Multi-Sensor Data Fusion for Modeling African Palm in the Ecuadorian Amazon

African oil palm ( Elaeis guinensis) is the most productive oil seed. Globally, the oil palm industry plans to double the area under cultivation to meet growing demands for both vegetable oils and biodiesel. Accurate assessment and monitoring of African palm extensification and intensification for both development and sustainability is crucial given that these crops are replacing the natural high-biodiversity forests as well as local subsistence agriculture. Using a simultaneous collection of RADARSAT synthetic aperture radar (SAR) and ground based digital video, we describe and model the spatial distribution of African palm and explore its lifecycle placing it in the regional ecological context of the Ecuadorian, Amazon. We evaluate the strengths and limitations of integrating RADARSAT texture information, Landsat ETM� , and digital video data to distinguish African oil palm plantations from other land-use and land-cover (LULC) categories. The grey-level co-occurrence matrix (GLCM) and a separate hybrid classification approach using a concatenation of SAR-optical products were tested. A significant improvement in the classification accuracy of African palm in the context of the Ecuadorian Amazon was obtained through the fusion of optical and RADARSAT texture measures as compared to single sensor