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Forests 2017, 8(8), 275; doi:10.3390/f8080275

Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

1
Department of Environmental Conservation, University of Massachusetts Amherst, 160 Holdsworth Way, Amherst, MA 01003, USA
2
Northeast Climate Science Center, University of Massachusetts Amherst, 233 Morrill Science Center, 611 North Pleasant Street, Amherst, MA 01003, USA
3
Department of Earth and Environment, Boston University, 675 Commonwealth Ave., Boston, MA 02215, USA
*
Author to whom correspondence should be addressed.
Received: 2 June 2017 / Revised: 19 July 2017 / Accepted: 22 July 2017 / Published: 31 July 2017
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
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Abstract

Introduced insects and pathogens impact millions of acres of forested land in the United States each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar) outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS) products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators. View Full-Text
Keywords: Landsat; time series; synthetic images; Continuous Change Detection and Classification (CCDC); defoliation; gypsy moth; Lymantria dispar Landsat; time series; synthetic images; Continuous Change Detection and Classification (CCDC); defoliation; gypsy moth; Lymantria dispar
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

  • Externally hosted supplementary file 1
    Doi: http://doi.org/10.5281/zenodo.801800
    Description: All Landsat time series products originating from this study have been made available as supplemental materials in a publicly accessible database.

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MDPI and ACS Style

Pasquarella, V.J.; Bradley, B.A.; Woodcock, C.E. Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series. Forests 2017, 8, 275.

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