In the context of increased pressures on land for food and non-food production, it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper, we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions shows that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like Land Parcel Identification Systems data, in regions is one of the challenges in this respect.

Wouter Meijninger, Berien Elbersen, Michiel Eupen, Stephan Mantel, Pilar Ciria, Andrea Parenti, et al. (2022). Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model. GCB BIOENERGY, 14(7), 735-755 [10.1111/gcbb.12939].

Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model

Andrea Parenti;Marco Acciai;Andrea Monti
2022

Abstract

In the context of increased pressures on land for food and non-food production, it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper, we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions shows that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like Land Parcel Identification Systems data, in regions is one of the challenges in this respect.
2022
Wouter Meijninger, Berien Elbersen, Michiel Eupen, Stephan Mantel, Pilar Ciria, Andrea Parenti, et al. (2022). Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model. GCB BIOENERGY, 14(7), 735-755 [10.1111/gcbb.12939].
Wouter Meijninger; Berien Elbersen; Michiel Eupen; Stephan Mantel; Pilar Ciria; Andrea Parenti; Marina Sanz Gallego; Paloma Perez Ortiz; Marco Acciai;...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/895751
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