Estimating Olive Tree Density in Delimited Areas Using Sentinel-2 Images

The objective of this study is to develop a method for estimating the density of olive trees in delimited plots using low-resolution images from the Sentinel-2 satellite. This approach is particularly relevant in certain regions where high-resolution orthophotos, which are often costly and not always available, cannot be accessed. This study focuses on the Extremadura region in Spain, where 48,530 olive plots were analysed. Data from Sentinel-2’s multispectral bands were obtained for each plot, and a Random Forest Regression (RFR) model was used to correlate these values with the number of olive trees, previously counted from orthophotos using machine learning object detection techniques. The results show that the proposed method can predict olive tree density within an acceptable error margin, which is especially useful for distinguishing plots with a density greater than 300 olive trees per hectare—a key criterion for allocating agricultural subsidies in the region. Although the accuracy of the model is not optimal, an average error of ±15.04 olive trees per hectare makes it a viable tool for practical applications where extreme precision is not required. The developed method may also be extrapolated to other cases and crop types, such as fruit trees or forest masses, offering an efficient solution for annual density estimates without relying on costly aerial images. Future research could enhance the accuracy of the model by grouping plots according to additional characteristics, such as tree size or plantation type.