Less is more: Optimizing vegetation mapping in peatlands using unmanned aerial vehicles (UAVs)
1. Northern peatlands are inaccessible wetlands that serve important ecosystem services to humans, including climate regulation by storing and sequestering carbon. Unmanned aerial vehicles or drones are ideal to map vegetation and associated functions in these ecosystems, but standardized methods to...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223000420 |
| _version_ | 1811163478403055616 |
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| author | Jasper Steenvoorden Harm Bartholomeus Juul Limpens |
| author_facet | Jasper Steenvoorden Harm Bartholomeus Juul Limpens |
| author_sort | Jasper Steenvoorden |
| collection | DOAJ |
| description | 1. Northern peatlands are inaccessible wetlands that serve important ecosystem services to humans, including climate regulation by storing and sequestering carbon. Unmanned aerial vehicles or drones are ideal to map vegetation and associated functions in these ecosystems, but standardized methods to optimize efficiency (highest accuracy with lowest processing time) are lacking. 2. We collected high-resolution drone imagery at three different altitudes (20 m, 60 m, and 120 m) of two Irish peatlands contrasting in pattern complexity and evaluated to what extent classification accuracy of vegetation patterns (microforms and plant functional types) changed using different flight altitudes, minimum segment size and training/testing sample size. We also analysed the processing time of all classifications to find the most efficient combination of parameters. 3. Classification accuracy was consistently high (>90 %) and estimated areas of both patterns were uniform among all flight altitudes, independent of pattern complexity. Minimum segment size and training/testing sample size were also important parameters affecting the efficiency of classifications. Total processing time from imagery capture to final map was 19–22 times faster with drone imagery at 120 m altitude than at 20 m, and seven times faster than at 60 m. 4. Our findings suggest that flying at the maximum legal altitude of 120 m is the most efficient approach for landscape-scale mapping of vegetation in peatlands or other ecosystems with similar short vegetation structure. We conclude that flying higher is always more efficient as long as the pixel size of drone imagery remains under the pixel size of the pattern under investigation. |
| first_indexed | 2024-04-10T15:06:05Z |
| format | Article |
| id | doaj.art-1a3fbc548c0f447094722d98595b0619 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-10T15:06:05Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-1a3fbc548c0f447094722d98595b06192023-02-15T04:27:35ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-03-01117103220Less is more: Optimizing vegetation mapping in peatlands using unmanned aerial vehicles (UAVs)Jasper Steenvoorden0Harm Bartholomeus1Juul Limpens2Plant Ecology and Nature Conservation (PEN), Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands; Corresponding author.Laboratory of Geo-information Science and Remote Sensing, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The NetherlandsPlant Ecology and Nature Conservation (PEN), Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands1. Northern peatlands are inaccessible wetlands that serve important ecosystem services to humans, including climate regulation by storing and sequestering carbon. Unmanned aerial vehicles or drones are ideal to map vegetation and associated functions in these ecosystems, but standardized methods to optimize efficiency (highest accuracy with lowest processing time) are lacking. 2. We collected high-resolution drone imagery at three different altitudes (20 m, 60 m, and 120 m) of two Irish peatlands contrasting in pattern complexity and evaluated to what extent classification accuracy of vegetation patterns (microforms and plant functional types) changed using different flight altitudes, minimum segment size and training/testing sample size. We also analysed the processing time of all classifications to find the most efficient combination of parameters. 3. Classification accuracy was consistently high (>90 %) and estimated areas of both patterns were uniform among all flight altitudes, independent of pattern complexity. Minimum segment size and training/testing sample size were also important parameters affecting the efficiency of classifications. Total processing time from imagery capture to final map was 19–22 times faster with drone imagery at 120 m altitude than at 20 m, and seven times faster than at 60 m. 4. Our findings suggest that flying at the maximum legal altitude of 120 m is the most efficient approach for landscape-scale mapping of vegetation in peatlands or other ecosystems with similar short vegetation structure. We conclude that flying higher is always more efficient as long as the pixel size of drone imagery remains under the pixel size of the pattern under investigation.http://www.sciencedirect.com/science/article/pii/S1569843223000420PeatlandsVegetation patternsunmanned aerial vehicles (UAVs)Heterogeneous landscapesRemote sensingMachine learning |
| spellingShingle | Jasper Steenvoorden Harm Bartholomeus Juul Limpens Less is more: Optimizing vegetation mapping in peatlands using unmanned aerial vehicles (UAVs) International Journal of Applied Earth Observations and Geoinformation Peatlands Vegetation patterns unmanned aerial vehicles (UAVs) Heterogeneous landscapes Remote sensing Machine learning |
| title | Less is more: Optimizing vegetation mapping in peatlands using unmanned aerial vehicles (UAVs) |
| title_full | Less is more: Optimizing vegetation mapping in peatlands using unmanned aerial vehicles (UAVs) |
| title_fullStr | Less is more: Optimizing vegetation mapping in peatlands using unmanned aerial vehicles (UAVs) |
| title_full_unstemmed | Less is more: Optimizing vegetation mapping in peatlands using unmanned aerial vehicles (UAVs) |
| title_short | Less is more: Optimizing vegetation mapping in peatlands using unmanned aerial vehicles (UAVs) |
| title_sort | less is more optimizing vegetation mapping in peatlands using unmanned aerial vehicles uavs |
| topic | Peatlands Vegetation patterns unmanned aerial vehicles (UAVs) Heterogeneous landscapes Remote sensing Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223000420 |
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