Using Landsat time series to characterize forest insect invasions
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1 Using Landsat time series to characterize forest insect invasions Valerie Pasquarella Postdoctoral Fellow // University of Massachusetts Amherst Research Associate // Harvard Forest APIPP / TNC Innovations in Invasive Species Prevention, Early Detection, and Management Summit July 10, 2018 Tannery Pond, North Creek, NY This work was supported in part by:
2 Forest pests and pathogens are a growing problem... a pest for every genus Dukes, J. S., Pontius, J., Orwig, D., Garnas, J. R., Rodgers, V. L., Brazee, N., et al. (2009). Responses of insect pests, pathogens, and invasive plant species to climate change in the forests of northeastern North America: What can we predict? Canadian Journal of Forest Research, 39(2), doi: /jxb/
3 Monitoring forest insect invasions & outbreaks The Landsat archive: Long Term Ecological Record Hungry, hungry caterpillars: The return of the gypsy moth Shifting the foundation: Hemlock woolly adelgid
4 Landsat The Landsat archive: Long Term Ecological Record
5 Modified from:
6 2008 Before: from $15 up to >$4,000 per image p012r (L5)
7 30 m spatial resolution 30 centimeters (high res. imagery) 30 meters (Landsat)
8 Broad multi-spectral bands
9 16-day revisit (8-day with two) png?w=254&h=
10 Gypsy moth Hungry, hungry caterpillars: The return of the gypsy moth
11 Millions of acres defoliated Defoliation in Massachusetts ( ) Prior to 1981, there were regular outbreaks about every 10 years Figure: J. Elkinton
12 Millions of acres defoliated Defoliation in Massachusetts ( ) E. maimaiga Figure: J. Elkinton
13 Rainfall (inches) Drought May June 1-20th Figure: J. Elkinton
14 Summer 2016: Surprise outbreak!
15 MODIS
16 Landsat Greenness time series (for one pixel)
17 Landsat Greenness time series (for one pixel) Use historical observations to estimate baseline
18 Landsat Greenness time series (for one pixel) Use historical observations to estimate baseline Condition = obs - pred RMSE
19 Landsat Greenness time series (for one pixel) Use historical observations to estimate baseline Condition = obs - pred RMSE
20 I. Modeling II. Monitoring III. Assessment Fit model to stable base period New image Find potential changes in condition Season-integrated metric of change in condition Predicted image Targeted field/aerial surveys (attribution) Near-real-time products (per scene) Annual disturbance map (larger swath)
21 June 2 Early spring No signs of defoliation Missed cloud shadows
22 June 18 Two weeks later Patches of damage in eastern CT
23 June 19 Two weeks later More extensive damage in RI/eastern MA
24 August 29 End of season Severity of impacts decreased
25 August 30 End of season Severity of impacts decreased
26 Total number of observations mid-may September, n = 16
27 Mean condition score
28
29 Landsat time series Aerial sketch overlay
30 Gypsy moth Spatially and temporally consistent reconstruction Continued monitoring
31
32
33
34 2x 2x Almost all low-level
35 Gypsy moth Spatially and temporally consistent reconstruction Continued monitoring Support risk and impact modeling
36 2018 Emerging patterns of low-level defoliation Less caterpillar mortality than anticipated What s going on in VT?? Preliminary results
37 Killing them slowly: Hemlock woolly adelgid Hemlock woolly adelgid Shifting the foundation: Hemlock woolly adelgid
38
39
40 Ellison, A. M., Barker Plotkin, A. A., Foster, D. R., & Orwig, D. A. (2010). Experimentally testing the role of foundation species in forests: the Harvard Forest Hemlock Removal Experiment. Methods in Ecology and Evolution, 1(2), LTER Hemlock Removal Experiment 90 x 90 plots, est He G L Hw Hemlock control Girdled (simulated HWA) Logged (simulated salvage) Hardwood control
41 Hw He G L Hw Hardwood control 2010 He G L Hardwood Hw Seasonal variability Low Wetness in Spring
42 Hw He G L Hw Hardwood control 2010 He G L Hardwood Hw Seasonal variability Low Wetness in Spring this site more deciduous
43 Girdled Logged Year 1 Year 1 Year 3 Year 3 Photos: A. Ellison
44 Hw He G L L Logged 2017 He G L Hemlock Birch Hw Abrupt change Loss of biomass
45 Hw He G L L Logged 2017 He G L Hemlock Birch Hw Abrupt change Loss of biomass
46 Hw He G L G Girdled 2017 He G L Hemlock Birch Hw Delayed response Standing dead biomass
47 G Hemlock Girdled 2017 Birch Delayed response Standing dead biomass
48 Hw He G L He Hemlock control 2017 He G L Hemlock Hw Infested in 2009
49 Hw He G L He Hemlock control 2017 He G L Hemlock Hw Infested in 2009
50 He Hemlock Hemlock-HWA 2017 Connecticut Much longer period of HWA infestation More gradual than simulated HeRE plots (as expected)
51 Hemlock woolly adelgid Identify alternate trajectories not always birch! Develop algorithm for detecting HWA impacts Validate composition shifts
52 Monitoring forest insect invasions & outbreaks Landsat provides long-term record of forest condition (plus opportunities to integrate with other sensors/datasets) Different pests different approaches Sometimes impacts, sometimes recovery Toward a comprehensive model of New England landscape
53 Questions? Methods results
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