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UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce.

Authors :
Allen, Benjamin
Dalponte, Michele
Ørka, Hans Ole
Næsset, Erik
Puliti, Stefano
Astrup, Rasmus
Gobakken, Terje
Source :
Remote Sensing. Aug2022, Vol. 14 Issue 15, p3830-3830. 16p.
Publication Year :
2022

Abstract

Numerous species of pathogenic wood decay fungi, including members of the genera Heterobasidion and Armillaria, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (Picea abies L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13). [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
15
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
158523889
Full Text :
https://doi.org/10.3390/rs14153830