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A minimum cross entropy approach to disaggregate agricultural data at the field level
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Orientador(es)
Resumo(s)
Agricultural policies have impacts on land use, the economy, and the environment and their analysis requires disaggregated data at the local level with geographical references. Thus, this study proposes a model for disaggregating agricultural data, which develops a supervised classification of satellite images by using a survey and empirical knowledge. To ensure the consistency with multiple sources of information, a minimum cross-entropy process was used. The proposed model was applied using two supervised classification algorithms and a more informative set of biophysical information. The results were validated and analyzed by considering various sources of information, showing that an entropy approach combined with supervised classifications may provide a reliable data disaggregation.
Descrição
Palavras-chave
Remote sensing imagery Supervised classification Maximum entropy Spatial disaggregation Distribution Maps Land cover Allocation Gis
Contexto Educativo
Citação
Editora
MDPI