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.
In the present document, methodologies for non-destructive techniques are developed to estimate the index of vegetable biomass of a study area that will undergo an intervention and whose location is near the city of Medell´ın, Colombia. The techniques proposed and compared are: multispectral photogrammetric images, photogrammetric images in the visible spectrum and analysis of SAR products. These techniques were chosen with the possibility of estimating the biomass index of a forest to perform an intervention of the study area, given that an urban construction will be carried out in this space.
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