Natalya Antonova , NCCN Catharine Thompson, NCCN Robert Kennedy, OSU*

1. Challenges of monitoring natural disturbance processes using remotely sensed data in North Coast and Cascades Network: comparison of approaches Natalya Antonova, NCCN Catharine Thompson, NCCN Robert Kennedy, OSU* • LandTrendr slides provided by Robert Kennedy

2. NCCN Monitoring Goals • Document landscape changes • When, where, what and magnitude • Status and trends • Prepare for and manage for landscape responses to climate change • Develop prediction tools • Test hypotheses

3. NCCN Monitoring Goals

5. Protocol for Landsat-Based Monitoring of Landscape Dynamics at NCCN Parks – Kennedy et al. 1994 2004 Subtract • Two different images 2. Select large changes in spectral values to indicate change Probabilities of Change

6. Tasseled-cap transformation of Landsat image Brightness: Red Greenness: Green Wetness: Blue Brt+Grn: Yellow/Orange Brt+Wet: Magenta Grn+Wet: Cyan Astoria

7. Change in Probability of Membership Dense broadleaf/ grass Conifer/Broad-leaf Mix Closed-canopy conifer Broadleaf tree/shrub Water/Deep shade Increasing TC Greenness Mixed Open: Bright Snow and ice Open: Dark Increasing TC Brightness Time 2 Time 1

8. Probability Thresholding FALSE POSITIVES FALSE NEGATIVES All spectral changes Artifacts Uninteresting* change Real change Sensor degradation, atmospheric contamination, geometric misregistration, sun angle variation Seasonality of vegetation (phenology), clouds, agricultural practices Sustained change in land cover or condition Threshold Mapped “no-change” Mapped “change”

9. North Cascades National Park ComplexJuly 29, 2005-Aug 17, 2006

10. Mount Rainier National Park Aug 14, 2005- Aug 17, 2006

11. Olympic National Park July 24, 2004- June 28, 2006

12. Validation - Errors of Omission a) b) c) d) e) TC2005 TC 2006 Change image 2006 NAIP Aerial Photo Polygons outlined in the validation process compared to change detected by the algorithm

13. Validation - Errors of Commission a) b) TC 2005 TC 2006 Polygons outlined in the validation process compared to change detected by the algorithm Change image c) d) e) Change image from east side of the study area

14. Subalpine Environments, Avalanche Chutes, Tree line, and River Disturbances 2006 2004 125 m Increase in conifer Increase in broadleaf Increase in vegetation Decrease in conifer

15. Summary: Current Protocol • Can detect change • Detected too much false change (clouds, shadows, agricultural dynamics) to provide meaningful results • Threshold level not sensitive enough to detect annual regrowth or low intensity, slow disturbance • Difficult to see change along narrow, long features of interest, due to misregistration errors • Upper elevation areas appear as pure speckle due to variable landcover and annual variation in phenology

16. Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) Rather than look for disturbance EVENTS, look for disturbance TRAJECTORIES Kennedy, R.E., Cohen, W.B., & Schroeder, T.A. (2007). Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110, 370-386

17. Segmentation • Goodness of fit to idealized curves • Allows for lower threshold levels • Greatly reduces amount of background noise

18. Cloud/Shadow Screening Merge Cloud Shadow Cloud Shadow Cloud Cloud

19. Poor-quality Images Olympic Peninsula 1996 1997 1998

20. Outputs Disturbance and recovery maps • Intensity/Magnitude • Year of onset • Duration

21. Current protocol vs. LandTrendr

22. Current protocol vs. LandTrendr ∑ = ~ 30,000 ha Original protocol detected ~100,00 ha of change between 2004 and 2006 within the OLYM study area

23. LandTrendr – Clearcuts: Forestlands north of Cle Elum, WA

24. LandTrendr - Insect disease/defoliation: • Olimpic N.P. 10+ yr starting 1990s 20+ yr Recent

25. LandTrendr - Avalanches

26. LandTrendr –Windthrow

27. LandTrendr - Fire

28. LandTrendr - Landslides

29. LandTrendr- Pros • Captures Pacific Northwest landscape dynamics well • Captures smaller changes that are still of interest • Already has long time series • 25 years of change • Provides additional products like intensity and regeneration • Includes Canada • Works for small and large parks

30. LandTrendr - Cons • Expensive to implement • Still need to interpret results (ascribe agent of change) • Develop methodology • Subsampling? • Modeling? • Validate every polygon in park? • Developed for forested areas • results have not been evaluated for subalpine vegetation

31. Existing Tools: C-CAP Data • NOAA- Coastal Change Analysis Program • Classified Landsat TM data • Every five years (1996, 2001, 2006 …) • Products: • Map of 21 classes • Map of change between classes • Accuracy of change classes varies between 75 and 95% • Focus on coastal areas

32. C-CAP Data Analysis - Example from SAJH

33. C-CAP vs. LandTrendr

34. C-CAP vs. LandTrendr (acres)

35. C-CAP vs. LandTrender – Rural Development

36. C-CAP vs. LandTrender - Fire

37. C-CAP vs. Landtrendr - Riparian

38. C-CAP -Pros • Free • Simple analysis to get results • Could provide “big picture” change detection outside park, particularly reductions in forest cover

39. C-CAP - Cons • Misses certain change types • Slow increase or decrease in vegetation, narrow features like riparian • Accuracy unknown, errors propagate • Long time delay for results (01-06 change available in 09) • 5 year interval too long for some types of change • Rivers, avalanche chutes • No control over product • Doesn’t cover Canada • Still need to ascribe agent to change

40. Current Efforts • Automatically assign disturbance agent based on: • Trajectory label • Location on landscape • Proximity to stream • Aspect • Elevation • Geology • Soil Type