[article]
| Titre : |
Mapping wild leek through the forest canopy using a UAV |
| Type de document : |
Imprimé |
| Auteurs : |
Marie-Bé Leduc ; Anders J. Knudby |
| Année de publication : |
2018 |
| Article en page(s) : |
70 |
| Catégories : |
[CBNPMP-Thématique] Télédétection
|
| Résumé : |
Wild leek, an endangered plant species of Eastern North America, grows on forest floors and greens up to approximately three weeks before the trees it is typically found under, temporarily allowing it to be observed through the canopy by remote sensing instruments. This paper explores the accuracy with which wild leek can be mapped with a low-flying UAV. Nadir video imagery was obtained using a commercial UAV during the spring of 2017 in Gatineau Park, Quebec. Point clouds were generated from the video frames with the Structure-from-Motion framework, and a multiscale curvature classification was used to separate points on the ground, where wild leek grows, from above-ground points belonging to the forest canopy. Five-cm resolution orthomosaics were created from the ground points, and a threshold value of 0.350 for the green chromatic coordinate (GCC) was applied to delineate wild leek from wood, leaves, and other plants on the forest floor, with an F1-score of 0.69 and 0.76 for two different areas. The GCC index was most effective in delineating bigger patches, and therefore often misclassified patches smaller than 30 cm in diameter. Although short flight times and long data processing times are presently technical challenges to upscaling, the low cost and high accuracy of UAV imagery provides a promising method for monitoring the spatial distribution of this endangered species. |
| Lien pérenne : |
DOI : 10.3390/rs10010070  |
| Permalink : |
https://biblio.cbnpmp.fr/index.php?lvl=notice_display&id=151548 |
in Remote sensing > 10 (2018) . - 70
Leduc, Marie-Bé, Knudby, Anders J.
2018
Mapping wild leek through the forest canopy using a UAV.
Remote sensing, 10: 70.
|
Mapping wild leek through the forest canopy using a UAV in Remote sensing, 10 (2018)
