Development of an algorithm for identification of sown biodiverse pastures in Portugal

ABSTRACT

 Sown biodiverse pastures (SBP) are a pasture system developed in Portugal. Until 2014, farmers were supported in installing and maintaining SBP, but tracking their locations has been lacking. To survey the country, remote sensing tools with machine learning were used. Here, we developed the first algorithm that combines remote sensing data with machine learning algorithms to identify SBP areas. The algorithm combines Landsat-7 and night-light spectral data with terrain and bioclimatic data. Remotely sensed data offer higher spatial resolution compared to bioclimatic data and also cover interannual variability. Gradient-boosted decision trees (XGB) and artificial neural networks (ANN) were the machine learning methods used. The overall classification accuracy, on an independent validation dataset, was 94%, with 82% producer accuracy and 85% user accuracy. The total estimated area of SBP in the Portuguese region of Alentejo region was 1300 km2 in 2013, which is similar to the total known installed area (approximately 1000 km²). The estimated spatial distribution is in accordance with the known distribution at the municipal level. These results are a critical first step towards the future development of remote systems for assessing the state of SBP and for compliance checks of farmer commitments.

KEYWORDS:

• Object-based classification
• remote sensing
• Landsat-7
• XGB
• artificial neural network
• cross-validation

Acknowledgments

This work was supported by Fundação para a Ciência e Tecnologia through project “GrassData - Development of algorithms for identification, monitoring, compliance checks and quantification of carbon sequestration in pastures” (DSAIPA/DS/0074/2019) and CEECIND/00365/2018 (R. Teixeira). The work was also supported by FCT/MCTES (PIDDAC) through project LARSyS—FCT Pluriannual funding 2020–2023 (UIDP/EEA/50009/2020) and by Fundo Europeu de Desenvolvimento Regional through project “GreenBeef - Towards carbon neutral Angus beef production in Portugal” (POCI-01-0247-FEDER-047050) and “Sheep 4.0 – Indústria 4.0 sustentável em ovinos de leite em pastagem” (POCI-01-0247-FEDER-069892).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data will be made available on request. The Python script used in this work is available on Github (https://github.com/tgmorais/SBP_identification).

Additional information

Funding

The work was supported by the European Regional Development Fund [POCI-01-0247-FEDER-047050]; Fundação para a Ciência e a Tecnologia [CEECIND/00365/2018]; by Fundo Europeu de Desenvolvimento Regional [POCI-01-0247-FEDER-069892].