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This work presents a methodology to classify croplands using a multitemporal/historical dataset of images and ground ancillary data referring to three consecutive years. An image processing/geographic information system as well as a database management system (DBMS) were used to make the integration of these multisource data. In order to evaluate the usefulness of a database for crop classification, the area under study was digitally classified by two groups of interpreters, using two methodologies: (a) the proposed methodology using maximum likelihood classification assisted by an historical/multisource database, and ( b) a conventional maximum likelihood classification only. Both results were compared using the Kappa statistics. The indices to both the proposed and the conventional digital classification methodologies were 0.669 (very good) and 0-472 (good), respectively. The use of the database rendered an improvement over the conventional digital classification. Furthermore, along with this study some problems related to multisource data integration are discussed.
Ortiz et al. (1997) studied this question.