessoa-PB.
Acknowledgements
The authors are pleased to acknowledge financial support from Fundação para a Ciência e a Tecnologia (grant UID/ECO/04007/2013) and FEDER/COMPETE (POCI-01-0145-FEDER-007659)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xavier, A., Fragoso, R., de Belém Costa Freitas, M. et al. An Approach Using Entropy and Supervised Classifications to Disaggregate Agricultural Data at a Local Level. J. Quant. Econ. 17, 763–779 (2019). https://doi.org/10.1007/s40953-018-0143-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40953-018-0143-6