Applications in plant sciences · 2026
Mapping Lupinus polyphyllus density and distribution along road verges using unmanned aerial vehicle (UAV)-based remote sensing.
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Blomqvist Elin L, Andersson Jan-Olov, Eckstein R Lutz, Haas Jan
Аннотация
PREMISE: Road verges function as refuges for semi-natural species, but they can also facilitate the spread of non-native plants such as Lupinus polyphyllus. Unmanned aerial vehicle (UAV)-based remote sensing is a promising tool for mapping these species; however, its application in roadside contexts remains limited.
METHODS: We developed and evaluated a UAV-based workflow to detect and map lupine presence and density along roads. Red-green-blue (RGB) imagery was processed into orthomosaics and classified using object-based and pixel-based approaches with support vector machines and random forest algorithms under multiple classification schemes.
RESULTS: The highest accuracy (88.8%) was achieved using pixel-based random forest classification and a simplified scheme. Flowering purple lupine showed strong classification performance (F1 score = 96.3%), while early-stage greenish individuals were harder to detect (74.5%). Spatial heatmap comparison revealed low overestimation (1.79%) and underestimation (1.47%).
DISCUSSION: Assuming optimal phenological conditions, UAV-based mapping provides a scalable and cost-efficient complement to ground surveys for the management of L. polyphyllus within infrastructure areas.
METHODS: We developed and evaluated a UAV-based workflow to detect and map lupine presence and density along roads. Red-green-blue (RGB) imagery was processed into orthomosaics and classified using object-based and pixel-based approaches with support vector machines and random forest algorithms under multiple classification schemes.
RESULTS: The highest accuracy (88.8%) was achieved using pixel-based random forest classification and a simplified scheme. Flowering purple lupine showed strong classification performance (F1 score = 96.3%), while early-stage greenish individuals were harder to detect (74.5%). Spatial heatmap comparison revealed low overestimation (1.79%) and underestimation (1.47%).
DISCUSSION: Assuming optimal phenological conditions, UAV-based mapping provides a scalable and cost-efficient complement to ground surveys for the management of L. polyphyllus within infrastructure areas.