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. 1.Two different images 2. Select large changes in spectral values to indicate change Subtract 19942004 Probabilities of Change

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

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

8. Probability Thresholding 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 Mapped “change” Mapped “no-change” ThresholdThreshold FALSE POSITIVES FALSE NEGATIVES

9. North Cascades National Park Complex July 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) TC 2005 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) c) d) e) TC 2005TC 2006 Change image Polygons outlined in the validation process compared to change detected by the algorithm Change image from east side of the study area

14. 125 m Subalpine Environments, Avalanche Chutes, Tree line, and River Disturbances 2004 2006 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 CloudCloud ShadowCloudCloud Shadow Merge

19. Poor-quality Images199619961998199819971997 Olympic Peninsula

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

21. Current protocol vs. LandTrendr

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

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

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

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