Work

Mapping Lupinus polyphyllus density and distribution along road verges using unmanned aerial vehicle (UAV)–based remote sensing

Year: 2026

Type: article

Abstract: 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.

Source: Applications in Plant Sciences

DOI: 10.1002/aps3.70080

Authors: Elin L. Blomqvist, Jan‐Olov Andersson, R. Lutz Eckstein, Jan Haas

Institution: Karlstad University

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Life on land, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,340