Detecting olive grove abandonment with Sentinel-2 and machine learning: The development of a web-based tool for land management

The abandonment of rural areas is an important environmental and socio-economic issue in Europe, threatening the stability and profitability of agricultural production. The identification and quantification of abandoned land is key for temporal and spatial monitoring of the process and for applying alternative management measures. Italy is one of the most important European countries for the production of high quality olive oil, accounting for a large slice of the current certificated production (i.e., PDO, PGI). In this study, we present a machine learning model (i.e., Random Forest) for the identification of abandoned olive tree groves using field observations and NDVI time series, tested in a typical agroecosystem in central Italy dominated by olive groves. An application for smartphones able to record the geographic position was developed and used to collect field points, which in turn were utilised to train the model. The data of NDVI from January to December 2020, calculated on Sentinel-2 images, were extracted for each monitoring point and gap-filled to obtain a 10-days interval time series. The Random Forest model used the annual NDVI time series as features and classified the sampling points in the test dataset with an accuracy of 0.85. The model showed a higher capacity of classifying cultivated than abandoned points, sensitivity being equal to 0.88 and specificity equal to 0.82. Results demonstrated the applicability of the combined approach for discriminating cultivated from abandoned olive tree groves, in case that the parcels destined for olive tree cultivation are known. A web-based tool was implemented to support land monitoring and management.