Land cover classification using data fusion to support sustainable coffee certification processes

Land cover classification is a remote sensing task that enables the visualization of different land uses. In the context of the sustainable coffee market, a land cover map is required as part of the sustainable coffee certification process. In this study, the land cover of a coffee production farm was classified into four categories: coffee, forest, civil infrastructure, and soil areas. Aerial images were acquired using a UAV equipped with visible and multispectral cameras. Image processing resulted in an orthomosaic for each camera, a vegetative index map (NDVI), and digital elevation models. Through statistical analysis and data fusion strategies, multithresholding and decision tree models—CART, Random Forest (RF), and Gradient Boosting (GB)—were trained and used to classify each pixel into one of the four categories. GB achieved the highest accuracy (94%), followed by RF (84%) and CART (83%). This study enhances the understanding of remote sensing methodologies and land use classification, specifically applied to the geographical particularities of the Colombian territory, and serves as a foundational step toward the application of agricultural technological innovation models in the country.