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2022 | OriginalPaper | Chapter

Abandoned Agricultural Areas: From Quantification to Qualification by Integration of GIS and Remote Sensing

Authors : Giuseppe Cillis, Valentina Santarsiero, Gabriele Nolè, Antonio Lanorte, Beniamino Murgante

Published in: Computational Science and Its Applications – ICCSA 2022

Publisher: Springer International Publishing

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Abstract

The agricultural areas abandonment has become one of phenomena that is most influencing the land transformation. This has been evident for some decades already, especially in inland mountainous and hilly areas of the Mediterranean where some types of agricultural activities have been abandoned as less profitable. The effects of this abandonment are not yet very clear but above all vary greatly in relation to morphological, geological and microclimatic characteristics of territory. The drivers of abandonment are also linked to these territorial characteristics with the addition of socio-economic factors. Therefore, studying this process from a spatial and geographical point of view is crucial. To do this, the integration of GIS and remote sensing in an open source environment represents a key approach as it allows to address the issues in a multi-temporal and multi-disciplinary way in an accurate way. In this paper, a case study of Southern Italy (Alto Bradano area - Basilicata region) has been chosen to quantify and qualify abandoned agricultural areas. First, a methodology for quantification of abandoned agricultural areas has been implemented through the time series analysis of spectral indices obtained starting from freely available satellite imagery, then a qualitative analysis has been carried out by relating abandoned agricultural areas to some spatial variables. In this way, we want to better define the drivers of change and contextualize them with respect to the local geography in order to have an overall view of the phenomenon of agricultural abandonment.

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Metadata
Title
Abandoned Agricultural Areas: From Quantification to Qualification by Integration of GIS and Remote Sensing
Authors
Giuseppe Cillis
Valentina Santarsiero
Gabriele Nolè
Antonio Lanorte
Beniamino Murgante
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-031-10450-3_30

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