Abstract:

The effective and regular remote monitoring of agricultural activity is not always possible in developing countries because the access to cloud-based geospatial analysis platforms or expensive high-resolution satellite images are not always available. Herein, using paid high-resolution satellite images first and then free medium-resolution satellite images, we aimed to develop a cost-free, affordable method for regularly mapping the spatial distribution of sicklebush (Dichrostachys cinerea), an archetypal allochthonous invasive plant in Cuba that is becoming impossible to control owing to its rapid growth in areas planted with sugar cane in the Trinidad-Valle de los Ingenios area (Cuba), a UNESCO World Heritage Site. Two types of images were used (WorldView-2 and Landsat-8); these were subjected to supervised classification, with accuracy values of 88.7% and 93.7%, respectively. Vegetation cover was first derived from the purchased WorldView-2 image, and this information was then used as the training field to obtain spectral signatures from the Landsat-8 free image so that Landsat images may be regularly used to monitor D. cinerea infestations. The results obtained in the spatial distribution map for sicklebush in the Landsat-8 images had an overall reliability of 93.7% and a Kappa coefficient reliability of 91.9%. These values indicate high confidence in the results, which suggests that sicklebush has invaded 52.7% of the total 46,807.26-ha area of the Trinidad-Valle de los Ingenios. This process proved extremely effective and demonstrated the benefits of using high-resolution spatial images from which information can be transferred to free satellite images with a larger pixel size.

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