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Forests 2018, 9(6), 357; https://doi.org/10.3390/f9060357

Detection of Annual Spruce Budworm Defoliation and Severity Classification Using Landsat Imagery

1
School of Forest Resources, University of Maine, 5755 Nutting Hall, Orono, ME 04469, USA
2
Department of Biological Sciences, University of Quebec in Montreal, Montreal, QC H3C 3P8, Canada
3
Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
*
Author to whom correspondence should be addressed.
Received: 10 May 2018 / Revised: 9 June 2018 / Accepted: 12 June 2018 / Published: 14 June 2018
(This article belongs to the Special Issue Protection Strategy against Spruce Budworm)
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Abstract

Spruce budworm (SBW) is the most destructive forest pest in eastern forests of North America. Mapping annual current-year SBW defoliation is challenging because of the large landscape scale of infestations, high temporal/spatial variability, and the short period of time when detection is possible. We used Landsat-5 and Landsat-MSS data to develop a method to detect and map SBW defoliation, which can be used as ancillary or alternative information for aerial sketch maps (ASMs). Results indicated that Landsat-5 data were capable of detecting and classifying SBW defoliation into three levels comparable to ASMs. For SBW defoliation classification, a combination of three vegetation indices, including normalized difference moisture index (NDMI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI), were found to provide the highest accuracy (non-defoliated: 77%, light defoliation: 60%, moderate defoliation: 52%, and severe defoliation: 77%) compared to using only NDMI (non-defoliated: 76%, light defoliation: 40%, moderate defoliation: 43%, and severe defoliation: 67%). Detection of historical SBW defoliation was possible using Landsat-MSS NDVI data, and the produced maps were used to complement coarse-resolution aerial sketch maps of the past outbreak. The method developed for Landsat-5 data can be used for current SBW outbreak mapping in North America using Landsat-8 and Sentinel-2 imagery. Overall, the work highlights the potential of moderate resolution optical remote sensing data to detect and classify fine-scale patterns in tree defoliation. View Full-Text
Keywords: forest pests; defoliation; spruce budworm; multi-spectral remote sensing; Acadian region; Maine; Quebec forest pests; defoliation; spruce budworm; multi-spectral remote sensing; Acadian region; Maine; Quebec
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Rahimzadeh-Bajgiran, P.; Weiskittel, A.R.; Kneeshaw, D.; MacLean, D.A. Detection of Annual Spruce Budworm Defoliation and Severity Classification Using Landsat Imagery. Forests 2018, 9, 357.

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