Abstract
Worldwide sustainable development is threatened by current agricultural land change trends, particularly by the increasing rural farmland abandonment and agricultural intensification phenomena. In Mediterranean countries, these processes are affecting especially traditional olive groves with enormous socio-economic costs to rural areas, endangering environmental sustainability and biodiversity. Traditional olive groves abandonment and intensification are clearly related to the reduction of olive oil production income, leading to reduced economic viability. Most promising strategies to boost traditional groves competitiveness—such as olive oil differentiation through adoption of protected denomination of origin labels and development of value-added olive products—rely on knowledge of the olive varieties and its specific properties that confer their uniqueness and authenticity. Given the lack of information about olive varieties on traditional groves, a feasible and inexpensive method of variety identification is required. We analyzed leaf spectral information of ten Portuguese olive varieties with a powerful data-mining approach in order to verify the ability of satellite’s hyperspectral sensors to provide an accurate olive variety identification. Our results show that these olive varieties are distinguishable by leaf reflectance information and suggest that even satellite open-source data could be used to map them. Additional advantages of olive varieties mapping were further discussed.
Highlights
Sustainable development at a worldwide scale is crucially dependent on changes in land use structure [1], with respect to the increasing global food demand and increasing land scarcity for agricultural production [2,3]
In terms of landscape dynamics, there have been three dominant agricultural land change processes with impact on biodiversity and nature values over the last half-century [4,5]: (1) In big cities surrounding areas, agricultural land was converted to urban use associated with rapid urbanization processes as a response to growing demographic demands [6]; (2) more economically productive areas have been intensified, incorporated into larger assemblages within developed countries [7]; and (3) unproductive farm areas were increasingly abandoned, reforested, or included in rewilding for nature values with the creation of nature reserves or parks [8,9,10]
The extraordinary accuracy achieved by our best performer model in the classification of most tested olive varieties is a clear substantiation that olive varieties yield distinguishable spectral reflectance patterns that can be used to identify them
Summary
Sustainable development at a worldwide scale is crucially dependent on changes in land use structure [1], with respect to the increasing global food demand and increasing land scarcity for agricultural production [2,3]. Agricultural land abandonment has been an increasing problem, mostly in Southern Europe throughout the last decades [11], contrasting with Northern and Western Europe where agriculture intensification/expansion is the dominating land-use change process [12]. The extent of such landscape changes raises deep concerns in European authorities since they could entail significant negative impacts. Additional concerns are raised regarding the loss of regional identities linked to the threat of cultural landscapes, essential for the sustainable development of specific communities [18,20,21,22]
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