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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2023.2238878 |
| _version_ | 1797771630563295232 |
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| author | Tiago G. Morais Nuno R. Rodrigues Ivo Gama Tiago Domingos Ricardo F.M. Teixeira |
| author_facet | Tiago G. Morais Nuno R. Rodrigues Ivo Gama Tiago Domingos Ricardo F.M. Teixeira |
| author_sort | Tiago G. Morais |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T21:39:27Z |
| format | Article |
| id | doaj.art-62ea932bd25b449cb4b7ae4a05aa97cb |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-03-12T21:39:27Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-62ea932bd25b449cb4b7ae4a05aa97cb2023-07-27T05:30:02ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542023-12-0156110.1080/22797254.2023.2238878Development of an algorithm for identification of sown biodiverse pastures in PortugalTiago G. Morais0Nuno R. Rodrigues1Ivo Gama2Tiago Domingos3Ricardo F.M. Teixeira4MARETEC − Marine, Environment and Technology Centre, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, PortugalTerraprima – Serviços Ambientais, Sociedade Unipessoal, Lda, Samora Correia, PortugalTerraprima – Serviços Ambientais, Sociedade Unipessoal, Lda, Samora Correia, PortugalMARETEC − Marine, Environment and Technology Centre, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, PortugalMARETEC − Marine, Environment and Technology Centre, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, PortugalABSTRACT 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.https://www.tandfonline.com/doi/10.1080/22797254.2023.2238878Object-based classificationremote sensingLandsat-7XGBartificial neural networkcross-validation |
| spellingShingle | Tiago G. Morais Nuno R. Rodrigues Ivo Gama Tiago Domingos Ricardo F.M. Teixeira Development of an algorithm for identification of sown biodiverse pastures in Portugal European Journal of Remote Sensing Object-based classification remote sensing Landsat-7 XGB artificial neural network cross-validation |
| title | Development of an algorithm for identification of sown biodiverse pastures in Portugal |
| title_full | Development of an algorithm for identification of sown biodiverse pastures in Portugal |
| title_fullStr | Development of an algorithm for identification of sown biodiverse pastures in Portugal |
| title_full_unstemmed | Development of an algorithm for identification of sown biodiverse pastures in Portugal |
| title_short | Development of an algorithm for identification of sown biodiverse pastures in Portugal |
| title_sort | development of an algorithm for identification of sown biodiverse pastures in portugal |
| topic | Object-based classification remote sensing Landsat-7 XGB artificial neural network cross-validation |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2023.2238878 |
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