Early Detection of Southern Pine Beetle Attack by UAV-Collected Multispectral Imagery

Author:

Kanaskie Caroline R.1ORCID,Routhier Michael R.2ORCID,Fraser Benjamin T.1ORCID,Congalton Russell G.1ORCID,Ayres Matthew P.3,Garnas Jeff R.1ORCID

Affiliation:

1. Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH 03824, USA

2. Institute for the Study of Earth Oceans and Space, University of New Hampshire Earth Systems Research Center, Durham, NH 03824, USA

3. Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA

Abstract

Effective management of bark beetle infestations requires prompt detection of attacked trees. Early attack is also called green attack, since tree foliage does not yet show any visible signs of tree decline. In several bark beetle systems, including mountain pine beetle and European spruce bark beetle, unpiloted aerial vehicle (UAV)-based remote sensing has successfully detected early attack. We explore the utility of remote sensing for early attack detection of southern pine beetle (SPB; Dendroctonus frontalis Zimm.), paired with detailed ground surveys to link tree decline symptoms with SPB life stages within the tree. In three of the northernmost SPB outbreaks in 2022 (Long Island, New York), we conducted ground surveys every two weeks throughout the growing season and collected UAV-based multispectral imagery in July 2022. Ground data revealed that SPB-attacked pitch pines (Pinus rigida Mill.) generally maintained green foliage until SPB pupation occurred within the bole. This tree decline behavior illustrates the need for early attack detection tools, like multispectral imagery, in the beetle’s northern range. Balanced random forest classification achieved, on average, 78.8% overall accuracy and identified our class of interest, SPB early attack, with 68.3% producer’s accuracy and 72.1% user’s accuracy. After removing the deciduous trees and just mapping the pine, the overall accuracy, on average, was 76.9% while the producer’s accuracy and the user’s accuracy both increased for the SPB early attack class. Our results demonstrate the utility of multispectral remote sensing in assessing SPB outbreaks, and we discuss possible improvements to our protocol. This is the first remote sensing study of SPB early attack in almost 60 years, and the first using a UAV in the SPB literature.

Funder

University of New Hampshire Collaborative Research Excellence (CoRE) Initiative

Publisher

MDPI AG

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