The objectives of this research were to develop and evaluate an approach for object-oriented mapping of banana plantations from SPOT-5 imagery, and to compare these results to banana plantations manually delineated from high spatial resolution airborne imagery. Cultivated areas were first identified through large spatial scale mapping using spectral and elevation data. Within the cultivated areas, separation of banana plantations and other land-cover classes increased when including image co-occurrence texture measures and context relationships in addition to spectral information. The results showed that a pixel size of 2.5 m was required to accurately identify the row structure within banana plantations, which enabled object-based separation from other crops based on texture information. The user’s and producer’s accuracies for mapping banana plantations increased from 73 percent and 77 percent, respectively, to 94 percent and 93 percent after post-classification visual editing. The results indicate that the data and processing techniques used offer a reliable approach for mapping banana plants and other plantation crops.

The objectives of this research were to develop and evaluate on approach for object-oriented mopping of banana plantations from SPOT-5 imagery, and to compare these results to banana plantations manually delineated from high spatial resolution airborne imagery. Cultivated areas were first identified through large spatial scale mapping using spectral and elevation data. Within the cultivated areas, separation of banana plantations and other land-cover classes increased when including image co-occurrence texture measures and context relationships in addition to spectral information. The results showed that a pixel size of <= 2.5 in was required to accurately identify the row structure within banana plantations, which enabled object-based separation from other crops based on texture information. The user's and producer's accuracies for mapping banana plantations increased from 73 percent and 77 percent, respectively, to 94 percent and 93 Percent after post-classification visual editing. The results indicate that the data and processing techniques used offer a reliable approach for mapping banana plants and other plantation crops.