View in EDS

Detecting and Mapping Invasive Species Across Riparian Corridors via Object Detection Approaches in UAV Imagery: An Example of Impatiens glandulifera.

Saved in:
Bibliographic Details
Title: Detecting and Mapping Invasive Species Across Riparian Corridors via Object Detection Approaches in UAV Imagery: An Example of Impatiens glandulifera.
Authors: Cook, Jack1 (AUTHOR) jrc15@aber.ac.uk, Roberts, Benjamin P.1,2 (AUTHOR), Labrosse, Frédéric3 (AUTHOR), Snooke, Neal3 (AUTHOR)
Source: Ecology & Evolution (20457758). Aug2025, Vol. 15 Issue 8, p1-15. 15p.
Subject Terms: *Riparian areas, *Introduced species, Object recognition (Computer vision), Impatiens, Geographic information system software, Computer vision, Remote sensing, Image processing software
Geographic Terms: United Kingdom
Abstract: Riparian zones in the United Kingdom have high species diversity but are prone to anthropogenic changes and alien plant invasions, like Impatiens glandulifera. However, identification can be challenging due to poor accessibility or visibility via tree canopies. UAVs provide a means to access previously inaccessible areas and capture imagery of the area. In this study, a method is introduced to identify the flowers of invasive species (Impatiens glandulifera) and map their locations using a computer vision framework and oblique image capture methods. The process includes thresholding images, image masking, blurring, ellipsoid shape search, noise reduction, and contour extraction. Locations are determined using camera parameters, EXIF data, and the average flower size, then converted into vector format for GIS software. This method is wrapped into a single executable program named the semi‐automatic thresholding tool (SATT). A validation set of 312 UAV images from the River Elwy, North Wales, showed high precision (79%–96%) and mean average precision (mAP) scores of 73%–86%. This demonstrates that the SATT consistently and correctly identifies Impatiens glandulifera flowers from UAV imagery, making it effective for identifying hotspots and targeting management techniques along riparian corridors. The tool has been wrapped into a single‐file executable program with a graphical user interface, enabling nonexperts to use the tool without the need of any software installation. Overall, the tool obtains consistent detection levels of abundance/or flower density across the study site. The tool also does not require an extensive amount of training data, and the intuitive design of the software enables nonexperts to utilize the tool and modify parameter values to adapt it to their needs. [ABSTRACT FROM AUTHOR]
Copyright of Ecology & Evolution (20457758) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
Description
Abstract:Riparian zones in the United Kingdom have high species diversity but are prone to anthropogenic changes and alien plant invasions, like Impatiens glandulifera. However, identification can be challenging due to poor accessibility or visibility via tree canopies. UAVs provide a means to access previously inaccessible areas and capture imagery of the area. In this study, a method is introduced to identify the flowers of invasive species (Impatiens glandulifera) and map their locations using a computer vision framework and oblique image capture methods. The process includes thresholding images, image masking, blurring, ellipsoid shape search, noise reduction, and contour extraction. Locations are determined using camera parameters, EXIF data, and the average flower size, then converted into vector format for GIS software. This method is wrapped into a single executable program named the semi‐automatic thresholding tool (SATT). A validation set of 312 UAV images from the River Elwy, North Wales, showed high precision (79%–96%) and mean average precision (mAP) scores of 73%–86%. This demonstrates that the SATT consistently and correctly identifies Impatiens glandulifera flowers from UAV imagery, making it effective for identifying hotspots and targeting management techniques along riparian corridors. The tool has been wrapped into a single‐file executable program with a graphical user interface, enabling nonexperts to use the tool without the need of any software installation. Overall, the tool obtains consistent detection levels of abundance/or flower density across the study site. The tool also does not require an extensive amount of training data, and the intuitive design of the software enables nonexperts to utilize the tool and modify parameter values to adapt it to their needs. [ABSTRACT FROM AUTHOR]
ISSN:20457758
DOI:10.1002/ece3.71921