Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping
A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (<i>Solidago</i> spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. Thi...
| Hlavní autoři: | , , , |
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| Médium: | Článek |
| Jazyk: | English |
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MDPI AG
2024-02-01
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| Edice: | Remote Sensing |
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| On-line přístup: | https://www.mdpi.com/2072-4292/16/4/636 |
| _version_ | 1827342861097500672 |
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| author | Bogdan Zagajewski Marcin Kluczek Karolina Barbara Zdunek David Holland |
| author_facet | Bogdan Zagajewski Marcin Kluczek Karolina Barbara Zdunek David Holland |
| author_sort | Bogdan Zagajewski |
| collection | DOAJ |
| description | A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (<i>Solidago</i> spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads to altered soil composition through the release of allelopathic chemicals, complicating control efforts and making it challenging to maintain ecological balance in affected areas. The research goal was to develop methods that allow the analysis of changes in heterogeneous habitats with high accuracy and repeatability. For this reason, we used open source classifiers Support Vector Machine (SVM), Random Forest (RF), and satellite images of Sentinel-2 (free) and PlanetScope (commercial) to assess their potential in goldenrod classification. Due to the fact that invasions begin with invasion footholds, created by small patches of invasive, autochthonous plants and different land cover patterns (asphalt, concrete, buildings) forming heterogeneous areas, we based our studies on field-verified polygons, which allowed the selection of randomized pixels for the training and validation of iterative classifications. The results confirmed that the optimal solution is the use of multitemporal Sentinel-2 images and the RF classifier, as this combination gave F1-score accuracy of 0.92–0.95 for polygons dominated by goldenrod and 0.85–0.89 for heterogeneous areas where goldenrod was in the minority (mix class; smaller share of goldenrod in canopy than autochthonous plants). The mean decrease in the accuracy analysis (MDA), indicating an informativeness of individual spectral bands, showed that Sentinel-2 bands coastal aerosol, NIR, green, SWIR, and red were comparably important, while in the case of PlanetScope data, the NIR and red were definitely the most important, and remaining bands were less informative, and yellow (B5) did not contribute significant information even during the flowering period, when the plant was covered with intensely yellow perianth, and red-edge, coastal aerosol, or green II were much more important. The maximum RF classification values of Sentinel-2 and PlanetScope images for goldenrod are similar (F1-score > 0.9), but the medians are lower for PlanetScope data, especially with the SVM algorithm. |
| first_indexed | 2024-03-07T22:15:40Z |
| format | Article |
| id | doaj.art-2376f9cab2fe46a8b6d320b59307dd62 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-07T22:15:40Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2376f9cab2fe46a8b6d320b59307dd622024-02-23T15:32:56ZengMDPI AGRemote Sensing2072-42922024-02-0116463610.3390/rs16040636Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species MappingBogdan Zagajewski0Marcin Kluczek1Karolina Barbara Zdunek2David Holland3Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, PolandDepartment of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, PolandDepartment of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, PolandApplied Research, Ordnance Survey, Southampton SO16 0AS, UKA proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (<i>Solidago</i> spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads to altered soil composition through the release of allelopathic chemicals, complicating control efforts and making it challenging to maintain ecological balance in affected areas. The research goal was to develop methods that allow the analysis of changes in heterogeneous habitats with high accuracy and repeatability. For this reason, we used open source classifiers Support Vector Machine (SVM), Random Forest (RF), and satellite images of Sentinel-2 (free) and PlanetScope (commercial) to assess their potential in goldenrod classification. Due to the fact that invasions begin with invasion footholds, created by small patches of invasive, autochthonous plants and different land cover patterns (asphalt, concrete, buildings) forming heterogeneous areas, we based our studies on field-verified polygons, which allowed the selection of randomized pixels for the training and validation of iterative classifications. The results confirmed that the optimal solution is the use of multitemporal Sentinel-2 images and the RF classifier, as this combination gave F1-score accuracy of 0.92–0.95 for polygons dominated by goldenrod and 0.85–0.89 for heterogeneous areas where goldenrod was in the minority (mix class; smaller share of goldenrod in canopy than autochthonous plants). The mean decrease in the accuracy analysis (MDA), indicating an informativeness of individual spectral bands, showed that Sentinel-2 bands coastal aerosol, NIR, green, SWIR, and red were comparably important, while in the case of PlanetScope data, the NIR and red were definitely the most important, and remaining bands were less informative, and yellow (B5) did not contribute significant information even during the flowering period, when the plant was covered with intensely yellow perianth, and red-edge, coastal aerosol, or green II were much more important. The maximum RF classification values of Sentinel-2 and PlanetScope images for goldenrod are similar (F1-score > 0.9), but the medians are lower for PlanetScope data, especially with the SVM algorithm.https://www.mdpi.com/2072-4292/16/4/636biodiversity<i>Solidago</i> spp.iterative classificationSupport Vector MachineRandom Forest |
| spellingShingle | Bogdan Zagajewski Marcin Kluczek Karolina Barbara Zdunek David Holland Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping Remote Sensing biodiversity <i>Solidago</i> spp. iterative classification Support Vector Machine Random Forest |
| title | Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping |
| title_full | Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping |
| title_fullStr | Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping |
| title_full_unstemmed | Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping |
| title_short | Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping |
| title_sort | sentinel 2 versus planetscope images for goldenrod invasive plant species mapping |
| topic | biodiversity <i>Solidago</i> spp. iterative classification Support Vector Machine Random Forest |
| url | https://www.mdpi.com/2072-4292/16/4/636 |
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