Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data

The Common Agricultural Policy of the European Union grants subsidies for olive production. Areas of intensified olivefarming will be of major importance for the increasing demand for oil production of the next decades, and countries witha high ratio of intensively and super-intensively managed olive groves will be more competitive than others, since theyare able to reduce production costs.It can be estimated that about 25-40% of the Sicilian oliviculture must be defined as “marginal”. Modern olivecultivation systems, which permit the mechanization of pruning and harvest operations, are limited.Agronomists, landscape planners, policy decision-makers and other professionals have a growing need for accurate andcost-effective information on land use in general and agronomic parameters in the particular. The availability of highspatial resolution imagery has enabled researchers to propose analysis tools on agricultural parcel and tree level.In our study, we test the performance of WorldView-2 imagery relative to the detection of olive groves and thedelineation of olive tree crowns, using an object-oriented approach of image classification in combined use with LIDARdata.We selected two sites, which differ in their environmental conditions and in their agronomic parameters of olive grovecultivation. The main advantage of the proposed methodology is the low necessary quantity of data input and itsautomatibility. However, it should be applied in other study areas to test if the good results of accuracy assessment canbe confirmed.Data extracted by the proposed methodology can be used as input data for decision-making support systems for olivegrove management.