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

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.

[1]  Keith N. Eshleman,et al.  Relationship of a Landsat cumulative disturbance index to canopy nitrogen and forest structure , 2012 .

[2]  P. Townsend,et al.  Spatial dynamics of a gypsy moth defoliation outbreak and dependence on habitat characteristics , 2013, Landscape Ecology.

[3]  Andrew M. Liebhold,et al.  Landscape characterization of forest susceptibility to gypsy moth defoliation. , 1994 .

[4]  D. L. Williams,et al.  Remote detection of forest damage , 1986 .

[5]  W. A. Patterson,et al.  Monitoring the effects of gypsy moth defoliation on forest stand dynamics on Cape Cod, Massachusetts: sampling intervals and appropriate interpretations. , 2008 .

[6]  R. Nelson.  Detecting forest canopy change due to insect activity using Landsat MSS , 1983 .

[7]  Clayton C. Kingdon,et al.  A general Landsat model to predict canopy defoliation in broadleaf deciduous forests , 2012 .

[8]  E. Crist.  A TM Tasseled Cap equivalent transformation for reflectance factor data , 1985 .

[9]  Philip A. Townsend,et al.  Estimating the effect of gypsy moth defoliation using MODIS , 2008 .

[10]  Lin Yan,et al.  An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery , 2016, Remote. Sens..

[11]  Kenton W. Ross,et al.  Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks , 2011 .

[12]  W. Cohen,et al.  Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection , 2005 .

[13]  Andrew M. Liebhold,et al.  Geostatistics and Geographic Information Systems in Applied Insect Ecology , 1993 .

[14]  Ronald J. Hall,et al.  4 Remotely Sensed Data in the Mapping of Insect Defoliation , 2006 .

[15]  Neil Flood,et al.  Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia , 2017, Remote. Sens..

[16]  Christopher E. Holden,et al.  An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations , 2016 .

[17]  J. Régnière,et al.  Climate suitability and management of the gypsy moth invasion into Canada , 2008, Biological Invasions.

[18]  Zhe Zhu,et al.  Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative , 2016 .

[19]  Patrick Hostert,et al.  Remote sensing of forest insect disturbances: Current state and future directions , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[20]  Erik W. Johnson,et al.  Quantifying error in aerial survey data , 2008 .

[21]  C. Woodcock,et al.  Continuous monitoring of forest disturbance using all available Landsat imagery , 2012 .

[22]  Gregory A. Elmes,et al.  Gypsy moth invasion in North America: a quantitative analysis , 1992 .

[23]  Kurt W. Gottschalk,et al.  Coping with the gypsy moth on new frontiers of infestation , 1987 .

[24]  Zhe Zhu,et al.  Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data , 2016 .

[25]  Andrew M. Liebhold,et al.  Invasive forest defoliator contributes to the impending downward trend of oak dominance in eastern North America , 2016 .

[26]  C. D. Rullán-Silva,et al.  Remote Monitoring of Forest Insect Defoliation -A Review- , 2013 .

[27]  D. Gray.  The gypsy moth life stage model: landscape-wide estimates of gypsy moth establishment using a multi-generational phenology model , 2004 .

[28]  Darrel L. Williams,et al.  Integration of Digital Elevation Model Data and Landsat MSS Data to Quantify the Effects of Slope Orientation on the Classification of Forest Canopy Condition , 1981 .

[29]  D. Pimentel,et al.  Environmental and Economic Costs of Nonindigenous Species in the United States , 2000 .

[30]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[31]  Curtis E. Woodcock,et al.  From imagery to ecology: leveraging time series of all available Landsat observations to map and monitor ecosystem state and dynamics , 2016 .

[32]  Christopher E. Holden,et al.  Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time , 2015 .

[33]  Christopher E. Holden.  Yet Another Time Series Model (YATSM): v0.3.1 , 2015 .

[34]  Andrew M. Liebhold,et al.  Historical Accumulation of Nonindigenous Forest Pests in the Continental United States , 2010 .

[35]  Gabriele Bitelli,et al.  Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use , 2016, Remote. Sens..

[36]  Darrel L. Williams,et al.  Use of Remotely Sensed Data for Assessing Forest Stand Conditions in the Eastern United States , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Andrew M. Liebhold,et al.  POPULATION DYNAMICS OF GYPSY MOTH IN NORTH AMERICA , 1990 .

[38]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[39]  Andrew M. Liebhold,et al.  Nonnative forest insects and pathogens in the United States: Impacts and policy options , 2016, Ecological applications : a publication of the Ecological Society of America.