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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