Potentials and Limitations of WorldView-3 Data for the Detection of Invasive <i>Lupinus polyphyllus</i> Lindl. in Semi-Natural Grasslands
Semi-natural grasslands contribute highly to biodiversity and other ecosystem services, but they are at risk by the spread of invasive plant species, which alter their habitat structure. Large area grassland monitoring can be a powerful tool to manage invaded ecosystems. Therefore, WorldView-3 multi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/21/4333 |
| _version_ | 1827677958371803136 |
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| author | Damian Schulze-Brüninghoff Michael Wachendorf Thomas Astor |
| author_facet | Damian Schulze-Brüninghoff Michael Wachendorf Thomas Astor |
| author_sort | Damian Schulze-Brüninghoff |
| collection | DOAJ |
| description | Semi-natural grasslands contribute highly to biodiversity and other ecosystem services, but they are at risk by the spread of invasive plant species, which alter their habitat structure. Large area grassland monitoring can be a powerful tool to manage invaded ecosystems. Therefore, WorldView-3 multispectral sensor data was utilized to train multiple machine learning algorithms in an automatic machine learning workflow called ‘H2O AutoML’ to detect <i>L. polyphyllus</i> in a nature protection grassland ecosystem. Different degree of <i>L. polyphyllus</i> cover was collected on 3 × 3 m<sup>2</sup> reference plots, and multispectral bands, indices, and texture features were used in a feature selection process to identify the most promising classification model and machine learning algorithm based on mean per class error, log loss, and AUC metrics. The best performance was achieved with a binary classification of lupin-free vs. fully invaded 3 × 3 m<sup>2</sup> plot classification with a set of 7 features out of 763. The findings reveal that <i>L. polyphyllus</i> detection from WorldView-3 sensor data is limited to large dominant spots and not recommendable for lower plant coverage, especially single plant detection. Further research is needed to clarify if different phenological stages of <i>L. polyphyllus</i> as well as time series increase classification performance. |
| first_indexed | 2024-03-10T05:53:58Z |
| format | Article |
| id | doaj.art-81ca9fb6ec984a8ca7bc93899bd41e5b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:53:58Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-81ca9fb6ec984a8ca7bc93899bd41e5b2023-11-22T21:32:03ZengMDPI AGRemote Sensing2072-42922021-10-011321433310.3390/rs13214333Potentials and Limitations of WorldView-3 Data for the Detection of Invasive <i>Lupinus polyphyllus</i> Lindl. in Semi-Natural GrasslandsDamian Schulze-Brüninghoff0Michael Wachendorf1Thomas Astor2Grassland Science and Renewable Plant Resources, Universität Kassel, Steinstraße 19, D-37213 Witzenhausen, GermanyGrassland Science and Renewable Plant Resources, Universität Kassel, Steinstraße 19, D-37213 Witzenhausen, GermanyGrassland Science and Renewable Plant Resources, Universität Kassel, Steinstraße 19, D-37213 Witzenhausen, GermanySemi-natural grasslands contribute highly to biodiversity and other ecosystem services, but they are at risk by the spread of invasive plant species, which alter their habitat structure. Large area grassland monitoring can be a powerful tool to manage invaded ecosystems. Therefore, WorldView-3 multispectral sensor data was utilized to train multiple machine learning algorithms in an automatic machine learning workflow called ‘H2O AutoML’ to detect <i>L. polyphyllus</i> in a nature protection grassland ecosystem. Different degree of <i>L. polyphyllus</i> cover was collected on 3 × 3 m<sup>2</sup> reference plots, and multispectral bands, indices, and texture features were used in a feature selection process to identify the most promising classification model and machine learning algorithm based on mean per class error, log loss, and AUC metrics. The best performance was achieved with a binary classification of lupin-free vs. fully invaded 3 × 3 m<sup>2</sup> plot classification with a set of 7 features out of 763. The findings reveal that <i>L. polyphyllus</i> detection from WorldView-3 sensor data is limited to large dominant spots and not recommendable for lower plant coverage, especially single plant detection. Further research is needed to clarify if different phenological stages of <i>L. polyphyllus</i> as well as time series increase classification performance.https://www.mdpi.com/2072-4292/13/21/4333invasive speciesWorldView-3grasslandmachine learningfeature selection |
| spellingShingle | Damian Schulze-Brüninghoff Michael Wachendorf Thomas Astor Potentials and Limitations of WorldView-3 Data for the Detection of Invasive <i>Lupinus polyphyllus</i> Lindl. in Semi-Natural Grasslands Remote Sensing invasive species WorldView-3 grassland machine learning feature selection |
| title | Potentials and Limitations of WorldView-3 Data for the Detection of Invasive <i>Lupinus polyphyllus</i> Lindl. in Semi-Natural Grasslands |
| title_full | Potentials and Limitations of WorldView-3 Data for the Detection of Invasive <i>Lupinus polyphyllus</i> Lindl. in Semi-Natural Grasslands |
| title_fullStr | Potentials and Limitations of WorldView-3 Data for the Detection of Invasive <i>Lupinus polyphyllus</i> Lindl. in Semi-Natural Grasslands |
| title_full_unstemmed | Potentials and Limitations of WorldView-3 Data for the Detection of Invasive <i>Lupinus polyphyllus</i> Lindl. in Semi-Natural Grasslands |
| title_short | Potentials and Limitations of WorldView-3 Data for the Detection of Invasive <i>Lupinus polyphyllus</i> Lindl. in Semi-Natural Grasslands |
| title_sort | potentials and limitations of worldview 3 data for the detection of invasive i lupinus polyphyllus i lindl in semi natural grasslands |
| topic | invasive species WorldView-3 grassland machine learning feature selection |
| url | https://www.mdpi.com/2072-4292/13/21/4333 |
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